WELCOME, EVERYONE, TO THE 18th
MEETING OF THE BRAIN NEUROETHICS
WORKING GROUP, NEWG, AS WE
AFFECTIONATELY CALL THIS GROUP.
I'M ANDREA BECKEL-MITCHENER,
DEPUTY DIRECTOR OF THE NIH BRAIN
INITIATIVE, AND I SERVE AS
DESIGNATED FEDERAL OFFICIAL FOR
TODAY'S MEETING.
I WANT TO EXTEND A WARM WELCOME
TO WORKING GROUP MEMBERS AND
CO-CHAIRS, CHRISTINE GRADY AND
NINA FARAHANY.
I HADN'T TO WELCOME OUR INVITED
SPEAKERS AND PANELISTS AND
GUESTS JOINING US TODAY VIA THE
MAGIC OF VIDEOCAST.
AS MOST OF YOU KNOW, THE NEWG IS
NOT A FORMAL ADVISORY GROUP LIKE
THE MULTI-COUNCIL WORKING GROUP
WHICH WILL MEET TOMORROW.
THE NEWG IS A WORKING GROUP OF
THE TEN ADVISORY COUNCILS OF THE
NIH INSTITUTES AND CENTERS THAT
PARTICIPATE IN THE BRAIN
INITIATIVE, AND IT WAS
ESTABLISHED TO IDENTIFY AND
REFLECT ON ETHICAL
CONSIDERATIONS RELATED TO
RESEARCH SUPPORTED THROUGH THE
BRAIN INITIATIVE, IN THIS ROLE
THE NEWG DISCUSSES AND
DELIBERATES ON ETHICAL, LEGAL,
SOCIETAL QUESTIONS THAT COME UP
IN THE CONTEXT OF BRAIN
INITIATIVE RESEARCH, AND OFFERS
HELPFUL PERSPECTIVES AND POINTS
TO CONSIDER.
AND TODAY'S VIRTUAL MEETING WE
WILL HAVE ITEMS ON THE AGENDA,
YOU ALL SHOULD HAVE RECEIVED THE
AGENDA, IT'S POSTED.
FIRST WE'LL HEAR AN UPDATE FROM
OUR BRAIN DIRECTOR DR. JOHN
NGAI, AND WE'LL DEVOTE THE
MAJORITY OF TODAY'S MEETING AT
THE INTERSECTION OF ARTIFICIAL
INTELLIGENCE, NEUROSCIENCE,
PHENOTYPING, ETHICS, THROUGH THE
PRESENTATION AND EXPLORATION OF
CASE STUDIES TO HELP SET THE
STAGE, WE'LL FIRST HAVE A
TECHNICAL OVERVIEW TO HELP
GROUND POSSESSION ETHICAL
CONSIDERSES IN THE --
CONSIDERATIONS.
WE'LL HEAR UPDATES FROM NEWG
MEMBERS ABOUT EVERYONE HAS BEEN
UP TO IN THE NEUROETHICS SPACE
SINCE WE LAST MET.
A FEW HOUSEKEEPING ITEMS, AS A
REMINDER THIS OPEN SESSION IS
BEING VIDEOCAST LIVE IN THE
PUBLIC DOMAIN, AND THE RECORDING
WILL BE ARCHIVED FOR LATER
VIEWING ON THE NIH AND THE BRAIN
WEBSITE.
PLEASE REMAIN ON MUTE UNLESS
YOU'RE PRESENTING.
DURING DISCUSSIONS WE ASK NEWG
MEMBERS, PLEASE TURN ON YOUR
VIDEO AND USE THE RAISE HAND
OPTION IF YOU HAVE QUESTIONS OR
COMMENTS, MODERATORS WILL CALL
ON YOU TO UNMUTE YOURSELF.
IT'S ESSENTIAL TO USE RAISE HAND
FEATURE BECAUSE RATHER THAN
TYPING IN THE ZOOM CHAT FOR OUR
VIDEOCAST ATTENDEES SO THEY CAN
HEAR AND SEE THE QUESTIONS.
LASTLY, NONE OF THIS CAN HAPPEN
WITHOUT THE INCREDIBLE TEAM WE
HAVE.
I ESPECIALLY WANT TO THANK DR.
NINA HSU, SPECIALIST FOR THIS
WORKING GROUP, THE SEEM MAKING
TODAY'S MEETING POSSIBLE
INCLUDES DOCTOR HENDRICKS,
DUFRAYA AND THE ROSE LI TEAM AND
VIDEOCAST TEAM.
I PASS TO CHRISTINE AND NINA FOR
OPENING REMARKS.
>> THANK YOU, ANDREA.
I JUST WANT TO SAY HELLO AND
WELCOME.
THANK YOU FOR JOINING US TODAY,
NEWG MEMBERS AND EVERYBODY ELSE.
IT'S A PRIVILEGE FOR ME TO BE
ABLE TO CO-CHAIR THIS GROUP WITH
NINA FARAHANY AND THE GREAT
PEOPLE YOU JUST HEARD ABOUT.
I WANT TO ADD MY THANKS TO NINA
HSU AND SASKIA HENDRIKS WHO MAE
THIS HAPPEN AND THE OTHER PEOPLE
ANDREA MENTIONED.
WE HAVE AN EXCITING TOPIC AND
GREAT SPEAKERS SO I'M LOOKING
FORWARD VERY MUCH TO THE
DISCUSSION AND SESSIONS THAT WE
HAVE.
NITA?
>> YOU COVERED IT, INCLUDING
THANKS TO WORK WITH YOU IN THIS
CAPACITY.
I'M EXCITED FOR THE DAY AHEAD.
THERE'S A LOT OF IMPORTANT
MATERIAL TO COVER.
>> ALL RIGHT.
THANKS.
WE'LL JUMP TO JOHN.
>> THANKS, EVERYBODY.
THE WORDS "DITTO" DON'T DO IT SO
I HAVE TO ADD MY THANKS TO
EVERYBODY MENTIONED.
NITA AND CHRISTINE, SASKIA
CHRIS AND DEB AND OTHERS FOR
MAKING SURE EVERYTHING WORKS.
OF COURSE TO THE NEWG MEMBERS
HERE TODAY FOR WHAT I'M SURE
WILL BE A GREAT DISCUSSION WITH
SPECIAL GUESTS.
SINCE I JOINED BRAIN OVER 4 1/2
YEARS AGO WHAT IMPRESSED ME WAS
BEING MINDFUL ABOUT WHAT WE DO
AND IMPACTS ON INDIVIDUALS AND
SOCIETY.
AS WE LOOK AT THIS CURRENT
ITERATION OF THE A.I. REVOLUTION
IT'S BECOME CLEAR TO ME THAT
"BRAIN" CAN AND SHOULD PLAY A
ROLE IN GUIDING OR AT LEAST
PARTICIPATING IN LEADERSHIP WAY
IN THESE IMPORTANT DISCUSSIONS.
LET ME START BY SHARING A FEW
UPDATES, IF I REMEMBER HOW TO DO
THIS.
YOU WOULD THINK I WOULD REMEMBER
HOW TO DO THIS BY NOW.
CAN YOU SEE MY SCREEN, YEAH?
TERRIFIC.
A FEW UPDATES.
WE LIKE TO START WITH RECAPS OF
RECENT EVENTS IN THE NEUROETHICS
DOMAIN AND WHAT OUR FOLKS ARE
DOING.
THERE WAS A MEETING IN APRIL AT
THE INTERNATIONAL NEUROETHICS
SOCIETY, DOCTORS CHURCHILL AND
NINA HSU WHO LEAD THE TEAM
ORGANIZED A WORKSHOP TO TALK
ABOUT DEMYSTIFYING BRAIN
INITIATIVE FUNDING, FUNDING
SCHOLARLY WORK IN THIS DOMAIN,
HAPPY AND PROUD ABOUT THAT.
THE GOAL WAS TO PROVIDE BETTER
UNDERSTANDING OF BRAIN
INITIATIVE IN THIS SPACE AND
GIVE INVESTIGATORS TOOLS TO
REACH OUT TO US TO ENGAGE -- TO
ENGAGE OTHERS IN THIS NETWORK
FOR RESEARCH IN NEUROETHICS, AND
I'LL TOUCH ON THAT BRIEFLY
BECAUSE THIS IS SOMETHING
SPREADING BEYOND JUST "BRAIN"
WHICH MAKES US QUITE HAPPY.
WE HAD A GREAT ANNUAL BRAIN
INITIATIVE CONFERENCE BACK IN
JUNE.
THIS IS OUR 10th ANNUAL
CONFERENCE.
WE HAD A LOT OF GREAT TALKS.
WE HAD A FUN PANEL WITH A BUNCH
OF LUMINARIES TALKING ABOUT NOT
JUST THE PAST BUT THE FUTURE OF
"BRAIN."
AGAIN, AS ALWAYS, WE MADE SURE
THAT THE TOPICS OF NEUROETHICS,
THE IMPACTS OF WHAT WE DO, WAS
FULLY EMBEDDED IN THE PROGRAM IN
MANY DIFFERENT WAYS.
WE HAVE THIS BUILDING OUT A
DECADE OF INNOVATION PANEL
DISCUSSIONS WITH DOCTORS FRANCIS
COLLINS, BARGMANN, NEWSOM, EDDIE
CHANG.
WE HAD SESSIONS LOOKING AT HIGH
DENSITY AND HIGH RESOLUTION
NEUROPHYSIOLOGICAL RECORDINGS OF
THE HUMAN BRAIN, OF COURSE BEING
MINDFUL OF THE ETHICAL
IMPLICATIONS OF THAT.
THERE WAS A GREAT PANEL ON
ADVANCING PARTICIPANT ENGAGEMENT
IN BRAIN-COMPUTER INTERFACE
RESEARCH, BEYOND STANDING ROOM
ONLY SPECIAL SESSION ON EMERGING
PERSPECTIVES ON EMBODIED
NEUROA.I. RESEARCH.
WE'LL BE GETTING TO THAT QUITE A
BIT AS THE DAY GOES ON.
ALSO, LAYING THE FOUNDATION FOR
NEW DATA AND ECOSYSTEMS CENTERED
AROUND THE RECENTLY LAUNCHED
BRAIN BEHAVIOR QUANTIFICATION
AND SYNCHRONIZATION PROGRAM
WE'RE EXCITED ABOUT.
YOU CAN ACCESS MUCH OF THE
CONTENT BY CLICKING ON THE QR
CODE OR GOING TO THE WEBSITE.
EVERYTHING NOW IS FINALLY
ONLINE.
UPCOMING EVENTS, THERE'S THIS
DEEP BRAIN STIMULATION THINK
TANK, UNIVERSITY OF FLORIDA,
ACTUALLY STARTING TODAY.
THERE'S THIS BRAIN-COMPUTER
INTERFACE COMMUNITY THAT JEN
FRENCH PLAYED A ROLE IN, IN
SEPTEMBER.
BROAD ONE ON HUMAN RESEARCH WITH
A.I. SPONSORED BY DEPARTMENT OF
HEALTH AND HUMAN SERVICES.
THEN BACK TO BACK WITH THE IBCI
COMMUNITY WORKSHOP, FDA-NIH
SPONSORED WORKSHOP ON DEVELOPING
IMPLANTED BRAIN-COMPUTER
INTERFACES, WE'LL HAVE A
PRESENCE THERE AS WELL.
THESE ARE ALL VIRTUAL OR HYBRID
SO WE ENCOURAGE YOU TO TUNE IN
AND SEE WHAT'S GOING ON IN THOSE
WORKSHOPS.
NEUROETHICS HAS BEEN A GOOD WORD
BEING SPREAD, THIS BUILDS AND
EXPANDS ON BRAIN'S FOUNDATIONAL
WORK IN NEUROETHICS, THE PURPOSE
OF THIS SPECIAL NOTICE IS TO
DRAW ATTENTION TO FOLK TO
ENCOURAGE THEM TO APPLY FOR
FUNDING IN THIS NEUROETHICS
SPACE, RELEVANT PARENT
ANNOUNCEMENTS LISTED BELOW,
CONTACT IS DR. NINA HSU AT
NINDS, QUITE EXCITED TO SEE THIS
FIELD GROW BEYOND BRAIN INTO
INSTITUTES AND CENTERS
PARTICIPATING IN BRAIN.
THERE'S A REQUEST FOR
INFORMATION.
THIS IS FROM THE COMMON FUND,
NOT FROM "BRAIN," BUT QUITE
RELATED TO THE DISCUSSION WE'RE
GOING TO HAVE TODAY ABOUT THE
CHALLENGES IN ADVANCING A.I.,
INTEGRATING CLINICAL IMAGING
WITH MULTI-LEVEL DATA, YOU CAN
TAKE A LOOK AT THAT, QR CODE ON
BOTTOM RYAN, DOCTORS FERRANTE
AND SHARMA ARE THE CONTACTS.
A FEW PUBLICATIONS THAT WERE
SUPPORTED BY THE BRAIN
NEUROETHICS R01 MECHANISM, A
PAPER SUPPORTED BY AN R01 TO THE
GROUPS LOOKING AT ADOLESCENT OCD
PATIENTS AND CAREGIVER
PERSPECTIVES ON POTENTIAL DEEP
BRAIN STIMULATION TREATMENT, HOW
DO WE ENGAGE WITH PATIENTS AND
FAMILIES.
A LOT OF DISCUSSION AROUND
NEUROMODULATION INFLUENCES ON
INDIVIDUAL PERSONALITIES,
IDENTITY AND AGENCY, RAISING
ISSUES AROUND PEDIATRIC
PATIENTS.
HERE THEY STUDIED 21 PATIENTS
WITH SEVERE OCD AND CAREGIVERS
THINKING ABOUT FUTURE DBS
TREATMENT, INTERESTING FINDINGS
DOVETAILS NICELY WITH TOPICALLY
COVERED IN FEBRUARY 2024 NEWG
MEETING ON ETHICAL
CONSIDERATIONS AROUND PEDIATRIC
NEUROSTIMULATION, LOOK UP THIS
PAPER AND YOU CAN LOOK AT THE
DISCUSSION THAT WE HAD BACK IN
FEBRUARY ON THIS MORE GENERAL
TOPIC.
THE SECOND PAPER I WANT TO
HIGHLIGHT FROM SARAH GOERING'S
GROUP, UNIVERSITY OF WASHINGTON,
LOOKING AT THIS ISSUE OF MORAL
ENTANGLEMENTS AS HUMAN RESEARCH
PARTNERS PARTICIPANTS ARE
ENGAGED IN HUMAN-BASED RESEARCH.
WE HAVE TO RECOGNIZE THAT
PARTICIPANTS HAVE SPECIFIC
VULNERABLABILITIES MADE MORE
COMPLICATION BECAUSE OF
RELATIONSHIPS AND TRUST WITH
RESEARCHERS, SURGEONS, WHAT HAVE
YOU.
THEY DELVE INTO THE ISSUE OF
MORAL ENTANGLEMENT, INTERESTING
AND TIMELY TOPIC FOR YOUR
CONSIDERATION.
THIS, AGAIN, DOVETAILS WITH A
WORKSHOP WE HELD BACK IN MAY OF
22, GOSH, IT'S ALREADY BEEN
2022, ON CONTINUING TRIAL
RESPONSIBILITIES, AND YOU CAN
LOOK UP THAT WITH THAT QR CODE
RIGHT THERE.
THAT'S IT.
I WANT TO GET US ON TO BUSINESS
TO THE FUN PART OF THE
DISCUSSION.
YOU CAN LEARN MORE ABOUT
EVERYTHING BRAIN BY SUBSCRIBING
TO THE BRAIN BLOCK.
WE PROMISE NOT TO CLOG YOUR
E-MAIL IN-BOX BUT YOU'LL GET
GOOD INFORMATION ABOUT STUFF
COMING UP, WEBSITE, GUIDE
NOTICES.
THE BEST SOURCE IS CONTACT YOUR
PROGRAM OFFICER.
WE'RE MORE THAN HAPPY TO TALK TO
YOU FOLKS AND SEE HOW WE CAN
ENABLE YOUR RESEARCH AND YOU CAN
FIND US AT BRAIN.GOV.
I WILL STOP THERE.
I DON'T KNOW IF WE HAVE TIME FOR
QUESTIONS OR COMMENTS OR WE
SHOULD JUST DIVE INTO THE GOOD
STUFF.
>> LOOKS LIKE NOT SEEING ANY
QUESTIONS, JOHN.
IF ANYONE HAS QUESTIONS OR
COMMENTS, OTHERWISE I'M GOING TO
JUST HAND THE WHOLE THING OVER
TO CHRISTINE AND NITA TO WORK
OUR WAY THROUGH THE AGENDA.
>> WE'RE LUCKY TO HAVE WITH US
SEVERAL PEOPLE TO GIVE US
BACKGROUND INFORMATION TO BE
ABLE TO GET STARTED ON THE
DISCUSSION OF THE ETHICS.
SO OUR FIRST SPEAKER WHO IS
GOING TO GIVE AN INTRODUCTION
AND OVERVIEW OF A.I.
NEUROSCIENCE AND ETHICS IS DR.
JOE MONACO FROM THE NINDS.
DR. MONACO?
>> GREAT, THANKS SO MUCH.
I'M GOING TO SHARE MY SCREEN.
I ASSUME YOU CAN SEE THAT.
THANKS FOR HAVING ME.
JOHN SAYS THIS IS THE GOOD
STUFF, AND I HOPE IT IS.
I WANT TO GIVE A VERY BRIEF KIND
OF OVERVIEW OF WHAT WE'RE
TALKING ABOUT WHEN WE'RE TALKING
ABOUT A.I. AND HOW WE CAN APPLY
TO NEUROETHICS.
IT'S FOUND MANY APPLICATIONS AND
WILL FIND MANY MORE IN THE
FUTURE.
I'LL INTRODUCE OUR SUBSEQUENT
SPEAKERS, DORIS T.S.A.O AND
PATRICK MINUTE MINEAUALT.
THE SECOND HALF WILL INVOLVE
CASE STUDIES, APPLYING A.I. TO
NEUROETHICS AND BRAIN HEALTH,
FOCUSING ON DEEP PHENOTYPING AND
TRAINING LARGE TRANSFORMER-BASED
NEURAL NETWORKS MODELS INCLUDING
MODALITIES LIKE EEG.
SO I WANT TO QUICKLY STEP BACK
AND SAY WHAT IS A.I., WHAT DO WE
MEAN?
PEOPLE HAVE DIFFERENT IDEAS, BUT
AT THE FOUNDATION MODERN
TECHNOLOGY THAT WE CALL A.I. IS
A COMPUTING TECHNOLOGY THAT'S
BASED ON A PARTICULAR MODEL THAT
WE CALLED ARTIFICIAL NEURAL
NETWORK MODEL, ANNs ARE
SIMPLE.
THEY ARE JUST NETWORK MODELS
ESSENTIALLY GRAPHS WHERE YOU
HAVE SIMPLE NODES AND YOU HAVE
CONNECTIONS BETWEEN THOSE NODES
AND THOSE CONNECTIONS ARE
WEIGHTED, AND THE WEIGHTS OF
THOSE CONNECTIONS ARE ADJUSTED
DUE TO LEARNING RULES APPLIED TO
DATA.
THE REASONS EXPLODED OVER 10 OR
12 YEARS, CERTAIN TECHNICAL
BARRIERS WERE SURPASSED.
FOR A LONG TIME THESE TYPES OF
MODELS COULD NOT BE SCALED UP
BECAUSE WE DIDN'T HAVE THE
COMPUTING HARDWARE TO
PARALLELIZE THE DATA, TO FLOW AS
FAST THROUGH THE SYSTEMS AS WE
NEED TO TEST LARGE SYSTEMS, BUT
AROUND 2012 WITH ALEX AND THE
COMPUTER VISION COMMUNITY THESE
BARRIERS WERE BROKEN AND NOW
THAT BASICALLY SET A.I. OFF TO
THE RACES, AS WE ALL KNOW.
WE CAN NOW THROW MORE AND MORE
DATA AND MORE AND MORE COMPUTE
AT THESE MODELS TO BUILD THEM
UP, AND THEY BASICALLY LEARN
FROM HIGH DIMENSIONAL COMPLEX
INPUT-OUTPUT RELATIONSHIPS THAT
ARE OTHERWISE VERY DIFFICULT TO
PROGRAM MANUALLY.
SO IN A SENSE THEY ARE EXPLORING
THE POSSIBLE PROGRAM SPACE TO
SOLVE PROBLEMS OF INTEREST.
AND SO THIS IS TECHNOLOGY THAT
WAS OBVIOUS, WIDE APPLICABILITY.
AND SO TO KIND OF PROVIDE THE
LONG VIEW HERE, THIS IS A
COMPLEX TIMELINE, IT'S NOT
CRITICAL TO UNDERSTANDING ALL OF
THE DETAILS HERE.
THE TOP PURPLE LINE THAT YOU CAN
SEE ARE LANDMARK ADVANCES IN
NEUROSCIENCE.
AND THE BOTTOM THREE TIMELINES,
BLUE, ORANGE AND GREEN, SHOW
DIFFERENT BRANCHES OF THIS
TECHNOLOGY THAT FALLS UNDER THE
UMBRELLA OF A.I., ANNs,
MACHINE LEARNING, STATISTICAL
LEARNING, AND RELATED METHODS.
AND SO THERE'S BEEN A FEW POINTS
IN THE LAST MANY DECADES
ESSENTIALLY WHERE SOME
INSPIRATION FROM THE BRAIN LED
TO ADVANCES IN THE A.I.
TECHNOLOGY, OR IN HOW WE
STRUCTURE THESE TYPES OF MODELS,
PARTICULARLY AROUND HOW WE TRAIN
THEM.
SO IT'S ONLY UNTIL THE '90s
WHEN THE ARABIC PROPAGATION RULE
WAS FORMALIZED, ABLE TO STABLY
TRAIN THESE NETWORKS TO DO
USEFUL THINGS.
WE'RE LOOKING AT 30 YEARS OF
HISTORY THERE.
SO THAT LEADS US TO THE PRESENT
WHERE WE HAVE 10 OR 12 YEARS OF
INFLECTION POINT IN MODERN A.I.,
OF COURSE WE NOW HAVE 10 YEARS
OF THE BRAIN INITIATIVE ITSELF,
SO THIS IS AN AMAZING POINT OF
CONVERGENCE REALLY BECAUSE NOW
WE HAVE THIS EXCELLENT
TECHNOLOGICAL TOOLBOX FOR
ANALYZING AND UNDERSTANDING VAST
QUANTITIES OF COMPLEX DATA, AND
THE BRAIN INITIATIVE HAS BEEN
TEN YEARS GENERATING AND
COLLECTING LARGE SCALE KNOWLEDGE
BASES AND DATASETS ON AN
UNPRECEDENTED SCALES USING
NEUROTECHNOLOGIES THAT IT HAS
INNOVATED.
SO WE CAN USE A.I.-BASED TOOLS
TO DO ALL SORTS OF THINGS TO
UNDERSTAND NEURAL DATA, SO WE
CAN LEARN COMPLEX MODELS OF
NEURAL ACTIVITY.
WE CAN LEARN COMPLEX MODELS OF
COMPLEX BEHAVIORS.
WE CAN TREAT STRUCTURE AND
ACTIVITY AS JOINT OPTIMIZATION
PROGRAM AND SEE WHAT COMES OUT
OF THAT, MAYBE FUNDAMENTAL
COMPUTATIONAL PRINCIPLES, A LOT
OF GREAT OPPORTUNITIES THAT ARE
IN THE CURRENT STATE OF THE ART
IN NEUROSCIENCE AND MANY MORE
GOING FORWARD.
THERE'S A GREAT REVIEW FROM WANG
A COUPLE YEARS AGO LAYING OUT
THE LANDSCAPE HERE, AND
ESSENTIALLY FOUR MAJOR KINDS OF
MODELS THAT WE CAN THINK ABOUT
THAT ARE IN USE, AND SO
BASICALLY, YOU KNOW, SINCE THESE
THINGS ARE JUST GRAPHS AND
NETWORKS OF NODES YOU CAN
ARRANGE THEM IN DIFFERENT WAYS
TO DO DIFFERENT THINGS.
SO THAT STRUCTURE, OFTEN YOU
WANT THAT TO MAP ON THE
STRUCTURE YOU'RE TRYING TO
SOLVE.
STOP LEFT, MULTI-LAYER
PERCEPTRONS, AND TOP RIGHT
RANDOM ASSORTMENT OF NODES THAT
ARE RANDOMLY INTERCONNECTED,
DYNAMIC CONNECTED NEURAL
NETWORK, TRAINED TO LEARN TIME
SERIES AND DYNAMICAL DATA
PATTERNS.
LOWER LEFT YOU CAN UNROLL A
RECURRENT NETWORK IN ORDER TO
APPLY IT TO SEQUENCES AND TIME
SERIES OF DATA SO YOU CAN MAKE
PREDICTIONS ABOUT WHAT TYPE OF
DATA OR WHAT DISTRIBUTION OF
DATA IS THAT YOU CAN EXPECT TO
COME NEXT.
AND THERE'S ALL SORTS OF WAYS
YOU CAN TAKE WHAT ARE OFTEN
CALLED INDUCTIVE BIASES,
STRUCTURES FROM BRAINS, APPLY TO
STRUCTURES OF NEURAL NETWORK
MODELS SUCH AS FOR VISION AND
CONVOLUTIONAL NEURAL NETWORKS.
DORIS WILL TALK MORE ABOUT THAT.
BUT THESE SPAN THE SPACE OF
PROBLEMS THAT WE CAN APPLY A.I.
MODELS TO.
IT'S IMPORTANT TO REMEMBER WHAT
THE SYSTEMS ARE.
AND SO BLAKE RICHARDS AND A
NUMBER OF CO-AUTHORS FROM A FEW
YEARS AGO LAID OUT A GREAT
FRAMEWORK CLARIFYING THAT
FUNDAMENTALLY THERE'S THREE
COMPONENTS TO ALL OF THIS STUFF,
WHICH IS ONE ARCHITECTURE, HOW
ARE NODES CONNECTED, WHAT DOES
THE STRUCTURE LOOK LIKE.
LEARNING RULE, HOW DO YOU
UPDATE, AND THE OBJECTIVE
FUNCTION, WHAT IS THE
MEASUREMENT OF PERFORMANCE, WHAT
IS THE TASK YOU'RE TRYING TO
LEARN, WHAT IS THE INPUT/OUTPUT
MAPPING YOU WANT TO COME OUT OF
THE MODEL.
THOSE ARE THE THREE ELEMENTS.
AND YOU CAN COME UP WITH
DIFFERENT WAYS OF PUTTING THESE
TOGETHER BUT THERE'S -- YOU CAN
KIND OF MAYBE INTUIT THAT
THERE'S NO FUNDAMENTAL GUARD
RAILS IN THIS KIND OF SYSTEM.
YOU TRAIN THIS UNTIL YOU DON'T
HAVE ANY MORE ERROR AGAINST YOUR
POLLED OUT TEST DATASET, BUT
THERE ARE NO FUNDAMENTAL
GUARANTEES WILL THE BIASES THAT
CAN COME, THAT CAN BE LEARNED BY
THESE MODELS.
THAT'S A MAJOR KIND OF CONCERN
AS FAR AS POTENTIAL ETHICAL
CONSIDERATIONS OF APPLICATIONS
HERE.
I WANT TO QUICKLY GO OVER HOW
CAN WE USE THESE IN
NEUROSCIENCE, WE KNOW ABOUT
CHATBOTS AND LLMs, THESE
MODELS CAN INGEST LARGE
QUANTITIES OF TEXT DATA, VERY
IMPORTANT, SO THOSE CAN BE USED
TO KIND OF EXPLORE THE
LITERATURE IN A WAY THAT CAN
HELP MODELS DEVELOP THEORIES,
EXPERIMENTALISTS UNDERSTAND WHAT
HAS BEEN DONE OR NOT DONE TO
DRIVE FUTURE SCIENCE.
YOU CAN USE THESE TYPES OF A.I.
MODELS TO SUPPORT OR FACILITATE
DETAILED IN SILICO MODELS THAT
INTERACT WITH THE PHYSICAL WORLD
IN IMPORTANT WAYS.
WE CAN THINK ABOUT THIS AS
ENCODING MODELS TO HELP US
ADVANCE THEORETICAL
UNDERSTANDING, DECODING MODELS
TO HELP PREDICTION AND CONTROL,
AND THINK ABOUT TRANSLATING
THOSE TYPES OF MODELS INTO, YOU
KNOW, REAL WORLD DEVICES
INCLUDING BRAIN-COMPUTER
INTERFACE, BCI, DBS AND NEURO
PROSTHETICS, A LARGE ROAD MAP F
POSSIBILITIES.
EXAMPLE OF GENERATIVE MODEL THAT
CAN BE TRAINED IN THIS SPACE, A
RESEARCH TEAM BASED OUT OF
UNIVERSITY OF TORONTO THAT
SHOWED YOU CAN TAKE A LARGE DATA
REPOSITORY OF MOLECULAR
SINGLE-CELL CHARACTERIZATIONS IN
THIS CASE 33 MILLION CELLS, AND
TRAIN A GENERATIVE FOUNDATION
MODEL WITH THIS MECHANISM, AND
IT CAN BE USED AS A BASE MODEL
TO PREDICT AND IMPUTE DATA AND
FIGURE OUT STRUCTURE OF WHAT
TYPES OF CELLS ARE RELATED TO
OTHER TYPES OF CELLS, AND
BASICALLY INFER THE STRUCTURE OF
CELLS IN THE HUMAN BODY.
SO THIS PARTICULAR STUDY WAS NOT
RESTRICTED TO JUST BRAIN CELLS
BUT EXACT SAME KIND OF METHOD
CAN BE APPLIED TO KNOWLEDGE
BASES AND CELL ATLASES SUCH AS
BEING CONSTRUCTED BY THE
TRANSFORMATIVE PROJECT OF THE
BRAIN INITIATIVE, AND THEY
PROVIDE A GREAT EXAMPLE WHERE
THEY PRE-TRAINED THIS LARGE BASE
MODEL BUT THEN THEY HAD A
DIFFERENT DOWNSTREAM USE.
THEY WANTED TO THEN APPLY THE
SAME BASE MODEL TO A DIFFERENT
PROBLEM.
SO THEY USED A CERTAIN FORM OF
TRANSFER LEARNING TO COME UP
WITH A SLIGHTLY NEW OBJECTIVE
FUNCTION, AND THEY WERE ABLE TO
FINE TUNE THE REPRESENTATIONS IN
THIS MODEL TO ANSWER DIFFERENT
KINDS OF QUESTIONS.
AND SO THAT'S ONE OF THE MAIN
QUESTIONS THAT, YOU KNOW, WE
WANT TO BE THINKING ABOUT AT THE
MEETING TODAY IS THE DIFFERENT
USES FOR DOMAIN-SPECIFIC MODELS,
FOUNDATIONAL BASE MODELS, OR HOW
TO FINE TUNE MODELS WITH NEW
DATA AND WHAT ARE IMPLICATIONS
FOR THOSE KINDS OF USES.
AND JUST AS ONE OTHER EXAMPLE OF
USING KIND OF THESE LANGUAGE
FOCUSED MODELS FROM A FEW YEARS
AGO, AT STANFORD IN THE
NEUROIMAGING RESEARCH COMMUNITY
USED NATURAL LANGUAGE PROCESSING
TECHNIQUES, MACHINE LEARNING
TECHNIQUES TO AGGREGATE OVER
18,000 STUDIES OF NEUROIMAGING
RESULTS AND ASSOCIATED DATASETS,
WITH fMRI AND PET DATA
MODALITIES, AND THEY WERE
ESSENTIALLY ABLE TO FORM A
BOTTOM-UNLEARNED ONTOLOGY OF
HUMAN COGNITION AND BRAIN
SYSTEMS THAT COULD BE LEARNED
OUT OF THE VAST AMOUNTS OF
LITERATURE AND ASSOCIATED DATA.
AND SO JUST BY THINKING DEEPLY
ABOUT HOW TO LINK KEY TYPES OF
DATA WE CAN CONSTRUCT, YOU KNOW,
AMAZING NEW KNOWLEDGE AND DATA
RESOURCES THAT CAN BENEFIT, YOU
KNOW, PARTICULAR COMMUNITIES AND
SCIENCE AS A WHOLE.
AND ONE OF THE MOTIVATIONS FOR
THIS KIND OF WORK INDIVIDUAL
SCIENTISTS IN THE LABS HAVE
HUMAN BIASES WHEN INTERPRETING
DATA.
IF WE HAVE THIS, YOU KNOW,
BASICALLY NEUTRAL OR SUPPOSEDLY
IMPARTIAL MODEL THAT'S BEEN
TRAINED ON, YOU KNOW, 18,000
STUDIES, IT PROBABLY AVOIDS
HAVING SOME OF THOSE HUMAN
BIASES THAT INDIVIDUAL
SCIENTISTS MIGHT HAVE, BUT OF
COURSE YOU DON'T ENTIRELY AVOID
THE PROBLEM OF BIAS.
SO WITH ALL THESE THINGS, AND
APPROACHES, THERE ARE
FUNDAMENTAL CHALLENGES.
WE'VE HEARD ABOUT HALLUCINATION
IN THESE MODELS.
DEPENDENCE ON LARGE PRE-TRAINING
DATASETS, INTENSIVE
COMPUTATIONAL REQUIREMENTS,
ENERGY USED TO TRAIN LARGE
MODELS IS NOT FUNDAMENTALLY
SUSTAINABLE.
THERE ARE GAPS IN REASONING
CAPABILITIES, AND SO THIS OTHER
BIASES, WHERE IN TRAINING
DATASET BASICALLY MODELS THAT
WILL INHERIT BIASES IN TRAINING
DATA, AND GENERATIVE MODELS CAN
PRODUCE HARMFUL OFFENSIVE OR
MISLEADING OUTPUT ON THE BASIS,
AND USERS HAVE DIFFERENT
ASSUMPTION ABOUT HOW GOOD THEY
ARE.
CERTAIN USERS MAY THINK THESE
ARE KNOWLEDGEABLE AND WILL TRUST
THEM MORE, OTHERS WILL BE
SKEPTICAL.
THIS USER AND MODEL INTERACTION
AS A LARGE EFFECT ON THE UTILITY
OF THESE MODELS IN NEUROSCIENCE.
THIS IS REALLY ALL DATA DRIVEN,
FROM DATA, IN THE BRAIN
INITIATIVE WE HAVE A LOT OF
DATA.
BRAIN INITIATIVE WAS FOUNDED ON
CORE PRINCIPLES ESTABLISHING
PLATFORMS FOR SHARING DATA AND
CONSIDERATION ETHICAL
IMPLICATIONS OF DATA SHARING,
ESPECIALLY IN HUMANS.
SO THIS IS THE CURRENT 2024
STATE OF THE DATA ARCHIVES.
WE HAVE LOTS OF DATA COMING INTO
OUR DATA TYPE SPECIFIC ARCHIVES
THE BRAIN INITIATIVE SUPPORTS IN
THE ECOSYSTEM.
OPEN NEURO AND DANDI, KEY PARTS
OF THE ECOSYSTEM.
LAST YEAR THERE WAS A MULTI-CASE
STUDY PAPER FROM THE BRAIN SHARE
PROJECT THAT TALKED ABOUT THE
BENEFITS OF SHARING HUMAN BRAIN
DATA, SO SOME GREAT INSIGHT IN
THIS PAPER WHICH I WANT TO
HIGHLIGHT.
THERE'S ANOTHER PAPER FROM
SASKIA HENDRIKS, WHERE THEY LAY
OUT A SPECTRUM OF RISKS AND
IDENTIFIABILITY PROBLEMS WHERE
IF YOU AGGREGATE DATA FROM
DIFFERENT SOURCES THAT ARE
SHARED THERE'S RE-IDENTIFICATION
RISKS OVER TIME AND SCALE,
APPLICABLE WHEN WE'RE THINKING
ABOUT MAKING TRAINING DATASETS
AVAILABLE FOR FUTURE A.I.
MODELS.
AND SO JUST TO REITERATE NIH
DATA MANAGEMENT SHARING POLICY
PUTS FORWARD GUIDELINES AND
PRINCIPLES FOR HUMAN RESEARCH
PARTICIPANT DATA, YOU CAN SEE
THOSE HERE AND GO TO PUBLISHED
NOTICE FOR MORE DETAIL ABOUT
THAT.
BUT THESE PRINCIPLES APPLY
DEEPLY AS WELL.
AS WE GO FORWARD I WANT TO
REITERATE THERE'S MANY SOURCES
OF BIAS, MANY SOURCES OF
PROBLEMS, WHEN WE THINK ABOUT
HOW TO USE AND INTERPRET A.I.
MODELS, IN NEUROSCIENCE IT'S
DIFFICULT TO ATTRIBUTE CAUSE IF
WE'RE THINKING ABOUT CLINICAL
APPLICATIONS, AND DIAGNOSTIC
TESTING, OR OTHER SIMILAR
AVENUES.
AND A LOT OF THIS COMES DOWN TO
PROVENANCE.
WE NEED TO KNOW WHERE THE DATA
COMES FROM AND THAT'S THE ONLY
WAY REALLY THAT YOU CAN KEEP
PEOPLE IN THE LOOP OF DOWNSTREAM
USES OF DATA AND DOWNSTREAM A.I.
MODELS BASED ON DATA.
AND WHEN YOU KEEP PEOPLE IN THE
LOOP THEN YOU HAVE SOME
ASSURANCE THAT YOUR MODELS WILL
AT LEAST BE GROUNDED IN THAT
INTERACTION.
AND SO I ALSO IN ADDITION TO THE
MEETINGS THAT JOHN HIGHLIGHTED I
WANT TO NOTE THAT EARLIER THIS
YEAR IN MARCH THE NATIONAL
ASSOCIATION OF SCIENCE ENGINEERS
HAD A GREAT WORKSHOP ON
NEUROSCIENCE AND A.I., A SESSION
ON REGULATORY POLICY RELATED TO
NEUROSCIENCE AND A.I., WHERE
JOHN AND NITA FARAHANY SPOKE, SO
THIS IS A VERY HIGH LEVEL BUT
GOOD DISCUSSION ABOUT
ESSENTIALLY WHAT ARE THE LATENT
VULNERABILITIES THAT CAN BE
UNCOVERED RELATED TO DATA
PRIVACY AND SECURITY WHILE
MAINTAINING ADVANCES IN
NEUROSCIENCE AND MEDICINE
EMPHASIZING PUBLIC ENGAGEMENT IS
IMPORTANT, AS WELL AS THE
REGULATORY SIDE OF THINGS.
AND UPCOMING AND NEXT MARCH,
SORRY, NEXT APRIL IN MUNICH,
THERE'S THE INS NEUROETHICS 2025
MEETING, ALSO FOCUSED ON THIS
THEME OF BRAIN AND A.I.
THERE'S THIS OTHER SIDE,
INVESTIGATING SCIENTIFIC CORE,
WHAT ARE THE SHARED FUNDAMENTAL
PRINCIPLES BETWEEN APPARENT
INTELLIGENCE OF THESE A.I.
MODELS AND THE INTELLIGENCE THAT
WE KNOW FROM BIOLOGY, HUMANS AND
OTHER ANIMALS, INTERESTING
QUESTIONS AND SCIENTIFIC
PERSPECTIVES EMERGING ALONG
THOSE QUESTIONS.
IN NOVEMBER WE'LL HAVE A TWO-DAY
HYBRID WORKSHOP TO BRING
TOGETHER MANY FIELDS AND
RESEARCHERS ACROSS CAREER LEVELS
TO REALLY DIG INTO AND EXPLORE
THIS KIND OF SCIENTIFIC
INTERSECTION OF NEUROSCIENCE AND
A.I., AND IT SHOULD BE EXCITING.
WE'LL HAVE THE WEBSITE UP SOON.
SIGN UP FOR THE BLOG.
I WANT TO HAND THE MIC OVER TO
DR. DORIS TSAO GIVING US AN
OVERVIEW OF THE TECHNICAL BASIS
OF THESE MODELS.
>> THANK YOU, JOE.
IT SAYS I CAN'T SHARE.
I'LL TRY AGAIN.
OKAY.
THANK YOU FOR INVITING ME TO
PARTICIPATE IN THIS WORKSHOP.
I'M EXCITED TO LEARN FROM ALL OF
OF YOU TODAY.
I'M A SYSTEMS NEUROSCIENTIST
STUDYING THE PRIMATE SYSTEM,
STEMMING FROM THE REVOLUTIONED
FIELD OF VISUAL NEUROSCIENCE.
YOU ARE RECOGNIZE THESE TWO
GENTLEMEN, CELEBRATING NOBEL
PRIZE FOR DISCOVERIES ABOUT
PRIMARY VISUAL CORTEX INCLUDING
LANDMARK DISCOVERY OF CELLS.
MOST OF YOU DON'T RECOGNIZE WHO
THIS IS.
THIS IS CHARLIE GROSS, RECORDING
ANTERIORLY IN THE VISUAL SYSTEM,
HE DESCRIBES A MARATHON
EXPERIMENT TRYING TO IDENTIFY
THE OPTIMAL STIMULATORY NEURON
DEEP IN THE VISUAL SYSTEM,
WRITES WE COULD NOT FIND A
SIMPLE PHYSICAL DIMENSION THAT
COULD EXPLAIN THE NEURON'S
RESPONSE, BECAUSE THE USES
HIERARCHICAL DISTRIBUTED
REPRESENTATIONS, CARRIED BY
BILLIONS OF NEURONS.
TURNS OUT THE BEST MODEL FOR
THESE REPRESENTATIONS ARE DEEP
NEURAL NETWORKS WHICH ALSO USE
HIERARCHICAL DISTRIBUTIVE
REPUTATIONS, HE DIDN'T HAVE
ACCESS TO THESE NETWORKS
UNFORTUNATELY IN 1972.
I PLAN TO GIVE A BASIC
INTRODUCTION TO THE TECHNICAL
VOCABULARY OF DEEP NETWORKS TO
MAKE SURE WE'RE ON THE SAME
PAGE.
I'LL EXPLAIN IN SOME DETAIL TWO
VERY INFLUENTIAL NEURAL
NETWORKS, CONVOLUTIONAL NEURAL
NETWORK AND TRANSFORMER, AND RUN
THROUGH MAJOR ACHIEVEMENTS, AND
ALL OF THIS SETS US UP TO
DISCUSS THE ROLE OF ETHICS IN
NEURO-A.I. WHICH JOE HAS GIVEN A
GREAT INTRODUCTION TO.
LET'S GET STARTED WITH A DEEP
DIVE INTO THE CONVOLUTIONAL
NEURAL NETWORK.
SIMPLE CELLS RESPOND ORIENTATION
AND LOCATION, COMPLEX CELLS
RESPOND TO A BAR AT SPECIFIC
ORIENTATION ANYWHERE WITHIN A
RANGE OF LOCATIONS.
PROPOSED COMPLEX CELLS ARE WIRED
BY POOLING TOGETHER SIMPLE
CELLS.
LATER FUKUSHIMA AND LECUN BUILT
A NETWORK WITH MULTIPLE LAYERS,
THE CONVOLUTIONAL NEURAL NETWORK
COULD RECOGNIZE HANDWRITING.
THE CURRENT REVOLUTION IN A.I.
STARTED IN 2012 WITH THIS PAPER,
BASICALLY TOOK LECUN'S NET AND
TRAINED IT.
THEY TRAINED THE NETWORK TO
CLASSIFY THE IMAGES, LABEL IT AS
A DOG, PARROT, ET CETERA.
THE RESULTING NETWORK CALLED
ALEXNET BLUE EXISTING BENCHMARKS
OUT OF THE WATER.
LET'S EXPLORE COMPONENTS OF A
CONVOLUTIONAL NEURAL NETWORK.
