I think the biggest thing humanity never

learns is the older generation lamenting

about the future generation as if the

future generation doesn't know anything.

They're rude. They're they're they're

forgetting the past. But if you look at

arc of history, of humanity, by and

large, we advance for the better. Now,

I'm not denying the atrocities. I'm not

denying the setbacks. I'm not denying

this. But fundamentally I'm a optimist

in humanity. I look at kids, they're

curious. Of course, they get massively

entertained by this technology, but they

also are starting to use it. What I

worry about are teachers and some

parents because I think our society

today and especially Silicon Valley are

not doing them a service. We're

forgetting about them.

Hey everyone. To celebrate the launch of

my new book entitled Protocols, I'm

pleased to share that I'll be hosting

three live events very soon. The first

live event is in New York City at Radio

City Music Hall on September 17th. The

second event is in Los Angeles at the

Dolby Theater on October 8th. And the

third live event is in San Francisco at

the Masonic on October 28th. At each of

these events, I'll be discussing topics

from the book and my favorite part,

taking questions directly from you, the

audience. To get tickets, you can go to

hubermanlab.com/events

and use the code protocols to get early

access. Again, that's

hubermanlab.com/events

and use the code protocols to get early

access to tickets. Welcome to the

Hubberman Lab podcast where we discuss

science [music] and science-based tools

for everyday life.

[music]

I'm Andrew Huberman and I'm a professor

of neurobiology and opthalmology at

Stanford School of Medicine. My guest

today is Dr. Fay Lee, a computer

scientist and professor at Stanford and

one of the pioneers and luminaries of

artificial intelligence and computer

vision. As you all know, millions of

people use AI chat bots to look up

information every single day. And of

course, many people are concerned about

AI, where it's going, and how it might

replace certain human jobs or degrade

our experience of life in one way or

another. Today we discuss from a

neuroscience perspective what

intelligence really is and the ways that

AI can and is being used for good

meaning to truly enhance learning health

and to enrich rather than diminish the

human experience. We start off by

talking about how human brains of all

ages learn new information. What rules

the brain follows in that process and

how AI because it is based on the

content of the internet both resembles

and falls short of what human brains can

learn. and we discuss exciting uses of

AI and robotics in medicine. To be

clear, FFE acknowledges and addresses

the many valid concerns about AI. But as

the director of the Stanford Institute

for Human- Centered Artificial

Intelligence, her goal is to make sure

that humans and humanity at large are

represented in where AI goes next. As

you'll soon hear, Dr. Fa Lee is an

extraordinary scientist and educator.

She has been called the godmother of AI

for her ushering in of AI technologies,

but also for her insistence that the

ethics and benevolent uses of AI stay

central to AI and robotics. So whether

you are young or old, today's

conversation will inform and empower you

to understand and use AI in ways that

truly benefit you and enrich your life.

Before we begin, I'd like to emphasize

that this podcast is separate from my

teaching and research roles at Stanford.

It is however part of my desire and

effort to bring zero cost to consumer

information about science and science

related tools to the general public. In

keeping with that theme, today's episode

does include sponsors. And now for my

discussion with Dr. Fay Lee. Dr. Fay

Lee, welcome.

>> Thank you. I'm excited to be here,

Andrew.

>> Yeah, this is a long time coming. And

yes,

>> you are a luminary in this AI field, but

I also consider you a neuroscientist and

computer scientist, and we share a

common path through vision science. And

so I'd like

>> and fellow colleagues

>> and fellow colleagues at Stanford. So

I'd like to start in vision. What is so

special about vision and seeing and

light as it pertains to AI and where

it's all going? Because I think for most

people those probably sound like very

divorced themes but actually that's

where it all starts.

>> Yeah. I see vision as a cornerstone of

intelligence in almost two parallel way.

One is what evolution has taught us. You

know what's the evolution of vision and

animal intelligence and human

intelligence. The other one is computer

vision and AI what that relationship is.

So I'll go into each evolution. I always

say that 540 million years ago animals

saw the first light. These are simple

sea ocean animals, trilobytes and and

the the cousins. And before that there

was very little sensing. Uh around that

same time tactile and haptics was

starting also to emerge in animal bodies

but but there was no hearing. There's no

you know smelling there's no but there's

absolutely no nervous system. But the

first photoreceptive

cells created a evolutionary force that

propelled animals to evolve because

sensing the external world changes your

self-perception changes the way your

relationship with the external world. To

put it simply, if you seek you can see

food, it changes your your life, right?

from a evolution point of view and you

become someone else's food and also

you're actively seeking food. You're

actively seeking mates and and and all

that. So really because of sensing and

perception evolution took a incredibly

accelerated pace in terms of uh animal

speciation. Fossil studies have told us

that 10 million years after the first uh

light for animals

was what we call the the big ban of

evolution or Cambrian explosion of

animal speciation. And fast forward I

think vision has always played a huge

role in not only in the early evolution

of animals but as well as um advanced

intelligence and how that emerged. You

and I are both vision student and and

scientists. It is estimated half of the

cortical AC activities in human brain is

involved in visual function.

Children were first visual before they

were verbal in development. So vision

really to this day plays a central role

in both the evolution of animal

intelligence as well as in the daily

life of human human life. Now in

parallel, vision as a uh as a discipline

or as a area of uh artificial

intelligence was really played a pivotal

role in what we see as this modern AI

moment in a couple of ways. First of all

is the the uh algorithms the neuronet

network algorithms. Neural network

algorithms were first computer

scientists start dabbling that in the

early 1950s.

And [snorts] Andrew, you might remember

what's happening on the neuros side in

the early 1950s

is that neuroscientists like Hubo and

Viso were starting to record visual

cells in malian brain and starting to

realize there is a hierarchical

structure of nervous cells that stack

against each other and pass

neuroinformation

across these hierarchy. And it goes from

you know collecting light from retina

all the way to recognizing there is a

shape in front of you. And that very

neuro architecture that we see in

mamalian brain is also part of the

inspiration of neuronet network

algorithm. Now today's neuronet network

algorithm runs on hundreds of billions

and even trillion of parameters. It has

the complexity that departs from what we

recorded in the mamalio uh brain or the

visual pathway but the origin is very

close to each other about half a century

ago um a little more than half a century

ago. That's one aspect of uh vision's

contribution to AI. There is another

aspect of vision's contribution to AI

that is also pivotal which is through

big data is that that comes closer to my

own work is that AI around the century

was a field of machine learning a lot of

different labs different research

scientists were were trying out

different algorithms and it's not just

neuronet network there are other methods

jargon words like Beijian methods,

support vector machine methods. It

doesn't matter what these methods are,

but it's a explorative phase that we're

trying to get these algorithms to work

so that we can empower the machine to

read or to see. A group of us computer

vision scientists

were struggling with these algorithms

and uh I was a very young faculty

um first year faculty 2006 at Princeton

and my students and I are looking at

these algorithms and how little data

were fed into these algorithms to learn.

