The Swyx Mixtape

From Lex Fridman: https://lexfridman.com/demis-hassabis/
Transcripts from Karpathy's https://karpathy.ai/lexicap/0299-large.html

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00:37:13.160 
So let's go to the basic building blocks of biology

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00:37:16.960 
that I think is another angle at which you can start

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00:37:20.200 
to understand the human mind, the human body,

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00:37:22.280 
which is quite fascinating,

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00:37:23.400 
which is from the basic building blocks,

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00:37:26.640 
start to simulate, start to model

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00:37:28.960 
how from those building blocks,

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00:37:30.480 
you can construct bigger and bigger, more complex systems,

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00:37:33.080 
maybe one day the entirety of the human biology.

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00:37:35.820 
So here's another problem that thought

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00:37:39.680 
to be impossible to solve, which is protein folding.

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00:37:42.720 
And Alpha Fold or specifically Alpha Fold 2 did just that.

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00:37:48.840 
It solved protein folding.

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00:37:50.320 
I think it's one of the biggest breakthroughs,

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00:37:53.400 
certainly in the history of structural biology,

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00:37:55.140 
but in general in science,

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00:38:00.240 
maybe from a high level, what is it and how does it work?

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00:38:04.840 
And then we can ask some fascinating questions after.

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00:38:08.700 
Sure.

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00:38:09.980 
So maybe to explain it to people not familiar

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00:38:12.880 
with protein folding is, you know,

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00:38:14.400 
first of all, explain proteins, which is, you know,

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00:38:16.980 
proteins are essential to all life.

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00:38:18.840 
Every function in your body depends on proteins.

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00:38:21.520 
Sometimes they're called the workhorses of biology.

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00:38:23.920 
And if you look into them and I've, you know,

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00:38:25.340 
obviously as part of Alpha Fold,

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00:38:26.660 
I've been researching proteins and structural biology

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00:38:30.200 
for the last few years, you know,

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00:38:31.760 
they're amazing little bio nano machines proteins.

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00:38:34.760 
They're incredible if you actually watch little videos

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00:38:36.460 
of how they work, animations of how they work.

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00:38:39.000 
And proteins are specified by their genetic sequence

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00:38:42.600 
called the amino acid sequence.

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00:38:44.280 
So you can think of it as their genetic makeup.

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And then in the body in nature,

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they fold up into a 3D structure.

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So you can think of it as a string of beads

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00:38:55.320 
and then they fold up into a ball.

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00:38:57.160 
Now, the key thing is you want to know

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00:38:59.100 
what that 3D structure is because the structure,

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00:39:02.480 
the 3D structure of a protein is what helps to determine

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00:39:06.120 
what does it do, the function it does in your body.

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00:39:08.580 
And also if you're interested in drugs or disease,

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00:39:12.320 
you need to understand that 3D structure

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00:39:13.980 
because if you want to target something

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00:39:15.840 
with a drug compound about to block something

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00:39:18.640 
the protein's doing, you need to understand

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00:39:21.120 
where it's gonna bind on the surface of the protein.

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00:39:23.440 
So obviously in order to do that,

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00:39:24.940 
you need to understand the 3D structure.

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00:39:26.720 
So the structure is mapped to the function.

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00:39:28.640 
The structure is mapped to the function

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00:39:29.880 
and the structure is obviously somehow specified

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00:39:32.560 
by the amino acid sequence.

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00:39:34.840 
And that's the, in essence, the protein folding problem is,

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00:39:37.420 
can you just from the amino acid sequence,

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00:39:39.620 
the one dimensional string of letters,

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00:39:42.560 
can you immediately computationally predict

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00:39:45.600 
the 3D structure?

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00:39:47.120 
And this has been a grand challenge in biology

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00:39:50.020 
for over 50 years.

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00:39:51.500 
So I think it was first articulated by Christian Anfinsen,

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00:39:54.360 
a Nobel prize winner in 1972,

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00:39:57.040 
as part of his Nobel prize winning lecture.

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And he just speculated this should be possible

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00:40:01.860 
to go from the amino acid sequence to the 3D structure,

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00:40:04.960 
but he didn't say how.

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00:40:06.060 
So it's been described to me as equivalent

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00:40:09.440 
to Fermat's last theorem, but for biology.

