Priscilla Chan and Mark Zuckerberg: Frontier AI + Virtual Biology To Solve All Diseases

[Music]

Hey everyone, welcome to the late in

space podcast. This is Allesio, founder

of Colonel Labs, and I'm joined by

Swixs, editor of Laid in Space.

>> Hello. We're so delighted to be in the

imaging institute of CZI with literally

CNZ. Welcome, Mark and Priscilla.

>> Thanks for having us. Thanks for getting

nerdy.

>> Yeah, we're excited to do this. we we so

don't often get to see this side of you

and so thank you for taking some time uh

out to talk about this and and it's like

you know sort of the 10y year

anniversary kind of of of CZI so I just

wanted to introduce people if if you

know people have not been caught up um

one of the interesting things that we

found out just from talking to your

teams is there's an interesting

difference between how you guys started

CZI and the Gates Foundation uh and I I

heard that Bill Gates is a mentor of

yours so maybe you could tell that story

on of like deciding to start CZI and

deciding to pursue basic science instead

of translational work. Well, I mean, I

think one of the core things for us with

CZI was just getting started earlier,

right? We got some advice that basically

philanthropy and doing science just like

any other discipline requires practice

and you're not going to be good at it

overnight. So um so we should just kind

of dig in and start doing a few

different iterations on it and see what

we enjoy and where we think we can have

an impact and go from there. So, um,

yeah, I mean, like you mentioned, I

mean, this is we're coming up on in

November, the 10-year anniversary of

when we started CZI. And, you know,

there's a lot of work that we've we're

really proud of that we've been a part

of, including, you know, work in

education and supporting communities.

But, you know, when we reflect on it, we

feel like the work that we've done in

science really has had the biggest

impact and in a lot of ways is

accelerating. And with um especially

with all the advances in AI that are

coming, I think the ability to have an

even bigger impact over the the coming

decade is um it it just seems really

clear like this is coming into focus.

So, you know, for the next period, we

really want to make science the main

focus of of what we're doing and and

specifically the Biohub organization

that we're really proud of this model

that we've helped pioneer that we can go

into detail on is really going to be

like the main focus of our philanthropy

and it's just something that we're very

excited about. Yeah, when we started 10

years ago, we had this idea like, okay,

I bring experience as a physician,

Mark's an engineer, and he builds things

and we have an an opportunity to give

back resources to make an impact on this

world. And we sort of just we tried a

bunch of things. And the thing that in

running a philanthropy I'm incredibly

envious of people who run companies is

that like you guys can have a dashboard

and there's like financial results and

people tell you if you're on the right

track on the wrong track and there's

clarity. But in philanthropy there's so

much you can do and it takes a long time

for you to get a sense of like what has

momentum? What are we doing that is

actually bringing all of our both skills

and resources to maximal impact. So over

the past 10 years, I would say we've

been getting a sense of what is that

thing that really allows us to have the

impact and makes the most of what we

bring to the table. And it it's really

been around AI and biology where we're

like, "Oh my gosh, this is it." And you

know, the ecosystem is big. And we

really think our ability to bring great

scientists, great AI researchers

together between the wet lab and the

compute, the ability to bring physicians

and patients into the picture. That's a

unique niche for us at the biohub. And

um that's we you know we need others to

take the work to translation. The Gates

Foundation has a strong focus on

translation and the field and we have

had a number of really awesome

collaborations and continue to where we

really look at sort of the basics

fundamental research and being able to

partner with someone who's thinking

about the translation layer is

incredible. We kind of see the first

decade and I would love to get your take

as a decade of creating data,

>> creating a science ecosystem and then

starting to work on some of the models

and the next decade maybe as more of the

applied modeling side.

>> At what point did you decide that just

doing the tooling was better versus you

know you could have cure malaria in

Africa too or some other disease you

know but

>> yeah I mean take a step back and this is

kind of related to your your first

question too. I mean there's like

Priscilla was saying the space is huge.

You know there are lots of other

philanthropies including Gates who I

think they would say that they're

primarily focused on public health and

sort of administering like once you know

what a what a cure is just getting it

out to the world is a huge thing too and

someone needs to do that and that's a

lot of work and a lot of resources and

it's good that that they're doing that.

Basic science is another completely

different part of the kind of innovation

funnel to enable that. And our view is

that the federal government basically

dwarfs everyone else in terms of how

much they invest through NIH. But

there's a certain pattern to how they

invest which is really enabling a lot of

individual investigators to do work. And

our kind of observation was that if you

look at the history of science, a lot of

major advances are basically preceded by

new tools or new ways of observing

things. Right? So the initial telescope

allowed a lot of advances in astronomy.

The microscope, the invention of that

allowed a lot of understanding of

biology. And similarly, I think we're at

a point in history where a lot of new

tools are being built. Computational

tools, tools to instrument the body in

different ways and and understand

things. And often those tool development

just takes a longer term time frame and

a sometimes a larger commitment of

capital including the way to do it isn't

necessarily just to make grants to a lot

of different people. you need to really

operate it yourself, which I think is

one thing that's different about the way

that we've operated than than others is,

you know, most times when you think

about philanthropy, you think about kind

of giving money away in terms of grants.

And a lot of what we're doing is

actually building up these institutes

and and kind of building labs to do that

kind of research our ourselves by

bringing in leading scientists and

engineers and and all that. But that's

kind of the strategy is um we feel like

there's a lot of new tools to develop.

