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Unexplainable is a science show about

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New episodes every Wednesday wherever you get

0:29

your podcasts. Hello

0:33

and welcome to Raising Health, where we explore

0:35

the real challenges and enormous opportunities facing entrepreneurs

0:37

who are building the future of health. I'm

0:41

Olivia. And

0:46

I'm Chris. Today's episode is with

0:49

Kim Branson, Senior Vice President of Artificial

0:51

Intelligence and Machine Learning at GSK. He

0:53

is joined by Vijay Pandey, founding General Partner

0:56

of A16Z Bio and Health. Together, they talk

0:58

about how AI has improved drug discovery and

1:00

development, as Kim walks through all the ways

1:02

AI can be deployed in the lab. AI

1:05

has become so useful because

1:07

we've got so much more measurement technology, right?

1:09

Cheap measurement technology, so we can do generalized

1:11

sequencing cheaply, we can do RNA sequencing, we

1:13

can do single-cell type stuff. You've got this

1:15

data, you've got more measurements now, but

1:17

you can't do anything with it unless you've got

1:19

machine learning. You know, my central hypothesis has always

1:22

been medicine has advanced, this new measurement technology has

1:24

come along. And as the measurement technologies get cheaper,

1:26

we learn more. They also get into

1:28

detail about what an ideal partner looks like

1:30

and what kind of solution is attractive to

1:32

a big biopharma, making this a particularly relevant

1:35

episode for builders in biotech AI. Because it's

1:37

honestly, you're selling the solution at the data styles and people

1:39

don't care whether it's like, you know, the mechanical

1:41

Turk type thing or whatever. You

1:44

can sell a random forest if you're trying to really call

1:46

random forest. Doesn't matter, it's not a neural network. You're

1:48

listening to Raising Health from A16Z Bio and

1:50

Health. Today

1:54

we have Kim Branson, the SVP and

1:57

global head of AI and machine learning

1:59

for GSK. So Kim, thank you so much

2:01

for being here. Welcome to Raising Health. Thanks, Vijay. It's great

2:03

to be here. Maybe one place to kick it off is

2:05

that I think people would love to get to know you. And

2:08

what got you interested in the space in

2:10

general? My guess is as a kid, you

2:12

like computers, but you must have also liked

2:14

medicine and biology. How does this all start?

2:16

I actually never done any biology. Never even

2:18

thought about it until I actually got to

2:20

university. And then I discovered molecular

2:23

biology and then bacterial pathogenesis. And

2:25

you have this interplay between the immune system and bacterial

2:27

stuff. And I was sort of hooked. And

2:29

yeah, you're right. Obviously, as a kid, I had played

2:31

with computers and liked computers. But I was far more

2:33

like a math, physics kind of kid. And

2:36

once I discovered this whole world, I

2:38

was sort of hooked. And then I discovered it wasn't just

2:40

sort of blobs, blobology. There was like structure. So

2:43

then x-ray crystallography and things. And once you sort of put all

2:45

those things together, it's

2:47

sort of a very seductive thing to come into

2:49

how things really work. So I

2:52

was sort of always drawn

2:54

by the allure structure of

2:56

biology and love all those sorts of

2:59

things. And then I realized between those

3:01

long hours during incubations and things

3:03

like that, I could just go and do

3:05

this instead. And just being

3:07

computations. Yeah, doing computation and doing small molecule drug

3:09

design. So when I had sort of finished my

3:11

PhD, even during my PhD, people were like, well,

3:13

computational drug design, does that work? And the answer

3:16

was, well, yeah. And it definitely is dying to

3:18

work more. And so the people I did my

3:20

research. But you're talking about, well, that's 20 years

3:22

ago. Yeah, so we're talking like, yeah, it makes

3:25

me feel old. This is like 99. So

3:29

I was working with Joseph

3:31

Agis and Peter Coleman and Brian Smith. And

3:33

these are the people behind the first Neuromunidase

3:36

drug, which actually was designed actually with computation.

3:38

So they used a program called Goodford's Grid

3:40

to map the protein surface and work out

3:42

to put this functional group there. And

3:45

it was all based off the original sugar

3:47

molecule. But it was actually designed using

3:49

a computational method. They were believers in it

3:51

because they had built something. It became

3:53

marketed as Relenza, I think, by GSK, ironically.

3:57

So yeah. Full circle. Yeah. Actually

4:00

after that, you went on to go to some

4:02

startups. Yeah. And also go on to pharma. Curious

4:04

to get your take on one versus other, how

4:07

you see that ecosystem. I did some sense of

4:09

time at Vertex. And I wrote a Vertex in

4:12

biology as you know. The best model we can build has

4:14

a figure of merit of accuracy, like 0.6, 0.7. If

4:17

it's 0.9, it looks great. There's

4:20

something wrong. This is pre-bing.