So I turned to cognitive neuroscience

literature per namely vision literature

and started to study how much humans

learn, how much humans can see and the

numbers were incredible. Humans were by

age six can learn tens of thousands of

different object categories and the

exposure to visual world is also

massive. Right? babies can see the mo

most of the time the moment they're

born. So they're inundated with this big

data. So we conjectured that the lack of

data was a huge part of the reason

that's the lack of progress in AI. So we

took a departure from everybody else who

are really focusing only on algorithm

and said that we need data. we need data

to drive these algorithms. So long story

short, we led this um image that project

that collected the first ever internet

scale large data set for the field of

artificial intelligence, but really

through the field of vision because

imageet is a collection of 15 million

images. And the goal of imageet was to

drive machines to recognize everyday

objects, you know, microphones, cups,

chairs. And that work converged with the

advances in neuronet network algorithm

as well as in GPU computing. And by 2012

that work uh that the convergence of the

three elements of modern AI became the

defining moment

of what um what modern AI is. I recall

somewhere around 2012 it seems there was

this debate at this vision course at

Cold Spring Harbor that was held every

other summer like could a computer learn

to recognize specific faces as well as

humans. Now I think most people would

say computers are actually much better

at it than humans are even though you

have these super super recognizer people

who are exceptional at this.

>> Could you tell us how is it that this

technology went from

a state basically where it would confuse

you and maybe a a a cousin or or even

someone that looks somewhat like you

could

>> or to the point where uh to the point

where now it is exquisitely precise.

>> How do we get here? I want to definitely

double triple click on the convergence

of this technology. I think around the

second decade of 21st century. So like

you said around 2012

the the huge convergence was the

capability of GPU computing which

basically accelerated or parallelized

computing so that you can have more

flops going through algorithms right you

need that speed then you also have a um

after many decades of research neuronet

network algorithm them is getting more

mature. Um you know starting as we said

1950s people start to um create these

very simple algorithm that behaves

similarly to neurons but much simpler.

Neurons as you know are very complex but

here the idea is that you have one unit

of node that takes some some input and

outputs another input and within it it's

just a function a very simple function.

So you stack them together. That's what

neuronet network is. But by by the time

it's in the um after you know around 20

uh 2010ish the maturity of these

algorithms have have gotten to a level

that it's it's becoming really good. But

also last but not the least the

recognition of big data. Internet

definitely fueled that. It made data

more available. But the reckoning moment

of wow big data needs to be part of that

equation. We need to use big data to

drive these algorithm to learn these

patterns. So this convergence of these

three things really set off um the the

the

revolution of AI. The specific moment is

also worth mentioning because you

mentioned face recognition is this image

net challenge. My lab put forward that

starting 2010 after we collected this

humongous data set, we at that point GPU

was not yet mature uh and and uh we put

out a uh public challenge for the

research community uh for for multiple

years in a row and invited people to

solve this major computer vision problem

called object recognition. The task was

very easy. We have a data set of a

thousand different categories of objects

and this data set is more than a million

images large. It's what we call the

testing data set and uh the task for the

algorithm is I'll show you a picture.

You have to name the the main objects

inside and if you guess right you're you

you get a point. If you guess wrong you

don't get a point. So that image that

challenge uh we later a couple of years

later benchmarked human performance by a

very smart graduate student at Stanford

and that was roughly 4%. So random

chance will be one over a thousand

>> right? So 4% for humans is not that bad.

The first few years machines were not as

good as humans. The turning point was

2012 the convergence of neuronet network

image net data set and GPU even that

year even though the error rate was was

cut um to oh by the way the human

performance error rate was 4%. Sorry I I

need to correct that the error rate was

cut down to to the teens. It wasn't

where human performance was. So this is

looking at images and and assigning a a

a

>> one out of a thousand labels.

>> Got it.

>> Yeah. But 2012 was so momentous that

year because the error rate from

previous algorithm dropped a lot by this

neuronet network algorithm. And we know

in the research community when something

this drastic happens it it means a

inflection point. But it still took

another

three years I remember by 2012 2016 for

the algorithm to beat humans in in

naming a thousand objects.

>> Could I ask you where this 4% error is

coming from in this very smart graduate

student? Is it that they don't recognize

the objects or it's a recognition

against time pressure? like they have to

they're being fed images fast enough

that occasionally they do an incorrect

assignment.

>> I don't think the time pressure was the

main issue even though for a graduate

student to do this I don't think they

want to do this forever. Um but I think

you know the the human brain as you know

has limited memory whether it's

long-term or short-term right so

retaining the patterns of a thousand

object classes even if some classes

you're you're familiar is is not that

easy

>> you know so so I think there is the

confusion and and also for example

different species of dogs gets really

close.

>> Mhm.

>> And that that's a challenge.

>> I'd like to take a quick break and

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I can see the rationale for doing this

in the vision domain. But has a similar

thing been explored with hearing with

sounds? I mean, it's, you know, as

humans, we we are amazing at recognizing

speech inflection, emotional tone,

things like that. But if I had to

discriminate, you know, even 15

different sound frequencies, I can tell

you as a non-m musician, um, it would be

very difficult for me.

>> Absolutely. I think that what you see is

the floodgate got open and every sub

area of AI whether it's speech

recognition sound recognition uh natural

language processing which more than

recognition uh vision all areas got

really a boost in terms of the

technology we have colleagues at uh

Stanford who are studying whale sound

right uh whale songs using machine

learning and AI AI now and speech

recognition is another area that did so

well in the early days of this AI

revolution and of course the technology

continues to um advance by the time the

transformer paper was uh published

around 2016 2017 it quickly showed that

it is even more powerful than the early

imageet AlexNet algorithm there it was

not the field of computer vision that

made the next big uh progress. It's the

field of natural language processing. So

because the recipe hasn't changed now we

have a even more powerful neuronet

network algorithm called transformer but

we have even more data on the internet

from at least more readily available

data on the internet in the form of

texts and now we have more powerful

GPUs. So companies like Open AI and

Google quickly rallied beyond this this

very important technology and um it

still took

about 5 years from

2017 to 2022 to get to the chat GPT

moment in natural language. But that's

yet another step forward. So I think for

people who are not computer scientists

nor neuroscientists, the um natural

human uh experience will perhaps

resonate with them and and maybe I can

just frame my question through that

lens. So when a child learns that

there's something called a kitty cat,

they go, "Oh, cat." Then they usually

drop the kitty part. They may say kitty

and then they learn cat.

