ALL RIGHT, WELCOME, EVERYONE,
I THINK WE'RE READY TO GET
STARTED WITH DAY TWO OF THE
WORKSHOP. I THINK WE HAD AN
EXCELLENT DAY YESTERDAY AND I AM
EXCITED TO HAVE PEOPLE HERE IN
THE ROOM AND WATCHING ON ZOOM
AND ON THE VIDEO CAST. I AM
BRIEFLY GOING TO GIVE YOU A,
WELL, I'M SUPPOSED TO GIVE YOU A
RECAP OF WHAT HAPPENED
YESTERDAY. A LOT HAPPENED
YESTERDAY AS YOU KNOW IF YOU
LOOKED AT THE AGENDA AND YOU
HAVE BEEN PARTICIPATING WE HAVE
A DENSE SCHEDULE FULL OF
EXCELLENT TALKS AND DISCUSSIONS
AND YESTERDAY CERTAINLY AFTER
THAT AND IT WAS A GREAT START TO
THE WORKSHOP. WE WERE EXCITED
TO HEAR VERY SUBSTANTIVE DEEP
CONVERSATIONS AROUND IMPORTANT
CONVERSATIONS AROUND NEUROAI AND
WHAT TYPES OF DATA ARE NEEDED
FOR DIFFERENT NEUROAI
APPROACHES. I AM SHOWING THE
AGENDA FOR DAY ONE RIGHT NOW
JUST TO REMIND US OF THE SESSION
STRUCTURE SO WE HAVE TWO OF
THESE MAIN SESSIONS PER DAY. WE
HAVE SESSION THREE AND FOUR
COMING UP TODAY. YESTERDAY
SESSION ONE WAS KIND OF BRINGING
TOGETHER A BROAD OVERVIEW OR
SURVEY OF DIFFERENT WAYS THAT
PEOPLE ARE THINKING ABOUT
NATURAL INTELLIGENCE AND AI AND
THAT REALLY IS THE CORE THEME OF
THIS WORKSHOP. I GAVE A STAGE
SETTING SET OR REMARKS
ACKNOWLEDGING THIS KIND OF VERY
LONG HISTORY OF COMPUTING BEING
THOUGHT OF AS HOW WE CAN USE
MACHINES, COMPUTING MACHINES TO
LEARN FROM DATA. AND IT'S TAKEN
A LOT OF DIFFERENT FORMS AND
ESPECIALLY IN THE LAST TEN YEARS
OR SO. WE HAVE CLEARLY REACHED
SOME SORT OF CONVERGENCE POINT.
WE'RE ONE DECADE INTO THE NIH
BRAIN INITIATIVE. WE HAD AN
OVERVIEW OF MANY OF THE BRAIN
DATA RESOURCES. WE HAVE A TRUE
WEALTH OF NEURAL BEHAVIORAL,
PHYSIOLOGICAL, MOLECULAR DATA
OUT THERE AND FROM OUR
LARGE-SCALE PROJECTS INCLUDING BICAN
MAKING MOLECULAR AND CELL-TYPE ATLASES
OF BRAINS OF ACROSS SPECIES FROM
FLIES TO HUMANS. AND WE SAW
EXAMPLES OF THAT AND HOW THAT'S
COMING TOGETHER AND HOW THAT CAN
POTENTIALLY BE USED TO
ACCELERATE A LOT OF THESE KIND
OF IDEAS THAT PEOPLE
HAVE BEEN THROWING AROUND IN THE
FIELD UNDER THE UMBRELLA OF
NEUROAI.
WE HEARD TALKS OF LIVING
SYSTEMS. BIO COMPUTING.
WE HEARD ABOUT DIFFERENT WAYS OF
THINKING ABOUT, YOU KNOW,
POTENTIAL USE CASES FOR
LARGE-SCALE AND SMALL SCALE
NEUROAI MODELS. IN THE
AFTERNOON IN SESSION TWO WE GOT
INTO THE DEEPER QUESTIONS ABOUT
METRICS THE SPACES OVER WHICH WE
DEFINE METRICS HOW AND WHEN THEY
ARE USEFUL. AND HOW TO THINK
ABOUT THE, YOU KNOW,
THERE'S A LOT OF COMPLEXITY IN
HOW WE MAP FROM ONE TYPE OF
MODEL TO OBSERVATIONS IN BRAINS
AND THAT WAS AN EXCELLENT
CONVERSATION I THINK. AND THIS
IS A CONVERSATION THAT'S BEEN
HAPPENING FOR A LONG TIME IN THE
FIELD. BUT THIS WAS I THINK
REALLY PULLING A LOT OF THREADS
TOGETHER. SO TODAY, KIND OF ON
THAT FOUNDATION WE'RE LOOKING
FORWARD TO EXPANDING THE SCOPE
IN SESSION THREE TO INCLUDE NEW
COMPLEMENTARY MODEL PLATFORMS
INCLUDING A FOCUS ON
NEUROMORPHIC COMPUTING,
NEUROMORPHIC ENGINEERING, BRINGING
PHYSICAL DEVICES INTO THE LOOP WITH
OUR SCIENTIFIC, EXPERIMENTAL PLATFORMS AND
THINKING MORE ABOUT
ENVIRONMENTAL AND SENSORY
FEEDBACK AS A KEY COMPONENT OF
NATURAL INTELLIGENCE AND SO THAT
WILL BE A GREAT SET OF
DISCUSSIONS TO OPEN UP THE SCOPE
OF NEUROAI. AND IN THE
AFTERNOON SESSION FOUR, WE'LL
START LOOKING AT TRANSLATION AND
HOW TO TRANSITION THESE IDEAS
AND THEORIES AND MODELS INTO
TECHNOLOGIES THAT CAN ADVANCE
OUR SCIENCE AND OUR TECHNOLOGIES
FOR BRAIN HEALTH. AT THE END OF
THE DAY WE'LL HAVE A WRAP UP OF
BOTH THE DAYS SESSIONS AS WELL
AS A WORKSHOP SYNTHESIS PRIOR TO
CLOSING REMARKS. AND SO I JUST
WANT TO QUICKLY REMIND FOLKS
THAT WHEN WE HAVE THE DISCUSSION
PANELS WE'LL HAVE A SERIES OF
SHORT TALKS THEN WE'LL HAVE
PANEL DISCUSSION DURING THE
DURING THE PANEL DISCUSSION
THERE'LL BE A SLIDE LIKE THIS WITH
A QR CODE ON THE LEFT AND YOU CAN
E-MAIL A RESPONSE THAT WE'LL SEE
IMMEDIATELY. LIKE I SAID, SO,
WE HAVE SHORT TALKS, PANEL
DISCUSSION AND THEN OPEN UP TO
AUDIENCE Q&A AND IF YOU HAVE A
BROAD FUTURE LOOKING OR
OPEN-ENDED QUESTION PLEASE SAVE
THAT FOR THE AUDIENCE Q&A. IF
YOU HAVE A VERY QUICK ONE
BETWEEN SPEAKERS, YOU CAN BE
WAITING AT AN AISLE MIC AND WE
CAN CALL ON YOU. IF YOU ARE ON
ZOOM, IF YOU'RE A ZOOM PANELIST
PLEASE REMAIN MUTED AND DISABLE
YOUR VIDEO UNLESS YOU'VE BEEN
PROMOTED TO SPEAK. OTHER ZOOM
PARTICIPANTS, JUST SUBMIT A
QUESTION, PLEASE USE THE Q&A
BUTTON AND IF YOU'RE ON VIDEOCAST
YOU CAN SEND A QUESTION
THROUGH THE SEND LIVE FEEDBACK
FORM AND WE WILL GET THAT
IMMEDIATELY. JUST TO REMIND YOU
WHERE YOU ARE. YOU ARE SEATED
IN NATCHER AUDITORIUM. I AM
TOLD TO REMIND EVERYONE THAT NO
FOOD OR BEVERAGE IS TO BE
PERMITTED INTO THE AUDITORIUM
AND MOST IMPORTANTLY, I AM ABOUT
TO HAND OFF TO COURTNEY PINARD TO
INTRODUCE THE EARLY-CAREER POSTER
BLITZES AND SO THOSE WILL BE UP
AT THE ATRIUM. I AM SURE YOU'VE
SEEN WHERE THOSE ARE SET UP.
BUT I WILL HAND IT OVER TO OUR POSTER
BLITZ AWARDEES. WELCOME TO THE STAGE.
MORNING, MY NAME IS COURTNEY
PINARD. I WORK IN THE DIVISION
OF NEUROSCIENCE AND I'M JOINED
BY THE POSTER BLITZ AWARDEES AND
MY COLLEAGUE JESSICA MOLLICK.
SHE IS A PROGRAM OFFICER
AT THE NATIONAL INSTITUTE
OF DRUG ABUSE IN THE BEHAVIORAL
AND COGNITIVE BRANCH.
AND SO I WANTED TO START WITH
JUST INTRODUCING THE BRAIN
NEUROAI TRAINING SUBCOMMITTEE
WHOSE MEMBERSHIP SPANS ACROSS AGENCIES,
NIH INSTITUDES AND TEAMS. SO THIS
SUBCOMMITTEE WAS RESPONSIBLE FOR
REVIEWING ABSTRACTS AND JESSICA
AND I LED THAT EFFORT. THE
POSTER BLITZ COMPETITION WAS A
GREAT SUCCESS. WE HAD OVER ONE
HUNDRED ABSTRACT SUBMISSIONS
FROM EARLY CAREER SCHOLARS FROM
HIGH SCHOOL ALL THE WAY UP UNTIL
POST-DOCTORAL CAREER STAGES.
AND I WANTED TO THANK EVERYONE
WHO SUBMITTED AN ABSTRACT.
THERE ARE MANY TALENTED
TRAINEES. AND AS YOU'LL SEE IN
THE PRESENTATIONS THIS MORNING
THE SUBMISSION TOPICS RANGE FROM
THEORY, MODELING AND PRINCIPLES
OF INTELLIGENCE TO BRAIN
INSPIRED ALGORITHMS AND
ROBOTICS, METRICS FOR MAPPING
MODELS TO BRAINS AND
NEUROMORPHIC COMPUTING SO
PLEASE, PLEASE ATTEND THIS
POSTER SESSION AFTER THE BLITZ
TO LEARN MORE.
FOR THE BLITZ COMPETITION
SCHOLARS NEEDED TO DEMONSTRATE
HOW THEIR RESEARCH CONSIDERS THE RECIPROCAL
INTERACTION BETWEEN NEUROSCIENCE AND AI RESEARCH. AND HERE
SCHOLARS WERE ASKED TO PREPARE A
ONE MINUTE PRESENTATION TO
SHOWCASE THEIR POSTERS DURING THE
SESSION WHICH FOLLOWS THE BLITZ
AS I SAID. SO I WOULD LIKE TO
START WITH JUST CONGRATULATING
THE BLITZ HONOREES. CIANA DEVEAU,
HARRISON ESPINO, LESLIE FAMULARO,
SEUNG JU KIM, DANEIL LEVENSTEIN,
JULIANA LONDONO ALVAREZ,
BRADLEY THIELMAN,
ERIC WANG FROM COLLEGE STATION
HIGH SCHOOL IN TEXAS,
QINGYANG ALICE WANG, AND
ALEXANDER WHITE FROM NATIONAL
TSINGHUA UNIVERSITY IN TAIWAN.
SO FIRST UP WE HAVE CIANA DEVEAU.
>> GOOD MORNING, EVERYONE, IN
OUR WORK WE SHOW A NEW ROLE FOR
THE DENSE LOCAL RECURRENT
CONNECTIONS IN LAYER TWO THREE
OF THE VISUAL CORTEX. HERE THEY
FILTER SEQUENCES OF INPUT. USING
TWO-PHOTON OPTOGENETIC
STIMULATION, WE CAN MIMIC NATURAL
MOVIE, PRESENTING IT IN THE PLACE OF THE IMAGE EITHER
AT THE CORRECT TIME POINT IN THE
MOVIE OR A RANDOM UNMATCHED TIME
POINT. WE FIND AMPLIFICATION OF
THE OPTO PATTERN ONLY IN THE CORRECT
CONTEXT OF THE MOVIE, WHILE IT IS
ATTENUATED IN THE UNMATCHED
CONTEXT. WE THEN EXAMINE IN
ARTIFICIAL RECURRENT NETWORK
TRAINED TO AMPLIFY SPECIFIC
INPUT SEQUENCES. AND FIND THAT
IT PRODUCES THE SAME CONTEXT-
DEPENDENT EFFECT. SO THE
BIOLOGICAL AND MODEL DATA ALIGN SHOWING
THAT THE SEQUENCE FILTERING IS
SPECIFIC, AMPLIFYING NATURAL
SEQUENCES WHILE SUPPRESSING OTHERS.
ADDITIONAL EXPERIMENTS EXPLORE MECHANSISM
UNDERLYING THIS FILTERING EFFECT.
SO PLEASE STOP BY THE POSTERS FOR
MORE DETAIL AND DISCUSSION.
THANK YOU.
>> HELLO, EVERYONE, I WILL
SHOW THIS BRAIN INSPIRED ALGORITHM FOR
AUTOOMOUS ROBOTIC NAVIGATION WHICH
SIMULTANEOUSLY MAPS THE ENVIRONMENT
AND PLANS COST-OPTIMAL PASSTHROUGH.
THIS IS A QUICK OVERVIEW OF THE
MODEL. AS WE FIRST FIND A
PATH BY PROPAGATING THROUGH A
GRID OF PLACE CELLS FROM START TO END AND THIS
IS SUPPOSED TO MIMIC HIPPOCAMPAL
PREPLAY. AFTER WE HAVE THE PATH, WE
COLLECT COST DATA, SO THINGS LIKE THE
ENERGY EXPENDED AROUND THE WHEELS,
TIME SPENT AROUND OBSTACLES, STEEPNESS
OF THE TERRAIN. AND ONCE WE HAVE THAT
INFORMATION WE CAN UPDATE THE PLANNER
WEIGHTS USING LEARING RULE CALLED E-PROP.
THIS CHANGES THE SPEED OF THE
CONNECTION PROPAGATED BETWEEN NEURONS, SIMILAR
TO ANOTHER INSPIRATION WHICH WAS
AXONAL-DEPENDENT PLASTICITY WHERE CHANGES
IN THE WHITE MATTER WILL CONNECT AFFECT
CONDUCTION VELOCITY. WE PUT THIS ON
A ROBOT AND ALLOW IT TO AUTONOMOUSLY
NAVIGATE FOR TWELVE HOURS.
WE FOUND TWO THINGS, ONE, WE CREATED
INFORMATIVE MAPS OF THE ENVIRONMENT AND
COMPARED TO SOTA PLANNING ALGORITHMS
LIKE A*, RRT*, IT ACTUALLY PERFORMED
BETTER AND PLANNED LOWER-COST
PATHS THAN TO ADAPTIVE PLANNERS LIKE D*LITE.
IF YOU WANT MORE INFORMATION
PLEASE STOP BY THE POSTER.
THANK YOU.
>> GOOD MORNING, EVERYONE, MY
MY NAME IS LESLIE FAMULARO, THIS
WORKSHOP A RECURRING THEME IS TO
INTRODUCE NEUROMORPHIC OR BIOINSPIRED
COMPUTING IN DEEP LEARNING. WE MADE
SUCH AN ATTEMPT. SPECIFICALLY
WE FOCUS ON AUDITORY NEUROSCIENCE. WE
TAKE A FORWARD MODEL FROM THE
COCHLEA TO THE BRAIN AND WE
COMBINE THAT WITH THE DEEP
NEURAL NETWORK THROUGH
DIFFERENTIALABILITY SO
THAT IT CAN BE UPDATED FROM THE
FRONT END WHICH IS OUR BIOINSPIRED
ARCHITECTURE AND A DEEP NEURAL
NETWORK AT THE SAME TIME. WE
USE THIS TO TRAIN MODELS FOR
ENGINEERING BASED TASKS LIKE
SPEECH RECOGNITION AND
ENHANCEMENT AND WE CONSISTENTLY
FIND OUR MODEL TO OUTPERFORM
DEEP LEARNING ALONE BOTH
PERFORMANCE AND ALSO ROBUSTNESS.
OUR PARAMETERS ARE ALSO
INTERPRETABLE BECAUSE THEY'RE
BASED ON NEUROSCIENCE AND WE'RE
EXPLORING APPLICATIONS ON PERSONALIZED HEARING,
CLINICAL APPLICATIONS HEARING AID FITTING. IF YOU
WANT TO HEAR ME STOP BY POSTER
THREE, THANK YOU.
>> OKAY. HELLO, EVERYONE. I AM
SEUNG JU KIM, MATERIALS SCIENTIST. MY RESEARCH
DEVELOPING NEUROMORPHIC HARDWARE
USING PULSE CMOS TECHNOLOGY. TRADITIONAL
TRANSISTOR-BASED COMPUTING IS
DIGITAL PROCESS-CENTRIC, RESULTING
IN MASSIVE ENERGY CONSUMPTION AND
INEFFICIENT COMPUTING. HOWEVER
NEUROMORPHIC COMPUTING USING
MEMRISTOR TECHNOLOGY CAN
COMPUTE A.I. EFFICIENTLY BY
USING ION MIGRATIONS TO UPDATE
THE SYNAPTIC WEIGHT, MUCH BETTER
THAN THE DIGITAL PROCESS
COMPUTINGS. SO MEMRISTORS CAN
REPRESENT MULTI-INFO WITHIN SINGLE
DEVICES. WE SHOWED THAT USING
HALIDE IONS, INSTEAD OF OXYGEN IONS
CAN, MIGRATE ION IN THE ENTIRE CELL
ACHIEVING LINAR SYNAPTIC UPDATING.
FOR MORE CHECK OUT MY POSTER.
THANK YOU.
>> YEAH SO IF YOU WERE TO ASK ME
WHAT DOES THE HIPPOCAMPUS DO. I
WOULD TELL YOU IT DOES TWO
THINGS. ONE AN ONLINE MODE
THAT REPRESENTS WHERE AN AGENT
OR ANIMAL IS IN THE WORLD AND
THIS IS WHERE YOU SEE PLACE
CELLS AND AN OFFLINE MODE IN
WHICH IT SIMULATES TRAJECTORIES
THROUGH THE WORLD WHICH WE OFTEN
CALL REPLAY. SO THE QUESTION IS
HOW DOES THE HIPPOCAMPUS DEVELOP COGNTIVE MAP
AND GENERATE REPLAY? SOMETHING THAT
CAN DO BOTH OF THESE THINGS FROM
EGOCENTRIC INFORMATION AVAILABLE
TO THE ANIMAL AND SO TO STUDY
THIS WE BUILT THIS GRID WORLD
ENVIRONMENT IN WHICH AN AGENT
MOVES AROUND AND COLLECTS
SENSORY INFORMATION FROM THE
VISUAL FIELD IN FRONT OF IT.
AND WE DREW FROM SOME RECENT
RESULTS IN A.I. AND COMPUTATIONAL
NEUROSCIENCE. SHOWING THAT WHEN
RECURRENT NEURAL NETWORKS LEARN
TO PREDICT SENSORY INFORMATION
THEY DEVELOP PLACE CELLS AND SO
THE FIRST THING WE DID WAS
REPLICATE THIS WORK. WE SHOW
THAT INDEED WE END UP WITH
THE EMERGENCE OF PLACE CELLS IN
OUR NETWORK BUT MORE IMPORTANTLY
WHEN WE ACTUALLY TURN OFF THE
SENSORY INPUT WE END UP WITH A
REPRESENTATION OF THE WORLD THAT
SIMULATES PLAUSIBLE TRAJECTORIES.
THIS SHOWS THE DECODED POSTERIOR
OF THE NETWORK DURING SLEEP WHICH REPRESENTS
A COHERENT LOCATION IN THE
ENVIRONMENT. AND THIS CAN MOVE
AROUND IN THE ENVIRONMENT
PRODUCING TRAJECTORIES THROUGH
THE WORLD INCLUDING THOSE THAT
THE AGENT HAS NEVER SEEN BEFORE
AS WELL AS OUTPUTS THAT LOOK LIKE
SENSORY INFORMATION THAT THE
AGENT WOULD HAVE SEEN IN THE
ENVIRONMENT. AND SO WE WANTED
TO KNOW HOW THE NETWORK IS
ACTUALLY DOING THIS, WE USE
TOOLS FROM NEUROSCIENCE TO LOOK
AT THE NEURAL MANIFOLD AND WE
FOUND THIS WAS HAPPENING BECAUSE
THE NETWORK LEARNED AN ATTRACTIVE
MANIFOLD OF SPACE AND THIS ONLY
EMERGED WHEN THE NETWORK LEARN TO PREDICT
SEQUENCES OF SENSORY INPUT NOT
SAY SINGLE OR NEXT STEP INPUT
DESPITE THE FACT THAT ALL OF
THESE NETWORKS LEARN SPATIALLY
TUNED CELLS AND THIS CORRESPONDED
TO THE EMERGENCE OF
REPLAY ONLY IN THE SEQUENTIAL
NETWORKS. SO THE ANSWER WE COME
UP WITH IS BASICALLY THAT THE
HIPPOCAMPUS COULD LEARN A
COGNITIVE MAP THRU LEARNING TO
PREDICT A SEQUENCE OF SENSORY
INFORMATION AS AN AGENT MOVES
THROUGH THE
ENVIRONMENT.
>> OKAY SO ATTRACTOR- BASED
MODELS USES ATTRACTORS TO
REPRESENT PROCESSES SO LIKE
MEMORIES CAN BE ENCODED AS FIX
POINTS WHILE LOCOMOTION HAS
TRADITIONALLY BEEN MODELLED
USING COUPLED OSCILLATORS. I THINK THIS
COULD OFFER AN ATTRACTIVE
UNIFYING NETWORK WHERE MULTIPLE
GAITS CAN BE ENCODED AS CO-EXISTING
LIMIT CYCLES. HOWEVER, THIS IS HARD
TO IMPLEMENT. BUT USING
MATHEMATICAL INSIGHTS FROM A
VERY SPECIAL FAMILY OF THRESHOLD
NEURAL NETWORKS, WE DEVELOP
SEVERAL ATTRACTOR-BASE MODELS
INCLUDING A 43 NEURAL NETWORK THAT
ENCODES A SEQUENCE OF QUADRUPED GAITS.
THE NETWORK TRANSITIONS TO THE NEXT GAIT WITHOUT
ANY INFORMATION ABOUT WHICH GAIT
COMES NEXT. AND I WILL BE VERY
HAPPY TO SHOW YOU THE
MATHEMATICS BEHIND IT AT MY
POSTER. THANK YOU.
>> MORNING, I'M BRAD, SO SCIENCE
AND COMPUTING BOTH NEED
ABSTRACTIONS AND WE BUILD THESE BY
FIRST PLACING SPIKES INTO TIME
BINS TO FORM POPULATION ACTIVITY
VECTORS BUT THERE'S NO REASON
FOR THE BRAIN TO RESPECT THESE
TIME BINS. TIME IN THE BRAIN IS
RELATIVE AND SPIKES ONLY MAKE
SENSE TO THE NETWORK THAT
GENERATED THEM. WE KNOW THAT
SYNAPTIC INTERACTION IS THE BASIS FOR NEURAL COMPUTATION.
MY POSTER PRESENTS A PROCESS THAT
UNIFIES ACTIVITIES FROM THE
CONNECTOME INTO A SINGLE
MATHEMATICAL OBJECT, THIS DIRECTED GRAPH
AND I WILL PRESENT AN EFFICIENT ALGORITHM
TO IDENTIFY ISOMORPHIC SUBGRAPHS TO THIS
DIRECTED GRAPH WHICH IS A HARD PROBLEM IN
GENERAL BUT THIS IS NOT THE
GENERAL CASE BUT I WILL ARGUE
THAT THESE ISOMORPHIC SUBGRAPHS ARE
CANDIDATES FOR ISOMORPHIC COMPUTATIONS
SO SEE ME AT POSTER 14.
>> HELLO, EVERYONE, MY NAME IS
ERIC WANG, ASSOCIATIVE LEARNING
IN WHICH THE CONNECTIONS BETWEEN
DIFFERENT STIMULI ADAPT TO THE
ENVIRONMENT AND OCCURS THROUGH
TWO WAYS OF VOLUME TRANSMISSION
AND WIRE TRANSMISSION. TO MIMIC
ASSOCIATIVE LEARNING I USE
ELECTRIC STIMULI TO EMULATE WIRE
TRANSMISSION AND PHOTO STIMULUS TO EMULATE
VOLUME TRANSMISSION. SO I WANT TO
ENHANCE THE EFFICIENCY OF THE
PROCESS SO I INCORPORATED
QUANTUM DOTS WITH A PVP FILM WHICH
WAS BETWEEN SILVER AND INDIUM TIN OXIDE.
I THEN MEASURED THE CURRENT OF
STIMULUS COMPARED TO BEFORE THE ASSOCIATION AND
IT SHOWED A SIGNIFICANT INCREASE
IN RESPONSE. THE RESISTIVE SWITCHING
ALSO SHOWED THE ASSOCIATION BETWEEN THE
ELECTRIC AND PHOTO STIMULI. I USED
CLASSICAL CONDITIONING AND IT SHOWED THE ELECTRIC AND PHOTO
STIMULI HAVE QUICK TRAINING
AND LONG RETENTION FOR
ASSOCIATIVE LEARNING. FINALLY
PUT THIS IN A NEURAL NETWORK AND
PERFORMED TESTS. AFTER THE ASSOCIATION, THE ACCURACY OF
THE RECOGNITION OF NUMBERS 0-9
WERE DRASTICALLY INCREASED USING A
SMALL DATA TRAINING SET. THANK
YOU.
>> HI, EVERYONE, SO HOW DOES THE
EXCITATORY-INHIBITORY STRUCTURE
CHANGES FUNCTIONAL COMPLEXITY OF
NETWORKS? I AM SHOWING
YOU TWO STORIES HERE WITH ONE
ON LEARNING IN THE POSTER. SO
THE UNIVERSAL APPROXIMATE THEOREM IS THE GUARANTEE
FOR THE SUCCESS OF DEEP LEARNING
TODAY. BUT IT WAS KNOWN THAT
HAVING INHIBITORY CONNECTIONS
IN THE NETWORK IS A REQUIREMENT
FOR THAT TO HOLD. HOW MANY
INHIBITORY NEURONS DO WE NEED? THAT IS
SHOWN ON THE RIGHT. WHERE WE
LEVERAGE THE RECENTLY AVAILABLE
LARVA DROSOPHILA EM CONNECTOME SET. THE HIGHER
FUNCTIONAL COMPLEXITY SIMULATED
BRAIN NETWORKS ARE COLORED BY
LIGHTER DOTS. AND THE MAIN TAKE
HOME HERE IS THE GREEN STAR
WHICH MARKS THE BRAIN E-I AS
STRUCTURED PROPERTIES THAT
MATCHES TO THE HIGHER FUNCTIONAL
COMPLEXITY REGION. I'M ALICE WANG
FROM HOPKINS, LOOKING FORWARD TO
SEEING YOU AT THE POSTER. THANK
YOU
YOU.
>> HOW IS THE BRAIN SO FLEXIBLE?
IT IS ABLE TO RESPOND RAPIDLY TO
UNEXPECTED STIMULI WITHOUT THE
NEED
>> SYNAPTIC PLASTICITY OR
REWIRING. THE -- THIS -- THE
HYPER FLEXIBILITY HINGES ON THE
RECURRENT STRUCTURE OF THE BRAIN.
THE -- ESPECIALLY MUTUAL
INHIBITION. THESE CONNECTIONS
ADD BIFURCATIONS SUCH THAT SMALL
NUDGES AND CONTEXTUAL INPUT CAN
SEAMLESSLY TRANSITION THE NETWORK INTO
COMPUTATIONAL MODES. HERE WE PUSH THIS TO
ITS LIMIT WE CONSTRUCT A FOUR
NEURON CIRCUIT CAPABLE OF
COMPRESSING 24 UNIQUE FUNCTIONS INTO A SINGLE CIRCUIT WITH A SET OFSYNPATIC WEIGHTS.
SIMPLY BY ADJUSTING THE BIAS CURRENTS OF
NEURONS, WE ARE ABLE TO SEAMLESSLY
TRANSITION BETWEEN ALL THESE. WE
ARE ABLE TO COMPUTE LOGIC FROM
THREE DIFFERENT TYPES OF INPUTS:
TEMPORAL, MAGNITUDE AND PHASE.
THIS IS A HARDWARE AGNOSTIC
APPROACH AND DOES NOT REQUIRE
ANY EXOTIC MATERIALS SO IF YOUR
INTERESTED IN LEARNING MORE
PLEASE STOP BY MY POSTER. THANK
YOU.
>> THANKS FOR YOUR ATTENTION
THIS MORNING. LET'S GIVE A BIG
ROUND OF APPLAUSE TO OUR POSTER
BLITZ HONOREES. I ALSO WANTED
TO THANK OUR SPONSORS FOR THEIR
CONTRIBUTION TO THE EARLY CAREER
SCHOLAR ACTIVITIES DURING THIS
WORKSHOP. IF EVERYONE WHO IS
PRESENTING A POSTER COULD JOIN
US BY THE STAIRWELL WE'RE GOING
TAKE A PHOTO AND PLEASE JOIN US
AT THE POSTER SESSION.
OKAY, WELCOME,
EVERYONE TO SESSION THREE. OF
THE WORKSHOP THIS SESSION IS
CHAIRED BY DR. BRAD WHO IS A DISTINGUISHED
MEMBER OF TECHNICAL STAFF AT SANDIA NATIONAL
LABS WHERE HE WORKS IN
NEUROSCIENCE TO ADVANCE
ARTIFICIAL INTELLIGENCE FOR
FUTURE SCIENTIFIC COMPUTING
APPLICATIONS. PRIOR TO JOINING SANDIA
IN 2011 HE WAS A POST-DOCTORAL
RESEARCH ASSOCIATE AT THE SALK
INSTITUTE FOR BIOLOGICAL STUDIES
WITH A PH.D IN COMPNEURO, MS AND BS FROM
FROM RICE UNIVERSITY AND I WELCOME
BRAD TO THE STAGE AND TO GIVE
OUR FIRST PRESENTATION. WELCOME, BRAD
>> BRAD: THANK YOU, JOE AND
THANK YOU, GRACE FOR HELPING
ORGANIZE THIS WONDERFUL WORKSHOP
THIS SESSION IS GOING TO BE A
LITTLE BIT OF A CHANGE FROM THE
DISCUSSIONS YESTERDAY WHICH WERE
LARGELY FOCUSED ON KIND OF MORE
STANDARD A.I. APPROACHES AND HOW
IT RELATE TO NEUROSCIENCE THIS
IS GOING TO FOCUS ON
NEUROMORPHIC COMPUTING AND WE'LL
LATER TALK ABOUT WHAT
NEUROMORPHIC COMPUTING LATER
THAN I WILL. I WILL TALK ABOUT
WHERE I THINK NEUROMORPHIC
COMPUTING CAN HELP US UNDERSTAND
THE BRAIN WHY WOULD THIS
TECHNOLOGY TODAY BE SOMETHING WE
WOULD WANT TO LOOK TO IN THE
NEAR FUTURE? SO I'M GOING TO,
LET'S SEE HERE -- SO I WANT TO
MOTIVATE THIS BY MENTIONING HOW
MODELING AND SIMULATION
HAS THE ABILITY TO PROJECT THE
FUTURE IF YOU WILL IS SOMETHING
THAT HAS ADVANCED TREMENDOUSLY
OVER THE PAST FEW DECADES.
TO THE POINT WHERE TODAY AS MANY
OF YOU ARE FAMILIAR WITH
HURRICANE PREDICTION. THIS WAS
HURRICANE MILTON THAT HIT TAMPA
A FEW WEEKS AGO.
WE HAVE MODSIM CAPABILITIES
THAT ARE TREMENDOUSLY EFFECTIVE
AT PROJECTING SEVERAL DAYS OUT
IN THE CASE OF A HURRICANE,
WHERE THINGS MAY HIT AND IN THIS
CASE IF YOU ARE FAMILIAR WITH
THE STORY THIS HURRICANE WENT
THE WRONG DIRECTION NORMALLY
THEY GO EAST TO WEST THIS WENT
WEST TO EAST AND YET THEY
PREDICTED LIKE FOUR DAYS IN
ADVANCE ALMOST EXACTLY WHERE THE
STORM WAS GOING TO HIT. AND
THIS IS AMAZING, IT SAVES MANY
LIVES, THIS IS A TREMENDOUSLY
IMPORTANT ADVANCE FOR OUR
SOCIETY. AND THIS LEVERAGES
LARGE SUPERCOMPUTERS. SOME OF
THE BIGGEST SUPERCOMPUTERS IN
THE WORLD ARE USED TO MAKE THESE
WEATHER PREDICTION SO THE
QUESTION IS WHY CAN'T WE DO THIS
FOR NEUROLOGICAL HEALTH? WE'RE AT NIH.
ARE WE NOT ABLE TO TAKE
ADVANTAGE OF LARGE-SCALE
SUPERCOMPUTERS IN A WAY THAT
WE CAN PREDICT A HURRICANE, USE
THAT TO PREDICT THE FUTURE
OUTCOME OF DISEASE AND HEALTH?
WE DO THIS FOR OTHER PARTS OF
BIOLOGY BUT FOR THE BRAIN IT'S
NOT QUITE AS EASY. SO, JUST
KEEPING THAT IN MIND THIS SORT
OF VISION OF HOW DO WE BRING
THIS SORT OF KNOWLEDGE THAT WE
HAVE OF THE NEURON AND CIRCUITS THAT THE BRAIN
INITIATIVE HAS BEEN INVESTING
WITH GREAT SUCCESS OVER THE LAST
DECADE AND ACTUALLY USE IT TO
POTENTIALLY MODEL THE BRAIN'S
FUTURE. WE HAD A DISCUSSION ON
FOUNDATION MODELS. I AM GOING TO
TALK ABOUT A MORE CLASSIC WAY OF
DOING THIS WHICH IS SIMULATIONS
AND THEN KIND OF SAY WHAT THE
CHALLENGES ARE WITH THAT AND
THEN LOOKING TOWARDS
NEUROMORPHIC COMPUTING AS A
SOLUTION BUT THEN ALSO KIND OF
INTRODUCING HOW NEUROMORPHIC
COMPUTING WHICH IS NEW HARDWARE
INSPIRED BY HOW THE BRAIN IS
CONSTRUCTED TO GIVE US A DEEPER
UNDERSTANDING OF HOW THE BRAIN IS
FUNCTIONING. SO GOING --
I CAN GIVE A FULL TALK ABOUT THE
CHALLENGES OF MODSIM OF THE BRAIN. WE HAD
A FEW DISCUSSIONS ABOUT THIS
BEFORE. I THINK IT'S, YOU KNOW,
THERE HAVE BEEN SOME VERY
NOTABLE EFFORTS IN THIS
DIRECTION IN THE HUMAN BRAIN PROJECT
FOR ONE, BEEN A LOT OF EFFORT IN
TRYING TO BUILD SIMULATIONS AND
I THINK QUITE SUCCESSFUL IN WHAT
THEY WERE TARGETING. AND THE
LESSONS THAT COME OUT OF THIS IS
THAT WE ABSOLUTELY HAVE TO HAVE
DATA AND MY PERSONAL EXPERIENCE
IN THIS AND LOOKING AT THE ADULT NEUROGENESIS
AND HIPPOCAMPUS, THE LIMITING FACTOR
VERY OFTEN IS THAT WE DON'T HAVE
A CONNECTOME FOR REGIONS WE WANT
MIGHT TO MODEL OR DETAIL ON ALL
THE CELL TYPES. MOST DISEASES
IN MENTAL HEALTH ESPECIALLY
RELATE TO MODULATORY
NEUROTRANSMITTERS WHICH ARE
DIFFICULT TO MEASURE IN VIVO,
THEIR BEHAVIOR AND THEIR
EFFECTS. WE NEED A LOT OF DATA
AND WE NEED MORE THAN JUST THE
CONNECTOME ALTHOUGH THAT IS A
TREMENDOUS START AND THERE'S
ANOTHER PROBLEM, THE BRAIN IS
REALLY BIG AND IS VERY COMPLEX.
AND I WOULD PROBABLY SAY AS MUCH
A CHALLENGE FOR THIS COMMUNITY
AS WELL AS FOR THE HIGH
PERFORMANCE COMPUTING COMMUNITY
THIS PROBLEM IS ONE EXTREMELY
CHALLENGING, RIGHT, SO IF YOU
LOOK AT THE TIMESCALES. OUR BRAINS
HAVE TEN TO THE FIFTEEN SYNAPSES GIVE
OR TAKE. OUR BRAIN IS FIRING AT
A HERTZ MAYBE MORE. SO THAT'S A
LOT PER SECOND. BUT, MOST
NEUROLOGICAL DISORDERS OCCUR
OVER WEEKS, MONTHS, EVEN YEARS
AND SO WE'RE TALKING ABOUT
TIMESCALES AND SIZE SCALES THAT
PUT IT ALTOGETHER. I NEED, NOT
JUST AN EXASCALE MACHINE, I NEED
MANY EXASCALE MACHINES, MANY TIMES A
MACHINE RUNNING FOR MANY MONTHS
AND MANY YEARS. THAT'S JUST
INTRACTABLE IF WE WANT TO DO
EVEN A SYNAPSE-LEVEL SIMULATION OF
MENTAL HEALTH DISORDER SO SOMETHING HAS
TO CHANGE HERE. AND THEN THE
FINAL THING IS WE DON'T HAVE A
GREAT UNDERSTANDING OF THE
BEHAVIORS OF THE BRAIN SO WITH
THIS IN MIND I WANT TO TALK
BRIEFLY ABOUT WHAT NEUROMORPHIC
IS. AND WE WILL HAVE MORE TALKS
THAT WILL GO INTO MORE DEPTH IN
THIS. NEUROMORPHIC CAN MEAN DIGITAL
IMPLEMENTATIONS THAT KIND OF USE
STANDARD FABRICATION
TECHNOLOGIES, WE CAN GET
AT SCALE. WE HAVE A SYSTEM AT
SANDIA THAT'S OVER A BILLION
NEURONS FROM INTEL AND WE'RE
GOING TO GET ONE SOON FROM SPINNAKER
, FROM THE SPINNAKER
PROJECT. WE ALSO HAVE ANALOG
SYSTEMS WHICH YOU'LL HEAR ABOUT
IN A LITTLE BIT. SO I WILL
FOCUS ON THE DIGITAL LARGE-SCALE
SYSTEMS. THIS IS WHAT WE LOOK
AT THE MOST. IT'S A LITTLE MORE
NEAR TERM BUT BOTH ARE
IMPORTANT. EVENTUALLY WE'LL
PROBABLY HAVE NON-SILICON
TECHNOLOGIES. WE'LL HAVE TO GO
INTO ANALOG SCALING, MEMRISTORS.
I WILL NOT TALK MUCH ABOUT THAT
BUT THAT IS THE FUTURE OF THE
FIELD AND SOMETHING WE WANT TO
KEEP IN MIND. SO WHAT CAN
NEUROMORPHIC COMPUTING DO? FOR
THE LAST FEW MINUTES I WANT TO
THROUGHOUT OUT A COUPLE OF THESE
IDEAS.
THERE ARE THREE THINGS I THINK
WE ARE INTERESTED IN. ONE IS
SIMULATE AT SCALE. THE SECOND IS
GUIDE HOW TO ANALYZE DATA, I'M
NOT GOING TO TALK AT ALL ABOUT
THIS. BRAD THEILMAN WHO HAS A POSTER
UPSTAIRS HAS A GREAT STORY ABOUT
THAT I ENCOURAGE YOU TO TALK TO
HIM. THE THIRD I WILL TOUCH ON
BRIEFLY IS MAYBE WE CAN GET A
BETTER INSIGHT INTO WHAT THE
COMPUTING OF THE BRAIN ACTUALLY
IS. SO VERY QUICKLY JUST AN
EXAMPLE OF SIMULATING SYSTEMS AT
SCALE. SO I MENTIONED, YOU
KNOW, HUMAN SCALE IS TEN TO THE
FIFTEENTHS SYNAPSES THAT IS NOT
GOING TO HAPPEN ANYTIME SOON BUT
FLYBRAIN, WE HAVE THE
CONNECTOME, THE BRAIN INITIATIVE
WAS INSTRUMENTAL IN SEEING THIS
COME OUT. THIS IS ONE OF THE
NINE NATURE PAPERS THEY HAVE
THAT TALK ABOUT THE MODEL OF THE CONNECTOME.
SO WE ASK THE QUESTION AND THIS IS
JUST KIND OF A SHOT IN THE DARK,
CAN OUR NEUROMORPHIC SYSTEMS
JUST OUT OF THE BOX JUST TAKE
THIS MODEL THAT WAS PUT UP
THERE, DATA SHARING, YOU KNOW,
THE WHOLE THING, CAN WE PUT THIS
ON THE LOIHI CHIPS WHAT WE HAVE? AND THE
ANSWER IS YES. USING SOME OF
THE SOFTWARE TOOLS THAT WE'VE
BEEN DEVELOPING FOR THIS IN
MIND, YOU KNOW, KIND OF THE WAY
OF STRESS TESTING THIS, WE WERE
ABLE TO TAKE THIS CONNECTOME AND
JUST, YOU KNOW, THEY PUT THEIR
MODEL ONLINE. WE SET THIS
THROUGH OUR TOOL CALLED FUGU AND
STACS. AND THERE'S A LITTLE BIT
OF WORK THAT GOES INTO THIS. I
WILL NOT GO INTO THE
NITTY-GRITTY DETAILS. THESE CHIPS ARE
NOT MADE TO HAVE BIOLOGICAL CONNECTOMES ON THEM.
WE'RE LEARNING A LOT ABOUT WHAT THE
MAPPING IS AND IT WORKS AND THIS
IS A BLOWUP OF THE PLOT. WE
STILL NEED TO DIVE INTO WHAT IS
ACTUALLY GOING ON HERE BUT THIS
IS ENCOURAGING. THIS IS A REAL
BRAIN, FULL BRAIN, ON A LOIHI CHIP WHICH
IS PRETTY EXCITING. SO THE FINAL
THING IS THIS IDEA OF CAN YOU
ACTUALLY USE NEUROMORPHIC TO GET
MORE EXPRESSIVE DESCRIPTIONS OF
COMPUTATION?
SO CAN WE GET AWAY FROM SIMPLE
BEHAVIORS I USE THIS IDEA OF
ANIMAL BEHAVIOR KIND OF LIMITS,
TO DISCRIMINATE BETWEEN TWO
OBJECTS. I WANT TO, YOU KNOW I
DON'T HAVE A LOT OF TIME BUT I
WANT TO SAY THAT A.I. ALONE IS NOT
GOING TO DO THIS. IF YOU'RE
FAMILIAR WITH WHY A.I. IS SO
POWERFUL THERE'S A LOT OF THINGS
LIKE UNIVERSAL FUNCTION APPROXIMATIONS,
THE TURING COMPLETENESS NEURON. CAN
DO ANYTHING SO THE FACT THAT A
NEURAL NETWORK CAN DO SOMETHING
DOESN'T MEAN THE BRAIN CAN DO
IT. THAT'S A FUNDAMENTAL THING
YOU HAVE TO KIND OF APPRECIATE
HERE. NEUROMORPHIC HARDWARE ON
THE OTHER HAND HAS REAL
CONSTRAINTS. IT HAS TO PERFORM A
COMPUTATION UNDER A FINITE SPACE
AND ENERGY BUDGET AND THESE
RESTRICTIONS ARE CRITICAL IN
MOVING FORWARD. AGAIN, I'M NOT
GOING TO GO INTO THE FULL DEPTH
OF THIS BUT THESE CHIPS HAVE NEURONS
THAT EXIST IN SPACE WITH A GRAPH AND
THAT IS SOMETHING THAT WE HAVE
TO FIT OUR ALGORITHMS TO TO BE
IMPACTFUL. SO WHAT WE HAVE
OBSERVED IS JUST A FINAL TEASE,
I'M HAPPY TO TALK ABOUT THIS AT
LENGTH IN DISCUSSION OR AT THE
BREAK WE HAVE SEEN REAL CASES
THAT NEUROMORPHIC HARDWARE SUCH
AS THESE LOIHI PLATFORMS CAN DO
PROBLEMS THAT REQUIRE SAMPLING
LIKE MONTE CARLO SIMULATIONS
OR MONITORING PROBLEMS THAT ARE
SPATIALLY CONSTRAINED AND LOCALIZED
OVER SPATIAL REGIONS. WE HAVE PAPERS
THAT HAVE LOOKED AT WHERE WE CAN LOOK
AT THIS PROCESS AS WELL AS
FINITE ELEMENT SIMULATIONS.
THESE MAP EXTREMELY WELL, FAR
MORE THAN ANYONE WOULD HAVE
GUESSED 10 YEARS AGO AND THEY
SUGGEST THAT THE BRAIN COULD
ACTUALLY VERY WELL BE DOING
COMPUTATIONS THAT ARE ANALOGOUS
TO THIS AND WE KNOW THEY DO
ESPECIALLY WHEN IT COMES TO STOCHASTIC
SAMPLING JUST TO KIND OF WRAP UP
THIS CHALLENGE OF COMPLEXITY,
TIMESCALES AND BEHAVIORS. I
BELIEVE NEUROMORPHIC COMPUTING
HAS A REAL POSSIBILITY OF, YOU
KNOW, CONTRIBUTING TO MOVING
FORWARD ALONG ALL THREE OF THESE
SORT OF CHALLENGES. ESPECIALLY
AS DATA STARTS BECOMING MORE
VIABLE GOING FORWARD. SO, WITH
THAT, I WOULD LIKE TO THANK YOU
VERY MUCH AND I -- HOPEFULLY THE
REMAINDER OF THE SESSION IS
GOING TO HELP GO INTO THIS IN
MORE DETAIL. THANK YOU.
>> PLEASE WELCOME KWABENA
BOAHEN FROM STANFORD UNIVERSITY.
>> YES, MICS ARE WORKING, DO I
ADVANCE OVER HERE? OH, I SEE,
YEAH, SO IT'S GREAT TO BE HERE
AND THANKS FOR THE INVITATION,
EVERYONE AND I'M LOOKING FORWARD
TO SOME MORE LIVELY DEBATE TODAY
LIKE WE HAD YESTERDAY. I AM
GOING TO TALK MORE ABOUT YEAH,
SO I'M GOING TO TALK MORE ABOUT
THE SUSTAINABILITY CRISIS THAT
A.I. IS FACING RIGHT NOW. AND ONE
OF THE PROMISES OF NEUROMORPHIC
ENGINEERING IS TO TRY AND MATCH
THE EFFICIENCY OF YOUR -- THE
HUMAN BRAIN. AND ONE OF THE
GOALS OF A.I. IS TO RECREATE HUMAN
INTELLIGENCE, RIGHT? SO THESE
TWO THINGS ARE RELATED. AND SO
WE ALL KNOW THAT, YOU KNOW,
MICROSOFT AND OPENAI ARE
PLOTTING THIS HUNDRED BILLION
DOLLAR A.I. SUPERCOMPUTER LAUNCHED
AS SOON AS 2038 AND EXPANDING
THROUGH 2030. WILL BE AS MUCH
AS 5 GIGAWATTS OF ELECTRICITY SUPPLIED BY FIVE NUCLEAR POWER PLANTSTO RUN MILLIONS OF GPUS.
SO WE HAVE TO ASK OURSELVES IS THIS
SUSTAINABLE AND IF WE WANT TO
FIX IT, WHAT DO WE HAVE TO DO?
A LOT OF PEOPLE FOCUS ON THE
AMOUNT OF ENERGY THAT GOES TO
COMPUTE BUT IT TURNS OUT THAT
MOST OF THIS ENERGY IS GOING TO
COMMUNICATION. AND TO
ILLUSTRATE THAT, I'M GOING TO
LIKE SHOW YOU A LITTLE TOY
NEURAL NETWORK. THIS ONE IS
A DEEP NET WITH TEN LAYERS.
EACH ONE OF THEM HAS TEN NEURONS
AND YOU ALL KNOW YOU COME IN
WITH THESE 10 INPUTS. YOU DISTRIBUTE THEM
TO THE TEN NEURONS IN THAT LAYER,
AND THEN YOU APPLY SOME
WEIGHTS. AND THEN YOU SUM THOSE
WEIGHTS, WEIGHTED INPUTS.
AND YOU THRESHOLD AND
THOSE ARE YOUR ACTIVATIONS THAT
YOU SEND TO THE NEXT LAYER. SO
THIS VECTOR IS YOUR MATRIX MULTIPLIER
USED TO DO THIS EFFECTIVELY AND
NORMALLY WE THINK OF THESE
NEURAL NETWORKS AS FEED FORWARD
BUT ACTUALLY MOST OF THE STATE
OF THE ART NETWORKS WE'RE USING
ARE NOT JUST FEED FORWARD THEY
HAVE SKIP CONNECTIONS, RESIDUAL
CONNECTIONS, SOMETIMES TOP-DOWN
FEEDBACK LIKE IN AN ENCODER DECODER
STACK OF A TRANSFORMER. SO YOU
HAVE TO COMMUNICATE THESE
SIGNALS AS WELL ACROSS SEVERAL
LAYERS. NOW, PEOPLE ARE FOCUSED
ON REALLY DOING THE COMPUTE
EFFICIENTLY AND THEY HAVE COME
UP WITH THIS CONCEPT OF
COMPUTE-IN-MEMORY. AND IN THIS CASE,
YOUR TEN BY TEN MATRIX IS STORED
IN A TEN BY TEN MEMORY ARRAY.
AND IN THIS CASE, YOUR
ACTIVATIONS ARE APPLIED TO WHAT
IS CALLED A WORD LINE OF THIS MEMORY.
VOLTAGE CHANGES THE
PROPERTIES OF THE MEMORY CELLS
SUCH THAT IT MAKES A CURRENT
THAT'S PROPORTIONAL TO SOME
PHYSICAL STATE IN THE CELL SO
THAT'S HOW YOU STORE YOUR WEIGHT
AND DO YOUR MULTIPLICATION AND
THEN YOU CAN BASICALLY SUM THESE
CURRENTS ALONG WHAT IS CALLED
THE BIT LINE AND YOU GOT YOUR
DOT PRODUCT BETWEEN A VECTOR OF
ACTIVATIONS AND A VECTOR OF
WEIGHTS FOR A PARTICULAR UNIT OR NEURON.
AND THEN YOU THRESHOLD AND YOU
SEND THESE ACTIVATIONS TO SOME
OTHER CROSSBAR. AND TO DO THAT
YOU USE THESE INTERCONNECTS
HERE. AND IF YOU HAD TO RUN
THESE OUTPUT FROM THIS CROSSBAR
, THIS MEMORY ARRAY TO ONE
SEVERAL LAYERS AWAY THEN YOU CAN
END UP RUNNING THESE WIRES AS
FAR AS ABOUT, YOU KNOW, TEN BY
TIMES TEN BECAUSE THEY RUN TEN
BY TEN AND THERE ARE TEN OF THEM
AND IF A FIXED FRACTION OF NEURONS
SIGNAL EACH INFERENCE, THEN YOU HAVE
A SET OF SIGNALS YOU'RE SENDING
OUT PROPORTIONAL TO THE NUMBER
OF NEURONS. SO THIS WILL SCALE IN ENERGY
BECAUSE THE ENERGY IS PROPORTIONAL TO
THE AMOUNT OF WIRE THAT YOU CHARGE
OR DISCHARGE AS THE NUMBER OF
NEURONS SQUARED. OF COURSE, YOU
HAVE MORE LAYERS OF WIRING
AND SO ON AND SO FORTH.
YOU CAN IMPROVE THIS BUT THE
IDEA IS THAT IF YOU HAVE A FIXED
FRACTION OF SIGNALS THAT ARE
TRAVELING A DISTANCE, THAT SCALES
LINEARLY WITH THE NUMBER OF NEURONS,
THEN YOU GET THIS QUADRATIC SCALE.
AND IT'S ALL COMING FROM THE
COMMUNICATION. NOT FROM THE
COMPUTER. THIS COMPUTE IS DONE
EFFICIENTLY IN THE CROSSBARS OR MEMORY ARRAYS.
NOW IF YOU LOOK AT THE BRAIN
IT'S MANAGED TO DO THIS TO
COMMUNICATE THESE SIGNALS
WITHOUT SCALING IN ENERGY QUADRATICALLY.
PEOPLE HAVE ACTUALLY MEASURED THIS
FROM A MOUSE'S 70 MILLION NEURON BRAIN
TO A HUMAN'S 86 BILLIONG NEURON BRAIN.