There's sort of been a hole in the

ecosystem where tool development and

kind of the 10 to 15 year runway that

you need to do that and often hundreds

of millions of dollars to build things

like the microscopes and that you're and

imaging that you're seeing in this

institute here. I think that that's been

sort of underfunded and that's where we

think that if we we do that kind of

work, it can just give all these other

scientists way more tools to accelerate

the pace of of of research, hopefully

discover cures, and then you have folks

who are focused on public health who

bring that out to the world and kind of

deploy it to everyone.

>> Yeah. I mean, our mission is to cure

prevent all diseases and like that's not

going to happen just in our four walls.

So the strategy has to be how do we make

every single scientist and everyone

better and more effective and you know

the strategy Mark talked about is sort

of where we landed on how to actually

maximally move the field forward.

>> Yeah. Yeah. The mission is cure prevent

all diseases. By the way, a lot of

people outside of the CZI worlds are

still kind of find this concept very

alien, but talking to the CZI people,

they really truly believe it. And uh

it's it's impressive how you picked the

right mission to motivate everyone to

work towards this enormous task.

>> Well, it's kind of a funny thing. I

mean, it's uh I mean I I kind of we like

to talk about the mission as like

helping scientists do it, right? Because

we're not actually curing the diseases.

We're just trying to build the tools,

advancing the

>> data models.

>> Yeah. Like basically accelerating

scientists work towards that. But you

know a funny thing about it is we had

this initial time frame of by the end of

the century and you know when you ask

biologists there's a lot of questions

around okay that's really ambitious are

we going to be able to do that and then

when you ask AI people um it's like that

should be really easy like why are you

so unambitious that you're you know

shooting for just the end of the the

century and I I do think that at the

pace that that AI is improving things. I

mean, I think it might be possible

significantly sooner than that. I mean,

I don't think it's it's necessarily

worth putting a a number on it or a

date, but I think that that's, you know,

to your point about the first decade

was, you know, sort of about doing work

like the cell atlas to to be able to

help understand basically all of the the

kind of specifics and data about all the

different configurations of every cell

in the body. When we did that, we kind

of had this vague notion that that would

be useful to advance science. But I

think that, you know, like a lot of

people in the tech industry, we we have

even been impressed by how quickly AI

has accelerated. But that ended up being

a really valuable thing to have done

over the last 10 years, especially for

where AI is now and now the models that

can get built with that.

>> But the thing that's interesting, don't

you agree, is like, okay, so from a

tech, I totally agree that in our

intersection of AI and biology, the AI

folks are like, yep, the biologists are

like, hm. Um, and I think it's actually

that confluence of conversations that

lead both the biologist to be like,

"Okay, I'm really uncomfortable about

this idea and timeline, but if I'm

really pinned down to think about it,

what are like you really force people to

think through like, okay, what are

actually the barriers? What would you

need to do?" And you're forcing that

conversation from the biologist side and

from the AI side really getting a sense

of okay what like data is not just data

you guys know this like you need to know

sort of how the data was collected and

from where and being able to connect the

AI researchers to the folks who are

actually gathering the data on a daily

basis makes their work better. And so

it's it's that conversation that's

happening here that um I think makes

people outside so excited about this cuz

it's credible and they sort of have work

really dug in and thought through how to

how that would work and um and they're

excited and they believe and believing

is the first step.

>> Believing is the first step. There's a

general pattern of software eating the

world and I think AI eating the world is

kind of like the next version of this. I

was talking with Garrett outside who

says he's a biologist, but you know, I

think he's using models like Sam from

from Meta.

>> You're like, you don't look like only a

biologist.

>> What does a biologist look like?

>> I don't know. I was thinking like just

working on models out there and that's

like biologists are working using

models, right? They're not just like in

imagination like just using in the wet

lab.

>> Yeah, that's totally. Yeah. I think one

of those things that you know

referencing the wet lab is one of the

the key approaches that you're pursuing

is turning things in uh pursuing the

virtual cell turning things from mostly

wet lab into something in silico. How

far along are we? I mean it's pretty

early right I mean I think the the first

step which I think is easy to overlook

is basically what Priscilla was talking

about of just getting these folks

together. It it almost it's worth taking

a beat just to talk about this just

because I think most people assume that

this is like obviously you would go do

that but it's somewhat novel in science

because of I think the way that a lot of

funding has been done that is basically

you grant individual teams small like

relatively small grants and people do a

lot of science independently. It is, I

think, pretty amazing how much progress

you can make if you just have people

from different disciplines sit together,

right? It's I mean, this is like over my

career. I mean, both at Meta and and

here, it's um it's like you have you

have teams that like are not working

together for some reason or they

disagree on something. It's like, okay,

physically just have them next to each

other and it like actually is super

helpful. So, here what are we doing?

It's not just bringing together the

biologists and the engineers which was a

core part of the initial Biohub model

but it was also unlocking the ability

for people to work together across

institutions. So the first Biohub that

we started out here between Stanford,

UCSF and and Berkeley allowed a lot more

collaboration between scientists and

engineers at those universities than was

in practice happening before. And it's

like you can look at this and be like,

"All right, that seems really obvious."

But it actually was sort of an

interesting and novel experiment and one

that I'm really happy to see others also

implementing because I think it's just

such a clear win. Um, just the kind of

the human side of bringing people

together and having them sit together.