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00:40:12.320 
You should, as somebody that very well might win

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00:40:15.120 
the Nobel prize in the future.

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00:40:16.560 
But outside of that, you should do more

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00:40:19.240 
of that kind of thing.

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00:40:20.080 
In the margin, just put random things

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00:40:22.160 
that will take like 200 years to solve.

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00:40:24.440 
Set people off for 200 years.

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00:40:26.000 
It should be possible.

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00:40:27.720 
And just don't give any details.

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00:40:29.040 
Exactly.

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00:40:29.880 
I think everyone exactly should be,

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00:40:31.500 
I'll have to remember that for future.

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00:40:33.520 
So yeah, so he set off, you know,

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00:40:34.800 
with this one throwaway remark, just like Fermat,

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you know, he set off this whole 50 year field really

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00:40:42.640 
of computational biology.

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00:40:44.400 
And they had, you know, they got stuck.

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00:40:46.240 
They hadn't really got very far with doing this.

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00:40:48.520 
And until now, until AlphaFold came along,

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00:40:52.500 
this is done experimentally, right?

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00:40:54.320 
Very painstakingly.

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00:40:55.500 
So the rule of thumb is, and you have to like

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00:40:57.440 
crystallize the protein, which is really difficult.

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Some proteins can't be crystallized like membrane proteins.

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00:41:03.060 
And then you have to use very expensive electron microscopes

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or X ray crystallography machines.

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00:41:08.200 
Really painstaking work to get the 3D structure

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00:41:10.680 
and visualize the 3D structure.

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00:41:12.400 
So the rule of thumb in experimental biology

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00:41:14.840 
is that it takes one PhD student,

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their entire PhD to do one protein.

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00:41:20.320 
And with AlphaFold 2, we were able to predict

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00:41:23.440 
the 3D structure in a matter of seconds.

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00:41:26.400 
And so we were, you know, over Christmas,

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00:41:28.700 
we did the whole human proteome

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00:41:30.240 
or every protein in the human body or 20,000 proteins.

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00:41:33.280 
So the human proteomes like the equivalent

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00:41:34.760 
of the human genome, but on protein space.

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And sort of revolutionized really

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what a structural biologist can do.

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00:41:43.300 
Because now they don't have to worry

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00:41:45.720 
about these painstaking experimental,

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00:41:47.960 
should they put all of that effort in or not?

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00:41:49.560 
They can almost just look up the structure

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00:41:51.120 
of their proteins like a Google search.

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00:41:53.280 
And so there's a data set on which it's trained

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00:41:56.880 
and how to map this amino acid sequence.

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00:41:58.800 
First of all, it's incredible that a protein,

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00:42:00.760 
this little chemical computer is able to do

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that computation itself in some kind of distributed way

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and do it very quickly.

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00:42:07.800 
That's a weird thing.

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00:42:08.840 
And they evolve that way because, you know,

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00:42:10.480 
in the beginning, I mean, that's a great invention,

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00:42:13.200 
just the protein itself.

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00:42:14.760 
And then there's, I think, probably a history

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of like they evolved to have many of these proteins

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and those proteins figure out how to be computers themselves

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in such a way that you can create structures

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that can interact in complexes with each other

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in order to form high level functions.

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00:42:32.660 
I mean, it's a weird system that they figured it out.

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00:42:35.520 
Well, for sure.

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00:42:36.360 
I mean, you know, maybe we should talk

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about the origins of life too,

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00:42:39.000 
but proteins themselves, I think are magical

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00:42:41.180 
and incredible, as I said, little bio nano machines.

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00:42:45.760 
And actually Leventhal, who was another scientist,

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00:42:50.280 
a contemporary of Amphinson, he coined this Leventhal,

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00:42:55.120 
what became known as Leventhal's paradox,

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00:42:56.820 
which is exactly what you're saying.

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00:42:58.320 
He calculated roughly an average protein,

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00:43:01.580 
which is maybe 2000 amino acids base as long,

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00:43:05.080 
is can fold in maybe 10 to the power 300

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different confirmations.

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00:43:11.480 
So there's 10 to the power 300 different ways

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00:43:13.320 
that protein could fold up.