GIVEN A SET OF INPUTS THE FIRST
STEP IS CONVOLVE WITH A FILTER,
CONVOLVE IS A WORD AVERAGING,
YOU GET THE OUTPUT AND THESE
WEIGHTS DEFINE THE FILTER.
PERFORM THIS WEIGHTED AVERAGING
LOCALLY ON A SMALL PATCH OF THE
INPUT, SAY 3 X 3 NEIGHBORHOOD.
AND YOU DO THIS REPEATEDLY
ACROSS THE ENTIRE INPUT ARRAY.
THE VALUE OF FILTER WEIGHTS ARE
PARAMETERS OF THE CONV-NET.
I'LL SHOW YOU HOW THEY CAN BE
LEARNED FROM DATA.
HERE IS A CONCRETE EXAMPLE OF
THIS COMPUTATION WHERE THIS
INPUT IMAGE IS CONVOLVED WITH
THIS FILTER TO GIVE ARRAY OF
OUTPUTS.
YOU CAN SEE ACTUAL FILTERS
LEARNED BY THE FIRST LAYER OF
ALEX-NET, 96 FILTERS, PLOTTED IN
EACH OF THE POSTAGE STAMP ARE
FILTERED WEIGHTS.
TO BUILD A DEEP CONVOLUTIONAL
NEURAL NETWORK YOU TAKE THE
OUTPUT AND PASS IT THROUGH A
GATING FUNCTION, S HERE.
YOU REPEAT THIS PROCESS WITH AS
MANY LAYERS AS YOU LIKE.
SIMPLEST FORM OF GATING FUNCTION
IS A RECTIFIED LINEAR USED,
OUTPUT ZERO IF INPUT IS
NEGATIVE, OTHERWISE PASSES ON
INPUT.
THIS NON-LINEARITY, NEURON
DOESN'T FIRE UNLESS MEMBRANE
POTENTIAL GOES ABOVE A CERTAIN
THRESHOLD.
WHY IS NON-LINEAR GATING
CRITICAL?
IN TERMS OF SIMPLE AND COMPLEX
CELLS, BUILD A COMPLEX CELL BY
ADDING UP A BUNCH OF SIMPLE
CELLS PREFERRING THE SAME
ORIENTATION, VERTICAL.
YOU CAN ASK WHY WOULDN'T THIS
COMPLEX CELL RESPOND TO
HORIZONTAL BAR?
THE REASON IS THAT EACH SIMPLE
CELL BY VIRTUE OF BEING A NEURON
WITH THRESHOLD FOR FIRING HAS
THIS NON-LINEAR GATING BUILT IN,
IF YOU PRESENT A HORIZONTAL BAR
ALL THREE INPUTS OF THE COMPLEX
CELL STAY SILENTS, EACH BELOW
THRESHOLD.
IF YOU PRESENT A VERTICAL BAR,
THEN YOU'LL GET A STRONG
RESPONSE.
SO THE NON-LINEAR GATING IS
CRUCIAL HERE.
IT'S THE MAGIC INGREDIENT IN A
ENABLES HIERARCHICAL COMBINATION
TO CREATE COMPLEX FEATURES.
AND AS A SIDE, MORE ABSTRACTLY.
TO BUILT A DEEP CONVOLUTIONAL
NETWORK YOU STRING BLOCKS
TOGETHER, KEY COMPUTATIONS
WITHIN EACH BLOCK ARE SPATIAL
FILTERING AND NON-LINEAR GATING.
FINAL OUTPUT IS END UNITS,
FIRING TELLS YOU PROBABILITY OF
INPUT IN IMAGE CLASSES.
ONE OF THE MOST IMPORTANT
FEATURES OF DEEP NEURAL NETWORKS
IS THE WEIGHTS OF THE NETWORK
ARE LEARNED FROM DATA.
AND THE IDEA IS TO UPDATE ALL
THE WEIGHTS TO MINIMIZE THE
ERROR ON A TRAINING SET.
IF WE KNOW THIS IS A GRAY PARROT
THIS OUTPUT WOULD HAVE A HIGH
ERROR, THIS WOULD HAVE A LOW
ERROR.
AND IN ORDER TO FIND THE SET OF
NETWORK WEIGHTS THAT MINIMIZE
THE ERROR PERFORM A PROCESS
CALLED STOCHASTIC GRADIENT
DESCENT.
COMPUTE THE ERROR, COMPUTE HOW
TO CHANGE THE WEIGHTS TO
DECREASE THE ERROR.
USING ALGORITHM THAT COMPUTES
GRADIENT OF ERROR WITH RESPECT
TO EACH WEIGHT.
BY FOLLOWING THE GRADIENT YOU
CAN MINIMIZE THE ERROR.
NOW WE UNDERSTAND HOW TO BUILD
AND TRAIN CONV NETS, PIONEERING
PAPER NEURONS OF DIFFERENT
LAYERS OF THE MACAQUE HIERARCHY
COULD BE WELL MODELS BY
DIFFERENT LAYERS OF CONV NET.
MACAQUE CORTEX HARBORS SEVERAL
LAYERS OF THE MAP, EXPLAINING
THE SPATIAL ORGANIZATION OF
VISUAL CORTEX USING CONV NET.
I'D LIKE TO TALK ABOUT
TRANSFORMERS WHICH REFER TO
ANOTHER TYPE OF NEURAL NETWORK
ARCHITECTURE.
A LOT OF SAME DEVELOPMENTS IN
A.I. ARE BASED ON TRANSFORMER
ARCHITECTURE LIKE ChatGPT.
IT'S WORKS FROM TEXT OR IMAGES
PATCHES, PASSES THEM THROUGH A
SERIES OF STAGES TO PRODUCE A
FINAL OUTPUT, FOR EXAMPLE
PREDICTIVE NEXT WORK.
HERE'S THE STRUCTURE OF A BASIC
TRANSFORMER BLOCK, N OF THESE
CHAINED TOGETHER, KEY MAGIC IN
MULTI-HEAD ATTENTION LAYER.
SO HERE IS HOW ATTENTION WORKS
IN A TRANSFORMER.
THINK OF ATTENTION AS ANALOGOUS
TO CONVOLUTION FILTER IN A CONV
NET, INSTEAD OF A LOCAL FILTER
IT COMPUTES BETWEEN ALL OF THE
INPUT TOKENS.
THIS CORRELATION IS COMPUTED
USING THIS EQUATION HERE WHERE
Q, K, AND V DENOTE PROJECTIONS
OF THE TOKEN STRING, YOU CAN
STACK COPIES OF THE BASIC MODULE
TO IMPLEMENT MULTI-HEAD
ATTENTION WHERE WEIGHTS AND
HEADS ARE INDEPENDENT.
INTUITIVE WAY OF UNDERSTANDING,
THINK OF SENTENCE STRING.
COMPUTES CORRELATION BETWEEN
WORDS, FOR EXAMPLE WEARING TO
JUMPSUIT HIGHLY CORRELATED.
THAT'S A QUICK TOUR THROUGH THE
TRANSFORMER.
NOW PRE-TRAINING AND FINE TUNING
WHICH JOE MENTIONED HAS A LOT OF
ETHICAL ISSUES INVOLVED.
THIS REFERS TO THE IDEA YOU CAN
TRAIN A NETWORK TO DO ONE TASK,
RETRAIN TO DO OTHER TASKS.
ALEX-NET WAS TRAINED ON OBJECT
CLASSIFICATION, CAN YOU TRAIN IT
ON DOWNSTREAM TASKS, YOU CAN
RETRAIN BY SIMPLY HOLDING ALL
THE WEIGHTS FIXED, EXCEPT FOR
THE FINAL LAYER, AND ALSO DO
MORE COMPLEX FINE TUNING BY
INSERTING NEW ADAPTER LAYERS
INTO EACH TRANSFORMER BLOCK
WHILE KEEPING THE WEIGHTS ON THE
OTHER LAYERS FIXED.
LET'S TALK BRIEFLY ABOUT MAJOR
ACHIEVEMENTS WHICH PATRICK WILL
DISCUSS.
THE SUCCESS OF DEEP LEARNING HAS
CAPTURED EVERYONE'S IMAGINATION.
ALEX'S GROUP AT U.T. AUSTIN
PUSHED FOR THE STATE OF THE
LARGE IN LANGUAGE DECODING,
DECODING FROM BRAIN ACTIVITY.
THE STORY WAS I GOT UP FROM THE
AIR MATTRESS AND PRESSED MY FACE
TO THE GLASS WINDOW, EXPECTING
EYES BUT SEEING DARKNESS.
DECODED, I WALKED TO THE WINDOW
OPENED THE GLASS, PEERED OUT,
DIDN'T SEE ANYTHING, LOOKED UP.
AGAIN I SAW NOTHING.
THIS IS REMARKABLE FROM HUMAN
fMRI DATA.
THE WAY THIS WORKS, TAKE YOUR
fMRI DATA, AND YOU LEARN THE
FEATURE TUNING OF EACH VOXEL.
AND THEN YOU USE YOUR LANGUAGE
MODEL, PRE-TRAINED LANGUAGE
MODEL, TO PREDICT A SET OF THE
MOST LIKELY POSSIBILITIES FOR
THE NEXT WORD.
YOU FEED POSITION BUILTS TO
PREDICT HOW THE BRAIN WOULD
RESPOND.
BY COMPARING YOU THEN DECODE THE
NEXT WORD.
SIMILARLY A GENERATIVE IMAGE
MODEL STABLE DIFFUSION DECODED
VISUAL IMAGES FROM fMRI DATA
WITH IMPRESSIVE ACCURACY.
WE'RE APPROACHING THE ABILITY TO
READ PEOPLE'S MINDS IN REAL
TIME, WE'RE NOT THERE YET.
THIS RAISES ALL KINDS OF ETHICAL
ISSUES.
NOW I WANT TO WALK THROUGH ONE
FRAMEWORK FOR THINKING ABOUT
ETHICS AND NEUROA.I. PROPOSED IN
"NATURE" IN 2017.
FIRST PILLAR IS IMPORTANCE OF
PROTECTING PRIVACY AND ENSURING
CONSENT OF THE BRAIN INFORMATION
IS THE MOST INTIMATE AND PRIVATE
OF ALL INFORMATION.
USERS SHOULD BE ABLE TO OPT OUT.
WHEN DRIVING FACTORS IS
INCREASING SCALE OF TRAINING
DATA, DATA GATHERED FROM SOCIAL
MEDIA, ENORMOUS AMOUNT OF DATA,
DIFFICULT OR IMPOSSIBLE TO KEEP
TRACK OF POTENTIAL PRIVACY
ISSUES.
ISSUES BECOME MORE TRICKLY WHEN
CONSIDERING APPLICATIONS TO
MEDICINE WITH PRIVATE HEALTH
INFORMATION.
THERE SHOULD BE A CONSENT
PROCEDURE TO SPECIFY WHO WILL
USE THE DATA, WHAT PURPOSES, HOW
LONG, REGULATIONS TO LIMIT
POSSIBILITY OF PEOPLE GIVING UP
DATA OR HAVING DATA WRITTEN TO
THE BRAIN FOR FINANCIAL
COMPENSATION, AND FINALLY SHOULD
ADOPT TECHNOLOGY TO ENSURE
PRIVACY AND ONE APPROACH IS
FEDERATED LEARNING.
INSTEAD OF GATHERING ALL OF THE
DATA IN ONE PLACE FOR MODEL
TRAINING, USE DECENTRALIZED
APPROACH, EACH PARTY TRAINS A
COMMON MODEL OF THEIR OWN
PRIVATE DATA, AND THIS IS USED
TO COMPUTE PART OF THE WEIGHT
GRADIENT.
ONLY THIS WEIGHT GRADIENT IS
SENT TO THE CENTRAL LOCATION
WHERE THEY ARE ALL AGGREGATED
AND BROADCAST BACK TO EVERYONE
SO EACH SITE CAN PERFORM THE
NEXT ROUND OF TRAINING IN
PRIVACY.
SECOND IS PROTECTION OF AGENCY
AND IDENTITY.
ABILITY TO CHOOSE ACTION AND
BRAIN MACHINE INTERACTION CAN
BLUR THE LINE.
A MAN TREATED FOR DEPRESSION
WITH BRAIN STIMULATORS BEGAN TO
WONDER ACTIONS WITH DUE TO
HIMSELF, HE SAID I'M NOT SURE
WHO I AM, RAISING FUNDAMENTAL
QUESTIONS.
WHAT MAKES US RESPONSIBLE?
AND IN CONTEXT OF MIND READING
TECHNOLOGY I TOLD YOU ABOUT
EARLIER, SUPPOSE ALEX HUTH USED
THE DEVICE TO READ THOUGHTS OF
APHASIC PERSON, WOULD THAT
PERSON BE RESPONSIBLE FOR THOSE
THOUGHTS AND ACTIONS OR ASCRIBE
PART OF THE AGENCY TO THE
LANGUAGE MODEL THAT'S GENERATING
THE PREDICTIONS OF THE NEXT
WORK?
THIRD ETHICAL CONCERN IS ALL
THESE NEUROA.I. TECHNOLOGIES
CREATE POSSIBILITY FOR VASTLY
ENHANCING HUMAN CAPABILITY, FOR
EXAMPLE CREATING SUPER
INTELLIGENCE, I'LL PROPOSE THAT
GUIDELINES SHOULD BE ESTABLISHED
TO SET LIMITS ON AUGMENTING
TECHNOLOGY ANALOGOUS TO THOSE
FOR CRISPR.
A.I. SYSTEMS CAN PRODUCE BIASED
RESULTS THAT REFLECT AND
PERPETUATE HUMAN BIAS IN
SOCIETY, FOR EXAMPLE FACE
RECOGNITION ALGORITHMS TRAINED
ON WHITE PEOPLE, AND ONE
APPROACH TO ACTIVELY REMOVE BIAS
HERE, ONE OF MANY, IS
ADVERSARIAL DEVISING, TACK ON A
SECOND NETWORK THAT ATTEMPTS TO
PREDICT GENDER FROM OUTPUT OF
THE FIRST NETWORK, AND YOU CAN
TRAIN YOUR MODEL TO MAKE THE
CORRECT PREDICTION WHILE SAME
TIME PREVENTING TH ADVERSARY
WITH THE BIASED FACTORS.
DEEP NEURAL NETWORKS CAPTURE
COMPLEX FUNCTIONS FROM INPUT TO
OUTPUT.
TWO INFLUENTIAL ARCHITECTURES
ARE CONV NET AND TRANSFORMER,
PRE-TRAINED FOR ONE APPLICATION,
FINE TUNED FOR OTHERS, DEEP
LEARNING ENABLED MAJOR ADVANCES
IN FUNCTION AND DECODING, AND
ETHICAL APPROACH TO DEVELOPMENT
SHOULD CONSIDER PRIVACY AND
CONSENT, AGENCY AND IDENTITY,
AUGMENTATION, AND BIAS.
NOW I'LL INTRODUCE THE NEXT
SPEAKER, AN A.I. RESEARCHERS,
SENIOR SCIENTIST AT A
BRIAN-COMPUTER INTERFACE AT
META, AND GOOGLE, FOUNDING CTO
OF NEUROMATCH.
TAKE IT AWAY, PATRICK.
>> THANKS SO MUCH FOR THIS
WONDERFUL INTRODUCTION.
AND IT'S GREAT TO BE HERE.
I'LL SHARE MY SCREEN.
HOPE YOU CAN SEE THIS.
I'M GOING TO BE TALKING ABOUT
THE MORE TECHNICAL ASPECTS OF
FOUNDATION MODELS FOR
NEUROSCIENCE.
SO I'LL START WITH FOUNDATION
MODELS IN GENERAL.
I'LL INTRODUCE A RUBRIC FOR
FOUNDATION MODELS, HOW THEY
DIFFER FROM CONVENTIONAL MACHINE
LEARNING MODELS, I'LL GO TO
TALKING ABOUT FOUNDATION MODELS
IN NEUROSCIENCE, BRING TAXONOMY
AND SHOWCASE EXAMPLES AND TALK
ABOUT THE OPPORTUNITIES AND
PITFALLS FROM THESE MODELS
INCLUDING CLASSICAL, ETHICAL
CONCERNING FOR MACHINE LEARNING,
AND NEUROSCIENCE, ADDITIONAL
CONSIDERATIONS REALLY SPECIFIC
TO FOUNDATION MODELS IN
PARTICULAR.
SO I DON'T NEED TO BELABOR THE
POINT, WE'RE ALL VERY IMPRESSED
BY THE PROGRESS OF A.I.
SEVERAL OF US USE FOUNDATION
MODELS EVERY DAY.
ON THE LEFT CLAUDE, ChatGPT,
ALPHA FOLD DEPICTED HERE, WE
HAVE SINGLE CELL RNAseq
FOUNDATION MODELS, AND RECENTLY
FOUNDATION MODELS HAVE BEEN
EXPANDED TO INCLUDE MODELS
FOREVIDEOS.
SORRY TO SAY THE PUPPIES PLAYING
IN THE SNOW DO NOT EXIST.
NOW, WHAT CONSTITUTES A
FOUNDATION MODEL?
ONE OF THE ELEMENTS IS THAT THEY
ARE PRE-TRAINED IN A SUPERVISED
WAY WHICH DORIS ALLUDED TO.
FINE TUNED ON DOWNSTREAM TASKS.
TRAINED ON LARGE SCALE DATA,
LARGE SCALE COMPUTE, CONTAIN A
LARGE NUMBER OF PARAMETERS.
THEY USE GENERIC ARCHITECTURES,
WE TALKED ABOUT CNNs,
TRANSFORMERS ARE POPULAR FOR
LARGE LANGUAGE MODELS AND
FOUNDATION MODELS IN GENERAL.
THEIR PERFORMANCE IS
PREDICTABLE.
WE'LL BE TALKING AND ALLUDED TO
SCALING LAWS IN THE REST OF THE
PRESENTATION.
THEY MAY DISPLAY EMERGENT
PROPERTIES NOT IMMEDIATELY
OBVIOUS FROM THE PRE-TRAINING
TASK.
FINALLY COMPOSABLE.
ONCE A FOUNDATION MODEL IS
TRAINED ITS WAYS CAN BE
LEVERAGED FOR ANOTHER KIND OF
TASK.
SO, WHAT ARE FOUNDATION MODELS
IN NEUROSCIENCE IN PARTICULAR?
WELL, THERE'S SEVERAL RECENT
PAPERS THAT CLAIM TO CREATE
FOUNDATION MODELS FOR
NEUROSCIENCE, IT CAN BE HARD TO
SEE THE FOREST FROM THE TREES.
HERE IS HOW I LIKE TO THINK
ABOUT THIS.
SO HERE IS A DIAGRAM OF AN
ANIMAL, IT COULD BE A HUMAN
INTERACTING WITH THE
ENVIRONMENT.
THERE'S A WORLD, ANIMAL
INTERACTS WITH THE WORLD,
CREATES INPUTS ON RETINA,
COCHLEA, FOR OLFACTION, THAT
DRIVES -- CREATES EXOGENOUS
INPUT WHICH GETS INTEGRATED INTO
THE STATE OF ITS BRAIN.
AND DETERMINES THE POSITION THE
ANIMAL'S BODY WHICH THEN
EVENTUALLY CHANGES THE CONDITION
OF THE WORLD.
AND HOWE OUT -- OUT OF THIS
SYSTEM WE CAN MEASURE IN LOW
DIMENSIONAL FASHION WITH
NON-INVASIVE, fMRI, EEG, OR
USING NEUROPIXELS.
SO FOUNDATION MODELS IN
NEUROSCIENCE TEND TO FOCUS ON
SOME SUBPARTS OF THIS GRAPH, AND
DIFFERENT KINDS OF FOUNDATION
MODELS FOCUS ON -- GENERALLY
FOCUS ON MODELING PAIRWISE PAIRS
OF THESE PARTS OF THE GRAPH.
SO, DORIS TALKED ABOUT THE
FAMOUS WORK FROM DAN YAMINS AND
JIM DICARLO, A LINK OF NEURAL
NETWORKS ON IMAGE CLASSIFICATION
AND REPRESENTATIONS, AND THE
VENTRAL STREAM OF THE VISUAL
CORTEX.
THERE ARE ALSO MODELS WHICH ARE
DIRECTLY DATA DRIVEN SO THEY USE
DATA FROM THE BRAINS OF ANIMALS
TO TRAIN A NETWORK, SO WE CAN
CREATE WHAT'S DEEMED A DIGITAL
TWIN OF A PARTICULAR SET OF
MOSTLY VISUAL AREAS.
THIS IS ADVANCED MOSTLY IN
VISION.
SOME INVERSE, GOING FROM
MEASUREMENTS BACK TO WHAT
STIMULUS WAS, POPULAR IN
DECODING STUDIES AND fMRI.
OTHER STUDIES FOCUS ON OUTPUT
RATHER THAN POSITION OF THE
BODY.
SO THESE ARE EXAMPLES FROM
DEEPLABCUT, A POPULAR TOOL IN
THE COMPUTATION.
SOME MODELS FOCUS ON MAPPING OF
MEASUREMENTS TO WHAT WOULD HAVE
BEEN THE BEHAVIOR SO WE'VE SEEN
TREMENDOUS ANSWERS IN
BRAIN-COMPUTER INTERFACES, IN
PARTICULAR FOR SPEECH
PROSTHESIS, SEE THIS IS A PAPER
JUST CAME OUT IN THE NEW ENGLAND
JOURNAL OF MEDICINE, THAT
SHOWCASES ABILITY TO READ THE
INTENSIVE SPEECH FROM A PATIENT
WITH A HIGHER ACCURACY THAN
WOULD BE POSSIBLE USING
SPEECH-TO-TEXT CAPABILITY SO
TRULY REMARKABLE CAPABILITIES
HAVE BEEN COMING IN THIS SPACE.
BUT I THINK THAT THE THING THAT
DIFFERENTIATES IS REALLY THIS
FOCUS ON MODELING THE SIGNALS
THEMSELVES, IN AN UNSUPERVISED
WAY.
NOW, WHY IS THIS IMPORTANT?
FIRST OF ALL, THIS REPRESENTS A
LARGE OPPORTUNITY BECAUSE THERE
ARE VERY LARGE SCALE BRAIN
ARCHIVES, JOE SPOKE ABOUT SOME
OF THEM, THERE'S OPEN NEURAL,
GHANDI, DABI, A LOT FUNDED BY
NIH, WE HAVE OVER 100,000 HOURS
OF OPEN NEURAL DATA, CREATING A
POSSIBILITY OF LEARNING GOOD
REPRESENTATIONS FROM EXISTING
NEURAL DATA IN UNSUPERVISED WAY
TO USE FOR DOWNSTREAM TASKS.
THIS IS ILLUSTRATED BY A DIAGRAM
FROM BLAKE RICHARDS WHERE ONE
COULD RETRAIN A NEURAL NETWORK
TO FIND REPRESENTATIONS OF
DISPARATE NEURAL DATA, WHETHER E
EGEG, CALCIUM IMAGING, FINE TUNE
FOR DIFFERENT KINDS OF
DOWNSTREAM TASKS INCLUDING
PREDICTING THE OUTCOME OF
CLINICAL TRIALS FOR
BRAIN-COMPUTER INTERFACE AND
PSYCHIATRIC DIAGNOSIS.
WHERE IS A MODEL THAT DOES SUCH
A THING LOOK LIKE?
A GOOD EXAMPLE IS THIS RECENT
PAPER THAT CAME OUT ABOUT A
MONTH AGO, TOWARDS A UNIVERSAL
TRANSLATORS NOR NEURAL DYNAMICS,
FOCUSED ON LEARNING
REPRESENTATIONS FROM DATA THAT
CAME FROM THE INTERNATIONAL
BRAIN LAB.
SO THIS IS A SET OF NEUROPIXEL
RECORDINGS IN MICE WHILE
PERFORMING A VISUAL
DECISION-MAKING TASK, AND THEY
HAVE RECORDINGS FROM MANY AREAS
ALL ACROSS THE MOUSE CORTEX.
I THINK THEY HAVE LIKE 194 AREAS
COVERED IN TOTAL.
SO THEY REALLY COVERED -- THEY
REALLY FOCUSED ON MODELING THE
RESPONSES, A HANDFUL OF THESE
AREAS, AND LEARNED USING A BUNCH
OF PRE-TRAINING, PRE-TEXT TASKS.
SO THESE TASKS INCLUDE TASKS
LIKE CO-SMOOTHING, THAT MEANS I
TAKE A CERTAIN NUMBER OF
NEURONS, PREDICT WHAT WOULD HAVE
BEEN THE RESPONSE OF A NEURON
WHICH IS LEFT OUT.
ALSO INCLUDES SOMETHING LIKE
CAUSAL PREDICTION, SO CAUSAL
PREDICTION IS TAKING PREVIOUS
ACTIVITY FROM A PARTICULAR SET
OF NEURONS AND PREDICTING WHAT'S
GOING TO HAPPEN NEXT IN THE
SEQUENCE.
AND SO ON AND SO FORTH.
AND THEY LEARN A GOOD
REPRESENTATION USING A
COMPLETELY GENERIC MODEL WHICH
IS TRANSFORMER AS DORIS ALLUDED
TO.
THE WEIGH THIS WORKS, IT'S A
VARIANT OF A PREVIOUSLY
PUBLISHED NEURAL DATA
TRANSFORMER WHICH WAS INTRODUCED
BY JOEL YE AND PANDAREINATH A
COUPLE YEARS AGO.
FOR EVERY CHUNK OF TIME THEY
PERFORM BINNING OF SPIKES, COUNT
THE TOTAL NUMBER OF SPIKES IN
EACH BIN, THEN THEY AGGREGATE
INTO ONE VECTOR, PERFORM A
LINEAR PROJECTION.
THAT CREATES A TOKEN.
SO, THEN ALL OF THESE TOKENS ARE
PUT THROUGH A DEEP NEURAL
NETWORK WHICH HAS MANY LAYERS OF
ATTENTION, IN ORDER TO TRY AND
RECONSTRUCT THE ACTIVITY THAT
WAS LEFT OUT.
THAT WAS MASK OUT.
THIS PERFORMS REASONABLY WELL.
NOW, AS YOU MIGHT EXPECT AND
YOU'LL SEE A LOT OF THESE PLOTS
IN THE FOLLOWING, I SHOULD SHOW
MY LASER POINTER.
THERE YOU GO.
SO, ONE VERY TYPICAL OUTCOME
FROM TRAINING THESE KINDS OF
MODELS IS PERFORMANCE INCREASES
AS WE ADD MORE AND MORE DATA
INTO THESE DATASETS SO AS WE
INCREASE NUMBER OF SESSIONS,
WHICH WERE USED FOR TRAINING
THESE MODELS, PERFORMANCE
INCREASES, IN THIS CASE AVERAGE
BITS PER SPIKE IS THE METRIC.
THEY HAVE FOUR PRE-TEXT TASKS,
LO AND BEHOLD THE MODEL BECOMES
BETTER AT FOUR TASKS, THAT'S
EXPECTED.
WHAT'S MORE INTERESTING IS WHAT
WE SEE HERE, WHERE NOT ONLY DO
YOU GET BETTER AT PRETEXT BUT
ALSO BETTER AT OTHER DOWNSTREAM
TASKS.
IN THIS CASE PREDICTING CHOICE
PROBABILITY OF THE MOUSE FROM
NEURAL ACTIVITY, AND IN THIS ONE
IT'S PREDICTING THE AMOUNT OF
MOTION ENERGY THAT CAME FROM ITS
WHISKERS.
SO, THEREIN LIES THIS IDEA MAYBE
WE COULD BUILD A UNIVERSAL
TRANSLATOR, SOMETHING THAT CAN
READ ARBITRARY NEURAL ACTIVITY
AND FIND MEANINGFUL MANIFOLD
THAT CAN THEN BE USED FOR
SEVERAL DOWNSTREAM TASKS FROM
NEURAL DYNAMICS.
NOW, LET'S GO THROUGH A QUICK
TOUR OF THE STATE OF THE ART IN
SCALING FOUNDATION MODELS FOR
NEUROSCIENCE, DORIS PRESENTED
SEVERAL ONES, I'LL PRESENT
SEVERAL OTHERS ALONG THE SAME
DIRECTION.
SO DORIS MENTIONED THE WORD FROM
ALEX HUTH UNIVERSITY OF TEXAS
DECODING AUDITORY INFORMATION,
RATHER TEXT, IN THE CONTEXT OF
LISTENING TO PODCASTS.
THIS IS NOW TRANSLATED TO EEG.
THIS IS A STUDY THAT JUST CAME
OUT THAT RECORDINGS FROM SINGLE
SUBJECT I SHOULD SAY SO HIGH
DENSITY EEG RECORDINGS FROM A
SING
SINGLE SUBJECT, THE SUBJECT WAS
SPEAKING DIFFERENT SENTENCES IN
JAPANESE AND WE TRAIN A NEURAL
NETWORK TO UNDERSTAND OR PREDICT
WHAT WAS A SENTENCE THEY SPOKE,
OUT OF 512 POSSIBLE SENTENCES.
SO CLASSIC MULTI-CLASS
CLASSIFICATION PROBLEM.
BUT WHAT THEY DID HERE WHICH WAS
VERY DIFFERENT FROM THE STATE OF
THE ART IS THAT THEY RECORDED
175 HOURS OF THIS SUBJECT,
PERFORMING THIS TASK.
THAT MEANS 7 HOURS A DAY FOR 5
FULL WEEKS, DAY IN, DAY OUT,
RECORDING FROM EEGs, WHAT'S
REMARKABLE IS THE SCALING CURVE.
DECODING ACCESS IF ONE WOULD
RECORD NORMAL AMOUNTS OF EEG
DATA, LET'S SAY 1 TO 10 HOURS,
WAS BELOW 5%, HOWEVER AS THEY
SCALED THIS DATASET UP TO 175
HOURS THEY WERE ABLE TO GET TO
50% ACCURACY ON THIS 512-WEIGHT
CLASSIFICATION, WHICH IS TRULY
ASTOUNDING.
NOW, THAT MEANS THAT MAYBE
THERE'S A LITTLE BIT MORE
INFORMATION IN EEG THAT'S BEEN
CONVENTIONALLY APPRECIATED.
NOW, OF COURSE IT'S NOT
PRACTICAL TO HAVE SOMEONE HAVE
TO TRAIN FOR 175 HOURS TO TRAIN
A DECODER, SO OTHER GROUPS ARE
FOCUSING INSTEAD OF CAPTURING A
REASONABLE AMOUNT OF DATA FROM
LOTS AND LOTS OF DIFFERENT
SUBJECTS, SO THIS IS A STUDY
FROM A PART OF MET A REALITY
LABS, ONE OF MY FORMER
EMPLOYERS, ELECTROMYOGRAPHY
DECODING.
AND THEY ALSO FOUND THAT THEY
COULD TRAIN A NETWORK, GENERIC
NETWORK, THAT WOULD TRANSFER
ACROSS DIFFERENT SUBJECTS AND
THAT WILL ALLOW THEM TO DECODE
GESTURES OR HANDWRITING FROM
RECORDINGS OF EMG ON THE WRIST.
NOW, NOTICE HOW THE
CLASSIFICATION ERROR DECREASES
IN A VERY PREDICTABLE WAY AS A
FUNCTION OF THE NUMBER OF
PARTICIPANTS.
IN THIS CASE THEY WENT UP TO
4800 TRAINING PARTICIPANTS, IN
THAT CASE THEY WERE ABLE TO GET
TO CLASSIFICATION ERROR OF LESS
THAN 10%.
AND SIMILARLY FOR HANDWRITING.
AS DORIS ALLUDED TO, THESE
TECHNIQUES HAVE ALSO BEEN NOW
ADAPTED FOR DECODING IMAGES
FROM fMRI, ONE IMPORTANT ASPECT
OF THIS PARTICULAR MODEL WHICH
SEEKS TO DECODE IMAGES FROM
VISUAL CORTEX IS THIS MODEL,
SDXL, STABLE DIFFUSION.
IT'S A DIFFUSION MODEL THAT
SERVES TO -- CAN BE USED TO
WRITE TEXT AND GENERATE IMAGES,
MIGHT HAVE USED DALI, FOR
INSTANCE, FOR THIS PURPOSE.
THIS MODEL WAS PRE-TRAINED,
ADAPTED IN THE CONTEXT OF
fMRI.
AND SO WE HAVE THIS
COMPOSABILITY WHERE WE CAN TAKE
POWERFUL MODELS AND INTEGRATE
THEM INTO LARGER MODELS FOR
BRAIN DECODING.
INDEED IN THIS CASE THE FIELD
HAS TREMENDOUSLY IMPROVED OVER
TIME.
THESE WERE STATE-OF-THE-ART
IMAGES TWO YEARS AGO.
THESE CAME OUT LAST YEAR.
AND THIS WAS WHAT WAS PRESENTED
IN THIS CURRENT PAPER, SO AS YOU
CAN SEE SEMANTIC CONTENT IS
PRESERVED, WE'RE STARTING TO SEE
MORE DETAILS.
YOU MIGHT COME UP WITH THE
CONCLUSION WE NEED TO THROW THE
DATA INTO A BIG PILE OF DATA AND
LINEAR ALGEBRA AND STIRRING THE
PILE UNTIL WE GET THE RIGHT
RESULT BUT OF COURSE THESE
MODELS ARE NOT MAGIC, AND THEY
RELY ON SCALING LAWS THAT NEED
EXPONENTIAL AMOUNTS OF DATA FOR
LINEAR INCREASES AND
PERFORMANCE, SLOPE READING
MATTERS, ILLUSTRATED BY THIS
RECENT PAPER THAT SHOWED THE
PERFORMANCE OF DIFFERENT
BRAIN-BASED IMAGING PHENOTYPE
PREDICTIONS FOR COMPUTATIONAL
PSYCHIATRY.
THEY SHOW THEY REQUIRE
PROHIBITIVELY LARGE SAMPLES TO
MAKE THIS WORK.
THEY USE U.K. BIOBANK DATA, AND
WHAT THEY DID IS TO TAKE DTI
DATA, fMRI, STRUCTURAL, TO
PREDICT COVARIATES ALSO
RECORDING IN DATASETS AND
EXTRAPOLATED TO FIGURE OUT WHAT
WOULD BE THE PERFORMANCE IF WE
HAD A MUCH LARGER DATASET THAN
EVEN WHAT WAS AVAILABLE, WHICH
IS, YOU KNOW, MORE OR LESS
50,000 PEOPLE.
AND IT'S NOT GOOD.
IT'S NOT PRETTY.
APART FROM AGE AND SEX,
OBVIOUSLY MUCH EASIER WAYS TO
GET TO THIS INFORMATION.
BUT WHAT I WANT TO SHOW HERE IS
PREDICTION ACCURACY FOR
DEPRESSION, FOR BALANCE ACCURACY
ACTUALLY.
WITH A SMALL NUMBER OF SAMPLES
ABOUT TO GET TO . 52 PREDICTION
ACCURACY, WITH A MILLION THEY
COULD INCREASE ACCURACY TO .6,
QUITE BAD COMPARED TO
ALTERNATIVE, WHICH IS A PIECE OF
PAPER.
SO, IN SUMMARY, THERE ARE MANY
USE CASES FOR FOUNDATION MOD
UNLESS NEUROSCIENCE, SINCE
UNSUPERVISED LEARNING IS MORE
SCALABLE THAN TRADITIONAL
SUPERVISED LEARNING, AND WE CAN
THEN FINE TUNE ON SMALL SCALE
DOWNSTREAM DATA FOR USE CASES WE
CARE ABOUT.
THERE'S IMPRESSIVE EXAMPLES OF
NEURAL DECODING, CREATING A
COMPOSABLE LEGO BLOCK MODEL OF
DEVELOPMENT WHICH CAN ACCELERATE
SCIENCE POTENTIALLY BUT IT'S NOT
A PANACEA.
SCALING LAWS NEED TO BE ON ONE
SIDE IN THIS COMPUTATIONAL
PHENOTYPING STUDY, AS WE SAW.
BRIEFLY I'LL SPEAK ABOUT SOME
ETHICAL CONSIDERATIONS FOR THIS
KIND OF MODEL.
AND I DO WANT TO SEPARATE THIS
DISCUSSION IN TERMS OF NUMBER
ONE BASIC SCIENCE, ANIMAL
RESEARCH, WHERE I THINK THE
BALANCE OF THE OUTCOMES IS
MOSTLY POSITIVE.
LESS ANIMALS INVOLVED IN
RESEARCH BECAUSE WE HAVE GOOD
DECODING MODELS, POTENTIALLY
FASTER ITERATION TIMES.
BUT OF COURSE WE'RE ALSO
INTERESTED IN CLINICAL
APPLICATIONS WHERE IT CREATES
ALL THE COMPLEXITIES OF HUMAN
SUBJECTS RESEARCH OF COURSE.
NOW, CLASSICALLY, PEOPLE HAVE
CONSIDERED ETHICAL
CONSIDERATIONS OF MACHINE
LEARNING AND NEUROSCIENCE, DORIS
MENTIONED SOME OF THEM,
FAIRNESS, BIAS, DATA OWNERSHIP,
CONSENT, PRIVACY, SECURITY,
AUTONOMY, AGENCY, THESE STILL
REMAIN WITH FOUNDATION MODELS.
YOU ABOUT A TOP OF THAT WE DO
ADD A NEW SET OF CONCERNS WHICH
ARE RATHER UNIQUE TO THIS
SITUATION OF FOUNDATION MODELS.
I THINK THE LARGEST ONE IS
INSCRUTABLEITY, HARD TO
UNDERSTAND WHY THE MODELS DO THE
THINGS THEY DO, IT BECOMES
DIFFICULT TO CREATE
COMPREHENSIVE EVALUATIONS OF
THESE MODELS.
A SECONDARY ISSUE HERE WHICH IS
EQUITY OF ACCESS TO DATA TOOLING
AND INFRASTRUCTURE.
WE RISK MARGINALIZING ACADEMIC
RESEARCH IF, YOU KNOW, WE NEED A
BIG GPU CLUSTER TO DO RESEARCH
ON THIS KIND OF MODELING, WHICH
RAISES SOME ISSUES ABOUT
AUDITING EXISTING MODELS OUT IN
THE WILD.
MANY OF THE MODELS THAT WE NOW
HAVE ACCESS TO ARE OPEN WEIGHTS
BUT NOT OPEN SOURCE SO WE DON'T
KNOW EXACTLY THE TRAINING DATA,
WHICH IS INSIDE OF THEM, NOR THE
WAYS IN THIS THEY WERE TRAINED.
MANY ACADEMIC LABS WITH NOT WELL
EQUIPPED TO FULLY UNDERSTAND AND
TRAIN THESE MODELS FROM SCRATCH,
WHICH LEADS THE MODEL TRAINING
TO THE HANDS OF MORE WELL
RESOURCED ENTITIES AND
COMPANIES.
AND WE MIGHT NEED TO CONSIDER
NEW FUNDING MODELS IN ORDER TO
SUPPORT THE CREATION AND
EVALUATION OF THESE KINDS OF
FOUNDATION MODELS.
SO I HAVE ANOTHER SLIDE.
I WILL SKIP IT BECAUSE I'M OUT
OF TIME.
I HAVE A LOT OF CONTENT THAT I
WASN'T ABLE TO GO THROUGH.
I WROTE THIS UP AS A BLOG POST
ON NEUROAI.SCIENCE WHERE YOU CAN
HAVE THE LINKS THAT I MENTIONED
HERE.
THANK YOU FOR HAVING ME.
I LOOK FORWARD TO YOUR
QUESTIONS.
>> WONDERFUL.
THANK YOU SO MUCH.
THANK YOU TO ALL THREE OF YOU,
JOE, DORIS, PATRICK.
I HOPE YOU DON'T MIND I USED
YOUR FIRST NAMES.
I'LL SEE IF ANYBODY HAS
QUESTIONS FOR THE GROUP.
AND I REALLY APPRECIATED THAT
LAST PART ABOUT DISTINGUISHING
BASIC SCIENCE FROM CLINICAL
APPLICATION BEFORE WE GET INTO
CLINICAL APPLICATION, I'LL SEE
IF PEOPLE HAVE QUESTIONS.
ANY QUESTIONS FROM THE GROUP?
>> I WANT TO ADD MY THANKS.
THAT WAS REALLY VERY
INFORMATIVE, GREAT WAY TO SET
THE STAGE FOR THE LATER
DISCUSSIONS.
AND FOR THOSE, PATRICK MENTIONED
A BLOG POST.
I ALWAYS TRY TO ENCOURAGED MY
STUDENTS IN MY TEACHING DAYS,
ALWAYS GOOD TO READ ASSIGNMENT
AHEAD OF THE LECTURE.
I DID THAT LAST NIGHT.
IT'S VERY INFORMATIVE.
AGAIN, THANK YOU FOR BEING HERE.
ANY QUESTIONS?
DR. KOROSHETZ?
>> HI.
MAYBE TANGENTIAL BUT GETS TO THE
POINT OF MAYBE ONE OF THE ISSUES
BEING WHAT PEOPLE WOULD ALLOW
THE DATA TO BE USED FOR, AND
BECAUSE THE DATA IS GOING INTO
THIS, YOU KNOW, GIGANTIC BANK,
PEOPLE ARE HAVING, YOU KNOW,
MULTIPLE DIFFERENT AGENDAS ON
FOLKS WHO ARE EXPLORING THIS
DATA, YOU KNOW, IF YOU HAD YOUR
DRUTHERS, WE TALKED ABOUT THIS
BEFORE, YOU KNOW, YOU WOULD WANT
TO HAVE SOMEBODY BE ABLE TO OPT
OUT OF THE RESEARCH BUT WHAT
WOULD THAT MEAN?
YOU SEND THEM AN E-MAIL AND THEY
SAY, I DON'T WANT MY -- I DON'T
REALLY WANT MY DATA USED FOR
THIS MODEL?
BUT IT'S OKAY FOR THAT MODEL.
SO THAT SEEMS THE RIGHT THING TO
DO BUT NOT FEASIBLE.
THE QUESTION COMES UP, WHETHER
OR NOT YOU COULD COLLECT DATA
FROM PEOPLE THAT WOULD THEN BE
ABLE TO BE PHUT PUT IN A.I. MOL
THAT WE TICKETS -- PREDICTS WHAT
KIND THEY WANT THEIR DATA TO BE
IN, WONDERING IF THERE'S AN A.I.
SOLUTION TO OUR CONSENT PROBLEM.
>> DO ANY OF THE SPEAKERS -- I
THINK OUR FIRST CASE MIGHT
INTRODUCE SOME RESPONSES TO THAT
QUESTION.
LET'S SEE IF ANY OF THE SPEAKERS
WHO WENT SO FAR WANT TO RESPOND
TO THAT.
>> I CAN SAY A FEW WORDS.
I DO THINK THAT WE HAVE TO BE
CAREFUL ABOUT HOW WE DESIGN
CONSENT FOR THIS KIND OF MODEL.
I DON'T KNOW THAT IT'S A GREAT
IDEA TO DIRECTLY TRY TO READ
THIS INFORMATION FROM THE BRAIN.
THAT SEEMS ALSO RIFE WITH
ETHICAL CONCERNS.
WHEN WE'RE DOING INFORM CONSENT,
IT IS VERY DIFFICULTY THINK FOR
PATIENTS TO FULLY GRASP WHAT
THEY ARE ACTUALLY SIGNING UP
FOR.
AND AS WE'VE SEEN IN THE
DEPLOYMENT OF DIFFERENT PRIVACY
LAWS FOR INSTANCE IN EUROPE,
WE'VE SEEN THAT GIVING CONSENT
TO SOMETHING AS SIMPLE AS
WHETHER ONE USES COOKIES OR NOT
ON A WEBSITE IS A COMPLICATED
ORDEAL.
WHEN YOU ADD THE EXTRA FACTOR OF
THE PRIVACY OF BRAIN DATA THAT
BECOMES VERY DIFFICULTY THINK
FOR A LAYPERSON TO MAKE INFORMED
CONSENT IN AN AUTOMATIC WAY.