>> And if they have enough interactions

with a cat, they'll realize what a cat

is. Even if they see it from the side,

from the back, and eventually if they

see a tail that looks a little bit like

a cat and it's, you know, behind some

books, you say, "What is that?" They're

very likely to say cat. Even if they've

also seen foxes and other animals with

tails, just based on their experience,

they're making a probability judgment.

And that's essentially what uh AI can

do. That's essentially what machine

learning can do. Mhm.

>> But it seems to me that there's a key

moment that had to happen in the

progression of, you know, from

calculators to the AI we have now to be

able to see an image of a tail and make

the reasonable assumption that it's most

likely a cat if it's indoors or

something like that because foxes

generally aren't indoors. This sort of

thing. So, at what point did machine

learning and AI gain the ability to do

kind of contextual learning and come up

with the most likely assignment of what

something is? Because it's one thing to

show apples and bananas and oranges,

they're all fruit. Okay, you could

distinguish them. You could distinguish

those from cars and trucks, etc. But

this object constancy piece

>> that if something is moving, you're only

getting a partial image. This isn't what

most people think of in terms of

intelligence, but it's part of what

makes our brains and the brains of other

animals, but especially our brains so

remarkable

>> and why we consider ourselves

>> probably the smartest species on earth

and if not the smartest and certainly

the best at technology development.

>> Yeah.

>> So when did AI achieve this and how was

that scripted into these computers to

allow them to do that? So let's just

take the problem very you you have

described it so well this problem of

seeing a glimpse of a cat tail and being

able to recognize cat right or or or a

sign of high likelihood there is a cat.

The interesting thing is Andrew

generations of

machine learning computer scientists

have tried this problem. So before today

that machines can reliably do it there

were different algorithm you know you

can imagine a common sense way of

thinking about this is oh maybe we

should recognize all the furniture to

know it's a indoor so it's unlikely to

be a fox. So though there are rules like

that that it was built into uh previous

generations of algorithms, there are

also rules like well let's only instead

of guess it's a cat, let's only guess

one out of the 10 potential animals, you

know, cat being one of them. That limits

the the the the search or guess uh space

and that would help. So many ideas were

tried.

So when was the moment it became much

more reliable is this current era when

the huge data that these algorithms have

learned let's take Gemini or GPD uh have

learned really created the capability in

the machines uh uh learned space so much

knowledge so much pattern that when

presented with this more or less maybe a

newish

photo of a cat's tail sticking outside

of a bookshelf. That pattern activated

the learned what we call learned weights

or learned parameters that put put the

machine's um assessment or or guess of

this this object closer to what it has

seen which is likely to be a cattail or

or just tail because there's just so

much data. Got it. This is where Andrew

as neuroscientists I think we depart

from human brain because that child who

learns about what you say kitty cat

would not have the chance to download

the internet of images of cat. They

likely have seen three cats, 10 cats at

most,

but yet they're able to identify that

tail as a cattail instead of a fox tail

through a different kind of learning

pathway. These are the mysteries we

haven't fully solved. But I I do want to

point out that departure between today's

AI algorithm that is learned with the

humongous amount of data versus how uh

humans have evolved. If we continue to

um ascend the kind of hierarchy from

simple object recognition to what you

and I would call higher order brain

functions like moving more towards what

most people they hear the word

intelligence and they just think oh it

must be some higher order thing

creativity imagination. Let's go to um a

middle step and then a and then a much

further step out. So staying with the

cat example, if a computer or a child

learns to recognize a cat through the

tail, the whole thing, whatever, and

they've seen a cat move, it's a very new

world at that point for that brain, that

child or that computer

>> because now

they know that the cat generally moves

in the direction of its head, not its

tail. These are simple simple learning

rules, right? It might go after mice,

but it might run from dogs. Maybe yes,

maybe no, and on and on. And so it seems

that the next layer up in terms of quote

unquote intelligence is to assign

likelihoods of direction to move,

directions not to move, other objects

that that object is likely to interact

with. This all sounds very basic to

people, but like this is how brains

learn and this is how machines learn. So

when was the next sort of big inflection

in terms of like giving a computer AI um

a picture of a cat and saying um uh

animate this cat for me, make it move

like a cat without giving it any

specific instructions about how to move

its limbs etc. But I would imagine that

was a pretty quick but a but a

remarkably important transformation in

this whole thing that we call AI because

that's what a brain does.

>> Yeah. So it's it's really funny you

asked this and you put it beautifully. I

never thought it to put it in this way

for a uh public audience but that moment

came when video become part of the

training data. So see again I'm going

back to the training data. So around

2023 very shortly after uh chatbt moment

multiple research teams start to put

video into the training data. Of course,

I'm not going to get into the nuance

stuff, the algorithm. There's a little

bit of uh changes and variations. So,

remember tw January 2024, Sora

was released and that's where people see

a video can be generated literally what

you just said. People can then type and

say

a cat running towards a mouse and then a

a a few second clip would be generated

and there would be a cat moving its leg

in a plausible way running towards the

mouse. At that time there were still

mistakes still to even today it's not

perfect but things gotten have gotten a

lot better but that opened the floodgate

of video generation as you described it.

So what happened there? What happened

there is actually not as revolutionary

as you might think because the bottom

line is it's still data. As a scientist,

I can tell you there are all kinds of

algorithm tweaks and changes and

improvements and and all that. But

overall, if you zoom out, it's still

part of this great neuronet network era,

right? But what happened is that we're

now able to um process video data in a

way again some clever engineering

tokenize it whatever you call it. And

now we can generate these short clips of

videos which is frames put together that

look like plausible cat movement. Now

you might ask does the algorithm know

the muscle structure of a cat's legs so

that when the algorithm shows that the

cat is mo moving in a plausible way with

the paws you know in a sequence I would

say the algorithm doesn't but what it

does have is so many video especially

cat on the internet so many videos of

cat so it learned what it should look

like so in a way humans do that most of

us without education would not know the

how muscles move in cats I still don't

know you know our colleagues in medical

school might know but we have just got

so used to seeing cats moving this ways

that we have a plausible idea of how

cats move so that is similar that's how

similar AI is the it's the statistics

It's the large amount of data that

showed you what is the plausible um

generation of cat movements.