THE ENERGY IS JUST A THOUSAND FOLD LARGER.
SO THIS IS A SLOPE OF ONE ON A
LOG-LOG SCALE OF NUMBER OF
NEURONS VERSUS ENERGY USE IN
GLUCOSE PER MINUTE. SO, YOU CAN
ASK YOURSELF, WHAT WOULD HAPPEN
IF BIOLOGICAL BRAIN SCALED QUADRATICALLY
INSTEAD OF LINEARLY? THAT
MEANS YOU EXTRAPOLATE THIS DATA
DOWN TO ONE NEURON, CHANGE THE
SLOPE TO TWO AND EXTRAPOLATE
BACK UP SO THAT'S WHAT YOU GET
AND SO IN THAT CASE, BY THE TIME
YOU GET TO 86 BILLION NEURONS
INSTEAD OF USING 25 WATTS YOU
WILL BE USING THREE TERAWATTS,
THAT'S THE ENTIRE U.S. ENERGY
CONSUMPTION. NOW THIS NUMBER
MAY SEEM OUTLANDISH, BUT, YOU
KNOW, STATE OF THE ART TRANSFORM
LARGE LANGUAGE MODELS OF ABOUT A
TRILLION PARAMETERS RIGHT NOW.
THAT'S LIKE A MOUSE-SCALE BRAIN AND
THEY WANT TO GET TO A
QUADRILLION WHICH IS A HUMAN-
SCALE BRAIN - A FACTOR OF 1000 -
TO TAKE YOU FROM FIVE
GIGAWATTS TO FIVE TERAWATTS.
GETTING THIS EXPONENT RIGHT, CHANGING
FROM QUADRATIC TO LINEAR IS CRITICAL
TO SUSTAINABLY FROM MOUSE SCALE TO
HUMAN SCALE. AND IT'S ALL COMING FROM
THE COMMUNICATION AND THIS IS TOUGH
BECAUSE, HOW DO YOU
REDUCE THE ENERGY OF
COMMUNICATION, YOU'RE GOING NEED
TO DO TWO THINGS. YOU SHORTEN THE
WIRES WHICH WILL USE LESS ENERGY
BECAUSE YOU USE LESS CHARGE TO
CHARGE THEM UP AND DOWN. AND YOU
REDUCE THE NUMBER OF SIGNALS,
LET'S LOOK AT THESE TWO THINGS
IF YOU DO BOTH OF THOSE THINGS,
YOU SHORTEN THE WIRES BY
BASICALLY STACKING THESE CROSSBARS
IN 3D SO THAT THOSE LONG
INTERCONNECTS ARE NOW GOING
VERTICALLY TO THE STACK THAT'S
GOING TO BE MUCH SHORTER AND BY
A SQUARE ROOT OF N IF YOU HAVE A
SYSTEM OF N NEURONS THAT YOU
ARRANGE IN THIS SQUARE ROOT OF N BY
SQUARE ROOT OF N BY SQUARE ROOT OF N CUBED.
THAT GETS YOU FROM QUADRATIC TO THREE
HALVES. TO GET FROM THREE HALVES TO
LINEAR YOU HAVE TO REDUCE THE
NUMBER OF SIGNALS YOU'RE SENDING BY
SQUARE ROOT OF N. SO INSTEAD OF A
LAYER WITH SQUARE ROOT OF N NEURONS
SENDING SIGNALS IT HAS TO SEND A
FIXED NUMBER OF SIGNALS, LET'S
SAY D. AS YOU MAKE THE NETWORK WIDER
AND DEEPER,YOU SEND THAT SAME D
SIGNAL BETWEEN LAYERS. THAT GET YOU
THE OTHER SQUARE ROOT OF N BUT YOU
CAN BASICALLY CHOOSE SPARSITY, RIGHT?
AND WITH THAT YOU CAN SCALE
LINEARLY INSTEAD OF QUADRATICALLY. THE FLIP SIDE OF
SENDING D SIGNALS IS WHEN A
LAYER OF SQUARE ROOT OF N NEURONS
RECEIVE D SIGNALS IT ALSO HAS TO
PRODUCE D SIGNALS. SO YOU NEED
NEURON MODELS THAT RESPOND MORE
SELECTIVELY THAN THEY DO RIGHT
NOW. NOW WITH THESE RELUS, HALF OF
YOUR ITEMS IN THE DATASET ARE
GOING TO ACTIVATE A NEURON.
ABOUT HALF OF THE NEURONS WILL
BE ADDED. WE'RE SAYING THAT
BASICALLY IT SHOULD BE MUCH
SMALLER THAN THAT AND SHOULD GET
SMALLER AND SMALLER FRACTION AS
YOU SCALE THESE SYSTEMS. AND SO
THAT'S THE SELECTIVITY SIDE. I AM
GOING TO WRAP THIS UP WITH I
THINK IN ORDER TO REALLY SCALE
THINGS SUSTAINABLY AND MATCH THE
ENERGY EFFICIENCY OF THE BRAIN,
WE HAVE TO SORT OF REVISIT WHAT
THESE ATEMS OF COMPUTATION AND
COMMUNICATION ARE BASICALLY.
AND I'M GOING TO REVIEW HOW OUR
CONCEPTIONS OF HOW THE BRAIN
LEARNS HAS EVOLVED OVER THE LAST
CENTURY, RIGHT? MOST OF THE
MODELS WE ARE BUILDING RIGHT NOW
ARE BASED ON THIS SYNAPTOCENTRIC
CONCEPT. AND THE THING
YOU COMMUNICATE IS THE
NONNEGATIVE PART OF THE
ACTIVATION AND THIS RELEASE TO
THESE DENSE SIGNALS AND THAT IS
BASICALLY ENERGETICALLY
UNSUSTAINABLE. PEOPLE IN THE
NEUROMORPHIC COMMUNITY, DECADES AGO,
MADE THE OBSERVATION THAT ACTUALLY
NEURONS COMMUNICATE WITH SPIKES.
OKAY. SO THE IDEA WAS TO GO TO
SPIKING TO SPECIFY THE ACTIVITY
BUT ACTUALLY THE THRESHOLD IN
YOUR RELU ACTIVATION COMES
FROM THAT THRESHOLD OF THE SPIKE
CONCEPT. IT DOESN'T CHANGE THE
FRACTION OF THE NEURONS THAT ARE
ACTIVATED IF YOU USE THE WEIGHTED SUM
EXTRACTION FOR YOUR BASIC ATOM OF COMPUTATION.
WE HAD TO THINK ABOUT HOW WE CAN
WITH A FIXED NUMBER OF SIGNALS,
ENCODE THE MOST NUMBER OF MESSAGES.
IF I HAVE THESE D SIGNALS
IN A LAYER. I CAN ACTUALLY
ENCODE A LOT MORE MESSAGES THAT
PAY ATTENTION TO WHAT
COMPUTATIONAL NEURONS IS ACTIVE
AS WELL AS THE ORDER IN WHICH
THOSE D NEURONS FIRE. THIS
LEADS TO THIS CONCEPT OF SPIKE
SEQUENCES AS THE ATOM OF
COMMUNICATION IN THE NERVOUS SYSTEM
AND OF COURSE THERE'S A LOT OF
INFORMATION OR A LOT OF DATA
SUPPORTING THIS NOW.
A LOT FROM THE HIPPOCAMPUS AND
ALSO, BUT THERE'S NOT MUCH
INFORMATION ABOUT HOW THESE
SEQUENCES ACTUALLY SORT OF
DECODED BY DOWNSTREAM NEURONS AND
SO ON AND SO FORTH AND THIS MAY
BE SOMETHING THAT THE DENDRITES
WITH ALL THE ACTIVE PROPERTIES
THAT YOU'VE HEARD ABOUT FROM YIOTA COULD
CONTRIBUTE TO SO THIS, THESE
ELEMENTS ARE REALLY REQUIRED FOR
US TO MATCH THAT SCALING, LINEAR
SCALING THAT THE BRAIN IS ABLE
TO ACHIEVE. AND I WROTE A
PERSPECTIVE OF THESE IDEAS AND
HOW DENDRITES COULD DO THIS
AND HOW THE WHOLE THING COULD
WORK IN THIS PUBLICATION, THIS IS THE
EIGHT MINUTE VERSION AND I'M OUT
OF TIME I GUESS AND I WOULD LIKE
TO THANK -- WE ARE MISSING THAT
LAST SLIDE. I UPDATED THE
SLIDES. I WOULD LIKE TO THANK
NSL FOR FUNDING THIS AND FOR THE
EFRI BRAID PROGRAM THAT GRACE SET
UP A COUPLE YEARS AGO. THANK
YOU VERY MUCH FOR YOUR
TIME.
>> PLEASE WELCOME JENNIFER HASLER
FROM GEORGIA TECH.
>> SO THANK YOU FOR HAVING ME
ME HERE AND THANK YOU FOR JUST THIS
WONDERFUL WORKSHOP SO THIS HAS
BEEN REALLY INTERESTING
DISCUSSIONS WE'RE HAVING HERE I
KNOW A LOT OF TIMES WHEN PEOPLE
SIT AND LISTEN USUALLY HEAR ME
TALK ABOUT STUFF LIKE OH, THERE
MUST BE SOME FLOATING GATES OR
DEVICES. WE ALWAYS DROP A
LITTLE BIT BUT I ACTUALLY WANT
TO TALK A LITTLE BIT MORE FROM A
BIGGER PICTURE ASIDE FROM THE
NEUROMORPHIC PERSPECTIVE AND IN
PARTICULAR, YOU KNOW, IF WE
WANTED TO START TALKING ABOUT
BUILDING BRAIN SCALE KIND OF
THINGS WHAT THAT WILL LOOK LIKE.
AND SO I WANT TO START FROM WHAT
WAS A PAPER THAT NOW IS ABOUT
ELEVEN YEARS OLD WAS SORT OF THE
ONE OF THE FIRST MAJOR SORT OF
NEUROMORPHIC ROADMAPS SAYING
COULD WE ACTUALLY TRY TO BUILD A
SILICON CORTEX? AND COULD WE DO
IT IN THAT SPACE AND THIS WAS
ACTUALLY A REALLY ROUGH THING
BECAUSE WITHIN NEUROMORPHIC
COMMUNITY AT THE TIME MULTIPLE
PEOPLE TRIED AND THAT TURNED OUT
TO BE THE PROBLEM EVERYONE WAS
KIND OF TERRIFIED BECAUSE IT
WOULD BE -- CREATE QUITE A LOT
OF FIRE SHALL WE SAY AND SO WITH
THIS KIND OF -- WAS ONE OF THOSE
FIRST DISCUSSIONS AND IT WAS
KIND OF IN THE AFFIRMATIVE AND
ENTIRELY IN SILICON. SO 7 TO 10
NANOMETER TECHNOLOGY. VERY
SPARSE CONNECTIVITY. EVERYTHING
THAT WE UNDERSTOOD AT THE POINT
LOOKED LIKE YOU COULD PROBABLY
DO THIS AND SOMETHING MIGHT BE
ABOUT A METER OR SO CUBED AND
50-100 WATTS OF POWER WHICH WAS
KIND OF AN INTERESTING
PERSPECTIVE OF IT AND ACTUALLY
THE FIGURES HERE ARE ACTUALLY
THE FIGURES FROM THAT. THERE
WAS A SIMILAR PAPER AFTERWARDS
FOR MORE PUBLIC CONSUMPTION BUT
THIS WAS AN INTERESTING THING.
ONE OF THE KEY CRITERIA WAS THE
ENERGY AND POWER WAS INCREDIBLY
IMPORTANT QUESTION HERE ALL THE
WAY THROUGH. IT'S ARGUABLY NOT
JUST IMPORTANT ON THE SILICON
SIDE AS A CONSTRAINT BUT ALSO IN
THE NEUROSCIENCE ITSELF AND SO
THERE'S VERY KEY PART. THE
INTERESTING THING IS I LOOK AT
IT ELEVEN YEARS AGO NOW AND
REALIZE THERE'S NOTHING I WOULD
CHANGE IN THE CONVERSATION WHICH
IS KIND OF GOOD OR TERRIFYING, I
DON'T KNOW. BUT IT'S ALSO A
CASE OF THE PROGRESS ON THIS
HASN'T BEEN ALL THAT FAST AND
THAT'S ONE OF THE QUESTIONS WE
MAY WANT TO ASK IN A
CONVERSATION LIKE THIS. HOW DO WE
START TO LOOK AT THAT? I WANT
TO WALK THROUGH THIS A LITTLE
BIT. I WOULD SAY ONE STARTS TO
GET INTO A CONVERSATION LIKE
THIS BECAUSE THERE'S BEEN AN
UNBELIEVABLE WORK DONE TO
UNDERSTAND THE NEUROMORPHIC
SPACE OBVIOUSLY LOTS OF THIS HAS
ITS ROOTS FROM PEOPLE LIKE
CARVER MEAD AND THE TEXTBOOKS
THAT WERE THERE AND THAT GENERAL
VIEW THAT WE SAW BOTH FROM
FEYNMAN BUT ALSO HOW CARVER
WOULD WRITE IT: IF YOU UNDERSTAND
IT YOU CAN BUILD IT AND I THINK
THAT IS HUGE. THIS IS A WHOLE
COMMUNITY, MULTIPLE COMMUNITIES
THAT SPAWNED. THERE'S SEVERAL
INDIVIDUALS THAT HAVE DONE
AMAZING WORK. AMAZING THINGS.
GERT AND KWABENA AND SEVERAL OTHER
PEOPLE IN THE ROOM HERE.
THERE'S BEEN A LOT
OF THINGS THAT HAVE BEEN BUILT
UP OVER DECADES ON THIS. A FEW
THINGS THAT DEFINITELY WE'VE
SPENT SOME TIME LOOKING AT,
OBVIOUSLY I'VE MENTIONED ALL OF
THE SINGLE TRANSISTOR SYNAPSE
CONCEPT WHICH IS THE FIRST KIND OF
NONVOLATILE CROSSBARS, ACTUALLY LED
TO THE NAMING OF COMPUTE-IN-MEMORY
MORE THAN TWENTY YEARS AGO. THE
CONCEPT OF TRANSISTOR CHANNEL
MODELS WHICH ALLOWS YOU TO HAVE
VERY TIGHT CONNECTIONS OF
SILICON. AND DISCUSSIONS ON
DENDRITES, ANALOG COMPUTING THEORY.
GIVE YOU AN OPPORTUNITY TO SAY,
COOL, WE CAN REALLY START TO
LOOK AT A MAP ON THE ENERGY
CONSTRAINT WHICH WAS ACTUALLY IN
THE ORIGINAL PAPER TALKING ABOUT
IF YOU LOOK AT THINGS IN TERMS
OF MAC: MULTIPLY-ACCUMULATE
YOU WANT TO BE CAREFUL OF
APPLES TO APPLES COMPARISON HERE
BUT IF YOU LOOK THE FACTOR OF 1000
IMPROVEMENT IN ENERGY EFFICIENCY FROM
THE FIRST DSPS IN THE LATE 70S
TO TODAY. THERE'S ABOUT A FACTOR
A THOUSAND OR MORE IMPROVEMENT.
WHAT WAS ORIGINALLY HYPOTHESIZED
BY CARVER MEAD IS JUST THE
ANALOG COMPUTING ITSELF WILL GET
YOU ANOTHER FACTOR OF A THOUSAND AND
NEUROMORPHIC COULD GO PAST THAT.
IT TURNS OUT THAT NOT IS THAT
ONLY TRUE EXPERIMENTALLY FOR NOW
TWENTY YEARS BUT NOW ALSO
THERE'S OTHER OPPORTUNITIES
PARTICULARLY AS YOU GO INTO MORE
NEUROMORPHIC ALGORITHMS, DENDRITES
AND SO FORTH THAT CAN ACTUALLY GET
MORE EFFICIENT. THAT EFFICIENCY THEN
STARTS TO LEAD YOU OUT INTO A
WHOLE RANGE OF QUESTIONS
PARTICULARLY IF YOU LOOK AT THE
DENDRITIC STRUCTURE. ONE OF THE
THINGS CLOSE TO MY HEART WHERE
WE STARTED TO REALIZE THAT YOU
COULD ACTUALLY DO THINGS WITH
DENDRITES THAT LOOK LIKE HIDDEN
MARKOV MODEL CLASSIFIERS AND THERE'S
A DIRECTION BETWEEN THESE
TECHNIQUES THAT EVEN PEOPLE IN
THE SPEECH COMMUNITY GO, THOSE
ARE HARD TO DO IN FULL
CAPABILITY AND WE REALIZED,
WAIT, THE NEURONS WITH THE
DENDRITES ARE DOING SUCH
AMAZING COMPUTATION AND ACTUALLY
BE ABLE TO DEMONSTRATE ENTIRELY
IN NEUROMORPHIC STRUCTURES. IT LEADS
YOU TO KNOW THAT ENCODING IS
CRITICAL. EVERY SPIKE MATTERS.
SPIKE RATES ARE QUITE LOW AND
THAT THERE'S A LOT OF IMPORTANCE
IN THAT. YOU SEE THAT IN THE
MODELS. I LOVE THE STUFF THAT
CAME OUT OF MARK TILDEN'S GROUP AND
ALSO TALKING ABOUT OPTIMAL PATH
PLANNING AND DOING THIS WITH HODGKIN-
HOXLEY NEURONS, AND THEY
DO MATTER FROM AN
ALGORITHMIC SENSE AND ALL THESE
THINGS CREATE OPPORTUNITIES BOTH
IN ENERGY EFFICIENCY AND MORE
IMPROVED ALGORITHMS ALL THE WAY
THROUGH. THE PROBLEM IS WE ONLY
HAVE A FEW OF THESE OVER TIME.
AND SO I THINK THE SPACE IS --
IN TERMS OF CONCEPTS IS REALLY
WHERE THERE'S A LOT FOR US TO
GROW ON. AND SO IF I WAS
LOOKING AT THIS, ON THE ROADMAP,
I MEAN, LIKE I SAID, THERE'S
STILL A REALLY GOOD MAP GOING
FORWARD. AND I THINK THE THINGS
THAT CARVER TALKED ABOUT MAKE
SENSE. I THINK WHAT'S REALLY
HELPED IS OUR INFRASTRUCTURE HAS
DONE WELL ON THE ANALOG AND MIXED
SIGNAL SIDE. WE HAVE A CONCEPTUAL IDEA
WE ARE IN GOOD SHAPE. EVEN IN THE
EARLIEST OF NEUROMORPHIC ENGINEERING
THERE WAS A SENSE THAT NEUROBIOLOGY
AND ELECTRONIC COMPUTATION SHOULD
BE BIDIRECTIONALLY
HELPING EACH OTHER. I KNOW
WE HEARD A LOT OF THAT ALREADY
AND SO I AM GLAD THIS IS ALL
CONSISTENT. THE ISSUE THEN
BECOMES THOUGH IS THAT THE
PROGRESS HAS ACTUALLY BEEN
RATHER SLOW AND HOW DO WE JUST
KIND OF KEEP PROGRESSING THIS?
AND I THINK THIS WAS A
CONCEPTUAL QUESTION. THERE'S
SOME THINGS THAT START TO MAKE
SENSE AND FROM SOME SORT OF
SMALL CONCEPTS AND SOME LARGER
CONCEPTS ON WHAT WE CAN BUILD
ONE OF THE FIRST THINGS I CAN
SAY RIGHT OFF THE BAT IS WE WANT
TO FOCUS ON WHAT WE CAN DO WITH
SILICON FABRICATION. IF WE WANT
TO PUSH THE ALGORITHMS WE WANT
TO FOCUS ON THE SPACES AND SEE
HOW MUCH WE CAN DRIVE IN THAT
DIRECTION.
WE NEED TO THINK ABOUT WHAT WE
CAN DO WITH MORE COMPLEX NEURON
MODELS, PARTICULARLY WITH DENDRITES,
WITH CHANNEL MODELS AND ALSO BE
CAREFUL WITH THE ENCODING. AS
MUCH AS WE CAN WE NEED TO BE
AVOIDING RATE ENCODING STRUCTURES,
INTEGRATE-AND-FIRE NEURONS, ONLY ASSUME THOSE
WOULD DEFAULT IN HIGH LEVEL
STIMULATION. THE
PROGRAMMABILITY IS ESSENTIAL. IF
YOU'RE GOING TO BUILD AN ANALOG
SYSTEM I WILL TELL YOU THAT YOU
ABSOLUTELY HAVE TO HAVE THE
ACCURACY SOMEWHERE. YOU WILL
JUST NOT LEARN ABOUT EVERY
ASPECT OF MISMATCH. AS MUCH AS
I'VE HOPED THAT I'VE NEVER
SEEN IT. THERE'S NEED TO TRAINE
STUDENTS AND HAVE IC FAB
AVAILABLE AND ALL THE TOOLS
AROUND IT AND WHAT I WOULD WANT
TO PROPOSE AROUND THIS IS
IMAGINE, YOU CAN START TALKING
ABOUT A NEUROBIOLOGICAL ALGORITHM OF
A CORTICAL COLUMN THAT YOU CAN USE
FOR AN ENGINEERING APPLICATION. TAKE THESE IDEAS OF A WHOLE
BUNCH OF PYRAMIDAL CELL-TYPE NEURONS
INTERNEURONS AND ACTUALLY WORK AT
THIS TAKING THE CORTICAL
COLUMNS WITH ALL THAT COMPLEXITY
AND KIND OF BUILDING THAT UP
INTO MULTIPLE HIGHER AND HIGHER
LAYERS OF COMPLEXITY AS WE BUILD
UP. I THINK THE OPPORTUNITY IS
HUGE I THINK THE FUTURE IS
BRIGHT AND I THINK IT'S NOW, YOU
KNOW, THE OPPORTUNITY TO REALLY
TRY SOME OF THESE IDEAS. THANK
YOU
YOU.
>> PLEASE WELCOME FRANCIS CHANCE
FROM SANDIA NATIONAL LABS.
>> HI, WE'VE BEEN TALKING ABOUT
THE VIRTUOUS CYCLE BETWEEN
NEUROSCIENCE AND ARTIFICIAL
INTELLIGENCE. I WANTED TO TALK
ABOUT THE CYCLE BETWEEN SCIENCE
AND COMPUTING AND WHAT KIND OF
QUESTIONS WE CAN ASK AND THEN I
WILL TALK ABOUT HOW THIS CAN
IMPACT NEUROAI.
SO FOR ME I THINK WE SHOULD BE
FOCUSING OR WE COULD ESTABLISH A
LOT OF SYNERGY BY FOCUSING ON
NEURAL PRIMITIVES.
AND I KNOW THIS WORD WILL GET
OVERLOADED AS WE GET INTO THIS
SESSION. I WANT TO KNOW WHAT
KINDS OF COMPUTATIONS,
BIOLOGICAL CIRCUITS COMPUTE
OVER AND OVER AGAIN ACROSS
DIFFERENT BIOLOGICAL SYSTEMS
TO ME, THEY ARE SLIGHTLY A LEVEL
HIGHER THAN BASIC OPERATIONS SO
I WOULD SAY THIS IS A LITTLE BIT
HIGHER OF THE ATOMS OF COMPUTATION
THAT KWABENA JUST TALKED ABOUT.
THEY'RE NOT THE BEHAVIOR
ITSELF. WE WANT TO UNDERSTAND
HOW THESE NEURAL PRIMITIVES
SUPPORT ANIMAL BEHAVIORS SO WE
UNDERSTAND HOW TO IMPLEMENT THEM
IN NEUROMORPHIC SYSTEMS TO SUPPORT
DIFFERENT TYPES OF TASKS.
ALL RIGHT, SO I'M GOING TO GIVE
YOU AN EXAMPLE OF MY OWN
RESEARCH TO GIVE YOU SOME IDEA
OF WHAT I MEAN BY PRIMITIVES AND
HOW WE USE THAT TO DRIVE
NEUROMORPHIC DEVELOPMENT AS WELL
AS THINK ABOUT HOW WE CAN IMPLEMENT
THESE NEURAL PRIMITIVES IN DIFFERENT
ALGORITHMS FOR NEUROMORPHIC TASKS.
I'VE BEEN INTERESTED IN COORDINATE
TRANSFORMATIONS. HERE IS AN
EXAMPLE OF THE BEHAVIOR THAT IS
SUPPORTED BY A TRANSFORMATION.
I WILL TALK ABOUT WHAT THE
COORDINATE TRANSFORMATION IS IN
A SECOND BUT WE HAVE A MONKEY,
IT'S TRAIN TO FIXATE AT A SPOT
IN FRONT OF IT. IT THEN NEEDS TO
REACH A TARGET THAT APPEARS AT SOME
ANGLE AND DISTANCE AWAY AS THE
FIXATION POINT IS MOVED TO
THE RIGHT AND THE MONKEY
TURNS ITS HEAD BUT NOT ITS BODY
TO MAINTAIN FIXATION AND THE
MONKEY NEEDS TO REACH OUT AND
TOUCH THAT TARGET AGAIN. THE
ARM'S TAKING A DIFFERENT
TRAJECTORY EVEN THOUGH THE SAME
IMAGE IS FALLING ON THE MONKEY'S
EYES SO THE MONKEY'S NERVOUS
SYSTEM NEEDS TO PERFORM
COORDINATE TRANSFORMATION FROM
THE VISUAL INFORMATION THAT'S
COMING IN WHICH IS GOING TO BE
EYE-CENTRIC COORDINATES AND
TRANSFORM THAT INFORMATION INTO
MOTOR COMMAND THAT'S GOING TO BE
BODY-CENTRIC COORDINATES OR
WHAT TRAJECTORY DOES THE ARM
NEED TO MAKE TO REACH THAT
TARGET. IF WE LOOK AT THE
LITERATURE THE MODELS GENERALLY
COMBINE TWO DIFFERENT TYPES OF
INFORMATION. VISUAL OR SENSORY
AND PROPRIOCEPTIVE INFORMATION.
AND THESE ARE COMBINED INTO BASIS FUNCTIONS
THAT REQUIRE INTERACTION AND
THEN THE OUTPUT IS READ OUT IN
THE DESIRED REFERENCE FRAME BY
SOME CONFIGURATION OF SYNAPTIC
WEIGHTS. I WILL TRY THIS ONCE
WHICH CAN BE CONFIGURED HERE
BETWEEN THE BASIS FUNCTIONS AND
THE OUTPUT LAYER. NOW THERE ARE
MANY MODELS OF THIS. I'VE BEEN
SPECIFICALLY INTERESTED IN
MODELS IN THE PARIETAL CORTEX, I
HAVE THREE REFERENCES HERE AND
I'VE BEEN INTERESTED IN THESE
BECAUSE THEY SPECIFICALLY
PREDICT THAT THE BASIS FUNCTIONS
ARE FORMED BY MULTIPLICATION
OF THE VISUAL INPUT BY THE
PROPRIOCEPTIVE INPUT AND THERE'S
EVIDENCE OF THIS IN THE
NEUROBIOLOGICAL LITERATURE THROUGH
THE GAIN FIELDS DISCOVERED BY
ANDERSON AND MOUNTCASTLE IN THE 1980S
WHAT WE HAVE HERE IS AN EXAMPLE
OF A VISUAL NEURON. THE FIRING
RATES ON THE Y AXIS. THE X AXIS IS
THE POSITION OF THE VISUAL STIMULUS
THE DIFFERENCE BETWEEN THE TWO TUNING CURVES IS THE DIFFERENCE INTHE HEAD POSITION
RELATIVE TO THE BODY. AND WHAT YOU
SEE IS IT'S MULTIPLICATIVE. THE
EFFECTIVE HEAD POSITION ON THE
VISUAL RESPONSE IS
MULTIPLICATIVE SCALING SO WE'RE
SEEING EVIDENCE OF THIS
MULTIPLICATION OF THE TWO
DIFFERENT MODALITIES OF INPUT.
ALL RIGHT, NOW I THINK FOR
NEURAL PRIMITIVES IT'S IMPORTANT
TO LOOK FOR COMPUTATIONS THAT
ARE RELEVANT FOR A WIDE VARIETY
OF BIOLOGICAL SYSTEMS. SO HERE'S
AN EXAMPLE FROM DROSOPHILA. I
WILL NOT GO THROUGH ALL THE
EXPERIMENTAL DETAILS BUT I WILL
NOTE IT'S A LINEAR RESPONSE
CURVE. AND THE DIFFERENCE
BETWEEN -- AND IT'S A LINEAR
RESPONSE CURVE NOT A GAUSSIAN TUNING
CURVE, IT'S A NAVIGATION TASK
NOT A REACHING TASK. AND THE
DIFFERENCE BETWEEN THE TWO
COLORED CURVES IS HEADING
DIRECTION RATHER THAN HEAD
POSITION. BUT AGAIN WHAT WE SEE
IS THE EFFECTIVE HEADING IS A
MULTIPLICATIVE SCALING OF THE
RESPONSE CURVE. SO IN MY OWN
RESEARCH WE HAVE FOCUSED ON WHAT
DRAGONFLIES NEED TO DO TO CATCH
PREY. WORK DONE BY CLAIRE PLUNKETT
FUNDED BY THE DOE THROUGH NSF CRCNS
POINTED OUT IN THE FUNDERING PANEL
YESTERDAY. GRATEFUL FOR OPPORTUNITIES
THAT THE CRCNS PROGRAM SUPPORTS.
IN THIS MODEL WE HAVE A COMBINATION
OF VISUAL INPUT AND HEAD POSITION AND
MULTIPLICATIVE INPUTS TO FORM BASIS
FUNCTIONS AND THE TURN DIRECTION
NEEDED TO INTERCEPT THE PREY IS
CALCULATED IN THIS OUTPUT. NOW WHAT
WAS INTERESTING WHEN I STARTED
WORKING WITH THE NEUROMORPHIC
ENGINEERS IS IMPLEMENTING -- YOU
THINK THE HEAVY LIFTING IS DONE
BY THE SYNAPTIC CONFIGURATION
BETWEEN THE BASIS FUNCTIONS AND
THE OUTPUT LAYER THAT'S
SOMETHING WE KIND OF KNOW HOW TO
DO. WHAT HAS BEEN ACTUALLY MORE
CHALLENGING IS THE
MULTIPLICATIONS HAPPENING IN THIS
TO FORM THESE BASIS FUNCTIONS.
IT'S NOT KNOWN HOW THIS IS DONE BY
BIOLOGICAL NEURONS. AND
IMPLEMENTING THIS IN THE
NEUROMORPHIC SYSTEM HAS ALSO
BEEN AN INTERESTING PROBLEM FOR
US. I WOULD LIKE TO TALK ABOUT
-- WE'VE BEEN EXPLORING DIFFERENT
APPROACHES.
HERE'S ONE EXAMPLE, THIS WAS
DEVELOPED BY SUMA CARDWELL AT
SANDIA NATIONAL LABS. I HOPE
SHE'S OUT THERE LISTENING. SHE'S
IMPLEMENTING SHUNTING INHIBITION
IN AN ANALOG NEUROMORPHIC DENDRITE.
YOU CAN THINK OF THIS AS THE OUTPUT
OF ONE OF THESE -- OH, ONE OF
THESE BASIS FUNCTION NEURONS AND
THE OUTPUT VOLTAGE WOULD BE THE
RESPONSE OF ONE OF THESE NEURONS
THE PEAK INPUT WOULD BE THE
VISUAL INPUT AND THE HEAD
POSITION WOULD BE CONTROLLING
THE OVERALL CONDUCTANCE OF THE
NEURON. WE'RE EXPLORING OTHER
APPROACHES SO WE CAN UNDERSTAND
THE ADVANTAGES AND DISADVANTAGES
OF THESE DIFFERENT APPROACHES
BUT THIS IS THE IDEA OF WHAT
WE'RE TRYING TO DO. LOOK TO THE
NEURAL SYSTEM, UNDERSTAND WHAT
COMPUTATIONS ARE IMPORTANT AND
THEN SEE IF WE CAN GET CREATIVE
ABOUT HOW TO IMPLEMENT THIS IN
NEUROMORPHIC HARDWARE. NOW I
STARTED BY SAYING I WANTED TO
TALK ABOUT THIS VIRTUOUS CIRCLE
BETWEEN NEUROSCIENCE AND NEUROMORPHIC COMPUTATION WHEN WE
TALK ABOUT THIS BACK AND FORTH
BETWEEN THE TWO FIELDS THERE'S
ALWAYS AN OPEN CHALLENGE OR AN
OPEN RESEARCH QUESTION OF WHERE
DO YOU LOOK IN BIOLOGY FOR
INSPIRATION AND I THINK WE
SHOULD START HAVING MORE
COMMUNICATION BETWEEN THE FIELDS
BECAUSE WE NEED TO BE
INTENTIONAL ABOUT WHY WE CHOOSE
THAT SPECIFIC FUNCTION, SORRY
THAT SPECIFIC BIOLOGICAL SYSTEM
FOR INSPIRATION. AND IN NEUROAI I
SEE AN OPPORTUNITY NOT JUST FOR
DEVELOPING ENERGY EFFICIENT
IMPLEMENTATIONS.
WHEN I STARTED PREPARING THIS
TALK I WAS EXPECTING TO SAY IF
WE HAVE A NEURAL-INSPIRED A.I.
MODEL IT MAKES SENSE TO HAVE
NEURAL-INSPIRED HARDWARE TO RUN
IT ON. BUT I THINK THERE'S AN
OPPORTUNITY HERE AS WE START
TALKING ABOUT HOW WE COMPARE
DIFFERENT SOLUTION IN BIOLOGICAL
SYSTEMS WITH DIFFERENT
APPROACHES THAT WE CAN USE IN
NEUROMORPHIC SYSTEMS, THERE'S AN
OPPORTUNITY TO CREATE A
FRAMEWORK FOR FACILITATING
COMMUNICATION BETWEEN THE
DIFFERENT FIELDS AND
SPECIFICALLY IF WE'RE TALKING
ABOUT HOW TO ABSTRACT BIOLOGY
FOR TRANSLATION INTO MODELS IN
OTHER COMMUNITIES WHETHER
IT BE NEUROMORPHIC OR
ARTIFICIAL INTELLIGENCE AND I
WILL LEAVE YOU WITH THAT.
THAT'S WHERE I THINK WE SHOULD
BE LOOKING FOR THE FUTURE.
>> PLEASE WELCOME SUEYEON CHUNG
FROM NYU AND THE FLAT IRON INSTITUTE.
>> OKAY. THANK YOU, EVERYONE. TODAY
I WILL DISCUSS HOW UNDERSTANDING
WHAT'S KNOWN AS NEURAL MANIFOLDS
CAN BRIDGE THE GAP BETWEEN
NEURAL SCIENCE AND A.I. LEADING TO
MORE EFFICIENT AND BIOLOGICALLY
ALIGNED A.I. SYSTEMS. WE ARE AT A
EXCITING MOMENT WHERE NEUROSCIENCE
AND A.I. ARE CONVERGING.
DEEP NETWORKS CAN PREDICT NEURAL
ACTIVITIES IN PRIMATE BRAINS
WITH REMARKABLE ACCURACY AND THIS
CONVERGENCE PRESENTS AN
OPPORTUNITY TO IDENTIFY A NEURAL
FRAMEWORK. FIRST I WOULD LIKE
TO PAUSE AND ASK WHAT ARE THE
GOALS OF THEORY AT THIS
INTERSECTION? WE ALL KNOW THAT
DEEP NETWORKS WORK VERY WELL
BOTH AS A MODEL OF THE BRAIN BUT
ALSO AS AN ENGINEERING SYSTEM.
ANOTHER WAY TO SEE THIS IS THEY
ARE ACTUALLY FULLY TRANSPARENT
AND WE HAVE ACCESS TO ALL OF
THEIR INTERNAL REPRESENTATIONS,
THE ARCHITECTURE THAT WAS USED
AND ALSO THE LEARNING RULES THAT
HAVE BEEN USED. SO MY VIEW FOR
THE ROLE OF THEORY AT THIS
INTERSECTION HAS TO DO WITH THE
IDEA OF THE EMERGENCE. AND ALSO
THERE'S MULTIPLE LEVELS OF
ABSTRACTION THAT WAS PRESENTED
EARLY -- PRESENTED EARLIER IN
THE TALK.
THERE'S A LOT OF COMPLEX INTERACTIONS
IN DEEP NETWORKS THAT WE DON'T
UNDERSTAND. THE CONNECTION
BETWEEN FEATURES AND UNITS AND
THE STRUCTURE OF THEIR INTERNAL
REPRESENTATIONS IN EACH LAYER
AND HOW THAT CONTRIBUTES TO THE
PERFORMANCE AFTER TRAINING. THIS IS
SIMILAR TO THE CHALLENGES THAT
WE FACE IN MODERN NEUROSCIENCE
IN INCREASING ACCESS THAT WE
HAVE WITH THE LARGE NUMBER OF
NEURONS AT THE SAME TIME HOW
FIRING RATES AND INDIVIDUAL
NEURONS AND THEIR INTERACTIONS
GIVE RISE TO REPRESENTATIONS AND
HOW THESE REPRESENTATIONS
IMPLEMENT TASK FUNCTIONS LIKE
SENSORY AND MOTOR FUNCTIONS.
THIS IS A SHARED CHALLENGE
BETWEEN A.I. AND NEUROSCIENCE AND
THE TASK FOR THEORY IS TO
FORMALIZE AND CONNECT THE
RELATIONSHIP BETWEEN THESE
PROPERTIES AND PHENOMENA AT
DIFFERENT SCALES AND PROVIDE
INTERPRETABILITY IN DESIGN
PRINCIPLES. ONE THEORETICAL
FRAMEWORK GAINING TRACTION IS CALLED
NEURAL MANIFOLDS FRAMEWORK. LET ME
EXPLAIN WHAT THAT IS. LET'S SAY
YOU'RE GIVEN AN IMAGE OF A DOG
AND THEN A POPULATION OF NEURONS
WILL RESPOND AND THAT BECOMES A
POINT IN THE SPACE IN THE NEURAL
ACTIVITIES AND THIS KIND OF
SPACE IS IN GENERAL HIGH
DIMENSIONAL. NOW IN REAL LIFE AN
IMAGE IS NOT REALLY STATIC.
LET'S SAY A DOG THAT YOU'RE
LOOKING AT IS RUNNING AND MOVING
AROUND THEN ALL OF THESE NEURONS
WILL RESPOND A LITTLE BIT
DIFFERENTLY. AND THIS PICTURE
OF NEURAL REPRESENTATION FOR AN
IMAGE OF A DOG ALSO WILL MOVE
AROUND IN THE NEURAL STATE SPACE
AND WE END UP GETTING A SET OF
NEURAL ACTIVITIES WITH AN
UNDERLYING GEOMETRY WHICH IS
WHAT WE CALL NEURAL MANIFOLDS
NOW WHEN YOU DISTINGUISH BETWEEN
TWO DIFFERENT OBJECTS OR
CONCEPTS LET'S SAY BETWEEN DOGS
FROM CATS THE PROBLEM OF THE
DISCRIMINATING BETWEEN DOGS FROM
CATS BECOMES A SEPARATING A DOG
MANIFOLD FROM A CAT MANIFOLD. WHAT
WE HAVE FOUND IN OUR WORK IS
THAT THE GEOMETRY OF THESE
NEURAL MANIFOLDS IS DIRECTLY
RELATED TO THE SYSTEMS CAPACITY
TO REPRESENT AND RECOGNIZE
OBJECTS. AND THIS WAS A
STARTING POINT FOR OUR NEURAL
MANIFOLDS FRAMEWORK AND IT GIVES
US A FEW KEY METRICS FOR
EFFICIENCY AND INTERPRETABILITY
FIRST, THE CAPACITY OF NEURAL
MANIFOLDS IS DEFINED AS
CATEGORIES, CAN BE REPRESENTED
PER GIVEN NEURAL POPULATION
SIZE. AND IT MEASURES THE
EFFICIENCY OF THE
REPRESENTATIONS OF THE BRAIN AND
A.I. SYSTEMS. THEN, THE GEOMETRICAL
PROPERTIES OF THESE MANIFOLDS
SUCH AS RADIUS, WHICH HAS TO DO
WITH SIZE AND ORGANIZATION OF
THESE MANIFOLDS CAPTURES A
STRUCTURE OF THEIR INTERNAL
REPRESENTATIONS.
SO IF OBJECT MANIFOLDS IN THE
NEURAL ACTIVITY OR
REPRESENTATIONS IN A.I. SYSTEM ARE
FLAT, SMALL, AND WELL SPREAD
OUT, AND NICELY STACKED OR
LINED, MEANING THAT THE OBJECT
MANIFOLD'S DIMENSIONALITY,
RADIUS AND LOCATION CORRELATIONS
ARE SMALL WHILE AXIS
CORRELATIONS ARE HIGH, THEN YOU
CAN REPRESENT MORE OF THESE
OBJECT MANIFOLDS WITH THE SAME
NUMBER OF NEURONS IN A GIVEN
NEURAL REPRESENTATION SPACE,
SUCH THAT YOU CAN DISTINGUISH
BETWEEN THEM. AND THAT RESULTS
IN HIGH CAPACITY OF OBJECT
MANIFOLDS SO THIS IS ACTUALLY
JUST LIKE PACKING THINGS IN THE
PHYSICAL SPACE EXCEPT THAT WE'RE
TALKING ABOUT NEURAL POPULATION
RESPONSES, CORRESPONDING TO
DIFFERENT OBJECT OR CONCEPTS.
AND THE IMPORTANTLY THIS ALLOWS
US TO DISTINGUISH BETWEEN
EFFICIENT AND UNTANGLED
REPRESENTATIONS VERSUS
INEFFICIENT AND ENTANGLED
REPRESENTATIONS AS MEASURED BY
THE MANIFOLD CAPACITY. WHILE
THE GEOMETRICAL MEASURES PROVIDE
INTERPRETABILITY SUCH AS THE
DATA CLOUDS THAT YOU'RE LOOKING
AT FROM THE RECORDED DATA IN THE
THE BRAIN OR THE -- FROM THE
FEATURES OR REPRESENTATIONS FROM
A.I. SYSTEMS, THEY ARE FLAT
OR COMPRESSED AND PROVIDES A
LANGUAGE AND METRIC TO TALK
ABOUT THE STRUCTURE IN THE
REPRESENTATION. SO WHEN WE
APPLY THIS FRAMEWORK TO VISUAL
DEEP NETWORKS, WE ACTUALLY END UP
SEEING A VERY CLEAR PATTERN. SO
AS WE MOVE THROUGH THE NETWORK
LAYERS, THE REPRESENTATIONS
BECOME MORE EFFICIENT. AS YOU
CAN SEE ON THE TOP LEFT, THE
MANIFOLD CAPACITY INCREASES
WHILE THE DIMENSIONALITY ON THE
TOP RIGHT AND RADIUS ON THE
BOTTOM LEFT AND THE CORRELATIONS
ON THE BOTTOM RIGHT, THEY END UP
DECREASING SO IT TELLS YOU THAT
THE REPRESENTATIONS IN A.I.
SYSTEMS ARE GETTING MORE
EFFICIENT AS A RESULT OF
CHANGING STRUCTURES IN THE
DISTRIBUTED REPRESENTATIONS.
AND THIS ALSO MATCHES WHAT WE
OBSERVE IN THE BIOLOGICAL VISUAL
SYSTEMS IN MONKEYS. SO NOW,
WE'RE APPLYING THIS FRAMEWORK ACROSS
MULTIPLE DOMAINS LIKE VISION,
LANGUAGE AND SPEECH, A.I. SYSTEMS
FOR INTERPRETABILITY AS WELL
AS INCREASING LIST OF EXPERIMENTAL
NEURAL DATASETS GOING FROM
MONKEYS TO HUMANS TO MICE AND
ALSO FROM VISION TO ADDITION TO
MEMORY AREAS LIKE HIPPOCAMPUS.
AND THESE APPLICATIONS ARE
REVEALING COMMON PRINCIPLES OF
HOW BOTH ARTIFICIAL SYSTEMS AND
BIOLOGICAL SYSTEMS DO THIS IN AN
EFFICIENT MANNER. AND THIS IS THE
BRAIN AND A.I. SYSTEMS USING
MANIFOLDS THEORY BUT IT ALSO
TURNS OUT YOU CAN USE THIS TO
BUILD MORE EFFICIENT A.I. SYSTEMS.
THE CLASSIC EFFICIENT CODING THEORY FROM HORACE BARLOW FOCUSED ON
HOW
THE BRAIN COMPRESSES RAW SENSORY SIGNALS.
MODERN FRAMEWORK EXTENDS THIS TO EFFICIENT REPRESENTATIONS FOR
COMPLEX TASKS FOCUSED ON DISCARDING IRRELEVANT INFORMATION. AND THIS
SHIFT FROM STIMULUS
EFFICIENCY TO TASK EFFICIENCY
NOT ONLY HELPS EXPLAIN NEURAL
RESPONSES IN HIGHER SENSORY
AREAS THAT ARE CLOSER TO COMPLEX
TASKS BUT ALSO IT ALLOWS FOR
TRAINING A.I. SYSTEMS WITH
EFFICIENCY PRINCIPLES AS AN
OBJECTIVE FUNCTION. SO BY
OPTIMIZING NEURAL NETWORKS TO
MAXIMIZE THE MANIFOLD CAPACITY
WHICH IS THEIR ABILITY TO
EFFICIENTLY REPRESENT OBJECT
VARIATIONS, WE ACHIEVE BOTH
STATE-OF-THE-ART CLASSIFICATION
PERFORMANCE AND BETTER PREDICTION OF
MACAQUE VISUAL CORTEX RESPONSES.
THIS SUGGESTS THAT MAXIMIZING
REPRESENTATIONAL EFFICIENCY IS A KEY
PRINCIPLE SHARED BY BOTH BIOLOGICAL
AND ARTIFICIAL VISUAL SYSTEMS. SO TO
SUMMARZE, THE THEORY OF NEURAL
MANIFOLDS OPENS THE BLACK BOX OF
BRAIN AND A.I. WITH INTERPRETABLE METRICS.
AND CAN ALSO EXTEND THIS TO COMPLEX
COGNITIVE TASKS, AND GUIDE DESIGN
OF BRAIN-ALIGNED HIGH PERFORMING
A.I. SYSTEMS. LOOKING AHEAD,
WE -- IN MY VIEW, WE NEED TWO
KEY ADVANCES. FIRST, WE SHOULD
BUILD ON OUR RECENT ADVANCES IN
NEURAL MANIFOLDS FRAMEWORK TO
BOTH THE THEORIES AND MODELS FOR
BROADER RANGE OF TASKS INCLUDING
CONTINUAL LEARNING AND EMBODIED
INTELLIGENCE AND SECOND I THINK
WE NEED TO BETTER BENCHMARK
DATASETS FOR NATURALISTIC
BEHAVIORS AND PHYSICAL
INTELLIGENCE. WHILE A.I. SYSTEMS
HAVE BENEFITED FROM DATASETS
LIKE IMAGENET AND COMPUTER VISION
I THINK NEUROSCIENCE CAN OFFER
UNIQUE ADVANTAGES IN THESE DOMAINS,
IN PHYSICAL INTELLIGENCE, THROUGH
ANIMAL EXPERIMENTS AND THESE ADVANCES
SUPPORTED BY SOPHISTICATED THEORETICAL
ANIMAL WILL PUSH OUR UNDERSTANDING
FORWARD. AND I HOPE I WAS ABLE TO
CONVINCE YOU THAT BY USING THE
THEORY OF NEURAL MANIFOLDS AS AN
INTERPRETABILITY TOOL WE CAN
OPEN THE BLACK BOX OF BOTH BRAIN
AND A.I. SYSTEMS AND BUILD
EFFICIENT BRAIN-ALIGNED AND SAFE
AND HIGHLY PERFORMANT A.I.
SYSTEMS. AND OF COURSE THIS
RESEARCH WAS POSSIBLE THANKS TO
THE SUPPORT OF BRAIN INITIATIVE,
SIMON'S FOUNDATION AND OTHER
FUNDERS AND THE COLLABORATION OF
EXCELLENT STUDENTS AND POSTDOCS
AND COLLEAGUES, THANK YOU.
>> PLEASE WELCOME OUR NEXT
SPEAKER DHIREESHA KUDITHIPUDI FROM UT SAN ANTONIO.
>> GOOD MORNING, EVERYONE. IT'S
BEEN AN INCREDIBLE TIME TO BE A
PART OF THIS NEUROMORPHIC
COMPUTING COMMUNITY BECAUSE WE
HAVE THIS OPPORTUNITY TO CHANGE
THE WAY WE ARE THINKING OF HOW
WE ARE DOING SYSTEM OR HARDWARE
DESIGN. AND IN THAT FRONT, OUR
OVERARCHING GOAL FROM OUR LAB IS
TO CREATE THE NEXT GENERATION OF
A.I. SYSTEMS THAT ARE CAPABLE OF
PERFORMING NATURAL TASKS BY WITH
REALLY HIGH ENERGY EFFICIENCY.
AND TO ALIGN THIS GOAL, OR TO
KIND OF HAVE A GRAND CHALLENGE
THAT FRAMES THOSE AND MAKES US
THINK HARD ABOUT THIS PROBLEM OF
BUILDING THE NEXT GENERATION
SYSTEMS WE'LL LOOK AT THE
PROBLEM OF CONTINUAL LEARNING
AND CONTINUAL LEARNING OR OFTEN
REFERRED TO AS LIFELONG LEARNING
IS A CHALLENGE IN WHICH AN AGENT
MUST LEARN FROM SEQUENTIAL OR
NONSTATIONARY DATA FROM
CHANGING DISTRIBUTIONS.
IF YOU LOOK AT THIS CARTOON
HERE, YOU HAVE THE MODEL WHICH
IS LEARNING A SEQUENCE OF TASKS.
TASK A, TASK B AND THEN IF
IT'S LEARNED-- IT COULD BE LEARNING
TASK C OR IF IT -- IT COULD HAVE
TASK A REINTRODUCED BUT QUITE
OFTEN CURRENT MODELS DO NOT DO
WELL WHEN THEY -- WHEN SUCH TASK
IS REINTRODUCED AND THERE IS
THIS PROBLEM REFERRED TO AS
CATASTROPHICIC FORGOTING AND
THIS IS NOT THE ONLY CHALLENGE
IN TERMS OF CONTINUAL LEARNING
THIS IS ONE OF THE MOST STUDIED
PROBLEMS.
SO HOW DO YOU ENSURE THAT THE
MODEL IS CONTINUALLY LEARNING
THROUGH ITS LIFETIME? SO IF YOU
LOOK AT THIS CARTOON AGAIN,
HERE, IF YOU LOOK AT THE
CONVENTIONAL A.I. MODEL, WHEN A
NEW TASK IS INTRODUCED TO THE
MODEL, IT IS NOT ABLE TO ADOPT
THAT TASK OR WHEN AN OLD TASK IS
REINTRODUCED IT IS
CATASTROPHICALLY FAILING.
WHEREAS WHAT WE WOULD LIKE TO
SEE IS WHEN THESE NEW TASKS ARE
INTRODUCED, THE MODEL IS ABLE TO
PERFORM FAIRLY WELL WITH PERHAPS
A SMALL REFRACTORY PERIOD OF
LEARNING, BUT ALSO IF A PREVIOUS
TASK IS RESPRINTRODUCED TO THE
MODEL WE WOULD LIKE TO IMPROVE
PERFORMANCE OVER THE PAST
PERFORMANCE. SO THAT'S A
DAUNTING TASK AND THERE ARE
SEVERAL STUDIES IN THE MACHINE
LEARNING COMMUNITY ON HOW TO
ADDRESS THIS PROBLEM. BUT WE
THINK THAT IS A HUGE OPPORTUNITY
HERE IN LOOKING AT THE LEARNING OR
PLASTICITY MECHANISMS THAT ARE
STUDIED IN THE NEUROSCIENCE
COMMUNITY AND HELP US BUILD THIS
HIGHLY FUNCTIONAL CONTINUAL
LEARNING MACHINES AND ONE OF THE
REASONS WE ARE HEAVILY
INTERESTED IN BUILDING THESE
SYSTEMS THAT ARE HIGHLY ENERGY
EFFICIENT. IF YOU LOOK AT THE
BIOLOGICAL BRAINS WE COULD SOLVE
SOME OF THESE PROBLEMS WITH VERY
FEW POWER. LIKE THE COMPUTATION
IS SHOWING JUST .1 WATTS OF ATP
REQUIRED. WHEREAS TO SOLVE
PROBLEMS OF SIMILAR SCALE OR --
A SCALE THAT IS OF INTEREST TO
THE CONTINUAL LEARNING COMMUNITY
HERE, YOU WOULD SEE THAT THERE
IS A SIGNIFICANT TRAINING COST
AND A SIGNIFICANT ENERGY COST SO
PERHAPS INSTEAD OF LOOKING AT
TRADITIONAL APPROACHES OF TAKING
THESE MODELS AND MAPPING ONTO AN
A.I. ACCELERATORS, WOULD THESE
NEUROMORPHIC SUBSTRATES OR
NEUROMORPHIC PLATFORMS SERVE AS
A NATURAL SUBSTRATE FOR US? WE
ARE LOOKING AT THAT AS AN
OPPORTUNITY. SO PERHAPS I KNOW
WE HAVE BEEN TALKING YESTERDAY
ABOUT THIS RECIPROCITY BETWEEN
NEUROSCIENCE AND A.I. BUT THERE IS
THIS OPPORTUNITY OF LOOKING AT
WHAT THESE MODELS BRING IN FROM
THE NEUROSCIENCE COMMUNITY, THE
LEARNING AND PLASTICITY MECHANISMS
OR THE HIERARCHICAL STRUCTURE OR
SPARSE, DISTRIBUTED REPRESENTATIONS.