So anyway, that I would say is kind of

step one or step zero and is probably

quite overlooked but is sort of a

fundamental part of the model that I

guess also goes back to this idea of

like we're not just kind of like

granting funds to other people. We're

building an institution. We're having

people sit together. So then you get

that and then you get these people who

are like half biologist, half AI

engineer because they they kind of have

some experience doing it. And I I don't

know. I mean, we can we can talk through

the specific models and and there's a

lot of exciting stuff there, but I'd say

it's um it's an early glimpse of where

this is all going. I think like you want

to kind of build up these models

hierarchically. So you give them a lot

of data about specific proteins and they

can model specific proteins in the cells

and then you can model different cell

behavior and then eventually you get you

kind of zoom out and you're modeling

like a virtual immune system or

something like that. And it's sort of

hard to simulate the immune system

without having a good understanding of

how a cell might work. And it's kind of

hard to understand or simulate how a

cell might work if you don't really

understand how the proteins interact. So

you kind of need systems that understand

data at all different levels of this and

then you kind of pull them together. And

then if you look at the different models

there's, you know, there are versions

that are kind of focused on all right

like which parts of the genome are are

kind of being expressed in different

ways. I mean the cryo model that I think

is very interesting that's built off of

the data here. the only model that I'm

aware of that's like a a spatial model

of like of basically like how how these

cells work and and you kind of you just

want to be able to look at stuff from

different perspectives and then put them

together and you build like a richer and

richer model of of kind of how these

cells work but we are definitely at the

beginning of this journey

>> but it's like slow and fast slow and

fast right so when we built the human

cell atlas we started 10 years ago it

was one of our first first RFAS and we

actually the first RFA was to fund the

methodologies of how you would get a

single cell transcriptton. And it took

us about 10 years to get to a place

where we had we now have one of the

largest uh corpus of RNA transcriptton

125 million cells cost a lot of money.

And the really cool thing we discovered

through that process was if we could

seed the effort and make it easy for

people to contribute, it happened.

That's cellene. We actually we're

responsible for maybe 25% of the data

and the rest of the ecosystem

contributed 75% of that. That's an

incredible asset and has been very

important in modeling work. Similarly,

if you look at Alphafold, they they they

built off publicly available data that

was collected for 30 years prior, right?

So that takes a long time. But now we're

doing the billion cell project and that

is taking months and at a fraction of

the price. You know, really slow to

fast, but it's a single dimension and

cells are so complicated. And here we're

looking like Mark said at the

three-dimensional imaging structures

that's and it's slow and expensive. But

with cryo with the cryo model, it will

get fast again and you just have to

repeat it. And so I think we'll get

growth spurts, but it's all happening

just faster and faster.

>> How do you think about the layers? So

you have compute, and we'll talk about

that later. On the data side, you build

these amazing microscopes. I learned

that they're all built for you by spec.

They're not offtheshelf things that

anybody.

>> How how much of a bottleneck is that

still? like can we convert the world of

atoms into bits now at

>> the right or do we need do we need more

work on the microscopes themselves too

>> I mean you're never done

>> right yeah

>> well speed for here speed has been a big

question of how just getting the process

through so here we've worked on sort of

the speed at which we can look at

tomograms and the sort of contrast and

resolution and that's where the laser

phase plate comes in so to be able to

make the data better and faster to get

the data Um, but it's a bottleneck in so

much as there's only a I I I don't know

the exact number. There are like maybe

tens of these microscopes in the world.

So, that's one bottleneck. And I think

really is uh like when I was saying it's

slow and then fast. There's so many

other dimensions that we don't have yet

of like the cool thing here is with the

transcriptto work we're looking at

cellular expression and with the imaging

work you're being you're able to

localize it in space and now you want to

connect those two but that's still like

two dimensions connected. Time is

another com dimension. We need to get

dynamic imaging in place.

>> Oh god

>> that's so much resolution. Yeah,

>> right. But like really cool biological

innovation. We need innovation in the

way we can look at things like stain

free, die free. So we can look at things

without sort of human intervention with

time as a dimension is another cuz like

we are not frozen slices. Um so I think

it's just continuously looking at what

the next dimension we want to sort of be

able to either understand deeply or

connect to our existing corpus of data

and knowledge. And obviously the the

ideal would be you want to increasingly

be able to image things inside living

cells, right? So I mean you can kind of

you can simulate it a bit by okay you

can take a cell out or or some culture

>> destructive.

>> It's like okay it's living for a little

bit or something but I mean you really

want to be able to kind of as much as

possible actually understand what's

going on in living organisms.

>> Can that be done? Is there what what

what it approaches?

>> Well the better it gets.

>> Well there's this cool methodology. So

there is a really highintensity X-ray

methodology you can use. The the organ

has to be dead. So like you can just

shoot X-rays, high intensity X-rays at

like a lung and understand at like a

sort of molecular level how the lung is

assembled and then you can correlate

that with living imagery, right? MRIs of

the lungs, CTS of the lungs and look at

the associations between the living

images in real patients with the sample

that you put into the highintensity

X-ray. So that's another example of like

correlating data types so that we can

get that sort of highlevel specificity

with clinical data that impacts humans.

But I mean in some level that's sort of

the point about building these AI

biological models is you can have a lot

of data and you can interpolate that on

that space and understand that and then

there's you know so one of the models

>> that again I mean this is it's really

early work and but the the R bio model

the idea of doing reasoning is that then

you don't just get correlation but you

get some understanding of like logic

over how these things get together too.

So yeah, I mean I I think it's probably

going to be a while and people don't

have great hypotheses on how you'd

actually do like molecular imaging like

of a cell deep inside a living organism,

but the goal is to be able to

approximate that as much as possible

with like this kind of surround view of

of of of different things that you can

image.

>> You guys like to see cool stuff. It's

not here, but at our San Francisco sky,

we do image see-through fish called

zebra fish.