>> Yeah. So when people have heard almost

certainly that the brain is a prediction

machine, it's a learning machine, this

is exactly what Yes. you're referring

to.

>> Yeah.

>> Let's go to a really far out there

aspect of brain function that we know

exists in humans, which is

>> thoughts

>> and creativity. M now there are probably

rules for thoughts and creativity.

They're a little bit harder to tack down

than um examples from the visual system.

Like if it's a tail and it's indoors is

likely a cat. This kind of thing, but

they're there. The rules are there.

>> If you use apple as an example,

>> we could have gone from lowle seeing an

apple

>> to midle seeing apple always drop, not

fly off. at highest level what is the

equation that governs the Apple's

movement

>> right so that's ascending to like a

higher order more reductionist analysis

>> what do you think about the idea that

while AI is indeed intelligent it can do

things that brains can do maybe even

>> well certainly things that individual

human brains can't do we know this by

virtue of beating humans at chess and

this sort of thing

the idea right now as I understand it is

that AI is trained on the internet,

>> images, discussions, videos, songs, but

that's not all of human cognition,

right? So, are there aspects of AI that

are whether or not it's chat or it's

claude or even the most powerful not yet

released machine learning and and AI

tools that don't have access to features

of human brain function yet because

they've never been uploaded to the

internet, at least not in a way that the

AI can pull out. So, for instance, you

know, you could put a symphony there and

it follows certain rules of music and

mathematics and sound like that that

makes sense, but you have thoughts all

day long and I have thoughts all day

long that don't quite mesh with language

in a way that I can just type them out

on the internet. Stay with me here. I

know this is a long question, but I feel

like this is the one thing you are

perfectly poised to answer, and I've

been waiting to ask you this for a year

and a half since I saw you in Utah. In

the world of art, we have this thing

called abstraction, right? And

occasionally somebody will come up with

a painting or a drawing that it doesn't

look like anything specific. This

happens in music, too, where you just

feel something like there's like a

fundamental rule or an emotion

associated with it. Like they've tapped

into some aspect of brain function, but

you can't say what it is.

I feel like this is the sort of thing

that is complicated for AI or for me to

understand how AI could do because you

can put that piece of art into AI and

say, you know, what fundamental feature

of human uh experience does this reveal

and it only has access to what's on the

internet. So, h how can you capture a a

complex constellation of feelings and

experience with AI? That seems to be the

gap for me. And I'm sure we'll get there

with AI, but I'm not seeing from

neuroscience to AI in any kind of direct

way. The same way we could ratchet

through visual motion, sadness,

happiness. You could pull out a lot of

things, but it's hard to get to these

higher order abstract representations

that can't be spoken or written down or

drawn. If I just say, give me your

example of whatever nostalgia for your

childhood home. You could write about

it, but those are just words. It's not I

can't understand your experience at a

first person level.

>> Totally. Andrew, I I know you put a lot

of thoughts into this question and I

think it's a very important question and

let's let's peel this one step at a

time. First of all, TLDDR short answer

is I agree with you that we do have to

be very careful recognizing what AI can

do, is likely to do, not conjecturing

over a 100 years or or whatever. I

recognize what you just said are these

extremely nuanced personalized

hard to characterize or not even

captured human cognitive behaviors and

because they were not captured they then

they were not uploaded on the internet

and we don't have today's AI doesn't

have a way to do that. So when you call

internet

which is the source of AI's data, let's

be very clear what is internet.

Internet is not some random thing.

Internet is the biggest collection of

human behavior

in multimodal forms. Let's break it down

further. Internet has the world's

population typing on it for many many at

this point multiple decades. That typing

is a sensing mechanism that captured

everything from teenager chit chitchat

all the way to deep scientific articles

who dig got digitized and get uploaded.

Right? So that capturing human language

is what internet is super good at.

Then internet captures

images. How? Because we now have digital

cameras. That's so prevalent in

smartphones and digital cameras. So that

humans love taking photos from, you

know, the cat in your house to selfies

to beautiful, you know, BBC captured

photos. Those also got uploaded in our

digital sphere. On top of that, there's

videos. Videos now has sound, has

movements that also got uploaded to our

digital sphere. On top of that, there's

music. We're not even getting into the

legal discussion of copyrights, but

let's just table that aside. I'm just

talking about the forms of data. The

speeches and and singing and music and

orchestra that also got uploaded into

the digital sphere. So now we have

created this humongous library of human

knowledge in words, human behavior in

videos, human expressions or even

nature's whatever in in sound and now AI

gets trained on that. That is why it's

so powerful. This is why especially in

the words front that AI can recognize

patterns can can synthesize patterns

because so much of this is already

there. But the thing that you just

talked about that when let's say Picasso

had that incredibly profound thought

about that particular way of expressing

that that portrait of the of the young

woman that thought has never been

captured.

In fact, as neuroscientists, if I ask

you which brain area did that thought

come from, you don't know, right? Is it

Broa? Is it V1? Is it motor? Is it

preffrontal? We don't know. Maybe it's

diffused everywhere because that thought

is so personalized, so special. You can

call it creativity, you can call it

emotion, you can call it whatever you

want. You can call it cat 231 whatever

name you can give it that thought is not

captured therefore it's not on the

internet therefore AI has not seen it

so that is where humans still remain so

unique but we also need to give credit

to AI because AI has learned so many

things it can combine information in

highly creative way did you Remember

move 37?

>> This is Alph Go. Right.

>> Right. Move 37 has symbolized AI's

creativity. I think it's both true but

can be taken out of context because that

was a game when Alph Go was plain Lisa

doll and in I think it's a third game

out of the five games that Alph Go as a

computer algorithm made a move that the

human masters of Go never thought about

and that is an incredible move right

because it really humans collect

collectively these are the masters never

thought about it. But if you really go

deep into what AI did there [snorts] it

was because first of all go is a highly

mathematical game. It it has very clear

mathematical objective very clear

mathematical rules in terms of move. So

when AI having a bigger compute and um

ways to retain how many moves it can uh

it can remember it was able to do things

that human brains don't typically do. So

is that called creativity? I think it is

but we do have to recognize that's a

special kind of creativity. I was

talking to a incredible mathematician of

our time and I was asking him about the

unsolved problem of mathematics and how

AI can contribute to that and he was

very positive. He said there are many

problems in today's mathematics.