THERE'S THESE FEATURES IN THE
NEUROMORPHIC COMPUTING SYSTEMS
THAT EARLIER SPEAKERS HAVE
MENTIONED ABOUT WHICH ARE
NATURALLY TUNED TO COMPUTE IN
MEMORY AND THEY CAN WORK WITH
PROBLEMISTIC LEARNING OR ON-
DEVICE AND LOCAL LEARNING. SO IF
YOU CAN KIND OF LOOK AT THIS AS
A NATURAL SUBSTRATE MAYBE THIS
IS AN OPPORTUNITY TO BUILD THESE
CONTINUAL LEARNING MODELS IN A
VERY EFFICIENT WAY AND DEPLOY
THEM IN THIS HARDWARE. SO THIS
SPACE IS REALLY A MULTISCALE
OPTIMIZATION PROBLEM. BECAUSE
IF WE GROUP THE DIFFERENT
MECHANISMS IN WHICH WE'RE
ADDRESSING CONTINUAL LEARNING
PARTICULARLY NEUROINSPIRED
MECHANISMS, YOU CAN GROUP THEM
INTO DYNAMIC ARCHITECTURES,
SYNAPTIC CONSOLIDATION OR REPLAY
MECHANISMS. AND EACH OF THESE
REQUIRES EITHER SIGNIFICANTLY
RESOURCE-GROWING MECHANISMS
LIKE YOU MIGHT NEED LOT OF
MEMORY IN ORDER TO STORE ALL OF
YOUR PAST EXPERIENCES: AND WHAT
TYPES OF MEMORY ARE THE RIGHT
ONES TO DO THIS?
AND YOU MIGHT NEED TO TRACK YOUR
SYNAPTIC TRACES FOR SOME OF
THESE CONSOLIDATION MECHANISMS
AND HOW DO I REPRESENT A
SYNAPTIC- OR NEUROGENESIS ON
HARDWARE? THE SUBSTRATE IS VERY
DIFFERENT COMPARED TO THE
BIOLOGICAL SUBSTRATE. THERE IS A
HUGE OPPORTUNITY HERE IN HOW WE
ARE THINKING ABOUT DESIGNING THE
HARDWARE SYSTEMS.
WE LOOKED AT SOME OF THE
CONSOLIDATION MECHANISMS LIKE,
FOR EXAMPLE, ACTIVITY DEPENDENT
METAPLASTICITY WHERE WE ARE
TRYING TO PRESERVE THE
PLASTICITY OF PREVIOUSLY ACTIVE
NEURONS AND IS REDUCED TO
PRESERVE OLD KNOWLEDGE AND WE
KIND OF REALIZED THIS IN A
HARDWARE SYSTEM AND THE
CHALLENGE HERE IS THAT THIS ONE
TECHNIQUE IS NOT ENOUGH TO
DESIGN A CONTINUAL LEARNING
MECHANISM. IT HELPS US IN GETTING
TO SOLVING SMALLER PROBLEMS IN
THE CONTINUAL LEARNING DOMAIN
AND THERE IS -- ANOTHER
TECHNIQUE WE LOOKED AT, FOR
EXAMPLE, CALLED SYNAPTIC
CONSOLIDATION WHERE YOU TRY TO
ACCUMULATE KNOWLEDGE OVER MULTIPLE
TIMESCALES SO THIS IS DIFFICULT
TO TRANSFORM FROM SHORT-TERM TO
LONG-TERM MEMORY AND TO ENSURE
THAT INFORMATION IS RETAINED. REQUIRE
MULTIPLE PLASTICITY EVENTS WHEN YOU
TRY TO REALIZE SOMETHING LIKE
THIS AND WHAT MY STUDENT FATIMA
HERE IS WHEN SHE LOOKED AT
REALIZING THIS IN A HARDWARE
SYSTEM, SHE REALIZED THERE IS
PERHAPS A NATURAL FEATURE IN
SOME OF THESE NONVOLATILE
MEMORY DEVICES. WHERE YOU COULD
UPDATE THE PROBABILISTIC
METAPLASTICITY INSTEAD OF
UPDATING THE WEIGHTS YOU ARE TRYING
TO UPDATE THE PROBABILITY OF THE
WEIGHT BEING UPDATED. SO IT COULD
REDUCE A COMPLEXITY OF YOUR
HARDWARE AND ALSO SAVE SOME POWER
AND ENERGY. SO CURRENTLY OUR
GROUP IS REALLY LOOKING AT
DIFFERENT SPACES OR PROBLEMS
WITHIN THE CONTINUAL LEARNING
DOMAIN. ONE OF THE BIG PROJECTS
WE ARE WORKING ON IS HOW DO YOU
ACTUALLY DESIGN THESE SYSTEMS IN
THE TEMPORAL DOMAIN? SO WE ARE
LOOKING -- WORKING CLOSELY WITH
A NEUROSCIENTIST IN
UNDERSTANDING THE TEMPORAL
SCAFFOLDING HYPOTHESIS THAT
HAPPENS DURING SLEEP AND HOW DO
WE BUILD NEW TYPES OF MODELS
USING THIS KIND OF HYPOTHESIS SO
THAT IT IS REALLY ENERGY
EFFICIENT IN LEARNING IN THE
TEMPORAL DOMAIN AND THANKS TO
GRACE'S BRAID PROJECT WE WERE
SUPPORTED THROUGH THAT EFFORT. WE
ARE ALSO LOOKING AT COMBINING
MULTIPLE OF THESE PLASTICITY
MECHANISMS AND ALSO LOOKING AT
LOCALITY AND PLASTICITY WITHIN
THE SPIKING NEURAL NETWORK
DOMAIN. WELL IN ORDER TO
ACHIEVE THIS, I THINK ONE THING
WE NEED IN THE STOPGAP IS --
>> ONE MINUTE WARNING.
>> LARGE-SCALE SPIKING NEURAL
NETWORKS AND PERHAPS A PLUG IN
PLAY APPROACH TO INTEGRATE THESE
KINDS OF MECHANISMS DIRECTLY
INTO OUR MODELS AND HARDWARE.
AND MAYBE WE COULD EMBED THIS IN
THESE KINDS OF PRECISION A.I. HEALTH
ASSISTANCE WHETHER IT'S THE
TWINS OR KINS I THINK THAT WILL
BE A HUGE OPPORTUNITY.
BUT WAIT, WHILE THIS SOUNDS
GREAT, BUT WE DO NOT HAVE ACCESS
TO THIS NEUROMORPHIC
INFRASTRUCTURE WHERE WE COULD
REALLY SIMULATE THIS LARGE-SCALE
MODELS. AND THIS IS WHERE WE
RECENTLY LAUNCHED A PROJECT CALLED
THE NEUROMORPHIC COMMONS. THIS IS
SUPPORTED BY THE NSF CIRC GRANT AND
THE IDEA IS TO BUILD THIS OPEN
SOURCE NATIONAL SCALE HUB AND
WORKING VERY CLOSELY WITH
INDUSTRIAL PARTNERS IN TRYING TO
GET ACCESS TO THE COMMUNITY.
BECAUSE RIGHT NOW, ACCESS TO THE
NEUROMORPHIC HARDWARE
INFRASTRUCTURE IS A BARRIER,
ENTRY BARRIER FOR A LOT OF SMALL
RESEARCH TEAMS OR ACADEMIC LABS
SO WE WANT TO REDUCE THOSE
BARRIERS AND HAVE PEOPLE
DISCOVER OR UNDERSTAND THESE
COMPUTATIONAL MODELS BETTER AND
HOPEFULLY HELP THE NEUROSCIENCE
COMMUNITY AND OTHER COMMUNITIES
IN THE LIFE SCIENCES AND THIS IS
A COLLABORATION BETWEEN
UNIVERSITY OF TENNESSEE, SAN
DIEGO, HARVARD AND SEVERAL OTHER
INDUSTRY PARTNERS BUT MORE
IMPORTANTLY THIS IS A COMMUNITY
DRIVEN EFFORT SO IF YOU'RE
INTERESTED PLEASE REACH OUT TO
US THROUGH THIS URL THAT'S
POSTED HERE. I WILL BE HAPPY
TO CHAT MORE AND THANKS TO THIS
AMAZING GROUP OF RESEARCHERS I
GET TO WORK WITH WHO ENTRUST ME
IN DOING THIS RESEARCH AND
THANKS TO OUR SPONSORS FOR
MAKING THIS HAPPEN. THANK YOU
>> PLEASE WELCOME OUR LAST
SPEAKER MITRA HARTMANN FROM
NORTHWESTERN UNIVERSITY.
>> THANKS VERY MUCH FOR THE
INVITATION, I NEED TO WARN THE
AUDIENCE WHEN WE TESTED SOME
VIDEOS THIS MORNING WE FOUND THE
COMPUTER WAS NOT ABLE TO KEEP UP
SO I MAY HAVE TO COUNT ON YOUR
GENERALIZED INTELLIGENCE TO
FOLLOW ALONG. WE THINK ABOUT
THE BRAIN AS A CLOSED LOOP
SYSTEM AND THIS PHOTOGRAPH
INDICATES WHY WE NEED TO TAKE
THIS VERY SERIOUSLY. BOTH OF
THESE ARE MAMMALS BUT THEIR
NEURONS ARE TUNED FOR COMPLETELY
SENSORIMOTOR SYSTEMS FOR THEIR NICHE
I AM GOING TO TELL YOU ABOUT THE
RAT VIBRISSAL SYSTEM AS A MODEL OF
EMBODIED INTELLIGENCE. AND WE CAN
SEE SOME NEW IDEAS AND RESOURCES
THAT THE SCIENTIFIC COMMUNITY
WILL NEED OVER THE NEXT FEW
YEARS. RATS DO SOME FAIRLY
COMPLICATED THINGS WITH THEIR
WHISKERS YOU CAN SEE RIGHT NOW
IT'S ABOUT TO PUT ITS PAW RIGHT
ON THE PLACE ON THE TINKERTOY
WHERE THE WHISKERS WERE
IMMEDIATELY BEFORE. AND THE
WHISKERS ARE ARRANGED IN VERY
REGULAR ROWS AND COLUMNS.
THEY ARE GIVEN LETTERS AND
NUMBERS. A-E,1-5 THE WAY THAT
THE WHISKERS ARE ACTUATED THEY
INSERT INTO FOLLICLES AND THESE
SLING MUSCLES PULL BACK. THE SKIN SERVES AS A FULCRUM AND THE
WHISKERS ARE PROTRACTED FORWARD.
THIS IS A CARTOON VERSION.
THERE'S ALSO A LOT OF EXTRINSIC
MUSCLES THAT ATTACH THE WHISKER TO
THE SKULL. NO SENSORS ALONG THE WHISKER LENGTH
BUT THE FOLLICLE IS REPLETE WITH
MECHANORECEPTORS. SO ALL SENSING
OCCURS AT THE WHISKER BASE AND THERE
ARE NO PROPRIOCEPTORS IN THE
WHISKER MUSCLES.
I'M GOING TO WALK YOU THROUGH
THREE STAGES OF THE SENSORY-MOTOR-
BRAIN ENVIRONMENT LOOP.
THE FIRST IS MUSCLES. THE SECOND
IS TACTILE. THE THIRD IS ENCODING
BY THE PRIMARY SENSORY NEURONS. AND
AT THE LAST BIT WE'LL LOOK TOWARDS
THE FUTURE, SO FOR MUSCLES THE
MOST I WILL HAVE A CHANCE TO
TELL YOU ABOUT IS WE'RE BUILDING
BIOMECHANICAL MODELS OF ALL THE
COMPLEX MUSCULATURE THAT MOVES
THE FOLLICLES AND IT'S A LOT
MORE COMPLICATED THAN THE ORIGINAL
DIAGRAM WILL SUGGEST. THAT'S
ALL I HAVE TIME FOR TODAY BUT
FOR THE TACTILE-MECHANICAL SIGNALS,
I WILL TELL YOU MORE.
TO UNDERSTAND THE SIGNALS IS TO
GET THE MORPHOLOGY RIGHT.
AND THIS INVOLVED A LOT MORE
THAN TAKING A THREE-DIMENSIONAL
SCAN. WE STARTED THIS WORK WAY
BACK IN 2011. OUR MODEL INCLUDES
WHISKERS AND SKULL FEATURES. AND
WE HAVE DONE COMPARATIVE
STUDIES ACROSS THE MOUSE, RAT
AND HARBOR SEAL. SO NEXT IS THE
MECHANICS. MY STUDENT NADINA ZWEIFEL
CREATED THE WHISKIT PHYSICS
SIMULATOR. THAT IS ALL THE TORQUES
MOMENTS AND FORCES AT THE WHISKER
BASE. EXAMPLE OF WHISKIT IN ACTION.
THIS IS A TYPICAL LABORATORY
EXPERIMENT WHERE IT IS WHISKING
AGAINST TWO PEGS AND THE MECHANICAL
SIGNALS FOR ONE SIDE OF THE FACE
IS IN THE NEXT VIDEO. MOMENTS IN RED,
FORCES IN BLUE. EACH CIRCLE REPRESENTS
ONE OF THE FOLLICLES. WHISKERS DOWN BELOW SHOW VECTOR MAGNITUDE.
SO HERE ARE THE SIGNALS ASSOCIATED
WITH THE RAT WHISKING AGAINST PEGS.
THESE ARE DATA THAT A BIOLOGIST WOULD NEVER BE ABLE TO OBTAINBECAUSE YOU CAN'T RECORD
THE MECHANICAL SIGNALS WITHOUT
INTERFERING WITH THE SIGNALS. SO
MORE INTERESTING IN A LABORATORY
EXPERIMENT IS THAT WE CAN GO OUT
INTO A DARK CHICAGO ALLEYWAY
AND LOOK AT A SIMULATED RAT
EXPLORING A DRAINPIPE. WE CAN
PUT THE ANIMAL IN ITS MORE
NATURAL ENVIRONMENT AND LOOK AT
THE SIGNALS BEING GENERATED
DURING THIS TYPE OF BEHAVIOR.
THEN WE CAN RETURN TO THE
LABORATORY AND LOOK AT SIGNALS
ASSOCIATED WITH A RAT THAT'S
BEEN TRAINED TO SEARCH FOR A
WATER REWARD. AND IN THIS CASE
WE HAVE THREE CAMERA VIEWS SO
WE'RE ABLE TO SUPERIMPOSE THE
MODEL ON TOP OF THE RAT IN THESE
CAMERA VIEWS AND TRACK THE
WHISKERS AND HEAD IN EACH FRAME.
AND THAT GIVES US -- THEN ALLOWS
US TO LOOK AT THE MECHANICAL
SIGNALS HERE ARE EXAMPLES JUST
FOR THE C1 WHISKER. WE HAVE ALL
THREE MOMENTS PLOTTED IN TIME.
THE IMPORTANT THING HERE IS
THESE ARE THE INPUT SIGNAL TO
THE PRIMARY SENSORY NEURONS THAT
ENCODE WHISKER-BASED TOUCH.
SO MOVING ONTO CODING BY PRIMARY
SENSORY NEURONS I WISH I COULD
PRESENT DATA FROM THE AWAKE
ANIMAL BUT WE'RE GOING RETURN TO
THE ANESTHETIZED ANIMAL. (VIDEO)
HERE WE HAVE TWO CAMERA VIEWS. WHAT WE CAN DO
THOUGH IS MANUALLY DEFLECT THE WHISKER AT
DIFFERENT ARCLENGTHS, SPEEDS, AND DIRECTIONS USING
A THIN 0.5 MM PENCIL LEAD. WE CAN THEN MERGE
THE TWO CAMERA VIEWS TO TRACK THE 3D MOTIONS
OF THE FLEXIBLE WHISKER TO LOOK AT HOW IT
MECHANICALLY RESPONDS DURING EACH TOUCH.
BECAUSE WE HAVE
THREE-DIMENSIONAL MODEL --
BECAUSE WE HAVE THESE MECHANICAL
MODELS WE CAN TRACK AND
RECONSTRUCT THE
THREE-DIMENSIONAL SHAPE OF THE
WHISKER, COMPUTE MECHANICAL
SIGNALS AT THE WHISKER BASE AND
ARE ABLE TO CHARACTERIZE THE
TUNING SPACES AND THEIR DISTRIBUTION.
SO THE UPSHOT, THE FINAL RESULT, THE
TAKE HOME FROM THESE EXPERIMENTS
THAT WE DID, IS THAT EACH
PRIMARY SENSORY NEURON IS
REPRESENTING MANY MECHANICAL
FEATURES SIMULTANEOUSLY AND THAT
RESULT CONTRASTS DIRECTLY WITH
THE TRADITIONAL VIEW THAT EACH NEURON
REPRESENTS A SINGLE KINEMATIC STIMULUS FEATURE. OUR
PRELIMINARY DATA SUGGESTS THAT
THIS WILL RESULT WILL GENERALIZE TO
AWAKE ANIMAL. THIS REPRESENTATION IN
PRIMARY SENSORY NEURON IS GOING TO
DIRECTLY CONSTRAIN THE COMPUTATIONS
PERFORMED BY MORE CENTRAL
NEURONS. SO I WALKED YOU THROUGH
THE THREE STEPS HERE. NOW WE'RE
GOING TO TAKE A LOOK AT THE
FUTURE. WHAT IS THIS FUTURE?
WELL THESE PRIMARY SENSORY
NEURONS ARE OF COURSE PROJECTING
TO SECONDARY NEURONS THAT WILL
COMPLETE THE LOOP. THEY PROJECT
BACK OUT TO FACIAL MOTOR NUCLEUS
NEURONS THAT CONTROL THE
WHISKERS BUT ALSO PROJECT TO ALL
OF YOUR FAVORITE MORE CENTRAL
STRUCTURES AND SO WHAT I WOULD
LIKE YOU TO IMAGINE IS HAVING
ACCESS TO THE RESPONSES OF THESE
SECONDARY SENSORY NEURONS, OVER
MILLIONS OF SIMULATIONS WITH
REALISTIC CLOSED-LOOP
BIOMECHANICS.
IMPORTANTLY WE DON'T REALLY NEED
THIS TO BE THE PERFECT RAT. WE
JUST NEED ITS PARAMETERS TO BE
IN THE RANGE OF MECHANICALLY
REASONABLE RATS. AND HERE WE
HAVE TAKEN INSPIRATION FROM THE
FIELD OF EVOLUTIONARY ROBOTICS.
SO LET'S LOOK AT FUTURE TOOLS,
RESOURCES AND APPROACHES. AS I
THINK ABOUT WHAT WE'RE GOING TO
NEED AS WE START TO TACKLE THIS
REALLY DIFFICULT PROBLEM OF THE
SECONDARY STAGE NEURONS, WE'RE
GOING TO NEED A LOT OF CIRCUIT
BREAKING. WE NEED FUNCTIONAL ANATOMY.
WE NEED TO CHARACTERIZE THESE
DESCENDING CONNECTIONS. WE NEED
RIGOROUS BEHAVIORAL QUANTIFICATION
BECAUSE SENSORY ACQUISITION
BEHAVIORS ARE GOING TO ESTABLISH
THESE MECHANICAL REGULARITIES
THAT THE BRAIN CAN EXPLOIT. WE
NEED TO TAKE A COMPARATIVE - I
THINK WE NEED TO TAKE A
COMPARATIVE APPROACH SO WE NEED
TO GO CROSS-MODAL. SO VISION IS
AN ALLOCTIVE SYSTEM BUT TOUCH
IS HOMEOACTIVE. AND THOSE ARE
IMPORTANT DISTINCTIONS THAT WILL
HAVE IMPLICATIONS FOR EFFERENCE
COPY AND THE SELF-PREDICTION OF
SELF-GENERATED MOTION.
WE ALSO NEED TO LOOK
ACROSS SPECIES. INVERTEBRATES
HAVE BEEN A BIG MODEL - THE RESULTS
FROM INVERTEBRATESHAVE BEEN A BIG
MODEL FOR MY WORK. WE NEED A.I. TO
PREDICT FAMILIES OF CIRCUITS TO
GENERATE TESTABLE HYPOTHESES. NEED
RIGOROUS APPROACHES FORVALIDATING MODELS
OF NEURAL POPULATIONS ACROSS DIVERSE
BEHAVIORS.
AND IMPORTANTLY THESE SOLUTIONS
MUST WORK ON HARDWARE THAT IS ON
THE ANIMAL NOT JUST IN
SIMULATION THAT MEANS WE NEED
MORE BIOMECHANICALLY REALISTIC
MODELS OF ALL OF THE DIFFERENT
STRUCTURES AND THEIR ROLE IN THE
SENSORY DATA ACQUISITION PROCESS.
BUT AS I MENTIONED YESTERDAY IN
ONE OF THE QUESTIONS, THE
SOLUTION THAT WE NEED BETTER
SOLUTIONS FOR THE SIM2REAL
PROBLEMS. ONCE WE START TO ADD REAL SENSORS, MOTORS WITH NOISE, ALLTHESE
NONSTATIONARITIES, NONLINEARITIES
WE MAY FIND WE NEED DIFFERENT CONTROL CIRCUITRY.
WE NEED BETTER MODELS OF COLLISIONS, CONTACT MECHANICS, FRICTION ANDFLUID FLOW.
HARDWARE MODELS HAVE BEEN AN
ESSENTIAL COMPONENT OF OUR WORK,
AND I WOULD LIKE TO SAY THAT --
SO ON THE LEFT HERE WE'RE
SHOWING AN ARTIFICIAL WHISKER
COMPARED WITH THE REAL WHISKER.
WE'RE DOING EXPERIMENTS IN SHAPE
EXTRACTION AND THE WAY I LIKE TO
THINK ABOUT IT IS THAT
NEUROSCIENCE HAS BEEN THE
INSPIRATION FOR ARTIFICIAL
INTELLIGENCE BUT LINKING BIO-
MECHANICS IS THE INSPIRATION FOR
EMBODIED A.I. SO WITH THAT I WOULD
LIKE TO THANK THE PEOPLE WHO DID
THE WORK SHOWN IN THE TOP ROW
AND MY CURRENT LABORATORY SHOWN IN
THIS THE BOTTOM ROW PARTICULARLY
THANKS TO BOTH NSF AND NIH BOTH
OF WHICH HAVE BEEN INCREDIBLY
IMPORTANT FOR DIFFERENT STAGES
OF THIS WORK. THANK YOU.
>> THANK YOU TO ALL OF OUR
SPEAKERS AND AT THIS TIME I
WOULD LIKE TO INVITE ALL FOUR OF
OUR DISCUSSANTS TO THE STAGE. I
WOULD LIKE TO REMIND FOLKS WHO
ARE WATCHING ON THE WEBINAR,
ZOOM OR THE WEBCAST YOU CAN SEND
QUESTIONS THROUGH THE ZOOM Q&A
OR THE VIEW CAST LIVE FEEDBACK
FORM OR IF YOU SEE THE QR CODE
ON THE SCREEN NOW YOU CAN SCAN
THAT AND SEND AN E-MAIL FOR
DISCUSSION. LET'S KICK OFF THE
PANEL DISCUSSION RIGHT NOW WITH
CHIARA, OKAY.
>> CAN YOU HEAR ME? FIRST OF
ALL, THANKS TO ALL OF YOU FOR
THE AMAZING TALKS, THEY WERE ALL
DIFFERENT AND ALL INSPIRING. SO
I HAVE A QUESTION THAT IS MOSTLY
FOR THOSE WHO DEVELOP
NEUROMORPHIC CIRCUITS AND
HARDWARE. THAT IS, VERY OLD
QUESTION BUT I AM TRYING TO
REPHRASE IT. SO WHICH CRITERIA
DO YOU USE TO CHOOSE WHICH
NEUROCOMPUTATION OR WHICH
NEUROPRIMITIVES YOU WILL
IMPLEMENT ON YOUR HARDWARE OR ON
YOUR MODEL AND THIS RELATES A
LOT TO THE OTHER QUESTION THAT
IS WHICH IS THE CRITERIA TO
CHOOSE THE RIGHT LEVEL OF
ABSTRACTION
ABSTRACTION IN THESE MODELS?
IT'S NOT JUST ABOUT THE HARDWARE
BUT WHAT YOU WERE USING,
SHUNTING INHIBITION. WHY
SHUNTING INHIBITION, WHY NOT
SOMETHING DIFFERENT?
YOU COULD HAVE USE OTHER
MECHANISMS? I DON'T KNOW.
>> I THINK FOR ME, WHEN
I CHOOSE WHAT -- I'M JUST
GOING TO CALL IT A PRIMITIVE,
WHAT PRIMITIVE I WANT TO USE.
OH MY GOSH, SEEING MYSELF DOWN
THERE IS REALLY SCARY. I WAS
LOOKING FOR A COMPUTATION THAT I
COULD LINK TO THE BEHAVIOR.
BECAUSE I WAS INTERESTED IN
HOW -- HOW THE CONSTRAINTS OF
THE BIOLOGICAL SYSTEM AFFECTED
THE MECHANISM. SO FOR ME, I SEE
A LOT OF VALUE IN INVERTEBRATES
WHICH I THINK MITRA ALSO ALLUDED
TO. BECAUSE IN INVERTEBRATES
THE CIRCUIT IS SMALL SO WE CAN
CHARACTERIZE THE COMPLETE
CIRCUIT AND SEE HOW THE
INFORMATION PROCESSING IS LINKED
FROM HARDWARE ALL THE WAY TO
BEHAVIOR. I THINK THAT WE NEED
TO BE CAUTIOUS NOT MIMICKING
EVERY ASPECT OF THE BIOLOGICAL
CIRCUIT JUST BECAUSE IT'S THERE.
SO I THINK FOR ME IT'S IMPORTANT
TO LOOK ACROSS DIFFERENT
SYSTEMS. WE DON'T KNOW WHAT THE
MECHANISM IS IN THE DRAGONFLY.
SHUNTING INHIBITION WE HAVE SEEN
TO DO MULTIPLICATION IN THE DRAGONFLY.
AND IT WAS SOMETHING THAT WE
REALIZED WE COULD IMPLEMENT IN
AN EXISTING NEUROMORPHIC
APPROACH SO THAT IS HOW THAT --
THOSE TWO PIECES CAME TOGETHER.
I THINK IT WILL BE IMPORTANT
GOING FORWARD TO UNDERSTAND HOW
THE CONSTRAINTS OF THE SYSTEM OF
THE BIOLOGICAL SYSTEM HAVE
AFFECTED THE SOLUTION THAT IS
IMPLEMENTED IN BIOLOGY AND
MAPPING THE CONSTRAINTS FACING
THE BIOLOGICAL SYSTEM AND THE
TASK TO WHAT WE WANT TO USE THE
NEUROMORPHIC SYSTEM FOR. SO FOR
EXAMPLE IN DROSOPHILA WITH THE
NAVIGATION TASKS THE BASIS FUNCTIONS
ARE DIFFERENT. IT MAKES THE SYSTEM
SMALLER BUT MAYBE LESS FLEXIBLE
THAN YOU MIGHT SEE IN MAMMALIAN
SYSTEM SO DEPENDING ON WHAT
WE WANT TO USE THE NEUROMORPHIC
SYSTEM FOR THAT CAN AFFECT WHICH
WHETHER WE LOOK TO MAMMALS OR
VERTEBRAE OR INVERTEBRATES FOR
THE SOLUTION. HOW ABOUT YOU,
DOES ANYONE ELSE WANT TO
ADD
ANYTHING?
>> I APPRECIATE THE QUESTION
BECAUSE IT'S A QUESTION THAT HAS
COME UP FOR OVER DECADES OF HOW
DO YOU CHOOSE OR STRATEGIZE.
THAT'S FAIR. I THINK FROM MY
PERSPECTIVE IS I SEE THIS AS A
CONTINUUM IN TERMS OF HOW
NEUROMORPHIC OR HOW SORT OF
ENGINEERING AND I THINK THIS
COMES WITH THE FACT THAT DOING
THIS EFFECTIVELY USUALLY MEANS
YOU'RE DOING SOME LEVEL OF
THINGS THAT ARE BOTH VERY
SERIOUS ENGINEERING THAT HAVE
ENGINEERING GOALS AND SOME
BECAUSE IT'S THE LOVE OF THE
NEUROSCIENCE AND SO WE'VE DONE
MANY THINGS THAT ARE VERY
CAREFUL ENGINEERING IC DESIGN
AND SO FORTH AND THERE'S ALSO
BEEN CASES WHERE WE LOOKED AT
THE NEUROSCIENCE AND SAID HOW
CLOSE CAN I MODEL CIRCUITS FOR
THIS?
TO BE FAIR PART OF THE REASON WE
DO IS A LITTLE WORD CALLED FUN.
WE ENJOY IT. WE DON'T TALK
ABOUT IT AS MUCH. BUT THAT THEN
YIELDS THE FRAMEWORK AND THE
PRIMITIVES THAT WE CAN WORK WITH
THAT WE SAY WHEN AN APPLICATION
SHOWS UP, IT VERY MUCH NATURALLY
FITS INTO THE CONVERSATION. AND
SO I THINK THAT CONTINUUM AND
THAT BALANCE IS IMPORTANT.
BECAUSE I DON'T THINK YOU CAN
JUST PICK ONE SPOT. YOU HAVE TO
KIND OF GET AN INTUITION OF THAT
WHOLE RANGE. OTHERWISE YOU'RE
USUALLY MISSING SOMETHING AND
MISSING AN OPPORTUNITY AND I CAN
PROBABLY TELL MULTIPLE STORIES
WHERE WE FOUND BY ACTUALLY
DOING -- BE DOING THAT IN TWO
DIFFERENT SPACES WE ACTUALLY
FOUND CONNECTIONS BETWEEN THEM.
AND THOSE WERE THE PLACES WHERE
WE HAD THE MOST INTERESTING
FEEDBACK INTO THE NEUROSCIENCE
QUESTIONS. BECAUSE OF THAT.
>> LET ME ADD TO THAT I THINK
EARLY ON WE WERE LOOKING AT THIS
PRIMITIVES HOW WOULD THEY HELP
US WHETHER THEY'RE NEURON
SYNAPSES OR DENDRITES OR ANY OF
THESE PRIMITIVES HOW WOULD THEY
HELP US GET TO AN ENERGY
EFFICIENT SOLUTION BUT I THINK
WE SHIFTED FROM THAT APPROACH
INTO LOOKING AT THIS GRANDER
PROBLEM AS A GUIDELINE FOR US
LIKE FOR EXAMPLE THIS CONTINUAL
LEARNING CHANNEL THAT WE ARE
LOOKING AT REQUIRES OR FORCES US
TO NOT JUST LOOK AT A SINGLE
PRIMITIVE OR AN ABSTRACTION THAT
WORK AT A CERTAIN LEVEL WHICH
YOU HAVE TO LOOK AT WHAT
JENNIFER WAS SAYING, THIS IS A
CONTINUUM. FOR EXAMPLE, YOU
COULD -- WE WERE THINKING LIKE
SYNAPSES AND NEURONS AS
PRIMITIVES BUT THEN WHEN YOU'RE
THINKING ABOUT ADDRESSING
CATASTROPHIC FORGETTING PROBLEM
YOU NEED SYNAPTOGENESIS OR PRUNING
IN A DYNAMIC FASHION AND YOU NEED
TO BE ABLE TO DYNAMICALLY
RECONFIGURE THESE UNITS. SO IF I
GO TO A LOWER LEVEL OF
GRANULARITY WOULD IT BE
BENEFICIAL OR NOT? I DO NOT
KNOW AT THIS POINT SO IT IS VERY
BENEFICIAL TO LOOK AT THIS AS A
PROBLEM YOU'RE TRYING TO SOLVE.
AND WHAT WOULD BE THE GUIDING
PRINCIPLES THAT NEUROSCIENCE
COULD OFFER AND GIVE US AN
AN
ADVANTAGE?
>> I THINK WHAT YOU'RE GETTING
AT IS REALLY IMPORTANT AND WHEN
YOU TALK ABOUT ABSTRACTION BOTH
AS A NEUROMORPHIC RESEARCH AS
WELL AS DOING MODELING BEFORE
ONCE YOU MAKE A DECISION ON
ABSTRACTION YOU PUT BARRIERS IS
HOW FAR YOU CAN GO AND I THINK
MITRA'S TALK WAS FANTASTIC BECAUSE
IT'S SHOWING THE RICHNESS OF THE
BEHAVIOR THAT NEURAL CIRCUITS
PERFORM. AND IF YOU MAKE AN
ABSTRACTED MODEL OF THIS SENSORY
CORTEX AND THE WHISKER PROCESSING
SYSTEM AND IT'S ON A NARROW
INTERPRETATION OF WHAT THE
WHISKERS ARE DOING, YOU ABSTRACT
IT AND A SIMPLE MODEL CAN DO X,
Y AND Z BUT YOU'RE NEVER GOING
TO GO FROM THAT ABSTRACTED MODEL
TO SOMETHING MORE RICH AND
COMPLEX. IT'S VERY HARD TO GO
BACK. I FEEL THE ABSTRACTION IS
ABSOLUTELY CRITICAL.
NEUROMORPHIC YOU PHYSICALLY HAVE
TO ABSTRACT BUT WITH MODELING
YOU MAKE AN INTENTIONAL DECISION
AS WELL. WE WANT TO BE
APPRECIATIVE OF THE FACT THAT WE
DON'T UNDERSTAND WHAT THE
COMPUTATIONS THE BRAIN IS DOING
MOST OF THE TIME. RIGHT? SO
THE BOX THAT WE'RE TRYING TO
CAPTURE IS KIND OF ILL-DEFINED
SO WE NEED TO BE -- AS YOU WERE
SAYING VERY SPECIFIC FOR THIS
TASK, FOR THIS THING WE'RE
TRYING TO DO, THIS IS THE
ABSTRACTION THAT WE HAVE TO DO.
BUT WE HAVE TO BE AWARE THAT IF
I WANT TO GO TO SOMETHING ELSE,
THAT ABSTRACTION MAY NOT BE
VALID AND I MIGHT BE MISGUIDED
BY USING AN ILL-FITTED
ABSTRACTION TO SOLVE A NEW
PROBLEM AND I DON'T THINK -- AT
LEAST IN THE COMP NEURO COMMUNITY,
WE DON'T TALK ABOUT IT NEARLY ENOUGH.
NEUROMORPHIC IS EARLY ENOUGH. IT'S
A GOOD THING TO TALK ABOUT NOW.
>> IT'S NOT ONLY IMPORTANT TO
CHOOSE THE RIGHT LEVEL OF
ABSTRACTION BUT ALSO TO CHOOSE
WHICH IS THE TASK IN A WAY THAT
IT'S NOT TOO SMALL, NOT TOO
LARGE AND TO FIND SOLUTIONS THAT
CAN THEN BE APPLIED AS WELL.
>> CARINA, YOUR QUESTION.
>> I AM GOING TO TRY TO SAY THIS
CONCISELY. I WAS STRUCK ACROSS
MANY TALKS ABOUT THE INTERPLAY
BETWEEN NEURAL REPRESENTATIONS,
FOR INSTANCE, THE MANIFOLDS OR
THE, YOU KNOW, SORT OF
REPRESENTATIONS THAT ARE MAYBE
PLAY WELL WITH GEOMETRIC
TRANSFORMATIONS AND THIS NEED TO
SPARSIFY THE NUMBER OF ACTIVE
NEURONS. I WAS STRUCK
BY KWABENA'S TALK ABOUT
HOW TO GET THAT LINEAR SCALING
AND SO I HAVE SORT OF A COUPLE
OF QUESTIONS IN THIS REGARD BUT
BROADLY SPEAKING HOW DO YOU SEE
THE, WHAT WE LEARN ABOUT NEURAL
REPRESENTATIONS INFLUENCING, YOU
KNOW, THE STYLE OF NEUROMORPHIC
HARDWARE THAT WE'RE COMING UP
WITH AND THEN SORT OF
SPECIFICALLY FOR SUEYEON, I WAS
CURIOUS IN YOUR NEURAL MANIFOLDS
HOW MANY NEURONS DOES A TYPICAL
MANIFOLD INVOLVE? IS IT HALF
THE NEURONS OR IS IT MUCH LESS?
ARE THE MANIFOLDS SOMEHOW --
SOMEHOW IMPLEMENTING A KIND OF
SPARSE CODING, COMBINATORIAL CODING
EFFECTIVELY IS THE QUESTION.
>> THANK YOU FOR THE QUESTION.
I THINK, I WAS ACTUALLY GETTING
INSPIRED BY A LOT OF
NEUROMORPHIC TALKS AND THINKING
ABOUT THE SIMILAR TYPE OF
QUESTIONS. TO ANSWER THE FIRST
QUESTION ABOUT THE CONNECTION
BETWEEN THE EFFICIENCY OF
REPRESENTATIONS AND NEUROMORPHIC
COMPUTATION SO I THINK THERE'S
SEVERAL INTERESTING DIRECTIONS
AT THE CONVERGENCE OF THESE
IDEAS. FOR EXAMPLE, WHAT COULD
BE TYPES OF REPRESENTATIONS THAT
EMERGE FROM COMPUTATIONS THAT
ARE IMPLEMENTED BY NEUROMORPHIC
COMPUTING. SO IF THEY PROVIDE
SOME SORT OF BIAS OR PREFERENCE
WHAT IS THE RESULT OF THOSE AND
THOSE QUESTIONS ARE INTERESTING AND
NOT ASKED AS MUCH. AND THE OTHER
CONNECTION IS, SO THE -- THE
IDEA OF MANIFOLD CAPACITY
DEFINITELY HAS TO DO WITH THE
REPRESENTATIONAL EFFICIENCY
MEANING THAT IT GIVES SOME SENSE
OF HOW MANY NEURONS OR UNITS ARE
REQUIRED TO REPRESENT THE GIVEN
NUMBER OF CONCEPTS WITH
CERTAIN GEOMETRY THAT'S MEASURED
FROM THE DATA AND AS WE SAW IN
EARLIER TALKS, THE NUMBER OF
UNITS, THE NUMBER OF NEURONS
REQUIRED HAS TO ALSO DO WITH
ENERGY EFFICIENCY OR ENERGY
THAT'S NEEDED SO I THINK THERE'S
ALSO DIRECT CONNECTION TO ENERGY
EFFICIENCY FROM THE
REPRESENTATIONAL EFFICIENCY AND
WHAT'S THE SECOND QUESTION
WAS....
>> I WAS JUST CURIOUS, THE
NEURON -- THERE'S A LOT OF ECHO.
I WAS CURIOUS OF THE NEURAL
MANIFOLDS THAT YOU SEE THAT YOU
OBSERVE WHEN YOU APPLY YOUR
FRAMEWORK TO DATA IF THEY TEND
TO BE SUPPORTED ON FEW NEURONS
IS THAT A KIND OF COMBINATORIAL, SPARSE?
>> IT ACTUALLY DEPENDS -- IT'S
KIND OF HARD TO ANSWER WHAT ARE
THE TYPICAL NEURONS THAT IS
REQUIRED FOR COMPUTATION BECAUSE
IT IS A FUNCTION OF THE GEOMETRY
OF THE DATA THAT WE SEE. AND
IT -- PROBABLY ALSO -- ACTUALLY
IT DEPENDS ON THE REGION IN THE
BRAIN THAT YOU'RE LOOKING AT.
IF YOU'RE LOOKING AT EARLIER
SENSORY REGION WHERE IS THE
INFORMATION HAS NOT BEEN
TRANSFORMED AS MUCH THEN THE
REPRESENTATIONS TEND TO BE
INEFFICIENT SO IN ORDER TO
REPRESENT SAME NUMBER OF
CATEGORIES YOU WOULD NEED A LOT
OF NEURONS WHILE IF YOU GO ALL
THE WAY DOWN TO I.T. OR THE
PREFRONTAL CORTEX THE
REPRESENTATIONS TEND TO BE MORE
ABSTRACTED WHICH WE KNOW FROM
THE GEOMETRY OF MANIFOLDS BEING
SMALLER AND SO ON THEN THE
MEASURED CAPACITY TENDS TO BE
HIGHER WHICH MEANS THAT YOU
WOULD NEED SMALLER NUMBER OF NEURONS
TO REPRESENT THE SAME NUMBER OF
CATEGORIES. THIS REALLY IS A
FUNCTION OF HOW MANY - THE ACTUAL
STRUCTURE IN THE DATA.
>> YIOTA, YOUR QUESTION, THANK YOU.
>> THANK YOU ALL FOR A WONDERFUL
SET OF PRESENTATIONS. I
APPRECIATE THAT WE WENT FROM
SUBCELLULAR MECHANISMS WITH
JENNIFER TO THE PRIMITIVES TO
MANIFOLDS TO CONTINUAL LEARNING
ALL THE WAY TO EMBODIMENT THAT
WAS REALLY FASCINATING FOR ME TO
SEE. AND I AM WONDERING
FOLLOWING UP WITH CARINA'S
QUESTIONS HOW YOU SELECT THE
RIGHT LEVEL OF ABSTRACTION TO
ADDRESS IN HARDWARE DOES THE
MATERIAL PLAY A ROLE? WE
HAVEN'T REALLY TALKED ABOUT WHAT
KIND OF HARDWARE DEVICES ARE YOU
USING AND WHETHER THE PLUSES AND
MINUSES OF THE MATERIAL DICTATE
ALSO THE LEVEL OF ABSTRACTION
THAT IS APPROPRIATE. THAT'S ONE
QUESTION AND THE SECOND ONE IS
WHAT DO YOU FEEL THAT YOU ARE
MISSING FROM THIS INTERACTION
THAT WOULD FACILITATE THE
DISCOVERY PROCESS FURTHER I CAN
SAY FOR SURE I DON'T KNOW THE
LIMITATIONS OF THE MATERIAL FOR
EXAMPLE, THAT'S MY FIRST
QUESTION, BUT IT COULD BE
SOMETHING ELSE. THOSE ARE THE
TWO THINGS I WOULD LIKE TO KNOW
>> I CAN TRY TO ANSWER BOTH
QUESTIONS. MATERIAL SCIENCE HAS
BEEN AN IMPORTANT PART OF OUR
WORK AND I'VE BEEN FORTUNATE TO
WORK WITH SCIENTISTS WHO HELP
DESIGN FOR US SOME ARTIFICIAL
WHISKERS THAT WERE DYNAMICALLY
SIMILAR TO REAL WHISKERS SO THAT WAS
AN IMPORTANT PART OF OUR WORK AND
TO THE LEVEL OF ABSTRACTION I
WAS STRUCK BY SOMETHING THAT
FRANCES SAID EARLIER THAT YOU
DON'T WANT TO REPLICATE ALL THE
BIOLOGICAL DETAIL AND I AGREE IN
MANY RESPECTS ALTHOUGH WE HAVE
PUT IN QUITE A LOT INTO OUR
MODELS I THINK THE QUESTION IS,
AGAIN, WHAT SORT OF TASK OR
BEHAVIOR ARE YOU REALLY
INTERESTED IN? GOING BACK TO
THE PICTURE OF THE CAT AND THE
RABBIT. ARE YOU INTERESTED IN
HOW THEY HOP AND HOW THEY --
STALK? OR ARE YOU INTERESTED IN
HOW THEY LEARN TO DO THAT? AND
IF YOU'RE INTERESTED IN ONE OF
THEM YOU NEED MORE BIOLOGICAL
DETAIL THAN THE OTHER AND WITH
THIS AUDIENCE IF YOU'RE
INTERESTED IN THE MORE GENERAL
ALGORITHMS LIKE HOW DO ANIMALS
LEARN WHAT I THINK MIGHT BE
HELPFUL IS A FEW REALLY GOOD
SYSTEMS THAT ARE IN REAL WORLD
INSTANTIATED SYSTEMS WHERE YOU
CAN TEST OUT THESE GENERAL
ALGORITHMS ON THESE PARTICULAR
SPECIES AND BEHAVIORS. SO
THAT'S HOW I SEE THE TWO
INTERACTING.
>> I MIGHT TAKE THE BAIT ON THE
NEUROMORPHIC HARDWARE MATERIALS.
SO THE NEUROMORPHIC FIELD HAS
LOOKED A LOT AT DIFFERENT
MATERIALS FOR POST-MOORE'S LAW,
NON-CMOS, AND MY OPINION IS I
THINK WE SHOULD LOOK AT
EVERYTHING. RIGHT? I MEAN, ON
THE MATERIAL SIDE, RIGHT, FOR
SCALING PURPOSES, FOR, YOU KNOW,
FUTURE OF COMPUTING AND SO FORTH
I THINK THAT AT SOME LEVEL THE
VISION AS I UNDERSTAND IT AND
YOU ALL LIVED IT MORE OF CARVER
MEAD IS THAT, YOU KNOW, BRAINS
ARE MADE OF CARBON, FATTY
MEMBRANES AND WATER AND SO
FORTH, RIGHT?
BUT THE COMPUTATION CAN BE
REALIZED IN SILICON, THAT
COMPUTATION CAN BE REALIZED IN
TANTALUM OXIDE MEMRISTORS, IN CARBON
NANOTUBE, IT CAN BE REALIZED IN ANY
NUMBER OF THINGS. IT'S GOING TO
MAYBE CHANGE THE CIRCUIT, MAYBE
CHANGE THE DEVICE PROPERTIES BUT
AT SOME LEVEL THE MATH, THE BIO-
PHYSICS BECOMES SOME MATERIAL
PHYSICS IN WHATEVER SUBSTRATE WE
USE AND BY MAKING THAT MAPPING
DOWN TO THAT -- WHETHER IT'S THE
NEURAL DYNAMICS, STOCHASTIC SYNAPSE,
OR THE NOISY WHATEVER IT IS THE
PROCESS OF THAT MAPPING THAT YOU
UNDERSTAND WHAT THAT PHYSICS IS
GIVING YOU. BUT I DON'T THINK
THE MATERIAL MATTERS TOO MUCH.
THIS MAY BE CONTROVERSIAL IT
MATTERS FROM A SCALING POINT OF
VIEW BUT FOR OUR PURPOSES HERE I
DON'T THINK IT MATTERS TOO MUCH.
CMOS IS GREAT BECAUSE WE CAN
BUILD IT REALLY EFFICIENTLY.
THAT'S WHY WE'RE FOCUSED ON IT A
LOT ON THIS STAGE BUT IN THE
FUTURE IT MAY VERY WELL CHANGE
TEN YEARS FROM NOW WE'LL
PROBABLY BE USING ALL SORTS OF
MATERIALS THAT WE DON'T USE
TODAY AND I DON'T THINK IT WILL
CHANGE MUCH WHAT THE QUESTIONS
ARE BUT I MIGHT HAVE A
DISAGREEMENT OVER HERE.
>> WE HAVE ONE MORE DISCUS ANT
QUESTION.
>> IS THERE -- YOU WANT TO -- IF
YOU HAVE -- LET'S FINISH QUICKLY
OR --
>> YEAH, SORRY. NO, I JUST
THINK JUST A COUPLE COMMENTS
THERE. YEAH, THE -- I DO THINK
THERE'S A LOT OF INTERESTING
THINGS WITH THE MATERIALS, LIKE
YOUR EXAMPLE MITRA.
I DO THINK THAT HOW WE IMPLEMENT
IS PROBABLY NOT TOO CRITICAL. I
WOULD AGREE WITH THAT STATEMENT
I DO THINK ON THE FLIP SIDE THE
SILICON SIDE IS PROBABLY OFTEN
BEEN UNDERLOOKED IN THE LAST
DECADE AND A HALF. AND I THINK
THAT IS A VERY SERIOUS PROBLEM.
AND PROBABLY HAS -- IS PROBABLY
SOMETHING WE DON'T WANT TO DO
BECAUSE I THINK THERE'S A LOT
THAT ACTUALLY FITS REALLY WELL
THERE. I THINK IT'S ALSO
INTERESTING BECAUSE THE SILICON
SIDE STILL, THE CAPABILITY THAT
YOU CAN GET OUT OF THE SILICON
SIDE IS STILL THE LEADING
IMPLEMENTATION BY FAR OF
ANYTHING ELSE THAT'S BEEN DONE.
SO I THINK IF WE WANT TO MOVE
FORWARD I THINK THAT'S GOING TO
BE -- THAT NEEDS TO BE IN OUR
CONVERSATION AND IN OUR
APPROACH.
>> OKAY. SO WHAT IS THE MINIMUM
SET OF COMPONENT THAT YOU
ABSOLUTELY HAVE TO HAVE IN YOUR
COMPUTING? SO NEURON OR SYNAPSE
OR LEARNING OR DENDRITE OR NOT?
>> PASSIVE DYNAMIC WALKERS. YOU
DON'T NEED ANY BRAIN AT ALL.
>> I WILL TRY AND TAKE THAT
QUESTION. I MEAN, IF YOU LOOK
ACROSS, RIGHT NOW WE HAVE THE
FLY CONNECTOME, SOMEBODY
MENTIONED THEY RECONSTRUCTED A
SYNAPSE EVERY NEURON, IN FACT
IT'S NANOMETER RESOLUTION, WE
GOT LIKE A CUBIC MILLIMETER OF
MOUSE AND OF HUMAN CONNECTOME AND THE
THING THAT STRUCK ME IN ANSWERING
YOUR QUESTION IS, THE FLY CONNECTOME
IS VERY DIFFERENT FROM THE HUMAN
CONNECTOME. A LOT OF US, YOU
KNOW, ME, I HAD NO IDEA THAT
FLIES WERE THIS DIFFERENT. MOST
OF THE NEURONS IN A FLY ARE NOT
POLARIZED SO THEY DON'T HAVE A
DENDRITE. THEY DON'T HAVE AN
AXON THEY HAVE SOMETHING CALLED
A NEURITE. IN OTHER WORDS YOU
CAN'T SEE WHICH DIRECTION THE
INFORMATION IS FLOWING. THE
MOST TYPICAL THING YOU FIND IN A
FLY IS YOU HAVE ALL TEN MICRONS
OF NEURITE THAT IS RECEIVING
FIVE TO TEN SYNAPSES, INPUT
SYNAPSES AND IT'S TURNING AROUND
AND MAKING AN OUTPUT SYNAPSE FROM
THAT SAME 10 MICRONS OF NEURON.
IT'S VERY CLEAR IN A FLY THE ATOM
OF COMPUTATION IS TEN MICRONS OF
NEURITE. INPUTS AND OUTPUTS ARE ALL THERE.
AND SO, WHY IS A FLY DOING THAT?
HOW HAVE WE EVOLVED TO DO THIS
POLARIZATION AND IS IT STILL THE
CASE THAT THESE TEN MICRONS
OF DENDRITE OR STILL THE ATOM OF
COMPUTATION BUT WE ORGANIZE THEM
IN A DIFFERENT WAY. AND OUR
COMMUNICATION BECOMES LONGER SO
WE HAVE SPECIALIZED AXONS TO DO
THAT AND SO ON AND SO FORTH.
BUT, YOU KNOW, SO THE QUESTION,
YOU KNOW, SOMEBODY MENTIONED
THIS, LOOKING ACROSS DIFFERENT
SYSTEMS AND DIFFERENT SOLUTIONS,
YOU KNOW, I THINK THAT THE
ANSWER TO THAT SHOULD BE COMMON
THE FLY AND THE HUMAN AND ALL
THIS STUFF. SO THAT'S TELLING
YOU SOMETHING. BRAD WHEN YOU SAY
YOU SIMULATED THE WHOLE FLY I
CAN'T IMAGINE HOW YOU CAN DO
THAT BECAUSE THERE'S NO SUCH
THING AS NEURONS WITH AXONS AND
DENDRITES IN A FLY.
THE ONLY THING I WILL SAY IS WE
HAVE TO BE HUMBLE ABOUT THE
ANSWERS TO THESE QUESTIONS AND
NOW WE HAVE DATA, IN FACT THE
ANSWER TO THIS QUESTION IS
SITTING ON SOMEBODY'S DESK. THERE'S
A PETABYTE DATASET THERE IF WE
EXAMINE IT CLOSELY WE REALLY
WILL BE ABLE TO ANSWER THESE
QUESTIONS AND COMPARE THEM.