>> THANKS.
WE'LL TALK ABOUT THAT MORE.
TIM?
>> HI.
I WANTED TO THANK THE THREE
SPEAKERS FOR THE PRESENTATIONS,
REALLY INFORMATIVE.
I HAVE A QUESTION, MORE FOR
PATRICK, CERTAINLY ANYONE IS
WELCOME TO COMMENT.
THAT'S ABOUT YOUR COMMENT ABOUT
INSCRUTABILITY OF MODELS AND
DATA GENERATED.
WHAT ARE ARE SOME WAYS OF
GETTING PAST THAT
INSCRUTABILITY, IS IT POSSIBLE
TO PUT IN STANDARDS HOW THE DATA
IS DEVELOPED AND INTERPRETED?
IS IT IMPORTANT TO DO THE
ANALYSIS FROM TWO DIFFERENT
APPROACHES, TWO A.I. APPROACHES,
IT'S GOING TO BE ALMOST
IMPOSSIBLE TO GO BACK AND DOUBLE
CHECK THESE TYPES OF DATA
OUTPUTS.
>> YEAH, ABSOLUTELY.
IN TERMS OF INSCRUTABILITY,
OFTENTIMES THE DATA IS CLOSED.
WHEN IT'S HUMAN SUBJECTS
RESEARCH AND BRAIN DATA, CONSENT
CONTAINS SENSITIVE HEALTH DATA,
GOOD REASONS TO PUT THAT DATA
BEHIND CLOSED DOORS.
BUT OF COURSE THAT MAKES IT
HARDER FOR RESEARCHERS TO THEN
AUDIT THESE MODELS, SO IF THE
DATA IS NOT AVAILABLE THAT'S
VERY HARD TO DO.
BUT EVEN IN THE CASES WHERE DATA
IS OPEN AND AVAILABLE IT IS VERY
HARD TO SEE WHAT'S INSIDE OF
THESE LARGE SCALE MODELS.
SO LLAMA WAS TRAINED, A LARGE
LANGUAGE MODEL FROM META, WAS
TRAINED ON 15 TRILLION TOKENS OF
TEXT, RIGHT?
SO JUST TO GIVE YOU AN IDEA, I
CALCULATED THAT THE LARGEST
MODEL COMMONLY READ, IT'S ABOUT
TWO MILLION TOKENS.
HIS LIFE'S WORK, EIGHT MILLION
PEOPLE'S WORK IN THIS DATASET,
NO HUMAN IS ABLE TO LOOK AT THIS
DATA ONE AFTER ANOTHER.
PEOPLE ARE MOVING IN THE FIELD
OF LARGE LANGUAGE MODELS TOWARDS
MECHANISTIC
INTERPRETABLEABILITY.
IT'S NEUROSCIENCE ON ARTIFICIAL
NEURAL NETWORKS, IT'S REALLY
EMERGING AS AN INTERESTING AREA
WHAT IS THE LOADING, HOW MUCH
DOES THAT INFLUENCE THE WEIGHTS
OF THE MODEL?
OR CAN WE CONTROL A MODEL IN A
SPECIFIC WAY?
AND I THINK KEEPING AN EYE ON
THIS RESEARCH IS VERY VALUABLE.
WE'VE SEEN A COUPLE INSTANCES
WHERE THERE HAS BEEN A DIALOGUE
BETWEEN NEUROSCIENTISTS AND
ARTIFICIAL INTELLIGENCE
RESEARCHERS.
I'M THINKING OF A PAPER BY GRACE
LINDSEY AND DAVID BOW THAT WE
SHOULD TEST THE TOOLS OF
NEUROSCIENCE ON A.I. AND MAYBE
BRING THAT BACK TO NEUROSCIENCE.
SO IT IS THE CASE THAT THERE
MIGHT BE SOME POSITIVE OUTCOMES
FROM LIKE REALLY ENGAGING IN
THIS RESEARCH ON, YEAH, BREAKING
APART MODELS AND FIGURING HOW
THEY WORK THE SAME WAY WE DO
NEUROSCIENCE ON REAL BRAINS.
>> INTERESTING.
>> OTHER QUESTIONS?
GO AHEAD, JOE, PLEASE.
>> I WANT TO ADD THERE ARE SOME
RECENT APPROACHES INCLUDE
ENSEMBLE MODELS, YOU HAVE A
SENSE OF HOW THEY ARE IN
CONFLICT OR CAN BE RECONCILED
AND SO IN THAT WAY YOU CAN
MITIGATE SPURIOUS VISIBILITY,
SOMETIMES INSCRUTABILITY IS A
PROBLEM BUT THERE'S SPURIOUS
SCRUTABILITY, MAY NOT BE
GROUNDED TO ACTUAL DATA, WE NEED
TO KEEP PEOPLE IN THE LOOP WITH
PROVENANCE AND DATA CONTROLS
LIKE WE DO FOR SHARING
ECOSYSTEMS.
>> THANK YOU.
TOR?
>> TERRIFIC TALKS, THANK YOU SO
MUCH.
THIS IS A BROAD QUESTION BUT I
WANT TO PUT IT OUT THERE.
THE MESSAGE THAT I'M GETTING IS
THAT IT'S A COMBINATION OF DATA,
SKILLS, COMPUTE RESOURCES, YOU
NEED ALL THOSE THINGS.
THAT'S THE KEY.
WE WANT THE RIGHT PEOPLE TO HAVE
ACCESS TO ALL THOSE RESOURCES TO
DO THINGS THAT ARE IN THE PUBLIC
INTEREST.
BUT OF COURSE IF PEOPLE -- IF
EVERYBODY HAS ACCESS TO ALL
THESE THINGS THERE WOULD BE
CRAZY THINGS THAT WOULD HAPPEN,
A LOT OF THE WRONG STUFF, USED
FOR THE WRONG PURPOSES.
SO I FEEL LIKE ONE OF THE BIG
PICTURE QUESTIONS FOR ME IS HOW
DOES ONE OR HOW DOES, YOU KNOW,
NIH CREATE SOMETHING WHERE ALL
THOSE RESOURCES ARE COMING
TOGETHER FOR WORK IN THE PUBLIC
INTEREST BUT, YOU KNOW, FOR
ACADEMIA BUT THAT ARE NOT MAYBE
SHARED IN THE WRONG WAYS.
>> WHEN YOU SAY CREATE SOMETHING
YOU MEAN LIKE GUIDANCE OF SOME
SORT?
>> WELL, YEAH.
>> A MECHANISM?
>> THINGS LIKE STRUCTURES AND
TRAINING PROGRAMS TO CREATE
ACCESS, LARGE FEDERATED
DATASETS.
>> RIGHT.
>> WHERE PEOPLE HAVE ACCESS TO
THOSE, THEY HAVE TRAINING TO,
YOU KNOW, GAIN SKILLS, SORT OF
RAPIDLY EXPANDING SPACE, ACCESS
TO COMPETE RESOURCES WHICH MOST
ACADEMIC RESOURCES DON'T HAVE ON
THE SCALE OF LARGE COMPANIES.
>> THANKS.
KAREEM?
>> HI.
THANK YOU FOR THIS FANTASTIC
PRESENTATION.
NICE TO SEE YOU, PATRICK.
I HAD A QUESTION RELATED TO
SCALING BECAUSE I THINK THIS IS
SOMETHING THAT'S VERY IMPORTANT
BECAUSE WE'RE SEEING THIS WITH
NLP BEFORE, THERE WAS A LEAP IN
THE PERFORMANCE WITH SCALING.
THE DYNAMICS OF SCALING ARE
SOMETIMES PREDICTABLE, CAN BE
ESTIMATED AS YOU SHOWED TOWARDS
THE END BUT SOMETIMES THEY ARE
NOT.
FOR EXAMPLE FACE TRANSITIONS
WHEN YOU GO UP WITH SCALE, ALL
OF A SUDDEN YOU HAVE A HUGE LEAP
IN PERFORMANCE, THINGS THAT ARE
SUPER DIFFICULT TO PREDICT.
MY POINT TO THE NEURO-A.I. SIDE
OF THINGS DEVELOPING MODELS FOR
NEUROSCIENCE, PUT YOUR BRAINS ON
THIS, IT ALSO HAS ETHICAL
IMPLICATIONS.
IN OTHER WORDS IF WE BUILD
MODELS THAT WORK AT A GIVEN
SIZE, MANAGEABLE SAY IN
ACADEMIA, BUT AS YOU POINT OUT
SOME UPSCALING MIGHT NOT BE
DOABLE IN THE FRAMEWORK OF A LAB
OR INSTITUTION, MIGHT REQUIRE
MORE RESOURCES, THAT THINGS THAT
MAYBE HIGH TECH COMPANIES, BIG
TECH COMPANIES HAVE ACCESS TO AT
THIS POINT.
IF WE DO BUILD THESE MODELS WE
WOULD LIKE TO PREDICT HOW WELL
THEY WILL PERFORM AT SCALE.
BECAUSE THAT'S VERY DIFFICULT TO
DO, MIGHT BE DECISIONS MADE IN
TERMS OF FUNDING, FOR EXAMPLE,
OF WHAT TYPE OF RESEARCH SHOULD
BE FUNDED BECAUSE IT IS LIKELY
TO LEAD TO PROGRAM SHIFT OR REAL
IMPROVEMENT IN PERFORMANCE, SO
HOW DO YOU SEE THINGS EVOLVING
SPECIFICALLY IN THE NEUROSCIENCE
CONTEXT NOW WITH THESE MODELS
AND HOW THEY SCALE?
>> YEAH, THAT'S A GREAT QUESTIO.
SO I THINK GENERALLY MODELS, WE
CAN PREDICT VERY WELL, BASED OFF
OF THE AMOUNT OF DATA, HOW IT IS
THAT LIKELIHOOD OR GOODNESS OF
FIT OF THE MODEL WILL EVOLVE AS
WE INCREASE THE NUMBER OF
PARAMETERS, AS WE INCREASE SIZE
OF THE DATA, AS WE INCREASE THE
SIZE OF THE COMPUTE.
FOR THE PRE-TRAINING TASK, WHAT
IS MUCH HARDER IS TO PREDICT
WHAT'S GOING TO HAPPEN ON THE
DOWNSTREAM TASKS, RIGHT?
AND THAT REQUIRES A KIND OF LEAP
OF FAITH, IF YOU WILL.
SO, THIS IS A PROBLEM THAT HAS
BEEN, YOU KNOW, DOCUMENTED IN
THE A.I. FIELD SO JUST TO GIVE
ONE EXAMPLE, IF YOU'RE ASKING
HOW GOOD IT WILL BE AT
PREDICTING THE NEXT TOKEN, MARY
HAD A LITTLE SOMETHING, YOU CAN
PREDICT THAT VERY WELL BY
TRAINING EVER LARGER MODELS AND
SEEING HOW THE LIKELIHOOD
CHANGES AS A FUNCTION OF THIS
MODEL SIZE.
BUT IF YOU'RE ASKING YOURSELF
HOW WELL WILL AN LLM BE ABLE TO
MULTIPLY TWO DIFFERENT NUMBERS
TOGETHER, TWO DIFFERENT LARGE
NUMBERS, THAT'S A LOT HARDER TO
PREDICT.
AND UNFORTUNATELY WE DON'T HAVE
GREAT WAYS OF PREDICTING SCALE
MODELS.
PEOPLE ARE TRYING TO DO THAT
BECAUSE THEY HAVE TREMENDOUS
INCENTIVES FROM MONETARY
PERSPECTIVE TO PREDICT HOW WELL
THIS MODEL WORKS ON SOME
ESOTERIC DOWNSTREAM TASKS.
IT FELT AN OPEN FRONTIER BUT WE
CAN DO SMALLER SCALE
EXPERIMENTS, PREDICT HOW WELL
WE'LL DO ON INITIAL PASS, IT'S
POSSIBLE TO CREATE SCALING LAWS
FOR DOWNSTREAM TASKS BUT
UNFORTUNATELY IF YOUR DOWNSTREAM
TASK IS ACCURACY OF .1% WITH
LARGEST MODEL, LARGEST DATASETS,
IS IT GOING TO GO THIS WAY OR
THIS WAY OR THIS WAY, IS IT
FLAT?
IT'S VERY HARD TO SEE.
>> THANKS.
CAROLYN, I'M GOING TO ASK YOU TO
HOLD UNTIL LATER BECAUSE WE'RE
BEHIND SCHEDULE.
I WANT TO GET US INTO THE FIRST
CASE, I HOPE YOU CAN RAISE YOUR
QUESTION LATER.
SO, OUR FIRST CASE IS GOING TO
BE ABOUT DEEP PHENOTYPING IN
MENTAL HEALTH FOR PSYCHIATRY
WHICH BRINGS SOME OF THIS
TOGETHER IN TERMS OF THINKING
ABOUT BOTH APPLICATIONS OF SOME
OF THESE MODELS THAT WE'VE HEARD
ABOUT YOU ALSO THE REAL HUMANS
INTERACTING IN NATURAL WORLD AND
REAL WORLD DATA.
I'LL ASK SUSAN WRIGHT TO
INTRODUCE SPEAKERS AND LET'S
JUMP IN.
>> FIRST SPEAKER IS DR. JUSTIN
BAKER, SCIENTIFIC DIRECTOR,
DIRECTOR OF THE LABORATORY FOR
FUNCTIONAL IMAGING AND
BIOINFORMATICS McQUEEN
HOSPITAL, ASSOCIATE PROFESSOR AT
HARVARD MEDICAL SCHOOL, USING
LARGE SCALE STUDIES AND DEEP
MULTI-LEVEL PHENOTYPING
APPROACHES TO UNDERSTAND NATURE
AND UNDERLYING BIOLOGY OF MENTAL
ILLNESS.
SHOULD I INTRODUCE THE SECOND
SPEAKER?
I'LL TAKE A PAUSE.
>> SURE.
>> GO AHEAD, DR. BAKER.
>> LET ME SHARE MY SCREEN HERE.
WELL, I'M JUSTIN BAKER.
THANK YOU FOR THE OPPORTUNITY TO
JOIN THIS INTERESTING SESSION
TODAY.
SO I'LL TALK ABOUT THIS IDEA OF
DEEP PHENOTYPING, GOING MUCH
BEYOND THE MORE SUPERFICIAL
CHARACTERIZATION OF PATIENTS
WITH MENTAL HEALTH CONDITIONS
AND MY COLLEAGUE DR. SILVERMAN
WILL FOLLOW FLESHING OUT ETHICAL
CONSIDERATIONS, SOME OF WHICH
HAVE ALREADY BEEN TOUCHED ON
THIS AFTERNOON.
SO WHERE WE START IS THINKING
ABOUT HOW DO WE MEASURE
SOMEONE'S MENTAL HEALTH.
HOW DO WE DETERMINE SOMEONE
MIGHT BE EXPERIENCING A MENTAL
HEALTH CONDITION?
AS WE'VE HEARD ALREADY WE CAN DO
THIS WITH MULTIPLE BIOLOGICAL
VARIABLES, THERE'S THE NOTION OF
PEOPLE WHO HAVE A CONDITION,
MEME WHO DON'T HAVE A CONDITION,
SO-CALLED CASES OF CONTROLS BUT
IN THE REAL WORLD THIS IS A MUCH
MORE COMPLICATED TRANSFORMATION,
MANY TYPES OF PSYCHIATRIC CASES,
THIS NOTION OF CONTINUUM FROM
HEALTH INTO DISEASE, AND FINALLY
THE NOTION OF COMORBIDITY, SAME
INDIVIDUAL MILD EXPERIENCE MANY
MENTAL HEALTH CONDITIONS AT THE
SAME TIME, AND SIMPLY PLOTTING
TWO OR EVEN TEN BIOLOGICAL
VARIABLES AND SEE WHERE
INDIVIDUALS MAYBE SIT WITHIN
THAT PARAMETER SPACE IS AN
APPROACH WHICH HASN'T PANNED OUT
AS WE TRY TO PREDICT OR ESTIMATE
SOMEONE'S DIAGNOSIS OR MENTAL
STATE BASED ON SUCH MEASURES.
SO THE APPROACH THAT WE'VE
TAKEN, AND ALIGNS WITH THE
TOPICS THAT WE'VE BEEN
DISCUSSING SO FAR, IS REALLY TO
USE MULTI-MODAL MEASUREMENTS AND
LATENT CONSTRUCT MODELS TO
ASSESS MENTAL HEALTH CONDITIONS
AND BEHAVIORS.
AND SO THE TYPES OF MULTI-MODAL
MEASUREMENTS WE CAN TAKE ARE
TRADITIONAL SELF-REPORT SUCH AS
SURVEYS.
WE CAN PULL IN DATA FROM BRAIN
RECORDINGS, MRIs, BLOOD
SAMPLES, PULL IN DATA FROM THE
ELECTRONIC HEALTH RECORD, BUT AS
I'LL TALK ABOUT TODAY THERE'S
ALSO MANY OTHER TIMES OF DATA,
RAW DATA SOURCES ONE CAN BRING
IN TO INFORM SOMEONE'S MENTAL
STATE, WHETHER IT'S VIDEO,
RECORDINGS FROM MICROPHONES,
RECORDINGS FROM WEARABLES, PHONE
SENSORS, THESE ARE ALL DATA
SOURCES WHICH IN TODAY'S WORLD
ARE SOMEWHAT UBIQUITOUS AND CAN
INFORM UNDERLYING MENTAL TATE.
LIKE WITH BRAIN RECORDINGS ONE
HAS TO MOVE THROUGH RAW DATA
SOURCES, BUT IN INDIVIDUAL CASES
WE WANT TO DO INTELLIGENT
FEATURE EXTRACTION OF THOSE RAW
DATA SOURCES, SO WHETHER THAT'S
EXTRACTING BRAIN SIGNALS SUCH AS
FUNCTIONAL OR STRUCTURAL MRI,
GENOMES USING NATURAL LANGUAGE
PROCESSING TO PARSE ELECTRONIC
HEALTH RECORD OR APPROACHES WE
CAN DERIVE FROM COMPUTER SCIENCE
SUCH AS COMPUTER VISION TO
EXTRACT INFORMATION FROM FACES,
AND OTHER TECHNIQUES TO TRY TO
PULL AND MAKE SENSE OF RAW DATA
SOURCES.
AGAIN, THE IDEA IS TO BE ABLE TO
MAP THOSE FEATURES INTO LATENT
CONSTRUCTS OF INTEREST, HOW DO
WE INFER SLEEP OR CIRCADIAN
PATTERNS FROM THOSE PATTERNS?
INFER EXECUTIVE FUNCTION,
LANGUAGE FUNCTION, LEVEL OF
AFFECT REGULATION, STRESS
SENSITIVITY, SO ON.
WE BELIEVE WHERE FUTURE MENTAL
HEALTH ASSESSMENT IS GOING IS
THAT THIS COMPREHENSIVE
PHENOTYPING APPROACH WHERE WE
POOL DIFFERENT RAW DATA SOURCES
AND MAP THEM INTO LATENT
PSYCHIATRIC VARIABLES WILL ALLOW
US TO THEN INTERFACE WITH
SOFTWARE THAT SYNTHESIZES ACROSS
DIFFERENT CONSTRUCTS, DETECT
CHANGES WITH LEVEL OF
INDIVIDUALS, FEEDS THOSE CHANGES
AND SYNTHESES BACK TO
INDIVIDUALS WHETHER IT'S THE
INDIVIDUAL WHO IS AT THE CENTER
OF CARE, THE PERSON WITH THE
CONDITION, OR SOMEONE WITHIN
THEIR CARE NETWORK, WHETHER A
FAMILY NETWORK, PEER, DOCTOR, SO
ON.
AND WE SEE THIS AS AN EVOLVING
FIELD WITH EVOLUTION IN TERMS OF
HOW WE MEASURE THESE THINGS, BUT
ALSO JUST AS IMPORTANT IS HOW WE
THEN DELIVER THEM BACK TO
INDIVIDUALS IN WAYS THAT ARE
ETHICAL AND LEAD TO CLINICAL
IMPROVEMENT OVER TIME.
AND SO I'LL START BY BRINGING UP
ONE KEY AREA, CHARACTERIZING
DYADIC INTERACTIONS, THE BREAD
AND BUTTER OF MEDICINE, HOW A
PATIENT AND THEIR DOCTOR ARE
GOING TO BE SHARING INFORMATION.
ONE CHALLENGE OF THIS IS THAT
THE WAY THIS IS DONE IN TODAY'S
MEDICINE, ESPECIALLY PSYCHIATRY,
IT'S VERY MUCH SUBJECTIVE.
TRAINING TO CONDUCT DYADIC
INTERACTION FOR MEDICAL DATA
EXTRACTION IS IT TAKES MONTHS TO
YEARS, ON TOP OF THAT ACTUALLY
ADMINISTERING THESE INTERVIEWS
AND SCORING THEM OR BEING ABLE
TO INTERPRET THEM CAN TAKE LONG
PERIODS OF TIME.
AND THEY ARE NOT WELL SUITED FOR
CAPTURING ROBUST POINT ESTIMATES
OF SYMPTOM SEVERITY ESPECIALLY
WHEN YOU CONSIDER PSYCHIATRIC
CONDITIONS HAVE MULTIPLE
DIMENSIONS TO THEM IN TERMS OF
AFFECT, PSYCHOSIS, ET CETERA.
AND SO TO DO THIS AT SCALE IS
SOMETHING WHICH TRADITIONAL
HUMAN INTERACTIONS IS PROBABLY
NOT WELL SUITED, ENDS UP BEING
EXPENSIVE AND UNRELIABLE AS A
RESULT.
SO, ONE OF THE AREAS WE'VE
ATTEMPTED TO INNOVATE IS BRING
TOGETHER COMPUTER SCIENCE WITH
CLINICIANS TO TRY TO COME UP
WITH MORE INTELLIGENT WAYS OF
CAPTURING THIS.
AND SO I'LL JUST HIGHLIGHT ONE
EXAMPLE OF THIS IS COLLABORATION
THAT WE'VE HAD WITH A GROUP OUT
OF CARNEGIE MELLON UNIVERSITY,
ORIGINALLY OUT OF UNIVERSITY OF
SOUTHERN CALIFORNIA,
MULTI-SENSE.
IT TAKES OFF THE SHELF CAMERAS
AND MICROPHONES AND USES TOOLS
READILY AVAILABLE FROM COMPUTER
SCIENCE, THIS TECHNOLOGY HAS
BEEN AROUND FOR MORE THAN A
DECADE NOW.
CAN IN REAL TIME EXTRACT FACIAL
EXPRESSION DATA, POSTURE DATA,
GAZE, THE WAY SOMEONE IS
SPEAKING, AND BE ABLE TO INFER
AND PROVIDE INPUTS BACK TO
POTENTIALLY CLINICIAN WHO COULD
MAKE CLINICAL DECISIONS IN REAL
TIME BASED ON THESE SIGNALS.
SO WE USING THIS TECHNOLOGY
WORKED DIRECTLY WITH SOME
CLINICIANS AT MCLEAN HOSPITAL
WHERE I'M BASED TO TRY TO TAKE
INFORMATION FROM A VERY BRIEF
CLINICAL INTERACTION THAT WOULD
CONTAIN THE SAME AMOUNT OF
INFORMATION ONE MIGHT BE ABLE TO
GET FROM HOURS OF INTERVIEWS IN
A RESEARCH CONTEXT.
WE CONSTRUCTED SEMI STRUCTURE
INTERVIEW WHICH TOOK QUESTIONS
OTHER PROMPTS THAT CLINICIAN
MIGHT BE ABLE TO USE IN THE
COURSE OF THEIR STANDARD
CLINICAL EVALUATIONS, AND BE
ABLE TO THEN COMBINE THOSE
PROMPTS WITH FACIAL EXPRESSION
DATA FROM BOTH THE PATIENT AS
WELL AS THE CLINICIAN.
SO YOU CAN SEE ON THE BOTTOM
HERE A HEAT MAP SHOWING HOW
THESE INDIVIDUAL PATIENTS IN
THIS CASE AND CLINICIAN
RESPONDED OVER THE COURSE OF A
VERY BRIEF 10-MINUTE
INTERACTION, WHERE AT THE
BEGINNING OF THE INTERVIEW THE
PATIENT'S ALONE IN THE ROOM, AND
YOU CAN SEE THE TOP OF THE HEAT
MAP SHOWS THEY ARE DOING A LOT
OF BROW RAISING, POTENTIALLY
LOOKING AROUND THE ROOM.
AS SOON AS THE DOCTOR ENTERS THE
ROOM THE PATIENT BEGINS TO
SMILE, SHOWN WITH CERTAIN ACTION
UNITS, SHOWING HIGHER LEVELS OF
ACTIVITY.
JUST LIKE YOU MIGHT WITH A
NEUROSCIENCE EXPERIMENT, HAVE A
VENN-RELATED POTENTIAL, HERE THE
POTENTIAL WE'RE EVALUATING IS IN
THE FACE.
WHEN THE DOCTOR ENTERS, THE
PATIENT SMILES.
IN RESPONSE TO EACH QUESTION WE
GET NOT JUST WHAT THE PATIENT
SAYS IN RESPONSE TO PROMPTS BUT
HOW THEIR FACE RESPONDS.
INTERESTINGLY WE ALSO CAN SEE
EXACTLY HOW THE EVALUATOR
BEHAVES.
THIS BECOMES LIKE A BEHAVIORAL
EEG, USE WHATEVER KIND OF
STIMULUS PROBES WE WANT TO, TO
EVALUATE A PATIENT OR
PARTICIPANT, RESEARCH
PARTICIPANT.
WE CAN READ OFF OR EVENTUALLY BE
ABLE TO INTERPRET AND IN A SENSE
DO SOME OF THE KIND OF MIND
READING YOU WERE HEARING FROM
THE EARLIER PRESENTATIONS SIMPLY
BASED ON HOW SOMEONE'S FACE
RESPONDS OR OTHER PARTS OF THEIR
BODY MIGHT RESPOND TO AN
EVALUATION.
THESE KINDS OF TECHNOLOGIES ARE
CREATED BECAUSE WE ASSUME ANY
ONE CLINICIAN ISN'T GOING TO
NECESSARILY BE ATTUNED TO ALL
THE SUBTLETIES OF MOVEMENT AND
BEHAVIOR A PATIENT MY EXPERIENCE
OR MIGHT BE MANIFESTING, BUT WE
COULD DEVELOP SYSTEMS THAT
PERFORM AS WELL AS THE BEST
CLINICIAN ON THEIR BEST DAY.
AND SO THEN TO BRING IN ANOTHER
SET OF INFORMATION WE WANTED TO
APPLY THIS TO FOLLOWING SEVEN
INDIVIDUALS WITH A NUMBER OF
POTENTIAL PSYCHIATRIC CONDITIONS
OVER A LONG PERIOD OF TIME,
NATURALISTIC PERIOD OF TIME OVER
WHICH THEY MIGHT EXPERIENCE
SIGNIFICANT CHANGES IN THEIR
MENTAL HEALTH.
SO WE SOMETIMES REFER TO THIS AS
THE ONE HUNDRED PERSON-YEAR
STUDY BECAUSE THE IDEA WAS TO
STUDY ONE HUNDRED INDIVIDUALS
WHO EACH HAD A CONDITION SUCH AS
DEPRESSION, BIPOLAR DISORDER OR
PSYCHOTIC CONDITION AND TRACK
THEM OVER A PERIODS OF TIME
LIKELY TO EXPERIENCE SIGNIFICANT
SHIFTS IN MENTAL STATE AND ASK
THEM TO REPORT ON THEIR
SUBJECTIVE EXPERIENCES WITH
SELF-REPORTS, AND THEN ALSO MEET
PERIODICALLY WITH CLINICIAN AND
COMBINE THOSE SELF-REPORTS AND
CLINICIAN EVALUATIONS WITH
OBJECTIVE MEASURES WE CAN DERIVE
FROM WEARABLE DATA, PHONE SENSOR
DATA, BEHAVIORAL TASKS ON A
DEVICE.
AGAIN, THE IDEA HERE IS TO
UNDERSTAND WHICH OBJECTIVE AND
SUBJECTIVE MEASURES ARE
CORRELATING WITH ONE ANOTHER,
AND TO WHAT EXTENT THOSE
COHERENT SYSTEMS ARE DEPENDENT
PERSONALIZED VERY SPECIFIC TO
THE INDIVIDUAL, OR MORE
GENERALIZED ACROSS INDIVIDUALS.
I THINK AS YOU'LL SEE AS PART OF
THIS WORK THIS BRINGS UP ETHICAL
CONSIDERATIONS AS WE MOVE
TECHNOLOGIES INTO A CLINICAL
REALM.
SO, IN THIS STUDY WE HAD BROAD
ELIGIBILITY, PATIENTS COULD
BE -- ANY ADULT, THEY HAD TO OWN
A SMARTPHONE, HISTORY OF
FLUCTUATION IN MOOD OR
COGNITION.
SO I'LL TAKE YOU THROUGH THE
INDIVIDUAL PARTICIPANT TIMELINE.
PEOPLE WERE FOLD OVER A YEAR,
USING THIS PARTICULAR WEARABLE
DEVICE, PHONE APPLICATION ON
THEIR PHONE, PULLING CONTINUOUS
DATA FROM THE WEARABLE AND PHONE
SENSORS.
PHONE SENSORS INCLUDED LOCATION
DATA, HOW THEY USED THEIR PHONE,
AS WELL AS CALL AND TEXT
METADATA, NOT THE CONTEXT OF THE
CALLS, BUT SIGNIFICANT OR
NUMBERS THAT THEY ARE CALLING OR
TEXTING WITH FREQUENTLY.
THEY ALSO WERE PERFORMING DAILY
ASSESSMENTS ON THEIR PHONE IN
TERMS OF ANSWERS TO SURVEYS,
PROVIDED DAILY VOICE DIARIES TO
PROVIDE MORE COLOR ON WHAT
EXACTLY WAS GOING ON WITH THEM.
FINALLY THEY WOULD HAVE PERIODIC
CLINICAL ASSESSMENTS THE WAY YOU
MIGHT EXPERIENCE IN YOU'RE A
PATIENT FOLLOWING UP WITH A
CLINICIAN EVERY MONTH OR SO AS
PART OF A STANDARD CLINICAL
VISIT, AND AS MENTIONED WE WOULD
COLLECT BEHAVIORAL DATA TO
CAPTURE INFORMATION ABOUT THEIR
COGNITIVE PERFORMANCE.
WITH THESE RAW DATA TYPES WE CAN
EXTRACT MANY DIFFERENT FEATURE,
SOME I'VE ALREADY MENTIONED.
AGAIN THIS IS A COMBINATION OF
THINGS WE CAN COLLECT BOTH
ACTIVELY MEANING THINGS THAT
PATIENTS HAVE TO RESPOND TO IN A
WAY THEY ARE AWARE OF BUT ALSO
PASSIVELY, SO THINGS ALTHOUGH
PATIENTS CONSENT, THEY ARE NOT
ACTUALLY AWARE OF HOW WE -- WHEN
AND HOW WE'RE PULLING IN RAW
DATA OR PASSIVE SENSING DATA.
I WON'T GO INTO IT TOO MUCH
TODAY, WE THEN MAPPED THESE
FEATURES INTO A NUMBER OF LATENT
CONSTRUCTS SUCH AS ENERGY LEVEL,
SLEEP, PROCESSING SPEED WITH
POTENTIALLY MECHANISTIC LINKS TO
OCCURRENCE OF THESE CONDITIONS
OVER TIME.
AND SO I'LL TAKE YOU THROUGH A
COUPLE OF CASE -- SORT OF
EXAMPLES OF INDIVIDUALS,
PARTICIPANTS IN THE STUDY TO
FUEL THE DISCUSSION.
SO HERE I'M SHOWING EXAMPLE OF A
SINGLE PARTICIPANT FOLLOWED OVER
MORE THAN TWO YEARS. .
SOMEONE WITH REPEATED PERIODS OF
DENSE DEPRESSION.
SO, WHAT I'LL HIGHLIGHT HERE,
THIS IS ONE OF THE FIRST
EXAMPLES OF WHAT IT'S LIKE IN
TERMS OF LINKING -- WE ALL HAVE
IDEAS POTENTIALLY OF WHAT
SOMEONE WITH DEPRESSION
EXPERIENCES BUT HERE IS AN
EXAMPLE OF DAY-TO-DAY CHANGES
THAT SOMEONE MIGHT SHOW.
SO YOU CAN SEE AT THE BEGINNING
OF THE STUDY THIS PARTICULAR
INDIVIDUAL WAS ENDORSING A LOT
OF POSITIVE VALENCE REPORTS,
ENERGETIC, SO ON.
A HUNDRED DAYS IN STARTED
REPORTING FEELING VERY NEGATIVE,
IRRITABLE, ASHAMED.
YOU CAN SEE THE NATURAL COURSE
OF DEPRESSION IN THIS
INDIVIDUAL.
YOU CAN SEE AFTER ABOUT DAY 200
SHE SHIFTS BACK TO POSITIVE
STATE, AGAIN TO NEGATIVE STATE,
SO ON.
YOU CAN SEE EPISODES BECOME
SHORTER OVER THE COURSE OF THE
STUDY.
I WILL MENTION OUR RESEARCH TEAM
WAS NOT TREATING THIS INDIVIDUAL
SO THIS WAS AN OBSERVATIONAL
STUDY.
SO WE'RE NOT COMMENTING ON THE
CAUSAL NATURE OF WHY SHE CHANGED
YOU ABOUT WE CAN NEVERTHELESS
STUDY HOW THIS INDIVIDUAL
SUBJECTIVELY AND OBJECTIVELY
VARIABLES WERE CHANGING.
WE CAN LINK UP THIS INDIVIDUAL'S
SELF-REPORT WITH
CLINICIAN-ADMINISTERED REPORT SO
MADRAS IS A COMMON DEPRESSION
RATING SCALE.
WHEN SHE BECOMES VERY SUBJECTIVE
DEPRESSED HER MADRS SCORE GOES
UP, BUT NOT SURPRISINGLY AS
THESE EPISODES BECOME MORE
FREQUENT TOWARDS THE LATTER
PART, WE LOSE THE GRANULARITY IN
HER MOOD SYMPTOMS BASED ON
CLINICIAN-RATED SYMPTOMS.
AS WE LINK REPORTS, I'M SHOWING
DATA EXTRACTED FROM THE WEARABLE
SHE WAS USING THROUGHOUT THE
STUDY.
AND WHAT WE'RE SEEING IN THESE
HEAT MAPS IS A REPRESENTATION OF
ACTIVITY, SO WHEN SHE'S AWAKE
YOU SEE THESE HOT COLORS, REDS
AND ORANGES.
WHEN SHE'S ASLEEP YOU CAN SEE
THE COOLER COLORS, THE BLUES.
WHAT'S APPARENT IN GOING THROUGH
THESE IS THAT WHEN SHE BECOMES
DEPRESSED, A NUMBER OF CHANGES
OCCUR IN HER SLEEP AND ACTIVITY.
ONE, YOU CAN SEE HERE IN THIS
SECOND PANEL OF THE HEAT MAPS,
HER SLEEP BECOMES LONGER, SO
SHE'S WAKING UP CONSIDERABLY
LATER IN THE MORNING, AS
COMPARED WITH FEELING WELL.
ALSO YOU'LL NOTICE IF YOU CAN
SEE THE COLORS HERE THAT WHILE
SHE'S AWAKE SHE'S ACTUALLY
CONSIDERABLY LESS ACTIVE DURING
HER DAY THAN WHEN FEELING WELL.
THIS AGAIN CONFORMS TO OUR
CLINICAL UNDERSTANDING OF
DEPRESSION WHICH IS THE IDEA OF
PSYCHOMOTOR SLOWING SO WHEN
INDIVIDUALS BECOME PROFOUNDLY
DEPRESSED THEY OFTEN HAVE VERY
REDUCED LEVELS OF ENERGY AND
JUST SIMPLY DON'T MOVE AS MUCH.
THEY MAY APPEAR TO SPEAK SLOWER,
MAY FEEL SLUGGISH DESPITE AS IN
THIS WOMAN'S CASE DESPITE
SLEEPING LONGER PERIODS OF TIME.
SO FROM THESE KINDS OF DATA WE
CAN THEN EXTRACT LATENT
CONSTRUCTS SUCH AS OVERALL LEVEL
OF ENERGY THAT THIS PERSON'S
MANIFESTING, SO WE CAN TAKE
SIMPLE DIFFERENCE BETWEEN HER
SLEEPING ACTIVITY AND WAKING
ACTIVITY TO COMPUTE ESSENTIALLY
LIKE A DAY-BY-DAY ENERGY
BALANCE, SO FOR INSTANCE IF
YOU'RE SLEEPING A LOT AND NOT
MOVING VERY MUCH, NORMALLY THAT
WOULD MEAN YOU HAVE AN EXCESS OF
ENERGY, WHEREAS IF YOU'RE NOT
SLEEPING VERY MUCH AND YOU ARE
VERY ACTIVE, THAT WOULD TEND TO
BUILD UP A DEFICIT IN YOUR
ENERGY BALANCE.
AND SO BASED ON THIS WE CAN
COMPUTE THIS LATENT ENERGY LEVEL
OR ENERGY BALANCE WHICH
INTERESTINGLY WHEN WE DO THIS IT
SHOWS THERE ARE THESE INFLECTION
POINTS IN HER ENERGY LEVEL BASED
EXCLUSIVELY ON THE ACTIVITY AND
SLEEP PATTERNS BUT INTERESTINGLY
ARE MATCHED CLOSELY WITH THE
SUBJECTIVE CHANGE SO WHEN YOU
SEE INFLECTION POINT IN ENERGY
BALANCE WE'VE COMPUTED BASED ON
ACTIVITY, IT CORRESPONDS
DIRECTLY WITH WHEN THIS PERSON
WAS EXPERIENCING THIS SUBJECTIVE
CHANGE.
YOU CAN IMAGINE THAT WHILE THIS
IS STILL VERY MUCH THEORETICAL,
YOU CAN IMAGINE THIS COULD BE
USED EVENTUALLY TO PREDICT WHEN
SOMEONE IS ABOUT TO ENCOUNTER A
CHANGE IN THEIR SUBJECTIVE
REPORTS AND POTENTIALLY USE THAT
TO PROACTIVELY TREAT SOMEONE FOR
DEPRESSION.
SO ANOTHER SOURCE OF DATA WHICH
WE THINK IS PROFOUNDLY USEFUL IN
THE COURSE OF EVALUATING
PATIENTS WITH MENTAL HEALTH
CONDITIONS IS LOCATION.
WE ALL MOVE THROUGH OUR LIVES
AND WHERE WE GO OFTEN DETERMINES
NOT JUST WHO WE'RE WITH BUT WHAT
KIND OF ACTIVITIES WE'RE
EXPERIENCING.
AND SO AS YOU MIGHT EXPECT WHEN
WE BEGIN TO MEASURE PEOPLE'S
LOCATION IN REAL TIME THAT
BRINGS UP A HOST OF POTENTIAL
ETHICAL ISSUES.
SO HERE I'M SHOWING ONE EXAMPLE
OF A PARTICIPANT WHO WAS
FOLLOWED FOR OVER TWO YEARS WITH
ALL THESE SAME MULTI-MODAL
MEASUREMENTS, AND IN THIS
EXAMPLE WHAT I'M SHOWING IS,
AGAIN, AS WITH THE PRIOR PLOTS,
WE'RE SEEING A 24-HOUR PERIOD OF
THIS PERSON'S LOCATION.
AND THEN EACH COLUMN SHOWS A
SINGLE DAY OF DATA.
WE'RE SHOWING HERE THE GRAY
LOCATION IS THE MOST COMMON
LOCATION THAT THIS PERSON
EXPERIENCES, WHICH IS THEIR HOME
LOCATION.
AND SO NOT SURPRISINGLY THIS
HOME LOCATION IS WHERE THEY ARE
SPENDING MOST NIGHTS.
IT'S WHERE THEY ARE SLEEPING.
YOU'LL NOTICE A NUMBER OF OTHER
LOCATIONS WHICH ARE -- WE CAN
COMPUTE DIRECTLY FROM GEOSPATIAL
DATA.
SO THERE'S A NUMBER OF
INTERESTING LOCATIONS THAT COME
OUT OF THIS PERSON'S LOCATION
TRACES.
ONE IS YOU'LL SEE THIS PATTERN
OF BLUE THAT WE SEE, SO A FEW
WEEKS INTO THE STUDY THIS PERSON
GOES TO ANOTHER LOCATION AND
STAYS THERE, SLEEPS THERE FOR
AROUND TEN DAYS, AND THEY GO
BACK TO THAT SAME LOCATION,
SEVERAL MONTHS LATER, AND YOU
CAN SEE THAT SAME LOCATION, THE
BLUE LOCATION, IS OCCURRING
SPORADICALLY THROUGH THE TIME
SERIES.
I WOULD NORMALLY OPEN UP IN CASE
ANYONE HAS GUESSES ABOUT THAT
LOCATION MIGHT REPRESENT, YOU
MAY HAVE THOUGHT ABOUT CLINICAL
CONTEXT WHAT A PLACE LIKE THIS
MIGHT MEAN FOR A PATIENT LIKE
THIS.
I'LL JUST SAY THIS IS A
HOSPITALIZATION, SO WE CAN
ACTUALLY INFER THAT NOT ONLY
FROM THE EXACT GEOSPATIAL
LOCATION, THE FACT THAT WE KNOW
WHERE THEY WERE GOING BUT EVEN
THE PATTERN OF WHERE THIS PERSON
IS GOING WE KNOW THAT THIS
PERSON IS ATTENDING THE HOSPITAL
DURING THE WEEK FOR THERAPY AND
CLINIC VISITS.
WE KNOW THEY ARE GOING TO THEIR
ALCOHOLICS ANONYMOUS MEETINGS IN
THE THINKS, AT THAT SAME
LOCATION, AND WE CANNOT JUST
INFER BUT ACTUALLY DEDUCE
DIRECTLY FROM THE GEOSPATIAL
DATA THAT THIS PERSON WAS AT THE
HOSPITAL AND WAS HOSPITALIZED
DURING THE TWO PERIODS.
THE NEXT LOCATION, SET OF
LOCATIONS TO DRAW YOUR ATTENTION
TO WAS THE TEAL LOCATIONS AT THE
BEGINNING.
SO, THIS IS A LOCATION, THIS
PERSON IS SPENDING TIME AT
PERIODICALLY THROUGH THE WEEK.
AND IF I SHOW YOU THIS AS A
FUNCTION OF DAY OF WEEK, WE CAN
SEE THIS IS A LOCATION THIS
PERSON IS SPENDING THEIR TIME ON
MONDAYS AND FRIDAYS FOR THE
FIRST PART OF THE STUDY, AND
THEN SORT OF DROPS OFF IN THE
MIDDLE OF THE STUDY, AND SO
BECAUSE OF THE TIMING THE PERSON
GETS TO THAT LOCATION AROUND
9:00 A.M., LEAVES AT 4:00 P.M.,
NOT HARD TO IMAGINE THIS IS A
PART-TIME WORK LOCATION.
WE CAN DEDUCE WHEN AND HOW OFTEN
THIS PERSON MAKES IT TO WORK,
CAN TELL WHEN THEY ARE LATE TO
WORK, WHEN THEY ARE MISSING WORK
FOR SOME REASON.
WE CAN ALSO SEE WHEN THIS PERSON
STOPS GOING TO WORK, SO WE CAN
DIRECTLY DETERMINE TIMES WHEN
THIS PERSON IS NOT ENGAGED IN
WORK OR SCHOOL.
WE CAN ALSO SEE THAT THERE'S
THIS PERIOD OF TIME IN THE
MIDDLE WHEN THIS PERSON STOPS
GOING TO WORK AT ALL AND THEY
ARE GOING BACK AND FORTH BETWEEN
THE HOME LOCATION AND THIS
HOSPITAL LOCATION.