As hard as they are, even as say a field

mentalist, I probably have forgotten

there are known methods in math that can

solve these problem because I have a

human brain. I don't remember I I don't

know all of math's, you know, solutions

in the past hundreds of years. even if I

were a a field medalist. So AI can help

us to solve these problems. But as a

mathematician, he was also telling me he

said, I don't know if AI can solve all

of math problems because some of these

math problems require solutions that

have not been invented

that will push creativity to a whole

different level. And this is where you

you know I'm we should be curious is it

going to be a human creativity or AI

would go through its iterations of uh of

improvement and get to a point of

creativity that humans don't have or is

it a combined creativity. My current

conjecture is hybrid is that humans

working alongside AI would help us to

solve these problems whose solutions

have yet to be invented. And then what

you said especially you touched on

emotion is even more personalized. This

is not necessarily logic. This is not

necessarily deduct deductive reasoning.

This is maybe Andrew you look at this

cup and say it's a great cup. What if it

evoked an emotion in me, a childhood

moment that a gray cup might mean

something that only me and my best

friend share? That is a completely

inaccessible

piece of information in my brain that is

never uploaded on the internet and no

matter how mighty AI is today cannot

access that. So that my reaction to this

cup and potentially what I would do with

it because of that piece of memory can

be completely different. You can call it

creativity. You can call it expression.

You can call it storytelling. You can

call it in many ways. But that's where

it AI cannot access.

>> I feel like at some point in the not too

distant future uh computers will have

access to our brain activity in

non-invasive ways. M

>> so you know like I might even imagine in

5 10 years I'm wearing something on my

head right now you can't see it

>> it's a very very fine hairet makes it

sound like it whatever like some

electrodes that are just there on the

outside of my skull not bothering me

sensing my activity inside the brain

maybe also sensing my heart rate

autonomic activity how alert I am and

comparing that yes to what I'm saying

and what I'm doing this is all totally

within reach and it's going to happen

you and I both know this and it's

probably already starting to scare

people, but let's let's let's keep it

benevolent, right? There's this world

where a computer that I own and I'm not

worried about data getting out or

anything like that. We've can manage

that problem is sensing all these

aspects of me and is picking up on the

fact that yes, what I say might be

important, but there are aspects of my

internal state and brain activity that

I'm not even aware of.

>> Yeah. and I can decide to collaborate

with this aspect of me and say, let's

let's come up with a really interesting

uh picture that I've never seen before,

but comes from some experience of mine

that's important based on whatever like

and and it could reveal that to me

because it has access to my

>> to unconscious features of my brain

activity. I think this is very likely to

happen in in the not too distant future.

And perhaps if people thought about it

within the bubble of their own

experience, like this isn't immediately

going to the internet or it's not going

to be used against them, you're actually

learning about yourself,

>> of course,

>> and and I feel most people have an

inherent interest in what's going on for

them also with other people, thank

goodness. But

>> they're I think like amazing. Like I

would love to know why

>> I trip up in certain ways and don't have

the best day or why some days I have the

best day or where ideas come from in me.

What states I could, you know, kind of

elaborate on, but I'm not going to know

how to do that except okay, one cup of

coffee good, one and a half a little

better, two is too much. If I like right

now, if you think about how primitively

we go about this, it's kind of crazy.

It's crazy. And everyone has a different

method and we all try and get this right

and then you've aged enough by the time

you get it right that then you have to

update it. And like we're probably not

getting the most out of our biology and

our brains at all right now.

>> No, we're not. And this is why I keep

saying this is why it bothers me when

people talk about AI. Some people make

it sound like it's replacing humanity.

But what we really what you describe is

about enhancing and augmenting humanity.

Right. This is where it doesn't even

have to go as sci-fi as a smart hairet

uh accessing your brain waves. Just AI

learning your patterns of writing

>> can already help you to be you know a

better communicator, a more effective

communicator, a more efficient

communicator and that is an empowering

capability that we could unleash in

today's AI. I think one of the most

important thing Andrew that as a

neuroscientist

and also faculty we know is agency is so

important for humanity. You know that

boils down to motivation, agency and

dignity at every individual level. And I

think we need to recognize that we need

to think about AI as a tool that helps

us in our agency. It does it should not

take away our agency and people who lead

in today's AI should not try to talk

like that this this work will take away

agency from people.

>> Yeah. I think people who are very

familiar with the technology whether

it's computers or it's biology or any

technology cars for that matter

we they become such nerds of that thing

that we forget that

>> it can be scary to people

>> and that the languaging around it is

essential it is.

>> And I remember a time in the early 90s

I'm sure you remember this too when

genetic testing was viewed as this thing

like would you want to have it? Would

you want to do a blood test? Because oh

my goodness, you might see something

that could really scare you. And that

discussion is happening now around, you

know, self-elected MRIs and things like

that. None of which people have to do.

>> But I come from the stance like more

information is better. But I've come to

understand that not everyone feels that

way. Some people don't want to know.

They don't want to know.

>> Yeah. But they should have the choice.

In the meantime, we should have enough

public education and communication to

let people know the pros and cons, but

not to deny them the choice and also not

to take away, you know, um, and and and

[clears throat]

say, well, since you don't understand

this, let me decide for you what's good.

That is not good, you know, and and the

rhetoric around AI right now is getting

really skewed because people who know

what this is tend to talk down at the

public. It tend to talk whether the

motivation is a positive one or negative

one. it there there's a rhetoric of you

guys don't know what this is and I will

tell you allow will make you whether

happy safe whatever it is and I will

decide for you these are not healthy and

not helpful. Yeah, I agree. And I think,

you know, one of the reasons for

starting this podcast was to showcase

the scientists and physicians who really

have a benevolence about them and they

have no interest in dumbing things down,

but they do have an interest in people

understanding things and many people

would feel that, you know, health

information is among the more important

things to understand. Absolutely.

>> Well, thankfully you're um you're

breaking the mold of of the, you know,

the phenotype you just described. um and

and there are a few others but you've

really uh you've been doing this at at

the highest levels really encouraging

people to think about the collaboration

that is AI the the agency that exists

and whether to use it or not to use it

and so forth

>> one of the agency I do think it's

important for individual humans whether

you're a student a teacher doctor a

policy maker is learn about this not

necessarily learn about how to code I

don't think that's it's necessary depend

on your job right So for example, if

you're artist or if you're a teacher or

doctor, you don't necessarily need to

code, but learn about what this l uh

this technology is, learn about how you

can use it yourself to empower yourself,

your [snorts] learning or your work or

your expression. By learning,

one feels more in control. By learning,

you're less scared of trying. And by

learning, you retain that agency. and

that dignity because at the end of the

day, no matter how advanced technology

is or medicine is, as humans, we want

that benevolence that helps us to live

better, keep our dignity, and and make

our community better.