THAT'S WHY I'VE BEEN QUIET. I
REALLY DON'T HAVE -- I HAVEN'T
EXAMINED THAT PETABYTE OF
DATA BUT THERE'S A GREAT
OPPORTUNITY HERE TO BE VERY
OPEN-MINDED ABOUT THIS, TO BE
VERY COMPARATIVE AND TO REALLY
GO FOR WHAT GIVES YOU THE
BIGGEST BANG FOR YOUR BUCK AND I
THINK THAT'S CHANGING THE WAY OF
THE EXPONENT WITH WHICH THINGS SCALE.
SO OVERALL, IF YOU BELIEVE THAT,
NEUROMORPHIC COMPUTING HAS SOMETHING
TO OFFER, THAT IS FUNDAMENTALLY
SUPERIOR TO THE KIND OF
COMPUTING WE'RE DOING NOW IT'S
LIKE GOING FROM CLASSICAL
COMPUTERS TO QUANTUM COMPUTERS
THAT AMOUNT OF CHANGING THE EXPONENT.
THE RIGHT INGREDIENTS WILL GIVE YOU
THAT. THE WRONG INGREDIENTS YOU
CAN DO WHATEVER YOU WANT YOU'LL
NEVER GET THAT. SO. YEAH.
>> ARE WE READY TO OPEN UP TO
AUDIENCE QUESTIONS? I WILL ASK
PEOPLE TO THE IN ROOM
MICROPHONES BUT I WOULD QUICKLY
LIKE TO BRING IN A QUESTION OVER
ZOOM ABOUT NEUROMORPHIC HARDWARE
VERSUS POTENTIALLY NEUROMORPHIC
NEUROAI SOFTWARE SO THIS QUESTION
MENTIIONS
HYPERDIMENSIONAL COMPUTING
THAT CAN POTENTIALLY
COMPLEMENT HARDWARE SOLUTIONS,
JENNIFER TALKED ABOUT HOW GOING
ANALOG GETS YOU A LOT OF THE WAY
THERE BUT NEURAL STRUCTURE GETS
YOU A LOT FARTHER. WHERE IS THE
BALANCE BETWEEN HYPERDIMENSIONAL
COMPUTING VERSUS HARDWARE OR OTHER
APPROACHES TO NEUROMORPHIC AS FAR AS
ATTAINING ENERGY EFFICIENCY?
WHERE DO YOU SEE IT AT THAT
BALANCE POINT?
>> I THINK SOMETHING LIKE HYPER-
DIMENSIONAL COMPUTING WHICH GOES
BY A FEW NAMES, VECTOR SYMBOLIC
ARCHITECTURE. IT'S AN ALGORITHM
FRAMEWORK. RIGHT? AND SO IT'S
JUST A MATHEMATICAL DESCRIPTION,
A WAY THAT FORMULATES THIS.
DEEP NETWORK IS ANOTHER ONE. I
THINK THERE'S A CHALLENGE OF HOW
DO YOU REPRESENT WHATEVER YOUR
MATHEMATICAL FRAMEWORK IS INTO
PRIMITIVES THAT UNDERLY
REPRESENTATION KERNEL
OF HARDWARE PERFORMANCE.
THE
HARDWARE HAS SOME SORT OF LEVEL
OF WHICH YOU PROGRAM IT. BE IT
NEURONS AND SYNAPSES OR
SOMETHING ELSE AND THERE'S A
COMPILATION CHALLENGE FOR ANY
MATH THAT WE WANT TO DEAL WITH
IN TRYING TO REPRESENT, YOU
KNOW, SAY, HYPERDIMENSIONAL
COMPUTING INTO SOMETHING THAT'S
COMPATIBLE WITH NEUROMORPHIC
HARDWARE AND THAT'S AN
INTERESTING AND OPEN QUESTION.
IT'S A LITTLE MORE OF A
CS MATH QUESTION BUT IT HAS A
LOT OF IMPLICATIONS ON, YOU
KNOW, KIND OF THE QUESTIONS WE
CAN ASK WITH IT.
>> QUICKLY ADD TO THAT. I THINK
A LOT OF THE LEADING A.I. SYSTEMS
ARE BASED ON FEEDFORWARD NETWORKS
AND TIME-DEPENDENT COMPUTATIONS TEND
TO BE SOLVED BY TRANSFORMER-LIKE
ARCHICTURES. COMPUTATIONS THAT ARE
CONTINUOUS, TIME DEPENDENT, OR
RECURRENT MIGHT BE REALLY WELL
IMPLEMENTED WITH NEUROMORPHIC
COMPUTING, JUST A SPECULATION.
>> GREAT. THANK YOU. GOING TO
JOE AT THE MIC.
>> HI. NO ONE REALLY OWNS THE
DEFINITION OF NEUROMORPHIC. BUT
THERE ARE A LOT OF DIFFERENT
DEFINITIONS OUT THERE. AND SO
I'M CURIOUS TO HEAR WHAT THE
PANEL WOULD DEFINE AS
NEUROMORPHIC COMPUTATION
AND WHAT ARE THE
ELEMENTS THAT MUST BE PRESENT
FOR IT TO BE CONSIDERED
NEUROMORPHIC VERSUS TRADITIONAL?
>> I NOTE THAT THE PARTICIPANT
GUIDANCE INCLUDED LET'S NOT GET
INTO TERMINOLOGICAL DEBATES BUT
IF THERE ARE QUICK ANSWERS THAT
ARE USEFUL AND GIVE US USEFUL
BOUNDARIES.
>> I WOULD SAY IT'S COMPUTING
THAT SCALES, ITS ENERGY
EFFICIENCY AS EFFICIENTLY
AS THE BRAIN.
>> THAT'S A VERY SHORT
DEBATE. [LAUGHTER]
OKAY. I APPRECIATE THAT. YOUR
MIC IS NOT ON.
>> HI, I'M HISHAM FROM THE UNIVERSITY
OF MICHIGAN SO MY QUESTION IS,
MAYBE A LITTLE BIT TANGENTIAL
BUT WE ARE AT THE NIH SO MY
QUESTION IS, WHAT DO YOU GUYS
THINK SOME OF THE CLOSEST I
GUESS HEALTH GOALS OR
DISCOVERIES COULD BE WITH
SOMETHING LIKE NEUROMORPHIC
COMPUTING? SORRY, I CAN'T HEAR
YOU.
>> IS THE QUESTION MORE ABOUT
HOW NEUROMORPHIC COMPUTING WOULD
BE BENEFICIAL IN THE HEALTHCARE
DOMAIN?
>> YEAH.
>> I THINK THERE ARE A COUPLE
OF EXAMPLES THAT I -- WE ARE
WORKING ON, LIKE, FOR EXAMPLE,
IN THE TRAUMA RESEARCH ACADEMY
WE HAVE SEEN LIKE, YOU KNOW,
THERE ARE A LOT OF THESE DATA
POINTS THAT ARE COLLECTED AND
YOU NEED TO BE ABLE TO MAKE
REALTIME DECISION MAKING IN A
MATTER OF 30 SECONDS. AND YOU
NEED TO BE ABLE TO DO SOME FORM
OF PREDICTION, SOME FORM OF
CLASSIFICATION IN A VERY DYNAMIC
ENVIRONMENT AND DATA IS SLIGHTLY
SPARSE. SO WE ARE SEEING THAT
THERE COULD BE AN OPPORTUNITY
HERE WHERE YOU COULD HAVE THIS
AS AN A.I. HEALTH ASSISTANT. AND
THERE ARE SEVERAL OTHER SMALLER
EXAMPLES THAT ARE SHOWN IN THE
COMMUNITY WHETHER THEY'RE USING
LIKE EEG DATA, EMG DATA, OR OTHER
FORMS OF DATA TO DO DIFFERENT
TYPES OF CLASSIFICATION OR SOME
FORM OF PREDICTION IN DIFFERENT
TYPES OF DISEASES, FOR EXAMPLE
ONE DOMAIN THEY ARE ALSO LOOKING
AT IS IN HOW YOU COULD CLASSIFY
DIFFERENT SUBTYPES OF
NEURODEGENERATIVE DISEASES.
THAT IS PROBABLY NOT AT A
REALTIME CLASSIFICATION PROBLEM.
THAT IS -- BUT THERE IS A LOT OF
MISSING DATA POINTS IN THERE AND
IT'S UNDERREPRESENTED OR
UNDERSAMPLED OR IMBALANCED. WE
SEE SOME OF THESE SPIKING
ALGORITHMS ARE REALLY GOOD AT
THAT AND MAYBE THE NEXT STAGE
WILL BE CONSIDERING DEPLOYMENT
ON NEUROMORPHIC HARDWARE. THESE
ARE SOME EXAMPLES WE ARE
THINKING OF.
>> WE DO HAVE A WHOLE SESSION
DEDICATED TO THESE KINDS OF
QUESTIONS THIS AFTERNOON,
SESSION FOUR. AND GIVEN THAT WE
ONLY HAVE A MINUTE LEFT AND I
SEE THREE PEOPLE STANDING, I
JUST WANT TO KEEP IT GOING.
>> I'M RUHIRDATTA, I'M NEW TO
NEUROMORPHIC COMPUTING. I HAVE A
QUESTION ABOUT THE THOR COMMONS.
IF YOU COULD ELABORATE A LITTLE
BIT. I HAVE SEEN THINGS BEING
IN SILICON
THAT ARE SPECIALIZED
NEUROMORPHIC HARDWARE BUT I'VE
ALSO SEEN ELSEWHERE THAT THERE'S
SOMETIMES SPIKING NEURAL
NETWORKS ARE IMPLEMENTED IN GPUS
THEMSELVES. AND SOMETIMES
THERE'S FPGAS. SO COULD YOU SAY A
LITTLE BIT MORE ABOUT WHAT THE
THOR COMMONS IS GOING TO BE
LIKE? PROFESSOR KUDITHIPUDI,
IF I SAID THAT RIGHT.
>> WAS THE QUESTION ABOUT HOW
HOW PERFORMANCE VARIES ACROSS
THESE PLATFORMS OR WHAT IS
THE --
>> OR WHAT KINDS OF PLATFORMS
ARE GOING TO BE AVAILABLE IN THE
THOR COMMONS?
>> OH, IN THE NEUROMORPHIC
COMMONS, SORRY.
>> YEAH.
>> THANK YOU. SO THE
NEUROMORPHIC COMMONS IS GOING TO
BE PROVIDING ACCESS TO PRIMARILY
NEUROMORPHIC HARDWARE NOT THE
GPU PLATFORMS. WE WOULD PERHAPS
BE OPEN TO THAT IDEA AT A LATER
PHASE. WE ARE IN CONVERSATIONS
WITH DIFFERENT INDUSTRY PARTNERS
BUT THE IDEA IS TO KIND OF
PROVIDE ACCESS TO LARGE-SCALE
SPIKING NEUROMORPHIC HARDWARE
WHERE PEOPLE CURRENTLY ARE NOT
ABLE TO REALIZE SOME OF THE
SPIKING NEURAL NETWORKS. IN
THIS LARGE-SCALE HARDWARE
PLATFORM SO WE ARE GOING TO WORK
WITH INDUSTRY PARTNER WHO CAN
GIVE US ACCESS TO THAT KIND OF
INFRASTRUCTURE.
>> AND WOULD THOSE BE
SPECIALIZED INDUSTRY PARTNER
LIKE BRAINCHIP?
OR WOULD THOSE BE LARGE-SCALE
TECH LIKE INTEL OR IBM?
>> WE ARE TALKING TO THE
START-UP ECOSYSTEM AS WELL AS THE
LARGER INDUSTRY PARTNERS.
BECAUSE THERE IS A LOT OF
HETEROGENEITY IN THESE PLATFORMS
AND WHAT THEY CAN PROVIDE IN
TERMS OF THE SIMULATION
CAPABILITIES AND THE SCALE TO
WHICH WE CAN SIMULATE. SO WE ARE
GOING TO GET ACCESS TO MULTIPLE
OF THOSE PLATFORMS AND SOME OF
THOSE COULD BE COMING FROM
ACADEMIC LABS AS
WELL.
>> GREAT. THANK YOU.
>> AND THANKS TO THE SPEAKERS
AND THE PANEL ISSIST --
PANELISTS AND TO EVERYONE FOR
THEIR QUESTIONS. I WILL RELEASE
EVERYONE TO LUNCH NOW BUT I WANT
TO MAKE A FEW QUICK
ANNOUNCEMENTS.
THE POSTER SESSION IS STILL
AVAILABLE DURING LUNCH. WE ARE
RESUMING AT 1:30 FOR SESSION FOUR.
I WOULD LIKE TO INTRODUCE OUR
SESSION CHAIR, GINA ADAM.
SHE'S AN ASSOCIATE PROFESSOR IN
THE ELECTRICAL AND COMPUTER
ENGINEERING DEPARTMENT AT GEORGE
MASON UNIVERSITY. LET'S WELCOME HER
TO GIVE US OUR FIRST TALK.
>> ESTEEMED COLLEAGUES.
IT'S MY PLEASURE TO WELCOME YOU
TO SESSION FOUR TOWARDS RECIPROCAL
BRAIN NEUROAI ADVANCES FOR
INTELLIGENT COMPUTING, ROBOTICS
AND NEUROTECHNOLOGY.
WE HAVE AN ESTEEMED SET OF
PRESENTERS AND DISCUSSANTS.
IT'S REALLY, REALLY GREAT TO
HAVE THE DISCUSSION AT THE END
OF THE WORKSHOP.
IT IS ALSO VERY NICE TO SEE THE
INTEREST THAT NIH HAS IN
COMPUTING AND HOW TO BRIDGE THE
GAP TOWARD NEUROAI.
THIS IS A TIMELY MOMENT BECAUSE
I THINK WE'RE IN A HISTORIC TIME
IN OUR DEVELOPMENT OF A.I.
THE CURRENT A.I. FRAMEWORK AS
DISCUSSED IN SESSION THREE ARE
ARE DISEMBODIED AND LARGE SCALE.
THEY STRUGGLE WITH THE SEVERE
ENERGY REQUIREMENTS.
A LOT OF REQUIREMENTS IN TERMS
OF HARDWARE FOR THE SERVER FARMS TO
RUN THESE VERY LARGE-SCALE
MODELS.
CURRENTLY THE A.I. FRAMEWORK HAS
DONE SIGNIFICANT ADVANCES TO
SUPPORT THE SCIENTIFIC RESEARCH.
BUT THEY ARE RESOURCE OBLIVIOUS
ALGORITHMS.
IT IS TIME TO TAKE THAT KNOWLEDGE,
BRING NEW KNOWLEDGE FROM NEUROSCIENCE
AND BIOMECHANICS AS DISCUSSED
IN THE PRIOR SESSION AND OTHER
TYPES OF DISCIPLINES AND THINK
HOW WE CAN DEVELOP PHYSICAL A.I.
FRAMEWORKS THAT CAN SUPPORT
ADAPTIVE COMPUTING, CONTINUAL
LEARNING IN A WAY THAT'S OPTIMIZED
TO PHYSICAL CONSTRAINTS.
THAT IS KEY TO NEUROAI WHEN WE
THINK ABOUT APPLICATIONS IN
ROBOTIC, NEURO-CONTROL, PROSTHETICS.
DISCUSSANTS AND PRESENTERS IN THIS
SESSION WILL TALK IN MORE DEPTH
ABOUT THEIR WORK.
SO, IN ORDER TO ACHIEVE THAT, WE
HAVE TO DRAW INSPIRATION FROM
THE BRAIN FOR THE DEVELOPMENT OF
NEUROMORPHIC COMPUTING SYSTEMS.
A GRAND CHALLENGE IN OUR TIME IS TO
DEVELOP THE ARTIFICAL NEURAL SYSTEMS
THAT PERFORM AT THE COMPLEXITY AND
ENERGY EFFICIENCY OF THEIR BIOLOGICAL
COUNTERPARTS.
WE HAD DR. JENNIFER HASLER TALKING
ABOUT A ROADMAP TO DEVELOP NEUROMORPHIC
SYSTEMS AND HOW ANALOG COMPUTING
AND NEUROMORPHIC TECHNIQUES
AND PRINCIPLES CAN BRIDGE THE GAP IN
TERMS OF POWER EFFICIENCY.
WHERE WE ARE TODAY IS THE RESULT
OF THE DEVELOPMENT IN THE PAST
FOUR DECADES IN NEUROMORPHIC
HARDWARE.
I WANT TO HIGHLIGHT SOME OF
THESE CONTRIBUTIONS. WE HAVE
CARVER MEAD IN THE '80S WITH WORK
ON TRANSLATING NEUROMORPHIC
PRINCIPLES IN ANALOGUE VLSI AND THE
DEVELOPMENT OF THE SILICON RETINA
FOLLOWING SIMILAR PRINCIPLES.
I WANT TO GIVE A SHOUT OUT
BECAUSE WE HAVE DR.
GIACOMO INDIVERI FROM ZURICH.
I WANT TO GIVE A SHOUT OUT TO
THE INSTITUTE OF NEUROINFORMATICS
ESTABLISHED IN THE MID '90S.
FROM THAT POINT ON, YOU SEE WE MOVE
FROM THE ERA OF SMALL-SCALE TO LARGE-
SCALE INTEGRATION OF NEUROMORPHIC
HARDWARE USING CMOS TECHNOLOGIES.
DIFFERENT FUNDERS AND COUNTRIES ARE
SUPPORTING THESE DEVELOPMENTS
WITH THE SPINNAKER IN EUROPE.
WE HAVE BRAINSCALES WAFERS, THE
SYNAPSE PROJECT FUNDED BY DARPA
AND NOW WE LOOK AT HOW TO
COMMERCIALIZE NEUROMORPHIC HARDWARE
DEVELOPMENTS.
I WANT TO POINT OUT THE LOIHI CHIPS
BY INTEL AND IT WAS DISCUSSED
IN SESSION THREE.
THINKING ABOUT CREATING THE
NEUROMORPHIC SYSTEMS AT SANDIA.
OTHER TYPES OF NEUROMORPHIC CHIP
ARE ALSO BEING DEVELOPED.
I WANTED TO GIVE A SHOUT OUT TO
GIACOMO INDIVERI AND THEIR
DEVELOPMENT OF THE DYNAP
CHIPS. SO WHAT'S NEXT? WHAT IS
COMING NEXT FOR PHYSICL A.I.?
WE HAVE THE NOBEL PRIZE IN
PHYSICS GIVEN FOR A.I.
DEVELOPMENT, WE THINK ABOUT WHAT
IS NEXT FOR THE PHYSICAL A.I.?
AND HOW DO WE CONTINUE THIS
DEVELOPMENT OF NEUROMORPHIC
HARDWARE FOR TRANSLATION TO
INTELLIGENCE AND COMPUTING, ROBOTICS,
AND NEUROTECHNOLOGIES? I
THINK THE EMERGENT DEVICES WILL
PLAY A KEY ROLE IN THE
DEVELOPMENT OF ENERGY EFFICIENT
AND COMPACT NEUROAI SYSTEMS.
IN ORDER TO INCORPORATE EMERGING
NEUROMORPHIC TECHNOLOGIES, WE
NEED TO HAVE A VISION FOR
VERTICAL INTEGRATION.
WE NEED TO THINK ABOUT THE STACK
AND BE ABLE TO HAVE CODESIGN
APPROACHES TO OPTIMIZE ANYWHERE
FROM MATERIALS TO DEVICES, TO CIRCUIT
DESIGN, PROTOTYPING, TO ALGORITHMS
DRIVEN BY BIOINSPIRATION.
I SHOW HERE THE DIFFERENT DEVICES
THAT WE'RE WORKING ON. WE'RE WORKING
ON MEMRISTIVE DEVICES BASED ON
OXIDE MATERIALS THAT HAVE SHOWN
SYNAPTIC PROPERTIES, SUCH AS STDP BEHAVIOR, SPIKING BEHAVIOR.
THIS IS A SINGLE DEVICE AND WE
HAVE HERE 20,000 DEVICES INTEGRATED
ON TOP OF TRANSISTOR CIRCUITRY
WHICH IS PACKAGED AND CONNECTED
TO CUSTOM PROTOTYPING BOARDS AND
INTEGRATED HERE WITH FPGA BOARD.
USER AT THE TOP LEVEL OF THE STACK
CAN PROGRAM THESE DEVICES ALL THE
WAY DOWN WITHOUT KNOWING HOW TO
DO THE MEASUREMENTS ON THE DEVICE
ITSELF. BUT BE ABLE TO ACCESS
EVERYTHING AT THE SYSTEM LEVEL.
THIS IDEA OF HAVING A USER
FRIENDLY NEUROMORPHIC SYSTEM
THAT WE CAN USE FOR PROTOTYPING
IS KEY.
I'M GLAD TO SEE THE INITIATIVES
IN BUILDING COMMUNITIES FOR
THIS NEUROMORPHIC HARDWARE AND
THIS WAS BROUGHT UP IN SESSION
THREE WITH THE THOR INITIATIVE.
AND OUR GOAL IS TO CONTRIBUTE TO
THAT AND BE ABLE TO HAVE DIVERSE
USERS AND STAFF BE ABLE TO
UTILIZE THE PLATFORMS AND
INTEGRATE THEIR OWN DEVICES AND
TECHNOLOGIES INTO THE SYSTEM.
FOR THIS, I WANT TO GIVE A SHOUT
OUT TO ALL OF MY STUDENTS WHO
HELPED OUT WITH THIS AS WELL AS
THE DIFFERENT FUNDING AGENCIES.
AND I WANT TO TAKE A FEW MINUTES
TO DISCUSS ANOTHER EFFORT IN
COMMUNITY BUILDING. THE 2024
NEUROMORPHIC COMPUTING FOR SCIENCES
WORKSHOP WAS SPONSORED BY THE DOE.
THIS WAS TWO MONTHS AGO AND WE HAD
ABOUT 70 PARTICIPANTS. THE VISION
WAS TO DISCUSS KEY RESEARCH NEEDS,
CHALLENGES, AND NEXT STEPS IN
BIO-REALISTIC NEUROMORPHIC CIRCUITS.
THREE THEMES WERE PRESENTED:
NEUROSCIENCE, MICROELECTRONICS,
LARGE-SCALE MODELING AND SIMULATION.
WE STRUCTURED OUR DISCUSSIONS
ACROSS FOUR KEY QUESTIONS, WHAT
ARE THE PRIMITIVES NEEDED TO CAPTURE
CRITICAL, BIOLOGICAL COMPUTING
MECHANISMS? WHAT ARE TECHNOLOGIES
NEEDED FOR DEMONSTRATION AND PROTOTYPING?
WHAT ARE THE CRITICAL CHARACTERISTICS
FOR SCALING SIMULATIONS? A CROSS
CUTTING DISCUSSION HAPPENED
REGARDING NEUROSCIENCE-BASED
BENCH MARK AND DATA SETS TO
EFFECTIVELY CHARACTERIZE THESE
NEUROMORPHIC CIRCUITRY AND SIMULATION.
WE CAME UP THIS SLIDE AT THE
CONCLUSION OF THE WORKSHOP FOR
ACCELERATING THE DESIGN, PROTOTYPING,
OF PRIMITIVES, TRANSFORMING THE
CONDUCTIVITY AND PIONEERING AN
INTEGRATED ECOSYSTEMS FOR SCALABLE
NEUROMORPHIC CODESIGN, LEVERAGING THE
NEUROSCIENCE-INSPIRED DYNAMICS AND
ALGORITHMS.
THE REPORT WILL COME UP SOON.
THANK YOU VERY MUCH.
>> THANK YOU VERY MUCH GINA FOR
SHARING THIS DOE WORKSHOP WITH US.
WE'LL INVITE CHIARA BARTOLOZZI
FROM IIT TO TELL US ABOUT EMBODIED
NEUROMORPHIC INTELLIGENCE.
>> THANK YOU FOR INVITING ME.
SO, EMBODIED IS A WORD THAT IS
RESONATING IN THIS MEETING AND
ALSO NEUROMORPHIC.
SO AS YOU UNDERSTOOD
FROM ONE OF THE LATEST QUESTIONS
HERE IN THE PREVIOUS SESSION,
THERE IS NO CONSENSUS ON WHAT
NEUROMORPHIC IS.
I WANTED TO START BY GIVING MY
OWN DEFINITION. I WANT TO UNDERSTAND
BIOLOGY AND THE COMPUTATION DONE IN
BIOLOGY TO BUILD BETTER TECHNOLOGY.
WHAT DOES IT MEAN AND IN WHICH
DOMAIN IS BIOLOGY BETTER THAN
TRADITIONAL DIGITAL COMPUTATION?
I WANT TO POINT TO THE TWO
FIGURES HERE THAT SHOW
THAT BASICALLY THE BRAIN IS NOT
A BUBBLE THERE, BUT EMBODIED IN A
BODY, CONNECTED TO A BODY THAT
ACTS.
SO.
THE BRAIN IS PART OF A SYSTEM
WITH BRAIN AND BODY. WHEN WE
THINK ABOUT NEUROMORPHIC TASKS,
THEY INTERACT WITH THE WORLD IN
REAL TIME. THIS IS WHAT TONY ZADOR
IN THEIR WORK ON THE EMBODIED TURING
TEST MENTIONED AS ANIMAL-LEVEL COMMON
SENSE SENSORIMOTOR INTELLIGENCE.
ONCE WE HAVE THAT, WE CAN USE
NEUROMORPHIC TECHNOLOGY TO
IMPLEMENT THIS, THEN THE OTHER
TASKS THAT ARE MORE
HUMAN-LIKE IN TERMS OF ABSTRACT
REASONING, LANGUAGE, THAT WOULD
BE ON TOP OF THIS SENSORIMOTOR
INTELLIGENCE.
WHAT DO WE NEED TO DO FOR INTERACTING
WITH REAL WORLD IN REAL TIME?
THAT'S WHAT ANIMALS DO, THEY GATHER
INFORMATION FROM THE EXTERNAL WORLD
AND THAT MEANS INTERPRET THE SENSORY
INFORMATION TO MAKE A DECISION
ON WHAT TO DO, WHICH ACTION TO
PERFORM IN ORDER TO ACHIEVE
THEIR OWN GOAL.
THEN THEY NEED TO CONTROL THEIR
BODY AND THERE WAS A BEAUTIFUL
EXAMPLE OF WHAT MITRA WAS
TALKING ABOUT TODAY. SO IF WE USE
NEUROCOMPUTATIONAL PRIMITIVES AND
NEUROMORPHIC, THE IDEA THAT WE HAVE
A SYSTEM THAT CAN INHERITS THE
QUALITIES OF BIOLOGY SYSTEMS TO
BE MORE EFFICIENT IN THE USE OF
RESOURCES.
SO IN THIS PRESENTATION, I'M
GOING TO SKIM THROUGH A FEW
EXAMPLES AND I WILL GO VERY
FAST, OF WHAT WE DID IN OUR LAB
IN FOUR DIFFERENT DOMAINS.
WE DON'T WANT TO REPRESENT A
FULL STATE-OF-THE-ART BUT IN
TERMS OF SENSING, I WAS HAPPY
YESTERDAY TO HERE ABOUT TOUCH
IT'S A SENSORY MODALITY THAT IS
DISCARDED BY MOST OF THE PEOPLE
IN VISION, BUT IT IS IMPORTANT.
WHAT WE WANT TO DO HERE WAS TO
DEVELOP CIRCUITS FOR ENCODING
IN THE HUMAN SKIN. WHAT YOU SEE
HERE IS A TEXTBOOK DESCRIPTION
OF MECHANORECEPTORS IN THE SKIN.
THEY ARE EVENT DRIVEN, PRODUCE SPIKES
UPON CONTACT. WE WANTED TO DO THIS
IN FAST, ADAPTIVE TYPE OF CELLS THAT
RESPOND TO THE CHANGE IN PRESSURE.
WE DID THAT WITH CMOS CIRCUITS
AND INTERFACE WITH PIEZOELECTRIC
SENSORS IN A WAY THAT IS SIMILAR
TO EVENT-DRIVEN SENSORS THAT MOST
OF US HAVE HEARD ABOUT.
HERE WE SHOW THE RESULTS WHERE
IF WE APPLY A CHANGE IN FORCE,
WE HAVE A DIFFERENT BEHAVIOR
OF DIFFERENT NEURONS THAT FIRE
ACTION POTENTIALS.
THIS IS TO SHOW YOU THAT THIS
WORKS AND WHEN THERE IS A CHANGE
IN THE OUTPUT, THERE IS A CHANGE
IN THE INSTANTANEOUS FIRING RATE.
SO THIS IS THE SENSING PART,
HOW WE GATHER INFORMATION.
IN TERMS OF PERCEPTION, IT'S
NICE BECAUSE WE BASICALLY
IMPLEMENT A MODEL THAT WAS CITED
YESTERDAY. IT'S THE ORIGINAL
ITTI-KOCK MODEL OF SELECTIVE
ATTENTION THAT SELECTS WHAT'S
INTERESTING IN THE VISUAL FIELD.
THIS IS ITTI-KOCK 2.0 BY ERNST
NIEBUR, HE USES BORDER OWNERSHIP AND
GROUPING CELLS TO USE THE CONCEPT
OF GESTALT OBJECTS OR PROTO-OBJECTS
TO ATTRACT ATTENTION. NOT ONLY IN
FEATURE SPACE THERE IS SOMETHING THAT
CAN ATTRACT ATTENTION, BUT ALSO, IF
THERE IS SOMETHING THAT COULD
REPRESENT AN OBJECT.
WE USED LEAKY INTEGRATE-AND FIRE
NEURONS AND SPIKING NETWORKS TO
IMPLEMENT THIS MODEL USING EVENT-
BASED VISION SENSORS. WE ALSO USED
COMPETITIVE STEREO MATCHING TO ADD
FEATURE SPACE FOR DEPTH PERCEPTION
SO AN OBJECT CLOSE TO THE CAMERA OR
ROBOT IS SOMETHING INTERESTING.
LONG STORY SHORT: THIS IS THE VIDEO
AND THE ROBOT CAN FIND THE
MOST INTERESTING PART IN THE
VISUAL INPUT.
I DON'T THINK I HAVE MUCH TIME,
BUT BASICALLY WE WORKED ON
CONTROL.
AND HERE WE WANTED TO SOLVE THE
PROBLEM OF INVERSE KINEMATICS.
WHEN THERE IS SOMETHING IN THE
SPACE, WHAT IS THE CONFIGURATION
OR PROPRIOCEPTION OF THE ROBOT
TO GET TO THAT POSITION.
WHAT I WANTED TO SHOW YOU HERE
IS THAT WHEN WE WORK IN ROBOTICS,
WE KEEP EXCHANGING INFORMATION
BETWEEN THE CLOCK-BASED DOMAIN
NON-NEUROMORPHIC AND THE
SPIKING DOMAIN THAT IS
NEUROMORPHIC. WE BUILD THIS SMALL
MODULE THAT HAS TO COMMUNICATE WITH
THE ROBOT THAT IS NOT NEUROMORPHIC.
IMPLEMENTING REINFORCEMENT
LEARNING WITH SPATIAL SEMANTIC
POINTERS FOR DECIDING WHEN THE
ROBOT HAD TO MOVE TO HIT DYNAMIC
TASK - HIERARCHICAL TASK. WE SHOW
THAT DEPENDING ON INITIAL VELOCITY
OF THE TASK, THE ROBOT IS
CAPABLE OF WAITING UNTIL THE
THE RIGHT TIME TO MOVE AND HIT
THE TARGET.
THESE ARE ALL SMALL THINGS IN
THE FOUR DOMAINS.
WHAT I THINK IS IMPORTANT IS
THAT WE PUT EVERYTHING TOGETHER.
WE HAVE END-TO-END SPIKING SYSTEM
WITH NEUROMORPHIC TECHNOLOGY TO
HAVE THE FULL STACK OF
BEHAVIOR BECAUSE IT'S COSTLY TO
MOVE FROM ONE DOMAIN TO THE
OTHER EVERYTIME AND OF COURSE WE
NEED TO SCALE UP THE TYPE OF
TASK WE ARE TRYING TO SOLVE HERE.
A COUPLE OF WORDS ON WHAT IS
NEXT. I THINK WE NEED TO
TAKE THE EMBODIMENT KEYWORD
TO THE NEXT LEVEL.
IT'S NOT ONLY A SENSORIMOTOR
LOOP, BU BUT WE NEED TO LOOK INO
ACTIONS THAT ARE PURPUSEFUL TO GATHER
INFORMATION. NOT ONLY OBSERVING
SOMETHING AND THEN PLAN THE ACTION
BUT ALSO PLANNING THE ACTION
TO OBSERVE SOMETHING. THIS PICTURE
OF THE ICUB ROBOT IS INTERESTING.
WHEN YOU NEED TO CLASSIFY SOMETHING
YOU CAN TAKE IT, MOVE IT, USE YOU
INFORMATION GATHERED FROM
DIFFERENT SENSORY MODALITIES TO
GATHER INFORMATION ON WHAT
YOU'RE HE DOING IN RELATION TO
YOUR OWN BODY. THIS CAN SIMPLIFY
THE PROBLEM OF CLASSIFICATION.
WHILE IN TRADITIONAL A.I. SYSTEMS,
YOU GET TONS OF DATA THAT YOU
DON'T KNOW HOW THEY ARE EVEN
ACQUIRED. BUT HERE YOU CAN USE YOUR
OWN ACTIONS TO ACQUIRE DATA AND
PROCESS THEM. THIS IS ACTIVE SENSING.
WE CAN ALSO THINK ABOUT
MORPHOLOGICAL COMPUTATION, POSITION
OF SENSORS CAN SIMPLIFY COMPUTATION.
ATTENTION ALLOWS YOU TO CALCULATE
WITH FULL RATE OF VISION, USE HIGHER
RESOLUTION IN THE CENTER AND LOWER
IN PERIPHERY TO MAKE COMPUTATION MORE
EFFICIENT AND NOT HAVING TO COMPUTE
A LOT OF DATA AT THE SAME TIME. BUT
THIS HAS TO BE COUPLED TO ATTENTION
TO WORK. WE SHOULD MOVE TO OTHER
PLATFORMS, WHERE THE BODY IS
PERFORMING COMPUTATION AND COUPLE
IT WITH THE NEUROMORPHIC COMPUTATION
TO HAVE REAL EFFICIENT SYSTEMS.
THESE ARE THE PEOPLE THAT DID THE
WORK AND I'M FUNDED BY EUROPEAN
AGENCIES.
>> THANK YOU SO MUCH.
[APPLAUSE]
IT'S GREAT -- THANK YOU SO MUCH
FOR THAT INFORMATION ON THE
EMBODIED COMPUTATION SETTING IT APART
FROM DISEMBODIED COMPUTATION.
WE'LL NOW BE JOINED BY JOE HAYS.
>> I'M JOE HAYS FROM THE
NAVAL RESEARCH LABORATORY.
GIVEN MY ATTENDANCE HERE, THERE
ARE A NUMBER OF THINGS I'LL BE
MESSAGES THAT YOU'VE ALREADY
HEARD.
BUT I THINK MY PRESENTATION WILL
BE A LITTLE BIT DIFFERENT
BECAUSE I'M GOING TO FOCUS ON
THE APPLICATION, I WON'T TALK
TOO MUCH ABOUT THE DETAILS OF
NEUROSCIENCE OR THE NEUROMORPHIC
IMPLEMENTATION.
HOPEFULLY THERE IS VALUE HERE.
OKAY, SO I COME FROM THE
SPACECRAFT ENGINEERING DIVISION
AT NRL.
OUR GROUP HAS DONE A LOT.
WE ONLY BUILD ONE-OF-A-KIND
SYSTEMS.
WE LAUNCHED OVER A HUNDRED
SATELLITES INTO ORBIT AND
EACH ONE IS UNIQUE.
WE HAVE A ROADMAP FOR OUR
ROBOTICS WORK.
THE LAST TWO DECADES OUR
DIVISION HAS BEEN FOCUSING ON
THE ABILITY TO ROBOTICALLY SERVICE
A SATELLITE WHILE IT'S IN ORBIT.
THE NAVY HAS A MAINTENANCE PLAN
FOR EVERYTHING THEY BUY EXCEPT
FOR SATELLITES.
SOME OF THE SATELLITES ARE A
BILLION DOLLARS.
TO NOT HAVE A MAINTENANCE PLAN
IS NOT A GOOD PLAN.
WE'VE BEEN DEVELOPING THE
CAPABILITY TO PERFORM MAINTENANCE
ON THESE SYSTEMS WHILE IN ORBIT.
THIS IS USING OLD TECHNOLOGY.
NO NEUROMORPHICS OR MACHINE LEARNING.
WE ARE SPRINGBOARDING OFF OF
THIS CAPABILITY AND I'LL TALK
ABOUT THAT.
BUT THIS SATELLITE WAS JUST
DELIVERED TO THE LAUNCH VEHICLE
AND IN ABOUT 12 MONTHS, IT
SHOULD BE LAUNCHED INTO ORBIT
AND FOR THE FIRST TIME WE'LL
SHOW THE WORLD HOW TO SERVICE A
SATELLITE WHILE IN ORBIT.
THERE ARE A NUMBER OF THINGS THE
SATELLITE CAN DO BUT I'M NOT
GOING TO SPEND TIME ON THAT.
I AM GOING TO TALK ABOUT HOW
WE'RE SPRINGBOARDING OFF OF THAT
WORK AND BUILDING NEUROMORPHICS
INTO THE ROBOTICS EFFORTS.
ONE AREA WE SPENT A LOT OF TIME
ON IS INVESTIGATING HOW TO
PERFORM MAINTENANCE ON A SHIP
WITH ROBOTS.
WE TAKE LIVE -- SOLDIERS OR
SAILORS OUT OF HARM'S WAY.
LIKEWISE WE LOOK TO CHANGE THE
PARADIGM ON HOW SATELLITES ARE
BUILT. AS SOON AS THE JAMES
WEB TELECOPE LARGEST SATELLITE
WAS PUT INTO SPACE, PEOPLE WERE
ASKING FOR A BIGGER VERSION.
AND WE'RE NOW INVESTIGATING TO
BUILD THOSE ROBOTICALLY IN SPACE
WHERE THEY'RE TOO BIG TO MASS
LIFT OFF OF THE EARTH.
YOU SEND UP AN AMAZON TRUCK AND
YOU HAVE A ROBOT IN ORBIT
CONSTRUCTING A STRUCTURE THAT IS
MUCH LARGER THAN IT COULD BE
BUILT AND LAUNCHED OFF THE
EARTH.
WE ARE VERY INVESTED IN OUR
RESEARCH RELATED TO CONTINUAL
LEARNING AGENTS AND WE HAVE TO
FOCUS ON POWER AND THEREFORE THE
EDGE INTELLIGENCE BEING DESIGNED
INTO THESE SYSTEMS IS VERY
IMPORTANT.
IN SPACE, POWER IS KEY.
MOST OF YOUR DECISIONS ARE MADE
OFF OF WHAT POWER IS AVAILABLE
TO YOU.
LIKEWISE, OUR SHIP MECHANIC ALSO
HAS LIMITED -- ACCESS TO LIMITED
POWER.
AS WE PUSH MORE AND MORE
INTELLIGENCE CLOSER TO THE
SENSOR AND ACTUATOR, THAT'S WHAT
WE CALL EDGE INTELLIGENCE, AS WE
PUSH THAT MORE AND MORE OF THAT
CAPABILITY TOWARDS THE SENSORS
AND ACTUATORS USING TRADITIONAL
COMPUTING, IT MAKES OUR POWER
CONSUMPTION AND OUR SIZE, WEIGHT
AND POWER OF OUR SYSTEMS GO UP.
THAT'S THE WRONG DIRECTION.
WE NEED TO GO DOWN.
SO WE'RE INVESTING A LOT OF TIME
AND ENERGY INTO NEUROMORPHICS
BECAUSE WE BELIEVE THAT TO BE
OUR BEST CANDIDATE TO BE ABLE TO
REALIZE EDGE INTELLIGENCE, BUT
STILL REDUCE OUR SWAP.
NEUROMORPHIC IS AN OVERLOADED
TERM. THERE IS NEUROMORPHIC
COMPUTATION AND CHIARA JUST
DID A GREAT JOB OF INTRODUCING
NEUROMORPHIC SENSING.
THEY ARE DISTINCT AND
DIFFERENT BUT HIGHLY RELATED.
THERE ARE MANY PEOPLE WHO DO
NEUROMORPHIC SENSING WITHOUT
COMPUTING AND VICE VERSA.
FROM A RESEARCH PERSPECTIVE,
OUR GROUP IS ENGAGED IN
A LOT OF THINGS.
I'M NOT GOING TO FOCUS ON ANY
RESEARCH TOPIC, BUT WHEN WE DO
BASIC RESEARCH, IT'S KNOWN AS
6-1 RESEARCH.
WE DO A LOT OF 6-1 RESEARCH IN
NEUROMORPHICS AND CONTINUAL LEARNING.
WE TRY TO BRING THAT BASIC RESEARCH
TO 6-2 RESEARCH AS WE'VE BEEN SHOWN
HERE.
AGAIN, CONTINUAL LEARNING.
WE'VE A TALKED ABOUT HERE IN THE
LAST DAY OR TWO.
SIMPLY DEFINED, IT'S THE ABILITY
TO INTEGRATE ANY CAPABILITIES
AND KNOWLEDGE WITHOUT LOSING
PREVIOUSLY LEARNED CAPABILITIES
AND KNOWLEDGE.
I LIKE TO POINT OUT IT'S DONE IN
A SINGLE LIFETIME.
WHEN WE TRAIN AGENTS TODAY, WE
DO IT IN BATCH MODE. TAKE A GRADIENT
STEP, RESET, COLLECT DATA, BATCH IT, TAKE ANOTHER STEP.
TRUE CONTINUAL LIFELONG LEARNING
DOES NOT BEHAVE THAT WAY.
I WOULD LIKE TO GIVE A SHOUT OUT
TO DHIREESHA. SHE LED A TEAM
THAT PUT OUT A GREAT
PAPER. HIGHLY RECOMMEND IT.
THEY DOCUMENT A NUMBER OF
BIOLOGICAL MECHANISMS THAT ARE
BELIEVED TO BE INVOLVED IN
CONTINUAL LEARNING AS WELL AS
THE KEY INDICATORS THAT YOU CAN
SEE ALONG THE TOP THAT DEFINE
CONTINUAL LEARNING AND
THE INTERSECTION OF THOSE
MECHANISMS WITH KEY INDICATORS
OR FEATURES.
AS A GROUP, WE ARE INVESTED IN
INVESTIGATING A NUMBER OF THESE
MECHANISMS.
AS A HIGH LEVEL, WHY DO WE CARE
ABOUT CONTINUAL LEARNING?
WE HAVE AGENTS LIKE A ROBOTIC
DOG OR MAINTENANCE ON THE SHIP,
WE DON'T HAVE TIME TO BRING IT
BACK INTO THE LAB TO TEACH
IT THE NEW TOOL OR TEACH IT A
NEW OBJECT THAT IT HAS TO
IDENTIFY.
WE NEED TO ALLOW A NON-PHD SAILOR
TO BE ABLE TO POINT A NEW OBJECT
OR TOOL AT OUR ROBOT AND LEARN
HOW TO IDENTIFY OR MANIPULATE IT
WITHOUT BRINGING IT BACK TO THE LAB
TO TEACH IT THIS NEW THING.
THOSE ARE CAPABILITIES THAT WE
NEED DRASTICALLY AND HENCE
MOTIVATION FOR INVESTIGATING THOSE
CAPABILITIES.
AGAIN, WE'VE HAD A LOT OF
DISCUSSION ABOUT EMBODIMENT
WHICH HAS MADE ME VERY HAPPY THE
LAST COUPLE OF DAYS.
I GO TO A LOT OF
MACHINE LEARNING CONFERENCES
AND THERE IS NO DISCUSSION ON
EMBODIMENT. WHEN WE GROUND ALGORITHMS
IN CONSTRAINTS OF OUR PLATFORM, WE
FACE ISSUES WE NEED TO SOLVE TO MAKE
TRUE CONTINUOUSLY LEARNING AGENTS.
AND JUST LIKE EVERYONE ELSE,
I'LL MESSAGE THE SAME THING.
THIS IS AN INTERDISCIPLINARY
EFFORT.
NEUROAI IS A WONDERFUL
COMMUNITY OF EXPERTISE AND WE
NEED TO COLLABORATE.
I'M NOT AN EXPERT IN
NEUROSCIENCE OR MACHINE LEARNING.
I'M A ROBOTICIST. I CONSUME WORK
THAT YOU ALL ARE DEVELOPING.
WE COULDN'T BE SUCCESSFUL
WITHOUT YOUR HELP.
HOPEFULLY WE'LL BE GIVING BACK
TO YOU AS WE APPLY THESE SYSTEMS
THAT WE'LL BE ABLE TO PROVE OUT
WHICH ALGORITHMS THAT WE USE BUBBLE
UP TO SOLVE OUR PROBLEMS AND THAT
WILL BE FEEDBACK FOR YOU ALL.
AGAIN, THE SAME DIAGRAM WE'VE
SEEN A HUNDRED TIMES IN THE LAST
TWO DAYS, BUT THESE TWO
COMMUNITIES ARE SUPPORTING ONE
ANOTHER.
AND I JUST ENCOURAGE THIS
CONTINUING COLLABORATION.
AND WE'LL DO GREAT THINGS GOING
FORWARD.
>> THANK YOU FOR A WONDERFUL
TALK, JOE.
I WOULD LIKE TO INVITE WILL
NOURSE TO TALK TO US ALSO ON
ROBOTICS.
>> THANK YOU FOR INVITING ME.
IT'S AN HONOR TO BE ON THE SAME
STAGE AS ALL THESE KEY SPEAKERS.
I'M WILL NOURSE AND I SERVE AS PART
OF A LARGE-SCALE NSF GRANT C3NS
COMMUNICATION, COORDINATION AND
CONTROL OF NEUROMECHANICAL SYSTEMS
I'M GOING TO TALK ABOUT TODAY IS
WHAT IT WOULD MEAN FOR ROBOTS TO
HAVE INSECT-SCALE INTELLIGENCE.
I ARGUE WE DON'T HAVE THAT YET AND
WHAT IT WILL TAKE FOR US TO GET THERE
FIRST I HAVE TO TALK ABOUT WHAT IS
THE CURRENT STATE-OF-THE-ART IN
ROBOTICS. THROUGH MODERN A.I.,
WE HAVE INTELLIGENT ROBOTIC
SYSTEMS THAT CAN DO ALMOST ANY
GENERAL TASK, RIGHT? THE WORK OF
MARCO HUTTER HAS BEEN INSTRUMENTAL.
ONE OF THE EXAMPLES OFTEN USED
AS A HARD PROBLEM FOR ROBOTICS
IS FOLDING LAUNDRY.
IN THIS LAST MONTH, THEY
DEMONSTRATE FOLDING UNSEEN
LAUNDRY.
THESE PROBLEMS ARE NOW BASICALLY
SOLVED.
WHAT I'M GOING TO TALK ABOUT
NEXT IS AUTONOMY.
THESE ARE GENERAL TASKS.
HOW AUTONOMOUS ARE THESE ROBOTS
TODAY? I'LL USE LENS OF MANAGEMENT.
IDEALLY WE WANT THE ROBOTS TO
WORK FOR US.
WE HAVE IT MANAGE THEM IN SOME
WAY.
THE LOWEST LEVEL OF MANAGEMENT
IS STEP-BY-STEP CONTROL OR
TELEOPERATION. WHERE THE USER
SPECIFY EXACT TRAJECTORY OF EVERY
INDIVIDUAL JOINT OR ROBOT SURGERY MACHINE.
THE NEXT LEVEL UP OF MANAGEMENT
IS GIVING AN EXACT SET OF STEP
THAT THEY CAN EXECUTE EVERYTIME.
THIS IS ROBOTIC MANUFACTURING
AND ASSEMBLY, ET CETERA.
WHERE ROBOTS ARE IN RESEARCH
RIGHT NOW AND WHERE I THINK
THEY'RE GOING TO BE IN PRODUCTS
SOON IS SOME KIND OF GENERAL
TASK.
YOU CAN SAY ASSEMBLE THIS PART
OR FOLD THIS PILE OF LAUNDRY.
ALL THESE DIFFERENT LEVELS OF
MANAGEMENT ARE USEFUL AND
IMPORTANT BUT THERE IS ANOTHER
FAMILY OF TASKS THAT WE NEED TO
GET INTO IF WE WANT ROBOTS TO
ACHIEVE INTELLIGENCE.
HERE IS A LIST OF RESPONSIBILITIES
AND I WANT YOU TO DO EACH OF THEM
AS THE NEED ARISES. THIS IS THE
IDEA OF HAVING AUTONOMOUS
CONTEXT-DEPENDENT BEHAVIOR
SELECTION.
THIS IS A LONG-TIME HORIZON
PROBLEM THAT WE DON'T SEE SOLVED
WITH ROBOTICS AT THE MOMENT.
IT'S WHERE WE WANT ROBOTS TO BE
AND ANIMALS DO THIS ALL THE TIME.
I'M NOT GOING TO TALK ABOUT HOW
YOU COULD SCALE TRADITIONAL A.I.
SOLUTIONS BECAUSE I THINK
PEOPLE IN THIS ROOM AGREE THAT
PURE SCALING IS NOT GOING TO SOLVE
ALL THE PROBLEMS. WHY DO I
SAY INSECT-SCALE INTELLIGENCE?
INSECTS ARE NOT JUST A STOP ON
THE WAY TO MAMMAL-TYPE INTELLIGENCE.
IT'S AN INCREDIBLE FEAT AND WILL
BE AN INCREDIBLE ACHIEVEMENT FOR
ROBOTS.
INSECTS HAVE GOAL-DIRECTED
MOTION. FRUIT FLIES SMELL ROTTING
STRAWBERRY IN THE BREEZE AND IS
ABLE TO FIND THAT SPECIFIC GOAL.
THEY HAVE LONG-TERM NAVIGATION. ABLE
TO TRAVEL LARGE DISTANCES FOR FOOD.
AS SOON AS THEY FIND THEIR FOOD,
THEY TAKE A STRAIGHT LINE BACK
TO THEIR HOME NEST WITH LIMITED
COMPUTATION. THEY DO COMPLEX MOTOR
BEHAVIOR. THIS IS A FRUIT FLY
CROSSING A CHASM LARGER THAN ITSELF.
COORDINATING ALL OF IT'S MUSCLES.
THEY'RE ADAPTIVE. YOU CAN TAKE A FLY
CUT OFF ONE LEG AND IT'S ABLE TO
IMMEDIATELY ADJUST ITSELF SO IT
DOESN'T LOSE STABILITY AND STILL
MAINTAIN NORMAL LOCOMOTION.
ANTS CAN ACHIEVE AND FORM LARGE
STRUCTURES AND WORK AS A LARGE
SCALE SYSTEM.
PUTTING THIS ALL TOGETHER,
INSECTS HAVE THE CAPABILITY OF
AUTONOMOUSLY SWITCHING AMONG THE
TASKS DEPENDING ON THE CONTEXT
OF THE ENVIRONMENT THEY'RE
LOOKING AT.
LOOKING AT INSECTS, ONE OF THE
PLUSES IS WE HAVE ALL 140,000
NEURONS IN THE BRAIN OF A FRUIT FLY,
23,000 VENTRAL NERVE CORDS
ANALOGOUS TO THE SPINE. WHAT'S
EXCITING ABOUT CONTEXT-DEPENDENT
ACTION SELECTION? WE KNOW THE BRAIN
REGION INVOLVED: THE CENTRAL COMPLEX.
IT HAS 3000 NEURONS AND BEEN STUDIED
FOR ITS ROLE IN PATH PLANNING AND
NAVIGATION.
ANOTHER OVERLOOKED ASPECT IS THE
CENTER OF THE BRAIN WHERE ALL
SENSOR INFORMATION CONVERGES
INTO THE CENTRAL COMPLEX AND
MOTOR COMMANDS COME OUT.
THIS REGION TAKES IN THE STATE
OF EVERYTHING IN THE WORLD AND
DECIDES WHAT THE ANIMAL HAS TO
DO NEXT. IT'S ONLY 3,000 NEURONS.
WHY CANT' WE TAKE THE CONNECTOME
PLOP IT ON NEUROMORPHIC HARDWARE
AND RUN IT ON A ROBOT AUTONOMOUSLY?