4:22

This is the ideas of Power Set and Bleco, if

4:24

you remember that, and a whole bunch of search things.

4:26

So I went. Some friends of mine were building a

4:28

search engine. And so I went to join

4:30

them because I wanted to play with large data

4:33

sets. And I learned a lot about

4:35

doing machine learning and things on very large data sets. And

4:39

that was really exciting because

4:41

the apps are up. There's not many encumbrances you

4:43

just get on with things. But then

4:45

you're inhibited by scale and capital and everything else

4:47

and those other types of things. Whereas

4:50

at a large company, there

4:52

is a lot more capital. But

4:54

also, it's a very big ecosystem. A lot of people are doing

4:56

various things. The trick is how you direct it. They

5:00

have different strengths and weaknesses. And for

5:02

me, I guess I could

5:05

bring what I learned from one to the other and

5:08

then back again. But once I

5:10

started the startup world, I stayed

5:12

in that bet for three companies.

5:14

For me, I still really

5:17

like that feeling of pace and

5:19

urgency. Yeah, in the startup. Yeah, yeah.

5:21

And if you carry that through, I

5:24

think in a large company, it's very easy to sit

5:26

back and go with ebb and flow. You

5:28

can do that if you do it today. If

5:31

someone else delays three days, then it doesn't delay a

5:33

month at the end. So do it,

5:35

do it now kind of thing, energy of startups.

5:37

And you recruit different type of people and things

5:39

like that. So what drew you to

5:41

GSK? What brought you there? So GSK was

5:43

interesting. So I was at Genentech at the

5:45

time. And I had a lot of friends at

5:47

GSK. And they

5:49

were wanting to Do AI and things. So They

5:51

asked me, how should we organize this? Where should we put someone?

5:54

They were dead set on a particular location. And at the time,

5:56

I was like, you're not going to find the people you want

5:58

there. And you should organize it this way. And

6:00

I was quite resisted going to Gsk because

6:02

if you know outside perceptions and you see

6:05

the same people at conferences that last twenty

6:07

years complaining about the same thing I want

6:09

my class right to be company for Yeah

6:11

yeah and so he eventually this is like

6:13

wages com an interview and so I went

6:15

to interview and I said have met how

6:17

I met some. People. I realize

6:19

actually. It's. Kind of very

6:22

different rides and then my and like realize that

6:24

they really a waltz in a phase of this

6:26

telling us up inside out these become don't reinvent

6:28

itself very often your hard to do yeah yeah

6:30

but they were really do and my were really

6:32

serious about doing it and so you know it's

6:34

an amazing how and I knew. He

6:37

how of the A through Daphne and other people

6:39

and you knew he was with and he gets

6:41

to write some python Ryan we get we get

6:43

his identity of oh yeah yeah he going to

6:45

see as an accident nearby Bananas well so he

6:47

can really right right coats and really like this

6:49

guy that she went missing people He knows what

6:52

it takes to do things and they're fully investing

6:54

in the things I thought were very important the

6:56

time which was you know these last year databases

6:58

coming on mine and vintage Nymex or he was

7:00

what way ahead in the in some of the

7:02

to give up crisper unlike the gene pool of

7:05

base generate data and saying we can discuss exclusion

7:07

data you can see the trend. So began the

7:09

machinery the make sentimental and so they weren't sort

7:11

of just like dabbling in like will have one

7:13

group and will do some contracts like move to

7:15

establish entire thing and it'll be caught. Why strategy

7:17

so how will that seems like an opportunity and

7:20

I you know I guess I was thinking that

7:22

time of doing another company or something else I

7:24

the worst case scenario did that on the job

7:26

process like it's not very hot and in an

7:28

interesting way there was a book running you probably

7:30

know that he were running up a book that

7:33

how many how long I would lost their like

7:35

to our really mans like two years interests. I

7:37

beat the odds. Now it's five years this

7:39

Monday. Know that that I've been there. You

7:41

go short squeeze on a if I if

7:44

I if an hour nice as I. Say

7:48

I've stayed on any. we built a lot. I

7:50

think of the I know How to Do things

7:52

in smaller companies. He had a build thing we can

7:54

track the peep when everything else but the next release

7:56

of challenges. How do you take a large organization create

7:59

something within a bit. They'd bring everybody along

8:01

for the ride and things like that, which

8:03

is a new challenge it was. And how

8:05

do you do that? I mean that's a

8:07

great topic I guess and Outlaw isn't just

8:09

for seep uses the company so that ratio

8:11

flight computation to communication right is really important.

