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welcome welcome everyone to the latest
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edition of the analytics at Wharton AI
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at Wharton podcast series we're doing a
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series here on artificial intelligence
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and today's episode is looking to be
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extremely exciting I'm happy to be
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joined today by two of my colleagues uh
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the first is Zab Elizabeth Johnson who's
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the executive director and Senior fellow
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of the wart Neuroscience initiative and
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my colleague Michael Platt who this will
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take a minute listeners who's the both
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the faculty director of the Wharton
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Neuroscience initiative he's the James S
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ree Prof pen integrates knowledge
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Professor he's in my home Department of
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marketing he's also in the department of
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Neuroscience in the Perman school of
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medicine and he's also in the psychology
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department in the School of Art Arts and
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Sciences so Michael and Zab Welcome to
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our
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podcast thanks for having us thanks Eric
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it's great to be here well why don't we
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start with the basics I know Zab I'll
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start with you um for our listeners that
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don't know of course they can go to your
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website and see all about it but what is
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the wart Neuroscience initiative and
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then we'll get into what it has to do
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with our episode today Ai and
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Neuroscience great uh so the wart
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Neuroscience initiative is a research
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center under analytics at Wharton here
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at the Wharton School um and really what
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we're doing is to help uh businesses
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individuals uh and Society RIT large
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understand why Neuroscience might impact
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their lives um so so we're working um at
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at in on all different levels uh both
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education of course here we are at the
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University of Pennsylvania um trying to
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lead the charge in in in encouraging
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curiosity um and engagement in the
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neurosciences RIT large we're not trying
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to change business students into
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neuroscientists but we want them to be
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aware um of of what the power of of
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looking under the hood is um at brain
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activity
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itself and uh and then we have research
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active res an active research portfolio
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a lot of times we do that in in
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conjunction with companies um to answer
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and uh and think about questions that
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haven't yet had an academic and practice
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partnership um to do bigger and and
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better things out in the wild um and of
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course engagement is like our third
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really our third pillar it's um it's
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encouraging people to think very broadly
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about this three-pound organ that they
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have in their inside their heads um and
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to think about how that might um impact
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their their lives their own individual
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work and um and how they live their
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lives so Michael maybe a question for
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you I've always said that you know
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people ask me what I do for a living and
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I say I'm a professor I'm a statistician
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but I really say I'm a professional data
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Chaser I chase interesting forms of data
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could you tell us since you and I are
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the same age we've been in Academia the
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same basic amount of time how has
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technology changed the field of
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Neuroscience because you know when I was
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a young researcher you had to put people
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over in an fmri machine if those even
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existed you'll even tell me if those
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existed 30 years ago we had to people in
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an fmri to get brain activity how is
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technology just changed even that aspect
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what you do it's a great question and I
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think that what what's interesting from
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the perspective of Neuroscience is the
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kinds of data that we get right so it's
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it's not directly observable data it's
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not data that people can um verbally
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Express typically because it's like we
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don't have good access to what's going
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on in our heads and and just the
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question just by being asked that
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changes our appreciation of that uh
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technology and Neuroscience is
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bewildering now so it is just every year
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every day just exploding the the Myriad
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ways in which we can measure and
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actually manipulate um brain structure
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and brain function the vast majority of