>> I'd like to take a quick break and

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to claim a free sample pack. The idea

that technologies can be connectors as

opposed to separators, I think, has to

sit at the center of the discussion.

Yes. And we all know who they are that

they're they're several of them. But the

big names in this field, you know, they

they are also in a developmental process

where they're learning how to be public

facing and it happens very fast. Like,

you know, the the microscope is on them

and the cameras are on them and and so

every every subtle dysfunction is

magnified. So I like to think that they

will mature quickly enough to realize

that and I think they are that some are

that the public needs to hear the

correct the true message but in a way

that makes them understand. That's the

the kind of dirty secret of medicine and

academia that you break this mold. I

like to think I break this mold is that

there's a power in not sharing how

things work. Yep.

>> But it doesn't serve anybody well at the

end of the day. Like you pull back the

veil and let people in and people feel

safer.

>> Yeah. There's a power in in not sharing.

There's also a power to say just trust

me I will tell you and the neither as

educators that is we don't go to our

lectures and say just trust me you know

2 plus 2 equals four. We actually say

here's how you break it down and learn

about it so next time you can do it

yourself. Right. I also think that

especially you are your podcast is so

important as part of public

communication education of knowledge. I

also think that we need to hear voices

of different different background,

right? So because there are plenty of

scholars, technologists, builders, uh

thinkers out there who have been dealing

with AI, using AI, thinking hard about

how to use AI to empower people, and

these voices are so important. Well,

certainly I'll take names of people to

to host in addition to you, but since uh

you're here, I'm going to go next to

something that I think most everybody

would agree would be a wonderful thing

if it existed and it's already starting

to happen, which is the use of AI to

augment health discovery, treatment of

disease, and so on. So, using the

AlphaGo example from before, and people

surely still remember the cat example,

those just follow certain rules. Alph Go

is very complicated set of rules, but if

you learn them, there's a constrained

set of rules.

>> With the cat, it seems unconstrained,

like infinite possibilities, but it's

constrained enough that machines and

humans can learn it really well.

>> When you start getting into medicine,

>> there are rules of medicine. There are

rules of science. You have a question,

you pose a hypothesis, you test the

hypothesis, you try and rule out your

hypo and so on like the the scientific

method. And in medicine, every field has

its methods. We observe, we observe

disease, we observe who recovers, we

have a case report, we do a randomized

control trial. So there are rules and

the internet knows these rules. So LLMs

can be used to mine health information

very well because there are constrained

rules. But I think you and I both know

because I also consider you a biologist

that the rules of biology are still

revealing themselves to us. Which is not

to say that the dermatologists,

neurosurgeons, and oncologists don't

know what they're doing, but they're

doing what they're doing within a

constrained set of rules that they

learned. And even if they continue to

learn and update them,

>> it's every month it seems now that a

discovery comes out that violates the

rule. Like I learned that action

potentials are unitary. They always look

the same. You either fire or not.

>> But there was a paper not but 12 years

ago that showed that the shape of an

action potential can vary quite a lot.

It was published in Nature. Mhm.

>> Everyone saw it and then no one wanted

to deal with it. It's just too much. It

changes the rule.

>> Neurons are supposed to be either graded

or all are one. And the all I mean it's

in every single textbook. So now if I

take a bunch of neural activity and I

give it the rule, oh well you know

action potentials can be big, they can

be small in the same neuron. It

completely confuses everything we

understand about neuroscience

>> and it just our understanding of the

brain just breaks down to zero. Yeah.

But if you gave AI the rule that it

could be, you know, a hundred different

shapes of this signal, well, AI could

probably do a lot more than even the

very very best graduate student at dare

I say Stanford or to be fair MIT or

Caltech. I don't think it can do it and

it can do it like in the duration of

this question, which admittedly is a bit

long. So, I'd like to get your thoughts

on how is it that humans in health care,

the general public and AI can

collaborate to help solve disease and

ideally come up with new rules for

discovery so that we can finally

understand our biology at a level that

can really change the course of humanity

for the better.

>> Yeah. No, Andrew, this is probably

perhaps you touch one of the most

exciting usage of AI, which is

scientific discovery. And in the case of

biio medicine, you know, scientific

discovery directly connects to human

health and diseases, I think we're we're

ready for complete re rewriting of how

scientific discovery can be done because

for ages, I don't even know how long, it

relies on smart humans retaining what

they have learned from other smart

humans and and and doing things at the

speed of our own muscles, I guess, you

know. Most likely of course there's like

super colliders and and all that but by

and large the the ways of doing

scientific discovery

human brain or scientists brain are the

only central character in this process.

Now we have a new tool whose brain that

can retain humongous amount of

information can help us synthesize

knowledge can go across disciplines in

ways that you and I cannot go. So for

example we happen to be both in the

vision neuroscience AI domain.

I know nothing about you know oactory

zero like I don't even know how to spell

most of probably the these words in that

our colleagues know right so it's so

hard for our brain but now we have a

tool that can break open so so I think

that

>> we need to change we need to use this

tool we absolutely I I was just thinking

150 or I don't know exactly when years

ago we electricity

changed everything in in in our life,

right? I'm sure that's a moment we were

thinking about how

the changes, the opportunities, the

scary moment. I think we have to come to

reckon that scientific discovery is one

of the most exciting opportunity for AI

and for health, right? How information

can be synthesized, how information can

be presented not only to clinicians but

also to patients and how patients can

participate in that process from

diagnosis to treatment is also there is

just so much we can do now. [snorts]

>> Yeah. I mean AI I won't say AI is better

than all doctors but AI was able to

disambiguate vertigo from low blood

pressure for me a few months back and

one of the people who got it wrong is a

ENT who works on the vestibular system

>> what information did you provide just

your subjective

>> my subjective experience over a day or

two

>> okay good

>> um turns out it was a medication that a

doctor had prescribed me that I had a

like a mild but adverse event and it's a

weird thing to step and feel like the

whole world's dropping down and then

kind of spinning and I thought my

goodness like feels like vertigo but I

remember dizzy and lightheaded or

different. So I started like looking

into that and then and um sure enough it

was a it was a blood pressure issue. It

brought brought my blood pressure excuse

me down too low

>> and but I consult we know some smart

doctors um none of these were at

Stanford. I will say that this is the

truth. But it was

>> we should just be intellectually honest.