8:14

So what I realize is that if. You

8:16

have to spend it's a long time. Explained

8:18

my what he seems intuitively obvious to you

8:20

to a large organization. has he been a

8:23

market A very busy people they will but

8:25

jobs to do and are coming to different

8:27

backgrounds to make some. and like most drug

8:29

discovery takes people from. All. Sammy

8:31

different disciplines together right path manufacture muthee like

8:33

it's entire thing. It's is complex endeavors anything

8:35

else and so they don't have had the

8:37

same background and training and and also different

8:39

ages. rotten never like tech only change a

8:41

lot So you got people living in different

8:44

stages that of what their experience with and

8:46

some I'm skeptical. We've got some in a

8:48

worked as as we work and compare it

8:50

to think so you've got people that on.

8:52

It. For a classic innovate and Emma when was not

8:54

very good. Now I'm like yeah but I say

8:56

nothing of the right a change. Yeah, I forgot

8:58

Moore's law, yeah you only got then you got

9:01

true believers near in of poverty those thing so.

9:03

What? It is about can expensing

9:05

people doing lot of messaging but

9:08

descend point. On I discovered you

9:10

can talk a lot. When. It

9:12

easy to south and build for bit so he

9:14

does basically like Caymans had said no we're not

9:16

working on this of the things cradle to show

9:19

for bubble built in the organization boat a whole

9:21

bunch of capability and then you at this phase

9:23

now it sort of the installation phase of the

9:25

technology right? yep where you now going to plug

9:27

into We've done with a pilot from things in

9:30

a ones that actually make it part of the

9:32

process but I think that's where you have to

9:34

communicate to communicate and communicate right and it takes

9:36

a long time cause is also geographically distributed time

9:38

zones things like that so he be surprise of

9:41

mouth time it takes. For a messy sir to

9:43

percolate but eventually if one's ever gets behind it

9:45

and you know then you give tenure should take

9:47

and then it on stuff. a dog fight for

9:49

the thought of capital One data and well that's

9:51

when when I have a farmer you've got all

9:54

that I have any discipline you want. You can

9:56

find an expert now and if I if they

9:58

don't know the answer they know decks that. who

10:00

does. I think the question now is like okay

10:02

we see around us but how is it going

10:04

to affect drug design I think was really cool

10:07

by what you're doing is you're doing this today

10:09

and was you could give some examples. Yeah so

10:11

I think everybody thinks or with I are successful.

10:13

You entering the disease and it will say this

10:15

talk and pop out the molecule. Make it and

10:18

we. And noom you know we went.

10:20

Even each test will test inventions humans right I

10:22

always bromides nice. I hear that last maybe don't

10:24

have to the me best years are well they

10:26

are so I think I think what we're seeing

10:28

is like. Your our group books or

10:30

the way across the process right? We have an

10:32

enterprise focus on. The. First thing is like

10:34

want to make the medicine against. right? You

10:37

could design the best dragon well with the

10:39

best pharmacokinetics and safety profile of the else

10:41

but if you're much lane among thing is

10:43

that the have a clinical effect my suffer

10:45

thing is it's the time we have these

10:47

last night databases of people sequenced and the

10:49

medical records and things like that and we

10:51

look for here people the disease here people

10:53

don't have the seasons are what's different about

10:55

them may lots of differences but what difference

10:57

is a really significant and in are they

10:59

drive ins disease so you might festival do

11:01

people that are diagnosed and not diagnosed net

11:03

one way of doing things. Or. We

11:05

can. Use. Machine. Then again,

11:07

so he isn't she. Many get on clinically meeting

11:09

say a bunch of people that may have a

11:11

different range of a site say live in management

11:13

all scarring, a liver or kidney function and rather

11:15

having a person score that by hand. we can

11:17

have a I model gone to that. The

11:21

gives us what to call a continuous train Him

11:23

it can take that information and do d was

11:25

right to so by you'd wake up was De

11:27

Vito. Why does Ossetia exactly right now we have

11:29

some other variants on that that were were interested

11:31

as well. My. But once.