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those Technologies are not really
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readily applied to humans because they
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would involve you know putting something
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in your head now that said there's an
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active race in the private sector even
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right now you know neuralink being you
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know one amongst many companies that is
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creating and building implants with a
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vision that someday you know maybe all
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of us will be uh perfectly happy to have
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uh some sort of Technology within our
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heads that could allow us to communicate
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with um machines computer com
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communicate excuse me with computers
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even communicate with each other but uh
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fmri still exists uh it's been around
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for some 40 years but um it is still a
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really great tool for peering deep into
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the brain uh but you know it's expensive
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it's cumbersome it's not very scalable
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you can't put it on a consumer's head
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while they're walking around you know
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shopping at Walmart or on your employees
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head while they're you know while
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they're at work so uh so we reserve it
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for specific kinds of of studies like to
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test hypothesis about you know why did
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somebody make a risk adverse decision
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versus not it provides kind of a
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foundation that we try to do uh from
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there is to use that as a springboard
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for applying other more scalable
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Technologies um that can be done you
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know with many more people in a lighter
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weight cheaper kind of fashion that's
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more uh more useful for business right
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and I think that's where we're in a
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really exciting place in the last decade
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was especially the last couple of years
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which is the development of very high uh
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High signal quality wearable
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neurotechnology and so there's a whole
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variety of of gizmos that are on the
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market or coming to Market and um and I
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think and we'll talk about this some
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more obviously but but yoking that
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combining that with advances in AI
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and machine learning puts us in position
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to um really capitalize on the ability
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to you know to under to measure brain
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activity in the real world you know at
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scale thousands maybe millions of people
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uh in a much wider array of activities
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than is possible in the laboratory so
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that's going to give us incredibly new
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insights I believe so one of the things
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as both of you know that we asked you to
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do before this episode was to write a
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set of questions that I could ask you
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and this is episode is no different
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matter of fact Zab and I joked before
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you got here Michael I she joked with me
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like who do you think wrote these
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questions us or chat GPT which is a
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perfect segue to my first question so um
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either one of you can answer this I
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don't matter of fact this is one of
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those times I'm asking a question where
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I actually really really don't know the
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answer so what is organic intelligence
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and how is this different from
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artificial intelligence so Zab I'll
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start with you what is what is organic
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artificial what does that mean to you as
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someone that's trained in Neuroscience
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so I think it starts with just thinking
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about organic matter right so so in
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general organic intelligence is used in
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the domain when it's carbon based um so
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that's what I have yeah so that's what
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you have the wet the wet gooey stuff
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that's inside right but also all of the
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animal kingdom has that um so so you can
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argue about intelligence right and and
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different metrics of intelligence and we
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can talk about that later but but in
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general it's it's this idea that it you
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know that you have a nervous system
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that's carbon based
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um that that you know has a certain kind
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of behavior um and integrates signals in
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a certain kind of you know chemical and
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electrical um system that's carbon based
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and I think in opposition to what we
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classically think of as ai ai is done in
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silica right um I mean that's the way it
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has been so far we'll see um if uh if we
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start to grow to to grow artificially um
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in the lab using wet stuff um that might