AND YOU CAN SEE TOWARDS THE END
OF THE STUDY THIS PERSON STARTS
TO GO TO A DIFFERENT PART-TIME
LOCATION, AS SHOWN IN THE BLACK.
AND SO THE FINAL SET OF
LOCATIONS I WANT TO DRAW YOUR
ATTENTION TO IS THIS ORANGE
LOCATION.
THIS IS A LOCATION THAT ALSO
THIS PERSON IS GOING TO,
SPENDING THE NIGHT AT, UNLIKE
THE HOSPITALIZATION LOCATION
THEY ARE SPENDING A FEW DAYS AT
A TIME AND GOING BACK TO THE
HOME LOCATION AND GOING BACK TO
THAT OTHER LOCATION.
AND SO AGAIN YOU MIGHT HAVE
THOUGHTS ABOUT WHAT THAT
LOCATION MIGHT REPRESENT.
BUT THIS IS A SIGNIFICANT OTHER,
SO THIS PERSON DEVELOPS A
SIGNIFICANT OTHER RELATIONSHIP,
WHICH INITIALLY THEY ARE
SPENDING THAT TIME IN THE
EVENINGS, AND THEN EVENTUALLY
THAT RELATIONSHIP GRADUATES TO
BEING MAYBE MORE SERIOUS
RELATIONSHIP, SO WE CAN SEE THAT
PERSON GOING TO THE SIGNIFICANT
OTHER RELATIONSHIP FOR A NUMBER
OF WEEKS, AND WE CAN SEE PERIODS
OF TIME WHEN THE RELATIONSHIP
MAYBE GOES AWAY FOR A PERIOD OF
TIME AND TOWARDS THE END OF THE
STUDY MAYBE PICKS UP AGAIN.
AGAIN SIMPLY BY REVIEWING
LOCATION TRACES WE CAN ACTUALLY
BEGIN TO SEE WHAT THIS PERSON'S
LIFE MIGHT BE LIKE.
THE WAY WE CAN THINK ABOUT THIS
IS THAT THIS IS A WAY OF
COLLECTING AUTOMATED MINIMALLY
BURDENSOME CONTINUOUS
ASSESSMENTS OF COMMUNITY AND
SOCIAL FUNCTIONINGS IN
INDIVIDUALS WITH SEVERE MENTAL
ILLNESS OR SEVERE PSYCHIATRIC
CONDITIONS.
THIS IS SEVEN INDIVIDUALS.
THIS ONE DIFFERS FROM THE
INDIVIDUAL BELOW IT WITH A
SPARSER SET OF LOCATIONS THAT
THEY VISIT.
AND SO WE COULD ACTUALLY BE ABLE
TO TELL REALLY WHAT ANYONE ON
THIS CALL'S LIFE LOOKS LIKE
BASED ON THE LOCATIONS THEY
ATTEND, OVER A PERIOD OF TIME AS
WELL AS SIGNIFICANT LIFE CHANGES
SUCH AS THIS PERSON WHO SHOWS
CHANGES IN WHERE THEY LIVE OVER
THE COURSE OF THE STUDY.
BECAUSE WE WERE TALKING ABOUT
INITIATIVES, WE'VE DONE THE SAME
WORK WITH INDIVIDUALS WHO HAVE
IMPLANTED BRAIN RECORDING
DEVICES.
THIS WAS A PROJECT PART OF THE
BRAIN SYNCHRONIZATION EFFORT.
THIS EFFORT WAS FOLLOWED OVER 18
MONTHS, SAME SELF-REPORTED AND
PASSIVE DATA, THEY HAD A DEVICE
IMPLANTED IN THE VENTRAL
STRIATUM, AS WELL AS IN THEIR
MEDIAL FRONTAL CORTEX WHERE WE
CAN BEGIN TO SEE NOT JUST WHERE
THEY ARE MOVING AND HOW THEY
FEEL BUT ALSO GET A
REPRESENTATION OF WHAT THEIR
BRAIN IS DOING AT THOSE
DIFFERENT TIMES.
AGAIN, THE CHALLENGE AND THE
OPPORTUNITY WITH THIS IS THAT
THESE KINDS OF DATA CAN ACTUALLY
BE DIRECTLY USED TO PREDICT AND
ESTIMATE CURRENT AND POTENTIALLY
FUTURE BEHAVIOR SIMPLY ON BASIS
OF RECOGNIZING PATTERNS AND
BEING ABLE TO HAVE PATTERNS OF
SUBJECTIVE AND OBJECTIVE DATA
AND LINK THOSE UP WITH NEURAL
DATA TO BE ABLE TO HAVE FULL
APPRECIATION OF NEURAL SYSTEMS
DRIVING THOSE BEHAVIORAL
CHANGES.
SO SIGNIFICANT PART OF THIS WORK
IS REALLY GEARED TOWARDS MOVING
BEYOND THE CARICATURE-BASED
ASSESSMENTS OF PSYCHIATRIC
CONDITIONS, SOMETHING THAT'S
MUCH MORE COMPUTATIONALLY
DEDEVELOPED, LEVERAGING
CONSTRUCTS AND ARTIFICIAL
INTELLIGENCE TO INFER MENTAL
STATES, BOTH CURRENT AND FUTURE
MENTAL STATES, IN WAYS THAT
COULD GUIDE TREATMENT PLANNING
FOR INDIVIDUALS WITH A RANGE OF
COMPLEX PSYCHIATRIC CONDITIONS.
AGAIN THIS BRINGS UP A HOST OF
ETHICAL CONSIDERATIONS, WHICH
WE'VE DONE A NUMBER OF PRIOR
SYMPOSIA ON THIS WORK.
IF YOU'RE INTERESTED, TO READ
MORE, I CAN DROP THIS,
POTENTIALLY SHARE THIS LINK WITH
OTHERS ON THIS VIDEOCAST.
BUT ALSO DR. SILVERMAN WILL GO
THROUGH SOME OF THESE IN MORE
DETAIL IN THE NEXT PRESENTATION.
>> THANKS, DR. BAKER, A GREAT
OVERVIEW OF DEEP PHENOTYPING IN
PSYCHIATRY.
WE'LL HOLD OFF ON QUESTIONS
UNTIL AFTER DR. SILVERMAN, NOW
GIVING A SUMMARY OF ETHICAL
ISSUES.
SENIOR IRBs CHAIR OF MASS
GENERAL BRIGHAM, ALSO THE
DIRECTOR OF ETHICS FOR MCLEAN
INSTITUTE OF INDUSTRY, CHAIR OF
THE EMBRYONIC STEM CELL RESEARCH
OVERSIGHT SUBCOMMITTEE,
ASSISTANT PROFESSOR OF
PSYCHIATRY AT HARVARD, RESEARCH
ETHICS IN PARTICULAR PERTAINING
TO RESEARCH CONDUCTED WITH
VULNERABLE POPULATIONS.
>> THANK YOU FOR THE
INTRODUCTION AND HAVING US TALK
ABOUT THIS, APPRECIATE THE
OPPORTUNITY, HOPE THIS DOVETAILS
WITH THE OTHER FURTHER TALKS.
PSOAS DR. BAKER DESCRIBED, I'M
GOING TO TALK MORE ABOUT SOME OF
THE ETHICAL ISSUES THAT COME UP
WITH DEEP PHENOTYPING, THIS
PARTICULAR PSYCHIATRIC RESEARCH.
WE'VE HEARD ABOUT SOME ALREADY
TODAY.
BUT JUST FOR THE SAKE OF TIME TO
HAVE SOME DISCUSSION I'M GOING
TO KEEP THIS AT A FAIRLY HIGH
LEVEL.
BUT THE BIG ISSUES THAT COME UP
REALLY RELATE TO INFORMED
CONSENT, PRIVACY AND
CONFIDENTIALITY, EQUITY
DIVERSITY AND ACCESS
PARTICULARLY WITH ALGORITHMIC
BIAS IN USING A.I. AND ML
MODELS, RETURN OF INDIVIDUAL
RESEARCH RESULTS, DUTY TO WARN
OR DUTY TO REPORT, AMONG MANY
OTHERS.
I WANT TO MAKE A QUICK NOTE HERE
ON TERMINOLOGY.
WE FOUND AS WE'VE DONE THIS WORK
THAT THERE'S A LOT OF DIFFERENT
PHRASES PEOPLE USE THAT I THINK
FOR OUR PURPOSES TODAY WE CAN
SAY ARE MEANING THE SAME THING.
WE USE THE PHRASE DEEP
PHENOTYPING, YOU SEE IN ONE OF
THE PAPERS WE USE THE PHRASE
DIGITAL PHENOTYPING.
WE ALSO HEAR THE PHRASE AND USE
THE PHRASE COMPUTATIONAL
PHENOTYPING.
THERE ARE PEOPLE WHO MIGHT ARGUE
THAT ONE OF THESE WORDS, ONE IS
JUST PHONE DATA OR ONE IS JUST A
DIFFERENT TYPE OF DATA BUT FOR
THE PURPOSE OF ETHICS TALKS
THINK OF THESE AS FAIRLY
INTERCHANGEABLE.
WE'VE WRITTEN A COUPLE PAPERS
ABOUT THIS, REALLY HAVE HAD THE
GREAT PLEASURE OF WORKING WITH
DR. BAKER AND ALSO CLOSELY WITH
FRANCIS SHEN WHO YOU MAY KNOW,
AND THE PLEASURE OF HAVING DR.
GRADY AND OTHERS PROBABLY HERE
TODAY PARTICIPATING IN SYMPOSIA
AND WORKSHOPS AND FUNDING FROM
NIH.
WE'VE PUBLISHED A COUPLE PAPERS.
THIS IS ONE WHICH CAME OUT OF
SOME OF OUR WORK THAT WAS TRYING
TO CREATE ETHICS CHECK LIST FOR
PEOPLE DOING THIS TYPE OF WORK
WITH THE IDEA THAT THERE REALLY
ARE NOT NECESSARILY RIGHT OR
WRONG ANSWERS AT THIS STAGE OF
THE GAME BUT THE KEY WAS TRYING
TO GET RESEARCHERS WHO WANTED TO
DO THIS TYPE OF WORK TO BE
THINKING ABOUT THESE ISSUES
BEFORE STARTING THEIR RESEARCH
STUDIES.
SO WE CREATED A SERIES OF
QUESTIONS IN EACH OF THE DOMAINS
I MENTIONED EARLIER, I PUT IT UP
HERE.
THIS IS ONE OF THE PAPERS THAT
WAS CIRCULATED TO YOU IN
ADVANCE.
I APPRECIATE THERE'S TOO MANY
WORDS ON THIS SLIDE FOR YOU TO
POTENTIALLY READ IT.
I WANT TO SHOW WHAT IT LOOKS
LIKE, ESSENTIALLY WHAT WE'RE
ASKING RESEARCHERS TO DO, TO
THINK ABOUT EACH OF THESE
DOMAINS AND GO THROUGH A SERIES
OF QUESTIONS TO SAY YES OR NO,
HAVE I CONSIDERED THESE ISSUES,
WITH THE GOAL THAT REALLY YOU
GET TO YES FOR ALL OF THESE OR
AT LEAST GET TO PENDING BEFORE
YOU COULD FEEL COMFORTABLE KIND
OF PROCEEDING WITH YOUR STUDY IN
THE MOST ETHICAL WAY POSSIBLE.
I'LL GO THROUGH SOME IN MORE
DETAIL TODAY, ALTHOUGH AS I SAID
WE WON'T HAVE TIME TO TOUCH ON
EVERY SINGLE ONE.
I THINK INFORMED CONSENT IS THE
ONE THAT WE SPEND THE MOST TIME
TALKING ABOUT, THIS CAME UP IN
EARLIER TALKS THIS MORNING.
JUST TO SAY QUICKLY THAT IF WE
THINK ABOUT THE HISTORY OF
PSYCHIATRIC ETHICS OR BASIC
FRAME FOR HOW WE THINK ABOUT
PSYCHIATRIC CARE OR
PSYCHOTHERAPY, THERE'S A
TRADITION IN PSYCHIATRY,
PSYCHOLOGY, PSYCHOANALYSIS, KIND
OF OUR TRADITION IN OUR
PSYCHIATRIC ETHICS, THAT
BASICALLY ALL OF THE INFORMATION
THAT WE OBTAIN CAME FROM THE
PERSON SITTING ACROSS FROM US.
AND YOU CREATED THIS FRAME OF
TREATMENT WHERE EVERYTHING WAS
SUPPOSED TO HAPPEN IN THE ROOM,
WITH THE PATIENT, AND THAT TIMES
OF REENACTMENTS YOU WOULD HAVE
IN THE ROOM WERE REFLECTIVE OF
THEIR LIFE OUTSIDE THE ROOM, AND
THAT WAS BY HAVING THOSE
REENACTMENTS AND TRANSFERENCE
AND COUNTER-TRANSFERENCE YOU
WOULD HELP THEM CHANGE THEIR
BEHAVIOR.
SO WE STARTED FROM A PLACE WHERE
BASICALLY ESSENTIALLY THERE WAS
A FORBIDDANCE OF EXTERNAL
INFORMATION I WOULD SAY.
OVER TIME OBVIOUSLY IT BECAME
RELEVANT AND IMPORTANT TO HAVE
COLLATERAL INFORMATION ABOUT OUR
PATIENTS.
SO WE WOULD OFTEN GET EXTERNAL
INFORMATION ABOUT OUR PATIENTS.
AND PSYCHIATRY AND PSYCHOLOGY
AND PSYCHIATRIC ETHICS HAD TO
ADAPT TO THAT.
HEY, WHAT DO YOU DO WHEN SOMEONE
CALLS YOU AND PROVIDES YOU WITH
INFORMATION ABOUT A PATIENT OR
WHAT DO DO YOU, ANY OF US WHO
TREATED PATIENTS LONG ENOUGH
HAVE BEEN IN THESE SITUATIONS,
WHEN YOU GET A FIVE-PAGE LETTER
FROM A PATIENT'S FAMILY MEMBER
THAT'S DESCRIBING ALL OF THE
STUFF YOU DIDN'T KNOW ABOUT WHAT
WAS GOING ON.
SO WE HAD TO GRAPPLE WITH HOW DO
WE DEAL WITH THIS EXTERNAL
COLLATERAL INFORMATION.
I SAY THAT JUST TO SAY I THINK
WHAT WE'RE TALKING ABOUT IN THE
FRAME OF PSYCHIATRIC CLINICAL
ETHICS IS REALLY JUST AN
EXPANSION OF THAT, HOW DO WE
DEAL WITH ALL OF THIS EXTERNAL
INFORMATION WE'RE NOW GETTING,
SOME OF WHICH IS COLLECTED
PASSIVELY, AS DR. BAKER SAID.
THE MAJOR DIFFERENCE THAT I
THINK ABOUT FROM LIKE A PURE
ETHICS STANDPOINT IS BASICALLY
THE IDEA OF AWARENESS OF
INFORMATION SHARING, RIGHT?
SO FOR MOST OF THESE EXAMPLES
THAT I'M GIVING IN THE PAST YOU
WOULD ASK FOR RELEASE OF
INFORMATION, YOU WOULD TELL
SOMEONE I'M GOING TO TALK TO
SO-AND-SO, I'M GOING TO GET
INFORMATION, MAYBE YOU WOULD TRY
TO HAVE THE PATIENT THERE TO BE
ABLE TO HEAR EXACTLY WHAT'S
SAID.
THERE'S REALLY A DIRECT HANDOFF.
IN OUR NEW WORLD THERE'S NO
DIRECT HANDOFF, INFORMATION
COMING FROM EVERYWHERE, ALL AT
ONCE, SO THE ABILITY TO KNOW
WHAT IS SHARED AND HOW THE
INFORMATION IS OBTAINED AND HOW
IT'S BEING USED IS VERY, VERY
DIFFERENT FROM THE PAST.
SOME RISKS I THINK THAT COME
FROM THAT, THE POTENTIAL RISKS
FROM DEEP PHENOTYPING, IS THE
IDEA OF DISCLOSING UNWANTED OR
UNINTENDED INFORMATION.
SO ANYONE WHO WALKS INTO A
DOCTOR'S OFFICE OR PSYCHIATRIC'S
OFFICE OR PSYCHOLOGISTS OFFICES
MAKE CHOICES CONSCIOUSLY OR
UNCONSCIOUSLY ABOUT WHAT THEY
CHOOSE TO SHARE.
MAYBE THERE'S INFORMATION THEY
DIDN'T WANT SHARED.
THERE COULD BE INACCURATE
INFORMATION.
YOU KNOW, DR. BAKER DIDN'T TALK
ABOUT IT AS MUCH BUT OFTEN THIS
DEEP PHENOTYPING INCLUDES
PULLING INFORMATION FROM SOCIAL
MEDIA, OTHER TIMES OF
INFORMATION, ESPECIALLY NOW MAY
BE WRONG, OR LIKE THE PUPPIES
PLAYING IN THE SNOW MAY BE FAKE.
PERMITTING UNINTENDED OR
UNWANTED SHARING OF INFORMATION
SO, AGAIN, YOU MAY NOT ACTUALLY
WANT CERTAIN INFORMATION SHARED
IN DIFFERENT WAYS, AND ONCE YOU
OPEN THIS BOX IT'S HARD TO
CONTROL ALL OF THAT.
THERE'S SOME FEAR POTENTIALLY
ABOUT BECOMING DEPENDENT OR
OVERRELIANT ON USING THESE TYPES
OF ELECTRONIC COMMUNICATIONS TO
COMMUNICATE WITH PROVIDERS THAT
MAYBE YOU WOULD ASSUME YOUR
PROVIDER IS GOING TO CATCH
SOMETHING BECAUSE THEY ARE
COLLECTING INFORMATION SO YOU
DON'T NECESSARILY COMMUNICATE
CONCERNS THE WAY YOU
TRADITIONALLY WOULD.
CONFIDENTIALITY AND PRIVACY
BREACHES OF COURSE, AND THEN
POTENTIAL TO IMPACT TREATMENT OR
THERAPEUTIC RELATIONSHIP AND
WE'LL TALK MORE ABOUT THAT.
I WILL SAY AS WAS SAID IN MY
INTRODUCTION, MY DAY JOB IS AS
AN IRB CHAIR.
I SPEND TIME THINKING ABOUT
RISKS AND RESEARCH STUDIES AND
HOW WE CATEGORIZE RISKS.
ONE THING I SAY AS I TALK ABOUT
THESE ISSUES, I BRING UP
POTENTIAL RISKS AND THEORETICAL
PROBLEMS, WHAT WE DO GENERALLY I
WOULD SAY CONSIDER MOST OF WHAT
WE'RE TALKING ABOUT TO BE IN TE
CATEGORY OF MINIMAL RISK.
THE REASON IS THE FEDERAL
DEFINITION OF MINIMAL RISK
POSTED HERE IS THOSE THE SAME
ENCOUNTERED IN DAILY LIFE.
AT THIS POINT, MOST OF US ARE
CARRYING AROUND SMARTPHONE
DEVICES, OR HAVE OTHER SMART
APPLIANCES IN YOUR HOUSE THAT
ARE LISTENING TO YOU 24/7, THAT
THE IDEA OF COLLECTING THIS DATA
OF NOT NECESSARILY KNOWING WHERE
THE DATA IS GOING, AND OF GIVING
PERMISSION TO USE THAT DATA WHEN
YOU SIGN, WHEN YOU AGREE TO USE
A PHONE FOR THE FIRST TIME, YOU
KNOW, WE ALL DO THAT ON A
DAY-TO-DAY BASIS WITHOUT I WOULD
SAY NECESSARILY WHAT WE WOULD
TRADITIONALLY CONSIDER TRUE
INFORMED CONSENT.
SO WE'VE GENERALLY TAKEN THE
POSITION MOST OF THIS DATA
COLLECTION AND USE IS IN THE
REALM OF MINIMAL RISK, DOESN'T
MEAN WE SHOULD IGNORE IT.
THAT'S WHY WE'RE HERE TALKING
ABOUT IT BUT JUST TO FRAME IT
FOR THE GROUP IN TERMS OF HOW WE
THINK ABOUT IT.
FROM OUR CHECKLIST SOME
QUESTIONS, HOW CAN WE BE
MEANINGFUL COMMUNICATE AND
TRANSPARENT ABOUT ARTIFICIAL
INTELLIGENCE ANALYSIS AND
INTERPRETATION, SO WE'RE BAD AT
INFORMED CONSENT FOR RESEARCH IN
GENERAL I WOULD SAY.
YOU HAND SOMEONE A 50-PAGE
CONSENT FORM.
THERE IS A LIMIT TO WHAT PEOPLE
ARE GOING TO BE ABLE TO
UNDERSTAND.
AND I THINK THIS TYPE OF WORK
POSES UNIQUE PROBLEMS FOR
CONSENT IN THAT PEOPLE MAY NOT
KNOW WHAT THEIR PHONES ARE
ALREADY COLLECTING ON THEIR OWN,
RIGHT?
WHAT'S ALREADY HAPPENING.
AND THAT'S ONE OF THE OTHER
QUESTIONS HERE.
HOW MUCH ARE WE REQUIRED TO
PROVIDE BACKGROUND INFORMATION
ON WHAT'S ALREADY HAPPENING WITH
SOMEONE'S DATA, IF SOMEBODY
DOESN'T KNOW IT.
IN SOME WAYS IN ORDER TO ACCEPT
MINIMAL RISK DETERMINATION THAT
I'M TALKING ABOUT SOMEONE WOULD
HAVE TO UNDERSTAND WHAT'S
HAPPENING ALREADY, AND SO HOW
MUCH EDUCATION DO WE HAVE TO
PROVIDE.
AND THEN AS DR. BAKER ALLUDED TO
A LOT IS PASSIVE DATA
COLLECTION.
SO WE CONSENT PEOPLE UP FRONT
BUT WHAT DOES THAT MEAN IN TERMS
OF ONGOING KNOWLEDGE ABOUT
WHAT'S BEING COLLECTED.
WE THINK ABOUT OBVIOUSLY THE
STUDY POPULATION, STUDY
POPULATIONS WE WORK WITH OFTEN
AT TIMES HAVE IMPAIRED DECISION
MAKING CAPACITY AND WE HAVE TO
THINK ABOUT HOW WE HANDLE
CONSENT OVER TIME, WHEN
SOMEBODY'S CAPACITY MAY
FLUCTUATE, WHETHER WE NEED
SURROGATE CONSENT, AND WHAT A
REASONABLE PERSON WOULD WANT TO
KNOW AND HOW DO WE REACH THAT.
I WILL SAY THAT WE HAVE BEEN
THINKING ABOUT THESE ISSUES FOR
A WHILE NOW, AND MOST OF THIS IS
STILL NOT EMPIRIC.
SO MOST OF THIS IS STILL
QUESTIONS THAT WE'RE ASKING BUT
WE DON'T REALLY KNOW EXACTLY
WHAT THE EVIDENCE BASED ANSWERS
WOULD BE.
SOME ADDITIONAL QUESTIONS THESE
ARE BEYOND THE SCOPE OF WHAT WAS
IN OUR PAPER BUT THINGS THAT
COME UP FOR ME, YOU KNOW, HOW WE
HANDLE EXCULPATORY LANGUAGE,
OFTEN COLLECTING IN DEEP
PHENOTYPING REQUIRES USE OF
THIRD-PARTY APPLICATIONS OR
DEVICES, ANDS SO THOSE OFTEN
HAVE TERMS OF USE, END USER
LICENSE AGREEMENT WAS
EXCULPATORY LANGUAGE AND WE'RE
IN A CATCH 22, WE STRUCKEL --
STRUGGLE WITH THAT.
AS DR. BAKER SHOWED WITH THE
DATA, YOU KNOW, HE CAN TELL YOU
BASICALLY YOU'RE GOING TO THE
BATHROOM, THIS PERSON IS GOING
TO THE BATHROOM AT THIS TIME.
THIS PERSON IS PROBABLY HAVING
SEX AT THIS TIME.
SHOULD WE BE ACTUALLY MORE
EXPLICIT IN CONSENT FORMS ABOUT
WHAT WE CAN SEE?
WHERE THE DATA IS STORED AND HOW
IT'S GOING TO BE SHARED I THINK
IS OBVIOUSLY CRITICAL.
THERE ARE REAL RISKS I THINK TO
ENSURABILITY AND POTENTIALLY
CRIMINAL LIABILITY FROM SOME OF
THIS DATA.
YOU KNOW, BEING ABLE TO IDENTIFY
WHERE SOMEONE WAS AT A
PARTICULAR TIME, BEING ABLE TO
IDENTIFY THAT SOMEBODY IS
SMOKING CIGARETTES OR USING
ALCOHOL, OTHER THINGS THAT MAY
NOT HAVE PREVIOUSLY BEEN IN THE
MEDICAL RECORD THAT COULD AFFECT
INSURABILITY, ET CETERA.
ONE OF THE BIG ONES IS HOW OFTEN
DO WE HAVE TO RECONSENT PEOPLE.
WE KNOW WE HAVE DATA THAT SHOWS
THAT WHEN YOU ENROLL PEOPLE IN
THESE RESEARCH STUDIES THEIR
BEHAVIOR DOES CHANGE BRIEFLY
BASICALLY BUT THEN IT
ACCOMMODATES.
IT GOES BACK TO KIND OF BEING,
YOU KNOW, NORMAL AFTER PEOPLE
ADJUST TO A PERIOD OF BEING
MONITORED.
WHAT DOES THAT MEAN?
DOES THAT MEAN THEY FORGOT THE
DATA IS BEING COLLECTED AND NEED
TO BE RECONSENTED OR IS THAT A
NORMAL HUMAN ACCOMMODATION TO
BEING MONITORED AND THEY
UNDERSTAND THAT IT'S THERE AND
THEY HAVE JUST GOTTEN USED TO IT
AND GOTTEN BACK TO NORMAL
BEHAVIOR, AND THEN, YOU KNOW,
WHAT WE DO, YOU KNOW, CHANGES,
RIGHT?
YOUR PHONE WILL TELL YOU I NEED
TO DO AN UPDATE NOW, RIGHT?
AND SO SOFTWARE PROGRAMS ARE
CHANGING, THE WAY WE CAPTURE
DATA CHANGES, HOW WE INTERPRET
THE DATA CHANGES, IT'S OFTEN NOT
PER
PERCEPTIBLE TO THE PERSON.
PRIVACY AND CONFIDENTIAL, THESE
ARE SOME QUESTIONS WE ASK BEFORE
STARTING A RESEARCH STUDY.
HOW DO WE BALANCE ROBUST DATA
COLLECTION WITH TRYING TO
PROTECT PRIVACY, HAVE WE
CONSULTED WITH SECURITY EXPERTS
ABOUT DATA FLOW?
A LARGE PART OF I WOULD SAY
ETHICS OF PRIVACY AND
CONFIDENTIALITY IS KNOWING WHERE
THE DATA IS AT ALL TIMES
ESSENTIALLY.
SO WHEN WE CONSULT WITH OUR
INFORMATION SECURITY TEAMS ABOUT
THESE RESEARCH STUDIES, THAT'S
WHAT THEY DO, THEY BASICALLY
FOLLOW AND SAY THE DATA GOES
FROM HERE TO HERE TO HERE TO
HERE, AND SOMETIMES THAT'S LESS
CLEAR THAN OTHERS, RIGHT?
BUT REALLY TRYING TO UNDERSTAND
THAT AS BEST AS POSSIBLE.
MAKING SURE WE HAVE A POLICY
ABOUT DE-IDENTIFICATION IF
THAT'S EVEN POSSIBLE ANYMORE.
AND PRIVACY THAT'S CONSISTENT
WITH BEST PRACTICES, THINKING
ABOUT THIRD PARTIES AND HIPAA
COMPLIANCE.
I DON'T SPEND A LOT OF TIMES
SPEAKING ABOUT REGULATIONS
BECAUSE TODAY OF TIME BUT
THERE'S REGULATORY ISSUES
ESPECIALLY IF DOING
INTERNATIONAL WORK, YOU GET INTO
OTHER REGULATIONS AS WELL.
AND THINKING ABOUT MAKING SURE
THAT THE RESEARCH IS COMPLIANT
IS ALSO CRITICAL.
EQUITY, DIVERSITY, AND ACCESS,
SO REALLY THINKING ABOUT IF THE
RESEARCH IS GOING TO REPLICATE
EXISTING OR GENERATE NEW BIASED
RESULTS, CONTRIBUTE TO
INEQUITIES, BASE ON LEGALLY
PROTECTED CLASS, CRITICALLY
IMPORTANT.
WE THINK THAT REALLY ONE OF THE
KEYS HERE IS AT A VERY EARLY
STAGE INVOLVING DIVERSE
COMMUNITY OF STAKEHOLDERS, AND
SO REALLY ASKING PEOPLE TO PUT
THEIR NICKEL DOWN ON WHETHER
THEY HAVE SOUGHT INPUT ON
RESEARCH DESIGN FROM APPROPRIATE
STAKEHOLDERS TO ADDRESS EQUITY
AND JUSTICE CONCERNS.
MAKING SURE THE RESEARCH PLAN
HAS ADDRESSED POSSIBLE PROBLEMS
WITH ACCESS, TO THE
TECHNOLOGIES, AND MAKING SURE
THE RESEARCH TEAM IS FULLY
TRAINED AND KNOWLEDGEABLE ABOUT
THESE ISSUES.
ALGORITHMIC BIAS WAS MENTIONED
EARLIER SO BUT I THINK RELEVANT
TO MENTION AGAIN, YOU KNOW, ONE
OF THE BIG QUESTIONS HERE IS
ESSENTIALLY ARE DATASETS BEING
USED, ADEQUATE REPRESENTATIVE OF
THE POPULATION THAT'S GOING TO
USE THE MODEL THAT'S BEING
DEVELOPED, I THINK THIS IS WHERE
ALGORITHMIC BIAS CAUSES THE
BIGGEST PROBLEMS, WHEN YOU TRAIN
AN ALGORITHM ON ONE POPULATION
AND THEN YOU APPLY IT TO ANOTHER
POPULATION THAT WAS NOT INCLUDED
IN YOUR DATASET, THERE'S A HIGH
LIKELIHOOD THERE'S GOING TO BE
PROBLEMS THERE.
THIS IS A TANGENT BUT I'M GLAD I
INCLUDED THIS BECAUSE THIS CAME
UP EARLIER.
I THINK THERE'S A REAL TENSION,
YOU KNOW, ON THE ONE HAND WE I
THINK AS KIND OF CERTAINLY AS AN
IRB PERSON, AS ETHICS PEOPLE,
YOU KNOW, GENERALLY PROMOTE
CONSENT AS KIND OF BEING, YOU
KNOW, THE KEY FEATURE TO HOW TO
DO THIS WORK MOST ETHICALLY.
YOU KNOW, CONSENT, I THINK THE
CAM EXAMPLE GIVEN EARLIER SHOULD
PEOPLE CONSENT TO EXACTLY WHICH
MODELS THEIR DATA ARE GOING TO
BE USED TO TRAIN ON, AND WAS
ALSO SAID EARLIER, AND YOU NEED
ENORMOUS AMOUNTS OF DATA TO
REALLY TRAIN THESE ALGORITHMS
WHILE AGAIN ENORMOUS AMOUNTS OF
DIVERSE DATA TO BE ADEQUATELY
REPRESENTED AND NOT BIASED BUT
AS SOON AS YOU IMPLEMENT
REQUIREMENTS FOR CONSENT THERE'S
A LIKELIHOOD THAT YOUR DATABASE
IS POTENTIALLY GOING TO BE LESS
REPRESENTATIVE, AND THAT YOU'RE
ACTUALLY GOING TO END UP WITH
MORE BIAS DATA, MORE BIASED
ALGORITHMS, MORE DISCRIMINATION,
SO THERE'S A REAL TENSION THERE
AROUND HOY DO YOU DEVELOP
THOUGHTFUL UNBIASED ALGORITHMS
THAT ARE TRAINED ON EVERYONE'S
DATA, WHEN, YOU KNOW, IT'S A
CHALLENGE TO GET EVERYONE TO BE
WILLING TO AGREE TO PARTICIPATE
IN RESEARCH GIVEN, YOU KNOW, THE
HISTORY THAT WE HAVE.
AND HAPPY TO TALK MORE ABOUT
THAT INTERESTING ISSUE.
ONE OF THE QUESTIONS HAVE THE
TOOLS THAT ARE BEING USED FOR
DATA COLLECTION BEEN ASSESSED
FOR PREEXISTING BIAS SO IF
YOU'RE USING FACIAL RECOGNITION
TECHNOLOGY WHICH WAS MENTIONED
EARLIER, ARE YOU PULSE OXIMETR,
CHANCES ARE THAT DATA IS GOING
TO BE BIASED AND CAUSE PROBLEMS
IN YOUR ALGORITHM.
FINALLY HAVE YOU BUILT IN DATA
BIAS MONITORING, TESTING,
MITIGATION STRATEGIES THROUGHOUT
YOUR AI/ML DEVELOPMENT LIFE
CYCLE.
I'LL SPEND THE REST OF THE TIME,
WATCHING THE CLOCK HERE, TRYING
TO MOVE QUICKLY, RETURN OF
INDIVIDUAL RESEARCH RESULTS
WHICH IS AN INTERESTING TOPIC.
THIS IS AGAIN A PLACE WHERE I
HAD THE PLEASURE OF WORKING WITH
DR. SHEN, DR. BAKER, DR. GRADY
AND OTHERS HERE, TALKING ABOUT
RETURNING RESULTS, IN PSYCHIATRY
FROM WHAT DR. BAKER PRESENTED
THERE'S A DESIRE TO RETURN
RESULTS, YOU CAN ESSENTIALLY
PREDICT POTENTIALLY WHEN
SOMEONE'S ABOUT TO HAVE A MANIC
OR DEPRESSIVE EPISODE SO
WOULDN'T IT BE NICE TO USE THAT
DATA TO INTERVENE SOONER,
ESSENTIALLY THE GOAL HERE IN
DOING THIS WORK.
BUT THE PROBLEM WITH RETURNING
INDIVIDUAL RESEARCH RESULTS IT'S
REALLY COMPLICATED WITH THIS
WORK.
I THINK THE TOPIC OF INDIVIDUAL
RESEARCH RESULTS RETURN IS NOT
UNIQUE.
I THINK THIS IS NOT A
CATEGORICALLY UNIQUE PROBLEM BUT
POSES CHALLENGES DUE TO THE
AMOUNT AND TYPE OF DATA.
SO DR. BAKER HIGHLIGHTEDDED THIS
BUT YOU CAN SEE IN THIS BOX ON
THE SCREEN THERE'S A TON OF
TYPES OF DATA THAT IS COLLECTED
AND SO IT RAISES QUESTIONS
AROUND, YOU KNOW, HOW MUCH
YOU'RE GOING TO RETURN AND WHAT
YOU'RE GOING TO RETURN, HOW
YOU'RE GOING TO RETURN IT.
EXISTING ETHICAL FRAMEWORKS WERE
INSUFFICIENT TO GUIDE
RESEARCHERS ON THIS.
JUST TO THINK ABOUT AT A HIGH
LEVEL I THINK THE ISSUES
ESSENTIALLY ARE RELATED TO
UNCERTAINTY ABOUT ANALYTICAL
VALIDITY, CLINICAL VALIDITY,
ACTIONABILITY, AND PERSONAL
UTILITY, AGAIN INCLUDING
POTENTIAL FOR BIAS.
QUICKLY ANALYTIC VALIDITY
MEANING IS THE ALGORITHM
ACTUALLY FUNCTIONING THE WAY
IT'S SUPPOSED TO?
IS IT PICKING UP AND COLLECTING
THE DATA AND PROVIDING THE DATA
THE WAY IT'S SUPPOSED TO?
ARE THE NUMBER OF STEPS THAT'S
REPORTING SOMEONE TOOK ACTUALLY
ACCURATE TO THE NUMBER OF STEPS
THEY TOOK?
AND IN CLINICAL VALIDITY, IS IT
ACTUALLY INDICATIVE OF A
CLINICAL SCENARIO, RIGHT?
SO THERE MAY BE THE ALGORITHM
MAY PICK UP ON SOMETHING RELATED
TO HEART RATE VARIABILITY, AND
COULD THAT BE, YOU KNOW, SOMEONE
EXERCISING OR COULD THAT BE
SOMEONE HAVING A CARDIAC EVENT
OR SOMEONE HAVING SEX, RIGHT?
WHAT THE VALIDITY OF THE KINDS
OF CLINICAL SCENARIO IS HAS TO
BE VALID IN ORDER TO RETURN THE
RESULTS.
ACTIONABILITY AND PERSONAL
UTILITY BASICALLY IS THERE
SOMETHING YOU CAN DO ABOUT THE
RESULTS AND/OR WOULD SOMEONE
FIND THEM PERSONALLY USEFUL?
ONE OF THE ISSUES IS THERE'S SO
MANY STREAMS OF DATA, YOU CAN
RETURN IT AT MANY TIME POINTS,
BASICALLY CONSTANTLY RETURNING
DATA, SOME DATA IS GOING TO
REVEAL POTENTIALLY SOCIALLY
STIGMATIZED, POLICEICALLY
SENSITIVE DATA, SOMEONE MIGHT
HAVE BEEN AT THE SCENE OF A
CRIME, ESSENTIALLY.
WE DON'T KNOW HOW RETURNING
DIGITAL PHENOTYPING RESULTS WILL
IMPACT BEHAVIOR.
YOU KNOW, IN THINKING ABOUT THIS
WORK WE CAME UP WITH EXAMPLES
WHERE WE THOUGHT THAT RETURNING
RESULTS WOULD IMPACT PEOPLE IN
ONE WAY, IT ACTUALLY ENDED UP
BEING THE OPPOSITE.
QUICK EXAMPLE, A SIMPLISTIC
EXAMPLE BUT RETURNING DATA FROM
A SLEEP WEARABLE TO PEOPLE IN
MEDICAL TRAINING, AND YOU WOULD
THINK WHEN YOU PROVIDED THEM
WITH DATA THAT SHOWED THEY WERE
SLEEPING LESS THAN THEIR PEERS,
I'M SORRY, SLEEPING, YEAH, LESS
THAN THEIR PEERS, THAT THEY
MIGHT, YOU KNOW, WANT TO CATCH
UP ON SLEEP ESSENTIALLY, RIGHT?
BUT ACTUALLY IT OFTEN HAS
OPPOSITE EFFECT, AND IT'S NOT
SLEEPING AND WORKING HARD IS
OVERLY PRIZED IN THAT
POPULATION.
AND SO IT ACTUALLY FOR SOME
PEOPLE MADE THEM SLEEP EVEN
LESS.
IT CAN BE VERY HARD TO KNOW
EXACTLY WHAT THE CONSEQUENCES
WILL BE.
AND THEN FINALLY THERE'S A LOT
OF THIRD PARTY DATA THAT'S
COLLECTED IN THIS DATA.
SO THERE'S POTENTIAL RISKS TO
THIRD PARTIES.
THESE ARE THE QUESTIONS THAT WE
POSED.
I'LL MOVE THROUGH THIS QUICKLY
FOR TIME.
BASICALLY ASKING IF PEOPLE HAVE
THOUGHT ABOUT THESE ISSUES IN
ADVANCE OF STARTING THEIR WORK.
AT THE END OF THE DAY WE DID
COME UP WITH A FRAMEWORK TO TRY
TO GUIDE PEOPLE.
I THINK IT RAISES MORE QUESTIONS
THAN ANSWERS BUT WE TRIED TO
BALANCE RISKS AND BENEFITS,
RESPECTING PERSONS BOTH RESEARCH
PARTICIPANTS AND THIRD PARTIES,
TRIED TO PROMOTE JUSTICE IN
RETURNING DATA.
WE DO THINK KIND OF AS A DEFAULT
PRESUMPTION THAT WE SHOULD BE
TRYING TO RETURN SOME OF THIS
DATA TO PROMOTE JUSTICE.
AND SO WE ENDED UP SAYING
ESSENTIALLY THAT IF SOMETHING IS
HIGH BENEFIT AND LOW RISK THAT
WE DO THINK YOU SHOULD BE
RETURNING RESULTS, IF IT IS LOW
RISK AND HIGH BENEFIT -- I'M
SORRY, LOW BENEFIT AND HIGH RISK
YOU SHOULD START WITH
PRESUMPTION OF NOT RETURNING
RESULTS.
AND EVERYTHING ELSE IS SOMEWHERE
IN THE MIDDLE.
SO NOTHING GROUNDBREAKING THERE
BUT TRYING TO JUST GIVE PEOPLE A
WAY TO THINK ABOUT THIS AND
REALLY ASKING PEOPLE TO CONSIDER
IF YOU'RE GOING TO DO THIS WHO
IS GOING TO RECEIVE THE RESULTS
AND WHO IS GOING TO PROVIDE
RESULTS, WHERE AND HOW
FREQUENTLY AND IN WHAT FORMAT
WILL IT BE RETURNED.
DUTY TO WARN OR REPORT, THIS IS
AT THE INTERSECTION, LIMITATIONS
OF OUR DUTIES OF PRIVACY AND
CONFIDENTIALITY.
AND THE RETURN OF INDIVIDUAL
RESEARCH RESULTS.
BECAUSE IF YOU'RE GOING TO USE
THESE RESULTS TO TRIGGER A DUTY
TO WARN OR DUTY TO REPORT YOU
HAVE TO THINK THE RESULTS ARE
ANALYTICALLY AND CLINICALLY
VALID, ABLE TO BE RETURNED AS A
RESEARCH RESULT, AND SO
ENCOURAGING PEOPLE TO THINK
ABOUT THIS IN ADVANCE, WHEN ARE
YOU GOING TO REACH THE POINT
WHERE YOU FEEL LIKE YOUR METHODS
ARE VALIDATED ENOUGH YOU HAVE TO
RETURN TO WARN OR REPORT.
ALSO WHAT ARE YOU GOING TO DO
WHEN YOU UNCOVER THINGS LIKE
CHILD PORNOGRAPHY OR OTHER
THINGS WHEN YOU'RE COLLECTING
THIS DATA WHICH IS INEVITABLE IF
YOU COLLECT ENOUGH DATA.
SO JUST A FEW TAKEHOME POINTS, I
THINK OUR ESTABLISHED GUIDELINES
AND REGULATIONS REALLY ARE
INSUFFICIENT TO GUIDE
RESEARCHERS ON ETHICAL
IMPLICATIONS OF PHENOTYPING AND
RESEARCH.
WE THINK AGAIN THE KEY TAKEAWAY,
YOU HAVE TO THINK ABOUT THESE
THINGS IN ADVANCE.
WE'RE ENCOURAGING RESEARCHERS TO
MAKE ETHICAL CHOICES EXPLICIT AS
PART OF DESIGN AND COLLABORATING
WITH THEIR IRBs, RELEVANT
STAKEHOLDERS, ET CETERA.
AND REALLY AT THE END OF THE DAY
THERE ARE A RANGE OF REASONABLE
CHOICES THAT RESEARCHERS CAN
MAKE RIGHT NOW.
FOR EXAMPLE ON RETURNING
INDIVIDUAL RESEARCH RESULTS, YOU
CAN MAKE CHOICES TO DO IT OR NOT
DO IT AND THAT COULD BE VERY
REASONABLE BUT I THINK PEOPLE
SHOULD BE REQUIRED TO THINK THIS
OUT AND GIVE RATIONALE BEHIND
THEIR DECISIONS TO THEIR IRBs
AND OTHER OVERSIGHT BODIES.
AGAIN, PROBABLY RAISING MORE
QUESTIONS THAN ANSWERS BUT
HOPEFULLY A WHIRLWIND TOUR
THROUGH THE ETHICS OF SOME OF
THE WORK WE DO AND AGAIN THANKS
FOR EVERYONE'S ATTENTION.
>> THANK YOU.