>> Zebra fish.

>> That's another It's another good

example. It's another good model. It's

>> like, all right, it's like how what's a

good way to imaging a living thing? It's

like take a see-through thing.

>> Take a see-through thing and then use a

model to say how does this see-through

thing actually relate to us, right? Like

I'm like not that interested in curing

disease. Cure, prevent, manage all

disease for zebra fish. I am very

interested.

>> Speak for yourself for zebra fish. Yeah,

>> Mark Mark's pro zebra fish. I'm okay on

zebra fish, but you you need to you

another application of large language

models is looking at how what is

conserved and what is actually relevant

and important to the way human biology

works in a fish model. And so being able

to have that translation be more

effective so we don't waste our time on

things that won't apply in a model

organism is another really interesting

way to elevate biology. On the data

side, can you just give an overview of

how far we are? Like what percentage of

all cells that we image and do we have

what's the distribution of them? You

know, like when you say 150 million to 1

billion cells, is that a lot? Is that

10%.

>> The funny thing is until recently, we

didn't know how many cell types.

>> I mean, that's kind of the wild thing. I

this was a big part of the cell atlas

project is like there wasn't even it's

kind of like imagine the periodic table

in chemistry but you you know it's like

it doesn't end

>> it well it's

>> you don't have the squares

>> we know it's billions we know there are

billions of cell types in a human and

we've only truly looked at a fraction of

them and we looked at it in largely

healthy cells and so like just the

number of permutations of like age well

species cuz not all research is in

humans Okay. So, species, ancestries,

like what is your sort of genetic

background, age, like babies are

different than old people, gender, all

of those things actually are

permutations. Environmental exposures,

all of those things are permutations on

the cell that actually you you want to

be able to understand in healthy and

disease states. I feel confident that we

are at the beginning of this. I'll ask a

little bit of um obvious question in

terms of the intersection of AI and bio

which is don't we want precision in

biology? Don't we want uh some grounding

in a world model maybe that we don't

normally get in a language model?

>> Yeah, I mean I I think that that's sort

of the the point of doing all the

measurement and being able to have all

this real. Like so you have the um the

diffusion model for generating cells

that we that we put out and it's like

one of the one of the recent models and

it's like it's cool because you can

basically you have a model now that you

can describe like the conditions and

it'll basically give you a synthetic

cell. But yeah, you you want it to be

increasingly grounded and that's a lot

of the point of the biology and the

engineering that we're doing is to be

able to have these different facets of

that. So the imaging institute is one

part that gets you the spatial data

that's that's very helpful and the work

that uh we're doing in the other biohubs

on cellular engineering and

instrumenting inflammation and things

like that. It's basically it's

scientific work to build new types of

tools that allow us to measure new types

of things that generate data that allow

us to ground the models in different

ways. One framing that we have on this

that I I think is is pretty interesting

is that you know there's this concept of

a frontier AI lab that is like okay it's

it's building AI models that are sort of

at the frontier of what's possible and I

think you can think about biology in

that way too and there's sort of a

concept of a frontier biology lab like

what is the idea of a you know it's a

like labs that are kind of at the

cutting edge of like building the most

advanced imaging like measuring you know

inflammation or cellular engineering in

the most advanced ways, whatever the the

problem space is that you're at. And

then I think that there's

this interesting problem space of what

happens if you're at the intersection of

those two areas, right? So I mean you

mentioned um the work that DeepMind did

on AlphaFold, which is great. Um that's

an example of a frontier AI lab using a

data set that was just generated by

other scientists like over decades,

right? But I think part of what we're

trying to unlock here with Biohub is the

idea of what actually what happens if

you do Frontier Biology and Frontier AI

in sync together and you're designing

the tools on the frontier biology side

in order to specifically collect and be

able to learn types of data that you

then want to feed into specific types of

models that you want to build so that it

can understand the the cells and the

body at different types of resolution.

And I think you can just kind of um I

don't know you it's like a much more

integrated approach that that allows you

know designing the things that you need

that that should eventually get towards

more grounding and not just allowing you

know folks who are good at AI to do the

best they can with whatever biological

data happens to be available. What's the

hill climbing in this scenario? So like

with language models you have benchmarks

you look at the benchmark you just make

that go better. With these things you

have to bring it back to the real world.

So as you build these models, like how

do you bring the two teams together to

give feedback?

>> I think it's very similar to what Mark

just said. You want to be able to

validate on the accuracy question. We

don't expect that these models, they

will get increasingly accurate, but you

want to be able to have feedback. And

it's not as easy as being like, you

know, this yeah, this output doesn't

make sense. You have to actually take it

to the wet lab, run the experiment, find

out if it actually happened as

predicted, and feed it back into the

model. And that's the virtuous cycle we

want to build to help the AI best serve

the biologists and the biologists be

part of continuously improving the

models.

>> From like a numbers perspective, in a

language model, you can run tens of

thousands of tests.

>> They're very false.

>> Yeah. And we have to build a lot of them

out.

>> Yeah.

>> Yeah. And then on going to the wet lab,

what do you think that's going to be

like the feedback cycle? Like as you

start to have more of these things to be

tested in the wet lab, do you feel like

that's going to be a bottleneck that

like we cannot take that many or

>> um I don't know the answer to that yet.

I think that the the throughput on sort

of established metrics in the wet lab is

actually getting quite fast. You can run

parallelized a lot of experimentation.

Um, so but it's not at the t easily at

the tens of thousands of verifications,

but it we'll have to we actually have to

see we'll probably need to be smart

about how we do it.