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that might change it might get in it you
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know and I think some people would argue
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that there is already still carbon in
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involved but I think the the sort of
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root of what people think of as this
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definition is is based on actually
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having a nervous system rather than not
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having a nervous system and doing it
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artificially and so another interesting
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question that you guys put down is what
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are some common traits shared between AI
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systems in the human brain so or let me
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ask another question should people that
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are building artificial IAL intelligence
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systems today do they have a team of
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neuroscientists working with them
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because in some ways if one of the goals
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of artificial intelligence is to mimic
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human intelligence shouldn't we actually
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understand how the human brain works
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before we try to build systems that are
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trying to mimic some aspects of that
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well I think there's several questions
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here which is what are the goals of AI
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like an AI researcher so one might be to
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mimic the properties of human nervous
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systems but maybe we can do it in a
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different way and I think that's what
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we're seeing now maybe we can even go
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beyond the capabilities of human uh
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intelligence so I think that there are
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there are a number of different
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um commonalities between the two so and
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it's kind of interesting when you think
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historically about where some of the
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basic algorithms in like machine
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learning like reinforcement learning
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came from they actually had an origin in
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Psychology and in neuroscience and we'
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it so it's kind of been you know this
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really interesting
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feedback between I never thought about
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that is would like what Pavlov did with
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his experiments would those be
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considered reinforcement learning that's
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the origins of of reinforcement learning
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and actually the you know the the the
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basic um reinforcement learning model
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was really written out here by um by a
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professor in Psychology Bob R scor back
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in the 19 uh 60s and early 70s so
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there's a you know pen has a really I
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think important part in the history of
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the development of of AI but um it's for
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a long time people thought that there I
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think that um we look at human brain or
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animal brains in general that yes
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reinforcement learning is important for
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learning to navigate and learning what's
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good and what's bad what to approach
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what to avoid but maybe it didn't go
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much deeper than that and then there
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were there sort of circuits and this is
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certainly true circuits that are have
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prescribed functions that are sort of
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built in if you will kind of hardwired
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and that AI with its V you know and the
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other thing is is that there's
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constraints on neural function okay
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we've got a three pound device in our
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heads 86 billion neurons 100 trillion
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connections but it's actually pretty
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limited honestly and it's limited by
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energetic constraints which I think
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we'll talk about in a bit um whereas
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AI you know what are the limits well how
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big is the data warehouse that you you
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know how many servers can you put
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underneath uh the hood of chat GPT and
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so so I think the thought was that like
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oh that Brute Force kind of approach you
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know in in Ai and machine learning
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couldn't deliver the kinds of
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intelligent creative um kind of thinking
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that human beings do and in fact that is
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what we're seeing and now when we look
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back at human brains we I think we're
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starting to rethink that
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conceptualization which is like uh well
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actually our brains have a ton of
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experience under the hood right so
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Evolution and then from the time you're
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an infant right and you're being
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bombarded with all this information and
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that's a lot of time uh for that system
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to kind of learn using SAR principles
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like gradient descent which is really at
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the root of AI and now when we go back
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and we look