11:33

You have that he didn't need were one of

11:35

the genetic variant do like biologically and the first

11:37

question is what cell types as acting that it

11:39

could be like several genes away and right so

11:41

you know predict which is which were the acting

11:43

and then it is a cave. what is it

11:46

doing. Is it making more of

11:48

the messenger or less the messenger or changing

11:50

spicing, right? So you get a different form

11:52

of the party And so we have methods

11:54

that they're missing money. methods that will predict

11:56

the directs. Now they are and so that's

11:58

in use in production to. The

12:00

health isn't it for the variants I and

12:03

so that helps as I do York I

12:05

dissing make movies Protein Pp was into had

12:07

this disease. Therefore, This gives a

12:09

clue on how much like the to these

12:11

right We have a. Lot

12:14

of different methods for looking at sell it,

12:16

imaging, sitting if you have occurred and things

12:18

like that we been predicting me. effect of

12:20

a small molecule can't like it on a

12:22

cellular phenotype. Arm. We have an

12:24

active learning systems for discovery and doing experiments

12:26

in biological models of disease and Sir Robin

12:29

making a small molecule tumult to molecule. Now

12:31

we can actually to sputtered that we just

12:33

turn up and down that the regime know

12:35

I any do paralyzes lung anymore. the one

12:37

and Bolland krispies him go to Talon. We

12:39

can turn up and down the things as

12:41

well when a continuous basis. We can go

12:44

from like small amounts, a large amount of

12:46

protein or tenant, turn it down slightly and

12:48

see what happens. He can modulate things like

12:50

that. And so we have a whole active

12:52

money system where we. Feed him he's are

12:54

they from here are hypothesis objects He's a

12:56

stuff on which to his like just trust

12:58

me I know happen as fast as you

13:00

get that in biology can't have you experienced

13:02

any. We can't do a genome wide screen

13:04

just turn it to try everything. So we

13:06

use this model that says okay I wanna

13:08

say i want my my. My

13:11

aren't expression profile to look like there's some

13:13

like some proteomic thing or maybe a functional

13:15

as I look like this and ultimately not

13:18

toxic and a tiger that's tackle like a

13:20

my Kamala against right So you michael those

13:22

things in. And. It

13:24

will do around of experimentation say well he has

13:26

information back. And in

13:28

by some of the I might saying by some

13:31

I conduct connectivity understanding. On. My second

13:33

a walk up there me take this guy

13:35

cause the common ancestry two parties are and

13:37

that is of the signal transduction works that's

13:39

about twenty percent faster we shine and doing

13:41

a random screen and as a going to

13:43

the clinic we use the seen running on

13:45

we accomplish pathology so we be writing competition

13:47

pathology for while we have a even as

13:49

he has a professorship of companies with alleging

13:51

that Geico to see Razvan hum who works

13:54

as closely and we use that the. First.

13:56

Of all we know, we do a lot of things with people's I you

13:58

know is that I've expressed in this issue. Going on holiday

14:00

right? And that's just yeah. you're staying with an

14:02

antibody and people are humor pathologist or Clinton basically

14:05

the I the about sixty three percent of the

14:07

slightest this you give it to few different people

14:09

inside as expressed high medium or like more when

14:11

the see money I can say how much it

14:13

express with counting brown pixels it's pretty easy Mac

14:15

my pillow thought can say would sell type it

14:18

is. right? And so then you

14:20

can the i just met office's gate. Frida.

14:22

Point is hop to do other things but then

14:24

once you've got that us a concise to get

14:26

responses me trawling and they will now which cell

14:29

types expressed in to respond more unique you got

14:31

more day in and use it right on the

14:33

model so there's things that that and we have.

14:35

We did a very heavily instrument advice you trolls

14:37

we measured lot more. Part L makes things and

14:39

everything else and before and very expensive ailments and

14:41

like will have you noticed. Me useful might. Not

14:44

most. Of it won't but some of will

14:47

be and then it would And we were would

14:49

look at that and analyze that data and find

14:51

this some sort of people that we know if

14:53

we low their vows as as entrances, level by

14:55

up as a function. Cute right and we know

14:57

which group it is. Inigo Okay now I can

14:59

find him as the brings in that state in

15:01

a country and the destroy them. My good to

15:03

go also for the audience. could you give a

15:05

sense for the delta between like which you can

15:07

do with Ml now a Gsk vs maybe the

15:09

way this would have been done ten years ago.

15:11

And. The Delta in terms of time or dollars

15:13

a pit wherever? Yes, yeah, I mean certainly for

15:16

some of these things you would have to do.

15:19

A lot more experiments, right?

15:21

So the genetics. Privee.

15:24

Didn't know which way the bearing was. Well, you'd have

15:26

to go an. Engineer a system and

15:28

make a model system and ten more been

15:30

up and had more went down all over.

15:32

express it or not and then make whole

15:34

bunch of Sergeant Recounts arm and all the

15:36

things about making continuous traits for discovering. Wanna

15:38

see a whole bunch? A radiologist has scored

15:40

a whole bunch of things manually and did

15:42

it all right. Net would take a long

15:44

time for that's a very easy paolo cost

15:46

right? Eat We can calculate the complex you

15:48

some of these are the things is. You

15:51

can go through. Fum. A very

15:54

small and heidi space. and try and find

15:56

out some Carl isn't that something like that that?