>> But I mean there's you know a lot of

people I think often take these things

to the extreme and are like okay pretty

soon if you have these models you're

just going to be able to run experiments

with the models without even having to

go to a wet lab. And it's like, no, I

mean, I think that that's kind of like I

think that that's sort of the biological

version of like eventually AI is going

to automate every single thing in

society. It's like, look at maybe you

get there, right? And I think that

there's like some chance over time, but

well before you do, you're going to be

able to have models that can help

generate hypotheses and scientists can

apply their taste on which ideas or kind

of suggestions come from this are worth

testing and then you test them and then

you feed it back into the model which I

think is basically the way that every AI

model is deployed into even other

places. Totally like you right now

because the wet lab is so expensive and

relatively slow compared to sort of

computational experimentation like

people are choosing like I need

something to hit. So people are going

for hypotheses or ideas that are like

you know uh to use a sports analogy like

singles or doubles and but like they

it's just too risky. They only have so

much grant funding and they need

something to help move their work along.

But like if we have a model that can

help derisk some of the bigger riskier

ideas that's going to move science

faster. Um and I think makes the science

and and those ideas both you know can be

sourced with AI as a tool but really

it's really about making the scientist

less hesitant to explore big ideas.

>> Yeah. Obviously that's a lot of the

success of the model CZI which is uh

serving this part of research that is

underserved because there was basically

no benefactor or no fun no funding

mechanism uh by which to do this. One

thing that we're announcing when we

release this podcast is this unification

of the sort of biohub model.

>> Um I think it's very analogous to the

foundation model and frontier lab

approach right where you bring together

people different disciplines. You have

much longer time horizons than than

anyone else. Uh, are there any other key

elements to the strategy of the Biohub

that you're taking?

>> Well, I mean, one thing that we haven't

talked about is the evolutionary scale

team and Alex Reeves and and his team

joining.

>> Let's talk about the announcement. Yeah.

>> Yes. I mean, I mean, this is like

probably the most talented team working

on AI and biology, right? And like at

the intersection of doing of like

basically good biology background and

also, you know, they've just been

working on

>> ES3.

>> Yeah. some of the top protein models for

a long period of time. Yeah. I mean, I I

think if you want to build a an

organization that is doing frontier

biology and frontier AI, you need to

have like worldleading AI researchers

and we're doing that by basically

combining the team that we have that's

already put out all the models that

we're talking about today plus having

the evolutionary scale team which is

just like very renowned um join and Alex

is basically going to be running the

program. So I think it's it's sort of an

interesting decision I I think to have

the AI person basically be running the

the overall program partnering with

these leading leading biologists I think

gives a sense of how optimistic we are

about the AI work being very fundamental

to this but we're very serious about

building out like a leading part a

leading lab on the AI side as well that

goes for both the talent and the compute

I think we were probably the first to

build out a large scale compute cluster

for um for biical research. I think now

um there are some others who are doing

it too but we're also building on that

and you know we really we plan to

release frontier models on this.

>> Do you see that as the 10year output

like in the next 10 years? We look back

at that yesterday they say it's faster

than that

>> but AI people are are

>> they're always in a hurry.

>> We have AGI in two years. So right would

that be a satisfactory result for you

guys? you fast forward 10 years, you

have like the best, you know, the three

best models in in biology or is there

like a further goal that you want to

have as an output of the foundation?

>> I have to bring it back to the patient.

I think like the AI models are I think

we will be very excited both if we have

great models and scientists are using

them, but you really want to make sure

that it's like accelerating clinical

impact like that's the goal, right? like

um that the AI models is a very

challenging milestone that we've worked

we are working very hard on and we will

get there but how do you actually take

those models and apply them to actually

change the way people live and uh

there's so there's two variants that I

think about in the application of these

models why are they important one is

like each one of our genetics is

incredibly diverse and different uh

first like first of all we are just all

the four of us are unique people but we

also have things like that are sort of

known indicators of disease and unknown

indicators of disease and I actually

find the variance of unknown

significance to be the most interesting

and the most frustrating say you know

someone that you love it's sort of a

diagnostic mystery they need to go in

and look at the genetics most likely

they'll come back and be like there are

these three things that are not usual

but we also don't know why and you're

like okay like should I panic, should I

not panic, like what do I do now? And

what you really want to do and I think

these models will be able to do is look

at those variants and actually model out

what is the impact in the different

cells, how it influences cellular

behavior and whether or not that is tied

to a pathway to disease or not. Like

that's a big deal and I think we should

be doing that. That is actually the

future of medicine where we think about

each one of your biology based on your

genetics, your exposure and how that

predisposes you or not to disease. Like

that's huge. And we want to be able to

see that clinical application. But we

can't. It's too expensive, too hard to

model each person, impossible to model

each person in the lab. But if we can

build models around this, it is

possible. And then we can start thinking

with extreme precision. And I'm just not

not just talking about rare disease.

Like there are like common diseases.

I'll just say depression. Right now it's

empirical, right? We just say like

you're depressed like here, let's try

this anti-depressant. And it's like

usually the one that the pat the

doctor's more familiar with or maybe one

that you've heard of, but like and then

you have to try it for months before

it's like did it work? Did it not work?

>> Months.

>> Yes.

>> That's the cycle. I I don't have

familiarity with this. That's horrible.