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00:43:14.800 
And yet somehow in nature, physics solves this,

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00:43:18.160 
solves this in a matter of milliseconds.

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00:43:20.520 
So proteins fold up in your body in, you know,

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00:43:23.080 
sometimes in fractions of a second.

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00:43:25.600 
So physics is somehow solving that search problem.

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00:43:29.080 
And just to be clear, in many of these cases,

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00:43:31.200 
maybe you can correct me if I'm wrong,

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00:43:33.040 
there's often a unique way for that sequence to form itself.

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00:43:37.680 
So among that huge number of possibilities,

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it figures out a way how to stably,

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00:43:45.320 
in some cases there might be a misfunction, so on,

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00:43:47.800 
which leads to a lot of the disorders and stuff like that.

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00:43:50.040 
But most of the time it's a unique mapping

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00:43:52.720 
and that unique mapping is not obvious.

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00:43:54.820 
No, exactly.

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Which is what the problem is.

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00:43:57.120 
Exactly, so there's a unique mapping usually in a healthy,

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00:44:00.720 
if it's healthy, and as you say in disease,

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so for example, Alzheimer's,

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one conjecture is that it's because of misfolded protein,

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00:44:09.000 
a protein that folds in the wrong way, amyloid beta protein.

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00:44:12.040 
So, and then because it folds in the wrong way,

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it gets tangled up, right, in your neurons.

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00:44:17.600 
So it's super important to understand

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00:44:20.560 
both healthy functioning and also disease

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00:44:23.600 
is to understand, you know, what these things are doing

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00:44:26.480 
and how they're structuring.

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00:44:27.600 
Of course, the next step is sometimes proteins change shape

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00:44:30.540 
when they interact with something.

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00:44:32.160 
So they're not just static necessarily in biology.

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00:44:37.200 
Maybe you can give some interesting,

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00:44:39.780 
so beautiful things to you about these early days

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00:44:43.260 
of AlphaFold, of solving this problem,

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00:44:46.160 
because unlike games, this is real physical systems

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00:44:51.280 
that are less amenable to self play type of mechanisms.

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00:44:55.640 
Sure.

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00:44:56.460 
The size of the data set is smaller

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than you might otherwise like,

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00:44:59.760 
so you have to be very clever about certain things.

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00:45:01.800 
Is there something you could speak to

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what was very hard to solve

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00:45:06.680 
and what are some beautiful aspects about the solution?

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00:45:09.920 
Yeah, I would say AlphaFold is the most complex

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00:45:12.800 
and also probably most meaningful system

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00:45:14.600 
we've built so far.

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00:45:15.860 
So it's been an amazing time actually in the last,

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you know, two, three years to see that come through

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00:45:20.520 
because as we talked about earlier, you know,

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00:45:23.200 
games is what we started on

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00:45:25.480 
building things like AlphaGo and AlphaZero,

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00:45:27.900 
but really the ultimate goal was to,

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not just to crack games,

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00:45:31.520 
it was just to build,

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use them to bootstrap general learning systems

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we could then apply to real world challenges.

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00:45:37.440 
Specifically, my passion is scientific challenges

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00:45:40.640 
like protein folding.

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00:45:41.920 
And then AlphaFold of course

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is our first big proof point of that.

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00:45:45.360 
And so, you know, in terms of the data

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and the amount of innovations that had to go into it,

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we, you know, it was like

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more than 30 different component algorithms

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needed to be put together to crack the protein folding.

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I think some of the big innovations were that

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kind of building in some hard coded constraints

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around physics and evolutionary biology

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to constrain sort of things like the bond angles

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in the protein and things like that,

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a lot, but not to impact the learning system.

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So still allowing the system to be able to learn

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the physics itself from the examples that we had.

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And the examples, as you say,

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there are only about 150,000 proteins,

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even after 40 years of experimental biology,

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only around 150,000 proteins have been,

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00:46:33.880 
the structures have been found out about.

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00:46:35.920 
So that was our training set,

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which is much less than normally we would like to use,

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but using various tricks, things like self distillation.

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00:46:43.840 
So actually using AlphaFold predictions,

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some of the best predictions

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that it thought was highly confident in,

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00:46:51.000 
we put them back into the training set, right?