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at what you might call neurons in a you
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know in an artificial neural network and
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real neurons in a neural network that
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often they have very similar properties
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which they seem to have arrived at
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through processes like gradient descent
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uh this is Eric bradow professor of
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marketing and statistics and vice dean
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of analytics here at the Wharton School
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and we're here today in our Ai and
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Neuroscience podcast uh Edition and I'm
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here talking to uh plat who's the James
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S ree professor of marketing Psychology
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and Neuroscience and zap Johnson who's
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the executive director of The W
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Neuroscience initiative and also a
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senior fellow so Zab let me ask you um
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how is artificial intelligence being
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used in your field of Neuroscience today
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whether it's from the I always say
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there's at least two sides to AI one is
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the more traditional one which is as in
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you know images can now be ingested by
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an engine and data can be now output
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from a like for example you could take a
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voxelized picture of voxelized blood
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flow from the brain jam it into an AI
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engine and out could shoot a big long
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Vector of stuff that's kind of the more
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traditional the other could be more in
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the generative AI way so any thoughts to
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our listeners about how AI is being used
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in Neuroscience yeah it's being used in
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so many different ways and uh so I'm a
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visual neuroscientist and and I think
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that some of the beginnings actually of
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the way that um that cognitive science
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vision science visual Neuroscience were
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coming together with AI and Engineering
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happened really early so so actually it
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was oftentimes through the
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neuroscientists that were thinking about
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Vision um how we see or how we can even
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train machines to see um that some of
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the very beginning of these algorithms
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emerged um and actually like you know I
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think deep neural Nets um and and you
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know CNN's for example like were like
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they were an outcropping actually from
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people in my dis IPL um and so like I I
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remember the ear one talking is that a
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cat right you know so you have some
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picture an image with a bunch of pixels
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and then it's putting it into some
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neuronet some compression engine and
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then it's got a you know encoder and a
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decoder I I agree with you I think
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Vision was probably one of the earliest
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ones that got people excited yeah and I
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think one of the really interesting
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things in that dialogue was was that you
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could get to the same you could get to
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the same answer you could actually make
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machine c um but it turned out to be
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fundamentally different from the way
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that that that uh that the human does
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right and so I think that some of the
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discoveries that are happening now are
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how can it inform actually how we
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understand neur neural processing um and
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one of the powers I think of AI is that
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it's a you know it can seek patterns um
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and even multiple answers to a single
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question that seems to you know push on
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the limit at least of single
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investigators or you know even you know
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even teams um and so I think we're about
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to learn much more um I think some of
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the most innovative work right now is
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happening where you can see communities
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and of of both AI researchers and
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neuroscientists back in dialogue with
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one another um some some recurrency um
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in that in that conversation and you
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know I think that I think we're in the
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very beginnings of of seeing what the
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power will be but I think um you know I
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think to give you some concrete examples
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um uh a couple of researchers uh uh
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yukiyasu kamatani who's at Kyoto
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University and Frank Tong who's at
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Vanderbilt University about 20 years ago
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a little over 20 years ago did some of
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the the foundational um work um to look
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and see whether you could de go decode
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um using algorithms um the information
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that people were seeing um that was that
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was sort of the beginning um and that
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was long before we had generative AI we