And and meanwhile, if it doesn't work,

it means the person's suffering. And

this applies to like almost every

disease, right? There has to be some

biological explanation as to why some

medications work and don't. So, can we

actually then look at each patient and

say based on who you are, we think this

medication is going to work best for

you. That's the future I want to live in

where we can actually understand

individuals as individuals and use the

biology and science very directly to

keep them well.

>> Yeah. So like if there's a name for this

tool that has the clinical impact

>> that is on the scale of the electron,

how do you envision it? I I guess like

um I I feel like it's almost going to be

uh the CZI app, I guess.

>> Oh well, it won't be uh first of all,

that's not what we're building right

now. We're building the basics. We're

understanding like cells and molecules.

Um, so I'm painting someone else. We're

painting a picture like we need

partnerships. This is you asked about

the ecosystem before like there are

experts along the way of this pathway

and so we sort of are at the fundamental

research side.

>> Yeah.

>> And you need to be able to partner with

folks to bring this all the way through

impact. But the way I think about it,

people call it different things, but

essentially you want to get to u

medicine where we it's truly precision

medicine. It's N of one. We're

understanding you and designing

therapeutics for you.

>> Yeah, I like the mission of RAR as one

as well. That's that's a great framing.

>> Do you feel like that's possible? Like

almost treating the body as like a

compiler. It's like because I know

exactly what it looks like. kind of know

exactly what's going to happen or is the

body just like there's too many outside

inputs and like over time it kind of

deviates from what you have.

>> Well, I think we'll see how far we can

get, but I I mean I'm pretty optimistic

that we'll be able to make a bunch of

progress and yeah, I mean there's like

you basically I mean what format does

this take technologically? I I would

imagine you're taking these different

types of virtual cell models and

eventually merging them into the

equivalent of like a biological omni

model. kind of like how on the language

model side you had people that did

language and then you know people who

did different kinds of media models and

perception and all that and then

eventually you just kind of merge that

and then you you

>> aim to get positive transfer by merging

it. So that way it's not just combining

capabilities but getting everything else

to be stronger. So yeah, I mean

technologically

I think that's basically what it looks

like is you know over whatever it is a 5

or 10 year period we're building up a

series of biohub models that like

increasingly get um all these different

dimensions of of data and capabilities

that can be used to help run individual

science experiments and potentially

eventually help with finding individual

therapies for patients. Although we're

going to be less on the clinical side,

we're going to be more on the kind of

scientific tool development side. And

the kind of main tool, if you will, is

this like these biohub virtual cell

models.

>> I would say 5 years ago, without sort of

the large language model supporting

this, I don't think it would have been

possible to really cuz biology is

incredibly complex. And what we're

essentially trying to do is break it

down from a discovery based science

where you kind of get lucky, you kind of

get clever and you sort of figure out a

hack to learn something new to really

making it closer to an engineering

problem of like this is how the system

works and when this breaks what happens

to the rest of the system. But like you

said, there's just there's far too many

dimensions for to for us to hold in our

brains. That's why we're so excited

about this intersection at this moment

because it is possible to consider so

many more dimensions matching the

complexity of biology.

>> What is the role of the doctor in that

future? Right? If you can like predict

everything out and then if you take

personal super intelligence seriously,

do you kind of distribute some of the

diagnosis and all of that work or how do

you envision that?

>> I've been thinking about this a lot. Um,

and I think one is the model's not going

to take you all the way. You're still

going to need to really look at

individual clinical situations and and

the doctor is going to be a form of data

input into the model, right? And so the

doctor there's some judgment that comes

into place, but there's already a lot of

models that make doctors really good at

what they do. For instance, looking at

your skin, like AI is really, really

good at detecting lesions in your skin

that are concerning, it's excellent.

Retinal issues, it is excellent. So, the

AI modeling and mapping is really,

really good. So, it's already happening.

So, I I think about like what should

future doctors be trained to do? And I

really think care and compassion and

sort of walking patients through

understanding I think understanding why

leads to trust in both the science and

in the clinical pathway and really

walking alongside patients on that

journey is you know it was the original

calling of physicians to be healers and

to be using great tools to heal

patients. Well, so bizai manner

ultimately is

>> I mean I also think you can zoom out

though from like the role of a doctor to

I think everyone wants the health system

to be more proactive and less reactive,

right? So that today it's like you show

up when you're sick and then like you

have someone treat you or understand

what's going on. I think the goal with a

lot of these systems is to be much more

proactive about this. So when we say

that the vision is to try to help

scientists cure and prevent all

diseases, it doesn't mean that there's

going to be like no bacteria in the

world and like no one ever starts to get

an infection. It's just that all right,

ideally you can kind of understand all

of that really early, right? Similarly,

if someone, you know, if someone gets a

mutation, it looks like it might become

cancerous, then you know, you can just

treat it a lot better if you if you know

that early rather than like showing up

to a doctor when it's, you know, already

metastasized and you you have a bunch of

of of issues on that. So, so I don't

know. I think that there going to be a

lot of opportunities to fundamentally

improve the health care system overall,

but I I I think I mean I agree with

everything that you said on this and I

also just think that like when we say

that that we think it's going to be

possible to prevent and cure all

diseases, it's not literally that no one

ever gets the beginning of a sickness.

It's just that it like it it kind of can

be managed in a way where everything is

sort of manageable.

>> I think we discover more diseases the

longer we live. Um, is it possible to

not die? Is, you know, obviously that's

a that's a meme that's coming to

fruition. If you theoretically cure all

diseases, maybe death is a disease.

>> Mark just said we had extreme alignment,

which I love. Thank you, honey.

>> This is this is one that we don't.