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To make the training set bigger,

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that was critical to AlphaFold working.

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So there was actually a huge number

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of different innovations like that,

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that were required to ultimately crack the problem.

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00:47:06.080 
AlphaFold one, what it produced was a distrogram.

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So a kind of a matrix of the pairwise distances

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between all of the molecules in the protein.

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And then there had to be a separate optimization process

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to create the 3D structure.

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00:47:23.640 
And what we did for AlphaFold two

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is make it truly end to end.

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So we went straight from the amino acid sequence of bases

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to the 3D structure directly

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without going through this intermediate step.

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And in machine learning, what we've always found is

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that the more end to end you can make it,

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the better the system.

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And it's probably because in the end,

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the system's better at learning what the constraints are

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than we are as the human designers of specifying it.

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So anytime you can let it flow end to end

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and actually just generate what it is

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you're really looking for, in this case, the 3D structure,

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you're better off than having this intermediate step,

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which you then have to handcraft the next step for.

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So it's better to let the gradients and the learning

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flow all the way through the system from the end point,

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00:48:09.000 
the end output you want to the inputs.

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00:48:10.880 
So that's a good way to start on a new problem.

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00:48:13.040 
Handcraft a bunch of stuff,

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add a bunch of manual constraints

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with a small end to end learning piece

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or a small learning piece and grow that learning piece

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until it consumes the whole thing.

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00:48:22.840 
That's right.

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00:48:23.680 
And so you can also see,

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this is a bit of a method we've developed

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over doing many sort of successful alpha,

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00:48:29.640 
we call them alpha X projects, right?

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00:48:32.200 
And the easiest way to see that is the evolution

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of alpha go to alpha zero.

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00:48:36.720 
So alpha go was a learning system,

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00:48:39.640 
but it was specifically trained to only play go, right?

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00:48:42.280 
So, and what we wanted to do with first version of alpha go

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00:48:45.360 
is just get to world champion performance

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00:48:47.520 
no matter how we did it, right?

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00:48:49.200 
And then of course, alpha go zero,

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we remove the need to use human games as a starting point,

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right?

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00:48:56.120 
So it could just play against itself

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from random starting point from the beginning.

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00:49:00.280 
So that removed the need for human knowledge about go.

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And then finally alpha zero then generalized it

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so that any things we had in there, the system,

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including things like symmetry of the go board were removed.

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00:49:12.240 
So the alpha zero could play from scratch

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00:49:14.600 
any two player game and then mu zero,

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00:49:16.440 
which is the final, our latest version

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00:49:18.360 
of that set of things was then extending it

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so that you didn't even have to give it

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the rules of the game.

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It would learn that for itself.

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00:49:24.880 
So it could also deal with computer games

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as well as board games.

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00:49:27.760 
So that line of alpha go, alpha go zero, alpha zero,

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00:49:30.400 
mu zero, that's the full trajectory

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of what you can take from imitation learning

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to full self supervised learning.

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00:49:40.440 
Yeah, exactly.

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00:49:41.640 
And learning the entire structure

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of the environment you're put in from scratch, right?

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00:49:47.640 
And bootstrapping it through self play yourself.

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00:49:51.840 
But the thing is it would have been impossible, I think,

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or very hard for us to build alpha zero

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or mu zero first out of the box.

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Even psychologically, because you have to believe

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in yourself for a very long time.

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You're constantly dealing with doubt

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because a lot of people say that it's impossible.

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00:50:06.680 
Exactly, so it's hard enough just to do go.

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00:50:08.640 
As you were saying, everyone thought that was impossible

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00:50:10.920 
or at least a decade away from when we did it

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back in 2015, 2016.

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00:50:17.320 
And so yes, it would have been psychologically

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00:50:20.960 
probably very difficult as well as the fact

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00:50:22.960 
that of course we learn a lot by building alpha go first.

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00:50:26.400 
Right, so I think this is why I call AI

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an engineering science.

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It's one of the most fascinating science disciplines,

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but it's also an engineering science in the sense

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that unlike natural sciences, the phenomenon you're studying

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doesn't exist out in nature.

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You have to build it first.

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So you have to build the artifact first,

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and then you can study and pull it apart and how it works.