15:58

He just gives them an advantage. No,

16:00

I need a New York. To do

16:02

it reliably making some things a very complex. Ryan

16:04

said a happy example is in Iowa and. It's

16:07

a base in work in like an Orang how

16:09

to type thing but you doing a lot of

16:12

it's compression type things ryan said it is thing

16:14

because. I. I become so useful.

16:17

Because. He got some more measurement technology

16:19

right seat measurement technology so he can

16:21

do. Beginning. July sequencing city we can

16:23

do our own i seek with using sulfide

16:25

stuff you can't be state. He got more

16:27

measurements now. But. You can't do the

16:29

with unless you got machine learning and I'm

16:32

in my central hypothesis always been medicines as

16:34

arts as new Measures attack on his come

16:36

along along and as amendment technologies get cheaper.

16:39

I'm we learn more so if you think about

16:41

cardiology right yes once cat cats that are measuring

16:43

things in the heart of a people can see

16:45

that. My story the first consider his bunkers he

16:47

does himself by oh yeah yeah yeah it is

16:49

like our that it would myself not as outs

16:51

absolutely kind of old school stuff is identify the

16:53

i'd of a have area are able to hum

16:55

yeah the guy the yeah I mean about the

16:57

pylori got a idea stranger my god what am

17:00

I think I might as an eye and they

17:02

said it's high they had a hypothesis back to

17:04

of they were actually so I know who they

17:06

want to try business and were no theoretical perfectly

17:08

or it outlasts now I'm but. Yeah I think

17:10

they're gonna go far as the I believe he

17:12

did their deaths as were enough experimentation and try

17:14

this at home Now I'm but from that either

17:16

you what blood pressure cos you're things now we've

17:18

got these right, that's enough pulse. My hunting for

17:21

that as I as I got cheaper we learn

17:23

more about cardiology right? And then that led to

17:25

discovery. So we've got more has been technology. Based.

17:27

In such high dimensionality on people that like the

17:30

item makes sense of it's an array of like

17:32

whole bunch of expression changes which suspects were around.

17:34

Baseline bus is a disease patients yeah right like

17:36

who can make sense of that So this we

17:39

could deny that we would if without machinery when

17:41

we would know what to do with it. Now

17:43

now I we have the various a linear things

17:45

and take up the most obvious stuff which might

17:47

be wrong. It's hard to compare in the event

17:50

of the data. Respect nine the change things Yeah

17:52

yeah I'm curious. Love our listeners are some people

17:54

that are doing start from the start businesses and

17:56

would have you learned. Would you take to start up

17:59

If you were to start. Now. Did. You

18:01

figure like a I sat In general, I'm. It's.

18:03

About data. It's an end

18:05

in itself about i'm unique data so what you want

18:08

is I think of a com prob my how I

18:10

see is people try and build something as like they

18:12

don't having that he's I'm on a set up to

18:14

do and I for we don't have exactly the data

18:16

we've got. This sort of said you saw this is

18:18

probably good as the people forget that the data they've

18:20

got is sometimes a proxy for the real thing and

18:23

what you say that's not the did that they doesn't

18:25

have the aren't easy going. Generate the data and actually

18:27

be under generate the data is gonna be critical cause

18:29

you're gonna run into thing at some point you run

18:31

out of and you need to generate the data to

18:33

build up and a bottle and and and. That also

18:35

becomes a competitive mode. Have some reckless you'd

18:37

have. it's not that you can generate. Plus

18:40

the public staff they've got in are you

18:42

bringing twice days that hate? ideally someone pc

18:44

the jury that they get a okay see

18:46

the data and in pays you to buy

18:48

them auto battle system here that that just

18:50

won't do that they would you just to

18:52

be clear, haven't But yeah, that's that's because

18:54

the data aspect is key in the joint

18:56

bid. Did it get more data right? Like

18:58

invest in that cause honestly more data. With.

19:00

A simple I were them is an easy sell, easy

19:03

install and like data wouldn't die in a look at

19:05

the Netflix sounds how was that One million different of

19:07

I Don't Daves to the table not with a smart

19:09

our them right us So I think that's a key

19:11

lesson is immense in there and then and then think

19:13

about the Ml and the fancy i wear them and

19:16

things that that's of wow look at lot of Com

19:18

is a come to asa look at like what unique

19:20

dated you have met and what's your been a degenerate

19:22

and what scaled you have I that I don't for

19:24

example I mean if you're pitches dislike all we've got

19:26

smart people give us your that it would be cool

19:29

stuff on my blog of smart people to rise. Of

19:31

the happened what was in what difference is

19:33

the data in your mind. I.