FUTURE FUNDING AND OPPORTUNITIES
CAN HELP US WITH. EVEN THOUGH
WE HAVE A CONNECTOME OF THE BRAIN
AND VENTRAL NERVE CORD, WE DON'T
HAVE A CONNECTOME OF ALL OF THAT IN
ONE ANIMAL. THIS IS IMPORTANT FOR
DESCENDING INFORMATION. COMING SOON.
ADDITIONALLY, WE NEED STATISTICALLY
SIGNIFICANT CONNECTOMES. TO
UNDERSTAND WHAT IS A COMPONENT
OF THIS INTELLIGENCE AND
INTERINDIVIDUAL VARIATION, WE
NEED LOT MORE CONNECTOMES TO LOOK AT
THE STATISTICS OF NEURAL MAPS.
ADDITIONALLY, SOME SMALL ELEMENTS
ARE MISSING BECAUSE THE VISION
ALGORITHMS CAN'T DETECT THEM.
THESE ARE ELECTRICAL OR EPHAPTIC
OR GAP JUNCTIONS. THEY ALLOW
BIDIRECTIONAL COMMUNICATIONS
OVER THE SAME CHANNEL.
THESE ARE EXPERIMENTALLY FOUND IN
DIFFERENT PATHWAYS IN INSECTS BUT
YOU CAN'T SEE THEM IN THE CONNECTOME.
IN MOTOR CONTROL, FIBERS BETWEEN THE
PERIPHERAL SENSORY SYSTEMS AND
VENTRAL NERVE CORD ARE TOO SMALL TO
BE PICKED UP SO THAT DATA IS NOT
THERE OR IT'S SPORADIC.
WE CAN DO COMPLEMENTARY ORGANISM
CONNECTOMES. LOOK FOR SIMILAR NERVOUS
SYSTEM BUT DIFFERENT BEHAVIOR. THE
PRAYING MANTIS IS A PREDATOR AND IT
HAS DIFFERENT PRIORITIES IT NEEDS
ITS CENTRAL COMPLEX TO DO. ONE OF
THOSE BIG ONES BEING PREDICTION.
IT HAS TO BE ABLE TO PREDICT THE
ACTIONS OF ITS PREY. HOW THE
CENTRAL COMPLEX DIFFERS ACROSS
DIFFERENT LEVELS OF ANIMAL BEHAVIOR,
MAY REVEAL CORE CONCEPTS OF INTELLIGENCE
ON THE HARDWARE SIDE, THE ENTIRE
CONNECTOME CAN BE PORTED INTO
NEUROMORPHIC HARDWARE - EXCITING.
WE STILL HAVE LIMITATIONS BEFORE
WE CAN RUN THIS ON PHYSICAL
SYSTEMS AND INTELLIGENT ROBOTS.
DUE TO THE FAN IN AND OUT CONSTRAINTS
OF CURRENT GENERATION NEUROMORPHIC
HARDWARE, IT REQUIRES MORE CHIPS THAN
THAN YOU WOULD EXPECT BASED ON NEURON
COUNTS. A CHIP WITH MILLION NEURONS
CAN'T FIT 140,000 CONNECTOME.
WE LACK EASILY AVAILABLE SYSTEMS FOR
RESEARCHERS INSPITE OF CLOUD-SCALE
SYSTEMS. AT THE END OF THE DAY, TO
PUT IT ON A ROBOTIC SYSTEM, WE NEED
TO HAVE PHYSICAL SYSTEMS IN HAND.
FINALLY, THE LAST THING I WANT
TO MENTION IS NEURAL COMPLEXITY.
EVEN THOUGH WE HAVE CONNECTOME
AND A POINT-TO-POINT MAP OF NEURONS,
A LOT OF NEURONS DON'T BEHAVE
AS POINT-TO-POINT. THIS IS A
PROJECTION PATTERN OF ONE SINGLE
NEURON IN THE FRUIT FLY, RING NEURON,
INNERVATING ENTIRE CENTRAL COMPLEX.
IN THIS PROJECTION, LOCAL COMPUTATION
HAPPEN WITHIN THE SYNPASE BEFORE
IT GETS TO THE CELL BODY. THIS CAN'T
BE IMPLEMENTED WITH A SINGLE SYNAPSE.
WE NEED MORE CONNECTOMES, ACROSS
MORE SPECIES AT HIGHER RESOLUTION.
WE NEED MID-SCALE NEUROMORPHIC
SYSTEMS TO CATALYZE ROBOTIC
INTELLIGENCE. I WANT TO THANK ALL
THE UNIVERSITIES INVOLVED
AND THE FUNDING OPPORTUNITIES.
THANK YOU.
>> THANK YOU.
THIS TIME I WOULD LIKE TO INVITE
KAI MILLER FROM THE MAYO
CLINIC.
>> IT'S A GREAT PLEASURE TO BE
HERE IN PARTICULAR, I WOULD LIKE
TO THANK DR. HWANG THE PROGRAM
OFFICER FOR INVITING ME.
I'M A CLINICIAN SO I SHOULD
START OUT BY SAYING THERE IS
DOMAIN EXPERTISE -- I HAVE LESS
EXPERTISE THAN THOSE IN THE
AUDIENCE BUT I CAN DESCRIBE THE
END APPLICATION FOR HEALTHCARE.
THESE ARE MY DISCLOSURES.
I WOULD LIKE TO THANK THE NIH
FOR SUPPORTING OUR WORK.
FUNCTIONAL NEUROSURGEONS ARE
DOCTORS WHO INVASIVELY INTERACT
WITH NEUROCIRCUITRY. WE THINK OF
THE BRAIN, THE SPINE AND PERIPHERAL
NERVE TO MEASURE AND MODIFY ITS
COMPUTATIONAL PURPOSE USING
ELECTRODES AND TISSUE
DESTRUCTION AND NOW WITH
EMERGING GENOMIC TOOLS.
TO BEGIN WITH, WE HAD OPEN
SURGERIES AND WE USED TO DO THINGS
LIKE INDUCE FOCAL STROKES.
BUT OVER TIME WE START
TO DELIVER HIGH FREQUENCY-POWERED
ULTRASOUND OR LASER LIGHT
TO DESTROY STRUCTURES INVOLVED
WITH CIRCUITRY AND STIMULATE.
THE AMOUNT OF INTERVENTION WE
HAVE IS CRUDE COMPARED TO
TECHNOLOGY ON OUR PHONES AND
COMPUTING.
MEDICINE DOES LAG AND THE
PURPOSE BEHIND THE LAG IS THE
NEED FOR SAFETY AND THE NEED FOR
VALIDATION PRIOR TO IMPLEMENTING
THINGS.
WHEN WE INTERVENE WITH THE
NERVOUS SYSTEM, IT'S IMPORTANT
TO REALIZE THERE IS A DIFFERENCE
BETWEEN TREATING THE SYMPTOMS OF
A DISEASE AND CHANGING THE
CIRCUIT DYSFUNCT DYSFUNCTIONS
THAT UNDERLIE THE DISEASE.
THAT MEANS WITH WE PUT DEVICES
IN THE BRAIN, MOST OFTEN WHAT
WE'RE DOING IS DELIVERING
ELECTRICAL CURRENT AT HIGH
FREQUENCY TO DISRUPT AN AREA.
MOVING FORWARD, WE WOULD LIKE TO
TREAT MORE NUANCE DISEASES THAN
WE DO NOW. MOVING FORWARD,
WHERE WE TREAT TREMORS, WE WANT
TO START TREATING PSYCHIATRIC
DISEASES, WE'RE GOING NO NEED
APPROACHES THAT MATCH THE
STATISTICS OF THE BRAIN.
I THINK OF THIS AS WHAT IT MEANS
TO BE NEUROMORPHIC AND ADAPT TO
THE STATE OF THE BRAIN SO WE CAN
MODIFY THE WAY THE BRAIN
COMPUTES IN REALTIME.
THE TYPE OF APPROACH FOR
NEUROMORPHIC SYSTEMS AND
EMBODIMENT MAY APPLY.
IF I SAY WHAT'S THE USE OF A
CLINICIAN?
I CAN PROVIDE SOME CONTEXT AND
DESCRIBE A NEED THAT WE HAVE,
BUT IT'S IMPORTANT TO REMEMBER
THAT THE MISSION STATEMENT OF
THE NIH AND MISSION STATEMENT OF
OTHER INSTITUTIONS THAT DELIVER
CARE IS TO MEET A CLINICAL NEED.
BUT THE MISSION FOR COMPANIES ARE
BOUND BY THEIR CHARTER TO MAKE A PROFIT.
WE HAVE TO KEEP IN MIND THAT THE
WAY WE DESIGN THINGS HAS TO
INTEGRATE INTO THE ECONOMIC
PICTURE WHICH PLAYS A ROLE IN
THE TYPES OF WAYS WE MAKE
DECISION IN THE CLINICAL CONTEXT.
THE ECONOMIC INCENTIVES DRIVE
WHICH TOOLS.
IT'S NOT NECESSARILY THE BEST
TOOL BUT THE ONE THAT WE HAVE
ACCESS TO.
ECONOMICS DRIVE THAT.
THAT'S NOT THE SAME THING AS
WHAT IS SCIENTIFICALLY COMPELLING.
I'M A SCIENTST AND A SURGEON.
THE THINGS THAT INTEREST ME
INTELLECTUALLY ARE DIFFERENT
THAN THE THINGS THAT INFLUENCE
THE DECISION I
MAKE ON HOW TO TREAT A
PARTICULAR CHILD.
A LOT OF US HERE THINK ABOUT THE
MESSAGING TO THE PUBLIC.
IT'S DIFFERENT THAN WHAT
PATIENTS NEED AND ECONOMIC
INCENTIVES.
IF WE SAY WHAT DOES ARTIFICIAL
INTELLIGENCE HAVE TO OFFER US
FUNCTIONAL NEUROSURGEON? I THINK WE
CAN USE IT FOR DISCOVERING STRUCTURE.
I DO ELECTROPHYSIOLOGICAL SIGNAL
DECODING. TRY TO UNDERSTAND HOW
GENETIC PREDISPOSITION DETERMINE
THE END PHENOTYPE. STRUCTURAL
SEGMENTATION, DOCUMENTATION,CHART
SYNTHESIS ARE THE MOST BORING THING
THAT WE DO.
THIS IS THE MOST STRAIGHTFORWARD
IMPLEMENTATION WE HAVE FOR LLMS
NOW. BUT FOR DELIVERING MEDICAL
CARE, THIS IS 50% OF THE PERSON
TIME IS DEDICATED
TO DOCUMENTATION AND CHARTING.
AND A LOT OF IT IS NOT AUTOMATED
IN INTUITIVE OR WORKABLE WAYS.
CLINICAL PREDICTION.
FORECASTING HOW THINGS CHANGE
OVER THE LIFESPAN.
I THINK I'VE HEARD A LOT ABOUT
ARTIFICIAL INTELLIGENCE WHERE
PEOPLE ARE THINKING ABOUT THIS
AHEAD OF TIME.
THIS IS JUST A SNAPSHOT TAKEN
FROM ONE OF OUR MANUSCRIPTS ABOUT
HOW WE MAKE A CONENSUS DECISION FOR
EACH INDIVIDUAL PATIENT WHO MIGHT
HAVE EPILEPSY.
WHAT WE TO HAVE IN THE FUTURE IS
DIGITAL TWINNING. USING A LARGE DATABASE OF PATIENTS,
WITHOUT RELYING ON CONVERSATIONS
BETWEEN EXPERTS, WE WANT TO HAVE
ALGORITHMS THAT HAVE THE PAST
KNOWLEDGE OF MANY, MANY PATIENTS
TREATED WITH EITHER STIMULATION
OR A RESECTION OR LASER ABLATION.
AMONG THIS PAST SET OF PATIENTS,
WHICH PATIENT IS THE CURRENT PATIENT
CLOSE TO? CAN WE MODEL IT BASED ON
PAST STATISTICS IN AN INTUITIVE WAY?
DIGITAL TWIN HAS A ROLE IN CLINICAL
MEDICINE AND DECISION-MAKING FOR PATIENTS.
FOR THOSE OF YOU WHO MAY NOT BE
FAMILIAR WITH WHAT IT'S LIKE TO
BE A PATIENT AND GO THROUGH THE
WORK FLOW, THIS IS HOW I THINK
OF ORGANIZING THINGS WITHIN AN
INSTITUTION.
THEY'LL SEE A PHYSICIAN WHERE A
DIAGNOSIS IS MADE. THERE IS A ROLE
FOR A.I. HERE. WE DO SURGICAL
PLANNING. WE PUT ELECTRODES IN ONE
SPOT IF I'M BURNING AN AREA OF
THE BRAIN OR IF I WANT TO USE
MOTION CAPTURE TO LOCALIZE AREAS IN
THE BRAIN, MACHINE LEARNING CAN PLAY
AN IMPORTANT ROLE. WE USE ROBOTICS
IN THE OPERATING ROOM. WE HAVE
ONLINE DIAGNOSTICS BASED ON IMAGING
ELECTROPHYSIOLOGY. THERE' A ROLE FOR
A.I. IN PREDICTING RESPONSE TO
INTERVENTION. ANOTHER APPLICATION
FOR DIGITAL TWINNING. IN THE NEXT
STEP NOW THAT WE'VE DONE THIS, CAN WE
PREDICT INDIVIDUAL CARE AFTERWARDS?
MY RESEARCH AND THE RESEARCH OF
MANY OTHER NEUROSURGEONS WHO ARE
SCIENTISTS IS WORKING ON THE NEXT
GENERATION OF IMPLANTABLE DEVICES.
PARAMETER OPTIMIZATION AND
CLOSED-LOOP CONTROL IS THE OBVIOUS
CLEAREST MOST COMPELLING ASPECT
OF A.I. FOR NEUROSURGERY.
WE NEED TO DISCUSS HOW TO INTEGRATE
INFORMATION AND MULTIMODAL DATA
ACROSS INSTITUTIONS FOR NEW THERAPY.
DOMINIQUE DUNCAN WHO SPOKE YESTERDAY,
THE TYPES OF ROLES THAT THESE
SCIENTISTS PLAY ARE IMPORTANT FOR
NEXT GENERATION THERAPY AND IMPORTANT
FOR THE NEW ALGORITHMS THAT WILL BE
DEVELOPED AND IMPLEMENTED.
I THINK THERE IS A KEY ROLE FOR
HAVING NEW ALGORITHMS THAT --
TAKING NEW ALGORITHMS AND
ALLOWING ONE TO IMPLEMENT THEM
FROM DATA THAT IS AGGREGATED
ACROSS A WIDE VARIETY OF
INSTITUTIONS.
THAT IS SOMETHING THAT THE NIH
IS UNIQUELY EQUIPPED TO DO BECAUSE
NIH IS WHAT INCENTIVIZES US
PHYSICIANS AND OTHERS -- WHAT
INCENTIVIZES US IN TERMS OF
DECIDING THE KIND OF RESEARCH WE'RE
GOING TO DO AND DECIDING HOW TO
DO IT AND GETTING FUNDS AND
RESOURCES WE NEED.
I WOULD LIKE TO GIVE A COUPLE OF
EXAMPLES ABOUT HOW WE DESIGN DEVICES
THAT INTERACT WITH THE BRAIN TO
PROVIDE A CONDUIT TO THE OUTSIDE WORLD: BRAIN COMPUTER INTERFACE.
INDUCING PLASTICITY TO HELP PEOPLE
WHO HAVE BEEN INJURED BY STROKE OR
EPILEPSY. TO MANAGE DISEASE SYMPTOMS
THIS IS OPEN LOOP STIMULATION NOW.
THINKING PARTICULARLY ABOUT EMBODIED
AND NEUROMORPHIC APPROACHES.
IMAGINE YOU HAVE A BRAIN, WE ALL
DO.
WE WANT TO SENSE FROM THE BRAIN
THESE TYPES OF DEVICE.
THERE IS A TECHNIQUE FOR
RECORDING INFORMATION IN THE
BRAIN. AN ELECTRICAL EXAMPLE:
WE HAVE SOME COMPUTATION AND
DECODING THAT GOES INTO THIS.
WE USE THAT FOR SOME SORT OF
INTERNAL/EXTERNAL EFFECT. THE
INTERNAL EFFECT WOULD BE BRAIN
STIMULATION. RETRAINING OR MANAGING
THE DISEASE WITH ELECTRODES
IMPLANTED IN THE BRAIN. WE CAN
INTERFACE WITH THE OUTSIDE WITH BCI.
ADOPTING AN EMBODIED OR NEUROMORPHIC
APPROACH MIGHT BE USEFUL. EMBODIED
MEASUREMENTS MATCHES THE INTERVENTION
THAT WE'VE PERFORMED TO THE PHYSICAL
SCALE OF THE SYSTEM. AND THAT COULD
BE LIMBS IN THE OUTSIDE WORLD,
THE SCALE OF ELECTRODES COULD
MATCH THE TYPE OF MEASUREMENTS AND
THE CIRCUITRY WE'RE INTERVENING
THE CIRCUITRY OF THE BRAIN IS
INSTRINSCALLY MULTISCALE. NOT JUST
A COLUMN WITHIN A NEURON. ITS ACROSS
SYNAPESES ACROSS REGIONS OF THE
BRAIN THAT SPREAD CENTIMETERS.
OUR HARDWARE SHOULD MATCH THE
PHYSICAL SCALES.
IN NEUROMORPHIC COMPUTING, THE
ALGORITHMS COULD BE THOUGHT OF AS
ALGORITHMS WHERE THE STATISTICS OF
THE ANALYSIS THAT YOU ARE PROCESSING
MATCHES THE NEUROLOGICAL STATISTICS.
THIS IS PRACTICAL -- I'M SORRY,
LET ME KNOW IF I'M RUNNING TOO
LONG.
MATCHING THE STATISTICS OF THE
BRAIN AND
MATCHING THE STATISTICS OF THE
BRAIN IS NOT STATIC.
THAT'S AN IMPORTANT THING TO
KNOW.
THE WAY YOUR BRAIN FUNCTIONS AND
THE NATURE OF THE CIRCUITS IN
YOUR BRAIN WHEN YOU ARE AWAKE
AND ASLEEP ARE DIFFERENT. THE WAY
IN WHICH YOU NEED TO INTERVENE
WITH THE BRAIN IN THOSE TWO
STATES IS VERY DIFFERENT.
THIS IS AN ARTICLE ABOUT
TRANSLATION FOR NEUROTECHNOLOGIES.
IT'S USEFUL. THERE'S A QR CODE.
THIS IDEA THAT EMBODIED HARDWARE
WOULD BE HAVING MICROELECTRODES
WHEN THE PHENOMENA CONCERN ACTION
POTENTIALS. OR IF YOU WANT TO
INTERVENE POPULATIONS, USE GRIDS ON THE BRAIN SURFACE.
THIS IS USEFUL FOR SIGNALS THAT AGGREGATE 10 -100,000 OF NEURONS
AND OBEY 1/F STATISTICS. YOU CAN LOOK
AT HOW BRAIN REGIONS INTERACT
DYNAMICALLY, STIMULATE AND MEASURE
RESPONSES FROM ONE AREA TO ANOTHER.
IF YOU'RE LOOKING AT THINGS LIKE
THE GENERAL HOMEOSTATIC STATE
OF THE BRAIN, THE WAY WE CONTROL
LARGE REGIONS FROM THALAMIC OUTPUT
TO CORTEX. YOU'LL THINK ABOUT
OSCILLATIONS. THESE ARE THINGS YOU
CAN PICK UP EVEN OUTSIDE THE HEAD.
THE SPATIAL SCALE OF ELECTRODES FOR
THESE CONTEXT ARE DIFFERENT FROM ONE ANOTHER
BUT THEY'RE TRYING TO INTRINSICALLY
MATCH THE ANATOMY OF THE BRAIN.
IT'S DIFFERENT FROM EMBODIMENT
OF LIMBS. BUT THE WAY YOU DESIGN
THE HARDWARE SHOULD MATCH IF
YOU'RE INSIDE THE BRAIN.
THIS IS AN EXAMPLE OF DECODING
VISUAL STIMULI.
YOU'LL SEE WHAT THE PATIENT IS
SEEING HERE.
THERE ARE ELECTRODES ON THE
OTHER SIDE OF THE BRAIN.
WE'RE MEASURING TWO TYPES OF
ELECTROPHYSIOLOGY. ONE THAT IS
FEEDFORWARD OUTPUT IN RESPONSE TO
VISUAL STIMULUS. THE OTHER IS THIS
1/F PROCESS REFLECTIVE OF LOCAL CIRCUITRY.
WE CAN GET REALTIME INFORMATION
FROM EACH LOCATION AND PLOT THEM
INTO A LARGER DIMENSIONAL FEATURE SPACE.
IT'S IMPORTANT IN THE BRAIN TO
REMEMBER THAT THIS FEATURE SPACE
IS NOT STATIC. CURRENT SYSTEMS FOR
DECODING THE BRAIN ARE RELIANT ON
DAILY CALIBRATION, ROUTINES THAT
DETERMINE NEW SETS OF PARAMETERS FOR ALGORITHMIC DECODING.
THIS IS BURDENSOME, NOT PRODUCTIVE.
MOST OF THE BRAIN COMPUTER
INTERFACING ONLY WORKS WHEN YOU
HAVE A SCIENTIST IN THE ROOM
WITH A PATIENT RECALIBRATING A
MACHINE EVERY DAY.
INSTEAD OUR MACHINES NEED TO
RECALIBRATE THEMSELVES FIRST OF
ALL BY TRYING TO DECODE THE
BRAIN IN A MULTISCALE WAY. YOU
NEED TO ENCODE NOT JUST THE DOMAIN
SPECIALIST AREA -- HAND AREA -- BUT
ALSO THE GENERAL STATE OF THE MOTOR CORTEX.
WE CAN PROBE, REPARAMETIZE DECODING IN REALTIME AND FEED OURALGORITHMS.
AND THIS IS WHAT WE SHOULD THINK OF
AS NEUROMORPHIC ALGORITHM WHEN DECODING BRAIN ACTIVITY.
THIS WOULD BE USEFUL FOR CLOSED-LOOP DEVICES THAT I WANT TO BEIMPLANTING INTO THE BRAIN.
[APPLAUSE]
THANK YOU VERY MUCH.
AT THIS
>> THANK YOU VERY MUCH.
AT THIS TIME I WOULD LIKE TO GET
GIACOMO INDIVERI'S RECORDING.
>> HELLO, I'M GOING TO PRESENT
AN OUTLOOK OF NEXT GENERATION
BRAIN MACHINES INTERFACES. UNLIKE
MACHINE WORKING WORKLOADS, THEY
HAVE STRINGENT HARDWARE REQUIREMENTS,
SMALL POWER BUDGET, LOW LATENCY, CLOSED-LOOP
MULTI-SCALE DYNAMICS, INPUTS WITH
A LARGE DYNAMIC AND VARIABLE RANGE.
FROM THE ALGORITHMIC POINT OF VIEW
THESE INTERFACES NEED TO BE ALWAYS
ON. SENSING DATA, DECODING, PROCESSING,
ENCODING, POSSIBLY STIMULATING
CONTINUOUSLY. UNLIKE CONVENTIONAL
WORKLOADS, THE TYPE OF DATA USED OR
IS AVAILABLE FOR THESE DEVICES IS
LIMITED.
THERE ARE NO LARGE DATA SETS
USED FOR TRAINING.
IN ADDITION, THESE DEVICES NEED
TO BE ADAPTED AND HAVE CONTINUAL
LEARNING, NOT INCREMENTAL LEARNING
AND CHANGING THEIR PROPERTIES
CONTINUOUSLY THROUGH TIME AS THE
DATA COMES THROUGH.
WITH STREAMING DATA, THE DATA IS
CALLED BATCH-SIZE ONE WHICH IS
NOT COMMONLY USED IN DEEP NETWORKS.
BUT THIS IS A HARD REQUIREMENT FOR
BRAIN MACHINE INTERFACES.
IF WE LOOK AT THE FIRST TWO
REQUIREMENTS, THE EXTREMELY
SMALL POWER BUDGET AND THE
ALWAYS ON PROCESSING, IT'S CLEAR
THAT NEXT GENERATION BMI NEEDS TO BE
DESIGNED WITH BOTTOM-UP, PHYSICS-
BASED APPROACH TO SQUEEZE EVERY JOULE
OUT OF THE SYSTEM. WE NEED TO USE
DIGITAL AND ANALOG CIRCUITS OPERATING
ON MULTIPLE TIME SCALES FOR ENDOWING
THESE SYSTEMS WITH THE ABILITY TO
PROCESS SIGNALS CONTINUOUSLY WITH
LARGE DYNAMIC RANGE, WITH STREAMING
DATA, CONTINUOUS LEARNING, AND SO ON.
ONE PROMISING APPROACH IS THE
NEUROMORPHIC COMPUTING ONE.
WE HAVE BEEN ANALYZING THESE MIXED
SIGNAL ANALOG / DIGITAL CHIPS FOR
SOME TIME. WE DESIGN CHIPS USING
ANALOG SUBTHRESHOLD CIRCUITS TO
IMPLEMENT SPIKING NEURAL NETWORKS.
TYPICALLY THE NEURON IS AT THE SIDE
OF THE ARRAY. A LARGE NUMBER OF SYNAPSES
RECEIVE INPUTS, ITEGRATE CURRENTS
USING ANALOG DYNAMICS TO PRODUCE
SPIKES. THESE CIRCUITS ARE SLOW. THEY
HAVE THE SAME TIME CONSTANTS AS
THE BIOLOGICAL SYSTEMS WE WANT THEM
TO INTERACT WITH: NEURONS, MUSCLES.
BECAUSE OF THE WAY THEY'RE BUILT BY
COPY PASTING MANY NEURONS TOGETHER.
THEY ARE MASSIVELY PARALLE AND
COMPATIBLE WITH EMERGING MEMORY TECHNOLOGIES.
HOWEVER, THEY ARE NOISY. ANALOG CIRCUITS
HAVE DEVICE MISMATCH. EACH NEURON IS
DIFFERENT. THEY'RE HIGHLY VARIABLE.
ONCE THESE ANALOG SIGNALS PRODUCE
AN ACTION POTENTIAL, WE CONVERT THEM
INTO SPIKES AND WE CAN USE DIGITAL
NETWORKS TO CONFIGURE THE WAY
THESE NEURONS ARE CONNECTED TO
EACH OTHER USING ROUTERS.
A POINT THAT IS IMPORTANT IS THAT
THESE NEURONS AND SYNAPSES ARE
VARIABLE. THERE IS A LARGE AMOUNT OF
DEVICE MISMATCH.
FORTUNATELY BIOLOGY CAN HELP US
BECAUSE IT HAS THE SAME TYPE OF
CHARACTERISTICS. NEURONS ARE VARIABLE
IN ANALOG CIRCUITS, HERE WE SEE
AN EXAMPLE OF CHIP MEASUREMENTS
COMPRISING 256 NEURONS. WE APPLY THE
SAME INPUT TO ALL NEURONS. NEURONS
ARE SLIGHTLY DIFFERENT WHEN THEY'RE
FABRICATED. THE TIME TO FIRST SPIKE
IS HIGHLY VARIABILITY -- THE
COEFFICIENT OF VARIATION IS ABOUT 20%.
NOW, HOW CAN WE DO A ROBUST
COMPUTATION USING THIS TYPE OF
VARIABILITY PRESENT IN OUR
COMPUTING SUBSTRATE?
ONE THING THAT COMES TO MIND IS
TO USE AVERAGING.
IF WE AVERAGE POPULATIONS OF
NEURONS, WE CAN REDUCE THE
VARIABILITY BY THE
SQUARE ROOT OF THE NUMBER OF
NEURNS WE AVERAGE WITH.
IF WE USE NONLINEAR DYNAMICS,
IT'S USEFUL AND IT'S BEEN SHOWN
THAT THESE TYPES OF NONLINEARITIES
CAN REDUCE VARIANCE BY A FACT OR N
NOT SQUARE ROOT OF N. IT'S POWERFUL.
THE OTHER MECHANISM FOR ENDOWING THE
SYSTEM WITH ROBUSTNESS IS LEARNING.
WE QUANTIFY THIS VARIABILITY IN
OUR CHIPS AND THOUGHT WHAT IF WE
AVERAGE TWO, FOUR, EIGHT NEURONS?
AND WE MEASURE THE COEFFICIENT
OF VARIATION AS A FUNCTION OF
POPULATION SIZE AND INTEGRATION TIME.
HERE THE COEFFICIENT OF VARIATION
GOES FROM 20% WITH ONE NEURON
TO 18% DOWN TO LESS THAN 1%.
ONCE WE HAVE THIS NUMBER, WE CAN
CONVERT INTO EQUIVALENT BIT RESOLUTION COLOR CODED IN THIS PICTURE.
IF WE NEED 8 BIT RESOLUTION, WE NEED
CLUSTERS OF 8 OR 16 NEURONS AND
AVERAGE OVER 100 MILLISECONDS
IT'S EASY TO HAVE HIGH PRECISION
USING HIGHLY VARIABLE COMPUTING
SUBSTRATES.
BIOLOGY USE POPULATION CODES.
WE KNOW FROM BIOLOGY HOW TO USE
POPULATION TO ENCODE SIGNALS. THIS IS
AN EXAMPLE MEASURED FROM NON-HUMAN
PRIMATES, DATA MEASURED FROM CAT
VISUAL CORTEX AND OUR CHIPS WHERE WE
HAD FEEDFORWARD OR RECURRENT NETWORKS
IN WHICH WE CAN AMPLIFY THE SIGNAL
AND SUPPRESS THE NODES WITH SELECTIVE
AMPLIFICATION. WE CAN USE A
POPULATION OF NEURONS, AVERAGE OVER
THE FIRING RATE TO GET A PRECISE
ESTIMATE OF THE FEATURE PRESENTED AS
INPUT. IT COULD BE THE TONE OF THE
SOUND OR STIMULUS OR THE
PRESSURE ON THE SKIN AND SO ON.
NOW THAT WE HAVE POPULATIONS, WE
CAN CREATE SYSTEMS AND
APPLICATIONS BUT HOW DO WE DO
THAT? HOW DO WE GO FROM BRIDGING
BETWEEN POPULATIONS OF NEURONS
AND CREATING ALGORITHMS FOR
PROGRAMMING NEUROMORPHIC SYSTEMS
TO SOLVE PRACTICAL APPLICATIONS?
HOW DO WE BUILD A NEUROMORPHIC
SOLUTION STACK? WE DON'T PROGRAM
COMPUTERS BY LOOKING AT SINGLE
TRANSISTORS, WE ABSTRACT WITH LOGIC GATES.
WITH HIERARCHY OF ABSTRACTIONS, WE GO
FROM MACHINE LANGUAGE TO COMPILERS TO
SOFTWARE. IN BIOLOGY, WE WOULD THINK
AT THE LEVEL OF NETWORKS. WE'VE BEEN
LOOKING AT DIFFERENT NETWORKS FROM
COMPUTATIONAL NEUROSCIENCE TO BUILD
A SOLUTION STACK. LACK OF TIME, WILL
MENTION WINNER-TAKE-ALL NETWORKS
AND NEURAL STATE MACHINES USED BY
THE COMMUNITY. WINNER-TAKE-ALL IS A
USEFUL COMPUTATIONAL PRIMITIVE
BECAUSE THEY CAN BE USED TO PROCESS
SIGNALS IN THE ANALOG DOMAIN LIKE
FOR SOUND, VISION, AND PRESSURE OR
NEURAL RECORDINGS. YOU CAN AMPLIFY OR
FILTER SIGNALS. BY CHANGING THE
PARAMETERS ON THE SAME NETWORK, YOU
CAN IMPLEMENT NONLINEAR BEHAVIORS:
SELECTIVE AMPLIFICATION, SIGNALRESTORATION,
MULTISTABILITY.
WE CAN GO FROM PROCESSING WAVES TO
MANIPULATING SYMBOLS, FROM SIGNAL
PROCESSING TO COMPUTATION.
IN NEURAL STATE MACHINES, BY HAVING
WINNER-TAKE-ALL NETWORKS, WE CAN HAVE
ELEMENTS WITH PERSISTING ACTIVITIES.
AND WE CAN SWITCH FROM ONE STATE TO
ANOTHER BY CHANGING THE INPUT. DATA
FROM CHIPS PROBE COGNITION IN NON-HUMAN PRIMATES.
USING POPULATIONS OF NEURONS IS ONE
WAY TO GET ROBUST COMPUTATION.
SINGLE NEURONS ARE NOISY.
LEARNING IS THE OTHER MECHANISM.
WE'VE IMPLEMENTED LEARNING RULES
USING POPULATIONS OF NEURONS.
INHIBITORY NEURONS ARE IMPORTANT IN
ROBUST LEARNING ALGORITHMS ON CHIP.
I CAN POINT YOU TO THE PAPERS
WITH MORE DETAILS THAN THIS.
BY COMBINING POPULATIONS OF NEURONS
LEARNING WE CAN GO FROM ALGORITHMS TO
APPLICATIONS. REMEMBER NEUROMORPHIC
COMPUTING NEEDS TO USE THE LANGUAGE
OF NEUROSCIENCE AND DYNAMICAL SYSTEMS
THE COMPUTATION NEEDS TO BE EXPRESSED
AS SIGNAL PROCESSING STEPS FROM WAVES
TO SYMBOLS. ALGORITHMS CAN BE CREATED
COMPOSING PRIMITIVES AND IMPLEMENTING
LEARNING. BY FOLLOWING THIS APPROACH,
WE'VE BEEN SHOWING THAT IT'S
POSSIBLE TO SOLVE SEVERAL TYPES
OF PROBLEMS IN THE DOMAIN OF BMI
GOING FROM HEART RATE VARIABILITY
TO EMG DECODING TO HFO DETECTION.
I WOULD JUST LIKING TO GO
QUICKLY OVER HFO IF
TIME ALLOWS TO TELL YOU THAT
IT'S POSSIBLE TO IMPLEMENT
NETWORKS THAT CAN SOLVE PROBLEMS
RELATED TO EPILEPTIC SEIZURES.
HFOS ARE HIGH FREQUENCY OSCILLATIONS
IN THE HIGH FREQUENCY BAND OF
EEG DATA AND TRAINED DOCTORS CAN
DISTINGUISH TRUE FROM FALSE HFOS.
WE CREATED A NETWORK TO DISTINGUISH
THIS BY IMPLEMENTING IT ON CHIP WITH
LOW OISE AMPLIFIERS, FILTERS, SNNS.
BY COMBINING THESE SIGNALS WITH
NONLINEAR SYNAPSES AND SPIKING NEURONS
WE'RE ABLE TO DETECT TRUE HFOS FROM
FALSE HFOS WITHOUT BACKPROPAGATION
WE DID HAVE TO MATCH THE TIME
CONSTANT OF THE SYNAPSES TO THE DATA.
JUST A SINGLE CHIP - 614 MICROWATTS
ONLY 78% ACCURACY IS ENOUGH TO GIVE
THE SURGEON AN EXTRA CUE FOR
MAKING A DECISION.
TO CONCLUDE, I WOULD LIKE TO
APPOINT OUT THAT IT'S POSSIBLE
TO BUILD INTELLIGENT BMIS AND
EFFICIENT INTELLIGENT BMIS.
IF WE FOLLOW THIS PHYSICS-BASED
APPROACH AND EXPLORE ALL THE
PROPERTIES OF THE SILICON, AND
ALSO IF WE LISTEN TO THE BRAIN
AND USE BIOPHYSICALLY REALISTIC
COMPUTATIONAL MODELS THAT ARE
WELL-MATCHED TO THE PHYSICS OF THE
COMPUTING SUBSTRATE AND TEST THEM.
THIS PROCESS OF DESIGNING MODELS
AND TESTING THEM AND REFINING
THEM IS SOMETHING WE'VE BEEN
DOING FOR THE PAST 10 YEARS AND
SOMETHING THAT CAN LEAD TO THE
NEXT GENERATION OF BRAIN MACHINE
INTERFACES.
I THANK YOU FOR YOUR ATTENTION.
I'M HAPPY TO TAKE QUESTIONS.
>> RALPH ETIENNE-CUMMINGS
FROM JOHNS HOPKINS UNIVERSITY.
>> I COME AT THE END WHICH MEANS
THAT MY THUNDER IS MOSTLY
STOLEN.
NONETHELESS I HOPE I CAN PULL IT
ALL TOGETHER IN A WAY THAT WILL
PROVIDE SOME DIRECTIONS ON WHAT
WE THINK NEUROMORPHIC IN MEDICINE
WILL LOOK LIKE GOING FORWARD. THIS IS
A READOUT FROM A NSF-NIH SPONSORED
WORKSHOP IN BALTIMORE IN OCTOBER.
WE WERE ESSENTIALLY LOOKING FOR
NEUROMORPHIC PRINCIPLES
ESSENTIAL FOR HEALTHCARE.
THIS WORKSHOP WAS ORGANIZED BY
THE FOLLOWING FOLKS.
IN PARTICULAR GRACE WAS ONE
OF THE ORGANIZERS AS WELL AND
KEPT US TO TASK TO MAKE SURE
THAT WE DID THE RIGHT THINGS.
IT'S A COMBINATION OF
ENGINEERS AND PHYSICIANS.
WE HAD THE GOOD FORTUNE OF A
SCIENCE WRITER
WHO WILL PUT TOGETHER THE
REPORT AT THE END, AND ALSO SHE
RAN THE QUESTIONING AND THAT WAS
INTERACTIVE WITH A LOT OF
DEBATES.
WHAT WAS THE WORKSHOP ABOUT?
IT WAS ABOUT BRINGING TOGETHER
COMMUNITIES THAT SPEAK ABOUT
WAYS THAT WE CAN LEARN FROM
NEUROSCIENCE AND BIOMEDICAL
ENGINEERING IN ORDER TO PUT
TOGETHER IDEAS THAT WOULD BE
HELPFUL IN SOLVING PROBLEMS IN
MEDICINE.
WE WANTED A ROAD MAP TO SAY
WHAT SHOULD BE THE POTENTIAL PLACES
FUNDING AGENCIES CAN THINK ABOUT
FUNDING GOING FORWARD.
THE ORGANIZATION -- YOU CAN SEE
HERE, IT WAS ESSENTIALLY BASED
ON A FLOW THAT STARTED CENTRALLY
FROM THE CORTEX.
WE TALKED ABOUT VARIOUS ASPECTS.
WE STARTED WITH A WONDERFUL
KEYNOTE SPEECH FROM TIM DENISON.
WE SAW THE REVIEW PAPER THAT KAI
TALKED ABOUT THAT THEY PUBLISHED.
TIM PROVIDED A FUNDAMENTAL REVIEW OF
WHAT CAN BE DONE AND WHAT HE LOOKS
FORWARD GOING FORWARD.
FROM THERE WE WENT INTO
PROSTHETICS AND ONE OF THE
HIGHLIGHTS FOR ME WAS THE WORK
THAT RANU JUNG IS DOING FROM THE
UNIVERSITY OF ARKANSAS. IT'S SOME OF
THE MOST ADVANCED REAL WORLD
NEUROPROSTHETICS. A LOT OF GREAT WORK
BY OTHER PEOPLE AS WELL.
AS WE MOVE TO THE NEXT DAY, WE
WENT INTO MATERIALS AND
ARCHITECTURES.
WE CLOSED OUT WITH APPLICATIONS
AND COMPUTATIONS.
AND, AGAIN, THERE WAS A LOT OF
DEBATE.
THERE WAS A LOT OF DISCUSSIONS
ABOUT WHAT IT MEANS AND WHAT ARE
THE PRINCIPLES.
THE WORKSHOP WAS SUCCESSFULLY
ATTENDED AND WE'RE PLEASED THAT FOLKS
ATTENDED IN THE NUMBERS THAT
THEY DID.
WE BASICALLY TRIED TO UNDERSTAND
WHAT NEUROMORPHIC ENGINEERING
MEANS AND HOW TO EXTRACT PRINCIPLES.
I USE THIS AS A MOTIVATOR FOR
INTERPLAY BETWEEN FUNCTION AND FORM.
HOW DO YOU MODEL THE FORM OF THE
NERVOUS SYSTEM AND/OR THE
FUNCTION OF THE NERVOUS SYSTEM
INTO ENGINEERED SYSTEMS AND TRY TO
BUILD SYSTEMS THAT APPLY TO A
PARTICULAR PROBLEM? DA VINCI
WHAT WILL WE SAW WAS THAT FROM A
NEURO-- BASICALLY LATE 80S
EARLY 90S THERE WAS A NOTION THAT
IF YOU LOOK AT CIRCUITS, YOU CAN
IMPLEMENT COMPUTATIONAL MODELS OF
NERVOUS SYSTEM THAT TAKE ADVANTAGE OF THE COMPUTATION IN INTEGRATEDCIRCUITS.
SOME OF THE FIRST NEUROMORPHIC
ENGINEERING DONE IN TERMS OF
BUILDING A NEURON DATES BACK TO
THE 1960S WHERE AN ELECTROCHEMICAL
CELL BUILT BY PAUL MUELLER WAS
A BILIPID LAYER, HE APPLIED ELECTRIC
SIGNALS TO OPEN AND CLOSE ION
CHANNELS TO GENERATE SPIKES.
THAT WAS ONE OF THE FIRST EXAMPLE
OF A NEUROMORPHIC SYSTEM. AND THEN
HE PUT TOGETHER A LOT OF
NETWORKS TO DO SPEECH
RECOGNITION AND SPEECH
DECOMPOSITION.
WHEN WE THINK OF THE PRINCIPLES,
THIS IS A LITTLE CONTROVERSIAL
BUT IT'S HOW I SEE IT.
WE LEARN FROM BIOINSPIRATION.
WE LEARN THE PRINCIPLES SUCH AS
COMPUTATION, MODEL, ALGORITHMS,
PROCESSES AND SO ON, LEARNING.
THE IMPLEMENTATION TO ME IS THE
NEXT STEP.
I'M AGNOSTIC.
I DON'T MIND WHETHER IT'S
IMPLEMENTED HARDWARE OR SOFTWARE
OR SOME OTHER MECHANISM, IT IS
WHAT IT IS.
ULTIMATELY WE GET TO THE
APPLICATIONS WHERE WE TALK ABOUT
EMBODIMENTS. WHAT WE SAW IN
THE CLINIC AND TO LEARN SCIENCE,
TO BASICALLY FEED THAT LOOP BACK AND
LEARN BACK WHAT WAS GOING ON IN
THE SCIENTIFIC NOTION TO BEGIN
WITH. THAT FLOW IS WHAT I SEE AS
ESSENTIALLY UNDERSTANDING THE
NEUROMORPHIC PRINCIPLES.
I LIKE THE SHORT AND SWEET
MORNING ABOUT BASICALLY COMING
UP WITH COMPUTERS, COMPUTATION
MODELS THAT SCALE IN THE SAME
WAY THE BRAIN DOES WITH POWER
EFFICIENCY OF THE BRAIN.
HOW HAS NEUROMORPHIC EVOLVED
OVER THE LAST 30 YEARS?
WE WERE EXTREMELY
HARDWARE-ORIENTED IN THE
BEGINNING. WE MOVED AWAY
INTO SENSORY PROCESSING
IN THE EARLY YEARS
THEN GOT INTO MATERIAL SCIENCE
AND THOUGHT ABOUT HOW TO DO
ACCELERATIONS.
NOW WE'RE STARTING TO THINK
ABOUT HOW TO DEAL WITH THE DATA
SCIENCE AND A.I. ELEMENT OF IT WHICH
IS STARTING TO PLAY A ROLE IN THESE
AREAS AND ULTIMATELY WE HAVE TO CARE
ABOUT APPLICATION, BIOMORPHIC
HEALTHCARE. I HAVE A STUDENT
AKWASI AKWABOAH HE'S GOTTEN THE BUG
OF OLD NEUROMORPH, SUPER INTO THE
CIRCUIT ASPECTS OF IT.
HE THINKS ABOUT IT FROM A
PERSPECTIVE WHERE HE SAYS I'M
TRYING TO SOLVE A PROBLEM HERE.
THE PROBLEM HE'S TRYING TO SOLVE
IS LOOKING AT THE SIMILARITY
BETWEEN TWO STREAMS OF SPIKES.
WE CAN APPLY THIS INTO A DEPTH
EXTRACTION INTO A STEREOOPSIS
SCENARIO. WE CAN USE IT FOR
SOUND LOCALIZATION BUT HE WAS
INTERESTED IN THE QUESTION OF IF
YOU HAVE ELECTRODES AND I WANT
TO MAKE SURE THAT I APPLY THE
RIGHT SPIKE TRAINS SO THAT I GET
THE RIGHT RESPONSE IN THE
TISSUE, HOW DO I DO IT?
I CAN RECORD LOCALLY FROM WHERE I
STIMULATE AND GET A PROFILE OF THE
IMPACT OF THE STIMULATION AND ADAPT
A DYNAMIC MODEL TO CHANGE THE
WAY THE SPIKES ARE DELIVERED IN
ORDER TO MAKE SURE THAT WHAT YOU
EXPECT TO GET IS WHAT YOU
ACTUALLY GET.
TO DO THIS, YOU HAVE TO EMBED
SOME CIRCUITS.
I THINK -- EMBED SOME CIRCUITS
IN THE ELECTRODE ITSELF IN ORDER
TO BE ABLE TO DO THESE TWO KINDS
OF COMPUTATIONAL COMPONENTS.
SO HE STAGES IT AS A LOSS
FUNCTION THAT HAS TO BE
MINIMIZED, IF YOU WILL.
AND THEN ALL THE DIFFERENT
COMPONENTS CAN BE IMPLEMENTED IN
DIFFERENT PIECES OF THE CIRCUIT.
WHAT IS ALSO INTERESTING TO ME
IS THE FACT THAT CERTAIN PIECES
COME FROM DIFFERENT PART OF THE
LITERATURE THAT SUPPORTS
NEUROMORPHICS OVER THE YEARS.
THIS IS A BIT FROM CELL PHONE CAMERAS
AND DOING ADDITION, DIVISION AND
MULTIPLICATION FROM THE EARLY
WORK ON COCHLEAR IMPLANTS.
THIS IS A BIT FROM THE ADAPTATION
CIRCUIT THAT TOBI DELBRUCK USED
IN ORDER TO GET A RANGE OF
VISUAL SENSING.
SO PUT TOGETHER NOW CAN BE
APPLIED IN AN ELECTRODE SCENARIO
WHERE YOU ADAPT ELECTRODE AND
SIMULATION CHARACTERISTICS BASED ON
CHANGES IN THE ENVIRONMENT
AROUND THE ELECTRODE.
LOVE THIS PAPER BY GERT CAUWENBERGHS
THAT MAPS ANGSTROMS TO METER
PARALLEL MAGNITUDES OF WHERE
NEUROMORPHICS PLAYS A ROLE.
WHAT IS COMPUTATION? WHAT ARE
ALGORITHMS? WHAT ARE THE INDIVIDUAL
COMPONENTS OF THE CIRCUIT AND THE ROLE OF SYNTHESIS?
THIS IS ONE OF WHERE WE LAY OUT
ALL THE PLACES THAT WE THINK
NEUROMORPHICS CAN PLAY A ROLE.
WHAT ARE THE DIFFERENT AREAS AND
WE DIVIDE IT INTO DIAGNOSIS, BIO-
SIGNAL ANALYSIS, NEURAL INTERFACES
AND NEUROMORPHIC TOOLS. RECOMMENDED.
COUPLE OF READOUT FROM THE WORKSHOP.
NUMBER ONE, DEFINITIONAL NOTION
ARGUEMENTS. WE UNDERSTOOD THAT
HEY, WE NEED NOT BE PARSIMONIOUSLY
ATTENDING TO DIRECT NEUROBIOLOGICAL
REPLICATION TO GET THE JOB DONE.
SOME DEGREE OF FLOW BETWEEN
PARSIMONY AND FUNCTIONAL. SO FORM
AND FUNCTION WHERE IT FITS IS DRIVEN
BY THE PROBLEM AT HAND.
WE TALKED ABOUT THE FACT THAT
THE TERM NEUROMORPHIC ITSELF --
THE TERM "NEUROMORPHIC" IS
PROBLEMATIC.
IT'S ASSUMED TO BE SOMEWHAT
EXPERIMENTAL EVEN THOUGH WE LIKE
A.I., NEUROMORPHIC GETS PEOPLE A
LITTLE BIT WORRIED FOR SOME
REASON.
THAT'S SOMETHING WE NEED TO
OVERCOME.
IT'S A HURDLE THAT WE NEED TO
GET OVER.
ONE AREA THAT BRAD WAS
POINTING OUT, WE LIKE
BENCHMARKING BECAUSE IT GIVES
US A SET APPROACH ON HOW GOOD AN
ALGORITHM IS BUT IT CAN BE
STIFLING BECAUSE WE'RE LOCALIZE
TO ATTEMPT ONE SET OF PROBLEMS
AND DON'T LOOK BEYOND IT.
AN ECONOMIST SAID USELESS
SCIENCE IS IMPORTANT.
IF WE'RE BOUNDED BY BENCHMARKS
ONLY, WE TENDING TO LOCALIZED
AND THAT CAN BE PROBLEMATIC.
THE ADVANTAGES THAT WE'VE HEARD
A LOT ABOUT.
MATERIALS WAS ALSO ANOTHER KEY
ELEMENT OF WHAT WE HEARD ABOUT.
THERE WAS A PRESENTATION BY
ZHENAN BAO FROM STANFORD ON
HOW TO APPLY FLEXIBLE, SOFT
MATERIALS TO DO REPLICATION OF
SKIN AND GET THE APPROPRIATE
BEHAVIORS YOU'D EXPECT IN DIFFERENT
PATHWAYS OF RECEPTORS OF FINGERS.
THAT WAS AMAZING.
BIOCOMPATIBILITY AND SELF-HEALING
MATERIALS IS SOMETHING THAT WAS ALSO
A VERY IMPORTANT COMPONENT
IN THE END, WHAT FOLKS WERE
KEYED ON IS THE FACT THAT WE
DON'T HAVE A GOOD WAY TO DEVELOP
THESE TECHNOLOGIES.
HOW TO DEVELOP MATERIALS THAT'S
SHAREABLE AND REPRODUCIBLE
HAT OTHER LABS CAN TAKE
ADVANTAGE OF AND THIS IS
SOMETHING THAT FUNDING AGENCIES
CAN HELP PUT TOGETHER.
CLINICAL TRANSLATION, WE GOT A
LONG LIST OF STUFF THAT HAS TO
BE RELEVANT TO THE CLINICAL
APPLICATION.
ESSENTIALLY IN THE END, IT'S HOW
DO WE GET MULTIMODEL AND CONTINUOUS
MONITORING? CONSIDER DIFFERENT
BIOMARKERS? SOMETHING KAI REFERRED
TO IN HIS TALK: CLINICAL WORK FLOWS.
MAKING SURE WHATEVER YOU DO, YOU HAVE
TO BE CONCERNED ON WHAT THE CLINICAL
WORK FLOW IS AND YOUR SYSTEM HAS TO
FIT INTO THAT AND REGULATORY
REQUIREMENTS PLAYS A BIG ROLE
A PLACE THAT IS RELEVANT IS
INFRASTRUCTURE FOR INNOVATION,
FABRICATION ACCESS. ENGINEERS OF A
CERTAIN GENERATION REMEMBER MOSIS.
WE COULD FABRICATE CHIPS QUICKLY. WE
NEED SOMETHING SIMILAR.
LASTLY, WHAT ARE THE PATH FORWARD?
LEVERAGING ALGORITHMS TO GET SIZE,
WEIGHT AND POWER IS OBVIOUS WAY TO GO
THINK PHYSIOMORPHIC. BEYOND NEURO.
CLINICAL NEEDS SHOULD DRIVE THE
APPLICATIONS INSTEAD OF
TECHNOLOGY.
GETTING AT SOLUTIONS.
BECAUSE OF THE TIME, I'LL LET
YOU GET THIS DISTRIBUTED.
>> THE VIDEO WILL BE AVAILABLE,
BUT NOT THE SLIDES.
>> THE VIDEO WILL BE
DISTRIBUTED.
MULTIMODAL DATA SETS FROM REAL
WORLD SITUATIONS WAS ANOTHER KEY.
IT WAS DEEMED TO BE REALLY KEY FOR
DEVELOPMENT.
THE LAST PART THAT IS CRUCIAL.
THEORETICAL ANALYSIS FRAMEWORK.
WITHOUT THAT, WE CAN'T
UNDERSTAND WHAT WE'RE SEEING AND
WE CAN'T INNOVATE AS WELL.
I'LL STOP HERE AND TAKE
QUESTIONS.