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were just thinking about algorithms and
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the power of pattern detection um then
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you know quickly after that um Jack
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gallon and Alex Huff um who Alex is now
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at the University of Texas Austin um and
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Jack who is who work you know whose lab
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he was in at the time at UC Berkeley
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were thinking about semantic meaning um
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and thinking about the kinds of brain
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activity that came up with semantic
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meaning but also um visuals um uh and
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movie decoding um and like they were
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starting to to use algorithms to look at
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the patterns of brain activity to help
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decode you know from a researcher
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standpoint what people had actually seen
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um and uh comani then did this really
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interesting thing where he actually
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decoded dreams so like one of the I
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think one of the interesting aspects of
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of that work is like you know telling
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you something about that maybe people
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actually have a really hard time
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reporting right um or it's impossible to
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to to to report but another kind of
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imagination or
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visualization so Michael let me ask you
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a follow-up question to what Zab said so
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I tend to be and maybe this is why I'm
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not a basic scientist well I'm a
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scientist I'm a basic scientist sort of
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um I tend to believe things that are of
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low-dimensional representation but maybe
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the world is really complex so uh Zab
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mentioned something about pattern
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recognition how much of like the future
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of what we're going to learn in
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Neuroscience is because we can take this
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very high dimensional or let's call it
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three-dimensional time series data put
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it into some AI engine
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and we're going to notice some 86
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dimensional interaction that no human
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could possibly find is the world built
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that way with 86 dimensional
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interactions or is it like no if you
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understood these I'll make it up I'm
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literally making it up and you'll
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correct all of my vocabulary if I if
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these neurons are voxelized areas if I
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put them into the right bracket then it
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really is just a three-dimensional
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four-dimensional thing what are we going
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to learn from AI
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wow that's a deep big question we have
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six to eight hours here on this
00:17:04
episode you you know I can talk for a
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long time I know you can so I think I
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have sort of two answers to that I mean
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I think that in on the one hand in the
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applied sense maybe it doesn't even
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matter but what it does allow us to do
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and there were three striking examples
00:17:18
of this kind of following what Zab
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talked about this year three major um
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discoveries Publications whatever you
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want to talk about was you're taking
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this very high dimensional data and
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you're reducing it and you're turning it
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into something useful so there was one
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study um you know was the culmination of
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Decades of work but by a group at lazan
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that basically took a guy who this
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gentleman who had a spinal cord uh
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injury he'd been paralyzed for you know
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a couple of decades uh you take the data
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out of the brain you feed it through an
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algorithm machine learning algorithm and
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now rather than trying to actuate a
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robotic exoskeleton or something like
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that uh you actually pipe it back into
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the spinal cord beneath the S side of
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the injury and now the guy can
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walk I mean you know breathtaking who
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knows how it's working what it's really
00:18:04
happening does it tell you how the brain
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works not necessarily but it's
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incredibly useful similarly uh for work
00:18:12
um out of Eddie Chang's group at at uh
00:18:14
UCSF allowing a woman who's been uh
00:18:17
unable to speak for you know again a
00:18:20
long time more than a decade uh due to a
00:18:22
stroke to actually have a conversation
00:18:24
real time with her uh with her husband
00:18:27
okay so you know speech is generally
00:18:29
taken to be the most important aspect of
00:18:31
being a human being those are the thing
00:18:32
the parts of the brain you want to avoid
00:18:34
um injuring in any kind of uh surgery
00:18:37
and now she can actually have a
00:18:39
conversation that is Meaningful and then
00:18:42
another decoding uh one that um kind of
00:18:45
building on what Zab talked about uh an
00:18:47
fmri study and fmri let's appreciate
00:18:50
it's not a great technology it's slow
00:18:52
it's sluggish it relies on blood flow so
00:18:54
not very precise right but um but in
00:18:58
that study uh the scientists were able
00:19:00
to uh decode what a person was reading
00:19:03
not word for word but the gist which I
00:19:05
think is really interesting not from
00:19:07
language areas but kind of from all over
00:19:09
the brain that was my question and uh
00:19:11
and they could then decode what they
00:19:13