I WANT TO OPEN THE DISCUSSION
FOR EVERYBODY ONLINE TO FOCUS ON
TWO POINTS.
ONE IS WHAT'S UNIQUE HERE IN
TERMS OF NEUROSCIENCE OR BRAIN
RESEARCH AND SOME OF THE ISSUES
THAT HAVE BEEN RAISED, AND I
THINK THERE'S SO MANY
INTERESTING ISSUES.
YOU'VE RAISED ALL -- ALL OF THE
SPEAKERS HAVE RAISED A NUMBER OF
THEM, REALLY GREAT, BUT FOCUSING
IN WHAT CAN WE THINK ABOUT IN
TERMS OF BRAIN RESEARCH AND
ARTIFICIAL INTELLIGENCE AND THAT
INTERSECTION?
AND I WANT TO START WITH THE
FIRST QUESTION I THINK FOR DR.
SILVERMAN.
YOU MENTIONED THAT, YOU KNOW, WE
SHOULD KNOW A LITTLE BIT MORE
ABOUT WHAT REASONABLE PEOPLE
WANT TO KNOW AND WHAT THEY ARE
WORRIED ABOUT IN THE PROCESS OF
CONSENT.
AND THAT WE SHOULD ALSO BE
EXPLAINING TO THEM WHAT'S GOING
TO HAPPEN TO THEIR DATA, ET
CETERA, TALKED ABOUT IT AS
COMPLEX CONTINUOUS SOMETIMES
PASSIVE A.I. MACHINE LEARNING
SYSTEMS, ET CETERA.
I GUESS MY QUESTION IS, IS IT
REALISTIC TO EXPECT PEOPLE TO
UNDERSTAND HOW THE A.I. AND
MACHINE LEARNING SYSTEMS THAT
THEIR DATA ARE EITHER BEING
INTERPRETED WITH OR FED INTO OR
BOTH, WHAT THAT MEANS TO THEM I
GUESS, AND IS THAT SOMETHING
THAT WE CAN OR SHOULD BE TRYING
TO ADDRESS THROUGH CONSENT
PROCESSES?
>> GREAT QUESTION.
I'M SURE EVERYONE HAS THOUGHTS
SO I'LL SUMMARIZE MY THOUGHTS.
I THINK -- SO I COME FROM AS YOU
ALL KNOW THE WORLD OF RESEARCH
ETHICS, AND SO WHEN I MADE MY
SLIDES AND KIND OF IN PARTICULAR
ORDER, REASONABLE PERSON
STANDARD, YOU KNOW, THINKING
ABOUT QUOTE/UNQUOTE ELEMENTS OF
CONSENT IT'S FOR RESEARCH
PARTICIPATION THAT I'M THINKING
ABOUT BUT THE INTERESTING THING,
JUST TO TAKE A STEP BACK AND
SAY, YOU KNOW, ESSENTIALLY I
THINK THE QUESTION YOU'RE ASKING
CAN OR SHOULD PEOPLE CONSENT TO
THE USE OF A.I. AND CAN THEY
REALLY UNDERSTAND IT, AND, YOU
KNOW, I WOULD ARGUE THAT
CLINICALLY THEY PROBABLY CAN'T,
AND PROBABLY DON'T WANT TO.
AND JUST TO GIVE A QUICK
EXAMPLE, LIKE IF YOU GO TO THE
ICU OF ANY OF OUR MAJOR ACADEMIC
MEDICAL CENTERS, AND YOU HAVE
SOMEONE WHO IS CONNECTED TO A
VENTILATOR, THOSE VENTILATORS
ARE MAKING DECISIONS SOMEWHAT ON
THEIR OWN AT THIS POINT.
WE'RE NOT GOING AND CHANGING
EVERY TINY SETTING THE WAY THAT,
YOU KNOW, MAYBE WE DID BEFORE I
WAS IN MEDICAL TRAINING, RIGHT?
AND NOBODY'S TELLING YOU THAT.
THEY JUST WANT TO MAKE SURE, YOU
KNOW, YOU WANT TO MAKE SURE THE
VENTILATORS WORK, RIGHT?
WE USE A.I. AS PART OF OUR DAILY
WORK.
ANYTIME YOU PULL UP GOOGLE MAPS,
APPLE MAPS, CHECKING THE
TRAFFIC, TO GET THERE AS QUICKLY
AS YOU CAN, THEY ARE USING A.I.
ALGORITHMS TO DO THAT, RIGHT IN
THE DO YOU CARE HOW THAT'S
WORKING?
OR THAT IT MIGHT BE WRONG OR
BIASED?
OR DO YOU JUST WANT TO KNOW THE
QUICKEST WAY TO YOUR DESTINATION
IN THE REASONABLE PERSON DOES
NOT CARE OR WANT TO KNOW THAT
INFORMATION.
WHEN WE THINK ABOUT WHAT THIS IS
EVENTUALLY GOING TO LOOK LIKE IN
CLINICAL CARE, I THINK WE MAY
NOT.
THE PLACE I DRAW THE
DISTINCTION, WHEN THERE'S A
PHYSICIAN INVOLVED OR NOT, A
CLINICIAN INVOLVED OR NOT, SO IF
YOUR CLINICIAN IS USING THESE
DIFFERENT TOOLS, USING JUDGMENT,
MOST PEOPLE WOULD JUST FEEL
COMFORTABLE CLINICIAN IS USING
THEIR JUDGMENT.
IF THERE'S A COMPUTER THAT'S
MAKING A DECISION WITHOUT MY
CLINICIAN ABOUT WHAT MEDICATION
I'M GOING TO RECEIVING ON
SOMETHING THAT, THAT FEELS
DIFFERENT AND PEOPLE WANT TO
KNOW.
ALL THAT SAID, THEN WHAT DOES
THAT MEAN FOR OUR CURRENT
RESEARCH STUDY?
WE HAVE TO HOLD OUR CURRENT
RESEARCH STUDIES TO A SLIGHTLY
HIGHER STANDARD SO I THINK WE
HAVE TO TRY TO AT LEAST LET
PEOPLE KNOW THAT A.I. IS BEING
USED.
BUT OF THE DEGREE TO HOW MUCH
THEY HAVE TO UNDERSTAND IS OPEN
FOR DEBATE.
>> OTHERS HAVE VIEWS.
THERE WAS A DISCUSSION IN THE
CHAT ABOUT EVEN IDENTIFIABILITY
IS NOT -- YOU CAN'T PROTECT THAT
COMPLETELY THROUGH A.I., FIRST
OF ALL, BUT, YOU KNOW, WE'RE
ALSO IN DEEP PHENOTYPING FOR
PSYCHIATRIC DISEASES THERE'S
SENSITIVITY IN SOME OF THOSE
DATA SO THAT'S A REALLY
COMPLICATED COMBINATION OF
FACTORS.
JIM?
>> THANK YOU.
I THINK MY QUESTION PLAYS ON
CHRISTINE'S QUESTION.
THAT'S WITH RESPECT TO PATIENT
PRIVACY.
WE HEARD EARLIER ABOUT A.I. AND
SOME OF THE ASPECTS OF FEDERATED
LEARNING, HOW ALGORITHMS
DEVELOP, LARGE DATASETS NEEDED.
IN TERMS OF FEDERATED DATASETS,
ARE THERE DIFFERENCES IN
THINKING ABOUT FEDERATED
DATASETS FOR NEUROPSYCHIATRIC
PATIENTS, IS THERE A DIFFERENT
STRUCTURE OF DATASETS TO HELP
PROTECT PRIVACY AS DATA IS
UTILIZED IN APPLICATIONS, ARE
THERE WAYS OF THINKING ABOUT
SPECIFICITY OF THOSE TYPES OF
STRUCTURES?
>> DR. BAKER, IF YOU WANT TO
WEIGH IN, I CAN CERTAINLY
COMMENT BRIEFLY.
>> IT'S A GREAT QUESTION.
ONE OF THE THINGS THAT WE TRY TO
DO IN TERMS OF BRINGING SOME
APPROACHES INTO MORE OF A
CLINICAL USE CASE IS THINKING
ABOUT LIKE THE NORMS, WHAT ARE
THE NORMS FOR FACIAL EXPRESSION,
WHAT ARE NORMS FOR SLEEP OR
LOCATIONS, TO REALLY DERIVE
POPULATION-LEVEL NORMS YOU NEED
TO BE ABLE TO HAVE DATASETS THAT
SPAN MANY, MANY STUDIES, RIGHT?
WE'VE STRUGGLED WITH HOW DO YOU
COMBINE FACIAL EXPRESSION DATA
ACROSS HUNDREDS OR THOUSANDS OF
STUDIES, WHEN WE CAN'T SHARE RAW
DATA, SIMILARLY WITH GEO
LOCATION.
PARTLY WHY I VISUALIZE LOCATION
DATA THE WAY I DID WAS WE'RE
ALWAYS TRYING TO COME UP WITH
WAYS RESPECTING GRANULARITY OF
THE LOCATION DATA WITHOUT
SHARING THE ACTUAL X, Y
COORDINATES.
I DON'T HAVE A GREAT ANSWER BUT
THIS IS ONE OF THE CHALLENGES,
ENCOURAGING PEOPLE TO SHARE
DE-IDENTIFIED FEATURES IN PUBLIC
DATASETS LIKE THE NIH DATA
ARCHIVE OR DO SOME KIND OF
FEDERATED LEARNING WHERE PEOPLE
CAN, YOU KNOW, KEEP THE
SENSITIVE DATA ON THEIR LOCAL
HIPAA-COMPLIANT SYSTEMS AND
STILL BE ABLE TO CREATE THOSE
POPULATION-LEVEL DISTRIBUTIONS.
>> I WOULD JUST ADD QUICKLY,
MAYBE A LITTLE CONTROVERSIAL,
ONE OF THE THINGS WE HAVE TO BE
CAREFUL OF IS I THINK SOMETIMES
WHEN WE TRY TO OVERPROTECT, YOU
KNOW, PSYCHIATRIC DATA OR
PSYCHIATRIC PATIENTS, WE
CONTRIBUTE TO STIGMATIZATION
ACTUALLY.
AND, YOU KNOW, IF WE OVERPROTECT
TO THE POINT THAT YOU CAN'T DO
THE WORK, THEN NO ONE IS GOING
TO EVER DO RESEARCH ON
PSYCHIATRIC CONDITIONS, RIGHT?
PEOPLE NEVER USED TO DO SUICIDE
RESEARCH BECAUSE IT WAS TOO
HARD.
RESEARCH DR. BAKER DOES ON
SCHIZOPHRENIC PATIENTS, PEOPLE
DIDN'T DO THAT WORK BECAUSE IT
WAS HOO HARD. -- TOO HARD, EVERY
EVERYONE WAS SO OVERPROTECTIVE.
WE HAVE A BAD HISTORY BUT WE
HAVE TO BE CAREFUL NOT TO BE TOO
EXCEPTIONALLISTIC WITH THIS TYPE
OF DATA ALSO.
>> TO FOLLOW ON THAT QUICKLY,
WE'VE DONE A LOT OF INTERVIEWS
WITH PATIENTS IN OUR STUDIES.
VERY FEW OF THE PATIENTS
THEMSELVES ARE CONCERNED ABOUT
THE PRIVACY AND CONFIDENTIALITY.
MORE OFTEN THAN NOT INSTITUTIONS
ARE BOTHERED ABOUT BEING SUED
AND SO ON.
I DO THINK WE NEED TO BE CAREFUL
NOT TO SO-CALLED PATERNALLIZE
OUR PATIENTS AND THESE
DECISIONS.
MOST PATIENTS THAT PARTICIPATE
ARE MORE THAN HAPPY TO SHARE
THAT DATA WITH OTHERS IF IT
HELPS SOMEONE.
>> JOHN?
>> THANKS TO BOTH OF YOU FOR
STIMULATING PRESENTATIONS.
A COUPLE THINGS PIQUE MY
INTEREST, ONE IS THE ISSUE OF
INFORMED CONSENT.
I'M STILL LEARNING, THE MORE I'M
REALIZING HOW LOADED A TERM IT
IS.
DO YOU CONSENT TO A.I. BEING
INVOLVED IN LOOKING AT YOUR
DATA, WHAT DOES THAT MEAN?
IT COULD MEAN ANYTHING TO
ANYBODY.
A.I. TODAY IS NOT GOING TO BE
A.I. TOMORROW.
IT'S AN INTERESTING THOUGHT.
ONE THING THAT YOU BROUGHT UP,
THIS INTERESTING IDEA OF RETURN
OF INDIVIDUAL RESEARCH RESULTS,
IT KIND OF JOGGED MY MEMORY
ABOUT THIS QUESTION AND MAYBE
IT'S A NAIVE QUESTIONS, WHO
ACTUALLY OWNS THE DATA AT THE
END OF THE DAY, RIGHT?
THIS I THINK WE TALK ABOUT DATA
PROVENANCE AND THINGS LIKE THAT
BUT WHO ACTUALLY OWNS THE DATA
AND MIGHT BE ENTITLED TO BENEFIT
FROM IT IN WAYS THAT MAYBE
SOMEBODY SHOULD OR SHOULDN'T BE
TELLING THEM THAT THEY MIGHT,
AND THEN AS YOU INVOLVE THESE
LARGE MODELS, YOU KNOW, MY DATA
IS MIXING WITH OTHERS, IT'S A
FASCINATING SET OF BLURRY
BOUNDARIES.
LOVE TO HEAR YOUR THOUGHTS ON
THAT.
>> IT'S A REALLY GREAT QUESTION.
I THINK DATA OWNERSHIP, IT'S NOT
JUST IN THE CONTEXT OF THIS
CONVERSATION, THIS COMES UP ON A
WEEK TO-WEEK BASIS.
I THINK SOMETIMES IT DEPENDS.
A CONCRETE ANSWER, SOMETIMES IT
DEPENDS ON WHO FUNDED THE WORK,
AND WHO COLLECTED THE DATA IN
TERMS OF WHICH THIRD-PARTY APP.
THE ACTUAL ANSWER CAN VARY
WIDELY, BUT I THINK FROM A
STANDPOINT OF INDIVIDUAL
RESEARCHERS THERE'S AN
INTERESTING PIECE IF YOU'RE A
HIPAA-COVERED ENTITY AND YOUR
WORK IS -- YOUR RESEARCH RECORDS
ARE PART OF YOUR DESIGNATED
RECORDS SET, RESEARCH
PARTICIPANTS ACTUALLY HAVE A
RIGHT TO ACCESS THEIR RECORDS,
RIGHT?
AND SO WE TALKED ABOUT THIS
ACTUALLY WITH PEOPLE WHO ARE
MORE EXPERT THAN I ON THE LAWS
AND REGULATIONS THERE BUT WE
TALKED ABOUT THIS FINE LINE
BETWEEN SAYING YOU'RE NOT
ALLOWED TO RETURN THOSE RESEARCH
RESULTS BECAUSE YOU MAY BE
VIOLATING SOME SORT OF OTHER
REGULATION OR ETHICAL NORM
AROUND, WELL, DOESN'T THE
PATIENT OWN THEIR OWN DATA AND
SHOULDN'T THEY HAVE THE RIGHT TO
SEE IT, SO THERE'S A TIGHTROPE
THERE THAT WE'RE WALKING WHERE,
YOU KNOW, WE'RE TRYING TO MAKE
DECISIONS TO ONLY SHARE DATA
WHEN WE KNOW THAT IT'S GOOD DATA
BASICALLY AND SO SOMEBODY ACTS
ON IT BUT ALSO RECOGNIZING THAT
WE HAVE TO BE CAREFUL, WHEN WE
SAY WE CAN'T.
>> GREAT.
SAMEER?
>> YEAH, THANKS.
TWO COMMENTS.
ONE QUICK ONE, TO ECHO JOHN SAID
A SECOND AGO ABOUT INFORMED
CONSENT, DR. SILVERMAN YOU'RE
TALKING ABOUT IN OUR LIVES WE
USE A.I. ALL THE TIME,
ACCEPTABILITY FOR MAKING
DECISIONS IS PROBABLY GOING TO
BE FAIRLY HIGH, TOTALLY AGREE.
JOHN, LIKE YOU SAID, THERE'S NO
SUCH THING AS INFORMED CONSENT
WHEN IT COMES TO THIS.
IT'S REALLY KIND OF POSSIBLE
COMPUTATIONALLY TO TRACE THE
BREAD CRUMBS FROM INPUTS.
I'M OKAY WITH THIS AND NOT THAT.
, THE SECOND COMMENT, I WANTED
TO FOCUS TOWARDS THE END YOU
TALKED ABOUT HOW, YOU KNOW, CASE
EXAMPLE I THINK WITH THE
PATIENTS WITH IMPLANTED DEVICE
ABOUT USING NOW A MUCH RICHER
STREAM OF INFORMATION AS YOU
SHOW IN TERMS OF BEHAVIORAL DATA
TELLING YOU REALLY PRECISELY HOW
ACTIVE YOU ARE, SOCIAL, MOBILE,
WHAT YOU'RE DOING.
YOU KNOW, THE CONCEPT OF USING
THOSE DATA IN A CLOSED LOOP
LET'S SAY MANNER TO TELL A
DEVICE HOW TO DELIVER
STIMULATION THAT'S GOING TO
CHANGE HOW SOMEONE BEHAVES OR
EMOTES, THIS IS ANOTHER QUESTION
ABOUT HOW TO USE A.I. OR JUST
KIND OF DEEP PHENOTYPE BEHAVIOR,
I THINK IT'S TRICKY.
THERE'S THIS RUSH TOWARDS THIS
HIGH STANDARD OF CLOSED-LOOP
NEUROMODULATION WHERE AUTONOMY
IS BETTER, THAT'S ONE THING.
IF YOU'RE DEALING WITH TREMORS,
PARKINSON'S DISEASE, DECISION
MAKING, OCD OR WHAT NOT, THE
INFORMATION IS CRITICAL AND I
LOVE HOW YOU PUT ANYTIME T
ERMS -- PUT IT IN TERMS OF
ALLOWING THAT TO BE AVAILABLE TO
A TEAM, HOW MUCH THAT GETS
INFORMATION WITH AN ALGORITHM TO
MAKE DECISIONS ABOUT THERAPY
DELIVERY IS A SEPARATE QUESTION.
I'D LOVE TO HEAR YOUR THOUGHTS
ON THIS.
>> YES, THAT'S A GREAT -- BOTH
COMMENTS ARE ON POINT.
IN TERMS OF THE SECOND ONE I
THINK THIS IDEA OF A CLOSED LOOP
SYSTEM WHICH TAKES BEHAVIOR IN,
MAKES A CHANGE TO THE BRAIN,
IT'S OBVIOUSLY ONE OF THE HOLY
GRAILS OF, YOU KNOW, IMPLANTED
SYSTEMS.
I WAY I THINK ABOUT IT IS FOR AT
LEAST THE WAY CLINICAL MEDICINE
WORKS IS WE ARE DOING THAT.
WE TAKE INPUTS, TRY TO MAKE A
CHANGE, CONSTANTLY ITERATING, WE
DON'T KNOW HOW A PARTICULAR
MEDICINE IS GOING TO AFFECT
SOMEONE, MAYBE SIDE EFFECTS AND
SO ON.
SO I GUESS MY INTUITION WOULD BE
THAT YOU DON'T AT LEAST AT THE
BEGINNING WANT A SYSTEM THAT'S
FULLY AUTONOMOUS.
YOU WANT TO WORK WITH A HUMAN IN
THE LOOP TO CONTINUE TO TWEAK
THE INPUTS.
AND KIND OF EXPLORATORY
EXPLOITED WAY SO YOU SETTLE ON
SOMETHING THAT SEEMS TO BE
WORKING, MAYBE MOVE IT MORE INTO
AN AUTONOMOUS MODE BUT IN EVERY
SITUATION YOU WANT TO HAVE
PERIODIC EXPLORATION, IS THIS
STILL DOING THE THING WE
EXPECTED IT TO DO, WHERE THERE'S
A HUMAN IN THE LOOP SAYING --
WHETHER THAT'S THE PATIENT
SAYING NO, THIS NO LONGER FEELS
GOOD, OR THE CLINICIAN, THIS IS
GOING TO CAUSE SIDE EFFECTS.
I'M NOT SURE WE'RE EVER GOING TO
GET TO A POINT SIMILAR WITH
SELF-DRIVING CARS, LIKE A FULLY
AUTONOMOUS SYSTEM.
I THINK THERE'S ALWAYS GOING TO
BE EDGE OR CORNER CASES WE
DIDN'T ANTICIPATE, WHERE THE
BRAIN OR THE BEHAVIOR OR THE
ENVIRONMENT IS CHANGING IN SUCH
A WAY WE'RE ACTUALLY OUTSIDE OF
WHAT OUR MODEL IS DESIGNED FOR
AND HAVE TO RECALIBRATE.
THESE ARE KEY THOUGHTS AS WE TRY
TO MOVE MORE TOWARDS THE CLOSED
LOOP SYSTEMS.
>> THANKS A LOT.
WE CAN ALSO QUESTION WHETHER
THAT'S A HOLY GRAIL, YOU KNOW,
OR NOT.
>> ABSOLUTELY.
>> AMY?
YEAH, THANK YOU BOTH SO MUCH FOR
EXCELLENT PRESENTATIONS.
FIRST I WANT TO START BY MAKING
A COMMENT THAT I APPRECIATE
CHRISTINE'S FRAMING AND
DISCUSSION AROUND LIKE WHAT IS
NEW HERE AND HOW CAN WE LEARN
FROM OTHER AREAS AND I'M
THINKING PARTICULARLY HOW MANY
OF THESE ISSUES THAT WE'RE
TALKING ABOUT NOW HAVE BEEN WELL
RESEARCHED, DEBATED, FIELD OF
GENETICS AND GENOMICS.
I APPRECIATED DR. SILVERMAN HOW
AND WHERE TO FRAMEWORKS FALL
SHORT AND WHY, WHAT'S UNIQUE, IN
THE CHAT SOME CONVERSATIONS
AROUND ISSUES OF CONSENT AND
CHALLENGES OF FUTURE UNCERTAINTY
IS SOMETHING THAT HAS BEEN WELL
STUDIED AND DO WE NEED RECONSENT
AND WHAT DO WE DO WITH MINORS
AND THOSE THINGS HAVE BEEN WELL
STUDIED IN THE FIELD OF
BIOETHICS AND OTHER AREAS.
SO JUST GENERAL ENCOURAGEMENT
FOR US TO LOOK AT THAT AND
REALLY PARSE OUT WHAT'S NEW OR
DIFFERENT HERE IN TERMS OF WHAT
NEEDS TO CHANGE.
TWO OTHER QUESTIONS/COMMENTS,
WHEN TALKING ABOUT PRIVACY, YOU
KNOW, SOME RESEARCH AROUND BRAIN
PRIVACY WE'VE FUND TO MY
SURPRISE PARTICIPANTS THAT WE'VE
SURVEYED ABOUT THIS ARE LESS
CONCERNED ABOUT THEIR BRAIN DATA
THAN THEY ARE THINGS LIKE TEXT
MESSAGE, THE CONTENT OF TEXT
MESSAGES AND THINGS LIKE THAT.
DR. BAKER MENTIONED THAT IT'S
VERY DIFFICULT TO OPT OUT OF THE
COLLECTION OR USE OF CERTAIN
TYPES OF DATA WHEN YOU HAVE THIS
HOLISTIC APPROACH TO A.I.-BASED
DATA COLLECTION AND I'M
WONDERING IF YOU HAVE THOUGHTS
IS AN OPT-OUT POSSIBLE IF
SOMEBODY SAYS I'M COMFORTABLE
BUT DON'T WANT YOU TRACKING X,
RIGHT?
EITHER MY TEXT MESSAGES OR MY --
WHATEVER IT IS.
IS THERE A WAY TECHNICALLY AND
ETHICALLY TO HAVE THEM OPT OUT
OF THAT?
AND THEN I GUESS MY SECOND
QUESTION REALLY RELATES TO SORT
OF THIS ACTIONABILITY ISSUE THAT
SAMEER BROUGHT UP.
ANOTHER THING WE'VE FOUND IN OUR
RESEARCH THAT OTHERS HAVE FOUND
AS WELL IS THAT WHEN WE TALK TO
PEOPLE WHO HAVE MENTAL HEALTH
DISORDER AND WE ASK ABOUT USING
AND SHARING BRAIN DATA THEY HAVE
SAID THAT'S THE WHOLE POINT OF
PSYCHIATRY, TRYING TO UNDERSTAND
WHAT'S GOING ON IN MY BRAIN SO
THE BETTER YOU CAN DO THAT, THE
BETTER IT IS TO COME UP WITH A
TREATMENT FOR ME.
THIS IDEA OF ACTIONABILITY, MY
QUESTION IS FOR DR. SILVERMAN,
WHETHER YOUR ANALYSIS AND
RECOMMENDATIONS ABOUT RETURN OF
RESULTS WOULD CHANGE DEPENDING
ON THE ACTIONABILITY AND DOES
THAT FIT INTO YOUR RISK/BENEFIT
ANALYSIS OR IS THAT CONSIDERED
DIFFERENTLY?
>> ALL GREAT QUESTIONS AND
POINTS, CERTAINLY APPRECIATE IT.
I THINK THAT WHEN WE THINK ABOUT
ACTIONABILITY IT HAD TO DO WITH
ESSENTIALLY I GUESS I WOULD SAY
TWO THINGS.
ONE IS THE ACUITY OF WHAT YOU'RE
POTENTIALLY PICKING UP ON, IS
THE ALGORITHM TELLING ME THAT
THIS PERSON WITH DEPRESSION
SPENT FIVE HOURS AT A LIQUOR
STORE AND NOW ON A BRIDGE FOR
TWO HOURS, JUST SEND A HUNDRED
TEXT MESSAGES IN 25 MINUTES,
THAT WOULD BE A SITUATION OF
PRESUMED VERY HIGH
URGENCY/ACTIONABILITY, ASSUMING
THERE'S ANALYTICAL AND CLINICAL
VALIDITY AND SO WE WOULD HAVE I
THINK A FAIRLY LOW BAR TO
WANTING TO, YOU KNOW, RETURN
THOSE RESULTS, WITH THE CAVEAT
THAT I MEAN ONE MIGHT ASK THE
QUESTION OR ARGUE WHY WOULDN'T
YOU JUST ALWAYS RETURN THOSE
RESULTS? WHY IS THERE EVEN
CONCERN?
OF COURSE THE PROBLEM IS THAT
THERE COULD BE FALSE POSITIVES,
AND THE SYSTEM IN WHICH WE
THE -- THE WORLD IN WHICH WE
LIVE IN, IF YOU CALL 911 ON
SOMEONE THE RESULT IS NOT ALWAYS
GOOD, RIGHT?
AND IF YOU GET HOSPITALIZED
INVOLUNTARY AGAINST YOUR WILL
THERE CAN BE TRAUMA AND
SEQUELAE.
SO IT'S NOT A PERFECT SYSTEM
WHERE WE HAVE -- I THINK THAT
PART OF THE ISSUE, THIS IS TO
ANSWER THE OTHER PART OF THE
QUESTION, CAN WE DO SOMETHING?
ACTIONABILITY IN MY VIEW ALSO
TIES INTO CAN WE DO SOMETHING
ABOUT IT, RIGHT?
THERE MAY BE PEOPLE -- I THINK
WE'RE -- IN THAT SITUATION
THERE'S PROBABLY SOMETHING WE
COULD DO.
I THINK IN SITUATIONS WHERE
SOMEONE HAS BIPOLAR DISORDER
STARTING TO RAMP UP AND GET
MANIC THERE'S PROBABLY THINGS WE
CAN DO BUT SOMEONE WHO HAS, YOU
KNOW, CHRONIC DEPRESSION THAT'S
HAVING VERY FREQUENT DEPRESSIVE
EPISODES, THERE MAY BE LESS WE
CAN DO AT TIMES, RIGHT?
JUST BASED ON THE STATE OF OUR
TREATMENT.
THAT'S WHEN I THINK ABOUT
ACTIONABILITY IN TERMS OF THE
DIFFERENT TIERS OF ACTIONABILITY
AND POTENTIAL OF FALSE POSITIVES
AND LIMITS OF WHAT WE CAN DO TO
HELP PEOPLE, IF THAT ANSWERS
YOUR QUESTION.
>> YEAH, THAT'S SUPER HELPFUL.
THANK YOU.
>> I CAN JUST COMMENT MAYBE ON
THE FIRST PART OF THE QUESTION
IN TERMS OF HOW DO WE LET PEOPLE
SELECTIVELY OPT OUT, THE SHORT
ANSWER IS, YES, WE HAVE ALL
SORTS OF WAYS WE CAN HELP
PARTICIPANTS OR PATIENTS
SELECTIVELY OPT OUT OF CERTAIN
DATA TYPES, FOR INSTANCE WE'RE
DOING A LARGE GLOBAL STUDY,
INDIVIDUALS WITH PRODROMAL
SCHIZOPHRENIA, OPTION OF
COLLECTING NO SENSOR DATA OR
COLLECTING EVERYTHING BUT
LOCATION DATA, IT'S WHAT WE
FOUND THAT VERY FEW PATIENTS OR
FEW PARTICIPANTS SELECTIVELY OPT
OUT OF LOCATION BUT THERE ARE
SOME WHO DO.
ALSO WE TRY TO TEACH THEM HOW
TO, YOU KNOW, YOU CAN GO INTO
YOUR PHONE AND TURN OFF LOCATION
ACCESS FOR GIVEN APPLICATION AND
SO ON SO THERE ARE WAYS TO DO
THAT.
THE OTHER POINT I'LL MAKE IS
THAT AS WE THINK ABOUT REALLY
SOME OF THESE IDEAS BRINGING
INTO A CLINICAL SETTING ONE IDEA
WAS THAT PERHAPS YOU CAN HAVE A
CLINICIAN AND PATIENT ONLY
COLLABORATIVELY TRACK A SPECIFIC
LOCATION AND NOT ALL LOCATIONS,
LIKE I WANT TO TRACK WHEN YOU GO
TO THE BAR BUT DON'T WANT TO
KNOW WHEN YOU GO TO SCHOOL OR
WHEN YOU'RE AT YOUR GIRLFRIEND'S
HOUSE, ET CETERA, WORKING ON
YOUR ALCOHOL USE DISORDER SO
OBVIOUSLY THE APP MIGHT COLLECT
MORE LOCATION DATA BUT IN TERMS
OF WHAT MAKES IT BACK TO THE
HUMAN REVIEWING IT WE CAN USE
VARIOUS FILTERING METHODS TO
SELECTIVELY CAPTURE CERTAIN
DATA, CERTAIN FEATURES FROM THAT
DATA.
>> GREAT.
KAREN?
>> THANK YOU FOR THOSE GREAT
TALKS.
I HAD A QUESTION REALLY RELATED
TO WHAT AMY WAS SAYING THAT, YOU
KNOW, WHAT IS THE DIFFERENCE
BETWEEN THE STUDIES AND OTHERS,
I GUESS CHRISTINE ALSO POSED TO
US, BUT ALSO INVITING US TO
THINK A LITTLE MORE EMPIRICALLY
ABOUT OUR WHAT-IFs, WONDERFUL
THAT YOUR AUTHOR ON THE CHECK
LIST PAPER, YOU ALREADY TALKED
ABOUT THIS RECENT THING, YOU
HAVE AN IDEA HOW YOU WORK WITH
PATIENTS ONE ON ONE,
PARTICIPANTS ONE ON ONE,
SELECTING FROM THEIR PHONE.
DO YOU HAVE -- CURIOUS, ONE,
WHAT KIND OF RISKS YOU CURRENTLY
STATE IN YOUR CONSENT FORM AS
RELATES TO SOME OF THE MINIMAL
RISK INFORMATION, YOU KNOW, USE
OF THESE MULTIPLE SENSORS,
PARTICULARLY PASSIVE DATA
COLLECTION, AND, TWO, I'M
CURIOUS IF YOU HAVE ANY ADVICE
ON BEST PRACTICES FOR
SYSTEMATICALLY HOW WOULD YOU
TELL OTHERS DOING THIS RESEARCH
BASED ON YOUR LEARNINGS HOW
YOU'VE BEEN ABLE TO ASSESS
VALUES OF YOUR PARTICIPANTS IN
THE SENSE WHETHER THEY BELIEVE
SOMETHING IS DIFFERENT ABOUT THE
COMBINATION OF BRAIN DATA AND,
YOU KNOW, SOMETHING THAT'S
AUTOMATED IN A.I.?
>> I CAN ANSWER THE FIRST PART.
SECOND IS IT'S A BIGGER LONGER
DISCUSSION I GUESS.
IN TERMS OF WHAT WE CURRENTLY
PUT IN MOST CONSENT FORMS IT'S
PRETTY VANILLA IN THE SENSE OF,
YOU KNOW, THE RISK IS BREACH OF
CONFIDENTIALITY IS THE MAIN RISK
THAT PEOPLE MIGHT EXPERIENCE
WITH HAVING THESE DATA BE
COLLECTED.
AS PART OF MOST OF OUR ONGOING
STUDIES WE DON'T RETURN
INDIVIDUALIZED RESEARCH RESULTS
TO PARTICIPANTS.
WE'RE STARTING TO EXPLORE HOW TO
DO THAT, BUT IN MOST OF THOSE
SITUATIONS IT'S MEDIATED BY
CLINICIAN, SO SOMEONE EITHER
THEIR DOCTOR OR ONE OF THE
RESEARCH STAFF WOULD GO OVER THE
RESULTS WITH THEM TO MITIGATE
POTENTIAL UNINTENDED QUESTIONS.
YOU MAY FIND ASSESSMENTS
EMOTIONALLY DISTRESSING WHEN WE
ASK ABOUT CERTAIN SYMPTOMS AND
POTENTIAL BREACH OF
CONFIDENTIALITY SO I WOULDN'T
SAY WE'RE SUPER EXHAUSTIVE IN
HOW WE FRAME THOSE RISKS.
>> I THINK IT'S INTERESTING YOU
MENTIONED EXAMPLE OF THE
LOCATION PIECE, THAT YOU TALKED
ABOUT DOING THIS.
WAS THAT PROMPTED BY YOUR TEAM
SIGHING, HEY, WE OUGHT TO START
DOING THIS WITH PARTICIPANTS OR
WAS IT ONE PARTICULAR
PARTICIPANT, I DON'T LIKE THIS
GPS PIECE AND YOU NEGOTIATING
SOMETHING WITH THEM?
>> WELL, PART OF IT WAS
MOTIVATED BY THE FACT THAT NIH
ACTUALLY REQUIRES, PART OF THE
GLOBAL STUDY I MENTIONED THE NIH
IS INCREASING IF YOU'RE FUNDED
BY NIMH DOLLARS, IT'S GOVERNMENT
PROPERTY IN THE NIH ARCHIVES, WE
WORD THAT WOULD HAVE A NEGATIVE
EFFECT ON PARTICIPANTS BEING
WILLING TO PARTICIPATE IN THESE
TRIALS, WHEREAS WE SAW IF THE
DATA COULD POTENTIALLY BE REALLY
USEFUL BUT REQUIRING THAT THE
ACTUAL RAW GEOSPATIAL DATA GO
INTO A PERPETUAL DATABASE STRUCK
US AS MAYBE A LITTLE BIT OF
GOVERNMENT OVERREACH, I DON'T
KNOW, SO WE DECIDED TO PUT THAT
BACK IN THE HANDS OF
PARTICIPANTS TO SAY THAT'S NOT
SOMETHING I'M COMFORTABLE WITH,
I'LL JUST LEAVE THAT OUT, AS A
WAY OF MITIGATING THAT CONCERN.
WE WERE PLEASANTLY SURPRISED
PARTICIPANTS DIDN'T SEEM THAT
CONCERNED ABOUT SHARING IT IN
GENERAL.
THAT SAID WE THINK THAT GIVING
PEOPLE THE OPTIONALITY IS THE
RIGHT WAY TO HANDLE THIS.
THE OTHER WAY WE ENVISION
HANDLING THIS IN THE FUTURE IS
THAT, YOU KNOW, APPLE AND GOOGLE
AND OTHER OPERATING SYSTEM
DEVICE MANUFACTURERS ARE
STARTING TO EMBED SOFTWARE TOOLS
INTO THE OPERATING SYSTEMS THAT
DO SOME INITIAL FEATURE
EXTRACTION SO THE RESEARCHERS
THEMSELVES DON'T EVER GET ACCESS
TO THE RAW DATA.
THEY ONLY GET ACCESS TO FEATURES
WHICH WOULD DE-IDENTIFY FOR
PARTICIPANTS.
THAT COMES WITH PROS AND CONS.
YOU DON'T NECESSARILY KNOW HOW
TO REGENERATE THOSE FEATURES,
YOU DO IT THAT WAY.
BUT WE'RE TRYING TO FIND WAYS SO
MOST CALCULATIONS CAN BE DONE ON
DEVICE, AT THE PARTICIPANT'S
PHONE SO WE CAN CONTINUE TO
ACQUIRE BEHAVIORAL DATA WITHOUT
INCREASING RISKS FOR
PARTICIPANTS.
>> GREAT.
THANK YOU SO MUCH FOR ALL OF THE
SPEAKERS.
I WISH WE COULD CONTINUE THIS
CONVERSATION BUT WE HAVE OTHER
THINGS ON THE AGENDA AND PEOPLE
NEED A BREAK AND PROBABLY SOME
PEOPLE HAVE TO LEAVE.
SO THOSE WHO CAN STAY, INCLUDING
SPEAKERS WE'LL HAVE MORE TIME
LATER TO DISCUSS WHAT THE
WORKING GROUP MIGHT BE ABLE TO
DO.
IF YOU CAN'T STAY AND HAVE
IDEAS, WE'D LOVE TO HEAR THEM.
THANK YOU SO MUCH FOR BEING HERE
AND EVERYBODY GETS A 10-MINUTE
BREAK BEFORE YOU COME BACK AND
DEAL WITH CASE NUMBER TWO.
>> I'D LIKE TO WELCOME EVERYONE
BACK TO TRAIN LARGE MODELS ON
BRAIN DATA.
I'M GRACE HWANG, PROGRAM
DIRECTOR FOR NINDS, THE BRAIN
INITIATIVE, ALONG WITH NITA
FARAHANY WE'LL CO-MODERATE THIS
SESSION.
LET'S INTRODUCE DR. KARIM JERBI,
PROFESSOR OF PSYCHOLOGY FROM
UNIVERSITY OF MONTREAL, CANADA
RESEARCH CHAIR, HEADS UNIFYING
NEURAL SCIENCE IN QUEBEC, ALSO
ASSOCIATE PROFESSOR AT MONTREAL
BASED A.I. RESEARCH INSTITUTE,
RESEARCH LIES AT THE CROSSROADS
BETWEEN COGNITIVE, COMPUTATIONAL
AND CLINICAL NEUROSCIENCE.
HE WILL WALK US THROUGH HIS
PAPER ON NEURO-GPT.
>> THANK YOU FOR THE NICE
INTRODUCTION, GRACE.
I'M GOING TO SHARE MY SCREEN.
I HOPE THIS WORKS OUT.
LET'S SEE.
OKAY.
WELL, THANK YOU SO MUCH FOR
HAVING ME.
I'D LIKE TO START BY LAND
ACKNOWLEDGMENT, ACKNOWLEDGING
THE LAND ON WHICH WE GATHER IS
THE TRADITIONAL AN UNCEDED
TERRITORY OF THE KANIEN'
KEHA:KA, THE MOHAWK.
I'M ALSO THANKING EVERYBODY THAT
MADE THIS POSSIBLE, LOOKING
FORWARD TO THE DISCUSSION WITH
DEBRA MATTHEWS, THIS DISCUSSION
CO-MODERATED BY NITA FARAHANY
AND GRACE HWANG AND THANKS TO
NINA HSU, THANK YOU FOR THIS
OPPORTUNITY.
I WOULD LIKE TO START BY GIVING
CONTEXT, SO AS GRACE MENTIONED
EARLIER THE CURRENT DIRECTOR OF
THE UNIQUE NEURO-A.I. CENTRE IN
MONTREAL, BRIDGING ARTIFICIAL
INTELLIGENCE, IN LINE WITH
TOPICS WE'VE HEARD AND DISCUSSED
TODAY, THE IDEA IS BRINGING
THESE TWO FIELDS TOGETHER,
HOPEFULLY FOR THE BEST OF
HUMANITY, PITFALLS OF THINGS WE
NEED TO THINK ABOUT.
IDEA TO USE KNOWLEDGE FROM
NEUROSCIENCE TO ENHANCE A.I.,
BUT ALSO TO USE A.I. TO IMPROVE
OUR UNDERSTANDING OF
NEUROBIOLOGY OF OUR SYSTEMS AND
MAYBE HOPEFULLY AT THE END OF
THE DAY IMPROVE CLINICAL CARE.
AND WITHIN THE CENTER WE CREATED
IN QUEBEC, THE IDEA IS TO MOVE
BEYOND NEUROSCIENCE ON ONE HAND
AND A.I. ON THE OTHER TOWARDS A
FORM OF INTEGRATION WHERE BOTH
ARE JOINTLY EXPLORED FOR THEIR
MUTUAL BENEFITS THROUGH THE
FIELD WE REFER TO AS BEING
NEURO-A.I.
WE HAVE 70 RESEARCHERS, EIGHT
ACADEMIC INSTITUTIONS ACROSS
QUEBEC, 100 RESEARCHERS IN
NEUROSCIENCE OR A.I. OR
INTERSECTION BETWEEN THE TWO
FIELDS.
THIS STARTED IN 2019, THIS IS
ALSO PART OF A LARGER MOVEMENT
IN CANADA WHERE THE CANADIAN
BRAIN RESEARCH STRATEGY IS
BUILDING THE SIX TRANSFORMATIVE
INITIATIVES AND NEUROA.I. IS ONE
OF THE INITIATIVES DEEMS TO BE
TRANSFORMATIVE BECAUSE A STRONG
IMPACT ON CLINICAL APPLICATION
AND IMPROVING QUALITY OF LIFE OF
EVERYBODY.
MOST PEOPLE HERE ARE AWARE OF
THE INTERFACE BETWEEN BIOLOGY
AND MACHINE WHEN YOU'RE TALKING
ABOUT INTELLIGENCE FROM A
BIOLOGICAL PERSPECTIVE OR
MACHINE PERSPECTIVE.
AS YOU KNOW, THE INITIAL
INSPIRATION FOR ACTUALLY
GENERATING WHAT WE REFER TO
NOWADAYS AS ARTIFICIAL
INTELLIGENCE COMES FROM THE
DESIRE TO BUILD SOMETHING THAT
IS BRAIN INSPIRED A.I., AND IN
MANY CASES TODAY WE NEED LESS OF
THIS, SOME PEOPLE BELIEVE A.I.
HAS BECOME A FIELD OF ITS OWN
AND NEUROSCIENCE OF BIOLOGY THAT
INSPIRED THE BEGINNINGS IS MAYBE
NO LONGER NECESSARY FOR SOME
TASKS THAT WE USE A.I. FOR.
MANY OF US ALSO BELIEVE THERE
ARE MANY APPLICATIONS FOR A.I.
THAT WOULD BENEFIT FROM BETTER
INTEGRATION OF BIOLOGY AND
NEUROSCIENCE KNOWLEDGE SO MORE
INSPIRATION FROM NEUROSCIENCE
AND COGNITIVE NEUROSCIENCE IN
PARTICULAR WOULD BE USEFUL.
THE OTHER DIRECTION IS USING
A.I. TO INFORM WHAT WE DO IN
NEUROSCIENCE BRAIN RESEARCH,
ALSO CLINICAL APPLICATIONS, AND
THIS IS WHAT WE REFER TO AS
A.I.-POWERED BRAIN RESEARCH.