>> This is one that I'm not sure we have

extreme alignment on. I in fact just

haven't thought about this one very much

because I think there is so much

>> there's other things to do

>> there. that there's so much to do in

terms of like I I you know I'm a

pediatrician. I think about babies and

like very sad things happen to very

small people and like I think a lot

about that and how do we like maximize

life quality and the things that harm

small people. I'm biased and I haven't

thought as much on the other end of the

spectrum but I don't know I'm 40 maybe I

should but I feel like I can still focus

on the little ones.

>> I think the strategy is the same right?

I mean, it's like we're we're basically

choosing to not focus on any specific

disease and like verticalize. Our

strategy is one of trying to accelerate

scientific progress overall. And I I

think that there are a lot of people who

are going to focus on each of these

individual things. So, I don't know,

>> but we don't have to cuz that's not our

strategy. Our strategy is to make sure

that we have tools that make people do

the best science possible out there.

I'll put to you that you know because of

because aging and and environments are

and mutations are so diverse like you

actually you have a high concentration

of grouping in in the early years and it

should have more diversity in terms of

uh the the the cell types and and the

problems that you face in in the later

years and so that I mean there might be

some imbalance in terms of uh where all

these things happen. Uh but I'm not you

know I'm not I'm not pitching in any

particular direction. Well, I mean I

mean I think it's clearly if you look at

the trend over the last I don't know

what it is 100 years. I mean there was

this flip and if you like pay attention

to the history of science where it

changed to kind of hypothesisdriven

scientific method of like we're going to

run tests and have controlled

experiments and since that happened the

average life expectancy has basically

increased by I think it's about a

quarter of a year every year over the

last hundred years. Now, a lot of that,

like Priscilla said, is basically making

it so that a lot of people don't die

young.

>> Yeah. So far, had somewhat less of an

impact on extending the maximum human

life expectancy. Although the oldest

people today, I do think in general are

older than the oldest people, you know,

20 or 30 or 40 years ago. But I think

that there's been a little bit less of

an increase there and more just um kind

of making it so that people don't suffer

and die prematurely from things. But I

mean there's other things that you want

to focus on here too. It's not just like

how long you live. It's like the quality

of the life while you're, you know, so

>> I think it's like

>> you can live a full life and have that

be high quality, you know, or you can

get sick in different ways that kind of

add up over time. And I I think like

there's lots of different ways to

improve. I mean it's um it's there's

like you know all these different

analogies that you can throw at this but

I think there's there's just a lot of

lot of room to improve here.

>> Yeah.

>> And then the other element I wanted to

come back to on the engineering side

which is when you presented a high

dimensionality problem you want to

reduce things into little boxes that you

can sort of manipulate at a higher

abstraction. And that's something I I

tried to do with uh the folks outside

and it's really we really struggled

because over here you're imaging on like

the atomic level and then you're also

worrying about proteins and then you're

also trying to build a cell model. Is

every abstraction leaky? Like where's

the boxes I can move around then and not

worry about it? Now my my physics

physics analogy is in the regular world

you don't have to worry about quantum

physics but here we kind of do. Mhm.

>> I think you want to build it up a little

bit hierarchically and like I when

you're trying to understand proteins,

understanding molecules makes a big

difference, but at some level you can

kind of just look at like correlations

in cells, but if you want to like really

have the most accurate model and if you

want to be able to reason about things,

then you probably also want to

understand proteins well. And then I

think that kind of extends. But yeah, I

mean that's part of the interesting

challenge of this is that it's not just

like one one resolution that you're

looking at it. I think in order to do it

well, it's yeah, I mean, you're you're

kind of you have some amount of

abstraction, but I think you want the

models just like language models are I

think how our brains work to basically

build up different levels of abstraction

and pattern matching. And I think that

that's here too and you basically just

need to be kind of like have some basic

excellence and understanding at each of

these different levels.

>> It's weird the the the number of levels

at which you have the telescope up and

down. It's uh mindboggling. Yeah, I

think like that's when when people say

dimensions, they typically mean

orthogonal dimensions, but here it's

sort of like nested and yeah,

>> there's different scales that are like

oddly different disciplines to

understand each specific scale and it's

like in a way that like the people who

are good at understanding one scale are

like

>> I've never spoken to people at the next

scale.

>> Yeah. Yeah. Yeah.

>> Yeah. Physics is there, chemistry is

here. Yeah. Bio's there. It's nice to

hear about it, but when you see it and

you meet the people, you're like, "Oh,

this is real." Like, and they they are

actually working together.

>> Yeah.

>> And then there's this goal of the

virtual immune system that you're

working towards. I would love to for you

to chat about that. And also like if

that happens, what should other people

build? So there's obviously, you know,

crisper and some of that technologies

like that people should maybe ramp

throughut for like how do you think

about the future? the virtual immune

system I think is is you know obviously

uh I I think of a subset of sort of the

generalized model eventually we'll get

to but the virtual immune system is

super interesting for a couple of

reasons one it's individual cells

interacting with each other there's you

know a a number of uh uh cells that we

don't even fully understand what they do

B cells T cells NK cells and so we can

use our current technologies to

understand these cells at a more

granular level. So that's cool from a

biology standpoint, but the clinical

impact is huge of understanding the

immune system because biology turns out

has already given us a way to keep the

body healthy and it also sometimes goes

ary and causes disease with autoimmune

disease, right? And so it's a very

complex system that has to stay in

balance and if it goes out of balance in

either direction, you get sick. It can

also go into your body and it's a

privileged system that is mobile and can

go into places like your brain, your

pancreas, your heart to sort of either

do maintenance or to collect signal.