were
00:19:14
thinking okay now as idiosyncratic to
00:19:16
each individual I couldn't take like my
00:19:18
brain decoder and put it on you probably
00:19:21
wouldn't work although we sh share a lot
00:19:23
of similarities but um I think those so
00:19:26
those are like ways in which the sort of
00:19:30
the data reduction dimensionality what
00:19:32
dealing with all that complexity is just
00:19:34
helpful and it's just useful right so
00:19:38
but I think that um in other ways some
00:19:40
of the work that we've done and and we
00:19:41
have a paper uh in review right now that
00:19:45
records which we recorded data from
00:19:48
thousands of neurons in monkeys these
00:19:51
monkeys are rather than being engaged in
00:19:53
a task they're actually just doing
00:19:55
Monkey stuff with each other so they're
00:19:57
engaged in totally natural behavior they
00:20:00
engage in like 27 different behaviors
00:20:02
usually in any kind of experiment it's
00:20:03
like one or two different things and the
00:20:06
data on the face of it looked kind of um
00:20:10
you know if you look neuron by neuron
00:20:11
which is what you would typically do it
00:20:12
looked kind of boring and not that like
00:20:15
didn't tell us that much you take all
00:20:17
that data together the pattern of data
00:20:19
across the you know and that's thousand
00:20:21
some dimensional data and you do
00:20:25
something we use map which is one way of
00:20:27
sort of dimensional
00:20:28
reduction and uh you pack that into
00:20:31
three dimensions and
00:20:33
suddenly all of the that population data
00:20:36
clusters into distinct all those 27
00:20:39
different behaviors and not just that
00:20:40
but like who you're doing it with who's
00:20:43
next to you what's going
00:20:45
on number you can and so and it's you
00:20:49
know it's kind of shocking but also
00:20:50
pretty amazing and so you know I think
00:20:52
that maybe what that tells us is that
00:20:54
even when dealing with the complexity of
00:20:57
the world which is is is Rich and
00:20:59
complex that the brain finds very
00:21:01
efficient answers right I mean it's had
00:21:04
hundreds of millions of years of
00:21:06
opportunity you know to do that and I
00:21:09
think that that's what we're seeing so
00:21:11
maybe in the last couple minutes we have
00:21:12
could you tell me about the application
00:21:14
since Michael I know you teach a course
00:21:16
in brain science for business that may
00:21:17
not be the exact title but it's probably
00:21:18
pretty close pretty close and Zab I know
00:21:21
you teach a course in visual marketing
00:21:23
that one I know is exactly right um
00:21:25
could you guys give me a sense of the
00:21:27
big application areas of
00:21:31
Neuroscience in business today that you
00:21:34
are seeing like what are the ones that
00:21:36
excite you the most well I mean I I this
00:21:39
so my course is really a sort of Gateway
00:21:42
course think of it that way it's sort of
00:21:44
like Soup To Nuts everything you need to
00:21:45
know but also like what and somewhat
00:21:47
idiosyncratic what are all the different
00:21:49
application areas where I think you know
00:21:51
Neuroscience either is already having an
00:21:52
impact or will have an impact where it's
00:21:54
already having an impact is in marketing
00:21:56
brand strategy I mean that is is a sort
00:21:58
of at this point a no-brainer if you are
00:22:01
not collecting neuro dat of some sort
00:22:04
you're leaving high quality data on the
00:22:06
floor that could you would make better
00:22:08
ads you would develop better Brands
00:22:10
you'd position them better we
00:22:11
demonstrate that just you know over and
00:22:14
over and over so you can turn that crank
00:22:15
and just do a much better job throw away
00:22:17
less money um the areas where I think
00:22:21
things start to get really interesting
00:22:22
and exciting are places like HR and
00:22:25
management where a more precise
00:22:29
scientific objective understanding of
00:22:32
people their individual talents traits
00:22:34
and motivators right and what it takes
00:22:36
to be really good at for example
00:22:38
whatever job that they are aspiring to
00:22:40
can help to make that match and identify
00:22:42
the training development Etc that can be
00:22:45
done to kind of get you from here to
00:22:47
there uh so we know companies waste tons
00:22:50
of money tons of time on this right
00:22:52
churn is is huge uh that's frustrating
00:22:55
for employees you know they're unhappy
00:22:56
so I think it's an opportunity for a
00:22:58
win-win same thing for teams right so
00:23:01
what you can do for individuals it's
00:23:03
more complicated than teams but we can
00:23:05
absolutely do that too so Zab I know you
00:23:08
guys have an upcoming conference I don't
00:23:09
know if my guess is this podcast will
00:23:11
not necessarily go up before it but
00:23:13
people could obviously there'll be
00:23:15
results from it there'll be video from
00:23:17
it there'll probably be summaries of it
00:23:19
all on the Wharton Neuroscience
00:23:20
initiative website um what's happening
00:23:23
at the upcoming conference it's
00:23:24
happening this exact Friday it is um
00:23:27
it's Friday November 3rd um and uh this
00:23:31
year's uh theme is on brain Capital
00:23:34
thinking about um all of the cognitive
00:23:37
um skills and we think about that as as
00:23:40
you know emotion and
00:23:43
creativity um and you know what I think
00:23:45
was classically thought of as as
00:23:47
cognition like what's necessary actually
00:23:50
to equip people to be productive members
00:23:52
across the entire lifespan um and to
00:23:55
think about the ways that we can make
00:23:57
Maybe devise uh strategies economic
00:24:01
strategies to to to to Really leverage
00:24:04
that sort of like a like a lunar Mission
00:24:07
but now uh thinking about cognition um
00:24:09
and and there's actually one segment of
00:24:12
the day's programming that's really um
00:24:14
taking a deep dive into this idea that
00:24:16
you know that Ai and human interaction
00:24:19
is coming fast um and that and that this
00:24:22
is really a a a moment to seize and
00:24:24
think about both you know an ethical but
00:24:27
also an optimization of what those new
00:24:30
teaming structures are going to look
00:24:31
like um how how can we really equip um
00:24:35
you know the human agent to think about
00:24:38
ways to to to do and live better um
00:24:43
given given this new role of AI uh
00:24:45
that's coming uh but we're also you know
00:24:47
we're also diving into other other
00:24:49
aspects of of brain development in
00:24:52
children and in aging right thinking
00:24:54
about the really the brain Capital
00:24:56
across the lifespan and how like even
00:24:59
early childhood ensures better cognitive
00:25:03
end points um more productivity across
00:25:06
the entire lifespan which will help
00:25:08
economic um and productivity and
00:25:11
businesses Thrive and individuals Thrive
00:25:13
um and and probably um you know build
00:25:17
better protections for for mental
00:25:19
illness and mental health deficiencies
00:25:22
well Michael in the last like 30 seconds
00:25:24
or so we have if we were all sitting
00:25:25
here 10 years from now what we looking
00:25:28
back and saying whether it's the
00:25:30
intersection of AI and Neuroscience or
00:25:33
Ai and humans what kind of problems do
00:25:35
you think gets solved in the next 10
00:25:37
years that just we were not capable
00:25:39
whether it's as you said because of more
00:25:41
data better servers what are the big
00:25:43
Frontiers in your world over the next 10
00:25:45
years yeah well I think that we are
00:25:47
going to see hopefully a lot of today's
00:25:51
um you know issues where brains go arai
00:25:54
right so like whether that's like
00:25:56
depression whether that's uh you know
00:25:58
neurodegenerative disorders um whether
00:26:01
that's the sort of Despair that we see
00:26:04
across the population that we're going
00:26:05
to make significant advances in that um
00:26:08
a lot of that's going to be techn
00:26:09
technology driven uh AI is going to be a
00:26:11
huge um uh Force for good I think in
00:26:15
this in terms of helping us to uh come
00:26:17
up with more creative ideas right and
00:26:20
help us select amongst those ideas and
00:26:21
then really important for translating
00:26:23
them into real solutions too well I'm
00:26:26
getting older by the minute so I'm
00:26:27
counting on you I'm counting on you both
00:26:29
so uh this has been the AI and
00:26:31
Neuroscience uh edition of the AI
00:26:33
podcast series here at the Wharton
00:26:35
School again I'm Eric bradow professor
00:26:37
of marketing and statistics and vice
00:26:38
teen of analytics and I'd like to thank
00:26:40
my guests Michael Platt and Zab Johnson
00:26:42
for our episode today thank you thanks
00:26:49
Eric