FOR THIS DIRECTION MACHINE
LEARNING HAS BEEN INCREASINGLY
USED AS A TOOL TO ADVANCE OUR
UNDERSTANDING OF BRAIN FUNCTION
AND DYSFUNCTION, AND THERE ARE
MANY WAYS THIS CAN HAPPEN.
AND RELATED ALSO TO TODAY'S
TOPIC, TWO WAYS THAT I'D LIKE TO
THINK ABOUT THIS IS SEEING THAT
A.I. CAN BE USED AS A TOOL TO
REVERSE ENGINEER THE BRAIN,
BUILDING MODELS, OF THE BRAIN
AND BRAIN FUNCTION.
IT'S A WAY FOR US TO UNDERSTAND
HOW THE BRAIN FUNCTIONS OR
DYSFUNCTIONS IN THE CASE OF
CLINICAL APPLICATIONS BUT ALSO
AS A DATA MINING TOOL.
AND DATA MINING TOOL IN THIS
CASE IS BASICALLY USING PATTERN
RECOGNITION OR WAYS OF EXPLORING
THE DATA THAT ARE VERY MUCH DATA
DRIVEN THAT COULD COMBINE ALSO
HYPOTHESIS DRIVEN BUT IN MANY
CASES STRONGLY DATA DRIVEN, VERY
MUCH RELATED TO OUR TOPIC TODAY
EXPLORING LARGE MODELS,
FOUNDATION MODELS FOR
NEUROSCIENCE.
SO THAT WAS MY QUICK
INTRODUCTION TO THE CONTEXT OF
THIS TYPE OF RESEARCH.
NOW I'D LIKE TO MOVE ON TO MORE
SPECIFICALLY RICHARD LEAHY AND I
WERE INVITED TO GIVE AN OVERVIEW
OF THE NEURO-GPT PAPER, A
PRE-PRINT NOT LONG AGO, WORK LED
BY A PhD student at Southern
California in electrical
engineering and a project in my
sabbatical a year ago as USC
collaborating with this team
that is also headedded by
Richard Leahy at USC.
YOU CAN SEE THE LIST OF OTHER
COLLABORATORS HERE.
I WANTED TO ACKNOWLEDGE THEM.
NOW BY WAY OF INTRODUCTION
YOU'VE SEEN THE GREAT OVERVIEW
OF THE FIELD OF FOUNDATION
MODELS FOR NEUROSCIENCE BY
PATRICK EARLIER TODAY, SO I WILL
NOT DELVE INTO MANY DETAILS BUT
WOULD LIKE TO GIVE A REFRESHER
OVERVIEW FOR THOSE NOT FAMILIAR
WITH SELF-SUPERVISED LEARNING IN
THE CONTEXT OF NEUROSCIENTIFIC
APPLICATIONS, THE IDEA THAT WE
MOVE FROM PRE-TRAINING A MODEL
TOWARDS APPLICATIONS WHICH WE
REFER TO AS DOWNSTREAM TASKS.
TO GIVE AN EXAMPLE
SELF-SUPERVISED PRE-TRAINING CAN
BE USE OF LARGE MEDICAL IMAGE
DATASETS, WITHOUT ANNOTATIONS
HENCE SELF-SUPERVISED, NOT USING
LABELS.
SELF-SUPERVISED LEARNING WILL BE
ABLE TO REBUILD FOR EXAMPLE
RECONSTRUCT DATA BY FILLING IN,
FOR EXAMPLE, MISSING GAPS IN THE
DATA.
WE CAN REMOVE PARTS OF THE DATA,
DO MASKING, TRY TO RECONSTRUCT
THEM.
THERE'S NO CLASSIFICATION TASK
PER SE IN THIS CASE HERE.
IT'S A SELF-SUPERVISED COMPONENT
WHERE WE'RE BUILDING A MODEL
THAT IS LEARNING
REPRESENTATIONS.
NOW, THAT IS FOLLOWED BY THE
SUPERVISED FINE TUNING ON
DOWNSTREAM TASKS.
THIS IS WHERE YOU TAKE THE MODEL
THAT HAS BEEN PRE-TRAINED ON
VERY LARGE DATASETS, AND NOW YOU
CAN FINE TUNE IT ON A
CLASSIFICATION TASK ON SMALLER
DATASET, FOR EXAMPLE, SMALL
MEDICAL IMAGE DATASET, AND NOW
YOU USE EITHER THE WHOLE MODEL
OR PARTS OF THE LEARNING IN A
SUPERVISED LEARNING CONTEXT TO
MAKE CLASSIFICATIONS.
THE IDEA BEING HERE THAT IF
YOU'RE TRAINING THE MODEL AND
DOING A PRE-TRAINING, ALSO
REFERRED TO AS PRETEXT, ON A
VERY LARGE DATASET THAT YOU
MIGHT LEARN REPRESENTATIONS,
MIGHT LEARN SPECIFIC THINGS
ABOUT THIS TYPE OF DATA, THAT
YOU WOULD NOT BE ABLE TO EXTRACT
FROM A SMALLER SUBSET OF DATA.
AND SO YOU START OFF INSTEAD OF
STARTING FROM SCRATCH
BUILDELLING A MODEL, APPLY AS A
CLASSIFIER ON A SPECIFIC TASK,
DOWNSTREAM TASKS.
IN THE CONTEXT OF THIS WORK,
OBVIOUSLY WE'RE GOING TO BE
TALKING ABOUT PRE-TRAINING
DATASETS AND THEN THE
PRE-TRAINING MODEL AND MOVE TO
THE DOWNSTREAM TASKS AS I JUST
EXPLAINED.
NOW HERE THE PRE-TRAINING DATA
AND THIS CONTEXT WAS TEMPLE
UNIVERSITY HOSPITAL EEG CORPUS,
THIS HAS OVER 25,000 RECORDINGS
FROM 25,000 SUBJECTS, EEG
RECORDINGS.
VERY DIVERSE RECORDINGS, OVER 40
CHANNEL CONFIGURATIONS OF EEG,
VARYING DURATIONS, AND DIFFERENT
PARTICIPANTS IN DIFFERENT
CLINICAL CONDITIONS.
DIFFERENT SAMPLING FREQUENCIES.
AND FOR THE SAKE OF THIS
PROJECT, WHEN WE AND PHILIP AND
WUJAY PRE-PROCESSED OVER 20,000
EEG RECORDINGS, 32 CHUNKS, EACH
CHUNK OF 2 SECONDS, ONE MINUTE
OF RECORDING PER BLOCK.
AND THEN WE USED THIS FOR THE
PRE-TRAINING MODEL.
AND THEN AFTERWARDS WE DO
DOWNSTREAM TASKS, ONCE YOU'VE
PRE-TRAINED THIS MODEL ON THE
LARGE CORPUS OF DATA EEG ACROSS
20,000 RECORDINGS, HOW WELL DOES
IT PERFORM AND COMPARE TO
SCRATCH ON THE TASK WE'RE TRYING
TO DO.
SO WHAT WAS DOWNSTREAM TASKS,
BCI, BRAIN-COMPUTER INTERFACE
COMPOSITION IV, PROVIDED BY
UNIVERSITY OF TECHNOLOGY IN
AUSTRIA, ONLY NINE SUBJECTS,
FOUR MOTOR IMAGERY TASKS,
IMAGINING LEFT HAND VERSUS RIGHT
HAND VERSUS FEET VERSUS TONGUE
MOVEMENTS.
SAMPLING FREQUENCY OF 250, 72
TRIALS PER TASK, TOTAL OF 288,
EMPHASIZING THIS IS A SMALL
DATASET, IT'S A CHALLENGE, ALSO
A BENCHMARK WITH DIFFERENT
MODELS FOR CLASSIFICATION USED
TO SEE HOW WELL THEY CAN
PERFORM.
SO THIS SERVES FOR US AS A GOOD
EXAMPLE, WELL, OKAY, IF I CAN
TRAIN A MODEL ON THOUSANDS OF
INDIVIDUALS, AND TRY TO LEARN
THE GIST OR REPRESENTATION OF
WHAT EEG DATA LOOKS LIKE, IF I
START NOW WITH THIS PRE-TRAINED
MODEL AND TAKE IT TO A TASK OF
TRYING TO GUESS HERE IN THIS
CASE MOTOR IMAGERY OF
PARTICIPANTS HOW WELL WOULD THAT
PERFORM COMPARED TO NOT AT ALL
HAVING A MODEL, STARTING
DIRECTLY BY THE DATA.
AND SO NOW VERY BRIEF OVERVIEW,
I WILL NOT GO INTO DETAIL OF THE
ARCHITECTURE BUT BASICALLY TWO
COMPONENTS, EEG ENCODER THAT'S
GOING TO TAKE IN THE EEG, RAW
EEG DATA AND GENERATE
EMBEDDINGS, THESE ARE SERVE AS
LOWER DIMENSIONAL AND DENOISED
REPRESENTATION, EXTRACTING
FEATURES, SIMPLIFYING PREDICTION
OF MASS CHUNK FOR GPT MODEL
RUNNING AFTERWARDS, ENHANCES ITS
PERFORMANCE THINKING ABOUT THIS
AS DENOISING, INJECTING TEMPORAL
CORRELATIONS, SECOND COMPONENT
OF THE PIPELINE IS THE ACTUAL
GPT MODEL ITSELF, AND AS THE
NAME INDICATES EMPLOYS
TRANSFORMER ARCHITECTURE.
AND IT'S GOING TO BE EXACTLY
LIKE, YOU CAN THINK OF THIS IN
ChatGPT, PREDICTING OR GPT
MODEL FOR LANGUAGE PREDICTING
THE NEXT WORD, IN THIS CASE
PREDICTING THE NEXT TOKEN, IN
THIS CASE EEG DATA, CAN I USE
PREVIOUS EEG CHUNKS OF DATA TO
PREDICT THE NEXT ONE LIKE A
LANGUAGE MODEL WOULD PREDICT THE
NEXT WORDS.
THIS SOMEHOW GUARANTEES
PREDICTION OF EEG EMBEDDINGS,
ALSO CONSIDERING CAUSAL TEMPORAL
RELATIONSHIPS, DIFFERS FROM BERT
AS OPPOSED TO GPT FOR INSTANCE.
SO, IF WE TAKE A QUICK LOOK AT
THE RESULTS, WE COMPARED RESULTS
TO OTHER METHODS USED FOR
CLASSIFICATION, AGAIN THIS IS A
MOTOR IMAGERY TASK.
IN OTHER WORDS THIS MEANS THE
BASELINE IF YOU HAD CLASSIFIER
THAT WOULD BE RANDOMLY GUESSING
WHAT TYPE OF MOVEMENTS THE
INDIVIDUAL WAS THINKING OF, YOUR
CLASSIFICATION PERCENTAGE WOULD
BE AT 25%, 0.25 WOULD BE CHANCE
LEVEL.
YOU CAN SEE I PRESENT THIS HERE
AS A PLOT ON THE RIGHT-HAND SIDE
HERE, THE BEST RESULT WAS
OBTAINED WITH NEURO-GPT, I DID
NOT GO INTO DETAILS, THREE WAYS
OF FINE TUNING, THREE FINE
TUNING STRATEGIES, THREE AT THE
BEGINNING, LINEAR, ENCODER,
LINEAR PLUS GPT, 64%, AGAIN THE
CHANCE LEVEL IS 25% HERE.
NOW, THE NUMBER PER SE IS NOT
VERY HIGH, IT IS BETTER THAN
STATE OF THE ART IN THE FIELD AT
THE TIME THIS PRE-PRINT WAS PUT
OUT.
BUT I THINK WE'LL GET BACK TO
THE DISCUSSION AFTERWARDS
BECAUSE, AGAIN, THIS IS A PROOF
OF PRINCIPLE.
THIS IS NOT TO SAY THIS IS WHERE
WE WANT TO BE, THIS IS A PROOF
OF PRINCIPLE OF TRAINING AN
ALGORITHM OR MODEL HERE ON LARGE
DATASETS AND THEN USING THAT AS
A STARTING POINT TO IMPROVE WHAT
WE CAN DO ON DOWNSTREAM TASKS.
SO, IN TERMS OF SUMMARIZING THE
MAIN MESSAGE OF THE PRE-PRINT,
THE STUDY SHOWS OR SHOWS HOW
APPLYING FOUNDATION MODEL CAN
IMPROVE EEG CLASSIFICATION
PERFORMANCE COMPARED TO MODEL
THAT WAS TRAINED FROM SCRATCH
FOR INSTANCE.
WE ALSO SHOW EVIDENCE FOR
GENERALIZABILITY OF THE
FOUNDATION MODEL AND ITS ABILITY
TO ADDRESS CHALLENGES OF DATA
SCARCITY AND HETEROGENEITY IN
EEG, SO SOMETHING, AGAIN, YOU
MIGHT HAVE CONDITIONS OR TASKS
WHERE YOU DON'T HAVE ENOUGH DATA
FOR YOU TO TRAIN A MODEL
RELIABLY TO BE ABLE TO DO ANY
CLASSIFICATIONS.
YOU
JUST CAN'T TRAIN A MODEL FROM
THE EXISTING DATA IN THAT TASK.
TRANSFER LEARNING OF THIS TYPE
HERE WOULD BE USEFUL.
OBVIOUSLY THE CODE IS AVAILABLE
FOR THIS WORK.
WE STARTED THINKING ABOUT
ETHICAL CONSIDERATIONS THAT
HOPEFULLY WE'LL BE DISCUSSING
THESE AND OTHERS TODAY YOU MIGHT
RECOGNIZE TWO SLIDES FROM
PATRICK'S PRESENTATION, THEY FIT
PERFECTLY WELL.
THERE ARE ETHICAL CONSIDERATIONS
THAT HOLD TRUE FOR CLASSIC
MACHINE LEARNING IN NEUROSCIENCE
ANYWAY NOT SPECIFIC TO THIS,
THEY HOLD HERE TOO,
CONSIDERATIONS FOR FOUNDATION
MODELS, PARTICULARLY IMPORTANT
WHERE WE NEED TO THINK ABOUT THE
FACT THAT OPEN WEIGHTS IS NOT
THE SAME AS OPEN SOURCE AND IN
SOME OF THESE GPT OR GPT-LIKE
MODELS THAT WE MIGHT BE USING IN
THIS ENDEAVOR MANY, FOR EXAMPLE,
GPT 3 OR 4, THE WEIGHTS ARE NOT
AVAILABLE, THIS IS AN IMPORTANT
ISSUE TALKING ABOUT ETHICS IF
YOU WANT TRANSPARENCY AT ALL
LEVELS OF THE PIPELINE.
ANOTHER -- TWO OTHER POINTS
WORTH DISCUSSING, I WAS HAPPY TO
SEE THIS IN A DISCUSSION EARLIER
ON TODAY, THE FACT THAT INFORMED
CONSENT IS A TRICKY CONCEPT, I
THINK HERE, BECAUSE IT'S
DIFFICULT TO BE INFORMED ABOUT
WHAT'S GOING TO COME LATER
SPECIFICALLY IN THE CASE WHERE
FIELD OF A.I. IS MOVING SO FAST.
SO INDIVIDUAL THAT PROVIDES
CONSENT FOR THE USE OF A.I. IN
REALITY DON'T REALLY KNOW WHAT
THEY ARE PROVIDING COULD -- COT
FOR, WE NEED GUIDELINES TO HELP
US IDENTIFY WHAT A CONSENT AT
THE TIME IMPLIES FOR WHEN
TECHNOLOGY CHANGES IN THE FUTURE
AND WHETHER THOSE -- THAT
CONSENT IS STILL VALID OR NOT.
I'M SURE THERE ARE LEGAL EXPERTS
AND EXPERTS IN NEUROETHICS THAT
HAVE AN OPINION ON THIS.
ALSO MY SECOND POINT HERE IS
DERIVING PRE-TRAINED MODELS WILL
PERPETUATE AND EVEN WORSE THAN
THAT ENHANCE BIASES THAT EXIST
IN THE TRAINING SET.
THIS IS AN IMPORTANT ISSUE HERE.
WE KNOW THIS.
WE'VE KNOWN THAT GARBAGE IN,
GARBAGE OUT IN OUR MODELS, BIAS
IN ALSO MEANS BIAS OUT.
AND WHEN WE ARE THINKING WHAT
HAPPENS WHEN WE DERIVE
FOUNDATION MODELS, WE NEED TO
REALIZE THAT, YEAH, THAT IF
WE'RE TALKING ABOUT PRE-TRAINING
MODELS, FOUNDATION MODELS FOR
NEUROSCIENCE, WE'LL BE LOOKING
FOR THE DATA OUT THERE THAT
EXISTS.
WHAT'S NEUROSCIENTIFIC DATA,
WHAT BRAIN DATA, EEG, MAG,
fMRI, WHAT OTHER DATASETS ARE
AVAILABLE, AND WE'LL REALIZE
QUICKLY THAT THERE'S STRONG BIAS
IN THE WAY THE DATA HAS BEEN
COLLECTED AND WHAT DATA IS
AVAILABLE.
THAT'S AN IMMENSE PROBLEM.
ALSO BEYOND THESE LIMITATIONS
THERE'S THE FACT THAT IN THE
DATA THAT IS AVAILABLE AND THAT
WE CAN USE FOR PRE-TRAINING AND
THAT'S GOING TO BE -- SOMEHOW
SHAPE THE PRE-TRAINING MODELS
THAT WILL BE USED IN THE FUTURE
IN THE DOWNSTREAM TASKS, THERE'S
ALSO MUCH MORE DATA AVAILABLE
ABOUT MORE COMMON NEUROLOGICAL
AND PSYCHIATRIC CONDITIONS THAN
FOR RARE DISEASES FOR INSTANCE
OR RARE CONDITIONS.
THAT IS ALSO A PROBLEM BECAUSE
WE MIGHT DEVELOP PRE-TRAINED
MODELS THAT ACTUALLY WORK VERY
WELL AS LONG AS WE ARE LOOKING
AT CLINICAL CONDITIONS THAT ARE
QUITE -- FOR WHICH WE HAVE A LOT
OF DATA AND THAT ARE, YEAH, THAT
ARE AVAILABLE ACROSS MANY
HOSPITALS THAT MADE THE DATASET
AVAILABLE, BUT IN RARE
CONDITIONS LIKE -- AND THIS IS
EXACTLY THE SAME PROBLEM WE
DISCUSSED WHEN WE TALK ABOUT
LARGE LANGUAGE MODELS, LOW
RESOURCE LANGUAGES, AND SOME
LANGUAGES ACROSS HUNDREDS THAT
EXIST IN AFRICA, ALSO INDIGENOUS
LANGUAGES ARE VERY GOOD EXAMPLES
OF SITUATIONS WHERE THE DATA
AVAILABLE IS SO SMALL COMPARED
TO THE MORE DOMINANT LANGUAGES
THAT THEY GET OVERSHADOWED AND
IN THE TRAINING, PRE-TRAINED
MODELS A BIAS GETS INJECTED INTO
THIS.
WHAT ABOUT INDIVIDUALIZED
MEDICINE?
WE'RE TALKING ABOUT DEVELOPING
MODELS FOR INDIVIDUALS THAT'S
GOING TO BE FINE TUNED, WHAT
GUARANTEES DO WE HAVE THAT
PRE-TRAINING ON A LARGE COHORT
AND THEN FINE TUNING TO
INDIVIDUAL WILL ACTUALLY BE
BETTER THAN JUST TRYING TO FINE
TUNE ON A SINGLE INDIVIDUAL?
THESE ARE OPEN QUESTIONS.
YES, THAT WAS ALL I WANTED TO
SAY TODAY.
THESE ARE MY ACKNOWLEDGMENTS.
I THANK YOU FOR YOUR ATTENTION
AND I HOPE I DIDN'T GO TOO MUCH
OVER TIME.
THANKS.
>> TERRIFIC PRESENTATION.
OPENED UP A LOT OF QUESTIONS FOR
EVERYONE.
BEFORE WE TAKE QUESTIONS I WANT
TO TURN TO DR. DEBRA MATHEWS TO
THE GUY -- GUIDE US THROUGH
ETHICAL THINKING, YOU ALREADY
STARTED TO INTRODUCE THOSE
ETHICAL QUESTIONS AND WE'LL TAKE
QUESTIONS AT THE END.
LET ME TURN IT OVER TO DR.
MATHEWS NOW.
>> TERRIFIC.
LET'S SEE IF -- ARE FOLKS SEEING
MY FIRST SLIDE?
EXCELLENT.
>> YES, WE SEE IT.
>> GREAT.
FIRST, THANKS.
I'LL ADD MY THANKS TO THE
ORGANIZERS FOR THE INVITATION,
ALSO HELLO TO MY FRIENDS AND
COLLEAGUES WHO ARE ON THE
COMMITTEE, ON THE CALL TODAY.
APOLOGIES, I WASN'T ABLE TO JOIN
UNTIL 1:00 SO I DON'T KNOW WHAT
I MISSED, AND THEREFORE HOW
REDUNDANT SOME OF WHAT I PLAN TO
SAY MIGHT BE BUT HOPEFULLY IT
WILL BE COMPLEMENTARY TO WHAT'S
ALREADY BEEN SHARED.
IT'S CERTAINLY I THINK BUILDING
NICELY, A LOT OF DISCUSSIONS
THAT HAPPENED SINCE I DID JOIN
THE CALL.
SO I'LL ALSO START WITH A LAND
ACKNOWLEDGMENT, MY HOME IN
BALTIMORE CITY ALONG WITH JOHNS
HOPKINS UNIVERSITY SITS ON
UNCEDED LANDS PISCATAWAY AND AD
SUSQUEHANNAK.
I'M GOING TO START WITH A BIT
MORE BACKGROUND ON A.I., ETHICS
AND GOVERNANCE, I WANT TO
INCLUDE IT IN MY SLIDES FOR
REFERENCE LATER IN CASE THAT'S
HELPFUL TO THE COMMITTEE.
OF COURSE THERE ARE LOTS OF
AMAZING OPPORTUNITIES OFFERED BY
THE IMPLEMENTATION OF ML BASED
METHODS IN MEDICINE AND IN
NEUROSCIENCE IN PARTICULAR,
HEALTH SERVICES MANAGEMENT,
PREDICTIVE MEDICINE, PATIENT AND
DATA DIAGNOSTICS, ET CETERA,
THAT COMES IN DEVELOPMENT USE,
IMPLEMENTATION COMES WITH BOTH
FAMILIAR AND NEW CONSTELLATION
OR HEIGHTENED SET OF ETHICAL
ISSUES.
FAMILIAR CHALLENGES INCLUDE MANY
THAT HAVE BEEN TALKED ABOUT
TODAY, CONCERNS ABOUT INFORMED
CONSENT, PRIVACY, INCIDENTAL
FINDINGS, RETURN OF RESULTS,
DATA OWNERSHIP, AND DATA
SHARING, INCLUDING DATA SHARING
UNDER MULTIPLE DIFFERENT SORT OF
ETHICAL REGIMES.
THE MOST SORT OF THE CURRENT
GOLD STANDARD OF COURSE ARE FAIR
PRINCIPLES, AND IMPORTANT TREND
OR IMPORTANT FORCE I WOULD SAY
OVER THE LAST COUPLE YEARS IS
INDIGENOUS DATA SOVEREIGNTY
MOVEMENT, AND I'LL TALK MORE
ABOUT THAT LATER.
BUT OFFERED A DIFFERENT SET OF
PRINCIPLES FOR DATA SHARING,
CARE PRINCE TELLS, COLLECTIVE
BENEFIT, AUTHORITY TO CONTROL,
RESPONSIBILITY AND ETHICS.
IT'S CLEAR THAT ETHICAL
PRINCIPLES ARE GUIDING YOU SHAPE
AND BUILD VERY DIFFERENT KINDS
OF DATA SHARING REGIMES.
DUAL USE, WE HAVE A LONG HISTORY
OF DEVELOPING NEW TECHNOLOGIES
FOR THE BENEFIT FOR EXAMPLE IN
MEDICINE OF THOSE AMONG US MOST
PRIVILEGED, AND THEN THE SAME
TECHNOLOGIES ARE TURNED TOWARDS
MARGINALIZED AND MINORITIZED
COMMUNITIES AND USED FOR
SURVEILLANCE.
IMPACT OF EARLY PUBLIC FAILURE
OF SOME NEW TECHNOLOGIES AND OF
A.I. IN PARTICULAR, FOR EXAMPLE
WHEN ChatGPT ENCOURAGED THE
REPORTER TO LEAVE HIS WIFE, AND
IMPORTANTLY FOR THE U.S. CONTEXT
AND IMPORTANT FOR THE
CONVERSATIONS THAT WE'LL HAVE
ABOUT CONSENT, AND RISK AND
BENEFIT, MANY PUBLICS HAVE NO
REASONABLE EXPECTATION OF
BENEFIT, BECAUSE HERE IN THE
U.S. WE DON'T HAVE A NATIONAL
HEALTH CARE SYSTEM, DON'T HAVE A
RIGHT TO HEALTH CARE, AND THAT
DOESN'T CHANGE WITH INCREASING
CALLS TO INCREASE DIVERSITY IN
RESEARCH.
I DON'T THINK WE HAVE SORT OF
FULLY -- IT'S LONG RECOGNIZED
BUT NOT SOMETHING WE FULLY
GRAPPLED WITH.
NOW, IN A.I. IN PARTICULAR,
THERE IS NOT NECESSARILY NOVEL
ISSUES, BUT IN NOVEL
CONSTELLATION OF ISSUES, THESE
INCLUDE -- ONE OF THE I THINK
MORE NOVEL AMONG THEM IS JUST --
YOU'VE HEARD ABOUT ALREADY
TODAY, SCALE, PERVASIVENESS,
INVISIBILITY OF TOOLS AND
EVOLUTION OVER TIME AS THEY ARE
USED.
ALSO IN CONTRAST TO OTHER
TECHNOLOGIES THAT WERE IN B
IOMEDICINE THAT WE'RE FAMILIAR
WITH, WHERE THERE ARE ETHICAL
ISSUES, MANY ETHICAL ISSUES OF
COURSE, AND THEY ARE OFTEN
AROUND HOW THOSE DATA OR
TECHNOLOGIES ARE USED.
IN A.I., THE ETHICS IS BAKED IN.
IT'S OFTEN BAKED IN
UNINTENTIONALLY.
AND IT CAN BE BAKED IN IN MANY
DIFFERENT WAYS, INCLUDING BIAS
TRAINING DATA WHICH I KNOW HAS
ALREADY BEEN DISCUSSED,
INAPPROPRIATE LABELING WHEN THE
PROXY YOU CHOOSE FOR THE THING
YOU'RE TRYING TO PREDICT IS NOT
THE RIGHT PROXY.
I'LL COME TO AN EXAMPLE IN A
SECOND.
NARROW OR INADEQUATE
FUNCTIONALITY, APPLE WATCH
INITIALLY HAD NO MENSTRUAL
TRACKER DESPITE THE FACT ABOUT
HALF THE POPULATION MENSTRUATES,
FAIRNESS HAS BEEN MENTIONED
TODAY, AND THEN THE
INSCRUTABILITY AND BLACK BOX
THAT COMES WITH SOME OF THESE
TECHNOLOGIES.
IN THE CONTEXT OF HEALTH CARE,
I'VE INCLUDED TWO EXAMPLES NOT
DIRECTLY NEURO-EXAMPLES BUT
STILL GIVE A SENSE OF KINDS OF
ISSUES A.I. PRESENTS IN THE
HEALTHCARE SPACE.
FIRST IS ABOUT THE LABELING
ISSUE THAT I MENTIONED, A VERY
FAMOUS PAPER, MANY OF YOU ARE
FAMILIAR WITH IT.
IN WHICH THEY -- OBERMEYER
DEMONSTRATED THIS TOOL BEING
USED BY LARGE HEALTH SYSTEMS WAS
USING HEALTH CARE COSTS AS A
PROXY FOR HEALTH CARE NEED AND
BECAUSE OF STRUCTURAL INEQUITY
IN THE U.S. HEALTH CARE SYSTEM,
IT WAS -- BLACK PATIENTS WERE
CONSIDERABLY SICKER THAN WHITE
PATIENTS.
THE SECOND EXAMPLE BY GOODMAN ET
AL., A.I.-GENERATED CLINICAL
SUMMARIES, ASKED AN LLM TO
GENERATE CLINICAL SUM SUMMARIEN
THE SAME RECORD REPEATEDLY,
SUMMARIES PRODUCED WERE
DIFFERENT, VARIED BY
ORGANIZATION, PHRASING,
INCLUSION OR EXCLUSION OF
PARTICULAR CLINICAL DETAILS,
WHICH COULD REASONABLY SHAPE
CLINICAL DECISION MAKING IN WAYS
THAT MIGHT BE CONCERNING.
AND THESE ARE THE KINDS OF TOOLS
THAT WILL ARE UNLIKELY TO FALL
UNDER FDA'S AUTHORITY.
OR UNDERGO CLINICAL TRIALS.
SO, AT THIS POINT WE HAVE MANY
EFFORTS AT GOVERNANCE OF A.I.,
SETS OF RECOMMENDATIONS, BUT
WE -- AGAIN I'M JUST BREEZING
THROUGH THESE BUT THESE ARE SOME
THAT MIGHT BE OF INTEREST TO THE
COMMITTEE IN PARTICULAR.
THIS ONE, THIS CAME OUT OF NIH,
OUT OF A WORKING GROUP OF THE
NEXTRAC, FOCUSED ON DATA SCIENCE
AND EMERGING TECH IN BIOMEDICAL
RESEARCH.
THIS REPORT CAME OUT A YEAR AGO,
AGAIN I HAVE THE RECOMMENDATIONS
HERE, I'M NOT GOING INTO DETAIL
THERE BUT IT'S AVAILABLE FOR
YOUR REFERENCE, AND I'M HAPPY TO
ANSWER QUESTIONS.
AND THEN OF COURSE THIS
DISCUSSION DRAFT THAT WAS
CIRCULATED TO THE COMMITTEE AND
IDENTIFIED CONDUCT, CODE OF
CONDUCT PRINCIPLES FOR USE OF
A.I. IN HEALTH CARE AND I'LL
MENTION ONE OF MY HOPKINS
COLLEAGUES WAS INVOLVED WITH
THIS WORK.
AND WE'RE -- THERE ARE MANY SUCH
DOCUMENT BUS WE'RE STILL IN THE
EARLY DAYS AND STILL VERY MUCH
IN THE SPACE OF ETHICS, NORMS,
GOVERNANCE, TRYING TO CATCH UP
EVEN A LITTLE BIT TO WHERE THE
SCIENCE IS BUT THE SCIENCE IS
NOT STOPPING.
IT JUST IS ACCELERATING.
AND GIVEN THE EXPERTISE AND ROLE
OF THIS COMMITTEE THERE'S NO
NEED FOR ME TO REHEARSE THE
ISSUES RAISED BY
NEUROTECHNOLOGY.
NEUROSCIENCE INDEPENDENT OF ITS
CONVERGENCE WITH A.I., LISTED A
FEW RELEVANT CONSIDERATIONS
HERE, SOME OF WHICH HAVE ALREADY
BEEN BROUGHT UP.
SO, BECAUSE OF LIMITATIONS OF
TIME, THERE'S A WHOLE BUNCH OF
ARGUE MENTATION AND SUPPORT THAT
I HAVEN'T SHOWED.
BUT I WANTED TO SUMMARIZE WHERE
I SEE US BEING IN TERMS OF THE
PATIENT AND PARTICIPANT SIDE OF
THINGS IN THIS RESEARCH AREA.
AND THE SCIENCE AND GOVERNANCE
FOR ETHICS SIDE.
SO, AS HAS MENTIONED, PATIENTS
AND RESEARCH PARTICIPANTS
GENERALLY SUPPORT AND ARE HAPPY
TO CONTRIBUTE DATA TO RESEARCH.
BUT THEY DO WANT TO BE ASKED.
THEY ARE ALSO LESS WILLING TO
SUPPORT AND CONTRIBUTE THEIR
DATA TO RESEARCH AND DEVELOPMENT
INVOLVING COMMERCIAL ENTITIES,
THIS WAS FOUND NOT ONLY IN
PUBLIC ENGAGEMENT THAT WE DID AS
PART OF THE NEXTRAC WORKING
GROUP BUT ALSO BY OTHERS.
I WANT TO POINT OUT, THIS WAS
ALLUDED TO, AS WELL, PATIENTS
WITH SERIOUS DISEASE AND PARENTS
OF KIDS WITH SERIOUS DISEASE
OFTEN HAVE A DIFFERENT
RISK/BENEFIT ANALYSIS OF
CONTRIBUTING TO -- CONTRIBUTING
DATA TO RESEARCH PARTICULARLY
WHERE DATA IS AT RISK OF
RE-IDENTIFICATION, ET CETERA.
LIKE THEY DON'T CARE, RIGHT?
THEY JUST WANT BENEFIT.
THEY JUST WANT FORWARD MOTION TO
BE MADE, PARTICULARLY OR OFTEN
PARTICULARLY IN RARE DISEASE.
SO, THIS DIFFERS ACROSS PEOPLE,
OF COURSE.
WE KNOW FROM QUITE A BIT OF
RESEARCH THAT THE PRIMARY DRIVER
OF PEOPLE'S WILLINGNESS TO
CONTRIBUTE IS TRUST IN THE
PERSON OR INSTITUTION ASKING
THEM AND ALTRUISM.
PEOPLE WANT TO CONTRIBUTE TO
BENEFIT FOR OTHERS.
FOR A VARIETY OF BOTH HISTORICAL
AND CONTEMPORARY REASONS, A BIG
SEGMENT OF OUR POPULATION IS
LESS WILLING TO CONTRIBUTE, AND
I MENTIONED BEFORE HAVE LESS
ACCESS TO BENEFIT THAT COMES
FROM THIS KIND OF RESEARCH.
AND THERE ARE INCREASING CALLS
FROM INDIVIDUALS AND COMMUNITIES
FOR RETURN OF VALUE.
SO WE ALSO HEARD THIS IN OUR
PUBLIC ENGAGEMENT WITH THE
NEXTRAC WORKING GROUP,
COLLEAGUES WORKING IN GENETICS
AND A.I., ET CETERA.
ON THE SCIENCE AND GOVERNANCE
SIDE OUR RULES WERE BUILD FOR
INTERVENTIONAL RESEARCH IN A
CONTEXT OF RARE EXPENSIVE
SEQUENCING AND DATA COLLECTION,
WHERE THERE WAS A REASONABLE
EXPECTATION THAT YOU COULD
DE-IDENTIFY DATA AND THAT DATA
COULD REMAIN PRIVATE AND SECURE,
SO INFORMATIONAL RISKS COULD BE
ELIMINATED.
AND CONSENT WAS THEREFORE NOT
NEEDED.
I THINK INFORMATION ECOSYSTEM
HAS CHANGED, THAT TECHNOLOGY HAS
CHANGED, AND WE NEED TO START
RESPONDING TO THAT.
ALSO IN THE U.S. THERE'S A
STRONG FOCUS ON AUTONOMY IN THE
U.S. AND WEST GENERALLY NOT
EVERYWHERE IN THE WORLD, BUT
AUTONOMY IS PRIZED OFTEN ABOVE
CONCERNS ABOUT JUSTICE AND
EQUITY.
AND WE DON'T HAVE GREAT WAYS OF
MEASURING IMPACTS OF RESEARCH
AND TECHNOLOGY AT THE POPULATION
LEVEL, AND IT'S NOT SOMETHING WE
GENERALLY TAKE INTO ACCOUNT IN
THE REGULATORY SPACE.
SCIENCE HAS EVOLVED
DRAMATICALLY.
THIS A.I. IS MOVING INCREDIBLY
RAPIDLY.
SO OUR ETHICAL AND GOVERNANCE
FRAMEWORKS NEED TO AS WELL.
THE SCIENCE DOESN'T EXIST IN A
VACUUM.
ALONDRA NELSON'S OF THE SOCIAL
LIFE OF DNA, THE FACT THERE ARE
MANY DIRECT TO CONSUMER
NEUROTECHNOLOGIES AVAILABLE, WE
ALL HAVE ACCESS TO ChatGPT, ET
CETERA.
PEOPLE'S UNDERSTANDING OF WHATS
GOING ON IN THE SCIENCE IS GOING
TO BE SHAPED BY THEIR EXPERIENCE
OF THESE TECHNOLOGIES IN THE
WILD.
FINALLY EQUITY DISTRIBUTION OF
RISKS AND BENEFITS IS IMPORTANT
NOT JUST AMONG RACIAL AND
ETHNICS GROUPS BUT RESEARCHERS,
AS ENVIRONMENT HAS SHIFTED RISKS
AND SHIFT AND WE HAVEN'T
NECESSARILY KEPT UP IN TERMS OF
THINKING ABOUT THE BENEFITS SIDE
OF THAT, ESPECIALLY WHEN YOU'RE
TALKING ABOUT UNCONSENTED DATA.
SO, THAT BRINGS ME TO THE PAPER.
AND I THINK IT'S BECAUSE OF THE
NATURE OF A.I. ML-BASED
TECHNOLOGIES AND OUR DUTIES
WITHIN THE HEALTH CARE SYSTEM TO
IMPROVE WELL-BEING WE NEED TO BE
THINKING NOT JUST ABOUT
INDIVIDUAL EXPERIMENTS OR MODELS
BUT THE LIFE CYCLE AND LIFESPAN
OF WHAT WE'RE CREATING.
SO, FROM -- AND THIS IS NOT
NECESSARILY EVERY SINGLE STEP.
THIS IS JUST SORT OF ONE SET
OF -- ONE WAY OF SLICING UP THE
PIE.
AND SO STARTING WITH DATA
COLLECTION AND, YOU KNOW, HERE
AGAIN THERE'S BEEN A FAIR AMOUNT
OF CONVERSATION ABOUT THIS, BUT
CONSENT, WHAT ARE THE DATA
TYPES, WHAT IS BEING DONE ABOUT
IDENTIFIABILITY, HOW ARE DATA
BEING STORED, HOW ARE DATA BEING
SHARED, ARE THEY BEING SOLD, WHO
OWNS THE DATA, WHO HAS ACCESS TO
THE DATA, HOW ARE THEY BEING
USED, ET CETERA.
I THINK EVEN THOUGH THERE ARE
INCREASING CALLS FOR A.I.-READY
DATASETS, THERE IS NOT CONSENSUS
ABOUT WHAT CONSTITUTES ETHICALLY
SOURCED DATA FOR A.I.
DATA PROCESSING, RIGHT, WHERE IS
THAT HAPPENING, AND THE SECURITY
OF THAT, HOW IT'S BEING CLEANED,
HOW IT'S BEING LABELED, OR IN
THE CASE OF THE PAPER CHUNKED,
I'LL COME BACK TO THAT IN A SEC.
MODEL DEVELOPMENT INCLUDING
ENVIRONMENTAL IMPACT OF THAT.
TESTING, APPLICATION
DEVELOPMENT, THESE ARE
DOWNSTREAM FROM WHERE THIS PAPER
IS BUT APPLICATION DEVELOPMENT,
IMPLEMENTATION, AUDITING FOR NOT
ONLY THE IMPACT OF THE ALGORITHM
ON INDIVIDUALS AND POPULATIONS
BUT ALSO DRIFT OVER TIME,
ENSURING THAT THE TOOL IS
STILL -- THE ALGORITHM IS STILL
DOING WHAT IT WAS INTENDED TO DO
IN THE FIRST PLACE.
AND THEN HIGHER LEVEL QUESTION,
WHOSE VALUES ARE INFORMING THE
DECISIONS THAT ARE MADE AT EACH
OF THESE STEPS.
SO IN THE PARTICULAR CASE OF THE
PAPER, FIRST IT'S IMPORTANT TO
NOTE I'M NOT SUGGESTING THERE'S
ANYTHING FUNDAMENTALLY FLAWED
ABOUT THIS WORK OR IT DOESN'T
CONFORM TO CURRENT STANDARDS BUT
WAS ASKED TO RESPOND TO THIS
PAPER AND THE IDEA OF TRAINING
MODELS ON HUMAN BRAIN DATA.
SO, GOING FROM THAT SORT OF LIFE
CYCLE OR LIFESPAN APPROACH THAT
I JUST DESCRIBED, STARTING WITH
THE DATA, THE TRAINING DATA CAME
FROM THE TEMPLE UNIVERSITY EEG
CORPUS.
THIS IS CLINICAL DATA, THESE ARE
DATA THAT WERE COLLECTED FROM
CLINICAL TESTING FROM 2002 TO
2013 AND BEYOND.
IT HAD THE 18 HIPAA IDENTIFIERS
REMOVED.
THE DATA WERE DE-IDENTIFIED IN A
VARIETY OF WAYS.
I BELIEVE IT'S THE CASE THAT
THIS TEAM ONLY USED THE EEG DATA
FROM THIS CORPUS, THE CORPUS
ALSO INCLUDES SUMMARY OF
CLINICAL HISTORY AND MEDICATION
HISTORY.
AND THE MEDICAL FINDINGS.
THE COHORT ITSELF IS PRIMARILY
AND EPILEPSY COHORT, SOMETHING
LIKE 87% OF THE RECORDS HAD
LABEL OF EPILEPSY, A SMALL
PERCENTAGE HAD STROKE AS AN
INDICATION.
I WAS UNABLE TO LEARN WHAT THE
DIVERSITY OF THE PATIENTS IN
THIS DATABASE ARE, NOT JUST
RACIAL, ETHIC, SOCIOECONOMIC BUT
BAKING IN A NEUROTYPICAL BIAS
TRAINED ON THESE DATA.
AGAIN, THESE WERE CLINICAL DATA
SO THIS HAPPENED WITH IRB REVIEW
BUT I IMAGINE THEY DIDN'T STATE
IT EXPLICITLY IN THE PAPER, I
IMAGINE A WAIVER OF CONSENT.
WITH REGARD TO CONTENT, AGAIN, I
THINK THE TEAM ONLY USED THE EEG
DATA BUT HERE IS ONE OF THE --
I'M A HUMAN GENETICIST BY
TRAINING, A LOT OF THINGS IS
SHAPED BY THAT.
ONE OF THOSE WAYS IS WITH REGARD
TO THE CONTENT, AND WHETHER OR
NOT WE'RE USING JUST BRAIN DATA
OR WE'RE ALSO USING THE OTHER
KINDS OF
MEDICAL/BEHAVIORAL/SOCIAL
TORRENTS OF HEALTH DATA THAT
MIGHT INFORM WHAT THE BRAIN DATA
ARE ACTUALLY TELLING US, RIGHT?
AND HOW MUCH DATA DO WE NEED TO
UNDERSTAND OR IMBUE BROADER
MEANING INTO THE BRAIN DATA.
ON THE CHUNKING, THIS IS MORE OF
A TECHNICAL QUESTION, AND
REVEALS MY LACK OF EXPERTISE IN
THIS PARTICULAR SPACE, BUT THE
CHUNKING THAT WAS DONE, RIGHT,
THE ANALOGY IN MY BRAIN WAS TO
DNA, RIGHT?
AND INTRONS AND EXONS.
IF YOU'RE CHUNKING IN A
PARTICULAR WAY ARE YOU
DISRUPTING MEANING IN THE DATA?
I KNOW THE CHUNKS EACH HAD 10%
OVERLAP IN THE FINAL GO, BUT HOW
DO WE THINK ABOUT THOSE KINDS OF
DECISIONS, RIGHT?
LABELING WASN'T USED HERE BUT
CHUNKING WAS.
HOW DO WE THINK ABOUT THE IMPACT
OF THOSE KINDS OF DECISIONS ON
THE MEANING WE'RE GETTING OUT OF
THE ALGORITHM AND ITS DOWNSTREAM
USES?
OF COURSE CONCERNS ABOUT PRIVACY
AND IDENTIFIABILITY, AND THIS
CAME UP IN THE CHAT EARLIER,
RIGHT?
THE QUESTION OF IF YOU PUT THE
DATA IN, CAN THE DATA THEN LATER
BE EXTRACTED, RIGHT?