>> THANK YOU RALPH, THIS IS
IMPORTANT. I WANT TO INVITE OUR
DISCUSSANTS TO JOIN US. I KNOW
YOU RUSHED OVER THE REGULATORY SLIDE
WE HAVE SOMEONE FROM THE FDA IN THE
AUDIENCE IF ANYONE HAS QUESTIONS
ABOUT THAT.
>> RALPH, COULD I ASK A QUICK
QUESTION?
I LIKED YOUR SLIDE WHERE YOU
TALKED ABOUT THE LAST STEP
BEFORE YOU GO INTO BIOMEDICAL
APPLICATIONS IS HARDWARE-SOFTWARE
INTERFACE. WE TALKED ABOUT DENDRITIC
COMPUTING. ARE THERE ANY NEUROMORPHIC
ALGORITHMS YOU WOULD LIKE TO SEE
INSTANTIATED IN HARDWARE SO THE
FIELD CAN USE THAT AT THE THAT LAST
LEVEL OF INTEGRATION?
>> SO IF I UNDERSTAND YOUR
QUESTION CORRECTLY, IN MY PAST
LIFE WHERE NOT SO LONG AGO I WAS
INTERESTED IN SPINAL CORD
STIMULATION TO RESTORE LOCOMOTION.
WHAT WE DID THERE IS WE BUILT
HARDWARE MODELS OF THE SPINAL CORD
AS AN EFFERENT COPY. AND USED THAT
TO STIMULATE THE SPINAL CORD TO
RECOVER LOCOMOTION ACTIVITIES.
I COULD TAKE THE CENTRAL PATTERN
GENERATOR MODEL AND USE IT
AS A MECHANISM TO STIMULATE.
MORE RECENTLY -- NOW WE CAN IMAGE
AN ENTIRE BEHAVING ZEBRA FISH DOING
PREY CAPTURE. YOU HAVE THE ENTIRE
NEURONAL CONNECTOME ALMOST
INSTANTANEOUSLY.
I WOULD LIKE TO SEE THAT
IMPLEMENTED INTO HARDWARE WHERE
YOU CAN TURN OFF DIFFERENT PIECES
AND SEE IF YOU DID THE SAME ABLATION
EXPERIMENT ON THE ANIMAL WHERE
THE ANIMAL WOULD CHANGE ITS
BEHAVIOR AS THE ANIMAL IN THE
MODEL.
THAT LINK BETWEEN A REALTIME
CONTINUOUS MEASUREMENT OF THE
NERVOUS SYSTEM AND THE TWIN AND
THE TWIN INTERACTING WITH THE TWO.
>> I WOULD LIKE TO FOCUS THIS
DISCUSSION TO REMIND US OF OUR GOALS
TO DISCUSS NEUROAI PRINCIPLES AND
TECHNOLOGIES ACROSS AGENCIES TO
TRANSFORM NEUROSCIENCE AND BRAIN HEALTH.
UNDERSTAND HOW OTHER AGENCIES COULD
BENEFIT FROM BRAIN DATA, KNOWLEDGE-
BASES. WITH THAT REFRAMING, I WOULD
LIKE TO INVITE JENNIFER TO ASK
HER PROVOCATIVE OR LINKING
QUESTION.
>> I GUESS I GET TO START.
SO, FIRST THING IS A LOT OF GOOD
TALKS.
IT WAS WONDERFUL TO SEE
EVERYTHING THERE AND THE WHOLE
CONVERSATIONS AROUND BETWEEN
BOTH THE EMBODIED COMPUTATION,
TALKING ABOUT THE MEDICAL
ASPECTS SIMULTANEOUSLY WITH THE
ROBOTICS.
ALL OF THESE THINGS ARE
INTERESTING.
ALSO THE DISCUSSIONS THAT RALPH
IN TERMS OF SHOWING THE
PREVIOUS OUTBRIEF, DISCUSSIONS
BETWEEN BIOHEALTH AND NEUROMORPHIC
ITEMS AND DIRECTIONS. IT WAS COOL.
AS I THINK ABOUT THIS FURTHER IS
GIVEN ALL OF THIS, YOU WERE
TALKING ABOUT THE NEUROMORPHIC
CONCEPTS.
HOW DO WE SCALE THIS UP TO BE
SOMETHING WE COULD USE IN SOME
OF THESE HEALTH AND MEDICAL
APPLICATIONS.
IN PARTICULAR, WHAT WOULD THAT
MEAN IN TERMS OF SCALING.
CERTAINLY, COMPUTING IS THERE AND
THERE ARE DISCUSSIONS THAT COME
FROM THE PREVIOUS SESSION BUT
ALSO IN THE SENSORS,
ACTUATION, AND
THE WHOLE PICTURE.
>> SO THE SCALING PART I SUPPOSE
IS A QUESTION OF WHAT
SPECIFICALLY YOU ARE REFERRING
TO.
IF YOU'RE REFERRING TO WHAT IS
INSIDE -- IMPLANTED OR ON THE
BODY, THE WEARABLES.
I WOULD ARGUE THAT THE SCALE IS
NOT AS IMPORTANT.
IT'S MORE OF A LOCAL, SMALLER
SYSTEM.
BUT REFERRING BACK TO
KWABENA'S TALK WHERE HE WAS
ESSENTIALLY SAYING EVEN IF WE
WERE TO ASSUME THAT WE CAN GO TO THE
CLOUD, GET ALL THE COMPUTATION THERE
THE TYPE OF COMPUTATION WE NEED TO DO
WOULD BE SO MUCH THAT THE IMPACT ON
THE ENVIRONMENT WOULD BE MASSIVE.
THAT'S WHERE THE SCALING IS
SUPER IMPORTANT TO MAKE THOSE SYSTEMS
MUCH MORE EFFICIENT AND AVAILABLE.
SO THAT WE CAN USE IT KIND OF
OFF-LOADING OF THE MEDICAL, THE
DETECTION OF CANCER IN THOSE
IMAGES AND SO FORTH.
EVEN IF YOU DO STUFF OFF THE
BODY, YOU OFF-LOAD IT, YOU STILL
NEED TO WORRY ABOUT SCALING AT
THAT LEVEL.
THAT'S WHERE I WOULD SEE IT.
>> GO AHEAD, GINA.
>> IN TERMS OF SCALING FROM COMPUTING
IN CLOSED LOOP PERSPECTIVE, I THINK
IT CONNECTS ALSO TO THE NECESSITY
TO HAVE CAPABILITIES TO INTEGRATE
WITH THE SENSORS, THE SIMULATION,
THE ACTUATION. THAT IS INFRASTRUCTURE
NEEDED TO BE ABLE TO DEMONSTRATE
THAT CLOSED-LOOP BEHAVIOR BETWEEN
SENSING, COMPUTING AND SIMULATION.
>> I WOULD LIKE TO JUMP IN ON
THAT.
WE'RE TALKING ABOUT SENSORS AND
ACTUATION. WE JUST CAN'T HAVE COMPUTING
ON THE CHIP. THE INFORMATION HAS TO
COME INTO THE CHIP AND COME OUT.
ONE OF THE BIG THINGS IS HOW DO
WE TRANSFORM THAT SENSORY
INFORMATION INTO A SYSTEM THAT
CAN BE HANDLED NEUROMORPHICALLY.
A LOT OF COMMERCIAL SENSORS ARE
NOT DESIGNED TO INTERFACE WITH THE
NEUROMORPHIC SYSTEM. NOT THAT THEY
CAN'T BUT THEY JUST AREN'T.
AN EXAMPLE, MOST MOTOR CONTROL AT
THE LOWEST LEVEL IS DONE WITH PULSES
AND TIMING.
THERE IS A MIDDLE LAYER IN
BETWEEN THAT TAKES DIGITAL SIGNALS
AND CONVERTS THEM INTO PULSES.
NO REASON IT CAN'T BE DONE
NEUROMORPHICALLY. THAT'S NOT HOW THE
SYSTEM IS CONSTRUCTED.
IN TERMS OF INTERFACING WITH ALL
THESE DIFFERENT ACTUATORS, WE HAVE
TO RECKON WITH THE MIDDLE LAYER
AND THE COMMUNICATION ASPECT.
>> ANOTHER CONSIDERATION FOR
SCALING, DR. HAYS SAID IF WE DESIGN
THE MOST SOPHISTICATED THING BUT
THAT A SAILOR CAN'T IMPLEMENT,
IT'S NOT GOING TO BE USED.
PHYSICIAL SURGEONS ARE NOT THE
END-USER. IT'S MAKING SURE THAT
THINGS ARE PACKAGED
APPROPRIATELY. WHEN WE DEVELOP
DEVICES, THE PACKAGING AND CONNECTORS
TAKE LONGER TO SOLVE --IT'S THE
ABILITY TO GET PACKAGING THAT WORKS
IN THE END USER AND COMMERCIAL SPACE.
THEN THE OTHER IS MAKING SURE
THAT PEOPLE THAT ARE GOING TO BE
USING IT, THAT IT CAN SCALE TO
THE RELATIVE MOST COMMON
DENOMINATOR NEEDS THAT DIFFERENT
PEOPLE HAVE.
>> GREAT QUESTIONS.
LET AS MOVE ON TO MYRIAM.
>> THANK YOU VERY MUCH ALL OF
YOU.
I LEARNED A LOT FROM THESE
INTERESTING TALKS.
WE LEARNED A LOT IN THIS WHOLE
TWO WEEKS ON HOW NEUROSCIENCE
WILL HELP NEUROMORPHIC.
WE'RE INTERESTED IN NEUROAI.
ONE THING THAT IS MISSING IS A.I.
IF YOU DON'T CARE ABOUT NEURO,
HEARING ABOUT THE PROBLEMS WE
CAN SOLVE, IF I'M SITTING IN
CONFERENCES THAT ARE COMPLETELY
HARDWARE OR A.I. ORIENTED,
THEY ARE TACKLING SIMILAR PROBLEMS
COMPLETELY WITHOUT
NEUROSCIENCE INSPIRATION.
HOW WOULD I COMPARE A NEUROINSPIRED
SOLUTION, FOR EXAMPLE AN EFFICIENCY
PROBLEM, WITH SOMEBODY WHO TACKLES
THE SAME PROBLEM WITHOUT NEURO
INSPIRATION. THAT'S ONE QUESTION.
I'M AMONG THE TEACHERS THAT HAVE
ONE QUESTION WITH TEN PARTS.
ALONG THIS LINE, I'M THINKING
OF, FOR EXAMPLE, GENERALIZABILITY
IS ISSUE IN ROBOTICS RIGHT NOW.
WHY DO I NEED TO HAVE A ROBOT
THAT ALWAYS RUNS ON A NEUROMORPHIC
CHIP BECAUSE OF GENERALIZABILITY?
WHY DON'T I THINK ABOUT HAVING
A.I. TAKING CARE OF ALL THE
PROBLEMS.
THE MOMENT THAT I NEED THAT
GENERALIZABILITY PIECE, I TURN ON
MY NEUROMORPHIC CHIP THAT HELPS
WITH THE ENVIRONMENTAL CHANGE
AND IT GOES BACK TO A.I.
MY QUESTION IS HOW CAN I HAVE
NEUROAI PLUS A.I.?
>> I UNDERSTOOD YOUR QUESTION.
AT LEAST IN MY SITUATION, I
CAN'T RUN A TRANSFORMER ON MY
HARDWARE.
IT TAKES WAY TOO MUCH POWER TO
PERFORM A SINGLE INFERENCE WITH
A TRANSFORMER. THEY'RE HUGE.
AND IT SUCKS DOWN LOTS OF POWER
JUST TO PERFORM INFERENCE.
I DON'T HAVE THAT POWER.
>> YOUR SITUATION IS SPECIAL.
>> I AGREE.
WE HAVE ROBOTIC SITUATION THAT
ARE NOT THAT LIMITED TO POWER
AND THEY CAN AFFORD IT.
>> FAIR ENOUGH.
IT'S STILL A LOT OF POWER.
I MEAN, THESE THINGS ARE SUCKING
DOWN 400 WATTS.
THAT'S A LOT OF POWER.
SOME ARE GETTING DOWN LOWER, BUT
I DON'T KNOW.
YOU KNOW, I KNOW MY SITUATION IS
DIFFERENT, BUT UNTIL THEY CAN
START GETTING SMALLER
TRANSFORMERS THAT ARE NOT SO
LARGE THAT REQUIRE SUCH LARGE
GPUS, I THINK IT IS A PROBLEM.
A LOT OF PEOPLE JUST SAY CONNECT
TO THE INTERNET. AND IT'S LIKE
WELL A LOT OF APPLICATIONS CAN'T
CONNECT TO THE CLOUD.
>> I COULD JUMP ON THAT TOO.
FUNDAMENTALLY I DON'T SEE A
REASON WHY WE CAN'T HAVE THE
HYBRID SYSTEMS OF NEUROAI AND
A.I.
RIGHT NOW WE'RE IN THE
EXPERIMENTATION PHASE.
IN MOST ENGINEERING CONTEXT WE
TEND IT USE THE TOOL BEST FOR
THE JOB.
IF ALL THE ROBOT HAS TO DO IS
CLASSIFY THERE IS A GRAPE OR
ORANGE IN FRONT OF ME, THERE IS
NO REASON FOR BUILDING A NEUROMORPHIC
SYSTEM FOR IMAGE CLASSIFICATION.
THERE ARE SOME ASPECTS THAT ARE
BETTER HANDLED IN NEUROAI AND
OTHERS IN NORMAL A.I.
I THINK SOME OF THESE EXAMPLES WOULD
BE MULTIMODAL, INTEGRATING OVER
MULTIPLE SENSORY SYSTEMS IN LONG TIME
HORIZON. THERE IS NO REASON WHY
WE CAN'T HAVE THE NEUROMORPHIC
COPROCESSOR THAT HANDLES THE
NEUROMORPHIC APPLICATIONS AND THE
A.I. PART. SYNERGY IS GOING TO ENABLE
POWERFUL SYSTEMS.
>> THAT'S A GREAT POINT.
I DO WANT TO POINT OUT A LOT OF
THE PRESENTATIONS DISCUSSED ABOUT
HAVING UNIFIED DATA EMBEDDINGS
BETWEEN SENSING, COMPUTING AND
ACTUATION. IT IS DIFFICULT TO BRING
THESE DIFFERENT CHIPS IN THE DIGITAL
DOMAIN. IT MIGHT BE A TRADEOFF THAT
WILL DEPEND ON THE APPLICATION.
>> I AGREE THAT SOMETHING
DOESN'T MAKE SENSE TO BE DONE THROUGH
NEUROMORPHIC, LIKE IMAGE CLASSIFICATION.
ON THE OTHER SIDE, AT NEURIPS, I
HEARD AN AMAZING PRESENTATION BY
I THINK LINDA SMITH.
SHE WAS LOOKING FROM NEWBORNS
TO INFANTS AT HOW THEY DEVELOP.
SHE STARTED SAYING NEWBORNS ARE
ATTRACTED BY STRONG EDGES,
FURNITURE, AND THEN THEY BECOME
ATTRACTED TO COLORFUL AND BRIGHT
THINGS AND THEN MORE DETAILS AND HOW
THEY INTERACT WITH OBJECTS WHEN
THEY PLAY. IT'S NOT A RANDOM
PASSIVE PRESENTATION OF STIMULI,
BUT IT HAS A STRUCTURE.
THEY REPEAT AND LOOK AT THE SAME
THING MULTIPLE TIMES, THEN SWITCH.
WHY CAN'T WE LEARN FROM THAT TO
DEVELOP SYSTEMS THAT ARE MORE
EFFICIENT THAT OPTIMIZE THE FACT
THAT WE NEED A LOT OF DATA.
THAT DON'T GENERALIZE. THEY'RE
BRITTLE AND NOT EXPLAINABLE.
MAYBE THAT'S NOT NEUROMORPHIC,
BUT TO ME IT'S AT APPROACH THAT
LOOKS INTO HOW CAN THE SYSTEM BE
BUILT IN AWAY THAT IS GENERALLY
TRYING NOT TO BURN DOWN THE
PLANET.
CLIMATE CHANGE, THEY IGNORE
THAT.
LET'S BUILD FIVE NUCLEAR PLANTS FOR
A.I. I THINK IT'S USEFUL, BUT MOST OF
THE TIMES IT'S MISUSED.
>> GIACOMO WANTS TO JUMP IN.
>> I HOPE EVERYONE CAN HEAR ME.
>> I'M GOING TO BE A LITTLE BIT
CONTROVERSIAL HERE AGAIN.
I THINK ALL A.I. IS
IS NEUROAI.
EVEN IN THE TRANSFORMER CASE,
THAT ATTENTION MECHANISM FOR PICKING
CAN BE CONSTRUCTED IN A NEUROMORPHIC
OR BIO-INSPIRED WAY.
I THINK IT'S NOT A DICHOTOMY
THAT I WOULD DRAW IS ONE THAT
WOULD SAY HEY, APPLY THE RIGHT
APPROACH THAT SOLVES THE PROBLEM.
THAT'S THE HYBRID SYSTEM.
>> GIACOMO, IF YOU CAN UNMUTE.
>> YES.
I AM.
I HOPE EVERYONE CAN HEAR ME.
I'M SORRY I COULDN'T BE THERE IN
PERSON.
AT LEAST I COULD SEE AND I AGREE
COMPLETELY WITH EVERYTHING SAID.
I WANT TO ADD AS IT HAS BEEN
MENTIONED, NEUROMORPHIC DOES
NOT HAVE TO REPLACE A.I. BUT IT
COULD COMPLEMENT A.I.
YOU HAVE SOMETHING OF THE ORDER
OF IT ALWAYS ON ACTING AS A
WATCHDOG.
IF ANYTHING REQUIRES MORE
COMPUTATIONAL POWER, THAT'S WHEN
EITHER MICRO PROCESSOR OR THE
WHOLE PROCESSOR OR GPU COULD BE
TURNED ON.
IT'S POSSIBLE TO USE BOTH.
IT MAKES SENSE IN AUTOMOTIVE
DRIVING AND OTHER INDUSTRIAL
APPLICATIONS TO SEE HOW TO USE BOTH.
TO WE FIND SOLUTIONS USING THE
NEUROMORPHIC APPROACH, WE HAVE TO
FIND THE SAME ROUTE THAT NATURE AND
EVOLUTION BECAUSE WE HAVE SAME
CONSTRAINTS. THEN WE MIGHT BE LED TO
SOLUTIONS THAT ARE MORE EFFICIENT
MORE VOLUME THAT OCCUPY LESS AREA
AND VOLUME.
I THINK THIS ALLOWS US TO FIND
OTHER SOLUTIONS, BUT THEN WE
SHOULDN'T THINK OF REPLACING A.I.,
WE SHOULD USE IT WHENEVER THAT
IS BEST THING TO DO.
WHENEVER THERE IS DIGITAL DATA,
IT DOESN'T MAKE SENSE TO USE
NEUROMORPHIC.
I MEAN, TRANSFERRING THE DATA IS
GOING TO BURN ALL THE POWER THAT
YOU WOULD SAVE.
LIKE RALPH LIKES TO SAY BURNING
1 AMP PLUS .1 MILLIAMP OR 1 AMP
PLUS .1 MICROAMP MAKES NO DIFFERENCE.
WE NEED TO GET THE HIGHEST
ACCURACY WITHOUT SACRIFICING
POWER.
>> WE HAVE THREE MORE
DISCUSSIONS IN SEVEN MINUTES.
OUR NEXT DISCUSSION --
>> HELLO, ARE YOU ABLE TO HEAR
ME?
>> WE CAN HEAR YOU.
>> SO, I JUST WANTED TO ASK A
BRIEF QUESTION SINCE WE'RE SHORT
ON TIME. I KEEP THINKING
ABOUT JET AIRPLANES.
THERE ARE THINGS THAT ENGINEERS
DO BETTER THAN WE DO.
WHAT THE MOST LOW HANGING FRUIT
OPPORTUNITIES FOR DIRECT
INTERACTIONS BETWEEN ANIMALS AND
HUMANS AND MORE ENGINEERED
SYSTEMS AND DO WE NEED MORE
NEUROMORPHIC TO MAKE THAT INTERFACE?
>> YOU CAN SCAN THE QR CODE OR
TYPE IT INTO THE ZOOM CHAT AND
WE'LL HAVE COME BACK TO YOUR QUESTION
WHEN WE HAVE IT IN WRITING.
CHRIS, YOU'RE NEXT.
>> I'LL TRY TO BRING LITTLE
SKEPTICISM
I ENJOYED ALL THE TALKS AND
EVERYONE IS FREE TO JOIN IN BUT
I WANTED TO PICK A FIGHT WITH
DR. MILLER.
I'M GLAD YOU BROUGHT UP THE
TENSION BETWEEN ECONOMIC
INTERESTS.
SCIENTIFIC INTERESTS, YOU SAID
PUBLIC INTERESTS AND I'LL THROW
THE INTEREST OF PEOPLE LIVING
WITH NEW NEUROLOGIC DISORDERS AS
WELL.
WE HAVE OVERLAP IN THE
NEUROTECHNOLOGY WORLD WHERE
THERE IS A CULTURE OF NEEDING TO BE
TECHNOLOGY-DRIVEN TO FEEL LIKE
THEY'RE EXCITE BUT IT PULLS AT
THE COMPETING INTEREST OF MAKING
SOMETHING SIMPLE AND SCALABLE
AND AFFECT IMPACT ON PATIENTS.
I THINK WE CAN AGREE WITH
LOWER-POWERED THINGS ARE BETTER
AND A.I. CAN HELP US WITH CERTAIN
TASKS BUT THERE ARE SOME THINGS
YOU SAID, THERE IS AN IMPLICIT
ASSUMPTION IF WE MAKE TECHNOLOGY
THAT IS MORE BRAIN-LIKE, NEUROMORPHIC
IT'S ADAPTIVE, IT'S CLOSED-LOOP AND
WILL BE BETTER FOR IMPLANTABLE NEUROTECH.
I'M TRYING TO THINK OF EVIDENCE
OF THAT. IT'S HARD TO THINK OF SOME.
WE NOW HAVE ADAPTIVE DBS FOR
PARKINSONS.
THERE ARE GAIS BUT THEY'RE
MODEST COMPARED TO REGULAR DBS.
IF WE WANT TO INDUCE PLASTICITY,
THERE MAY NOT BE A
NEUROMORPHIC SIGNAL FOR THAT.
THERE ARE EXAMPLES OF TRYING TO
STOP SOMETHING LIKE
DEFIBRILLATION IS NOT CARDIOMORPHIC.
IF YOU'RE NOT TALKING ABOUT
PROSTHETICS, MANY OF THE NEURO-
TECHNIQUES IS TO STOP SOMETHING
NEURODEGENERATION, EPILEPSY.
I WANT TO BE A BELIEVER, BUT I
ACTUALLY DON'T KNOW WHERE THIS
ASSUMPTION COMES FROM.
WE PROBABLY CAN'T ANSWER THIS
RIGHT HERE, BUT WHAT IT BRINGS
UP TO ME IS THERE IS A NEED FOR
A SIGNIFICANT SCIENTIFIC PROGRAM
LIKE WHAT DO WE DO WITH ALL THIS
TECHNOLOGY ONCE WE HAVE IT. SOLUTIONS
ARE NOT TECHNOLOGY LED, BUT LED BY NEEDS.
I WANTED TO ASK, WHAT DO YOU
THINK ABOUT THE NEED FOR A MAJOR
SCIENTIFIC PROGRAM TO GO
ALONGSIDE THIS TO FIGURE OUT
WHAT TO DO WITH ALL THE
TECHNOLOGY AND WHAT WOULD SUCH A
PROGRAM HAVE TO LOOK LIKE?
WHAT WOULD BE ITS FEATURES?
>> WHAT I CAN SAY IS I GUESS THE
CLOSEST THING WE HAVE TO A TRUE
CLOSED LOOP SYSTEM IS RESPONSIVE
NEUROSTIMULATION FOR EPILEPSY.
IT'S AN IDEA THAT YOU
CONTINUOUSLY RECORD A STREAM OF
INFORMATION FROM THE AREA IN THE
BRAIN WHERE YOU THINK THE SEIZURE
IS STARTING FROM. THE RUDIMENTARY
FILTERING PROCESS ON DEVICE AND IT
TRIGGERS A SINGLE OR MULTIPLE
PULSES OF ELECTRICAL STIMULATION
IN RESPONSE TO A DETECTED EVENT.
WE DON'T KNOW IF THE CLOSED LOOP
ASPECT HELPS AT ALL.
BUT WE KNOW, FOR EXAMPLE, THAT
BECAUSE WE WERE RECORDING AND
TRACKING OVER TIME, WE NOW
UNDERSTAND THAT THERE ARE STRONG
DIURNAL PATTERNS TO SEIZURES AND THE
LIKELIHOOD THAT PATIENTS WILL
HAVE A SEIZURE.
AND THAT WAS SOMETHING THAT WE
DIDN'T KNOW UNTIL WE HAD THIS
SYSTEM.
BUT THE SENSITIVITY OF
STIMULATION IS PROBABLY GETTING
BETTER AND IT'S HARD TO -- YOU
NEED TO AGGREGATE A LOT OF DATA
BEFORE YOU CAN SAY CONCLUSIVELY
THIS IS MAKING PATIENT
SEIZURES BETTER.
THE LIKELIHOOD THAT SOMEONE WILL
HAVE A SEIZURE IS DEPENDENT UPON
THEIR PARTICULAR FORM OF
EPILEPSY.
WE KNOW THAT BECAUSE OF THESE
CLOSED-LOOP DEVICES.
THAT DOESN'T MEAN THAT'S GOING
TO CHANGE THE CIRCUIT FOR
EPILEPSY, BUT IT DOES MEAN BY
HAVING A CLOSED-LOOP SYSTEM FOR
AN INDIVIDUAL PERSON KNOW WHAT
THEIR SEIZURE PATTERNS ARE AND
ADJUST THE PLANS FOR STIMULATION
AND RESPONSE. THAT'S RUDIMENTARY
IN TERMS OF MATCHING THE STATISTICS
OF SEIZURES, BUT IT'S DIFFERENT
FROM PERSON-TO-PERSON.
THAT WOULD BE ONE EXAMPLE.
THE MEDTRONIC DEVICE I THINK
THAT THEY'RE TRYING TO PUSH A
PRODUCT BEFORE THEY FIGURE OUT
THE APPROPRIATE MECHANISM, THERE
ARE BETTER APPLICATIONS THAN
PARKINGSON'S FOR CLOSED-LOOP DEVICES.
FOR EXAMPLE THE WORK COMING OUT
OF THE UNIVERSITY OF FLORIDA THAT
AYSE GUNDUZ SHOWED THAT YOU CAN
DECODE THE ONSET OF TICK AND
STIMULATE IN RESPONSE FOR PATIENTS
THAT SUFFER FROM TOURETTE SYNDROME.
JERKING OF THE BODY IS COMMON.
WE CAN DETECT THE IMPENDING NEED
TO EXERCISE A TICK.
I GUESS THAT DOES CLOSE THE LOOP
BUT THAT'S AN APPLICATION WHERE
THE DEVICE IS NEEDED.
THE POPULATION AND SIZE FOR
PEOPLE IMPLANTED IS SMALL
ENOUGH WHERE THAT INS'T THE MAIN
APPLICATION THAT'S BEING CHASED.
>> MY QUESTION IS MORE LIKE, IN
BOTH OF THOSE EXAMPLES, THE TYPE
OF STIMULATION DELIVERED IS VERY
NON-BIOLOGICAL.
IT'S A DISRUPTIVE STIMULATION.
IF THERE IS AN ASSERTION THAT IF
WE MAKE THEM MORE BIOLOGICAL AND
MAKE THEM MORE LIKE NEUROMORPHIC
IN SOME WAY THAT IT CAN BE
BETTER, HOW DO WE TRY AND GET
ANSWERS TO QUESTIONS LIKE THAT
BEFORE A COMPLETE HARDWARE
PACKAGE THAT IS ABLE TO DO IT
HAS BEEN BUILT AND ALL THAT TIME
AND MONEY HAS BEEN INVESTED SO
WE CAN GUIDE THE DEVELOPMENT OF
THINGS LIKE THAT WITHOUT HAVING
TO REACT TO TECHNOLOGY HANDED TO
US.
>> SO SOME THINGS WE DO
CURRENTLY -- AND PEOPLE MAY OR
MAY NOT BE FAMILIAR WITH THIS,
BUT FOR EPILEPSY, WE IMPLANT
STEREO-EEG WITH DEPTH ELECTRODES
THROUGHOUT THE BRAIN AND TRY TO
CHARACTERIZE WHERE A SEIZURE IS
COMING FROM. THEN WE DO TEST THERAPY
FOR A COUPLE OF DAYS TO SEE IF IT
HELPS. THE SAME APPROACH IS ADOPTED
FOR DEPRESSION AND OCD ACROSS A
NUMBER OF INSTITUTIONS. WE'VE DONE
THIS POST-ANOXIC BRAIN INJURY.
WE HAVE A GOOD TEST ENVIRONMENT.
I THINK THAT REPRESENTS EXTRA
SURGERY AND ONE TO TWO WEEK
INPATIENT STAY.
THE IDEA WE SHOULD DELIVER THE
MOST COMMON STIMULATION PATTERNS
AT A FIXED FREQUENCY.
A LOT OF THESE HAVE PATHOLOGICAL
BIOMARKERS ARE OSCILLATORY WITH A
FIXED TIME SCALE. IT DOESN'T MAKE
SENSE TO TREAT SOMETHING THAT HAS
A FIXED TIME SCALE AND TREAT IT
WITH SOMETHING THAT DOESN'T.
THERE ARE ACTUALLY TOOLS OUT THERE
AVAILABLE ON A RESEARCH BASIS
FOR COMPANIES.
WE AND OTHERS, I THINK YOU GUYS
AT LEAST WERE PUTTING IN A PROPOSAL
ON THIS POTENTIALLY WITH US.
THE IDEA THAT YOU DELIVER
STIMULATION IN NEW WAYS, I THINK
THERE ARE TOOLS FOR THAT.
THOSE ARE NOT COMMERCIAL GRADE
TOOLS BUT THEY COULD QUICKLY
BECOME THAT.
SORRY FOR TAKING SO LONG.
>> AND I WANT TO ADDRESS ONE
THING.
YOU WERE TALKING ABOUT CENTRAL
STIMULATION.
BUT THERE IS ANOTHER ASPECT
WHICH IS CENTRAL PERIPHERAL
WHICH I THINK IS MORE RELEVANT
IN SOME SENSE.
THINKING ABOUT BLOOD PRESSURE'S
LINK TO FOLKS WITH SPINAL-CORD
INJURIES.
THERE IS A MASSIVE LINK THERE.
IF YOU COULD MONITOR BLOOD
PRESSURE, IT DOESN'T EXIST
TODAY AT LEAST NOT WELL.
AND USE THAT TO DETERMINE WHEN
TO STIMULATE THE NERVOUS SYSTEM
IN ORDER TO BASICALLY REACH
APPROPRIATE EQUILIBRIUM, THAT'S A
PLACE NEUROMORPHICS COULD BE A BIG DEAL.
>> VAGAL NERVE STIMULATION HAVE HAD
THAT FOR THE LAST SEVEN YEARS
THE CURRENT GENERATION DETECT
RAPID INCREASES IN HEART RATE AND
WILL DELIVER STIMULATION IN RESPONSE
IT'S A PERIPHERAL SENSOR.
>> THAT'S HEART RATE.
>> RIGHT.
>> I'M DOING POORLY ON TIME
MANAGEMENT.
WE'RE FOUR MINUTE OVER THE
SCHEDULED BREAK TIME AND WE
STILL HAVE KAREN WHO HASN'T HAD
A CHANCE TO ASK HER QUESTION.
DO WE WANT TO TAKE TIME FROM THE
4:00 WRAP-UP TO CONTINUE SOME OF
THESE QUESTIONS?
>> YES, WE CAN DO THAT.
FOR SURE.
>> WE HAVE A QUESTION COME IN
AND WE WILL HAVE TO TAKE THAT
ONE AT 4:00.
I'M SORRY FOR THE PEOPLE STANDING
PATIENTLY.
WE CAN TAKE YOUR QUESTION AT
4:00.
THERE WILL BEING A RAPID FIRE
WRAP-UP BECAUSE THIS DISCUSSION
IS SO INTERESTING.
THANK YOU, EVERYBODY.
WE'LL RESUME AT 4:00 PM.
WELCOME BACK PANELISTS
AND DISCUSSANTS.
WE'RE GOING TO CONTINUE WITH
SESSION FOUR.
FOR ANYONE WHO HAS QUESTIONS
THAT -- WE'LL PROBABLY NOT HAVE
TIME TO ASK ALL YOUR QUESTIONS,
SO FEEL FREE TO SCAN THE QR CODE
AND ASK YOUR QUESTION THERE.
WE'LL BE ABLE TO INCLUDE THAT IN
OUR POST WORKSHOP REPORT.
IF WE'RE REALLY AMBITIOUS, WE'LL
FIND SOMEONE TO ANSWER THOSE
QUESTIONS.
LET ME GET STARTED WITH WHERE WE
LEFT OFF.
BING HAD A QUESTION.
MACHINES HAVE CAPABILITIES WE
DON'T HAVE, RADARS, WHEELS AND
JET ENGINES.
WHAT ARE LOW-HANGING FRUIT FOR
INTERFACING HUMAN COMMUNITIES WITH
ENGINEERED SYSTEMS. AND DO WE NEED
NEUROMORPHIC COMPUTING TO DO THIS?
>> I DON'T THINK THE ARGUMENT IS
THAT WE NEED ALL ASPECTS OF
BIOLOGICAL COMPUTATION TO DO
THESE TASKS.
IT IS THE CLASSIC CASE OF BIRDS
FLAP AND PLANES USE ENGINES.
THE KEY THING TO KNOW IS THERE
ARE ASPECTS OF BIOLOGICAL
INTELLIGENCE THAT WE CANNOT
CURRENTLY DO WITH THE STRATEGIES
WE'RE CURRENTLY USING.
WE'RE GOING TO HAVE TO FIGURE
OUT WHAT THOSE PRINCIPLES ARE IN
NEURAL SYSTEMS.
FOR ME PERSONALLY, I'M NOT
CONVINCED THAT THE SPIKE IS
IMPORTANT AND NECESSARY FOR
THESE KINDS OF
NEURALL COMPUTATIONS.
BUT NEUROMORPHIC AND NEUROAI
SYSTEMS -- WE HAVE TO FIGURE OUT
THE CORE PRINCIPLES BEFORE WE
CAN DISTILL THOSE CORE
PRINCIPLES DOWN.
>> I THINK THIS DOVETAILS INTO
THE QUESTION I ASKED BEFORE WE
ENDED THE SESSION, WE'RE TALKING
A LOT ABOUT TECHNOLOGY BUT THERE
ARE FOUNDATIONAL SCIENCE
QUESTIONS THAT WE HAVE TO ASK
ESPECIALLY IN THE DOMAIN OF
HUMAN INTERFACE EXAMINING
NEUROTECHNOLOGY ABOUT WHAT THE
VALUE OF THE SYSTEMS IS AND HOW
TO USE THEM BEFORE THEY ARE
BUILT BASED ON THE PERSPECTIVES
OF CLINICIANS AND LIVED
EXPERIENCE FOLKS AND WHAT THE
NEEDS ARE SO WE'RE NOT JUST
DESIGNING A TECHNOLOGY AND THEN
TRYING TO FIGURE OUT WHAT NEEDS
IT MIGHT BE ABLE TO MEET, BUT
WE'RE DESIGNING TO THE NEED
RATHER THAN LETTING THE
TECHNOLOGY LEAD.
I THINK THERE IS A NEED FOR A
FOUNDATIONAL SCIENCE PROGRAM TO
GO ALONG WITH ALL THE THINGS
THAT WE'RE DISCUSSING SO WE'RE
BUILDING THINGS THAT ARE GOING
TO BE USEFUL AND NOT JUST
ASSUMING THEY'LL BE USEFUL ONCE
THEY ARE BUILT.
>> ALL RIGHT. MAKES A LOT
OF SENSE ALONG THE LINES OF
PARTICIPATORY DESIGN.
I THINK THE NEXT QUESTION THAT
WE HAD WAS REALLY A COMMENT FROM
GIACOMO.
GIACOMO, ARE YOU STILL ON LINE?
>> YES, YES, I AM.
>> HE'S IN EUROPE.
SO THE NEUROMORPHIC COMMUNITY IS
PRAGMATIC. SO IF THERE'S A BENEFIT
IN USING NEUROMORPHIC PROCESSING ON
NON BIOLOGICAL DATA SUCH AS RADAR, WE'LL DO IT.
BUT IF THERE IS A NEED TO USE
CONVENTIONAL COMPUTING, WE'LL DO
THAT AS WELL.
ONE THING DOES NOT RULE THE
OTHER OUT.
THE ONE PLACE WHERE THE
NEUROMORPHIC APPROACH WILL HELP
HERE IS THE NEED TO DO THINGS AT
LOW POWER ONLINE ESPECIALLY
WHEN THE SENSORY DATA IS SPARSE.
OH, THERE IS GIACOMO.
NOTHING ELSE TO ADD?
>> NO.
>> THEN I'LL TURN IT OVER TO
KAREN WHO HAS BEEN PATIENTLY
WAITING TO ASK HER PROVOCATIVE
OR LINKING QUESTION.
>> THANKS TO EVERYBODY FOR
COMING BACK AND FOR SPEAKERS TO
STAY ONLINE ESPECIALLY GIACOMO.
I KNOW IT'S LATE.
FIRST I WANTED TO COMMENT ON
WHAT CHRIS SAID BECAUSE IT'S SO
IMPORTANT THAT THE TYPES OF
QUESTIONS WE DECIDE TO ASK WITH
NEUROAI THEY'RE NOT
MORALLY NEUTRAL. A DECISION ON
WHAT WE'RE MODELING, WHAT WE CHOOSE
TO USE THE SCIENCE FOR, AND WHAT WE
CLAIM TO BE SAYING, THESE ALL COME
WITH EMBEDDED VALUES AND CONFLICTS
AND NEED TO BE DETERMINED IN ANY
NEURO AI EFFORTS WITH THE COMMUNITIES
FOR WHOM THIS MIGHT MATTER MOST
INCLUDING THE BUILDERS AND
FUTURE USERS.
I'M THINKING ABOUT GIACOMO.
I'M GLAD YOU'RE STILL HERE AND
RALPH I'M GLAD YOU'RE STILL HERE.
I WANT TO TALK ABOUT BCIS.
IN THE NEUROETHICS LITERATURE,
WE'VE HAD DISCUSSION ON BCIS
IN GENERAL AND HOW THEY
INTERVENE WITH THE BRAIN AND
WHAT MEANS FOR INDIVIDUALS, THEIR
IDENTITY, ATYPICAL SAFETY CONCERNS.
NOT JUST IMPLANTING A DEVICE BUT
WHAT IT DOES TO INFLUENCE THEIR
PERCEPTION OF THE WORLD ET
CETERA.
SCIENTISTS WHO WORKS ON BCI'S
REPORT SHARING THOSE CONCERNS AND
THEY'RE TRYING TO FIGURE OUT
WHAT TO DO WITH THAT.
A LOT OF THE SOLUTIONS ARE
AROUND IN ETHICAL LEGAL CATEGORY,
CONSENT.
THAT'S A GOOD START BUT PEOPLE
OFTEN MISTAKE THAT CONTENT IS A
LIABILITY DEVICE.
I HAVE YET IN ALL MY YEARS
WORKING SEEN ONE WRITTEN AT AN
APPROPRIATE HUMAN READABLE LEVEL.
WHAT I ADVOCATE FOR IS A
HUMAN-IN-THE-LOOP ETHICS BY DESIGN
APPROACH. BRINGS ME TO AN OPERATIONAL
QUESTION ON HOW TECHNOLOGY
WORKS AND HOW IT MIGHT BE USED
CLINICALLY.
THE FIRST IS A BASIC ONE.
WE SAY OPERATIONALLY, THERE ARE
GREAT THINGS ABOUT BCIS.
THERE IS POTENTIAL ENERGY
EFFICIENCY AND MAYBE MORE
REAL-TIME CAPABILITIES.
IT'S SMALLER.
THERE IS EVEN THE POSSIBILITY
THAT THERE ARE MORE NATIVE
CONNECTIONS YOU CAN MAKE WHEN
INTERFACING WITH THE BRAIN.
EVEN POTENTIAL FOR EDGE
COMPUTING OR A SECURITY FEATURE.
THIS IS SOMETHING WE TALKED
ABOUT THIS MORNING.
MANY BCIS OFTEN CONNECT
BETWEEN UNSECURE INTERNET
CONNECTIONS OR BLUETOOTH.
I WONDER ABOUT -- THERE ARE
BENEFITS TO AUTONOMY.
YOU CAN RESTORE SOMEONE'S
AUTONOMY AND AGENCY TO OPERATE
IN THE WORLD WITH THESE DEVICES.
BUT DOES NEUROMORPHIC COMPUTING
IT'S POTENTIAL TO NATIVELY INTERACT
EXACERBATE TENSIONS ABOUT SAFETY FEATURES?
IT MIGHT BE INTERACTING WITH THE
BRAIN AND OTHER WAYS WHERE IT
MITIGATES THOSE TYPES OF
CONCERNS?
I MENTIONED SAFETY ISSUE AND I'M
CURIOUS IF THOSE QUESTIONS CAME
UP AT THE WORKSHOP YOU WERE
TALKING ABOUT, RALPH.
>> THE CYBERSECURITY ASPECT CAME
UP.
AND IT WAS SOMETHING THAT WAS
PERCEIVED AS BEING AN AREA OF
INVESTMENT HASN'T BEEN ENOUGH
INVESTMENT INTO STUDY.
ALSO FROM THE PERSPECTIVE OF
STANDARDS.
THAT WAS ANOTHER COMPONENT.
I THINK IEEE IS DEVELOPING
STANDARDS ON A.I. AND SPECIALLY
ON NEUROAI WITH CONNECTION
BETWEEN NERVOUS SYSTEM AND A.I.
THERE IS NO SOLUTION JUST YET.
SO MOVING AWAY KIND OF THAT'S
THE HIGH LEVEL, THE METHOD SIDE
OF WHAT WE CAN DO TO PROTECT THE
USERS OF THE DEVICES.
MAYBE THIS IS WHERE THE OLD
SCHOOL NEUROMORPHS CAN BE HELPFUL.
THE HARDWARE IS SO SPECIFICALLY
DESIGNED IN A WAY TO INTERFACE
WITH THE NERVOUS SYSTEM AS WELL AS
TO MIMIC IT IN SUCH A WAY THAT IT
TRIES TO USE THE PHYSICS OF
COMPUTATION IN ORDER TO SOLVE THE
PROBLEM AT HAND.
THERE IS TYPICALLY NOT A LOT OF
EXTERNAL CONNECTIONS.
THERE IS NO USB OR BLUETOOTH
ELEMENT TO IT.
MAYBE IN THAT SENSE WE CAN
MITIGATE SOME OF THE DANGER BY
HAVING NOT THE TRADITIONAL
INTERFACES, BUT MORE SPECIFIC
INTERFACE THAT JUST CLOSES THE
LOOP.
THE EXAMPLE THAT I GAVE WHERE
WE'RE WATCHING AN ELECTRODE AND
ADAPTING LOCALLY, THAT HAS NO
NEED FOR EXTERNAL
COMMUNICATIONS.
SO THE SAFETY ASPECT ON HACKING
INTO IT AND SO ON CAN BE
AVOIDED.
BUT THE OTHER SIDE IS WHAT IF IT
RUNS OFF THE RAILS?
WHAT IF THE CONTROL LOOP
IS OUT OF TRACK?
HOW DO YOU SOLVE FOR THAT?
>> THAT'S A POINT I HOPE GIACOMO
WILL ADDRESS.
>> EXACTLY.
I AGREE COMPLETELY THE
NEUROMORPHIC TYPE OF SYSTEMS
HAVE LESS OF A PROBLEM.
IT'S LESS OF THIS INTERPRETABILITY.
THEY ARE MORE INTERPRETABLE THAN THE
BLACK BOX APPROACH OF STANDARD A.I.
THERE IS STILL THE PROBLEM OF
HAVING A SYSTEM, ESPECIALLY IF
IT HAS ADAPTATION PROPERTIES OR
ON-CHIP LEARNING PROPERTIES OF HAVING
A SYSTEM THAT IS AUTONOMOUS AND
THAT INTERACTS WITH ANOTHER
AUTONOMOUS SYSTEM, THE REAL BRAIN
OR REAL NERVOUS OR PERIPERAL SYSTEMS.
IT'S A PROBLEM THAT WE ARE
STARTING TO FACE ESPECIALLY ALSO
BECAUSE IN THE NEUROMORPHIC
COMMUNITY, SOME OF US ARE TRYING
TO LINK THE ARTIFICIAL NEURONS
WITH REAL NEURONS EVEN ORGANOIDS.
SO YOU CAN IMAGINE THERE ARE
MANY ETHICAL QUESTIONS THAT
ARISE IN THIS DOMAIN.
I CAN TELL YOU WE'VE BEEN
INVOLVING PEOPLE THAT ARE
EXPERTS IN ETHICS IN OUR
WORKSHOPS AND DISCUSSIONS TO TRY
TO START TO WORRY ABOUT THESE
THINGS EVEN BEFORE WE START TO
DESIGN THESE CIRCUITS AND CHIPS
THAT WILL EVENTUALLY MIGHT END
UP IN BRAIN MACHINE INTERFACE.
I AGREE, IT'S AN IMPORTANT PROBLEM.
THE COMMUNITY IS AWARE OF THE
PROBLEM.
CHIARA LED A WORKSHOP IN THE
NEUROTECH AND A.I. INITIATIVE SHE
LED WITH HER COLLEAGUES IN
EUROPE SOME YEARS AGO.
EVEN IF THE PAST RECENT YEARS
WE'VE BEEN INVOLVING COLLEAGUES
I DON'T HAVE A SOLUTION TO THE
PROBLEM.
>> I THINK IT'S GOOD TO HEAR
YOU'RE THINKING ABOUT IT AND I
WANTED TO POINT OUT THIS IS AN
OPPORTUNITY BECAUSE IT'S NOT A TOPIC.
HBP HAS BEEN INVOLVED THAT'S HOW
WE'VE INTERACTED.
WE'VE LOOKED AT ETHICAL ISSUES.
BUT IT'S NOT A PART OF THE
CONVERSATION OF IMPLANTABLE BCI,
FDA COMMUNITY COLLABORATORY.
THIS IS SOMETHING TO DISCUSS TO
UPGRADE OUR PROCESSES AS WE
EXPLORE THESE THINGS.
>> MAY I ASK A QUICK QUESTION.
FOR PACE MAKERS AND SO ON, THAT
IS ALSO HAS THE SAME KIND OF
RISKS, IF YOU WILL AS THE BCI
SCENARIO.
WHAT DO THEY DO THERE?
>> I SAY THAT IF YOU HAVE A
HEART TRANSPLANT, YOU'RE
PROBABLY STILL YOU.
IF YOU GET A BRAIN TRANSPLANT
YOU ARE NOT YOU.
NOT TO DISPARAGE THE HEART, BUT
I THINK THE TYPES MUCH CHANGES
ALSO MIGHT BE DIFFERENT. THEY
MIGHT BE SLOWER OR FASTER.
THERE ARE THINGS WE DON'T UNDERSTAND
EVEN WITH REGULAR BRAIN COMPUTER
INTERFACES, THE EFFECTS OF TIME.
THERE ARE CHALLENGES WITH THE
BRAIN BECAUSE IT'S -- WELL THERE
ARE A LOT OF THINGS SPECIAL
ABOUT THE BRAIN.
OTHER THINGS TO THINK ABOUT WITH
THE THESE MORE SOPHISTICATED
TECHNIQUES AS THEY RAPIDLY EVOLVE.
WE DO NOT HAVE ANYWHERE IN THE WORLD
A GOOD STANDARD FOR ENSURING
BACKWARDS COMPATIBILITY AND UPGRADES.
IT WOULD BE IMPORTANT TO
CONSIDER THAT AS WELL.
I RECALL CHRIS AND I HAVE A
FRIEND, BRANDY ELLIS, WHO HAS
A DBS IMPLANT.
SHE SAID PUBLICLY BEFORE SHE
MISSED THE OPPORTUNITY TO GET AN
UPGRADE. IT WAS RIGHT BEFORE
GETTING IMPLANTED AND NOW SHE HAS
A LESS SECURE DEVICE. THERE AREN'T
WAYS TO CHANGE THAT FOR HER.
HER DEVICE WORKS BEAUTIFULLY FOR
HER AT THE SAME TIME.
WE DON'T HAVE MECHANISMS IN
PLACE FOR THAT.
MANY KIND OF BCIS WILL HAVE
SIMILAR PROBLEMS.
>> AND I GUESS WITH PERIPHERAL
SYSTEMS LIKE RETINAL IMPLANTS.
WE HAD THE ARGUS SYSTEM BEING
DISCONTINUED NOW PEOPLE WANT IT
AND CAN'T GET TO IT.
THERE ARE TWO SIDES OF THE
PROBLEM.
>> WITH THE RETINAL IMPLANT,
PEOPLE WHEN THE COMPANY WAS SOLD
WERE NOT ABLE TO GET UPGRADES OR
REPLACEMENTS FOR THEIR
TECHNOLOGY ANYMORE AND THEY
BECAME BLIND AGAIN.
I HEARD SOMEBODY TELL ME THE
SAME THING WITH A SPINAL CORD
STIMULATOR THAT RESTORED THEIR MOVEMENT
WHEN THEIR EQUIPMENT STOPPED
WORKING THEY BECAME PARALYZED
AGAIN. IT'S DEVASTATING.
>> I WOULD LIKE TO ASK A
QUESTION ON ZOOM.
I WOULD LIKE TO DISAGREE WITH
THE IDEA THAT WE FIRST NEED TO
UNDERSTAND THE PRINCIPLES BEFORE
WE UNDERSTAND THE TECHNOLOGY.
UNDERSTANDING BY BUILDING IS
STILL A VALID APPROACH.
UNDERSTANDING COMES FROM BITS
AND PIECES AND FINDING LIMITATIONS
AND UNDERSTANDING WHAT IS CORE.
THIS IS SPICY.
I WANTED TO GET THE PANEL'S TAKE
ON THAT.
>> SOUNDS LIKE THAT MAY BE
ADDRESSED TO ME.
I DON'T DISAGREE WITH THAT.
YET THERE IS A LOT OF VALUE IN
THE ITERATIVE BACK AND FORTH OF
THIS ALL.
AND TO BE CLEAR, I SEE A LOT OF
VALUE FOR NEUROAI AND
NEUROMORPHICS IN A LOT OF AREAS.
MOST OF MY COMMENTS ARE DIRECTED
TO NEUROTECHNOLOGY AND BRAIN IMPLANTS
THE HONEST ANSWER
IN THE WORK WE DO RIGHT NOW, IF
YOU GAVE ME A NEUROMORPHIC
DEVICE, THERE WOULD BE A LOT OF
TIME, ENERGY AND MONEY THAT
WOULD HAVE GONE INTO THE
DEVELOPMENT OF IT AND WE DON'T
KNOW WHAT TO DO WITH IT RIGHT NOW
TO MAKE THE LIVES OF OUR PATIENTS
BETTER.
IT'S POSSIBLE THAT THAT DEVICE
WOULD HAVE BEEN DESIGNED AND
BUILT FOR A SPECIFIC PURPOSE
THAT IS NOT MOST NEEDED IN THAT
APPLICATION.
I LOVE THE TECHNOLOGY
DEVELOPMENT.
I AGREE IT'S AN ITERATIVE
PROCESS BUT I'M TRYING TO MAKE
THE STATEMENT THERE IS A LOT OF
VALUE IN THE PARALLEL LINE OF
SCIENTIFIC INQUIRY TO UNDERSTAND
WHAT WE CAN DO WITH THE
TECHNOLOGY, NOT IN PLACE OF IT,
BUT NOT JUST TECHNOLOGY
DEVELOPMENT IN PLACE OF DOING
THE SCIENCE EITHER IS WHAT I'M
TRYING TO ADVOCATE HERE.
WE CAN END UP WITH A PLACE WHERE
THE TECHNOLOGY IS DESIGNED TO A
NEED RATHER THAN NEEDS TRYING TO
FIT INTO A TECHNOLOGY DESIGN
THAT'S BEENS SEPARATED FROM IT.
I DON'T WHOLESALE DISAGREE, BUT
I'M TRYING TO ADVOCATE THERE IS
SOMETHING ELSE THAT NEEDS TO BE
PART OF THE DISCUSSION.
>> I HAD A QUESTION WHICH IS
THE INTERESTING THING ABOUT THIS SET
OF TALKS IN PARTICULAR THE
NEUROTECH TALKS AS WELL AS THE
EMBODIED ROBOTICS TALKS.