That's it's built in. So if we can

understand this system, we can use it to

keep people healthy. We already kind of

do. So there's CARTT cells uh where we

reprogram tea cells to go in and fight

cancer. In our New York Biohub, we're

doing cellular engineering to say like,

"Hey, can you go in to this person's

heart, check if they have uh plaques

that are causing problems, read it into

your DNA, self lice, and then we can

read out the signal of cellulfree DNA

and give us a binary answer, yes or no.

Then we can uh put in other engineered

cells and imagine where you go in and

you clear out the plaques using

engineered immune cells that are your

own." That is incredible. That is a tool

that like is is realistic too. I know it

sounds sci-fi. It is realistic. It is

happening. And then on the other end of

understanding the balance like so many

autoimmune diseases, MS, lupus, those

are the ones examples of ones we know. I

I think there are other things that are

autoimmune that we don't understand like

dementia can have a autoimmunity can

play a large role in that. And so if we

can understand the fine balance that the

system needs to be kept in, then we can

actually impact a lot of the ways the

the human body is maintained. Um so I

think it's both interesting from a

biology perspective and feasible to

model and probably one of the highest

sort of now impact systems if we can

learn how to manipulate.

>> Amazing. But it's only one system,

right? I mean it's I think it's like the

>> So it's a subset. If you're focused on

curing and preventing diseases, the

immune system is a pretty important one.

And I think it's also interesting for

all the and unique in a lot of the ways

that you said, but there's like lots of

other parts of the body to understand

too.

>> And I think we're running out of time.

So we have two questions to close. One,

again, 100 years maybe is too long,

right? What would it take to do it in 50

in 25? And to make those happen, like

what should other people build to

support um your work? I mean, I think a

lot of this is going to end up coming

down to how far a lot of these AI

methods get, right? I think that there's

like people have there's just this

constant ongoing debate around what are

the time frames for getting to very

strong AI. And I think if you if you get

that, then I think it's pretty

optimistic that with the right

investments in frontier biology, you

should be able to get these systems that

can allow you to have virtual cells that

allow you to do the kind of precision

treatments and preventative care that

can achieve this kind of mission um

significantly sooner. But at the end of

the day, I think a lot of that time

frame will probably come down to the AI

time frame. There's obviously a ton of

stuff to do in biology. But but it's so

it's not I mean like I think that what

what other people do. I mean other

people doing more frontier biology and

and helping to collect that this type of

data and solve these problems is super

helpful to that too. It doesn't

automatically happen. But I guess if

we're, you know, predicting whether it's

going to take 10 or 20 or 40 years, that

is probably more a function of the pace

of AI development than it is a pace of

the pure biology side.

>> Yeah, I was going to agree with you. So

I think it's a a lot needs to it's I

think we're on a path to gather get a

lot of important biological data through

uh advances in laboratory technique but

it's not a given like and there are

different groups that are expert at this

all across the nation and across the

world and so we need to be continuing to

push the research and the methodologies

and I want to say that like you know the

cell atlas was not glamorous work people

were not going to get their 10ear track

paper by sort of analyzing the 100th 120

millionth cell. That is just not it,

right? And so rethinking the way that

this work gets done in a collaborative

like doing big things together in

science like that's what built is going

to need to happen to sort of get the

knowledge we need to build models that

give us this type of insight. I guess

one thought on on like the type of

biology that I think should get done is

there is a certain orientation around

choosing problems that will help

generate data that can help make the

models a lot smarter. I think that

there's a you do that when you are very

optimistic about the pace of progress

and what AI is going to enable because

the classic reason that scientists

generated data sets is so that they

could basically look through the data

sets to make advances. So it is a little

bit of an inversion in the thinking

which is like I'm now going to do this

so I can like help train this other

thing to be better and create more

advances. And I think in a world where

you really believe that there's going to

be very significant AI progress, I think

more frontier biology should be done in

that way. But these data sets aren't

going to get created by themselves.

There's a lot of work that needs to get

done and a lot of investment there. And

at some level, you could probably have

the smartest AI model in the world, but

if it doesn't actually have the data to

understand this stuff, it's like, okay,

you can't just like reason from first

principles about about all these things.

I mean, a lot of human knowledge comes

empirically, not from first principles

reasoning. I think that more, this is

kind of the whole biohub network idea

that we're building. And I've been

really happy to see other folks,

especially a lot of people in

technology, I think, have this

orientation, too. They're, you know,

they they believe a lot in AI. They

believe in the technological progress.

They've generated some some significant

wealth building their companies and now

they're investing in in science

research. And I think that's great. And

I think doing it in this way where

you're like building up these networks

to solve specific to basically build

specific tools that generate data that

make the models better. It's one

approach. It's not like it's not that

all science should go in that direction,

but it's one of the things that I'm

quite optimistic about that I I think is

going to make a very big difference.

>> Cool. That's probably all the time we

have, but I'll just leave it to you guys

for any calls to action, anything that

you want biologists or engineers to

check out. I mean, check out the models.

Check out the models, the tooling. Yeah.

I mean, it's they're they're early, but

I think it's they're um they're it's

kind of an interesting sense of where

things are going and, you know, we'd

love feedback on it, and it'll kind of

just help this feedback loop of of like

what we should build next.

>> Yeah, I would say let's do this

together. Like, we need lots of people

coming together to do this work.

>> Well, thank you for organizing it and

solving and curing all diseases.

try to help others do it.

>> All right. Thank you guys.

>> Thank you.

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