19:35

Think is the. Either. You've

19:38

got a. It's uniquely

19:40

suited to understand so it's clean, right?

19:42

You got this batch effects. You can

19:44

generate more that and you can control

19:46

the sampling aspects of it. I think

19:48

those things like a key you. Could.

19:50

Then basically understand how your method

19:52

might behave under different scenarios where

19:55

what would which may have rocket

19:57

us and biases and as systems

19:59

example interesting. You're in the abstract, those are

20:01

kind of the key things you would wanna kind of

20:03

point and then we you got that in the Ml

20:05

thing doesn't really matter because it's almost. you sign the

20:07

solution in the days of and people care whether it's

20:09

like you know the the mechanical turk type thing or

20:12

whatever we as you can sell a random forest minority

20:14

cool random forest if you know it doesn't as on

20:16

a new network or whatever L hundred and one of

20:18

our assignments I goes along rak get their hands if

20:20

they did all on the horizon for the place you

20:22

visit based on average to start with every night rider

20:25

yes a she always compare your information gain of of

20:27

a random forest that could be depressing from a. Skill

20:30

or either that or or in any of the

20:32

other simple method suggests even desserts a similar neural

20:34

net. It was logistic regression basically. yeah yeah yeah

20:37

I mean that same thing I've it's it's funny

20:39

actually it's how I think that will go full

20:41

circle. By the way there's of his love of

20:43

the a complex stuff on we'll see what happens.

20:46

Yeah I think we people realize that I'm on.

20:48

This is great paper like by David Hand by

20:50

which we may have said long time ago by

20:52

the could classify a complex the an illusion of

20:54

progress. It's very old square malware he looks at

20:57

some you see of on stuff and that end

20:59

of a sophisticated. Mythical Macinnis Vm but it's

21:01

like this year disposition any discomfort analysis at

21:03

the are a bunch of things and he

21:05

very toy datasets but saying that like facing

21:07

the simplest of gets you there and that

21:09

extra five percent gains you need with extra

21:11

complexity. I it's and still true now I

21:13

think they'll think the thought his work out

21:15

how good the thing needs to be before

21:18

you start. So Tokyo Macula got like if

21:20

he could do this, do these things yes

21:22

I would use it and then go work

21:24

out to do that don't build a thing

21:26

and and sidewalk got that with an hour

21:28

to work it out and eat. Maybe you're

21:30

far off or maybe there are some criteria

21:32

right? and I were you. It's top secret,

21:34

the pure. you know what's stopping when to

21:36

start shipping and but so to start had

21:38

to differentiate data. yep at what would you

21:40

wanna see in the algorithm to get excited

21:43

I want to see. Well

21:45

characterized robustness and reliability criteria. Right arm

21:47

don't give me to point estimates you

21:49

me a point estimate with some measure

21:52

of confidence as her right like you

21:54

gotta have that are many minutes se

21:56

tricky to do sometimes they are higher

21:58

than I can still. I mean

22:00

like I still. Think

22:02

that's what I'm missing. My papers out there

22:04

is like well my things better he is

22:06

my the his my table his my stuff

22:08

in ball which is naturally by the my

22:10

stuff and by the way different of the

22:12

third different place for my criticism by this

22:14

other thing is like the lot of people

22:16

stuff the ten percent difference that like I

22:19

don't care about worth the cost me by

22:21

inspired know is if you like point one

22:23

in asia not gonna not so yeah you

22:25

want the I'm the precision cove right I'm

22:27

is really mirza of a you say but either

22:29

way out what you need to be at

22:31

home to make it many be different and then

22:33

how will the engineered and in time plug

22:35

into my system housing work right out the in

22:37

the installation stuff could often knit the people

22:39

who use it in a company in in

22:41

this be will have to operate and run and

22:44

had remote control for model drift in everything

22:46

else and so you're. Selling.

22:48

To a user. But the people

22:50

buying and installing it that are also part the

22:52

decision process are like the engineering thoughts ego thing

22:54

to a cafe, how we make it easy for

22:56

them to use and I would just they can

22:58

be around people like stick in the Cause to

23:00

get my cloud. You. Know

23:02

that we have run on PRAM like if the

23:04

think of different ways to bring it to the

23:06

market and that depends on the industry. Like

23:09

the more ways you habit the make it easy

23:11

if he would integrate and monitoring watson it's security

23:13

than let's stop the is it is for them

23:15

for to be fixed with thing. I.