THIS WAS A FOUNDATION MODEL.
WE KNOW THAT THEY CAN SPIT OUT
TRAINING DATA.
SO CONCERNS ABOUT THAT IN THIS
CASE.
RISK/BENEFIT AND RETURN OF
VALUE, WHAT ARE QUESTIONS
DRIVING THIS RESEARCH, WHOSE
VALUES AND EXPERIENCE ARE
INFORMING THE QUESTIONS AND HOW
IS THE MODEL ULTIMATELY -- AGAIN
THIS IS PROOF OF PRINCIPLE BUT
DOWNSTREAM HOW MIGHT IT BE USED,
WHAT KINDS OF APPLICATIONS,
AGAIN GIVEN WE KNOW IN THIS CASE
THAT THE FOLKS WHOSE DATA
TRAINED THE MODEL DID NOT GIVE
CONSENT, HOW DO WE THINK ABOUT
RETURN OF VALUE GIVEN THE
POTENTIAL RISKS THEY HAVE TAKEN
ON WITH REGARD TO
RE-IDENTIFICATION.
I'VE INCLUDED A NUMBER OF
CITATIONS FOR THINGS THAT I
MENTIONED IN THE TALK AND THAT
OTHERWISE INFORMED MY
PRESENTATION.
AND WITH THAT I LOOK FORWARD TO
THE DISCUSSION.
>> THAT WAS A GREAT PLACE FOR US
TO BEGIN THE CONVERSATION.
I KNOW THERE ARE A LOT OF
QUESTIONS.
GRACE AND I WILL LAUNCH THE
CONVERSATION, AND I THINK I'LL
START WITH A QUESTION FOR DR.
JERBI, PICKING UP ON WHAT SOME
OF WHAT DR. MATHEWS WAS
DISCUSSING.
I FOUND THIS PAPER FASCINATING,
BUT I WANT TO START WITH JUST
THE BASIC ISSUE OF BIAS, OF
UNDERSTANDING, YOU KNOW, HOW
GIVEN THAT EEG DATA
TRADITIONALLY IS INHERENTLY
BIASED IN MANY CASE, THAT OVER
TIME WE'VE SEEN THAT CERTAIN
GROUPS OF INDIVIDUALS ARE
EXCLUDED, THAT THIS IS CLINICAL
DATA SO LIMITED ALREADY TO A
SUBSET OF INDIVIDUALS, HOW ARE
YOU ADDRESSING BIAS IN THE MODEL
OR HOW DO YOU PLAN TO ADDRESS
BIAS IN THE MODEL GOING FORWARD.
I'LL START THERE.
I HAVE AUDITIONAL QUESTIONS BUT
WANT TO MAKE SURE EVERYBODY ELSE
HAS A CHANCE.
>> THANK YOU SO MUCH.
THANK YOU, DEBRA, ALSO.
THERE WERE A LOT OF QUESTIONS.
I HOPE WE GET TIME TO GO THROUGH
ALL THESE.
SO I'LL START WITH YOUR
QUESTION, NITA.
SO, FOR NOW NOTHING HAS BEEN
DONE.
THAT'S THE PROBLEM.
THE REASON PARTLY IS THAT -- SO
IN TERMS OF GETTING A
PROOF-OF-CONCEPT THAT THESE
THINGS CAN WORK, THAT YOU CAN
TAKE LARGE AMOUNTS OF DATA,
GENERATE GENERIC INFORMED MODEL
THAT BASICALLY IS ABLE TO
GENERATE SOMETHING LIKE EEG,
BECAUSE IT'S LEARNING TO
REGENERATE EEG, IT'S TRYING TO
CREATE THAT SO LEARNING
REPRESENTATIONS, WHAT DOES EEG
DATA LOOK LIKE?
JUST THE MERE FACT OF TRYING TO
GENERATE THAT AND THEN SAY CAN
THIS BE A STARTING POINT I CAN
FINE TUNE AND MAKE IT OTHER ON
OTHER CLASS
CLINICAL APPLICATIONS, PCI, --
BCI AND SO ON.
THERE'S INCREASING INTEREST TO
BUILD THESE MODELS, COMPARE
THEM, FIND FUNCTIONAL TRICKS TO
MAKE THEM MORE ROBUST, MORE
RELIABLE, AND IDEALLY MORE FAIR
DOWN THE LINE.
BUT IN GENERAL PEOPLE GO RIGHT
TO LARGER DATASETS THAT EXIST
AND THIS IS ONE OF THE LARGER
ONES THERE, WHICH COMES WITH
LIMITATIONS AND I'M 200% IN
AGREEMENT WITH EVERYTHING SAID
ABOUT LIMITATIONS THAT ACTUALLY
IN THIS DATASET BUT APPLIES TO
THE WHOLE FILED OF COLLECTED EEG
DATA.
WE'RE VERY FAR FROM HAVING
SUFFICIENT DATA COLLECTED ACROSS
DISEASES, ALSO ACROSS GEOGRAPHIC
GROUPS, UNDERREPRESENTED
MARGINALIZED GROUPS, SO THERE'S
A PROBLEM INHERENTLY WITH
EXISTING DATA.
SO WHETHER WE'RE TALKING ABOUT
JUST DOING REGULAR RESEARCH ON
EXISTING EEG DATA OR DEVELOPING
MODELS THAT'S INFORMED BY EEG
DATA THAT EXISTS THIS SAME ISSUE
STILL PERSISTS, IT'S NOT
SPECIFIC TO I THINK BUILDING
THESE MODELS BUT WE HAVE TO BE
AWARE OF IT BECAUSE IT CAN HAVE
DOWNSTREAM IMPLICATIONS.
WHEN WE CLAIM OR WHEN SOME
RESEARCHERS CLAIM NOW THEY HAVE
A VERY POWERFUL MODEL TRAINED ON
DATASETS, YOU CAN FINE TUNE AND
RUN AND APPLY TO MANY
APPLICATIONS, WE ALWAYS NEED TO
KNOW THAT DATA THAT WAS USED HAS
ITS OWN BIAS AND WE NEED TO TRY
AND MITIGATE.
ONE WAY OF DOING THAT IS
OBVIOUSLY TRY AND HAVE LESS --
MORE -- THAT DATA THAT IS USED
FOR TRAINING THAT WE CAN HAVE
ACCESS TO DATA THAT SOURCES FROM
THE BEGINNING, AT LEAST REDUCES
BIAS.
I WOULD SAY THAT'S THE PROBLEM
OF THE WHOLE FIELD OF
NEUROSCIENCE IN GENERAL WHICH
GOES BEYOND THESE MODELS.
NOT SURE THAT ANSWERS THE
QUESTION.
>> VERY HELPFUL.
IT WILL BECOME I THINK A BIG
CHALLENGE PARTICULARLY FOR EEG
DATA GIVEN IT'S OFTEN EXCLUDED
PEOPLE WITH DIFFERENT HAIR
TYPES, SO IT WILL BECOME MORE
PROBLEMATIC IN THIS SETTING THAN
OTHERS.
CAROLYN, LET ME TURN TO THE
CO-MODERATOR FIRST TO JUMP IN
WITH A QUESTION OR IF SHE'S
AHEAD WITH US TAKING QUESTIONS
FROM OTHERS.
>> I HAVE A QUESTION.
I COULDN'T HELP BUT NOTICE ON
YOUR RESULT SLIDE THERE WERE
NON-NEUROGPT METHODS THAT DID
REACH 60% ACCURACY IN PREDICTION
OF EEG DATA, WONDERING IF THEY
WERE TRADITIONAL APPROACHES THAT
DOESN'T REQUIRE THE CARBON
FOOTPRINT OF TRAINING LARGE
LANGUAGE MODELS.
DOES IT MAKE SENSE TO GO IN THE
DIRECTION NEEDING MORE DATA,
CREATING MORE BIAS, CONTINUING
EVENTUAL SUSTAINABILITY ISSUE,
USING LARGE LANGUAGE MODEL, OR
SHOULD WE TAKE A STEP BACK AND
SAY, YOU KNOW, GIVEN THE
HETEROGENEITY OF THIS EEG DATA,
YOU SAID 60% ACCURACY WITH
GPT-2, WHAT IS THE TRADEOFF,
WHEN DO YOU LOOK AT ALTERNATIVE
TECHNIQUES THAT DON'T REQUIRE
ALL THIS DATA?
>> GREAT QUESTION, GRACE.
I AGREE FULLY THAT IT HAS TO BE
WORTH IT AND WE NEED TO MAKE
SURE GAINING FROM IT MORE THAN
ANY RISKS THAT WE INCUR BY DOING
SO.
SAME RATIONALE APPLIES TO, FOR
EXAMPLE, DEVELOPING LARGE
LANGUAGE MODELS.
PEOPLE CONTINUE TO DEVELOP THESE
HUGE MODELS, IT'S COSTLY, HAS A
LARGE CARBON FOOTPRINT.
THERE'S INCREASING INTEREST IN
LOOKING AT SLMs RATHER THAN
LLMs, SMALL LANGUAGE MODELS,
BEING MORE EFFICIENT.
AT OUR RESEARCH CENTER AT UNIQUE
WE'RE PUSHING FOR EXPLORING
MAYBE LEARNING MORE HOW IS THE
BRAIN SO EFFICIENT, SOMETHING
THE BRAIN IS REALLY GOOD AT,
IT'S A COMPLEX NEURAL NETWORK,
ALSO BIOLOGICAL, BUT SUPER
EFFICIENT.
WE'RE STILL LACKING THAT
EFFICIENCY WHEN WE TALK ABOUT
ARTIFICIAL NEURAL NETWORKS.
THERE'S A LOT TO BE LEARNED FROM
THE BRAIN, BUT ALSO FOR SURE A
WHOLE SUB, OR NICHE IN THE
FIELD, LOOKING INTO OPTIMIZING,
INCREASING PERFORMANCE WITHOUT
INCREASING THE SIZE AND
COMPUTES.
ALSO BECAUSE EVEN IF IT'S
UNFORTUNATE, IF IT COMES FROM
INDUSTRIAL AND FROM COMPANIES
THAT ARE TRYING TO MAKE MONEY,
EVEN THAT'S GOING TO BE A FIELD
WHERE FOR THESE THINGS TO BE
FUNCTIONAL AND FOR THEM TO BE
DEPLOYED ON CELL PHONES AND
PEOPLE TO TRAIN MODELS LOCALLY,
IT'S GOING TO BE A PUSH TOWARDS
MORE EFFICIENT MODELS.
I THINK THAT'S GOING TO COME
DOWN THE LINE.
THIS IS WHY STUDIES LIKE OURS
AND A FEW OTHERS OUT THERE AND
MORE IN THE NEXT YEARS I THINK,
YES, THE CURRENT RESULTS IF THAT
WAS THE END GOAL, PROBABLY IT'S
NOT WORTH IT.
INCREASE IN TWO, THREE, FOUR
PERCENT, I DON'T THINK IT'S
WORTH IT.
THE IDEA IS THAT WE'RE FIGURING
OUT HOW TO DO THIS IN THE BEST
WAY POSSIBLE SO THAT -- IN A FEW
I DON'T KNOW, WEEKS, MONTHS,
A.I. GOES SO QUICKLY WE'LL HAVE
SOMETHING THAT ACHIEVES
SUBSTANTIAL IMPROVEMENT THAT
ULTIMATELY BENEFITS THE HUMANITY
AND ALSO CLINICAL APPLICATIONS
AS WELL AS BASIC RESEARCH.
>> MAYBE THERE WILL BE MORE WAYS
TO BOOTSTRAP ETHICS.
>> I SEE CAROLYN WAITED
PATIENTLY SO LET ME TURN TO HER.
>> THANK YOU, NITA.
GOOD TO SEE YOU HERE TODAY.
I HAVE QUESTIONS ABOUT
ALGORITHMIC BIAS, WHICH IS A
TOPIC THAT WAS BROUGHT UP BY
SEVERAL SPEAKERS THROUGHOUT THE
DAY.
ONE OF THEM IS RELATED TO NITA'S
QUESTION, AND ALSO EXTENDS FROM
CHRISTINE'S EARLIER PROMPT ABOUT
FOCUSING ON ASPECTS THAT MIGHT
BE VERY SORT OF UNIQUE OR
SPECIFIC TO NEUROSCIENCE.
SO MY FIRST QUESTION IS ARE
THERE ASPECTS OF ALGORITHMIC
BIAS IN AN A.I. MODEL THAT ARE
UNIQUE TO BRAIN DATA?
WE TALKED ABOUT SOME
EEG-SPECIFIC AREAS BUT THINKING
MORE BROADLY THAN EEG ARE THERE
AREAS THAT ARE UNIQUE TO BRAIN
DATA BEING FED INTO A MODEL AS
COMPARED TO OTHER KINDS OF HUMAN
BIOLOGICAL DATA?
I THINK IT WAS INTERESTING TO
HEAR DEBRA MENTION NEUROTYPICAL
SIGNATURES, I DON'T KNOW, MAYBE
SPATIOTEMPORAL NATURE OF BRAIN
DATA BUT CURIOUS ON TO HEAR
SPEAKER THOUGHTS ON THAT.
SECOND QUESTION, RESEARCH
APPROACHES TO CLEANING UP OR
MITIGATING UNWANTED BIAS IN A
TRAINING DATASET, PERHAPS
INSERTING ETHICAL CONSIDERATION
AND ADJUSTMENT FROM THE
PIPELINE, I THINK THE SECOND
QUESTION MAYBE RELATES MORE TO
BIASES THAT ARE CHARACTERISTIC
IN A GROUP DATASET AS OPPOSED TO
INDIVIDUAL DATASETS, THINGS LIKE
BALANCING DEMOGRAPHIC
REPRESENTATION IN A TRAINING SET
OR I THINK ANOTHER SPEAKER
MENTIONED THE DOMINANT VERSUS
RARE NEUROLOGICAL QUESTIONS.
QUESTIONS ABOUT UNIQUENESS AND
SECOND QUESTION ABOUT MITIGATION
PERHAPS BY TARGETING DATA
PREPARATION PIPELINE.
>> THANK YOU FOR THAT QUESTION.
SO, I AGREE THESE ARE IMPORTANT
TOPICS.
FOR THE FIRST QUESTION, WHICH IS
MITIGATION, SECOND WAS THE
UNIQUENESS, YES.
SO, THERE'S AN OVERLAP.
THERE ARE A FEW THINGS THAT ARE
UNIQUE, THAT ARE SPECIFIC TO
THIS TYPE OF DATA, NEUROSCIENCE
DATA OR BRAIN DATA, I WOULD SAY
FOR EXAMPLE ACCESS TO DATA
COLLECTION.
SOME COUNTRIES WHERE YOU DON'T
HAVE fMRI MACHINE, YOU WON'T
HAVE fMRI DATA COLLECTED IN
SOME CASES ACROSS THE WORLD, AT
LEAST NOT AS OFTEN AS ELSEWHERE.
GEOGRAPHICALLY ACROSS THE PLANET
LOOKING AT WHAT BRAIN DATA WE
HAVE, IT WON'T BE THE SAME
COVERAGE EVERYWHERE.
IF WE'RE RELYING ON EXISTING MRI
DATASETS ACROSS THE PLANET TO
DERIVE A MODEL OF MRI DATA THAT
WE'RE USING AS A PRE-TRAINING
MODEL THAT'S GOING TO COME FROM
THERE, OTHER AP MILES -- APPLIS
TO OTHER TIMES OF DATA, RICH
COUNTRIES.
SO, YEAH, MAYBE I'LL ROLL BACK A
BIT.
IT'S NOT SO MUCH IT'S SO UNIQUE
I WOULD SAY ABOUT THE BIAS THAT
COMES INTO THE BRAIN SCIENCE BUT
WHAT MIGHT BE UNIQUE WHEN WE'RE
TRYING TO BUILD MODELS THAT
WOULD SERVE AS STARTING POINT,
DIFFICULT TO KNOW WHAT WENT INTO
THE MODELS BECAUSE IT MIGHT BE
USEFUL FOR SPECIFIC DOWNSTREAM
APPLICATIONS BUT NOT AT ALL
USEFUL AND ACTUALLY DANGEROUS
FOR OTHER DOWNSTREAM
APPLICATIONS, THAT'S SOMETHING
WHERE THERE'S SOME KNOWLEDGE
ABOUT NEUROSCIENCE OR CLINICAL
APPLICATIONS THAT GETS LOST,
MAYBE WHEN THESE MODELS ARE
BEING BUILT.
AND TO YOUR SECOND QUESTION OF
WHETHER THERE ARE WAYS OF
MITIGATING, IN PRE-PROCESSING, I
THINK SO, APPLIES TO IN GENERAL
PROCESSING EEG OR NEUROSCIENCE
DATA IN GENERAL.
A LOT OF WORK NEEDS TO BE DONE,
IN PRE-PROCESSING, IDEALLY FROM
THE DATA COLLECTION IN GENERAL.
THAT'S SORT OF LIKE WHERE I
THINK MOST OF THE PROBLEM COMES.
AND SO DESIGN OF STUDIES,
EXPERIMENTS.
>> YES, I'LL ADD MY THOUGHTS AS
WELL.
ON THE QUESTION OF UNIQUENESS, I
DO -- I DON'T THINK THIS IS --
THIS ISN'T BRAND NEW.
THERE ARE AS AMY MENTIONED,
THERE ARE ANALOGIES IN OTHER
SPACES, DNA IN PARTICULAR,
RIGHT, BOTH GENETICS AND OUR
BRAINS WE FEEL DETERMINISTIC
ABOUT.
AND FEEL LIKE THEY ARE SPECIAL
IN WAYS THAT OTHER ORGANS OR
OTHER PARTS OF OUR BODIES ARE
NOT.
AND THAT SORT OF EMPHASIS ON THE
TWO KINDS OF DATA IS DIFFERENT
THAN OTHER KINDS OF DATA.
THE CHANGING OVER TIME IS ONE
WAY IN WHICH BRAIN DATA ARE
QUITE DIFFERENT THAN DNA, RIGHT?
ON THE QUESTION OF MITIGATION,
AND THE DATA PREPARATION
PIPELINE, OF COURSE HUGE
QUESTIONS ABOUT DATA COLLECTION,
FROM INDIVIDUALS, RIGHT, WHO ARE
DIVERSE ACROSS MULTIPLE SPECTRA,
BUT ALSO THE QUESTION OF WHICH
DATA ARE WE COLLECTING, RIGHT?
THIS IS THE GENETICS COMMUNITY
IS FACING THIS RIGHT NOW, RIGHT,
WITH THE NATIONAL ACADEMY'S
REPORT ON RACE, ETHNICITY, AND
POPULATION, DESIGNATORS USED IN
THE DEVELOPMENT OF GENETIC
RESEARCH, I THINK THERE'S
ANALOGY HERE
ANALOGY HERE TO WHAT OTHER KINDS
OF DATA THAT I ALLUDED TO IN MY
TALK, NECESSARY TO UNDERSTAND
WHAT IS GOING WITH THE BRAIN
DATA, RIGHT?
YOU CAN'T ISOLATE THE DATA FROM
EVERYTHING ELSE.
YOU NEED TO UNDERSTAND THE
ENVIRONMENTAL FACTORS, ET
CETERA, THAT ARE SHAPING WHAT'S
HAPPENING IN THE BRAIN.
YEAH, I THINK I'LL STOP THERE.
>> THANK YOU.
>> THANKS, CAROLYN.
FEEL FREE TO RAISE HANDS.
OTHERWISE WE HAVE LOTS OF
QUESTIONS AND YOU'LL END UP
HEARING GRACE AND MY QUESTIONS
ALL DAY LONG HERE.
A SECOND QUESTION FOR DR. JERBI,
BRIDGING BETWEEN THE FIRST CASE
STUDY AND THIS ONE TO SEE IF
THERE'S A DIFFERENCE BETWEEN
APPLYING, YOU KNOW, GENERAL
FOUNDATIONAL MODEL TO QUESTIONS
OF NEUROSCIENCE AND DIGITAL
PHENOTYPING VERSUS CREATING
CUSTOM MODEL THAT'S TRAINED ON
EEG OR OTHER BRAIN DATA AND
SIMILARLY TO MAKE PREDICTIONS
ABOUT THE FUTURE.
YOU MENTIONED IT'S PREDICTING
THE NEXT TOKEN, SO TRYING TO
IMAGINE PREDICTING THE NEXT
TOKEN IN EEG SEEMS LIKE YOU
COULD ACHIEVE THE SAME THING
FROM THE DIGITAL PHENOTYPING, IF
NOT MORE PRECISELY OR
DIFFERENTLY BY BEING ABLE TO SEE
PATTERNS OVER TIME THROUGH
LARGER DATASETS OF WHAT COMES
NEXT, NEURODEGENERATION OR IF
THAT IS MENTAL ILLNESSES OR
OTHER TIMES OF PATTERNS THAT
COULD BE DETECTED FROM RECORDING
OF EEG THAT WOULD BE UPLOADED
SAYING WHAT COMES NEXT.
IS THAT RIGHT THAT EVENTUALLY
THESE MODELS COULD ACHIEVE THAT
SAME CLINICAL SIGNIFICANCE?
AND LET ME START THERE, WHETHER
OR NOT THAT'S ACCURATE, YOU
COULD DO THE SAME THING AND
ACHIEVE IT BY PREDICTING WHAT
COMES NEXT IN THE EEG DATA
POTENTIALLY IN WAYS YOU WOULDN'T
DO WITH DIGITAL PHENOTYPING
BECAUSE IT'S MORE FORECASTING IN
THE FUTURE, MORE ACCURATELY THAN
THE FIRST CASE STUDY.
>> THANK YOU FOR THE QUESTION.
THIS ALLOWS ME TO CLARIFY ONE
THING ABOUT THE NEXT TOKEN
PREDICTION OR NEXT WORD
PREDICTION, LIKE LANGUAGE MODEL,
FOR INSTANCE.
TO THE QUESTION WHETHER ON
LARGER SCALE WE CAN USE THESE
MODELS TO EXPLORE CONVERSION IN
NEURODEGENERATIVE DISEASES, I
THINK THIS IS PART OF THE
APPLICATION THAT CAN BE
DOWNSTREAM TASK BUT THE NEXT
PART, CHUNKING DATA, THAT'S USED
IN THE FIRST PART OF THE
PRE-TRAINING, TO TRAIN A MODEL
TO LEARN REPRESENTATIONS OF WHAT
EEG DATA IS LIKE YOU REMOVE
PARTS OF THE DATA AND YOU TRY TO
LET IT, ITSELF, PREDICT WHAT
WOULD BE THE CONTINUATION OF
LET'S SAY EEG SENTENCE IN A WAY,
PREDICT THE NEXT WORK OF EEG.
BUT THE MORE IT BECOMES CAPABLE
OF DOING THAT IN THAT PROCESS
IT'S NOW LEARNING REPRESENTATION
OF EEG DATA.
NOW YOU HAVE A MODEL THAT'S
PRE-TRAINED BUT NOT PREDICTING
WHAT'S GOING TO HAPPEN TO THE
INDIVIDUAL IN THAT DATASET.
IT'S JUST THAT HAS LEARNED BY --
AS IF YOU'RE LEARNING A GAME AND
SOME COMPONENTS ARE REMOVED AND
YOU'RE TRYING TO GUESS MISSING
PARTS AND END UP WITH A HOLISTIC
VIEW.
>> I UNDERSTOOD THAT FOR THE
TRAINING.
I WAS ASKING THEN FOR THE
APPLICATION PART, ONCE IT HAS
LEARNED THAT, PRESUMABLY COULD
TAKE AN INDIVIDUAL NEW, FEED IT
IN AN EEG REPORTING FROM
SOMEONE, PARTICULARLY
LONGITUDINAL DATA, TO SAY, YOU
KNOW, BOTH PICKING UP SUBTLETIES
WE COULDN'T NECESSARILY PICK UP
BY HAVING BEEN TRAINED ON HUGE
DATASETS ABOUT THE CURRENT
INFORMATION THAT IS BEING
INFORMED BY THAT EEG BUT ALSO
BEING ABLE TO FORECAST BY HAVING
SEEN IN, YOU KNOW, IF IT'S BEEN
TRAINED ON LONGITUDINAL DATA,
WHAT COMES NEXT, RIGHT, IN
PREDICTING AND FORECASTING
NEURODEGENERATION OR ONSET OF
MENTAL ILLNESS OR OTHER
FEATURES.
>> IT'S POSSIBLE AND EXCITING
VENUE FOR THE FUTURE.
TIME SCALE AT THE MOMENT DOING
NEXT PREDICTION ARE NOT THE
SAME.
WE'RE NOT AT TIME SCALES OF
YEARS.
THEIR ARE TIME SCALES OF SECONDS
OF DATA.
BUT ABSOLUTELY.
THAT WOULD BE SOMETHING THAT MAY
BE A SOURCE, IDEA FOR RESEARCH
TRYING TO USE LONGITUDINAL DATA,
TRYING TO DEVELOP THE SAME
ALGORITHMS DOING PREDICTIONS BUT
ON WAY LARGER TIME SCALES, THAT
WOULD INVOLVE A WHOLE OTHER
SCHEME FOR TOKENIZATION, MIGHT
BE INTERESTING TO PURSUE.
>> A FOLLOW-UP QUESTION.
>> GO AHEAD.
>> ABOUT THE PARTICULAR DATA.
AS I MENTIONED, THE DATA ARE
PRIMARILY IN EPILEPSY DATASET,
SO YOU ALREADY HAVE DATA THAT
PRESUMABLY HAVE MEDICAL EVENTS
IN THEM.
ARE YOU ABLE TO PREDICT THOSE
MEDICAL EVENTS GIVEN THESE ARE
THE DATA YOU'VE ALREADY TRAINED
ON?
>> UM, SO, WE DIDN'T TRY THAT
BUT THE POINT HERE IS THAT SO
THE LENGTH OF THE DATA, THEY
WERE REALLY SHORT.
>> RIGHT.
>> YOU HAVE TWO SECONDS OF DATA,
MULTIPLE CHUNKS.
SO -- AND THE FACT THAT IT HELPS
IMPROVE, NOT TO A LARGE EXTENT,
MODELS COME PRIMARILY FROM
CLINICAL CONDITIONS, THEY DID
HELP IMPROVE ON THE
CLASSIFICATION TASKS UNRELATED
TO ANY CLINICAL CONDITIONS,
ACTUALLY SHOWING WE'RE CAPTURING
SOMETHING THAT REPRESENTS
SOMETHING ABOUT THE INHERENT
LATENT REPRESENTATION OF EEG
DATA.
>> COULD YOU CLARIFY THAT POINT,
SINCE THE QUESTION ON CHUNKING
DID COME UP IN DEBRA'S TALK?
DID YOU EXHAUSTIVELY RUN YOUR
ANALYSIS OF CHUNKS AND DIDN'T
MATTER HOW MUCH OVERLAP THERE
WAS IN TERMS OF ACCURACY?
>> IF I REMEMBER CORRECT CORRE,
MIGHT HAVE BEEN MENTIONED, MAYBE
NOT.
WE TRIED, MAYBE SIX SECONDS OF
DATA WITH DIFFERENT DEGREES OF
OVERLAP, ONE USED FOR THE
REPRESENTATION WAS THE ONE THAT
WORKED BEST BUT DIDN'T SEEM TO
MAKE A BIG DIFFERENCE.
THIS TOUCHES UPON A VERY
IMPORTANT QUESTION NOT RESOLVED
YET FOR THE FIELD OF EEG AND
BUILDING FOUNDATION MODELS, WHAT
IS THE BEST TOKENIZATION SCHEME.
WE KNOW BUILDING UNITS MAKE UP
LANGUAGE, A SENTENCE, WE CAN
LOOK FOR THAT.
WHAT IS A BUILDING UNIT FOR EEG
TIME SERIES?
I THINK THIS IS SOMETHING FOR
NOW PEOPLE ARE TAKING WILD
GUESSES, IT WORKS OKAY WITH ONE
SECONDS, MAYBE 200 MILLISECONDS
WORKS BEST, WE'RE LOOKING WITH
STUDENTS INTO INFORMED WAYS
EXPLORING BUILDING BLOCKS OVER
TIME FOR EEG, AND SO WE'LL SEE
IF THAT IMPROVES THE RESULTS
DOWNSTREAM.
>> NOW, SHIFTING SLIGHT GEARS
HERE BUT STAYING ON THE WHAT CAN
BE DONE WITH IT, EARLIER TODAY
WE WERE TALKING ABOUT EXTENSION
OF THE WORK FROM PLACES LIKE
ALEX HUTH'S LAB, TO BE ABLE TO
TRANSLATE EEG TO TEXT, AND I
KNOW THAT THE LAB IS WORKING,
THEY PRESENTED EARLIER, ABOUT
THE ABILITY TO TRANSLATE ACROSS
PEOPLE WITH MINIMAL TRAINING
DATA FROM ANY INDIVIDUAL PERSON.
SO MY QUESTION IS ASSUME YOU GET
TO THE PLACE WITH MUCH HIGHER
ACCURACY WITH MODEL YOU THAT YOU
CREATED, CAN YOU THEN SAY, YOU
KNOW, HERE IS A CHUNK OF EEG,
TRANSLATE THIS INTO WHAT THE
PERSON WAS THINKING AT THE TIME,
GIVEN THAT IT IS AN EEG LANGUAGE
MODEL THAT IS, YOU KNOW, IT
SPEAKS EEG?
>> SO I THINK WE'LL SEE WORK
LIKE THIS THAT COMES OUT.
THE QUESTION IS ALWAYS GOING TO
BE HOW RELIABLE IT IS.
WHEN YOU HAVE THE GROUND TRUTH,
YOU HAVE THE LABELS, YOU CAN
CHECK BACK.
BUT YOU ALWAYS HAVE THE SAME
ISSUE WITH MACHINE LEARNING AND
PREDICTIONS.
IF YOU HAVE AN 80% -- WE NEED TO
FIND MORE SHORT WAYS OF
IDENTIFYING ACCURACY IN THIS
SPECIFIC CONDITION -- SMART WAYS
OF IDENTIFYING ACCURACY.
>> THEORETICALLY POSSIBLE NOT
HIGHLY ACCURATE.
>> YES, I SEE THE QUESTION IN
THE CHAT, DEBRA'S QUESTION ABOUT
THE LABELS AND GROUND TRUTH, IN
THIS CASE LABELS WOULD BE FOR
EXAMPLE IF YOU COLLECT DATA
WHERE YOU ARE ASKING INDIVIDUALS
TO EITHER SAY SOMETHING OR THINK
ABOUT A STATEMENT, SO TO THE
EXTENT THAT THEY ARE FOLLOWING
INSTRUCTIONS YOU COULD BUILD A
DATASET WHERE YOU HAVE THAT TYPE
OF LABELING WHERE THEY WERE
INSTRUCTED TO THINK ABOUT THIS
OR THINK ABOUT THAT OR WERE
SHOWN IMAGES OF ANIMALS VERSUS
HOUSES, THEN YOU'D BE OUT OF THE
--
>> WHOSE MODEL HAS GROUND TRUTH?
HERE IS ALL OF THE, YOU KNOW,
PODCASTS A PERSON IS LISTENING
TO, AND HERE IS, YOU KNOW, THE
DATA, SO THEN THEY CREATED
TRANSFORMER THAT IS ABLE TO
TRANSFORM THAT FROM ONE
INDIVIDUAL TO THE NEXT
INDIVIDUAL.
SO I'M ASSUMING A WORLD IN WHICH
WE HAVE EEG WITH GROUND TRUTH
FOR LANGUAGE WONDERING THE
EXTEND TO WHICH YOUR MODEL
ALLOWS ME TO TAKE PUBLICLY
AVAILABLE EEG DATA, FEED IT IN,
SAY WHAT WAS THE RESEARCH
SUBJECT THINKING AT THE TIME
THIS WAS RECORDED.
>> IT'S A TOUGH QUESTION.
I DON'T KNOW IF THAT'S -- SO I
DON'T KNOW HOW RELIABLE THAT WAS
GOING TO BE.
SO WHETHER WE'LL BE, YOU KNOW --
WE'LL GET A ROUGH ESTIMATE, IT'S
SOMETHING LIKE ONE OF THE
REASONS I'M IN NEUROSCIENCE AS A
KID MY DESIRE WAS TO BUILD A
MACHINE TO IN REAL TIME BUILD A
MACHINE OF IMAGES WHILE PEOPLE
ARE DREAMING.
>> MY NIGHTMARE, YOUR DREAM, I
LOVE IT.
>> SO, YES, I THINK WITH ENOUGH
DATA, HIGH QUALITY DATA, WE
MIGHT BE GETTING CLOSER TO
THINGS LIKE THIS, WITH THE MODEL
WE'RE BUILDING, YOU CAN SEE THE
SCALE, HOW MUCH DATA THIS WILL
REQUIRE.
>> YOURS DOESN'T TRANSLATE FROM
EEG TO TEXT, RIGHT?
MEANING I COULDN'T UPLOAD IT
INTO THE -- IT'S GOING TO
PREDICT OUT IN EEG, NOT IN
LANGUAGE, SO YOU NEED SOMETHING
THAT WOULD TRANSLATE BETWEEN THE
NEURO-GPT AND LANGUAGE MODEL.
JOHN, I'M SORRY.
I SEE YOUR HAND IS UP.
>> THIS IS SOMETHING WE TOUCHED
ON BEFORE.
NITA, YOUR NIGHTMARE, KARIM'S
DREAM.
>> I THINK IT'S INCREDIBLE, IN
THAT SENSE.
>> WHAT'S SOBERING, WHEN HUTH
PUBLISHED HIS FIRST PAPER IT
TOOK FOUR OR FIVE DAYS OF
TRAINING IN THE SCANNER, WHO IS
GOING TO DO THAT?
IF THEY DISTRACTED THE PERSON OR
DIDN'T HAVE THEM CONCENTRATE IT
DIDN'T WORK.
AND THEN NEXT THING, WE TURN
AROUND, HE HAD A TRANSFORMER
THAT COULD GET IT.
THIS SPEAKS TO THINGS ARE MOVING
SO FAST AND HOW DO WE - PROTECT
IS THE WRONG WORD BUT PREPARE
OURSELVES FOR THAT
INEVITABILITY.
A BCI BRAIN TO SPEECH NEURAL
PROSTHESIS THIS WEEK, MANY YEARS
WORK, GREAT WORK, ALWAYS
REQUIRED EXTENSIVE TRAINING AND
VERY LIMITED VOCABULARY, GOOD
ACCURACY.
BOOM, THIS THING WAS PLUG AND
PLAY, REQUIRED ESSENTIALLY NO
TRAINING.
AFTER THE FIRST HALF HOUR THEY
GOT 50 WORDS AT CLOSE TO 100%
ACCURACY, THEY GOT UP TO 125,000
WORDS, 95% ACCURACY WITH MAYBE A
DAY OR TWO OF TRAINING.
THIS IS MIND BOGGLING.
IT SPEAKS TO THE POWER OF
ALGORITHMS AND THESE MODELS AND
IT'S JUST INTERESTING TO ME,
WE'RE GOING TO HAVE TO FIND A
WAY TO NOT GET IN THE WAY OF THE
PROGRESS BUT ALSO PREPARE
OURSELVES WHAT IT'S GOING TO
BRING US.
THESE KIND OF THINGS BRING IT TO
LIFE.
>> WHAT WE NEED TO TRANSITION
THE CONVERSATION TO NEXT AND
EXACTLY ON TIME, SO THANK YOU.
>> WE'VE DONE THIS BEFORE, NITA.
>> I KNOW YOU HAVE.
NEXT STEPS FOR NEWG AND TAKE
THIS EXCITING RESEARCH WHICH IS
EXTRAORDINARY, HOW QUICKLY IT'S
TRANSFORMING, THIS PAPER, WHEN
IT CAME OUT, INSTANTLY GRABBED
MY ATTENTION, FASCINATED BY THE
WORK.
THANK YOU BOTH FOR A RICH AND
INTERESTING CONVERSATION.
THANK YOU, DR. MATHEWS, FOR
HELPING US TEE UP NEXT STEPS,
THANK YOU FOR GROUNDBREAKING
RESEARCH BOTH EXCITING,
IMPORTANT, KEEPS US IN BUSINESS
OF TRYING TO FIGURE OUT WHAT
NEXT.
AND LET'S TRANSITION THE
CONVERSATION THERE.
I THINK NINA HAS A SLIDE TO GET
US STARTED.
LET'S START HERE WHICH IS TO
REALLY THINK ABOUT WHAT ARE THE
NEXT STEPS AND WHERE DO WE AS
NEWG WANT TO JUMP IN, LIKE IF
IT'S DEVELOPING A SET OF POINTS
TO CONSIDER FOR BRAIN-FUNDED
RESEARCH?
IS IT, YOU KNOW, POTENTIALLY
OPENING UP AREAS FOR FUTURE
FUNDING OPPORTUNITIES?
IS IT WORKING TOGETHER TO
DEVELOP A PAPER THAT GOES BEYOND
POINTS TO CONSIDER?
DO WE THINK THERE ARE UNIQUE
ISSUES TO GRAPPLE WITH HERE THAT
ARE DIFFERENT AND WARRANT
JUMPING INTO THE NEXT STEPS?
LET ME OPEN THE FLOOR TO
CONVERSATION.
I DON'T SEE ANYONE BECAUSE OF
THE SLIDES.
MAYBE WE COULD TAKE THE SLIDE
DOWN.
THERE WE GO.
CHRISTINE, PLEASE.
>> I WANTED TO ASK DEBRA SINCE
SHE WENT THROUGH SLIDES WITH
FRAMEWORKS AND GUIDELINES AND
MEETINGS AND THINGS THAT HAD
COME UP WITH LOTS OF I DON'T
KNOW PRINCIPLES AND ETHICAL
FRAMEWORKS, THINGS TO THINK
ABOUT, I GUESS GIVEN ALL OF
THAT, I KNOW ALL OF US ARE --
I'LL SPEAK FOR MYSELF, ALL OF US
I THINK ARE TRYING TO GRASP SOME
OF THE SPEED OF THIS RESEARCH
AND WHAT IT MEANS FOR ALL THE
KINDS OF THINGS WE THINK ABOUT
BUT GIVEN THE SORT OF WEALTH OF
ATTENTION TO THIS MATTER, THE
NUMBER OF PEOPLE COMING UP WITH
FRAMEWORKS, DO YOU THINK THERE'S
SOMETHING THAT THE NEUROETHICS
WORKING GROUP OF BRAIN CAN
UNIQUELY CONTRIBUTE?
WE'RE NOT GOING TO HOLD YOU TO
IT.
I JUST WANT YOUR OPINION ON IT.
>> SURE.
I MEAN, YES, I THINK THERE IS.
I'LL GIVE A COUPLE REASONS.
ONE, THERE HAS BEEN A TON OF
WORK, CONSENSUS BUILDING ABOUT
RELEVANT PRINCIPLES, THERE
CONTINUE TO BE DISAGREEMENTS
ABOUT TO WHOM AND WHICH STAGES
OF THE PROCESS THE PRINCIPLES
APPLY, WHO IS RESPONSIBLE FOR
UPHOLDING THEM, RIGHT?
IT'S GOING TO -- BECAUSE A.I. IS
NOT ONE THING, IT'S MANY
DIFFERENT THINGS, THERE IS WORK
THAT NEEDS TO BE DONE IN EACH
INDIVIDUAL CONTEXT TO HELP GUIDE
PEOPLE TO FIGURE OUT, OKAY, HERE
ARE PRINCIPLES THAT SHOULD BE
GUIDING THIS STUFF, WHAT DOES IT
MEAN TO TRANSLATE INTO DECISIONS
FOR THIS TECHNOLOGY AT THIS
STAGE OF DEVELOPMENT?
I THINK THAT'S THE KIND OF SORT
OF MORE GRANULAR GUIDANCE WE
NEED.
>> WOULD YOU SAY THEN SORT OF I
DON'T KNOW SIMILAR TO WHAT WE
DID TODAY, YOU KNOW, PICK A
COUPLE VERY SPECIFIC CASES AND
THINK ABOUT MAYBE WHAT THE
PRINCIPLES ARE, WHAT FRAMEWORKS
ARE, HOW THEY DO OR DON'T APPLY,
USE THAT AS ILLUSTRATIVE TO THE
QUESTIONS THAT YOU'RE SAYING
STILL NEED TO BE ADDRESSED LIKE
HOW DOES, YOU KNOW, WHO DO THEY
APPLY TO, WHO IS SUPPOSED TO
UPHOLD THEM?
>> NO, I THINK THAT WOULD BE A
GREAT STARTING PLACE.
THE FIRST CASE, THE SET OF
QUESTIONS EACH INVESTIGATOR
SHOULD BE ASKING ABOUT VARIOUS
ASPECTS OF RESEARCH, I THINK
THAT'S THE KIND OF GUIDANCE
FOLKS NEED.
OKAY, I GET THIS IS CONTENTIOUS
BUT WHAT DO I DO ABOUT THAT?
>> RIGHT.
THAT'S HELPFUL.
THANK YOU.
>> YEP.
>> I SEE ONE TAKER FOR INTEREST
IN THE POTENTIAL TO DEVELOP
COMMENTARY.
SOUNDS LIKE THERE'S OPEN SPACES
TO BE DEVELOPED.
TOR, YOU HAVE YOUR REAL HAND UP
WHICH IS CONFUSING BUT I'M HAPPY
TO CALL ON YOUR REAL HAND
INSTEAD OF THE VIRTUAL ONE.
>> I HAVEN'T FIGURED OUT HOW TO
RAISE MY HAND IN THIS INTERFACE.
>> FAIR.
REAL HANDS WORK TOO.
>> ONE OF THE TEAMS THAT'S
EMERGED THAT I WAS INTERESTED IN
IS THE TRADEOFFS AMONG ETHICAL
PRINCIPLES, AND I SORT OF SEE
THIS AS A GRAPH IN MY MIND,
MULTIPLE PRINCIPLES, YOU KNOW,
OPEN SCIENCE CONFLICT WAS
PRIVACY, MAYBE FOUR, FIVE, SIX
AT LEAST DIFFERENT PRINCIPLES WE
DISCUSSED.
I WOULD ADVOCATE FOR THAT AS,
YOU KNOW, SOMETHING THAT COULD
BE MADE EXPLICIT BECAUSE OFTEN
IT'S, YOU KNOW, WE HAVE THESE
COMPETING PRINCIPLES, ONLY ONE
OR TWO IS IN OUR MIND OR
ATTENTION AT ANY GIVEN TIME.
IF YOU COULD SEE HOW THEY TRADE
OFF, RIGHT?
AND BE EXPLICIT OR CONSCIOUS
THOUSAND TO CHOOSE A TRADEOFF
BETWEEN CONFLICTING PRINCIPLES
IN ANY GIVEN SPACE, WHERE I
THINK A.I.-RELATED THINGS, THERE
WILL BE A DIFFERENT SOLUTION
THAN OTHER KINDS OF DATA SO I
LIKE THAT.
>> NOT ALWAYS EASY BUT WITH MORE
PRACTICAL GUIDANCE THAT
IMPLEMENTS PRINCIPLES AND WHERE
CONFLICTS CAN REVOLVE CAN BE
INSTRUMENTAL AND HELPFUL AND HOW
THEY ARE DIFFERENT ACROSS
CONTEXTS.
AMY?