IT BRINGS TO THE FRONT THAT WE
CAN DEVELOP TECHNOLOGIES IN A
GENERIC SENSE UP TO A POINT.
AT WHICH POINT YOU NEED TO
BUCKLE DOWN AND INVEST ON ADVANCING
THE TECHNOLOGY READINESS LEVEL (TRL).
FOR A SPECIFIC TECHNOLOGY,
REDUCE ALL THE RISK ASSOCIATED
WITH ITS USE IN A GIVEN
APPLICATION.
AT WHICH POINT IT STOPS BEING
USEFUL FOR OTHER THINGS OR IT MAY
BECOME USEFUL FOR OTHER THINGS BY
LUCK. AT WHAT POINT DO WE SWITCH
FROM DEVELOPING CONCEPTS? THIS
APPLIES TO NEUROAI BROADLY AND
NEUROMORPHICS SPECIFICALLY.
WE CAN DO SOMETHING GENERAL
WHERE WE'RE ALL LIKE I PREFER
THIS OR THAT BUT WE'RE ALL
CHASING THE SAME GOAL IN THE
DISTANT FUTURE.
AT SOME POINT YOU HAVE TO SWITCH
AND COMMIT TO A TECHNOLOGY AND
INVEST TIME AND RESOURCES TO
VALIDATE THAT AND IF YOU'RE NOT
CAREFUL, YOU BUILD A SOLUTION
WHICH IS CUSTOM TO THAT
SIX-LEGGED ROBOT AND IT'S NOT
GOOD FOR ANYTHING ELSE.
WHICH MAY NOT BE WHAT WE WANT.
IF YOU WAIT LONG ENOUGH, YOU
BUILD SOMETHING WHERE THAT IS
INCREDIBLY INFORMATIVE AND
TRANSFERS ACROSS.
HOW DO WE IDENTIFY THIS HAND-OFF
POINT OR IS IT EVEN A HAND-OFF
POINT, AND BASICALLY DO THIS IN
A -- I THINK WHAT YOU'RE
ADVOCATING IS EYES OPEN THAT THIS IS
GOING TO HAPPEN
AS WE MATURE THIS TECHNOLOGY AND
BECOME INTENTIONAL ABOUT IT AS
OPPOSED TO CRASHING INTO A WALL
AND SAYING MAYBE I SHOULDN'T
HAVE DONE THAT.
>> IF I CAN ADD TO THAT.
I THINK IT'S IMPORTANT TO
DISTINGUISH IN TERMS OF
NEUROMORPHIC PLATFORMS.
THERE IS THE NEUROMORPHIC APPROACH
WHICH I THINK IS WHAT KWABENA IS
PROPOSING FOR NEUROMORPHIC SUPREMACY
WHERE WE AIM FOR LARGE-SCALE GENERAL-
PURPOSE TYPE EVER BRAIN INSPIRED
COMPUTING SYSTEMS AND THEN THERE'S
THE NEUROMORPHIC APPROACH OF THE
EDGE COMPUTING, ULTRA LOW POWER,
VERY SPECIALIZED SENSORY PROCESSING
SYSTEMS. FOR THE SENSORY PROCESSING
SYSTEM SIDE OF THINGS, THOSE
WOULD BE REALLY DEVELOPED FOR A
SPECIFIC APPLICATION.
THERE IS NOTHING YET, ALL THE
THINGS WE HAVE AVAILABLE NOW ARE
RESEARCH PLATFORMS.
THIS IS WHAT MIKE DAVIS SAYS FOR
LOIHI, SAME FOR SPINNAKER 2, AND
CHIPS FROM HOPKINS, SAN DIEGO, ZURICH.
THEY ARE ALL RESEARCH PLATFORMS
AS SOON AS A CUSTOMER COMES WITH
SOME SPECIFICATIONS, PROBABLY
WE'D STRIP OUT MOST OF THE
THINGS ON THESE PLATFORM AND
MAKE IT SPECIFIC TO THAT
PARTICULAR APPLICATION, BUT NOT
FOR ANYTHING ELSE. IF WE WANT
TO REDUCE POWER CONSUMPTION, WE
CANNOT HAVE GENERAL-PURPOSE
PROGRAMMABILITY. IT'S A TRADE OFF.
BUT THIS APPLIES ONY TO EMBEDDED
SENSORY SYSTEMS, FOR EXAMPLE, BRAIN MACHINE INTERFACES
AND NOT TO THE MORE LARGE-SCALE
NEURAL PROCESSING SYSTEMS THAT
KWABENA ADVOCATES.
>> TO THAT POINT, I WAS GOING TO
POINT OUT THAT THIS ISN'T NEW TO
ANY TECHNOLOGY.
THERE HAS ALWAYS BEEN A VALLEY
OF DEATH BETWEEN -- ON THE
INNOVATION LIFE CYCLE.
THERE IS A VALLEY OF DEATH FOR
PRETTY MUCH ANY TECHNOLOGY.
ONE COULD ARGUE THAT THE
RESEARCH COMMUNITY AND ARGUABLY
GOVERNMENT FOSTERS THE
GENERATION OF THE IDEAS BUT IT'S
INDUSTRY THAT DOES THAT
REFINEMENT AS TO WHICH SOLUTION
IS GOING TO BUBBLE UP OR NOT.
AND THAT REFINEMENT PROCESS IS
REALLY -- IF YOU LOOK AT
HISTORY, IT'S BEEN THE SURVIVAL
OF THE FITTEST OF THE INDUSTRY
ORGANIZATIONS.
THOSE THAT SURVIVE AND THOSE
THAT DON'T.
PROBABLY NOT THE BEST ANSWER,
BUT IT IS WHAT WE SEE EVERY DAY
WITH DIFFERENT TECHNOLOGIES.
>> IN SOME WAYS AND MAYBE I'M
BEING OVERLY BROAD.
A.I. IS A NEW FIELD.
NEUROAI IS A NEWER FIELD AND
NEUROMORPHIC, THERE'S NOT LIKE AN
EXISTING INDUSTRY OUT THERE DOING IT.
I HEAR WHAT YOU'RE SAYING, IF I COME
UP WITH A NEW WIDGET FOR THE AUTO-
MOTIVES INDUSTRY, THERE'S AN EXISTING
STRUCTURE FOR IT. BUT THIS IS NOT OUR
CASE. WE HOPE THINGS START THINGS UP.
IS THERE A WAY TO BE MANIPULATE THIS
VALLEY OF DEATH FROM WHERE WE'RE
SITTING NOW GIVEN THAT
INDUSTRY HAS TO FORM
ORGANICALLY AS WE LOOK FORWAR?
>> CAN I ADD SOMETHING FURTHER
ON THAT.
ONE OF THE THINGS BROUGHT UP WAS
THAT WHEN WE START TO GET REAL
APPLICATIONS, IT HAS TO BE
EXTREMELY SPECIFIC IN ITS
APPLICATION.
YET, IF I WERE TO LOOK AT A
DESIGN PERSPECTIVE, IT'S NOT
WHAT WE SEE IN PRACTICE.
IT IS QUITE EXPENSIVE TO DO FULL
NEW IC DESIGN STARTS.
IN PRACTICE WHAT YOU SEE IS EVEN
ON THE DIGITAL SIDE, THERE ARE
MANY DIFFERENT COMPONENTS THAT
ARE CONFIGUREABLE OF SOME FORM AND
STILL EFFICIENT.
THERE IS A REASON WE HAVE FPGAS
AND NOT CUSTOM CHIPS IN THOSE SPACES.
THERE IS A REASON THAT WE HAVE
MICRO PROCESSORS EVERWHERE BECAUSE
EVERYONE CAN CUSTOMIZE IT WITH
GOOD EFFICIENCY.
I THINK THAT'S GOING TO BE TRUE
WITH A LOT OF ANALOGUE
COMPUTING.
WE'LL NEED THAT TO MAKE IT AT ALL
AFFORDABLE.
AND FURTHER -- THERE ARE ALWAYS
BE ONE OR TWO APPLICATIONS THAT
WILL BE FINE.
BUT YOU CAN USUALLY COUNT THOSE
ON A FEW FINGERS.
>> THAT'S WHAT I WAS ABOUT TO
SAY. THE MOST SUCCESSFUL
NEUROMORPHIC TECHNIQUE IS THE
LOGITECH MOUSE. IT'S VERY SPECIFIC.
YOU MOVE A MOUSE ON A SCREEN.
>> IT WAS DONE MORE THAN 25 YEARS AGO
SO. SO TODAY WE'VE HAD
A BIT INTENTIONALLY THAN YESTERDAY.
I THINK WE WANTED TO FOCUS ON TODAY
WAS THIS SORT OF, QUESTION ABOUT HOW,
NEUROMORPHIC TECHNOLOGY, EMBODIMENT,
SOME OF THE SORT OF NEUROTECH THINGS WE JUST DISCUSSED,
MIGHT PLAY INTO
THE SORT OF BIGGER NEUROAI SORT OF QUESTIONS.
RIGHT.
AND HOPEFULLY WE GOT
A SENSE OF BOTH THE WHERE, FOR PEOPLE
WHO AREN'T AS FAMILIAR WITH
THESE FIELDS, YOU GET A BETTER,
SENSE OF WHAT THESE FIELDS ARE ABOUT,
WHAT THE QUESTIONS ARE.
BUT I THINK ALSO A SENSE OF THE OPPORTUNITY.
I THINK RIGHT NOW WHAT WE'RE SEEING IS WE'RE CLEARLY GAINING,
THROUGH THE BRAIN INITIATIVE AND OTHER EFFORTS.
NEUROSCIENCE HAS REACHED A POINT WHERE IT IS ABLE TO OFFER
SYSTEM LEVEL UNDERSTANDING OF
WHAT'S GOING ON IN THE BRAIN.
BUT AT THE SAME TIME,
THE NEUROMORPHIC FIELD IN PARTICULAR
IS REACHING SCALES, WHICH IS ABLE TO ABSORB
THOSE SORT OF DETAILS AND COMPLEXITIES
OUT OF NEUROSCIENCE.
AND SO I THINK THIS IS PRETTY EXCITING TIME.
I MEAN, I THINK EVEN TWO YEARS AGO,
DARE I SAY, I DON'T THINK THIS WORKSHOP,
OR AT LEAST TODAY, WOULD HAVE BEEN AS POSSIBLE.
THIS IS A VERY EXCITING TIME,
OBVIOUSLY IN NEUROAI, BUT ALSO FOR
THE ROLE OF NEUROMORPHIC COMING BACK
INTO NEUROSCIENCE.
SO I'M
SO, YOU KNOW, I THINK, WELL,
YOU KNOW, THE SLIDES AREN'T SUPER IMPORTANT,
BUT I THINK WHAT WE REALLY WANT TO KIND OF POINT OUT
IS THAT THERE IS THIS SORT OF VIRTUOUS CYCLE.
AND I THINK IT'S, YOU KNOW,
WE'VE BEEN TRYING TO POPULATE IT. RIGHT.
I THINK WE'VE SEEN EXAMPLES OVER THE LAST, YOU KNOW,
STAGE WORTH OF TALKS, A COUPLE OF SESSIONS WHERE,
YOU KNOW, WE'RE SEEING THINGS LIKE,
NOT ONLY CAN WE PUT NEURAL MODELS IN THE CONNECTOME
ON NEUROMORPHIC HARDWARE TODAY AND THEN USE THAT IN SIMULATION,
BUT WE CAN TAKE SOME OF THESE ACTIVE THEORETICAL FRAMEWORKS
LIKE THE MANIFOLD, YOU KNOW, AND, AND THINGS LIKE THAT.
AND WE ACTUALLY CAN INTERROGATE A.I. MODELS, CAN,
YOU KNOW, INTERROGATE NEUROMORPHIC SYSTEMS
AND USE THESE NEUROSCIENCE TOOLS TO UNDERSTAND
WHAT'S GOING ON IN THE NEUROAI FIELD.
BUT WE CAN ALSO UNDERSTAND.
AND I THINK THE OTHER THE OTHER THING IS, IS THAT,
THE NEUROMORPHIC FIELD IS POSITIONED
TO GREATLY IMPROVE FROM THESE NEUROSCIENCE IDEAS.
RIGHT? I THINK, YOU KNOW,
THOSE OF US WHO'VE BEEN DOING NEUROMORPHIC FOR A WHILE
AND SOME FOR, FOR A VERY LONG WHILE,
YOU KNOW, WE'VE HAD THIS SORT OF BELIEF
THAT WE WERE GOING TO HIT THIS POINT
THAT THE ENERGY COSTS OF THE WORLD ARE GOING TO
OVERWHELM MUCH OF, OF,
WHAT'S WHAT'S GOING ON.
AND I THINK NEUROMORPHIC NOW
REACHED THE POINT WHERE WE'RE ABLE TO, TO IMPACT THAT.
BUT I THINK IT'S GOING TO REQUIRE THE SORT OF BRAIN,
YOU KNOW, REAL BRAIN INSPIRATION TO COME BACK INTO THE PROCESS
AND DO IT.
AND I THINK THIS IS,
WHAT I WOULD SAY IS WE WANT AS MUCH
INPUT FROM THE NEUROSCIENCE FIELD AS POSSIBLE.
AND I DO BELIEVE THE NEUROAI COMMUNITY IS BEST POSITIONED
TO BE THAT LIAISON.
OH, PERFECT.
THANK YOU.
SO ANYWAY, WE HAVE A LOOP EVERYONE LIKES THE LOOP.
ACTUALLY NEVER USED A LOOP BEFORE IN A SLIDE
BEFORE THIS MEETING, I'M GOING TO START USING IT.
I THINK.
SO ANYWAY, THE FIRST SESSION, JUST AS A BRIEF RECAP, RIGHT?
YOU KNOW,
I THINK I SAID MOST OF THIS, RIGHT.
I DO THINK THAT THIS IDEA OF, OF EMBODIMENT,
AND I THINK, YOU KNOW, WE CAN DEBATE
WHEN EMBODIMENT REALLY MEANS.
BUT I THINK THIS IDEA THAT
THE BRAIN DOES NOT EXIST IN ISOLATION, EVEN IN,
YOU KNOW,
NOT NECESSARILY LOOKING AT, INTERVENTIONS,
JUST UNDERSTANDING HOW THE BRAIN WORKS.
WE HAVE TO UNDERSTAND HOW IT INTERACT,
HOW THE BRAIN IS INTERACTING WITH THE WORLD AROUND IT.
AND THIS IS A WAY OF BUILDING MODEL SYSTEMS
IN ORDER TO UNDERSTAND THINGS.
AND I THINK AT THE SAME TIME, WE CAN LOOK AT HOW A.I.
YOU KNOW, JUST EVEN MAINSTREAM DEEP NETWORKS.
AND SO FORTH ARE INTERACTING WITH THE WORLD
AND SIMILARLY INTERROGATE THOSE USING
THE NEUROSCIENCE TOOLS THAT WE HAVE DEVELOPED TODAY.
AND I THINK THAT,
SOME OF THAT IMPACT WAS VERY WAS VERY IMPACTFUL.
IMPORTANT.
SO, YOU KNOW, I DO THINK THAT THE, THE, THE IMPACT, YOU KNOW,
IS FEEDING BACK INTO NEUROSCIENCE,
BUT ALSO GOING OUT AND MAKING THE WORLD A BETTER PLACE.
BUT ALSO.
GETTING INTO NEURO HEALTH AND NEUROTECHNOLOGY.
AND THAT'S WHERE I THINK WE'LL HAND OFF TO GINA.
YES. THANK YOU VERY MUCH, BRAD.
ACTUALLY, MY, FOLLOW UP SLIDE, IS,
USING I DON'T KNOW IF THIS IS WORKING AS.
A THE SPACE. FOR.
YES. FOR SESSION FOUR,
WE TOOK THAT, CONCEPT OF IMPACT
AND HOW NEUROAI CAN SUPPORT ADVANCES IN DIFFERENT
MEDICAL AND HEALTHCARE RELEVANT APPLICATIONS.
AND, ONE CONCEPT THAT WAS DISCUSSED TIME
AND TIME AGAIN WAS THIS IDEA OF EMBODIMENT
AND HOW THAT HAS TO TRANSLATE INTO END-TO-END SYSTEMS,
WHERE NEUROMORPHIC IS A PART OF IT.
IT HAS TO BE INTEGRATED WITH MULTIMODAL SENSORS.
IT HAS TO HAVE REALLY HIGH FAN-IN
AND FAN-OUT COMPUTING, BIOLOGICALLY REALISTIC
ACTIONS AND DECISIONS, SIMULATION IN A CLOSED LOOP.
SO THIS IDEA OF HAVING THE NEUROMORPHIC
COMPUTING AS PART OF A CLOSED LOOP,
IT CAME FROM, FROM THE EMBODIMENT PIECE.
AND ALSO TO, TO THAT EXTENT,
BEING ABLE TO HAVE TWINS BY,
OF THE BIOLOGICAL REGIONS, DIGITAL TWINS, HARDWARE TWINS
TO SUPPORT
THE, THE RESEARCH AND DEVELOPMENT IN THESE
END-TO-END SYSTEMS.
ANOTHER IMPORTANT PIECE WAS WHAT ARE THE APPLICATIONS
THAT, ARE, SUPPORTIVE, AND WHERE WE SEE
THE NEUROMORPHIC COMPUTING ADVANTAGE, WHERE IS NEUROAI,
BRINGING IN ITS CONTRIBUTION FOR NIH RELEVANT APPLICATIONS.
AND WE ENDED UP OUR DISCUSSIONS FOR SESSION FOUR WITH THAT.
THERE WERE A LOT OF DISCUSSION ABOUT,
DIAGNOSTICS AND NEURAL INTERFACES,
THE DIFFERENT BIO SIGNAL ANALYSIS, ROBOTICS, ETC.,
BUT THAT COMES WITH DATA SETS AND SUPPORTING
DATA THAT IS ABLE TO, TO PROVIDE
RESEARCHERS AND ENGINEERS WITH
METRICS AND WAYS OF ACTUALLY DEVELOPING THESE TECHNOLOGIES.
AND, IN TERMS OF METRICS, A LOT WAS DISCUSSED, OF COURSE,
ENERGY EFFICIENCY IS A BIG PIECE,
BUT ALSO THE IDEA OF PERFORMANCE IN TERMS OF ACCURACIES,
REPROGRAMMABILITY, VALIDATION, SAFETY,
CLINICAL BENEFITS, USABILITY, RELIABILITY.
WHAT'S THE TECHNOLOGICAL MATURITY?
AND THE INTERPRETABILITY AND THE EXPLAINABILITY OF THE MODELS?
AND, WE HAD A LIVELY DISCUSSION
ABOUT THE SAFETY AND ETHICS OF, OF THIS,
AND, THE,
THE LAST PIECE OF IT WAS, WHAT WOULD BE NEXT?
WHAT SHOULD COME NEXT?
AND, THE DISCUSSION
ALTERNATED AND PROPOSES TWO COMPLEMENTARY
RESEARCH PROGRAMS.
ONE MORE ON THE FOUNDATIONAL PIECE
OF IDENTIFYING WHAT WOULD BE THE POTENTIAL KEY
APPLICATIONS FOR NEUROAI.
AND, THE OTHER ONE WAS ON TECHNOLOGY DEVELOPMENT
ON SUPPORTING, THESE DEVELOPMENT OF NEUROAI SOLUTIONS.
SO, WITH THIS, I'M LOOKING FORWARD TO THE WRAP UP
FOR THE ENTIRE WORKSHOP AND TO SEE THE BIG VISION
FOR THE FUTURE.
I'M GOING TO HAND IT OFF TO PAUL MIDDLEBROOKS.
ALL RIGHT, SO,
WE'RE GOING TO HAVE A DISCUSSION TO WRAP UP THIS SESSION BEFORE
JOHN COMES OUT AND REALLY WRAPS IT UP.
MAYBE ONE WAY I'LL OPEN
THIS DISCUSSION UP IS BY NOTING THAT,
THERE WASN'T A TON OF THEORY,
IN ALL OF THE TALKS.
AND SO THEY'RE SORT OF TWO QUESTIONS.
ONE IS,
SPEAKING TO WHAT DORIS YESTERDAY NOTED
WHEN SHE SAID, DON'T DISCOUNT OUR INTUITIONS AS BIOLOGISTS
AS WE ARE DOING RESEARCH.
WITH NEUROAI AND USING ARTIFICIAL
INTELLIGENCE MODELS, THERE'S A SLIGHT RISK.
YOU KNOW, THESE DATA DRIVEN APPROACHES,
THERE'S A SLIGHT RISK
THAT WE LOSE SOME OF THAT CREATIVITY AND INTUITION,
AND WE'RE JUST LISTENING TO THE MODELS.
SO ONE QUESTION IS HOW DO WE MAINTAIN THAT INTUITION
AS WE PUSH FORWARD IN NEUROAI?
I WOULD BE HAPPY TO RESPOND
TO THAT BY SORT OF EXTENDING THE QUESTION, PERHAPS.
SO YESTERDAY, BLAKE
POSED
A SORT OF DICHOTOMY OF APPROACHES, ONE OF WHICH
WAS APPLYING THEORETICAL IDEAS, AS YOU'RE SAYING,
AND THE OTHER ONE WAS TAKING SORT OF A,
BOTTOM UP APPLYING DATA, TRYING TO MAKE DO
WITH WHATEVER WE HAVE JUST TO BUILD THINGS THAT WORK.
AND I THINK TALKING ABOUT,
AND MAYBE I COULD JUST SUGGEST TALKING ABOUT THAT TENSION
AND HOW IT INTERSECTS
THE THINGS WE'VE BEEN SEEING OVER THE PAST FEW DAYS.
I THINK THIS YOU'RE POSING THIS DICHOTOMY IN BETWEEN,
YOU KNOW, BIOLOGY AND THEORY.
WHAT WE DO, FOR EXAMPLE, IN, IN THE, YOU KNOW,
NEUROMORPHIC WORKSHOPS, IN TELLURIDE,
IN CAPO CACCIA AND OTHERS, A NEW ONE IN, IN BANGALORE.
WE KEEP INVITING BIOLOGISTS,
THAT DESCRIBE WHAT HAPPENS, WHAT THEY OBSERVE,
WHAT I THINK IS
NOT MISSING, BUT
NEEDED IS TO HAVE
A SMOOTHER TRANSITION BETWEEN WHAT
A BIOLOGIST OR A NEUROSCIENTIST OBSERVES
AND DESCRIBES,
WITH THEORIES,
MATHEMATICS, COMPUTATIONAL NEUROSCIENCE
THAT TRANSLATE THAT INTO SOMETHING
THAT WE CAN IMPLEMENT IN NEUROMORPHIC TECHNOLOGY.
WE ARE TRYING TO DO THIS EXERCISE.
SOMETIMES WE DON'T HAVE THE RIGHT BACKGROUND TO DO SO.
SO IT'S THAT'S WHY
IT'S A REALLY MULTIDISCIPLINARY TYPE OF COMMUNITY.
SO, SO IF YOU HAD I THINK WE CAN COME BACK
TO THE SAME TOPIC, BUT JUST JUMPING OFF OF THAT.
IF YOU HAD TEN YEARS
HOW WOULD YOU DESIGN A PROGRAM TO GET WHAT
YOU NEED?
IT'S.
YOU MEAN DESIGN A PROGRAM IN TERMS OF.
IMPLEMENTING, YOU KNOW, INFRASTRUCTURE, PEOPLE TRAINING?
YOU WERE TALKING ABOUT WHAT YOU NEED.
SO HOW I MEAN, WHAT WE, YOU KNOW, HERE AT THE NIH, RIGHT?
WE NEED TO TALK ABOUT IMPLEMENTATION.
SO HOW WOULD THIS BE IMPLEMENTED?
ONE FIRST STEP
IS THESE WORKSHOPS WHERE WE HAVE PEOPLE COMING TOGETHER.
AND I THINK YESTERDAY IT WAS MENTIONED,
WE DON'T NEED TO HAVE PEOPLE TRAINED IN EVERYTHING,
BUT WE NEED TO HAVE PEOPLE
THAT UNDERSTAND EACH OTHER THAT USE THE SAME VOCABULARY.
AND THAT'S NOT THE CASE YET.
WHEN DIFFERENT PERSONS
FROM DIFFERENT COMMUNITIES TALK ABOUT,
USING CERTAIN KEY WORDS, THAT KEYWORD HAS DIFFERENT MEANINGS
IN DIFFERENT COMMUNITIES.
AND, AND SO THAT'S WHY IT'S IMPORTANT.
TO CREATE AN INFRASTRUCTURE WHERE WE CAN EXCHANGE IDEAS
AND COMPETENCIES. AND THAT,
KIND OF FACILITATES THE SYNERGIES.
I'LL AMPLIFY
THAT A LITTLE BIT, AND THEN I'LL GO BACK
TO THE ORIGINAL QUESTION FOR A MOMENT.
I THINK IT
I WOULD ACTUALLY SAY, YES, WE DEFINITELY NEED TO HAVE WAYS
TO GET WITHIN A PROGRAM HOW PEOPLE COMMUNICATE.
AND IF IT WERE ME, I WOULD DEFINITELY TRY TO HAVE A PROGRAM
WHERE YOU HAD AT LEAST A COUPLE CORE TEAMS
THAT WERE FAIRLY BROAD THAT WOULD THEN ALL BE
COMMUNICATING TOGETHER, PROBABLY ALL INTERFACING
WITHIN THE SAME SORT OF HIGH- LEVEL FRAMEWORK AND TOOLS.
I THINK THAT A COUPLE TIMES THIS HAS BEEN DONE IN THE PAST
HAS BEEN INCREDIBLY EFFECTIVE.
BECAUSE IT'S NOT JUST EDUCATION ACROSS THE BOUNDARIES,
BUT EVERYBODY USING THAT CAPABILITY.
ALL THE WAY ACROSS.
AND SO YOU'D WANT TO HAVE A COUPLE BROAD TEAMS TO
BE ABLE TO DO THAT
SO I COULD IMMEDIATELY SEE
THAT WHEN WE BUILD A TEN YEAR PROGRAM
TO DO SOMETHING ALONG THOSE LINES.
THE OTHER COMMENT YOU MENTIONED, WHICH WAS,
YOU KNOW, THE QUESTION OF THEORY.
I MEAN, I THINK
CERTAINLY THEORY MATTERS, BUT I THINK WHERE YOU'RE
GOING WITH THAT IS, YOU KNOW, IT WAS THE DATA DRIVEN,
DRIVEN APPROACH VERSUS WHAT CAN WE FIGURE OUT DIRECTLY.
AND I WOULD SAY ANYTIME WE TRY TO BUILD SOMETHING.
AT LEAST I, FOR MY SENSE OF IT,
ANYTHING THAT I CAN DEFINE EXPLICITLY
RIGHT FROM THE BEGINNING. I WOULD USE THAT.
YOU KNOW, I DON'T WANT TO MAKE IT A COMPLETE BLACK BOX IF I
DON'T NEED TO DO THAT.
I ONLY PUT MORE BLACK BOXES WHEN I NEED TO.
GOOD EXAMPLE WOULD BE IF I'M DOING NEUROMORPHIC
STRUCTURES
AROUND, SAY, SPEECH RECOGNITION,
I ALREADY KNOW THE FRONT END VERY WELL.
THE MODEL FROM IS RELATED TO THE HUMAN COCHLEA.
I ALREADY KNOW WHAT THAT LOOKS LIKE.
I ALREADY KNOW WHAT THE FIRST COUPLE LAYERS LOOK LIKE.
I GO BUILD THIS AND THEN I GO ON TOP OF THAT.
AND THAT'S USEFUL BECAUSE THE MORE THIS
IF I'M BUILDING A TRUE EMBEDDED STRUCTURE, I
WANT TO
SIMPLIFY THE BLACK BOX AS MUCH AS I CAN
OR ALL SORTS OF OTHER REASONS.
WHICH I.
IMAGINE, PATRICK, YOU HAVE SO YOU HAVE INDUSTRY EXPERIENCE.
AND I THINK OF THESE THINGS IN TERMS
OF, OF FOR EXAMPLE, I ASKED MY PI
YESTERDAY
VIA TEXT WHAT TOOL WOULD YOU NEED?
AND HE SAID A SOFTWARE ENGINEER
AND I COMPLETELY AGREE WITH HIM.
AND I IMAGINE YOU MIGHT HAVE THOUGHTS ABOUT THAT.
YES. I AM A REFORMED SOFTWARE ENGINEER.
I DID DO THAT FOR A LITTLE BIT THOSE, INTERESTING.
SO RIGHT AFTER MY POSTDOC,
I GOT A JOB AS A SOFTWARE ENGINEER AT GOOGLE,
AND DID THAT FOR A LITTLE BIT UNTIL THEY REALIZED
THAT THEY SHOULD REALLY, YOU KNOW, TAKE ME OFF
OF THE REAL MACHINES THAT DO THE REAL THINGS.
BUT IN ALL SERIOUSNESS, IT WAS I THINK I REALLY,
ENLIGHTENING EXPERIENCE
AND SEEING WHAT WAS THE SHIFT
IN PARADIGMS FOR DOING ENGINEERING,
ESPECIALLY FOR SOFTWARE ENGINEERING.
AND SO I THINK AS WE'RE CONTEMPLATING THIS WORLD
IN WHICH MAYBE WE HAVE LARGER TEAMS,
WE HAVE CENTRALIZED ENGINEERING,
MAYBE WE HAVE LIKE A HUB- AND-SPOKES MODEL IN WHICH WE DO,
YOU KNOW, CENTRALIZED RESEARCH.
AND MANY PEOPLE ARE BROUGHT IN TO DO THIS.
AND WE'LL NEED TO EMBRACE,
THAT KIND OF FRAMEWORK FOR RESEARCH, SOFTWARE ENGINEERING
THAT IS MORE ROBUST
THAN THE KINDS OF APPROACHES THAT WE USE NOW, BECAUSE
IF WE WANT TO FACILITATE BOTH THE DISCOVERY SCIENCE,
AS WELL AS THE ENGINEERING THAT'S NECESSARY,
WE'LL NEED TO BRING THESE DISCIPLINES TOGETHER.
BUT RIGHT NOW, IT IS HARD.
IT IS CHALLENGING FOR PEOPLE IN LABS TO HIRE
GOOD RESEARCH SOFTWARE ENGINEERS FOR INSTANCE.
WHY IS IT ALWAYS A CHALLENGE?
BECAUSE THEY CAN MAKE MORE MONEY ELSEWHERE?
THAT'S A BIG REASON.
YEAH, ABSOLUTELY.
IT'S ALSO,
IT IS ALSO LIKE A CAREER TRAP.
IF SOMEONE HAS THE POSSIBILITY OF GOING INTO INDUSTRY RIGHT NOW
AND CLIMBING UP THE LADDER AS SOFTWARE ENGINEER, YOU KNOW,
THEY CAN, THEY HAVE LOTS OF POSSIBILITIES FOR GROWTH.
IF YOU ARE
INSIDE OF A RESEARCH LAB AND YOU'RE A SOFTWARE ENGINEER,
MUCH HARDER TO FIGURE OUT WAYS
TO GET, PEER REVIEW,
TO HAVE PEER MENTORSHIP.
YOU DON'T HAVE NECESSARILY POSSIBILITIES
TO GROW WITHIN THE ORGANIZATION.
OFTENTIMES THERE WILL BE 1 OR 2 RESEARCH
SOFTWARE ENGINEERS WITHIN A SILO, WHICH IS THE PI
IS THE SILO.
AND, IN THIS CASE, SO WHAT ARE YOUR OPTIONS
FOR ADVANCEMENTS?
YOU'LL MAKE ABOUT A QUARTER OF THE SALARY
THAT YOU WOULD MAKE AN INDUSTRY.
AND SO YEAH, I MEAN, IT'S, IT IS OFTEN AND I SHOULD SAY THAT,
IT OFTEN TIMES IT'S NOT EVEN
POSSIBLE TO FUND THESE POSITIONS.
SO, SO YEAH, THERE'S A FEW PLACES
THAT HAVE REALLY REALIZED THE ROLE OF CENTRALIZED ENGINEERING.
IT'S JUST TO NAME A FEW
I THINK HHMI JANELIA IS ONE PLACE THAT HAVE EMBRACED THIS
AT THE ALLEN INSTITUTE,
SOME OF THE NEWER INSTITUTES
IN CHARLOTTESVILLE COMING ONLINE THAT ARE TRYING
TO DO A NEW SCIENCE AT THE ART INSTITUTE
ARE REALLY TRYING TO EMBRACE THIS, BUT OTHERWISE IT'S,
WE'RE LIMITED BY, YOU KNOW, THIS BOTTLENECK WHERE,
ESSENTIALLY IT'S GRAD STUDENTS THAT ARE DOING THAT WORK
OF RESEARCH, SOFTWARE ENGINEERING.
AND, YOU KNOW, MAYBE SOME OF THAT WORK
IS NOT BEST DONE BY GRAD STUDENTS.
YEAH, I AGREE WITH THAT.
BUT SO IS THE SOLUTION JUST TO CENTRALIZE EVERYTHING
IN INSTITUTIONS THEN?
NO.
IS THE SOLUTION TO CENTRALIZE EVERYTHING
IN INSTITUTIONS,
LIKE THE EXAMPLES THAT PATRICK WAS GIVING
ABOUT HOW ESSENTIALLY IT'S
A MANAGEMENT PROBLEM, RIGHT?
I. YEAH, I MEAN, I THINK THERE'S REALLY HARD
INSTITUTIONAL DESIGN QUESTIONS THERE ABOUT WHO IS THE SOFTWARE
ENGINEERING GROUP AND WHO ARE THEY REPORTING TO,
AND HOW CLOSELY ARE THEY INTEGRATED WITH THE SCIENTISTS.
YOU KNOW, THAT ALSO INTERSECT WHAT PATRICK IS SAYING
ABOUT CAREER PATH AND MENTORSHIP.
AND SO I DON'T HAVE ANY REALLY GOOD ANSWERS,
BUT I THINK THE TECH FIRMS HAVE THOUGHT ABOUT THIS,
ABOUT HOW, YOU KNOW, WHO'S A RESEARCH SOFTWARE
ENGINEER, WHO'S A DEVOPS PERSON, WHO IS A, YOU KNOW,
A TESTING PERSON.
HOW DO YOU SCALE UP?
WHO HOW DO THEY PARTNER WITH,
YOU KNOW, DATA SCIENTIST DOING THE, PROTOTYPING?
AND I THINK IT'S, YOU KNOW, I DON'T HAVE ANY ANSWERS,
BUT I THINK IT'S WORTH THINKING ABOUT THOSE ISSUES.
THEY'RE REALLY IMPORTANT.
WE CONTINUE.
I'M STILL STUCK ON YOUR INTUITION QUESTION,
BUT I THINK WE'VE KIND OF GONE IN ANOTHER -- AN INTERESTING PATH.
BUT I HEARD IT
MAYBE FROM A DIFFERENT PERSPECTIVE.
WHAT DORIS WAS SAYING, I MEAN, IT WAS KIND OF TO ME,
IF SHE'S HERE, SHE CAN TELL US WHAT SHE MOST MEANT.
BUT I WAS THINKING THAT
IT'S ACTUALLY KIND OF, MAINTAINING A CERTAIN
HUMILITY AND CURIOSITY ABOUT YOUR KNOWLEDGE SET.
AND I THINK YOU HAD ASKED THIS QUESTION IN THE BEGINNING.
YOU HAD SAID,
WHAT DO OUR TRAINEES NEED?
AND SOMEBODY SAID, CURIOSITY.
AND YOU'RE LIKE, WELL, YOU NEED CURIOSITY FOR EVERYTHING.
AND I WAS LIKE, YOU KNOW, I'M
THINKING, ACTUALLY, I'VE BEEN HEARING AS AN OUTSIDER
AND I HAVE FELT SO LUCKY TO THOSE OF YOU WHO'VE SHARED
WITH ME YOUR EXPERIENCES,
YOU KNOW, I'VE HEARD A
LOT OF CULTURAL CONFLICT ACROSS DIFFERENT DISCIPLINES.
I LOVED TALKING TO THE STUDENTS AT THE POSTERS.
IF ANY OF YOU ARE HERE, THAT WAS REALLY MY HIGHLIGHT.
BECAUSE I ASKED THEM ABOUT THEIR EXPERIENCES
ON WHAT IT WAS TO BE IN THIS INTERSECTION OF A SPACE.
HOW LONELY THAT IS.
ALSO, AND HOW,
YOU KNOW, THEY'RE LUCKING OUT WITH SOMEBODY WHO'S OPEN MINDED
TO LET THEM TRY SOMETHING ELSE AND WHAT THEY HAVE MAYBE,
YOUNG UNDERGRADS OR WHATEVER
AREN'T THE BEST SOFTWARE ENGINEERS
FOR OTHER REASONS, I DON'T KNOW, BUT WHAT I THINK THAT,
MY EXPERIENCE FROM MY OLD PROFESSORING DAYS
WAS THAT ACTUALLY
STUDENTS ARE REALLY GREAT ABOUT BEING CURIOUS,
AND THEY DON'T HAVE THOSE INGRAINED ASSUMPTIONS
THAT GET HAMMERED INTO YOU BY THE TIME YOU GET YOUR PHD,
WHERE YOU'RE REALLY INDOCTRINATED
IN A MEDIEVAL SYSTEM OF BELIEFS OF YOUR MENTOR,
YOU KNOW, YOU JUST KIND OF
AND IT'S REALLY HARD TO BREAK OUT OF THAT.
AND TO THE POINT,
I THINK SO, LIKE MIKE WAS MAKING ABOUT MARK.
SORRY, MARK,
I APOLOGIZE THAT YOU WERE TALKING ABOUT
ONCE YOU GET THERE'S THIS OTHER,
THIS RELATES TO WHAT YOU WERE SAYING TO PATRICK.
WHAT I ALSO HEARD FROM THE STUDENTS,
WHICH MANY OF YOU BEEN SAYING HERE IS THAT, YOU KNOW,
WE ARE NOT PREPARING THEM FOR CAREERS IN NEUROAI.
AND IT'S NOT THAT WE NEED TO HAVE A PHD IN NEUROAI.
IT'S JUST THAT
MAYBE THEY NEED INSTITUTIONS THAT ARE BUILT
MORE AROUND HAVING RESEARCH AND EDUCATION TRACKS.
YOU KNOW,
MAYBE THERE NEED TO BE INCENTIVE STRUCTURES
THAT SUPPORT INNOVATION
AND EVEN CULTIVATE TOWARDS UNDERSTANDING WHAT IT WOULD MEAN
TO BE A DEVOPS PERSON OR WHATEVER.
YOU KNOW, THEY DON'T REALLY LEARN A
LOT OF THAT UNTIL THEY LEAVE.
AND I DON'T KNOW THAT
WE'RE SETTING UP A GOOD BRIDGE FOR THEM TO LEAVE ACADEMIA.
IF WE'RE SETTING UP A GOOD BRIDGE FOR THEM,
FOR THOSE TYPES OF CAREERS WHERE THEY'RE NEEDED,
OR EVEN TO DO MORE INNOVATIVE WORK IN THE UNIVERSITY.
SO BLAKE STARTED THE CONFERENCE BY BRINGING UP
ALAN TURING AND JOHN VON NEUMANN AND HOW THEY
DIED IN THEIR EARLY 50S.
SO I KNOW THIS IS A TERRIBLE QUESTION.
BUT, YOU KNOW, SO TEN YEARS ON WITH THE BRAIN INITIATIVE,
WE CAN THINK IN TERMS OF THE NEXT TEN YEARS,
OR WE COULD THINK IN TERMS OF, LET'S SAY, 50 YEARS.
DO YOU HAVE ANY SENSE OF THE VISION
WHERE NEUROAI WILL BE AND HOW TO GET THERE
IN 50 YEARS, BARRING THE SINGULARITY?
ASSUMING THE SINGULARITY
DOES NOT HAPPEN. OH.
OH, I THINK IT WILL BE IN A POST AGI WORLD IN 50 YEARS.
I DON'T THINK THAT THAT'S SUPER
THAT IT'S THAT CONTROVERSIAL.
SO I THINK OUR WORLD IS GOING TO BE VERY DIFFERENT.
MAYBE NOT IN TEN YEARS, BUT
WELL, YEAH.
I MEAN, REALLY I'M ASKING MORE
AND MORE ABOUT A TEN YEAR VISION.
AND 50 IS A RIDICULOUS NUMBER, I REALIZE. BUT,
I MEAN, DO YOU HAVE A SENSE OF WOULD YOU KNOW WHAT GOAL
YOU WOULD WANT TO ACHIEVE BY TEN YEARS FROM NOW?
I GUESS I'D BE I'D BE HAPPY TO TAKE A TAKE A SHOT.
SO, ONE OF,
YOU KNOW,
I THINK I'D START BY TALKING ABOUT, LIKE,
THE ARC OF BASIC RESEARCH ON THE WHOLE. RIGHT.
SO IN THE EARLY 70S, RICHARD NIXON LAUNCHED A WAR ON CANCER,
AND THAT LED TO A BUNCH OF MONEY GOING TO NIH.
AND, NIH USED A LOT OF THAT MONEY FOR
BASIC SCIENCE RESEARCH INTO
CELL BIOLOGY TO UNDERSTAND HOW CELLS WORKED BETTER.
AND DECADES LATER, THAT LED TO NEW KINDS OF CURES FOR CANCER.
YOU KNOW, IMMUNOTHERAPY DRUGS, YOU, YOU IMMUNOTHERAPIES
YOU CAN SAY, CAME RIGHT OUT OF A LOT OF THAT FUNDING.
AND SO I THINK THAT'S
THE NATURE OF BASIC SCIENCE THAT IT'S YOU INVEST IN
UNDERSTANDING SYSTEMS WITH THE POINT OF BEING A,
YOU KNOW, SORT OF MEDIUM TO LONG TERM DEVELOPING
IMPROVEMENTS IN HUMAN HEALTH AND HUMAN SOCIETY.
AND WHAT WOULD THOSE BE?
AND SO I THINK THAT'S A GOOD PLACE
TO START ABOUT THINKING ABOUT, LIKE,
WHAT ARE WE THINKING ABOUT FOR NEUROAI?
I SEE A LOT OF THAT,
THE BASIC SCIENCE THAT WE'RE TALKING ABOUT HERE
AND THE AMAZING WORK
THAT MANY PEOPLE ARE HERE DOING TO ADVANCE
KNOWLEDGE OF NETWORKS IN THE BRAIN.
IT'S REALLY POINTING TOWARDS
FUTURE UNDERSTANDING OF BRAIN DISEASES.
AND WE KNOW THAT A LOT OF I MEAN, I THINK IT'S,
I THINK MANY OF US THINK THAT MANY BRAIN PATHOLOGIES
ARISE FROM NETWORK PROPERTIES.
AND UNDERSTANDING DISEASE,
, A VARIETY OF DISEASES FROM THE EFFECTS
OF ALZHEIMER'S TO PARKINSON'S TO SCHIZOPHRENIA, ETC., ETC.
ARE GOING TO ARISE FROM UNDERSTANDING OF NETWORKS.
AND THE WORK THAT WE HAVE TALKED ABOUT HERE OVER THE PAST
TWO DAYS IS VERY MUCH UNDERSTANDING NETWORKS,
AND I THINK MY SENSE OF THE VISION FOR NEUROAI OVER
THE NEXT,
LET'S SAY, 10 TO 20 YEARS, IS REALLY
THAT WE'RE GOING TO START TO UNDERSTAND BRAIN DISEASES
BETTER AS WE UNDERSTAND BETTER HOW BRAIN NETWORKS WORK.
AND A.I. YOU KNOW, PRINCIPLES ARE GOING TO INFORM THAT.
WHAT DO YOU MEAN BY NETWORK PROPERTIES?
COULD YOU JUST EXPAND ON THAT?
YEAH.
SO I HAVE
YOU KNOW, I STARTED MY CAREER STUDYING
SINGLE NEURONS, AS I THINK PAUL DID, AND MANY OF US DID IN,
YOU KNOW, IN ANIMALS THAT ARE BEHAVING
AND CORRELATING THE PROPERTIES OF SINGLE NEURONS WITH BEHAVIOR.
AND WE HAVE LEARNED ENORMOUS AMOUNTS FROM THAT.
PEOPLE LIKE, YOU KNOW,
HUBEL AND
WIESEL TO BILL NEWSOME AND ETC., ETC.
HAVE ADVANCED A LOT OF OUR KNOWLEDGE OF LIKE
THE NEURAL BASIS OF BEHAVIOR.
AND OVER THE PAST TEN YEARS, THANKS IN PART TO TOOLS
DEVELOPED BY THE BRAIN INITIATIVE,
WE HAVE RECORDED FROM MANY, MANY, MANY NEURONS.
AND WE'RE STARTING TO UNDERSTAND POPULATION LEVEL PHENOMENA
AND HOW GROUPS OF NEURONS WITHIN AREAS WORK TOGETHER
AND HOW CIRCUITS CREATE THAT FUNCTION.
YOU KNOW, WHETHER IT'S SYNAPSES OR DIFFERENT CELL TYPES
AND ALSO HOW NETWORKS, EXTEND ACROSS BRAIN REGIONS, YOU KNOW,
AND PEOPLE HERE LIKE KANAKA HAVE WORKED,
AMONGST MANY OTHERS, HAVE WORKED ON THESE THINGS.
AND SO WHEN I SAY NETWORK PROPERTIES, I'M
TALKING ABOUT THAT.
LINKING CIRCUITS TO THE ACTIVITY OF POPULATIONS OF NEURONS
THAT GIVE RISE TO COMPUTATIONS AND ULTIMATELY BEHAVIOR.
SO I WOULD.
FRAME THIS.
IN A COUPLE OF WAYS,
BUT I THINK I WOULD SAY I WOULD LIKE TO SEE A REAL
I'D LIKE TO SEE IN TEN YEARS,
A SILICON VERSION OF MULTIPLE LAYERS OF
OF CORTICAL LAYERS, LIKE TRULY WITH THE FULL PYRAMIDAL.
SO, I MEAN, I MEAN REALLY BIOPHYSICAL CLOSE TO
THIS SUCH THAT
THEN WE CAN USE THAT FOR ENGINEERING APPLICATIONS.
I THINK THAT IT DOES A
NUMBER OF THINGS.
ONE, AND BY THE WAY, WE COULD ALSO DO THIS FOR A COUPLE
OTHER DIFFERENT STRUCTURES IF WE WANTED TO.
I'M JUST TO USE THIS TO START WITH.
SOMEBODY SAYS, I WANT TO TAKE THE FLIERS.
WE'LL TALK ABOUT THAT TOO. IT'S COOL.
BUT I THINK IT IS A COUPLE THINGS.
ONE IS IT CERTAINLY OBVIOUSLY GETS ALL THE
CIRCUITS UP THAT UP AT A CERTAIN LEVEL.
I THINK IT ALSO GETS YOU TO A POINT WHERE
WE CAN ACTUALLY USE THIS
FOR THIS APPLICATION, BECAUSE THE TECHNOLOGY BE READY.
BUT I THINK MORE IMPORTANTLY, IT FORCES.
US TO ASK A LOT OF
QUESTIONS,
TO INTEGRATE OUR UNDERSTANDING OF WHAT DO WE MEAN BY
WHAT HAPPENS WHEN I
IN TERMS OF THE ENGINEERING, IN TERMS OF THE COMPUTATION
OF, SAY, MULTIPLE PYRAMIDAL CELLS,
CORTICAL COLUMN AND SO FORTH?
AND I THINK THAT WILL
CREATE A
HUGE ENTHUSIASM IN TERMS OF A WHOLE SET OF THINGS
IN COMPUTATIONAL NEUROSCIENCE.
THAT PROBABLY WILL CHANGE THE WAY WE TALK ABOUT
THE MODELS.
THE WAY WE TALK ABOUT, YOU KNOW,
NEURAL ENCODING
ALL THE WAY UP AND DOWN THE CHAIN. THAT WILL PROBABLY
-- IT WILL FINALLY HELP TO INTEGRATE THINGS AS WELL AS
BRING A LOT OF COMMUNITIES TOGETHER.
SO IF I HAD A TEN YEAR PLAN, THAT'S WHAT I WOULD GO FOR.
OH, SORRY.
SO, IF ANYONE HAS QUESTIONS, PLEASE COME GO TO THE MIC,
AND ASK QUESTIONS. SO THIS IS IT'S AN OPEN
DISCUSSION, NOT A CLOSED DISCUSSION.
ANY ANSWERS TO THE QUESTIONS PAUL RAISED?
IT'S A TWO WAY STREET.
CAN I CONTINUE ALONG THIS LINE?
OKAY.
SO I THINK WE HAVE LIKE,
PRETTY LIKE ALREADY WE'RE MAKING A LOT OF PROGRESS ON THE FLY.
AND SO I WOULD NOT BE SURPRISED IF WE COULD REALLY SAY
THAT IN TEN YEARS THE FLY WOULD BE SOLVED.
WE'RE ALREADY ABLE TO MEASURE FROM ABOUT 10% OF THE NEURONS
IN THE MOUSE.
PEOPLE HAVE SAID THAT
BUILDING THE CONNECTOME FOR THE MOUSE
WOULD TAKE ABOUT 15 YEARS, BUT THOSE ESTIMATES HAVE
BECOME MUCH SHORTER AS EXTENSION MICROSCOPY IS, HAS IMPROVED.
SO I THINK THE VIRTUAL MOUSE,
YOU KNOW, THE KIND OF GRANULARITY
THAT WE'RE SEEING IN FLIES, I THINK
MIGHT BE FEASIBLE
JUST SO WITHIN THE NEXT TEN YEARS.
FOR NON-HUMAN PRIMATES AND FOR HUMANS,
I THINK THAT WE WOULD NEED TO VASTLY
INCREASE OUR CAPACITIES TO RECORD FROM NEURONS.
SO IF YOU LOOK AT, YOU KNOW,
THE SO-CALLED MOORE'S LAW FOR, FOR NEURAL RECORDINGS, IT
DOUBLES ABOUT EVERY FIVE YEARS WHEN IT COMES TO ELECTRODES.
IF WE WANT TO GET, YOU KNOW, PRETTY REASONABLE COVERAGE
OF THE ENTIRE CORTEX,
IT WOULD NEED TO INCREASE AT A MUCH FASTER RATE THAN THAT.
BUT THAT'S, YOU KNOW, WITHIN THE REALM OF POSSIBILITY,
IF WE PUT THE ENGINEERING EFFORT, INTO IT
AND I THINK THAT, WE'LL GET
WE'LL PROBABLY BE ABLE TO GET AT THAT POINT RECORDINGS FROM
YOU KNOW, TENS OF PEOPLE AND TENS OF ANIMALS
INTERACTING AND SOCIAL INTERACTIONS,
WIRELESSLY RECORDING.
I COULD DEFINITELY SEE THAT HAPPEN.
WILL WE NEED THOSE
RECORDINGS, THOUGH, IF WE HAVE FOUNDATION MODELS?
YEAH, OF COURSE WE NEED THOSE RECORDINGS.
AND JUST TO BE CLEAR, YOU KNOW, WHEN I WAS MENTIONING
100,000 HOURS, YESTERDAY THAT WE ALREADY
HAVE, AS,
ANDREAS CORRECTLY POINTED OUT, YOU KNOW, A LOT OF
THIS IS DATA IN LOW ENTROPY SITUATIONS.
A LOT OF THIS IS DATA WHEN PATIENTS ARE MONITORED,
FOR INSTANCE, WHERE THEY'RE NOT DOING
ANY PARTICULAR TASK AND WE DON'T HAVE THE LABELS FOR THEM.
SO, SO IT'S IMPORTANT TO CAVEAT THIS.
WHAT I'M SAYING IS THAT WE HAVE A GOOD BASIS LIKE,
IT FEELS TO ME LIKE THE FIRST TEN YEARS
OF THE BRAIN INITIATIVE WAS REALLY LIKE ABOUT
BUILDING A STRONG FOUNDATION, HAVING ALL THESE NEW
NEUROTECHNOLOGIES, HAVING ALL OF THESE DATA ARCHIVES.
BUILDING THE INFRASTRUCTURE TO BE ABLE TO GET,
TO BE ABLE TO,
PUT METADATA ON DATA THAT ALREADY EXISTS, SO THAT WE
CAN ACTUALLY LEVERAGE THIS DATA.
AND NOW I THINK IT'S THE RIGHT
TIME TO KIND OF PUT IT INTO HIGH GEAR.
IF ANYONE WANT TO COMMENT ON THAT BEFORE WE TAKE A QUESTION.
OKAY.
QUESTION.
HI. THIS IS A GREAT DISCUSSION.