23:17

Know you still my coding same time so

23:19

I know you like playing with a eyes

23:21

or and what have you done your own

23:23

hands Recently moved and the one thing I

23:25

bear with me is a i can these

23:27

emails reports fine I'm over the various types

23:29

of things and like eighty one question when

23:32

I was one thing it's like I come

23:34

by their at all so. I

23:36

got realize like I just thing that like

23:38

dumpster might these emails clear whether since then

23:40

title onto a photo my laptop and I'm

23:42

on the Lm as my quest honor and

23:44

in it for me via email to sense

23:47

to me write an essay like that's one

23:49

thing I built recently. I'm. In. I'm

23:51

pretty interested in Ny and of the long

23:53

contacts windows the the stop us as like

23:55

I'm. Do. Need a long contacts minute Fatah

23:57

spanning Or do you need a whole bunch of specialist June.

24:00

The lens we're in an age of for dry

24:02

yeah yeah I'm going to search yes no yes

24:04

and look such as are things such as fundamentally

24:06

changed because you know what in like easy like

24:08

find me to document I'll read it now it's

24:10

like he's my question you read the document for

24:13

me and telling my answer is obvious nissl version

24:15

of actually this Lm for reasoning over multiple scientific

24:17

documents that I write myself russians impact on our

24:19

the prototype and put it together and now it's

24:21

a tie I still think is do have that

24:23

kind of build things I think also to. It.

24:26

Keeps you think emails aspects of also

24:28

think it also helps you know when

24:31

your mum bedroom for search for the

24:33

company. Ride. You're kicking the tires and

24:35

it regularly have you found it difficult to do something that

24:37

now that he would do as per be slowing them down

24:39

says nothing a lot about had I spit bit of the

24:41

duration Zoc with have. Any Mccroskey is Kate

24:44

that has eluded me running chi that he

24:46

would access data and get data and think

24:48

about things right in Iowa can take some

24:50

three weeks to get a thing to answer

24:52

simple question than yeah, that's gonna modify down

24:55

the chain, you know, and you're after another

24:57

quarter what he called us before they've done

24:59

something right. Now. Okay, so

25:01

let's it's five years from now. Yes, and we're

25:03

doing other podcasts of Will Do and sooner. But

25:05

my guess is five years. What are the examples

25:08

that you're excited about them? that you've done. A.

25:10

Guy obviously that hasn't happened yet. sometimes I

25:12

think like a month would you think really

25:14

think you'll get I think will have most

25:17

but I will cope. Computational methods every drug

25:19

be have an aunt were have software the

25:21

sits around it adding it can be true

25:23

of everybody is making we're going to see

25:25

it does suck at it will sit around

25:27

every dragon with say he's do that I'm

25:29

what their coffee to be at baseline. More.

25:32

Likely what they like to be. plus one

25:34

cycling drug because it's very difficult to predict

25:36

people's responses. A Python maybe buy them will

25:38

be better at it. I think we'll do

25:40

we will know a lot more about I'm

25:42

I I I think we're really understand how

25:44

on this image therapy Gsk Famous really action

25:46

a mean programming company right vaccine that way

25:48

the program immune system. right? And

25:51

with single of different vaccines I think we

25:53

will understand really the whole the my i

25:55

always like twenties and a patient's respond by

25:57

and is amazing response but with twenty percent

25:59

I'm not. That what I mean disease. I

26:01

think we'll see get a lot more aspect

26:03

with software but I think also the ages

26:06

single cell and perturbation genomics is just getting

26:08

started. Or I like it when

26:10

it's we didn't give case be doing excellent

26:12

Would a to growth rise if I started.

26:14

You know and like with doing what we're

26:16

doing when we are generating large scale put

26:19

a basin datasets purely to become like either

26:21

lookup tables. I don't have a dune experiment.

26:23

right? But more important now an infant stable

26:25

kind thirty minutes from and for I like the

26:27

Human Genome Project becomes of lookup table Yeah yeah

26:30

exactly right. So you building most of the things

26:32

I think in five years time what will happen.