>> But it's just remarkable. And when I ran

it back to them, they were like, "That's

really incredible." You know, had you

not been on the phone with me and in my

clinic, I would have been able to do

some additional testing to be fair. But

this was zero cost. It took a morning to

know if I drank some uh electrolytes at

what I would have thought would be

excessive level that by two hours later,

I would be fine. Now, of course, there's

the possibility of a placebo effect

here, but two hours later, I was fine.

>> And so, it's also very consoling to the

patient

>> to have this. And so, it's not to say

don't go to a doctor, but it it's

incredible. I mean, this exists now.

>> Doctor can use this tooling. By the way,

I have a very interesting example. You

know that we have to reschedule this uh

our conversation because my father was

going through a surgery right at

Stanford uh with an incredible surgeon.

But the surgery was done by a robot, the

Davinci robot system because it was a

liver surgery and the surgeon,

incredible surgeon was driving the

robot. So it was a deep human machine

collaboration. After the surgery, I

asked the surgeon, I said, "Do you

imagine if say you've done a million,

which is impossible for a surgeon, but

human surgeon, but let's collect all of

human surgeons uh for for this liver,

this type of liver surgery data. Can we

possibly train a automatic AI to do

this?" The answer was not clear. So we

went a little bit down the rabbit hole

because liver is a very complicated

organ. It's extremely vascular. It has a

lot of vessels and everybody's liver is

very different. So given the reality of

how many patients undergo liver surgery

per year, even if you aggregate um the

world's liver patient um surgeries, you

might not have enough data to train

these algorithm. So this speaks of a

very important fact that um AI learns

from patterns. When the patterns are not

abundant,

then we have to be careful. We have to

know how to use AI or how not to use AI.

You know in this case that having a

human collaborating with the robot is

way better than a underlearned robot

doing the surgery by itself. But the

same issue might be true for surgeons

because how many surgeries a surgeon can

get trained on. So these are

opportunities that humans and AI can

totally collaborate with and might

reveal the best result. Right now the

future remains to be seen. Can we create

a artificial simulation of a liver that

we can now train infinite possibility?

These are all incredibly open scientific

possibilities that is waiting ahead of

us. But then there are

uh situations like your situation where

the vertigo versus low blood pressure

probably have been reported so many

times that in the database there's

enough of that that AI has learned that.

So we can then now take advantage of

that for people who don't have immediate

access to doctors.

>> Amazing. Is your father's surgery went

okay?

>> It did. It actually

lost

>> 10x less blood than a typical surgery

>> uh thanks to the laparoscopic capability

of a robot surgery.

>> I'd like to talk a little bit about some

features that we think are uniquely

human that may or may not be. You'll

tell me. These are genuine questions,

not loaded questions. And then I'd also

like to get educated on how AI is

structured to allow these things to

happen. For instance, intuition. We all

like to think of intuition as this like

mystical very like it certainly is

powerful, but this thing that like we

own that no one can take from us that

can't be mimicked kind of thing. But I

could also break intuition down to be

well, it's my experience over time. It's

a data set

coupled to some bodily and brain

sensations and some prediction cues like

the last time I felt this this happened.

The last two times I felt that things

didn't work out that way so I'm going to

go this ways. I mean that you could

assign these rules to a computer. But

there are other aspects of our deeper

self if I can refer to them that way.

Like we don't know where intuition is

mapped in the body could do an imaging

experiment but you're not going to

collect all the neurons and hormones and

everything simultaneously. who don't

really have like a location or even a

network to to point to like things like

creativity, intuition, premonition, the

idea that you know you really sense

something is coming on but it hasn't

happened yet.

What sorts of rules can AI get that

could give it these sorts of

capabilities? And here I'm want to talk

about it in the context if you will of

energy. So whatever this thing is, it's

like mitochondria driving cells more

around one thing versus another, the

same way fear or happiness would, right?

We were just talking about energy. But

within AI systems, and I'm not a

computer scientist, within AI systems

and GPUs, can we actually allocate more

energetic flow through particular

learning rules? So we could tell maybe

someday you know based on everything you

know about

my sister who I love you know what is

your intuition about how our uh brother

sister relationship will evolve over

time and what is your sense about what

would be great for us to do perhaps for

our birthdays this year that's different

than before giving and it only has

access to the internet can it actually

become sort of mindlike or mindbody like

and come up with a sort of sense of what

might actually be worthwhile or does it

just need more and more prompts like

it's just going to keep asking me

questions so I'm actually doing the

work.

>> Such a interesting question Andrew. So

um I do want to separate intuition from

creativity for the sake of argument here

and maybe we'll come back to merging. So

let's talk about this intuition of given

my sibling love

what's going to happen right is it

really intuition so today when you go to

a AI chatbot you're going to prompt you

know I'm a Stanford professor and a um a

um neuroscientist

um give me this information that is

already called context I don't know if

you call it intuition but because you

gave that piece of information. The AI's

answer for you is already going to be

different if I type that I'm a 14 year

old teenager, you know, loving race

cars. Even if we ask the same question,

it'll have customized answer. That is a

mathematical

I wouldn't call it energy. I want to be

that is just a mathematical

uh fact of how these um these algorithms

takes these context and tailor the the

the outputs and it's called context.

It's not that deep in the in computer

science. That's one type of intuition

that is fairly shallow because you

already are able to use language to

describe it or you can say I'll upload

an image that that also is is already

expressable and then AI gets it. The

deeper intuition you just said is like

you don't even know where they come

from, right? Like is it because I smell

something? Is it hormones? Is it you

know the the mixture of mood? Is it my

breakfast?

That intuition,

what would AI do with it? That is what I

would say is inaccessible. There's no

sensory apparatus yet that can glean

that data and feed it to

not only AI cannot even feed it to, you

know, for example, sometimes as a couple

you might have moment that you're just

rubbing each other in the wrong way.

>> Never. No, I'm just kidding. Yeah, of

course.

>> If you're really familiar with each

other, you kind kind of can sense it,

but you can't quite tell. Maybe you just

leave quietly, leave that person alone.

So that means whatever that intuition

that person has, they could not even

express it in words or or a gesture to

give it to another person to use as a

piece of information. So when you cannot

even access that

neither a human a different human nor a

machine

can can do anything about it because

there's no access to that highly

individualized intuition.

There's no technology that can do that

till you say we put brainwave collectors

or you know skin conductance

sensors. I mean by the time we do those

maybe they become accessible. So we have

to recognize. So so what I'm trying to

say here is it's not what's not very

deep is is the data accessible

you know either through language or

through picture or through imaging or

through brain waves whatever it is it

needs to be an accessible piece of

information. If it's accessible

then

if we have collected enough of that you

can train machines with or if a machine

is well trained it can like you said in

a private way forget about privacy uh uh

uh breach but in a private way the

machine can probably you use it. What

I'm trying to do, Andrew, here is not to

make it sound mystical,

>> but try to give it a scientific

process to describe if it were to

happen, how would that happen?