ONE QUESTION IS IN THE NEXT TEN YEARS,
WOULD IT ALSO BE POSSIBLE TO INTEGRATE NON-
ELECTRICAL SIGNALS AND NON-NEURONAL CELLS
AT THE SYSTEMS LEVEL INTO THESE MODELS?
WOULD THAT BE HELPFUL?
JENNIFER I WAS GOING TO ASK YOU THAT AS WELL.
I MEAN DO
WE JUST NEED THE PYRAMIDAL NEURONS OR WHAT LEVEL OF?
I WOULD THINK YOU I MEAN CERTAINLY.
THE ELECTRICAL SIGNALS YOU'D LIKE THERE.
I THINK I WOULD LOVE TO SEE, YOU KNOW ALSO WHERE
IT MAKES SENSE, THE CHEMICAL SIGNALS
AND OTHER RELATED THINGS.
CERTAINLY THE DISCUSSIONS AROUND SOME OF THE NON NEURAL BLOCKS,
WHICH ARE ACTUALLY DOING MODULATIONS AT,
YOU KNOW, MINUTES TO LONGER TIMESCALES
I THINK IS ABSOLUTELY
PART OF THAT CONVERSATION.
I THINK IT HAS TO BE PART OF THAT CONVERSATION
IF WE'RE GOING TO BE REAL ABOUT THIS.
SO EXACTLY WHAT THAT ROADMAP
IS, I THINK
IT'S STILL A LITTLE LESS THAN OBVIOUS, BUT
I'D LIKE US TO GO DOWN THAT PATH.
ANDRES.
YEAH.
SO I WHAT I THINK A BIG, BOLD VISION.
I THINK WE NEED A BOLD NEW
VISION FOR THE NEXT TEN YEARS AND NOT CONTINUE
IN ADDITION TO CONTINUE DOING WHAT WE'RE CURRENTLY DOING.
I THINK WE NEED SOMETHING NEW, BOTH AT THE STRUCTURAL LEVEL
AND IN TERMS OF THE SCALE OF THE PROJECT.
AND IT DOESN'T HAVE TO BE ONE. IT COULD BE MULTIPLE.
BUT HERE IS THE ONE IDEA THAT I WOULD ADVOCATE FOR.
IF YOU LOOK AT THE LAST TEN YEARS, WHAT WE'VE ACHIEVED,
AS IT WAS SAID MANY TIMES, IS REALLY NEUROTECHNOLOGY
TO BE ABLE TO DO EXPERIMENTS AT SCALE AND ALSO IS
DEEP LEARNING, RIGHT?
I MEAN, A.I. HAVE SOLVED PROTEIN FOLDING.
IT HAS DONE AMAZING STUFF.
AND I THINK IN THE NEXT TEN YEARS
WE NEED TO BRING THESE TWO THINGS TOGETHER.
BUT TO DO THESE, WE NEED TO DO A LOT OF LARGE SCALE DATA COLLECTION.
AND I THINK TO DO THAT WELL, IT NEEDS TO BE DONE
MOSTLY IN A CENTRALIZED WAY, WHERE WE'RE GOING TO BE ABLE
TO DESIGN THE RIGHT
EXPERIMENTS WHENN THE ANIMALS ARE GOING TO BE ENGAGED
IN COMPLEX TASKS.
SO THEN WE CAN GATHER THIS DATA AND THEN BUILD, AS IT WAS
SAID, THE FOUNDATION MODEL.
AND I THINK THAT FOUNDATION MODEL
IS GOING TO BE ABLE TO BE USED BY EVERYBODY,
BECAUSE YOU CAN THINK OF IT LIKE AN API
WHERE YOU'LL BE ABLE TO GO IN
RUN IN SILICO
EXPERIMENTS, DESIGN EXPERIMENTS, AND, AND TEST THEM BACK
IN YOUR OWN LAB.
SO I DO THINK WE NEED LIKE, AND I THINK
THIS IS THE RIGHT TIME TO DO IT.
WE NEED LIKE A BIG INVESTMENT IN NEW TYPE OF INVESTMENT
FROM THE FEDERAL GOVERNMENT, LIKE THE GENOME PROJECT.
THAT'S GOING TO SAY, OKAY,
IN THE LAST TEN YEARS WE'VE HAD NEUROTECHNOLOGY.
NOW WE HAVE DEEP LEARNING. LET'S BRING IT TOGETHER.
AND I SEE THIS AS HEDGING OUR BETS IN THE FOLLOWING WAY.
EVEN IF A.I. IN THE NEXT FIVE YEARS LIKE I SAW, YOU KNOW,
OPENAI, ANTHROPIC AI'S CEO HAD A PODCAST YESTERDAY
AND HE CLAIMED THAT IN 2 OR 3 YEARS WE'LL HAVE AGI.
SO YOU CAN IMAGINE THAT THE
IF WE CREATE AGENTS THAT BECOME AUTONOMOUS,
THEY'RE GOING TO BE IN SILICO
FOR THE NEXT FEW YEARS, LIKE WE'LL BE SOME VERSION OF
LARGE LANGUAGE MODELS.
SO IF WE BUILD THESE FOUNDATION
MODELS, NOT ONLY SCIENTISTS WILL BE
ABLE TO MINE AND DO EXPERIMENTS IN SILICO,
BUT THESE A.I. AGENTS CAN THEN GENERATE HYPOTHESES.
IF, ON THE OTHER HAND, THESE PATHS TOWARDS AGI
GET STUCK AND THE SCALING LAWS DON'T WORK,
THEN THE TYPE OF EXPERIMENTS THAT WE NEUROSCIENCE CAN DO,
WE'RE GOING TO PROVIDE A NEW DIMENSION OF NEURAL DATA
TO REALLY HELP A.I.
AND THERE'S THE WHOLE ISSUE OF BUILDING FOUNDATION
MODELS FOR NEUROPSYCHIATRIC DISEASES.
BUT I DO THINK WE NEED SOMETHING MORE OF A BOLD VISION,
SOMETHING THAT'S
GOING TO BRING NEW TYPES OF FUNDING TO BE ABLE TO DO THIS.
AND IT WILL INVOLVE
MANY PARTS OF THE GOVERNMENT LIKE NIH, OF COURSE.
DOE, DARPA, DOD
ANYWAY, THAT'S.
COMMENTS?
SO I,
I GUESS I,
I THINK ABOUT THIS VISION, I APPRECIATED WHAT YOU ACTUALLY
I WAS VERY MOVED ANDREAS WHEN YOU WERE TALKING EARLIER
ABOUT HOW USEFUL OR MAYBE NOT USEFUL THE DATA THAT WE HAVE IS.
I THINK I WAS STRUCK WITH HOW LIMITED YOU SAID IT MIGHT BE.
I THINK YOU'VE REPEATED THIS TOO.
LIKE WE NEED THE LABELS ON IT.
WE EITHER WE LACK METADATA, ETC.
SO I AGREE WITH A BOLD VISION
AND I WOULD SUPPORT A VISION ALSO THAT
TAKES CARE OF THE PROCESSES IN WHICH IT DOES THAT.
AND ONE OF THAT THING THAT CAN BE DONE
SOONER IS THIS IS SOMETHING I WAS ASKING,
IS SHE HERE? DOMINIQUE?
ANYWAY,
SOMETHING WE WERE TALKING ABOUT WAS WITH THE EXISTING DATA
OF ALL THOSE EIGHT
REPOSITORIES THAT WERE DISCUSSED AND INTRODUCED, OF THOSE,
HOW REPRESENTATIVE DO WE KNOW THOSE ARE?
AND SO SOME OF THEM ON PURPOSE,
CERTAIN KINDS OF DEMOGRAPHIC INFORMATION
IS NOT PUT ON THEM FOR AVOIDING RE-IDENTIFICATION
BECAUSE THEY'RE FROM SO FEW PEOPLE.
BUT OTHERS, HAVE SEX DATA ON THEM, FOR EXAMPLE.
AND WE ALREADY KNOW OF THE HUMAN DATA, AT LEAST THAT MAYBE,
YOU KNOW, IT'S MAYBE 2% OF THAT DATA ACTUALLY IS,
FEATURING PEOPLE WHO ARE
TYPICALLY UNDERREPRESENTED IN SCIENTIFIC STUDIES.
SO I THINK THAT
AND I'M NOT SAYING THAT MEANS WE CAN'T USE THE DATA AT ALL,
BUT I THINK THAT MEANS
THAT WE NEED TO BE VERY CLEAR ON WHAT WE'RE USING
IF WE START TRYING TO BUILD MODELS ON THAT,
IF WE START TRYING TO MAKE CLAIMS
ABOUT HOW THE BRAIN WORKS.
THE OTHER PIECE THAT I WOULD SAY
IN THIS BIG CONVERSATION, WE ALREADY MENTIONED AGI.
WELL, WE DIDN'T TALK ABOUT WHAT I MENTIONED EARLIER IN MY TALK
WAS RESPONSIBLE CONCEPTUALIZATION.
A LOT OF THE TERMS INHERENT IN A.I. ARE BORROWED.
YOU KNOW,
THEY ARE BORROWED FROM BRAIN
OR THEY'RE BORROWED TO REFERENCES TO HUMANS.
SOME OF THAT'S BY DESIGN,
BUT SOME OF THAT'S
KIND OF MORE CONVENIENT, LIKE INTELLIGENCE, AUTONOMY,
MOTIVATION. AND ALTHOUGH SCIENTISTS,
SOME SCIENTISTS MIGHT CLAIM THAT THEY HAVE
THE SAME DEFINITION AS OUR PEERS,
WE HAVE SEEN HOW THEY ACTUALLY DON'T.
AND IF YOU'RE NOT INTENTIONALLY
USING THOSE TO CALL UPON HUMAN FEATURES,
THEN WE MIGHT THINK TWICE ABOUT HOW WE'RE SPEAKING ABOUT THEM.
BECAUSE OUR PEERS, AND ESPECIALLY THE PUBLIC,
WHEN THEY HEAR US TALK ABOUT WHAT WE'RE BUILDING
AND THE TYPES OF TECHNOLOGY WE ARE TRYING TO CREATE,
WE'LL HEAR SOMETHING THAT'S ALONG THE LINES
OF THE POPULAR IMAGINATION, WHICH ACTUALLY SCIENTISTS
ARE JUST AS VULNERABLE TO FOLK PSYCHOLOGY AND CULTURAL
NOTIONS, TOO. SO I THINK THAT WOULD BE
AN IMPORTANT PART OF THAT VISION.
AND THE LAST PIECE, I HOPE,
IS THAT THERE'S AN ETHICS- BY-DESIGN APPROACH
WHERE WE'RE ASKING MORE QUESTIONS,
NOT JUST ABOUT RESPONSIBLE CONCEPTUALIZATION AND DATA,
BUT HAVING HUMANS IN THE LOOP AS WE THINK ABOUT APPLICATIONS
FOR THEM IN HEALTH.
YEAH.
I SO, WHAT KAREN IS SAYING ABOUT,
YOU KNOW, BEING CAREFUL ABOUT DATA AND PUBLIC OUTREACH
AND THINKING ABOUT HOW WE SPEAK TO THE PUBLIC AND COMMUNICATE
WHAT WE'RE DOING AND THE BROAD SCOPE OF OUR VISION AND,
MAKING, YOU KNOW, THINKING ABOUT CAREFULLY
ABOUT HOW TO COMMUNICATE, I THINK IS SUPER IMPORTANT.
I ALSO WANTED TO JUST RESPOND TO ANDREAS
AND APPARENTLY SAY SOMETHING THAT MIGHT BE SOMEWHAT
UNPOPULAR IN THE ROOM BASED ON THE REACTION TO HIS COMMENTS.
I
THINK WE
CERTAINLY NEED A BROAD VISION
AND WE NEED TO BE ABLE TO COMMUNICATE IT.
THAT IS INCREDIBLY ESSENTIAL.
I ALSO THINK WE NEED MULTI-SCALE PROJECTS.
I THINK WE SHOULD BE VERY CAREFUL OF BIG INVESTMENTS IN
SINGLE PROJECTS.
AND I, I THINK THE BRAIN INITIATIVE,
AS ORIGINALLY ENVISIONED HERE IN THE US,
DID AMAZING WORK FUNDING SOME SMALL TEAMS
OF SMALL NUMBERS OF PIS AND SOME BIGGER TEAMS
THAT LASTED LONGER AND SOME REALLY BIG PROJECTS.
AND FROM MY PERSPECTIVE, A LOT OF THE,
YOU KNOW, WE'RE TALKING ABOUT
INTEGRATING PEOPLE HERE, ABOUT INTEGRATING THEORY
AND BRINGING THEORY AND EXPERIMENTS TOGETHER.
THE BRAIN INITIATIVE WAS INCREDIBLY SUCCESSFUL
AT BRINGING TOGETHER SMALL GROUPS OF PIS
TO REALLY DO DEEP WORK ON THEORY AND EXPERIMENT TOGETHER,
AND FUNDING LOTS OF DIFFERENT TEAMS LIKE THAT.
AND I THINK THAT HAS BEEN A SUPER SUCCESSFUL MODEL.
AND AND SO, YES, BOLD VISION.
YES, A BIG PROJECT. YES. SOME BIG PROJECTS.
BUT ALSO LET'S THINK ABOUT MULTI-SCALE TEAMS
AND BRING PEOPLE TOGETHER TO THINK ABOUT DEEP STUFF
THAT IS GOING TO HAVE REALLY BIG BASIC RETURNS.
CAN I JUST ADD SOMETHING TO THAT?
I'M ALSO VERY INSPIRED BY ANDREAS'S VISION,
WHICH I THINK BUILDS ON THE SUCCESSES
OF THE BRAIN PROJECTS OVER THE LAST
TEN YEARS, BRAIN INITIATIVE OVER THE LAST TEN YEARS.
BUT IN SORT OF SUPPORT OF WHAT YOU WERE SAYING,
OVER I MEAN, THE WAY THE BRAI
INITIATIVE STARTED ESPECIALLY WAS WITH A BUNCH OF SMALL TEAMS
THAT WERE PUT TOGETHER OFTEN IN RESPONSE TO SORT OF, CALLS
FOR THE DEVELOPMENT OF NEURO- TECHNOLOGIES OF VARIOUS SORTS.
AND SO I ACTUALLY THINK THAT,
THE, THE IDEAL SITUATION WOULD BE IF WE HAD BOTH.
WE SORT OF MOVE FORWARD WITH THE EXCITING RESULTS.
WE BUILD ON THE EXCITING RESULTS THAT WE ALREADY HAVE,
AND THEN WE SORT OF TARGET
THE AREAS WHICH ARE NOT READY FOR BIG SCIENCE YET.
FOR EXAMPLE,
THERE WERE A LOT
OF REALLY INTERESTING TALKS ON
HOW DO WE BUILD ENERGY EFFICIENT COMPUTATION.
BUT I DON'T THINK WE'RE THERE YET.
I MEAN,
I THINK THERE WERE
A LOT OF INTERESTING TALKS, BUT I DON'T THINK
THAT THERE'S ANYTHING YET THAT WE'RE READY TO SCALE UP.
AND I THINK ONE OF THE MAJOR CONSENSUSES FROM SORT OF,
YOU KNOW, THE,
THE VARIOUS SESSIONS WAS THAT THAT IS ONE, YOU KNOW, ENERGY,
THE INEFFICIENCY OF -- SORT OF THE CURRENT PARADIGM
IS GOING TO BE ONE OF THE BIG PROBLEMS THAT WE FACE.
AND I DON'T THINK
THE SOLUTION TO THAT IS PUT TOGETHER ONE BIG TEAM.
THE SOLUTION, I THINK, IS TO PUT OUT CALLS FOR MANY,
MANY TEAMS TO USE AS MANY APPROACHES
AS WE CAN TO FIGURE OUT WHAT THE SOLUTION IS.
AND I THINK, YOU KNOW, WE CAN IDENTIFY
A BUNCH OF SORT OF
SIMILAR PROBLEMS
THAT WERE TOUCHED ON AT THIS MEETING THAT WOULD BENEFIT FROM,
I THINK, THE SORT OF SMALLER HOPE,
NOT EVEN HYPOTHESIS DRIVEN, BUT BUT SORT OF ARTISANAL SCIENCE,
YOU KNOW, LET A THOUSAND FLOWERS BLOOM
APPROACH THAT I THINK YOU'RE ADVOCATING.
SO I THINK IT DEPENDS ON WHAT THE,
WHAT STAGE WE'RE AT RIGHT NOW FOR A GIVEN PROJECT,
OR A GIVEN SORT OF SET OF QUESTIONS, THAT WOULD BE MY
TAKE ON IT
AND GO,
YOU CAN GO AHEAD
SO YEAH.
SO I JUST LIKE TO POINT OUT ONE THING, TONY, WHICH,
OF COURSE,
I DON'T THINK THAT ANDREAS WAS EITHER SAYING, LIKE,
WE SHOULD SUCK ALL THE AIR OUT OF THE ROOM
AND JUST GIVING MONEY TO HIM.
I CAN ASSURE YOU.
HE MIGHT BE. HE MIGHT BE SAYING THAT.
YOU MIGHT BE SAYING.
ALL RIGHT,
BUT, YOU KNOW, ONE EXAMPLE I SHOWED IN, IN MY SLIDES,
YESTERDAY WAS, THIS LARGE LANGUAGE MODEL LLAMA.
RIGHT.
AND THAT'S AN ARTIFACT THAT WAS CREATED
BY A VERY COORDINATED RESEARCH TEAM.
THERE'S 500 PEOPLE ON THAT PAPER, AND IT'S BEEN CITED,
YOU KNOW, 10,000 TIMES IN A YEAR.
IT'S A BIG ARTIFACT THAT IT'S USED BY A LOT OF PEOPLE. AND,
IT IS LIKE, WELL, BEYOND THE CAPACITIES
OF A SINGLE LAB THAT'S SUPPORTED BY A FEW ARE R01S.
RIGHT.
SO I THINK IN THAT SENSE, IF WE LOOK AT
WHETHER IT'S THE LARGE HADRON COLLIDER,
WHETHER IT'S, THE, THE HUMAN GENOME PROJECT
OR WHETHER IT'S THESE NEWFANGLED MODELS THAT ARE CREATED
BY THESE, FOUNDATIONAL TEAMS INSIDE OF BIG TECH,
IT REQUIRES A LOT OF ENGINEERING AND A COORDINATED EFFORT,
WHICH DOESN'T MEAN THAT YOU HAVE TO SUCK THE AIR
OUT OF THE ROOM AND
AND STOP SUPPORTING FUNDAMENTAL AND BASIC SCIENCE.
IT CAN BE BOTH SPIKE AND SLAB, AS I LIKE TO CALL IT.
PANAYIOTA.
HI, SO ACTUALLY MY COMMENT
WAS FOR THE PREVIOUS QUESTION, BUT MAYBE I'LL BRING IT BACK.
HAVING TO DO WITH, WHAT JENNIFER SAID ABOUT, YOU KNOW, BUILDING
THE CORTEX IN A HIGH LEVEL DETAIL ONTO, HARDWARE AND,
WONDERING WHETHER THERE IS A REALLY BIG RISK THERE.
SIMILAR TO WHAT THE HUMAN BRAIN PROJECT WAS TRYING TO DO
BY SIMULATING, LET'S SAY, A VERY BIG CHUNK OF THE CORTEX
AND IN MY VIEW, AND THE VIEW OF MANY OTHERS
ACTUALLY FAILED TO DELIVER,
BECAUSE IN THE END, YOU CAME UP WITH A MODEL
THAT WAS PRETTY MUCH AS COMPLEX AS THE REAL THING.
AND IT WAS VERY HARD TO UNDERSTAND
WHAT THE SIMULATED COLUMN IS ACTUALLY COMPUTING OR DOING.
AND I THINK IT IS IMPORTANT TO KEEP IN MIND
THAT THERE IS ALWAYS A TRADE OFF
BETWEEN A HIGH LEVEL OF COMPLEXITY
AND AN EASY INTERPRETABILITY OR READOUT OF THE FUNCTION,
AND ESPECIALLY WHEN WE WANT
TO BRING THE NEUROAI, LET'S SAY, INTO THE GAME.
I THINK WHAT IS REALLY MISSING, AND IT IS IMPORTANT HERE,
IS TO HAVE A COORDINATED EFFORT THAT AIMS AT EXTRACTING THE KEY
COMPONENTS OF BIOLOGY THAT ARE IMPORTANT FOR BUILDING,
LET'S SAY, MORE EFFICIENT SYSTEMS AS, AS TONY PUT IT OUT.
SO I THINK WE, WE REALLY MISS,
LET'S SAY A THEORETICAL COMPONENT HERE
THAT TALKS TO THE BIOLOGIST,
TALKS TO THE HARDWARE EXPERTS, AND TRIES TO FIGURE OUT
WHICH ARE THE IMPORTANT ELEMENTS THAT WE WANT TO KEEP
TO CREATE SUCH EFFICIENT SYSTEMS.
AND TO MY KNOWLEDGE, THERE IS NO SUCH COORDINATED,
APPROACH ANYWHERE IN EUROPE OR IN THE US.
AND, YOU KNOW, DEFINING THE MISSING ELEMENTS
WOULD BE REALLY, YOU KNOW, USEFUL AND IMPORTANT.
YES. SO THANK YOU, BY THE WAY,
THANK YOU FOR BRINGING ALL THAT UP.
THAT WAS VERY HELPFUL.
AND ALSO THE HISTORY,
I THINK WHEN I WAS TRYING TO GIVE MY SHORT ANSWER, PART OF
I ACTUALLY REALLY APPRECIATE THAT
BECAUSE I THINK THE THEORETICAL
FRAMEWORK OF THAT AND THE GROUNDING OF THE CORE
CONCEPTS IS ESSENTIAL.
AND I THINK PART OF THE REASON I DROVE
THAT IS SAYING,
LET'S SEE, LET'S ACTUALLY MAKE THIS BE USEFUL FOR
SOMETHING THAT'S.
ENGINEERING APPLICATION
THAT WOULD FORCE YOU TO DO EXACTLY THE THINGS YOU'RE SAYING
SO IMPLICITLY.
I STRONGLY AGREE, AND I'M GLAD YOU BROUGHT IT UP AGAIN.
I THINK THAT WOULD BE
A FUNDAMENTAL PART OF THAT KIND OF EFFORT,
WHETHER DONE AS A LARGE EFFORT OR A SMALL EFFORT.
BUT THANK YOU.
YEAH.
ZACH, SORRY ABOUT THE WAY
THAT WORKS.
I, I AGREE PANAYIOTA WITH MANY OF THE, THAT
INVESTING IN SOME PARTICULAR HARDWARE IS PREMATURE
BEFORE YOU HAVE SOME CHEAPER SIMULATIONS OF THINGS
THAT YOU CAN VALIDATE.
BUT THE THING THAT I THINK
WE REALLY, REALLY NEED IS TO UNDERSTAND LEARNING.
WE DON'T UNDERSTAND HOW WE LEARN.
WE DON'T HAVE THE TECHNOLOGICAL TOOLS
TO MEASURE THE MECHANISMS OF LEARNING IN THE BRAIN.
AND WHEN WE LOOK AT WHAT DEEP LEARNING IS DOING,
IT'S REALLY SUCCESSFUL, BUT IT'S NOT REALLY EFFICIENT.
IF YOU LOOK AT THE USUAL GRADIENT DESCENT RULE,
WE KNOW THAT IT JUST BOBS ALONG
ON THESE LONG VALLEYS WHEN IT COULD USE A BETTER RULE.
AND THERE ARE SOME ALTERNATIVES
TO STRAIGHTFORWARD GRADIENT, RIGHT.
NATURAL GRADIENT AND THINGS LIKE THIS.
AND MAYBE SOME OF THOSE TRICKS ARE THINGS THAT THE BRAIN DOES.
IT'S ABLE TO MAKE FASTER LEAPS THAN FIDDLING ALONG
WITH LITTLE INCREMENTAL CHANGES AND GIVING US EUREKA MOMENTS.
SO I FEEL LIKE THAT IS A GOOD TEN YEAR PROJECT,
BRINGING THE TECHNOLOGY TO MEASURE SYNAPTIC CHANGES
AND NEUROMODULATION ON A LARGE SCALE
THAT WOULD ALLOW US TO UNDERSTAND EUREKA MOMENTS.
AND OF COURSE, THE CHALLENGES
THAT YOU ONLY GET ONE CHANCE TO MAKE A FIRST IMPRESSION.
SO EVERY TIME YOU'RE STUDYING
A EUREKA MOMENT, YOU CAN ONLY STUDY IT ONCE.
SO WE NEED TO FIND SOME PARADIGMS
WHICH ALLOW US TO GET ANIMALS TO DO LOTS OF EUREKA MOMENTS.
AND THAT'S HARD, BUT I THINK IT'S FEASIBLE.
SO YOU'RE ECHOING WHAT BLAKE RICHARD SAID YESTERDAY.
YES, EXACTLY WITH BLAKE, TOTALLY ON THE SAME PAGE.
DO YOU THINK MEASURING
SYNAPTIC STRENGTH ACROSS ALL CONNECTIONS IS THE SOLUTION?
WELL,
I THINK YOU ALSO NEED THE MODULATORS,
BECAUSE I DON'T THINK THAT
THE ONLY EFFECT IS JUST PURELY LOCAL.
I THINK THAT THAT WOULD MISS A WHOLE LOT OF STRUCTURE.
IS THAT DOABLE IN TEN YEARS, YOU THINK?
DEFINITELY. MAYBE NINE.
OKAY, SO ACROSS 10 MILLION NEURONS.
YEAH.
YEAH, 10,000 SYNAPSES.
SO IF YOU WANT TO, LIKE GET ALL OF THEM,
WE JUST NEED TO SAMPLE FROM WHAT'S LIKE A TRILLION SYNAPSES.
SURE.
THAT SOUNDS -- I JUST
THAT, IT SOUNDS SUPER EXCITING,
TO BE ABLE TO TRACK LEARNING, BUT AGAIN, IT
DEPENDS ON
TECHNOLOGICAL DEVELOPMENTS, THE ABILITY TO MEASURE THINGS
AT THE,
SUBCELLULAR LEVEL, TO MEASURE THE SYNAPTIC STRENGTH,
TO BE ABLE TO TRACK ANIMALS OVER THAT EXTENDED AMOUNT OF TIME.
MEASURING SYNAPTIC STRENGTH IS NOT TRACKING LEARNING, THOUGH,
RIGHT?
THAT'S WHERE THE THEORY COMES IN.
IF YOU JUST MEASURE SOMETHING, IT DOESN'T,
WITHOUT A THEORETICAL FRAMEWORK, IT DOESN'T NECESSARILY TELL
YOU ANYTHING ABOUT THE PROCESS THAT'S HAPPENING.
RIGHT.
BUT I MEAN, THE FIRST ORDER
YOU WOULD EXPECT TO SEE THE LEARNING IN THE SYNAPSES.
AND THAT'S SAYING THAT THERE'S NOT OTHER STUFF GOING ON THAT,
BY THE WAY, I SUPPORT THAT IDEA.
I'M JUST PLAYING DEVIL'S ADVOCATE.
YEAH.
BUT THAT BRINGS UP THE IDEA OF, OF DOING CHRONIC RECORDINGS.
RIGHT.
AND, AND BRINGING UP NEW TECHNOLOGIES
IN ORDER TO CREATE CHRONIC RECORDINGS.
AND DOING THAT REQUIRES
VERY SIGNIFICANT ENGINEERING.
GO AHEAD.
SO, AS WE'RE TALKING ABOUT
GATHERING MORE AND MORE DATA, YOU KNOW, YOU'RE DOING THE FLY
CONNECTOME, AND THEN THE MOUSE CONNECTOME
SOON,
THESE HUGE INITIATIVES, ALL OF ALL OF WHICH I FULLY SUPPORT.
BUT I'M CURIOUS HOW PEOPLE THINK ABOUT C ELEGANS,
BECAUSE WE KNOW ALL 302 OF THOSE NEURONS
AND WE CAN'T BUILD IT YET.
THERE ARE SIMULATED ONES.
YEAH, CERTAINLY.
THERE'S OPEN WORM.
YEAH.
THAT'S IN -- THAT'S ON A COMPUTER.
WE HAVEN'T BUILT IT.
RIGHT. WE HAVEN'T EVEN BUILT THE SCALED MODEL OF IT.
WIRELESS MEANING?
SO TO REPEAT FOR THE AUDIENCE WHO MAY NOT BE ABLE TO
HEAR, HE'S SAYING WE DON'T KNOW THE WIRELESS DIAGRAM
WHICH MEANS
BUT THERE ARE.
NEUROTRANSMITTERS THAT ARE NOT GOING THROUGH SYNAPSES.
YEAH THAT'S TRUE.
SO HOW DO WE THINK ABOUT THAT.
WHY DO WE
WE WANT TO THINK ABOUT TRYING TO SOLVE THAT?
DO WE THINK IF THAT'S A PROBLEM FOR 302 NEURONS,
DO YOU THINK THAT'LL BE ONLY
AN ESCALATED PROBLEM FOR LARGER NUMBERS?
RIGHT.
SO DO YOU THINK THAT SPIKES REPLACE
THAT COMPLETELY?
THEY USE GRADED TRANSMISSION.
BUT I BELIEVE THAT'S FOUND IN MAMMALS SOMETIMES.
IT'S NOT A DOMINANT WAY.
IT'S NOT THE DOMINANT WAY.
IT'S TRUE.
I'VE JUST.
HEARD IT THAT.
PEOPLE WHO WORK ON A DAILY RECORD FOR THEIR.
DOES ANYONE HAVE C. ELEGANS THOUGHTS?
I MEAN, THERE ARE PEOPLE WORKING ON SIMULATING C ELEGANS,
BUT I TAKE YOUR POINT ABOUT THE BODY AS WELL.
I MEAN,
I THINK THAT'S QUITE IMPORTANT
BECAUSE I WAS IMPRESSED WITH HOW MUCH EMBODIMENT WAS DISCUSSED.
I'M REALLY NOT JUST TRYING TO BEAT THE EMBODIMENT DRUM.
I'M TRYING TO SAY THAT
THERE PROBABLY
IS THE WIRELESS SYSTEM OR, YOU KNOW, THESE NEUROMODULATORS
THAT ARE AT PLAY IN INVERTEBRATE SYSTEMS ARE THAT
SEEM ALSO LIKELY TO BE IN PLAY IN MAMMALIAN SYSTEMS.
THAT MAY COMPLICATE MATTERS TO A DEGREE THAT WE DON'T YET
AND AREN'T YET ABLE TO ANTICIPATE.
SO I'LL JUST CAVEAT.
CAN I ADDRESS THAT? THAT'S A GREAT POINT.
AND INTERESTINGLY, MAYBE IRONICALLY,
THERE WAS A STUDY PUBLISHED LAST FEW MONTHS THAT ACTUALLY USED
A.I. ANALYSIS OF GENE EXPRESSION STUDIES TO FIGURE OUT THE LIKELY
TARGETS OF THESE NEUROMODULATORS THAT ARE ACTING AT A DISTANCE.
SO I THINK THE THE ROLE OF NEUROMODULATION
IS GOING TO BE CRITICAL.
IT'S NOT ALL GOING TO BE IN THE STATIC
CHEMICAL SYNAPSE WIRING DIAGRAM.
BUT THAT'S NOT TO DISCOUNT THE
THE POTENTIAL POWER OF THESE SYSTEMS.
IF YOU, AS DORIS SAID,
USE YOUR INTUITION TO GET CREATIVE ABOUT IT.
I THINK WE CAN ACTUALLY GET PRETTY FAR WITH IT.
SO IT'S A GREAT POINT
AND ONE THAT I THINK WE DISCUSSED OFFLINE A BIT.
BUT THERE ARE APPROACHES TO KIND OF GET AT THIS PROBLEM.
SO, THEY'RE NOT
INSOLUBLE. AND THAT COULD BE QUITE
INSTRUCTIVE IF WE PUT OUR MINDS TO IT.
TO KIND OF PIGGYBACK ON THAT,
BUT KIND OF GO TO THE OTHER EXTREME.
RIGHT.
SO WE'RE TALKING ABOUT DOING THESE KIND OF VERY LARGE SCALE
NEURAL ANALYSIS, LARGER SCALE CONNECTOME
OVER MULTIPLE INDIVIDUALS, RECORDING
FROM LOTS OF NEURONS OVER LONG PERIODS OF TIME.
WE ALREADY HAVE A BIG DATA PROBLEM.
AND THIS IS AN EVEN BIGGER DATA PROBLEM.
RIGHT?
I REMEMBER AT EITHER
THE LAST BRAIN INITIATIVE MEETING
OR THE ONE BEFORE THAT, SOMEONE
I THINK IT WAS DIRECTOR NGAI SAID THAT, YOU KNOW,
IF YOU LOOK AT
TRYING TO MAKE A CONNECTOME OF SOME OF THESE BIGGER
MAMMALIAN SYSTEMS, RIGHT, LIKE MOUSE, RAT GOING UP, PROBABLY
I THINK IT WAS THE RAT.
IT IS GOING TO
GENERATE MORE DATA THAN HAS BEEN GENERATED
BY HUMANITY SINCE THE STONE AGE, RIGHT?
THIS IS IT'S AN ORDER OF INCREASE IN DATA COMPLEXITY.
SO I FEEL LIKE A BIG PORTION OF HOW WE MOVE
INTO THIS NEXT PHASE OF NEUROSCIENCE
IS EVEN DEVELOPING EVEN MORE TOOLS
AND METHODOLOGIES AND THEORIES ABOUT HOW YOU ANALYZE
THESE BIG, EVEN BIGGER ASPECTS OF DATA, I GUESS.
DOES ANYBODY HAVE THOUGHTS
ABOUT KIND OF THE LARGER EXPLOSION OF DATA?
YEAH, I MEAN, I THINK THE DATA THING IS
SOMETHING THAT IS GOING TO GROW AND WE'RE GOING TO HAVE TO
DEDICATE ENGINEERING RESOURCES TOO, NO QUESTION.
AND ALONG THE LINES OF SOFTWARE ENGINEERING,
THE SOFTWARE ENGINEERING DISCUSSION
WE ALREADY HAD, I, I JUST WANT TO MAKE ONE COMMENT
ABOUT THE C ELEGANS, POINT, WHICH I THINK IS GREAT.
RIGHT?
WE KNOW THINGS ABOUT THE C ELEGANS WIRING
AND WHAT YOU KNOW, IT'S A FAIR QUESTION TO ASK WHAT WE HAVE,
WHAT KIND OF PROPERTIES WE HAVE LEARNED ABOUT THE,
YOU KNOW, THE HUMAN BRAIN, LET'S SAY, FROM THAT.
AND I THINK IT JUST REALLY EMPHASIZES
THE TASK THAT THIS NEUROAI WORKSHOP AND CONCEPT
IS REALLY PUTTING FORWARD. RIGHT?
I MEAN, AS WE GO, LOOK AT WHAT'S HAPPENED WITH A.I.,
WITH MACHINE LEARNING SYSTEMS OF ALL SORTS,
AS YOU GO TO MORE AND MORE AND MORE AND MORE PARAMETERS,
YOU GET EMERGENT PROPERTIES THAT LEAD TO THINGS LIKE,
YOU KNOW, MODERN TRANSFORMERS
AND REALLY LARGE FOUNDATION MODELS.
AND WE KNOW THAT AS YOU SCALE THEM UP,
THE CAPABILITIES INCREASE.
AND I THINK THAT GOING FROM THE STUDY OF THE C ELEGANS
NERVOUS SYSTEM
TO THE FLY WIRING DIAGRAM THAT WE'VE SEEN TO, YOU KNOW,
SMALL MAMMALS AND LARGER MAMMALS AND,
SCALING UP. I THINK THERE'S A VERY
EXCITING VISION OVER THE NEXT,
YOU KNOW, 10 OR 20 YEARS THAT WE ARE GOING TO TAKE
THESE DATA SETS AND UNDERSTAND EMERGENT PROPERTIES THAT ARISE
WHEN YOU START WIRING UP LARGE NUMBERS OF NEURONS
INTO NETWORKS, INTO SYSTEMS LIKE OUR BRAINS.
AND TO ME, THAT'S A REASON WHY THIS WHAT'S GOING ON
HERE IS SO EXCITING. REALLY?
MAYBE ONE LAST QUESTION.
WE HAVE ABOUT A MINUTE AND A HALF.
OKAY.
I'M THE LAST QUESTION.
OKAY.
SO BESIDES THE LEARNING, I THINK ANOTHER THING
THAT WE MAY NEED OR PAYING ATTENTION IS MEMORY.
SO, I ATTENDED, SFN, AND, IN THE NEUROSCIENCE FIELD,
IT'S NOT JUST LEARNING HOW BRAIN ENCODE THE INFORMATION
AND STORES THE
INFORMATION IN OUR NEURAL SYSTEM. IT'S STILL VERY IMPORTANT
QUESTIONS AND WE HAVE NOT ANSWERED THAT YET
IN THE NEUROSCIENCE PART. AND IN THE DEEP LEARNING
OKAY
SO BASICALLY PEOPLE
WORKING ON THE LEARNING, LEARNING AND LEARNING,
BUT HOW CAN USE THIS AS KIND OF THE TECHNIQUE
ON NEUROMORPHIC TECHNIQUE TOOLS TO
ANSWER HOW BRAIN STORES THE MEMORY.
AND ALSO I THINK THAT THESE ARE VERY IMPORTANT
BECAUSE MEMORY, IN MY OPINION,
MEMORY IS RELATED TO
LEARNING.
WHEN I TRY TO REMEMBER HOW I LEARN SOMETHING, BECAUSE I
LEARN AND I MEMORIZE SOMETHING, OKAY, LIKE A 1 PLUS 1 EQUALS 2.
AND, WHEN I
FACED A SIMILAR QUESTIONS THAT THEN I RECALL SOME MEMORY.
OKAY.
SO BASICALLY I THINK THAT'S HIGHLY RELATED TO EACH OTHER.
AND ONOTHER POINTS, THAT IS,
I THINK, FOR THE WHOLE DIGITAL SYSTEM,
WE ALREADY IN THIS KIND OF DIGITAL WORLD,
USING DIGITAL COMPUTERS FOR SO MANY YEARS.
AND THE PEOPLE USE THIS MEMORY
WHEN YOU TALK MEMORY WITH THE CIRCUIT DESIGN
GUYS AND THE NEUROSCIENCE GOT FUNDAMENTALLY DIFFERENT
CONCEPT.
SO WHEN WE TALK ABOUT THE MEMORY IN THE CIRCUIT
IS KIND OF
HOW DO WE STORE THE INFORMATION? 010101 USING DEVICE?
OKAY.
THAT'S HOW THE MEMORY
AND ARCHITECTURES KNOW HOW TO ACCESS THAT.
BUT IS THAT SAME CONCEPT IN THE NEUROSCIENCE,
IN THE NEUROMORPHIC COMPUTING? WE USE THE SAME CONCEPT?
IF YOU USE THE SAME CONCEPT,
ACTUALLY, THAT'S THE FUNDAMENTAL DIFFERENT. I DO NOT THINK,
THERE IS
SOME 0101 STORED IN THE NEURAL SYSTEM.
SO WHEN WE TALK ABOUT MEMORY,
MAYBE WE NEED TO THINK OUT OF THE BOX AT A LEAST
AND NOT, IT'S NOT
EQUIVALENT TO THE 0101 OR ANY
MEMORY DEVICES LIKE SRAM IN
THE DIGITAL SYSTEM.
SO THAT'S SOMETHING I WANT
TO PICK UP.
AND, ANOTHER THING IS I THINK ABOUT MEMORY IS ALSO
SOME KIND OF CONCEPT THAT WE NEED TO TAKE A PAY ATTENTION.
THANK YOU VERY MUCH.
ANYBODY WANT TO COMMENT ON THAT?
KAREN? I'M SORRY.
I WAS GOING TO
STEER US TOWARD ETHICAL CONSIDERATIONS AS WELL,
BUT I THINK WE NEED TO YIELD THE FLOOR TO, JOHN.
SO THANK YOU GUYS FOR
JOINING ME UP HERE IN A THOUGHTFUL DISCUSSION.
HI EVERYONE, SORRY FOR THE DELAY.
SO I WANT TO ANNOUNCE
THE WINNERS OF THE POSTER PRESENTATION AWARD.
FOR FIRST PLACE, WE HAVE
ADITYA NAIR, FROM CALTECH.
MACHINE LEARNING
GUIDED DISCOVERY OF AN
INTRINSIC
LINE
ATTRACTOR
DON'T FALL OFF THE STAGE
OH. REPEAT THE TITLE.
YEAH, SURE.
MACHINE LEARNING GUIDED DISCOVERY OF INTRINSIC LINE
ATTRACTOR
THAT'S ALL RIGHT.
NEXT WE HAVE WE HAVE.
THERE'S TWO RUNNER UPS.
TO XINHE ZHANG HARVARD UNIVERSITY,
DECODING BRAIN INTRINSIC DYNAMICS FOR NEUROAI.
YEAH. YOU.
RIGHT.
THAT'S NOT THE RIGHT ONE.
SO THERE'S A FEW.
AND THE SECOND RUNNER UP IS HARRISON ESPINO
FROM THE UNIVERSITY OF CALIFORNIA, IRVINE.
AND HIS POSTER WAS A RAPID ADAPTING AND CONTINUAL LEARNING,
SPIKING NEURAL NETWORK PATH PLANNING
ALGORITHM FOR MOBILE ROBOTS.
THANK YOU EVERYONE.
HERE WE GO.
THANK YOU.
THEY'RE GREAT. THANKS, EVERYBODY, FOR HANGING IN.
IT'S BEEN AN INTENSE, TWO DAYS, AND I THINK,
I HOPE YOU'LL ALL AGREE WITH ME.
THERE'S A HUGE AMOUNT OF EXCITEMENT IN THIS AREA,
AND I TRUST YOU BELIEVE THAT BRAIN INITIATIVE CAN
AND WILL BE PLAYING A ROLE HERE.
SO THIS IS KIND OF WHAT WE'RE QUITE INTERESTED IN.
I JUST WANTED TO LEAVE YOU WITH A FEW POINTS TO CONSIDER.
LOOKING AHEAD.
YOU KNOW, MANY, MANY QUESTIONS CAME UP OVER THE COURSE
OF THE LAST TWO DAYS. WE HAD SOME GREAT WRAP UP.
SUMMARIES JUST NOW.
BUT ONE THING TO THINK ABOUT IS HOW CAN NEUROAI BRIDGE
ALL THESE MULTIPLE SCALES, BOTH FROM THE EVOLUTIONARY
TIME SCALE TO REAL TIME TO THE MILLISECOND TIMESCALE,
CONSIDERING THE COMPUTATIONAL
ELEMENTS FROM DENDRITES TO NEURONS.
WE JUST HAD AN INTERESTING DISCUSSION
ABOUT NEUROMODULATION, WHICH ALSO CAME UP
CONSIDERING ALL THE CELL TYPES
IN THE CIRCUITS, NOT JUST THE NEURONS.
ASTROCYTES, BELIEVE IT OR NOT,
ARE THERE TO PLAY A VERY IMPORTANT ROLE.
AND USING ALL THIS TOGETHER TO UNDERSTAND THE BASIS
FOR BIOLOGICAL INTELLIGENCE, AND IN TURN, THAT CAN BE USED
TO DESIGN BETTER
ARTIFICIAL INTELLIGENCE.
WE NEED TO CONSIDER HOW CAN THE CROSS-MODAL DATA
INTEGRATION, STANDARDIZATION AND METRICS.
VERY IMPORTANT THAT YOU NEED TO, THAT WE UNDERSTAND
WHAT WE'RE TRYING TO MEASURE AS WE GO ALONG.
HOW CAN USE THAT TO LEVERAGE LARGE SCALE BRAIN DATA
AND OTHER DATA TO UNDERSTAND, SENSORIMOTOR INTERACTIONS
CRUCIAL FOR THEORETICAL ADVANCES AND PRACTICAL APPLICATIONS.
WE ARE INTERESTED
IN APPLICATIONS IN ADDITION TO THE THEORY AND THEORY
IS, OF COURSE, AT THE CORE OF WHAT WE'RE GOING TO BE DOING.
AND VERY
IMPORTANT THAT WE NEED TO BE VERY MINDFUL ABOUT
INCLUDING EVERYTHING WE DO, ETHICAL FRAMEWORKS,
NOT AS AN ADD ON, BUT FROM THE VERY BEGINNING
AS WE GET INTO THESE AREAS THAT ARE GOING TO BRING US INTO
DOMAINS THAT I DON'T THINK WE CAN NECESSARILY IMAGINE
WHERE WE'LL BE IN TEN YEARS.
SO IT'S VERY, VERY IMPORTANT FOR US TO BE, MINDFUL
ABOUT AND INTENTIONAL
ABOUT HOW WE THINK ABOUT THESE ISSUES AS THEY AFFECT HUMANS
AND QUITE FRANKLY, THE PLANET, NOT TO BE OVERLY DRAMATIC.
AND THEN FINALLY,
I CAN'T OVERSTATE THE IMPORTANCE OF OUR APPRECIATION
THAT THIS IS A VAST RESEARCH ECOSYSTEM. NIH BRAIN,
WE'RE JUST A PART OF THIS.
WE HOPE TO BE IN A LEADERSHIP POSITION
BECAUSE OF
THE INTELLECTUAL RESOURCES THAT WE
HAVE MANAGED TO PULL TOGETHER.
PUSHING THIS FIELD FORWARD IN A WAY
THAT I THINK CAN FULFILL ITS TRUE POTENTIAL
WILL REQUIRE THE COLLABORATION ACROSS AGENCIES,
NOT JUST FEDERAL AGENCIES, BUT ALSO, NONPROFITS,
OTHER FUNDERS, PHILANTHROPY,
AND THAT WE WILL HAVE TO BUILD AN ECOSYSTEM
THAT IS BUILT ON OPEN SHARING OF RESOURCES
FOCUSED ON TRAINING THE NEXT GENERATION OF
NEUROAI RESEARCHERS.
IT'S INTERESTING
WHEN WE TALK ABOUT MENTORING THE NEXT GENERATION,
I MEAN, WHAT THE HECK CAN I TEACH
SOME OF MY TRAINEES ABOUT THIS FIELD?
THEY KNOW MORE THAN I DO,
BUT I THINK IT'S A MATTER OF GIVING THEM THE SPACE
AND THE FREEDOM TO EXPLORE THESE,
NEW DOMAINS TO TO BE THE NEXT SET OF CREATORS.
AND OF COURSE, TRANSLATING THESE INSIGHTS
FROM BASIC TO BROADER APPLICATIONS.
SO THESE ARE
SOME SOMEWHAT GRANULAR THINGS I'D LIKE YOU TO THINK ABOUT.
BUT JUST OVER THE LAST YEAR, IN LISTENING
TO THE LAST OF THE DISCUSSIONS, I JUST HAD A FEW OTHER THOUGHTS
THAT IF YOU'LL ALLOW ME TO
JUST GO A LITTLE BIT MORE META.
SO PERMEATING A LOT OF THESE DISCUSSIONS
WAS THE ISSUE OF SCALING AND SCALABILITY.
AND I'VE COME TO APPRECIATE
OVER THE LAST FEW YEARS THAT ABOVE A CERTAIN
THRESHOLD, SCALING DOESN'T JUST ALLOW YOU TO DO MORE,
IT ALLOWS YOU TO DO DIFFERENT.
RIGHT?
SO IT OPENS UP A WHOLE NEW SET OF PARADIGMS
FOR UNDERSTANDING COMPLEX PROCESSES
AND SCALING UP THE ENTERPRISE.
AND THIS APPLIES HERE
IN PARTICULAR.
LET'S NOT DISCOUNT
THE ROLE OF TECHNOLOGY DISRUPTIONS
AND DRIVING THE FIELD FORWARD IN A NONLINEAR WAY.
SO, THERE WAS A MENTION
JUST IN THE LAST SESSION ABOUT MOORE'S LAW.
YOU KNOW, CAN WE YOU KNOW, WHAT DOES MOORE'S LAW LOOK LIKE
FOR ELECTRONICS?
WHAT DOES IT LOOK LIKE IN TERMS OF WHAT WE'VE LEARNED ABOUT
THE BRAIN IN TERMS OF CELL TYPES?
AND I THINK WE'VE EXCEEDED MOORE'S LAW.
BUT REALLY IT'S NOT A LINEAR.
OR EVEN A WELL-BEHAVED PROCESS
BECAUSE IT DEPENDS ON TECHNOLOGY DISRUPTIONS.
IF YOU LOOK AT SOME ADVANCES, FOR EXAMPLE, IN OUR ABILITY
TO DO,
DNA SEQUENCING FROM SLAB GELS, WHICH I RAN BACK
WHEN I WAS A STUDENT, RIGHT, TO CAPILLARY SEQUENCING,
THERE WAS A HUGE DISRUPTION
IN HIGH THROUGHPUT SEQUENCING
THAT ALLOWED US TO ASK QUESTIONS
THAT PEOPLE COULD NOT HAVE IMAGINED
WHEN WE WERE DEBATING WHETHER TO SEQUENCED GENOME TO BEGIN WITH.
AND SO,
NEUROPIXELS
AND OTHER HIGH DENSITY
RECORDING TECHNIQUES, IF THEY'RE DONE
IN AN INTELLIGENT AND A SMART WAY,
CAN REALLY OPEN UP NEW PARADIGMS FOR STUDYING
HOW NEURAL DYNAMICS
THAT ARE GOING TO BE SO IMPORTANT FOR GENERATING,
NEURO-INSPIRED A.I. ALGORITHMS
NEURO-INSPIRED PLATFORMS AND SO ON.
I JUST WANTED TO SAY AGAIN,
IN TERMS OF
THE IMPORTANCE OF DISRUPTIONS AND DRIVING THE FIELD
FORWARD, IT'S NOT GOING TO BE A LINEAR OR REGULAR PROCESS.
THE QUESTION,
I THINK FOR ALL OF US TO CONSIDER
IS HOW TO BUILD A RESEARCH
ECOSYSTEM THAT'S FRIENDLY FOR DISRUPTION.
YOU CAN'T TELL WHEN THE NEXT DISRUPTION
IS GOING TO COME, RIGHT.
BUT WE CAN CREATE AN ECOSYSTEM THAT'S FAVORABLE FOR IT.
AND AGAIN, THAT INCLUDES
TRAINING THE NEXT GENERATION OF LEADERS
AND GIVING THEM THE FREEDOM TO BE CREATIVE
AND TO TRUST THEIR INTUITION TO BRING OURSELVES
INTO NEW DOMAINS.
AND THEN, OF COURSE, THE QUESTION IS, WHAT ROLE
WILL ARTIFICIAL INTELLIGENCE AND NEURO-INSPIRED
ARTIFICIAL INTELLIGENCE PLAY IN SHAPING SUCH AN ECOSYSTEM?
AND THEN MY NEXT TO LAST POINT IS
DON'T FORGET THE VALUE OF GROUND TRUTH INFORMATION.
RIGHT?
WE'RE GOING TO BE BUILDING ALL THESE MODELS.
WE'RE GOING TO BE FINDING THESE LATENT PATTERNS OF
AND NEURAL DYNAMICS.
BUT AT THE END OF THE DAY,
THEY HAVE TO BRING US BACK TOWARDS SOME REALITY.
AND THEN FINALLY, I WANT TO BRING UP,
WHAT'S BECOME A BIT OF A MANTRA IN THE BRAIN INITIATIVE.
AND IF YOU LOOK AT SOME OF THESE BIG PROJECTS THAT WE STOOD UP,
INCLUDING THE CELL ATLASING PROJECTS,
AND I HOPE GOING FORWARD INTO THE CONNECTIVITY
PROJECTS AND OTHERS AND HOPEFULLY INTO THE A.I. FIELD.
OUR MANTRA IS THINK BIG, START SMALL AND SCALE FAST.
AND WITH THAT, I'LL LEAVE YOU. THANK YOU AGAIN FOR BEING HERE.
THANKS TO THE ORGANIZERS,
AND OUR CONTRACTORS FOR MAKING ALL THIS HAPPEN,
ESPECIALLY, IN TERMS OF THE ORGANIZERS,
ESPECIALLY JOE AND GRACE,
FOR PUTTING THIS TOGETHER, IT WAS A HUGE LIFT.
BUT I THINK THIS REALLY GETS US THINKING IN A GOOD WAY
ABOUT HOW TO SHAPE THE FUTURE IN THIS FIELD.
AND TO CREATE A NEW FIELD
FROM WHAT WE'VE BUILT SO FAR.
SO THANKS AGAIN, EVERYBODY. HAVE A SAFE TRIP BACK.