26:34

I think we'll be doing. If

26:36

you're extremis more informed of experiments but

26:39

I think also the loops into observation

26:41

cohorts limited things and people were not

26:43

things you management which is learning about

26:45

the disease or will it be running

26:47

trials limit disease. Add to. It

26:49

public, private and a mix of data so I

26:51

think that's going to be full scheme ever been

26:53

we doing that when I think you know this

26:55

whole. Wheat thing of to these

26:58

heterogeneity will actually sought to really understand what

27:00

that means night and also kind of Iraqi

27:02

sort of serious obese decipher. But yeah I

27:04

think we'll know a little bit more about

27:06

what's happening. I'm and this is where I

27:08

think interestingly. Ml. Will start to

27:10

collide with really the. You

27:13

know that kind of mechanistic modeling abolish the we

27:15

know a lot about medicine. we know organ systems

27:17

and things like that. We have to start with

27:19

just gene expression in the thing and until our

27:21

our the nothing about by the rights to the

27:24

person the south things interact we can have these

27:26

the structure price so I'm think this is where

27:28

that sort of systems mass an approach would become

27:30

very useful as well so we will have a

27:33

lot more morals I think that be used in

27:35

place for discovery and systems now as well To

27:37

witty thing is sort of the rate limiting step

27:39

in getting nailed Advancing this in our he could

27:42

be people. To be money? well and a that me

27:44

up the try to answer it. Is data

27:46

but the real answer is day with outcomes right?

27:48

Say like I can deny all the pet of

27:50

a snow day or I want the what I

27:52

really want is like. In. A sample

27:54

from the wealthy people with humble think with off

27:56

I want as what happens when I'm I was

27:59

a clinical trial. Curbing Someone right now we're

28:01

therapy with we Can Happen and Weather conditions

28:03

Yeah, at which? Yeah. And I think that's

28:05

where we want that. we need the Anthem

28:07

dataset. Outcome Daters is is the goals

28:09

and they have Fry is the rare estate

28:11

or that's really like one divine to some

28:13

zoo. Com is hop as I can generate

28:15

data in humans at scale him safe ethical

28:18

manner. That data past if you measure was

28:20

different things. We start instantly. Charles Moore. To.

28:22

Me to complete ways to be brick dragon.

28:24

Two hundred people who eat more of them

28:26

closely which emits everything we can write like

28:28

and bring that back and there is a

28:30

patient burden. You can overload them and is

28:32

a was attending between anniversaries do that right

28:34

fashioned. but that data with outcomes that's the

28:37

right them. except for the know what we

28:39

did them you bomb as as as as

28:41

that and you know how they do these

28:43

progress on what happened to them So how

28:45

can we sort of minimise Obama? That's where

28:47

I think that we either need to create

28:49

larger public private consortium or public thing like

28:51

that. In a way everybody. Wants to do is

28:53

like out to can I have odious case clinical data

28:55

for this trial and I mean if a company like

28:57

know cause I have a psyche eyes and of a

29:00

my uni committee but I have no I'm I wanted

29:02

on using it right now to develop things but what

29:04

we actually need is I think we need to do

29:06

large scale of xvi so cohorts the palm and there

29:08

are these things exist now but the palm is what

29:10

they measure is limited by the by a had an

29:13

ipod the had the time. We. Use you

29:15

gonna look at something else and I can

29:17

walk a new thing. How I measured that

29:19

when we need to bank samples my and

29:21

sometimes you in a bank the samples and

29:23

kind of invoke slackers law like a to

29:25

become proceed from bad to analyzing and then

29:27

my that is true. I guess it's Richard.

29:29

Rider is is and sometimes you can't do

29:31

with friends you middlesex we should be establishing

29:34

like it's not just genetic sequencing, an electronic

29:36

medical records, Is. Sexy can possible, but

29:38

also we'd be falling people over time

29:40

like we're even really know what the

29:42

mean system looks like overtime. right?

29:45

It's kind of bunkers I We just don't

29:47

know what the average evolution immune system does.

29:49

That's the right limiting steps and then you've

29:51

got treatments and you could outcomes. I for

29:53

those people, If the missing

29:56

link me the or that the drug discovery mean

29:58

gentle had more those sorts of thing I I

30:00

think we would we will be in a better

30:02

place and I think that's was going to

30:04

come by but that's actually what honestly I think

30:07

is the work of you know, government and industry.

30:09

the phone, those sorts of things that maybe there's

30:11

a time exclusive access and things like that but

30:13

eventually they made me pay. If I become public

30:16

resources they become a common good a new different

30:18

ways people trying to construct those for I think

30:20

it if you look away machine They've had

30:22

message strides as when is cheap by electing data

30:24

from a suburb thirty Me that was standing right.

30:27

Yes, the good advice of the thing for this

30:29

datasets. I'm we. The company that we we do

30:31

put out they assess machine learning challenges that Europe's

30:33

and things like that we've done them. You know

30:36

the genius guy challenge her pet have a sense.

30:39

Of he does a few of them. You can see the munchies.

30:41

could I mean the put out a prize of things like that

30:43

and he could make you can make real money. So Kim thanks

30:45

so much for joining us! Reason: it's been lot of fun with

30:48

it. Think.

30:56

You been listening to raising? The

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