>> YEAH, GOING BACK TO THIS IDEA
OF LIKE WHAT'S DIFFERENT HERE,
WHERE SHOULD WE SEE COORDINATING
EFFORTS VERSUS WHERE WE NEED
SORT OF SILOED EFFORTS WHICH
THERE'S PROBABLY A PLACE FOR
BOTH OF THEM, I WONDER IF
THERE'S A WAY TO GET LIKE
INVENTORY OF WHAT'S HAPPENING
ACROSS NIH WITH THIS SO I KNOW
FOR EXAMPLE LIKE I THINK NHGRI,
OR MAYBE THE COMMON FUND, THEY
ARE FUNDING GRANTS ON
DEVELOPMENT OF A.I. TOOLS, AS
PART OF IT CONSORTIUM OR EACH
GROUP HAS TO DEVELOP ETHICAL
FRAMEWORK FOR A.I., I IMAGINE
THERE ARE OTHER SORT OF CALLS
LIKE THIS IN OTHER INSTITUTES
AND CENTERS AND WONDER IF
THERE'S A WAY TO AT LEAST HAVE
AN UNDERSTANDING OF WHAT'S
HAPPENING AT THE NIH LEVEL WITH
REGARD TO THIS AND WHERE SORT OF
WE NEED TO BE PARTICIPATING IN
THOSE CONVERSATIONS VERSUS WHERE
THERE'S LIKE UNIQUE OPPORTUNITY
FOR US RELATED TO BRAIN DATA?
>> GOOD QUESTIONS.
JIM AND WALTER.
>> THANKS, NITA.
DISCUSSION, FOCUSING MY THOUGHTS
ON THE IDEA OF CLINICAL VERSUS
FUNDAMENTAL RESEARCH A.I. AND
THOSE APPLICATIONS.
WE HEARD ABOUT LANGUAGE, EEG,
BUT SO MUCH MORE TIMES OF BRAIN
DATA THAT WILL IMPACT US, IF WE
GET SYNAPTIC QUESTIONS THERE MAY
BE PROPENSITIES TO PARTICULAR
TYPES OF PRECISION MAKING,
THINGS THAT COME OUT OF THE
THIS.
WHAT KINDS OF BRAIN DATA SHOULD
WE THINK ABOUT IN TERMS OF
PERHAPS FEDERATED DATABASES, HOY
IT'S GOING TO BE IMPACTED BY
A.I., AND SO I THINK SOMETHING
ALONG THOSE LINES WHERE WE SORT
OF THINK ABOUT CLINICAL VERSUS
FUNDAMENTAL SCIENCE, AND THE
BROAD EXPANSE OF THE TYPES OF
NEURODATA THAT'S COMING FROM THE
NEUROTECHNOLOGIES, NOT JUST
RELATED NECESSARILY, AND NOBODY
IS SUGGESTING THIS, BUT I
WOULDN'T WANT TO SEE IT RELATED
SOLELY TO, SAY, EEG OR fMRI OR
THAT BECAUSE THERE'S SO MANY
MORE TIMES OF DATA THAT WILL BE
IMPACTFUL PARTICULARLY
MULTI-MODAL DATA.
>> FAIR.
GOOD AND IMPORTANT POINTS.
WALTER?
>> I WAS WONDERING, I'M REALLY
GOOD AT MAKING WORK FOR OTHER
PEOPLE, IN TERMS OF, YOU KNOW,
THE PAPER THAT WENT AROUND ON,
YOU KNOW, WHAT WAS IT, CODE OF
CONDUCT, PRINCIPLES FOR
ARTIFICIAL INTELLIGENCE, HEALTH,
HEALTHCARE, BIOMEDICAL SCIENCES,
IT WAS PRETTY GENERIC.
I WAS WONDERING IF OVER TIME WE
SHOULD BE LOOKING AT CREATING
SUCH A CODE OF CONDUCT FOR BRAIN
INITIATIVE WORK THAT CERTAINLY
WOULD INFORM YOU WILLS HERE AT
NIH BUT ALSO IF WE CAN WORK OUT
THE KINKS AS WE GO ALONG COULD
SERVE AS SOMETHING THAT WOULD BE
VALUABLE GLOBALLY.
AND ANYWAY THAT WAS ON MY TO-DO
LIST FOR YOU GUYS.
BUT THE OTHER THING WAS
MENTIONED, YOU KNOW, I'M
CO-CHAIRING THE A.I. PROJECT,
THE COMMON FUND ONE WITH GRACE
HERE, LOOKING AT INCORPORATING
IMAGING INTO OTHER TIMES OF --
TYPES OF DATA TO MAKE HEALTH
PREDICTIONS, WE HAVEN'T GOTTEN
OFF THE GROUND LOOKING AT
ETHICAL PRINCIPLE, AGAIN ON
ANOTHER TO-DO LIST, SO, YEAH,
THERE'S A LOT -- WE'RE GOING TO
LEARN A LOT.
HOPEFULLY WE DON'T MAKE TOO MANY
BAD MISTAKES.
WE'LL LEARN A LOT AS WE MOVE
INTO THIS SPACE.
>> IT'S GREAT TO THINK ABOUT
DEVELOPING A SET OF POINTS TO
THAT ARE COULD BE USEFUL NOT
JUST FOR BRAIN BUT GLOBALLY, I
WOULD SAY GIVEN HOW RAPIDLY THE
FIELD IS PROGRESSING THAT'S
PROBABLY GOT TO BE A LIVING
DOCUMENT THAT CHANGES AS THINGS
CHANGE.
REMINDS ME OF ANONYMOUS SPERM
AND EGG DONATION, NOBODY THINKS
THERE'S THE POSSIBILITY OF
ANONYMITY IN THAT SPACE, IT'S
NOT A LET'S FIND WAYS TO LAYER
ANONYMITY OR PROVIDE PEOPLE WITH
ANONYMITY, IT'S AN UNDERSTANDING
THERE'S NO SUCH THING AS
ANONYMITY, THAT CHANGED
FUNDAMENTALLY IN LIGHT OF
CHANGES IN TECHNOLOGICAL
POSSIBILITIES.
AND SO I THINK WE'RE RAPIDLY
APPROACHING THAT SPACE HERE AND
THE QUESTION IS GIVEN THAT HOW
DO YOU START TO SHIFT GEARS AND
PROVIDE UPDATED GUIDANCE WITH
UNDERSTANDING THAT WE'RE IN A
DIFFERENT ERA.
>> HOPEFULLY A.I. CAN DO THAT.
>> WE'LL ASK ChatGPT TO WRITE
THAT FOR US.
NOT GOOD AT ETHICAL GUIDANCE.
>> DIDN'T HAVE THE RIGHT
TRAINING SET YET.
>> RIGHT MODEL SPEC FOR BEING
ETHICAL.
>> WALTER, INTERESTING POINT.
WE DO HAVE OUR NEUROETHICAL NINE
GUIDING PRINCIPLES, I THINK NINE
OF THEM.
THEY ARE A GREAT FRAMEWORK BUT I
THINK ESPECIALLY GIVEN THE
DEVELOPMENTS OVER THE LAST EVEN
YEAR, I THINK IT'S PROBABLY GOOD
TIME FOR THIS GROUP TO REVISIT
AND UNDERSTAND WHAT NEEDS
REFRESHING, WHAT NEEDS FLESHING
OUT, I THINK THE KINDS OF ISSUES
WE'RE DISCUSSING TODAY PROBABLY
WEREN'T REALLY OUT THERE AT THE
SURFACE IN MOST PEOPLE'S MINDS
FIVE YEARS AGO.
A GREAT THING FOR THIS GROUP TO
LOOK AT.
POINTS TO CONSIDER WOULD BE
GREAT.
WE DID HAVE THIS WORKSHOP, AND
THERE IS ACTUALLY A MANUSCRIPT
FLOATING AROUND.
DATA PRIVACY.
WE STARTED SCRATCHING THE
SURFACE OF SOME OF THESE ISSUES
YOU'RE TALKING ABOUT TODAY.
A LOT HAS HAPPENED SINCE.
THIS IS A TOPIC WITH US FOR THE
DURATION OR FORESEEABLE FUTURE
AND BEYOND, AND GOING TO GET
MORE INTERESTING TO SAY THE
LEAST.
WE DO NEED TO GET OUR HEADS
AROUND THIS.
I THINK THIS IS A GREAT
DISCUSSION.
I THINK A COMMENTARY POINT TO
CONSIDER, SCHOLARLY ARTICLES
WOULD BE GREAT.
AT LEAST I WANT TO KNOW MORE.
WE'LL BE HAVING THIS NEURAL A.I.
WORKSHOP THAT JOE AND OTHERS,
GRACE ARE CO-ORGANIZING FOR THIS
FALL.
THAT'S GOING TO RAISE SOME
INTERESTING ISSUES.
MAYBE WE CAN THINK ABOUT DIVING
INTO THIS MORE DEEPLY BOTH HERE
AND PERHAPS AT ANOTHER
NEWG-SPONSORED WORKSHOP TO DIG
IN.
THERE'S A LOT HERE.
I WANT TO BE ABLE TO TAKE
LEADERSHIP ON A LOT OF THESE
ISSUES.
SOME THINGS ARE GOING TO BE
PARTICULARLY FOR BRAIN BUT ALSO
TRANSLATABLE ACROSS THE BOARD
BECAUSE WE'RE CLOSER TO THE EDGE
OF, HOW SHOULD WE SAY,
UNINTENDED AND UNWANTED
CONSEQUENCES.
I WILL THROW THAT OUT THERE,
SOMETHING FOR THIS GROUP TO
THINK ABOUT.
ABOUT MAYBE WE COULD THINK ABOUT
LAYING OUT A YEAR OR TWO
STRATEGY FOR DIVING IN OR
GETTING OUR HEADS AROUND THIS.
>> GREAT POINT.
THAT'S WHY I THOUGHT WE NEED A
LIVING DOCUMENT.
THERE'S SOME THINGS WE KNOW
ALREADY WHICH ALLOW US TO
REVISIT AND SAY UPDATED THINGS.
MY POINT ABOUT THE GAMETE
ANONYMITY, I THINK WE'RE ALMOST
THERE.
>> THERE'S NO ANONYMITY THAT CAN
BE GUARANTEED ANYMORE, RIGHT.
>> RIGHT.
GIVEN WE CAN ACCEPT THAT FACT
THEN THE QUESTION IS WHAT IS THE
UPDATE CONSENT AND GUIDANCE THAT
NEEDS TO BE PROVIDED.
SOME THINGS WE CAN ALREADY
APPROACH WITH UNDERSTANDING OF
HOW RAPIDLY THINGS HAVE CHANGED.
AND SOME WE NEED TO LEARN MORE.
THERE'S A LOT OF EFFORTS
HAPPENING ACROSS THE WORLD IN
THESE CASES AS CONVERGENCE.
CHRISTINE, PLEASE JUMP IN.
>> I WAS GOING TO -- YOU WERE
JOKING ABOUT THE TRAINING DATA
FOR ETHICAL GUIDANCE FOR
ChatGPT WOULDN'T BE SO GOOD.
I THINK THAT'S RIGHT BECAUSE SO
MANY OF THE THINGS THAT WE HAVE
FOCUSED ON IN ETHICS LIKE
INFORMED CONSENT, IT'S A
QUESTION MARK ABOUT HOW IT
SHOULD WORK IN THIS SETTING.
I MEAN, WHAT ARE WE TALKING
ABOUT WHEN TALKING ABOUT THAT?
OR DECREASING RISK BY
DE-IDENTIFYING THINGS.
THE KINDS OF THINGS THAT HAVE
BEEN OUT THERE NEED TO BE
REVISITED.
AND I WAS -- I LIKE THE CONCEPT
THAT WAS RAISED EARLIER TODAY
ABOUT INSCRUTABILITY, AN
IMPORTANT CONCEPT IN TERMS OF
HOW THIS -- HOW THESE
TECHNOLOGIES GIVE US INFORMATION
THAT CAN BE USEFUL.
>> AGREED.
WE HAVE SOME WORK CUT OUT FOR
US, THINGS WE CAN START.
JOHN, SOUNDS LIKE SOME COULD BE
FLESHED OUT FOR ADDITIONAL
WORKSHOPS BUT YOU PUT ON THE
TABLE POSSIBILITY OF US HERE AT
NEWG DOING ADDITIONAL WORKSHOP
THAT MIGHT FOCUS OUR EFFORTS
MORE SPECIFICALLY AND IN A MORE
TARGETED WAY.
LET ME INVITE OTHER NEWG MEMBERS
TO SAY WHAT DO YOU THINK ABOUT
THAT, ABOUT ADDITIONAL WORKSHOP
TO TAKE A DEEPER DIVE.
SASKIA HAS A LOOK OF, YOU KNOW,
DISCOMFORT AND UNEASE.
BY ALL MEANS, SPEAK UP IF THAT'S
MORE WORK THAN TRANSLATION TO
BENEFIT AT THIS POINT.
LET'S GET PEOPLE TALK BECOME
WHAT WE NEED TO GET TO THE NEXT
STEP TOGETHER OF BEING ABLE TO
AT LEAST REVISIT AND UPDATE
GUIDANCE OF THE THINGS WE KNOW
NOW THAT COULD BE USEFUL.
I KNOW IT'S LATE IN THE DAY.
I KNOW IT'S THE START OF THE
SEMESTER FOR A LOT OF PEOPLE.
BUT WE'RE INVITING MORE WORK ON
YOUR PLATE SO NOW IS A GOOD TIME
TO SPEAK UP.
>> BEFORE WE AGREE TO BURY
OURSELVES IN MORE WORK, YOU
MENTIONED AS WELL IN THE CHAT,
THERE ARE ONGOING SYMPOSIA,
WORKSHOPS, CONFERENCES ON THE
AREA.
AGAIN, LET'S BE INTENTIONAL
ABOUT FINDING SOMETHING AROUND
THAT IS BOTH IMPORTANT FOR US
AND EVEN UNIQUE BUT NOT BEING
COVERED BY SOMEBODY ELSE SO WE
DON'T JUST EXPEND EXTRA ENERGY
TO NOT GREAT EFFECT.
THERE'S A LOT TO DO.
I'M SURE WE CAN FIND SOMETHING
PARTICULAR TO US TO ADD TO THE
CONVERSATION AND NOT REPEAT
OTHERS.
>> I'D LOVE TO HEAR FROM OTHER,
INCLUDING SASKIA, BUT NOT ONLY
SASKIA, OTHER MEMBERS OF THE
NEWG, WRITING UPDATES, A
WORKSHOP, INVENTORY, WHICH ONES
ARE YOU INTERESTED IN IS MY
QUESTION.
>> SASKIA, YOU CAN CLARIFY THAT
LOOK OR --
>> WELL, SO, I'M CURIOUS WHAT
OTHERS ON THIS CALL THINK ABOUT
IT FEET LIKE THERE'S A LOT OF
DIFFERENT -- THIS IS A BROAD
SPACE, IN TERMS OF ETHICS
THERE'S A LOT OF TOPICS THAT
CAME UP.
I WONDER ABOUT PRIORITY SETTING,
AMONG ALL THE DIFFERENT
QUESTIONS THAT HAVE ARISEN
BECAUSE I LOVE THE IDEA OF A
WORKSHOP OR PAPER BUT I THINK IF
WE COULD THINK ABOUT RIGHT NOW
WHAT ARE THE MOST IMPORTANT
UNRESOLVED QUESTIONS, ESPECIALLY
AS THEY RELATE TO BRAIN IN THIS
SPACE, I THINK THAT WOULD BE
REALLY HELPFUL AND FOCUSING OUR
NEXT EFFORTS.
SO I DON'T HAVE ANSWERS BUT THAT
WAS WHAT I WAS WONDERING ABOUT.
>> IS THERE A WORKSHOP, THE
POST-ANONYMITY WORLD, RIGHT?
I MEAN -- WE'RE GOING TO START
CALLING ON NEWG MEMBERS TO GET
YOU TO SPEAK UP AT THIS POINT.
WE'RE COMING SOON FOR THE
ROUNDTABLE BUT IF YOU'VE NOT
SPOKEN, WEIGH IN AND TELL US
WHAT YOU THINK.
IS THERE A PARTICULAR SLICE THAT
YOU'RE FOCUSING ON THAT YOU
THINK WE SHOULD BE FOCUSING ON.
I SEE JEN PUT IN THE CHAT BEST
PRACTICES, FROM INDUSTRY.
I AGREE IT WOULD BE HELPFUL,
OUTPUT DELIVERABLES, THAT'S ONE
OF THE DIFFERENCES BETWEEN
UPCOMING WORKSHOP TO PRESENT
MORE SCHOLARLY, FOCUS ON DIVE
INTO SCHOLARLY DETAILS VERSUS A
SPECIFIC GROUP OF DETAILS TO PUT
OUT, INFORMED BY THAT WORK, BUT
TRANSLATIONAL WORK.
AMY?
>> TRYING TO ANSWER SASKIA'S
QUESTIONS, THERE'S A LOT OF
WORK, I WAS GOING TO SAY MY
FIRST REACTION ONE OF THE MOST
PRESSING ISSUES IS AROUND SORT
OF HEALTH EQUITY AND DISPARATE
SORT OF BURDEN.
BUT I ALSO THINK THERE'S A LOT
OF WORK BEING DONE IN THAT
SPACE.
ONE OF THE THINGS WE TALKED
ABOUT TODAY THAT I THINK MAYBE
THERE'S LESS WORK BEING DONE IN
IS AROUND LIKE DATA GOVERNANCE
AND RETURN OF RESULTS, AND, YOU
KNOW, SOME OF THE QUESTIONS
RAISED ABOUT, YOU KNOW, DUTY TO
REPORT, DUTY TO WARN, LIKE WHAT
WE DO WITH ALL OF THE DATA THAT
WE'RE COLLECTING, AND SO THAT'S
ONE SUGGESTION, I'M NOT -- MAYBE
THERE ARE PEOPLE OUT THERE WHO
ARE WORKING VERY DILIGENTLY ON
THOSE PROBLEMS BUT I'VE SEEN
LESS OF IT THAN I HAVE IN SORT
OF THE EQUITY AND
REPRESENTATIVENESS, BIAS SPACE.
>> WALTER PLEASE JUMP IN.
>> YOU'RE MUTED, WALTER.
>> MY BACKGROUND, I WOULD BE
MOST WORRIED THAT THE CONFUSION
AROUND THE CONSENT FORM IS GOING
TO EVENTUALLY STALL EVERYTHING
TO A HALT UNTIL THAT CAN BE
SORTED OUT.
SO I WOULD SAY IF WE COULD JUST
FOCUS ON GUIDELINES FOR CONSENT,
THAT COULD BE SEEN AS A
CONSENSUS AND PUBLISHES, THAT
IRBs COULD LOOK AT AND SAY,
OKAY, I CAN AGREE WITH THIS,
OTHERWISE I THINK THE IRBs ARE
GOING TO BE COMPLETELY CONFUSE
AND THROW UP THEIR HANDS.
>> COULD I ASK WHEN YOU SAY THAT
YOU MEAN CONSENT FOR THE USE OF
A.I. WITH DATA, WITH NEURODATA?
>> CORRECT.
YEAH, WE'VE TOUCHED ON THAT IN
THE PAST BUT I THINK THAT'S
PROBABLY THE THING THAT I CAN
SEE HITTING US FIRST.
>> WELL, SEEING CONVERSATION
SLOWING, IT MAY BE TIME TO
TRANSITION TO DISCUSSION OF
UPDATES.
HOPEFULLY THAT WILL GET EVERYONE
TALKING ABOUT WHAT'S ON THEIR
PLATES, WHAT WE SHOULD BE AWARE
OF, AND SOME SHARING.
DOES THAT MAKE SENSE?
ANYBODY HAVE ADDITIONAL THOUGHTS
BEFORE WE DO THAT?
OKAY.
LET'S TRANSITION TO THIS PART OF
THE CONVERSATION WHICH IS TO DO
ROUNDTABLE UPDATES.
>> SHOULD WE MODERATE SO YOU CAN
SHARE?
>> I WAS GOING TO HAND IT OVER
TO CHRISTINE TO MODERATE.
OR JOHN, YOU CAN MODERATE,
WHOEVER WANTS TO MODERATE.
YES, BY ALL MEANS, SOMEBODY TAKE
OVER.
>> I WANTED TO HEAR FROM
CHRISTINE TOO.
IS THAT OKAY?
>> SURE, ABSOLUTELY.
>> TWO, FOUR, SIX, EIGHT, TEN
FOLKS.
IS WINSTON HERE?
WE'RE MOSTLY STILL HERE.
I'M GOING TO GO DOWN THE LIST,
I'LL FINISH WITH THE CO-CHAIRS,
HOW ABOUT THAT?
>> SOUNDS GREAT.
>> WINSTON, I DON'T SEE YOU
HERE.
>> I'M HERE.
>> THERE YOU ARE.
YEAH, YEAH, ARE YOU READY?
>> COME BACK TO ME.
>> THERE'S LIKE EIGHT-PLUS,
MAYBE TWO MINUTES EACH, THAT
WOULD BE GREAT.
JEN, DO YOU HAVE A FEW THINGS TO
SHARE WITH US?
>> MAYBE NOT.
>> I DO, YES.
SORRY ABOUT THAT.
SORRY FOR THE DELAY.
YES, ACTUALLY IF YOU DON'T MIND,
WE'D LOVE TO GIVE A QUICK
UPDATE.
WE LAUNCHED THE IMPLANTABLE
BRAIN-COMPUTER INTERFACE
COLLABORATIVE COMMUNITY IN
MARCH, WE'RE UP TO 200 MEMBERS
NOW.
MANY OF THOSE ON THE CALL HERE
HAVE JOINED SOME OF OUR WORKING
GROUPS.
WE ENCOURAGE MORE TO JOIN.
WE DO HAVE A WORK GROUP
DEDICATED TO ETHICS, NEURAL
DATA, NEURAL SECURITY.
I KNOW THAT ETHICS CAN GO INTO
AREAS OF BRAIN AND COMPUTER
INTERFACE, BUT I THINK THE
LOW-HANGING FRUIT WHERE WE ARE
RIGHT NOW IS IN TERMS OF DATA
AND SECURITY AT THIS POINT.
IT'S OPEN TO THE PUBLIC TO JOIN.
WE'RE LOOKING FOR A LOT OF
DIVERSE MEMBERS TO JOIN OUR
CONVERSATION AND REALLY HAVING
OUTPUTS.
KEEP IN MIND WE DEFINE IMPLANT
AND INTERFACES MORE NARROWLY
THAN A LOT OF PEOPLE DO.
WE WERE JUST LOOKING AT THOSE
SENSING FROM THE BRAIN AT THIS
POINT, EVENTUALLY WE'LL BE ABLE
TO BROADEN THAT OUT BUT RIGHT
NOW IT'S JUST FOCUSED ON THAT.
FOR THOSE THAT MIGHT NOT BE
PRESENTED WITH THE COLLABORATIVE
COMMUNITY, IT IS AN EFFORT TO
BRING TOGETHER DIVERSE
STAKEHOLDERS TO SOLVE SYSTEMIC
ISSUES.
WE WENT THROUGH A YEAR PROCESS
OF DEVELOPING THE COMMUNITY AND
LIAISON TO BRING IN FEDERAL
PARTNERS TO SPEAK ACTIVELY AND
WORK ACTIVELY WITHIN OUR WORK
GROUPS ALONG WITH THOSE FROM
INDUSTRY, RESEARCH, PATIENT
ADVOCACY ORGANIZATION, AND
OTHERS.
>> YOUR KICKOFF WORKSHOP WILL BE
THIS SEPTEMBER 18, CORRECT?
CHEVY CHASE?
>> THAT'S CORRECT.
IT WILL BE IN SILVER SPRING.
>> I'M SORRY, SILVER SPRING.
>> YEP.
AGAIN, THAT'S OPEN TO THE
PUBLIC, SO I'M GOING TO PUT OUR
WEBSITE INTO THE CHAT WHERE YOU
CAN LEARN MORE AND BE ABLE TO
REGISTER INURE EVENT. EVENT --T
EVENT.
WE'RE REQUIRED TO HAVE PUBLIC
MEETINGS SO WE'RE WELCOMING YOU
ALL TO JOIN, AND JOIN OUR
CONVERSATIONS, AND REALLY WE'RE
FOCUSED NOT ON JUST DISCUSSIONS
BUT BE ABLE TO HAVE OUTPUTS THAT
CAN REALLY BRING IMPLANTED
BRAIN-COMPUTER INTERFACES
AVAILABLE IN A SAFE AND
EFFICACIOUS WAY.
>> LOOKING FORWARD TO THAT.
OKAY, AMY?
>> I DON'T HAVE SPECIFIC
UPDATES, I DON'T THINK.
SORRY, ARE YOU CALLING ON ME?
YES.
>> YES, SORRY, AMY MCGUIRE.
>> I DON'T HAVE SPECIFIC UPDATES
TO SHARE WITH THE GROUP.
>> OKAY, GREAT.
APPRECIATE YOU BEING HERE.
CAROLINE MONTOJO.
>> WE'VE HAD A FEW UPDATES THEY
ASKED ME TO SHARE SINCE IT'S
RELEVANT TO OUR WORK AT THE DANA
FOUNDATION.
I'LL GO THROUGH THESE
EFFICIENTLY BUT ALSO SHARING
SOME LINKS IN THE CHAT SO PEOPLE
CAN HAVE MORE CONTEXT.
FIRST IS THAT A NEW EPISODE OF
NEUROSOCIETY STORIES WAS
RELEASED YET, FRANCIS CHEN,
PRODUCED BY DANA FOUNDATION,
HIGHLIGHTS CONNECTION THROUGH
GRANTEES, PARTNERS, OUTSIDE THE
DANA FOUNDATION NETWORK
DEDICATED TO CONNECTING BRAIN
SCIENCE TO SOCIETY.
I'LL DROP IN THE LINK TO THAT
HERE.
SO FYI A FEW LINKS COMING.
SECOND UPDATE IS ABOUT THE DANA
CAREER NETWORK IN NEUROSCIENCE
AND SOCIETY, THIS IS
INTERESTINGLY ALSO LED BY
FRANCIS CHEN, ORGANIZING A
SECOND CAREER FAIR THAT'S TAKING
PLACE SEPTEMBER 9-12, SO IF YOU
HAVE FOLKS IN YOUR LAB, TRAINEES
THAT MIGHT BE ATTENDED, THERE
YOU GO.
IT'S COMING.
THAT'S FOR THE CAREER FAIR.
AND THEN ANOTHER SHOUT OUT FOR A
HASTINGS CENTER SERIES OF
ARTICLES AND ESSAYS ON ETHICAL,
LEGAL, SOCIAL ISSUES PRESENTED
BY IMAGING NEUROSCIENCE,
PUBLISHED A FEW ARTICLES IN THAT
SERIES THROUGH THE HASTING
CENTER REPORT, A SHOUT OUT TO
THOSE ON THE STEERING COMMITTEE,
JENNIFER, WINSTON, JOE, SARAH,
JONATHAN AND OLIVER FOR
PARTICIPATING IN PLANNING OF
THAT.
AND THEN THE OTHER PIECE IS THAT
THE U.N. GENERAL ASSEMBLY THIS
COMING SEPTEMBER THE THEME OF
THAT ASSEMBLY EVENT WILL BE ON
NEUROSCIENCE IN SOCIETY, A SIDE
OF THE U.N. GENERAL ASSEMBLY, ON
SEPTEMBER 20th THE DANA
FOUNDATION WILL HOST A SPECIAL
PANEL ON APPLYING NEUROSCIENCE
IN THE COURTROOM, SO WE'RE
FORTUNATE TO HAVE A FEW
PARTICIPANTS, DEBRA DENNO FROM
FORDHAM UNIVERSITY, DIEGO
RODRIGUEZ FROM COLOMBIA, OLIVER
ROLLINS FROM M.I.T. AND JUDGE
GLORIA TAN FROM MASSACHUSETTS
JUVENILE COURT.
AND FOR THIS EVENT IT'S OPEN
BOTH IN PERSON AS WELL AS
VIRTUAL.
SO IF ANYBODY IS INTERESTED BUT
CAN'T ATTEND IN PERSON IN NEW
YORK CITY THERE WILL BE A
VIRTUAL OPTION.
THE LAST PIECE IS THAT SOCIETY
FOR NEUROSCIENCE THIS COMING
OCTOBER THE DANA FOUNDATION WILL
BE SPONSORING THE DIALOGUE
BETWEEN NEUROSCIENCE AND SOCIETY
SO THIS IS AN EVENT THAT HAS
TAKEN PLACE PREVIOUSLY AT FSN,
AND THEY WENT TO A HIATUS
PERIOD, NOW RESTARTING AGAIN
THIS COMING OCTOBER.
SO IT WILL BE A PLENARY SESSION
SO HOPEFULLY YOU CAN MAKE IT TO
THAT.
THAT'S IT.
>> THANKS, CAROLINE.
YOU FOLKS HAVE BEEN PRODUCTIVE
AND BUSY.
THAT'S GREAT.
THANKS SO MUCH.
SAMEER, ARE YOU STILL WITH US?
MAYBE NOT.
WE'LL COME BACK.
JEN?
WHAT'S NEW ON YOUR END?
>> THANKS, JOHN.
THERE'S NO NEUROETHICS UPDATE ON
MY PART.
>> OKAY.
WE CAN HAVE OTHER DISCUSSIONS AT
ANOTHER TIME.
SID, WHAT'S NEW?
>> WELL, IT'S BEEN A LONG TIME
SINCE WE'VE MET SO I DON'T
REMEMBER WHAT I TOLD YOU ABOUT
LAST TIME.
BUT I HAVE A LITTLE BOOK ON
PHILOSOPHICAL MEDICAL AND LEGAL
CONTROVERSIES ABOUT BRAIN DEATH
THAT CAME OUT LAST WINTER.
ANOTHER ONE COMING OUT VERY SOON
ON RESEARCH WITH NON-HUMAN
PRIMATES.
I HAVE A PAPER IN THE JOURNAL OF
COGNITIVE NEUROSCIENCE WHICH
DEVELOPED OUT OF A TALK I GAVE
AT AN NIH FRONTIERS OF
CONSCIOUSNESS WORKSHOP FROM LAST
SUMMER, AND THAT'S ON ENTITIES,
UNCERTAINTIES, BEHAVIORAL
INDICATIONS OF CONSCIOUSNESS, IN
HUMANS, ANIMALS, AND A.I.
AND, OH, I'VE BEEN GIVING
INTERVIEWS ABOUT NEURAL LINK
EVERY TIME THEY IMPLANT A NEW
PATIENT, AND THERE'S A LOT OF
HYPE COMING OUT ABOUT THEIR
BRAIN IMPLANT SO I'VE BEEN
TALKING ABOUT PATIENT SAFETY
ISSUES.
VERY MUCH INFORMED BY THINGS
THAT I HAVE LEARNED IN NEWG
WORKSHOPS AND MEETINGS ON BRAIN
IMPLANTS SO HOPEFULLY ABLE TO
SPREAD SOME OF THAT INFORMATION.
>> TERRIFIC.
PLEASE LET US KNOW WHEN --
CONGRATULATIONS ON THE SECOND
BOOK, I THINK LAST TIME WE MET
THE PREVIOUS BOOK HAD JUST BEEN
RELEASED BUT LET US KNOW WHEN
THE SECOND BOOK COMES OUT.
WE'D LOVE TO LEARN MORE ABOUT
THAT.
>> WILL DO.
>> TERRIFIC.
KAREN?
HI.
YES, I WAS TRYING TO REMEMBER
WHEN WE LAST MET, LIKE SYD.
I THINK IT WAS FEBRUARY.
I WANTED TO SHARE ONE PAST EVENT
FROM JUNE, ACTUALLY.
JOHN, I SAW YOU THERE.
WE HOSTED THE INAUGURAL
NEUROETHICS HACK-A-THON,
PROBABLY THE FIRST, OFFICIALLY
DUBBED AT SUCH.
THE FOCUS ON NEURO-A.I. WHERE WE
INVITED -- WE CAPPED AT 80 FOR
REGISTRATION TO COME IN AND WORK
ON NEAR TERM FICTITIOUS SLIGHTLY
FUTURE-LOOKING NEURO-A.I.
SCENARIOS, WORKING OUT
SOLUTIONS.
TECHNICAL HACK-A-THONS LOOKING
TO SOLVE A TECHNICAL PROBLEM,
HERE ALSO ASKING THEM TO DESIGN
SOCIAL SOLUTIONS AND THESE COULD
BE POLICY PIECES, COULD BE
WHATEVER.
WE HAD THIS METHODOLOGY WE
UTILIZE AND PILOTED THERE.
ANYWAY, A PUBLICATION WILL BE
FORTHCOMING THIS FALL.
I BRING THAT UP WE FOUND IN
REVIEWS OF THE EVENT PEOPLE,
THIS IS LARGELY POSTDOCS,
GRADUATE STUDENTS, MAYBE A FEW
FACULTY SPRINKLED IN, LARGELY
ACADEMIC COMMUNITY, BUT THEY
REALLY ENJOYED THE EXERCISE OF
WORKING THROUGH A REAL CASE, AND
IT MADE ETHICS TANGIBLE.
IF WE WANTED TO DO SOMETHING
WITH YOUR NEURAL A.I. EFFORT THE
CASE STUDIES WORKS AS A WAY TO
COME UP WITH TANGIBLE EXAMPLES
AND I LOVE ACTUALLY SYD'S POINT
ABOUT US UTILIZING TOOLS.
WE HAVEN'T DONE MUCH WITH THE
NEUROETHICS ROAD MAP.
THANKS TO JIM FOR HIS STRONG
LEADERSHIP ON THAT.
WE HAVE SOME PIECES, WE COULD
ADD THINGS, ADD CASE STUDIES TO
BUILD TANGIBLE SOLUTIONS.
TACKLING CONSENT IS ONE THING
BUT THERE'S SOME REALLY DAILY
THINGS THAT COULD BE APPROACHED
IN THE RESEARCH PROCESS.
AND SECOND THING, CAR OLYN PUT
THE LINK IN, BRAIN HEALTH,
SHARING A PANEL, INSTITUTE OF
THE NEUROETHICS WITH OUR THINK
AND DO TANK PARTNERING WITH
INTERNATIONAL CENTER FOR FUTURE
GENERATIONS AND WE'VE BEEN
DEVELOPING STRATEGY FOR THE E.U.
ON PARTICIPATORY GOVERNANCE IN
NEUROTECHNOLOGY, THERE'S A LOT
OF NEUROTECHNOLOGY GOVERNANCE
CONVERSATIONS, EXCELLENT ONES,
LARGELY DEVOID OF PUBLIC
ENGAGEMENT, MEANINGFUL PUBLIC
ENGAGEMENT OPPORTUNITIES SO
WE'RE TRYING TO PILOT A FEW
METHODS TO SHARE AND PUT FORWARD
A STRATEGY LOOKING FORWARD TO
THE NEXT FIVE-YEAR AGENDA FOR
THE E.U. BUT HOPE THESE ARE
HISTORICAL MODELS TO BE USED
GLOBALLY.
>> THANKS VERY MUCH, KAREN.
GREAT ACTIVITIES THERE.
WINSTON, ARE YOU BACK?
>> YES.
>> COOL.
>> SORRY, I HAD TO POP OUT WITH
A CONFLICTING MEETING.
ONE THING WE'RE ALMOST READY TO
FULLY ANNOUNCE, I CAN GIVE A
PREVIEW HERE, WITHIN THE
INTERNATIONAL NEUROETHICS
SOCIETY WE'VE BEEN INTERESTED IN
BUILDING SOME BRIDGES BETWEEN
NEUROETHICS AND CLINICAL
COMMUNITY SO FOLKS IN NEUROLOGY,
PSYCHIATRY, NEUROPSYCHOLOGY,
NEUROSURGERY, ADDRESSING THE
BRAIN AND NERVOUS SYSTEM, SO
WE'RE BUILDING A CLINICAL
NEUROETHICS AFFINITY GROUP,
PLANNING AN EVENT THAT WILL BE
RELATED TO CAREER AROUND FUNDING
FOR PEOPLE FROM CLINICAL
BACKGROUNDS INTERESTED IN THE
NEUROETHICS AND ETHICS SPACE.
I'LL LINK UP WITH YOU ABOUT THE
CAREER NETWORK, LOOKING AT
EVENTS IN THE FUTURE SOME
RELATED TO COMMON PEDIATRIC
ISSUES, YOU KNOW, ACROSS THESE
BRAIN-BASED SPECIALTIES AND
OTHER INTERNATIONAL ISSUES AS
OLD.
HOPE TO HAVE MORE TO SAY IN THE
COMING WEEKS.
>> GREAT TO SEE NEWG MEMBERS
BEING ACTIVITY IN THIS SPACE,
GREAT MOMENTUM.
LET'S SEE.
CHRISTINE?
>> SORRY.
TWO SHORT THINGS FOR ME.
I THINK MOST OF YOU PROBABLY SAW
BUT SASKIA AND I HAD BEEN
INVITED TO CONTRIBUTE TO A
SERIES, IN THE NEW ENGLAND
JOURNAL, FUNDAMENTALS OF MEDICAL
ETHICS.
WE WROTE A PAPER ON ETHICS,
HIGHLY INNOVATIVE RESEARCH ON
BRAIN DISEASES.
COMMENTARY, IT'S A COMMENTARY,
WITH CASES, KAREN.
CASES.
SO JUST THAT.
THE OTHER THING I WANTED TO
MENTION IS THAT WHEN NEUROETHICS
GROUP USUALLY MEETS IN JANUARY,
FEBRUARY, AND THEN AUGUST, BUT
WE'RE NOT GOING TO MEET IN
JANUARY/ FEBRUARY OF 2025.
WE'LL HAVE OUR NEXT MEETING IN
MAY OF 2025.
YOU'LL GET MORE INFORMATION
ABOUT THIS SOON BUT I WANTED TO
LET PEOPLE KNOW THAT WAS GOING
TO HAPPEN.
>> RIGHT.
THANKS FOR THE REMINDER.
WE'LL SEND OUT INFORMATION ON
THAT SHORTLY.
NITA?
>> THANKS, JOHN.
A BUNCH OF THINGS, LET ME GO
THROUGH THEM QUICKLY.
FIRST, NEXT WEEK I'LL BE IN
PARIS AGAIN.
WE HAVE THE SECOND MEETING OF
THE UNESCO GROUP ON
NEUROTECHNOLOGY, GOING INTO IN A
WE HAD THE FIRST DRAFT WHICH WAS
RELEASED TO THE BROADER PUBLIC.
WE RECEIVED OVER 7,000
INDIVIDUAL COMMENTS.
THERE WERE OVER 25 GLOBAL
CONSULTATIONS RUN AS WELL.
MANY OF YOU PARTICIPATED BOTH
THROUGH THE CONSULTATION
PROCESS, ALSO THROUGH COMMENTS.
AND WE HAVE BEEN OVER THE PAST
FEW WEEKS INCORPORATING MANY OF
THOSE COMMENTS INTO AN UPDATED
DRAFT.
THAT DRAFT HAS TO BE COMPLETE BY
STATUTORY REQUIREMENTS BY THE
END OF NEXT WEEK FOR IT TO BE
TRANSLATED INTO SIX LANGUAGES
THAT THEN GOES THROUGH A
YEAR-LONG POLITICAL PROCESS.
LOOK FOR THE UPDATED DRAFT
COMING OUT SOON.
SECOND IS THE UNIFORM LAWS
COMMISSION HAS JUST LAUNCHED A
STUDY COMMITTEE ON MENTAL
PRIVACY, AND IN PARTICULAR
ADDRESSING UPDATED LEGAL
MOVEMENTS IN A -- THAT IS HAVE
HAPPENED IN THE UNITED STATES,
PROTECT OF NEURAL AND COGNITIVE
DATA.
IF YOU'RE INTERESTING IN SERVING
AS BE A OBSERVER, REACH OUT TO
ME.
WE'RE INTERVIEWING POTENTIAL
REPORTERS TO SERVE ON THE
COMMITTEE.
THAT WILL -- IF ALL GOES
ACCORDING TO THE WAY THE
PROJECTS GO IT WILL BE A
YEAR-LONG STUDY COMMITTEE, GOING
INTO A DRAFTING COMMITTEE,
PROPOSE DRAFT LEGISLATION FOR
STATES.
THIRD, THERE'S AN AMERICAN LAW
INSTITUTE AND EUROPEAN LAW
INSTITUTE PROJECT, JOINT
PROJECT, LAUNCHING ON DEVELOPING
PRINCIPLES OF BIOMETRICS, MOIST
ON COGNITIVE BIOMETRICS,
INFERENCES DRAWN ABOUT BRAIN AND
MENTAL STATES.
AGAIN, IF YOU'RE INTERESTED IN
SEARCH SERVING AS OBSERVER LET
ME KNOW.
WE CONCLUDED APPLIED ETHICS PLUS
PROGRAM PARTNERING WITH REAL
WORLD ORGANIZATIONS LIKE THIS
SUMMER UNESCO OECD AND OPEN A.I.
AND OTHER PROBLEMS, BRINGING
REAL WORLD PROJECTS TO --
PROBLEMS TO STUDENTS, A NUMBER
WERE NEUROETHICS RELATED.
IF YOU HAVE A PROBLEM YOU'RE
INTERESTED TO BRING TO TALENTED
AND INTERESTING DUKE STUDENTS TO
WORK ON COLLABORATIVELY, REACH
OUT AND LET ME KNOW.
THE RESULTS OF A LOT OF THOSE
REAL WORLD PROBLEM-SOLVING
PRACTICES ARE UP ON OUR WEBSITE.
AND THE LAST IS THAT I HAVE A
PAPER COMING OUT WITH MARCELLO
AND PATRICK IN NEURON NEXT
MONTH, ON NEURAL DATA AND
COGNITIVE BIOMETRICS, HOW WE
OUGHT TO THINK ABOUT EXPANDING
THE CATEGORIES AND WHAT THE
LEGAL APPROACH, IT HAS A
TREMENDOUS AMOUNT OF USEFUL
TABLES AND DATA THAT PATRICK
CREATED THAT I HOPE WILL BE A
USEFUL RESOURCE FOR THE
COMMUNITY.
THAT'S IT.
>> THANKS, NITA.
DID I MISS ANYBODY?
GREAT.
AGAIN, IT SHOWS HOW MUCH
MOMENTUM IS BEING BUILD AROUND
THIS AREA.
THERE ARE MANY THINGS WE DO
UNIQUE TO BRAIN, UNIQUE FOR NO,
EVENTUALLY TRANSLATABLE ACROSS
THE FEET IN NEUROSCIENCE AND
BIOMEDICAL SCIENCE.
THANK YOU FOR BEING HERE, IN
PARTICULAR TO THE FOLKS WHO GAVE
PRESENTATIONS AND PARTICIPATED
IN DISCUSSION.
AS ALWAYS, IF YOU'RE LIKE ME YOU
LEARNED A LOT TODAY WITH FOOD
FOR THOUGHT.
THANKS TO THE FOLKS WHO MADE
THIS HAPPEN.
WE MENTIONED THEM EARLIER.
WITH THAT I WILL TURN IT OVER TO
ANDREA TO CLOSE THE MEETING OUT.
>> THANKS, JOHN.
ADDING MY THANKS TO ALL.
AND JUST A COUPLE REMINDERS.
AS WAS JUST MENTIONED, MEETING
WILL BE MOVED TO MAY SO THERE
WON'T BE THAT JANUARY MEETING,
YOU'LL BE BETTING INFORMATION.
WE'RE SHARE INFORMATION ON THE
NEURAL A.I. WORKSHOP JOE
MENTIONED, REGISTRATION LIVE IN
THE NEXT WEEK OR SO.
FINAL REMINDER THAT THIS
VIDEOCAST FOR THOSE THAT WEREN'T
HERE ALL DAY AND WANTED TO CATCH
UP OR MAY HAVE JOINED LATE
VIDEOCAST WILL BE AVAILABLE,
POSTED SHORTLY ON THE NIH
VIDEOCAST SITE AND THE BRAIN
WEBSITE.
WITH THAT, WE CAN CLOSE OUT THIS
MEETING.
AND WE HOPE TO SEE YOU AT
VARIOUS OTHER MEETINGS BUT
CERTAINLY WHEN WE MEET AGAIN AS
THE NEWG IN MAY.
THANKS, EVERYONE.
[END OF PROGRAM]