>> Yeah. Because um pattern recognition

based on big data sets and rules get us

a long way is what I'm hearing. And we

earlier we were talking about where

doctors fail and robots and machines

perhaps do better or they collaborate to

do better than either one alone. You

know I as a neuroscientist you spend a

lot of time looking at cells at some

point in your career. And it's amazing

how like the electrophysiologists for

decades if not longer you develop an

intuition. I'm not really a

physiologist, but I learned to recognize

cells based on like kind of these things

that were not written up in any papers.

But like if there was kind of a like a

like a straighter edge along this thing

and it had a certain shape and

roundness, like I tell you right now,

that's a transient offpha cell in the

retina. Eventually, we we developed

genetic labels to reveal that that was

true in every case. But then you also

saw some that didn't fit the rule.

Machines can learn that, computers can

learn that. And with all that

information from all those papers, now

we have a pretty good parts list of the

retina.

>> Cool. That works. And then you can apply

rules like they fire this way, they fire

that way. Okay, I'm good with all of

that. What I think I was trying to get

to with intuition, and I probably didn't

give the best example, is like what are

some internal states of humans that are

really hard to imagine machines could

recapitulate, but perhaps they can like

motivation. Do machines, do robots get

motivated? We have rules of motivation.

Like when I'm really motivated to do

something, we call that urgency, a state

of urgency. And I might move faster to

do it. Less activation energy. You say,

"Let's go." I stand up a little bit

faster. Machines could like go quicker

in a certain direction. But can you say,

"Hey, I want you to seek this out, but

with a heightened level of urgency, or

are they just constrained by the

mathematical rules they can work with?"

>> So you could build this in the

mathematics. So certain things whether

you call it motivation or in machine

learning world we call them objective

functions you can build certain things

into math for example now you go to say

JBT it has different mode like think

deeper mode or or like give me a quick

answer mode if you don't know how this

works you're like oh this is interesting

one has more urgency that gives me a

quicker answer the other one has to go

deeper into the search, right? And and

take longer to give me the answer. So,

as a human, if you anthropomorph

anthrop morph morphalize it too much,

you might call it urg urgency or

motivation. But the truth is this is

just a different kind of um objective

for the uh algorithm. You can say, well,

the the one that think quicker has a

time limit or token limit. the one that

thinks slower can activate a different

part of the model that would take

longer. So it become actually

mathematically very dry and not that

deep. But for a human you can call that

motivation or urgency. But let's go

deeper because you're asking something

deeper than than that, right? Is that

there are cognitive states that humans

you truly just whether it's motivation

or urgency or fear or love that is very

hard to access and express. And do

machines have it today? No. Let's make

it very clear. we tend to imagine that

the machines feel or or they're not they

don't have that data they don't have

that mathematical objective function so

they can say when the machine says I'm

sorry you're so sick today it's very

different from how your friend says it

to you because the machine said that

because it has learned through pattern

when someone tells it I'm sick you

should say I'm sorry you're sick instead

of I'm so glad you're sick because that

data exists. Whereas your friend who

hears that, they genuinely want your

well-being. They love you. They want

they don't want to see you suffer. They

have that empathetic

feel of, "Oh, wow. If you're in pain,

I've experienced pain." So that's it's

not mirror neuron, but it's at least a

memory of what pain means. The machine

doesn't have any of that. So we do need

to make sure we differentiate

uh that. So a lot of what drives human,

what ticks human, what triggers human is

doesn't exist in today's machine. We

operate fundamentally different from

today's AI and we have to recognize that

respect that and this is where public

communication is so important. We cannot

confuse the public about this.

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I feel like people assume there's an

emotion, a person or whatever inside of

the AI chatbot because we're so language

oriented. It's talking to us. It's

writing things to me. And we do that

more now than we did 30 years ago. Yeah.

>> Certainly, we've gotten very accustomed

to receiving communications in fairly

deprived language. Texts are not like

extensive pros. Language has changed.

Modes of communication have changed.

more deprived as opposed to more

enriched.

>> Yeah.

>> But at some point soon, I'm guessing

faces are going to start to enter the

picture.

>> Uh no pun intended. Um like how far off

are we from? Like if you or I were to

text the other person, oh uh see you on

campus for coffee next week at this

time.

>> How soon is it that that text is going

to be actually a photo or video like

image of you just talking to me telling

me that? I mean, this would be trivial

to do nowadays.

>> The technology is there. Mhm.

>> But we have to now look zoom out a

little bit and look think about the

social parameters, the legal

implications. I mean, humans are capable

of doing a lot of things with our tools,

but we don't do all of them. For

example, today any car manufacturer can

say every Friday the brake doesn't work.

This is a trivial technology. There's a

clock in the car's computer and it just

turns off the brake every Friday. But we

don't do that because it has deeply bad

implications to our human society.

That's where rules comes in, laws come

in, social norm comes in, morality comes

in and I think this is where we exit the

pure technical discussion of AI and need

to enter the social discussion of AI.

Mhm. Well, let's do that because one

thing that I know about biologists or

technologists is they like to go fast

cuz it it's exciting. It's the next

edge, right? I remember long ago I had a

friend he was studying viruses and ways

of putting uh these weren't infectious

disease viruses. These were viral

vectors for getting genes expressed as

experimental tools in animals. But there

came the opportunity to actually put the

rabies virus, a modified rabies virus

into Drosophila, into fruit flies.

>> Oh my god.

>> Now, that's fine and good in my opinion

if you are absolutely certain, 100%

certainty that that is a nonfunctional

version of the rabies virus because you

can put other cargo in there and do all

sorts of important experiments on,

believe it or not, disease and things

like that.

>> But if there's just one fruitly that

somehow an escaper and you get the

actual rabies virus.

>> There's the potential it mates with

another and then they eventually find

the others. I don't know if this would

be a dominant or recessive situation,

but

>> now you have fruit flies with rabies and

those things move really fast. So,

there's a reason why you don't do that

experiment.

>> But it was exciting for them to think

about and then they got denied, right?

For good reason. I was grateful, right?

Go to any biology department, you're

going to see some fruit flies flying

around. They love vinegar, by the way.

you know, so they're coming to your

salad. But the point here is that

technologists love to go fast. They love

sensing that next edge of things. So how

is it that