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And so places where I think that's about
to happen and it hasn't happened yet.

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Um, one of them is interpretability. I

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think interpretability is both the key

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to steering and making safe AI systems

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um and and we're about to understand and

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interpretability contains insights about

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intelligent optimization problems
and about how the human brain works. Um,

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I've said and I I'm really not joking.

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Chris Ola is going to be a future Nobel

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Medicine Laurette. I'm serious. I'm

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serious because a lot of these I used
to be a neuroscientist. A lot of these

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mental illnesses, the ones we haven't
figured out, right? Schizophrenia or the

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mood disorders, I suspect there's some
higher level system thing going on and

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that it's hard to make sense of those
with brains because brains are so mushy

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and hard to hard to open up and interact
with. Neural nets are not like this.

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They're not a perfect analogy,
but as time goes on, they will be a better

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analogy. In AI, it's really something
where like in three three to six months,

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you can really learn a lot about how
these systems work. It's not not so

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complex. And I think uh I think it's
probably worth learning if you think

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it's plausible that these systems are
going to become that powerful because I

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think we need our best and brightest to

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kind of I don't know have their game

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face on or something for uh the

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challenges and the opportunities that
that the technology might bring. where I

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had like my personal runway. I I ran out

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and so I was like, "Okay, I think that

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I'm going to need six months of stealth

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study to have a shot at getting a job."

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At that point, it was Deep Mind
or Google Brain were the two places to do

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work there. Or Merie Merie was the third
one that I was like looking at. So, I

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was like, if I want to help out with that,
those are the three places to look

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at. I don't have any of the skills yet.

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I need six months of self-study to feel
like I would not be a drag on them and

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like actually be helping instead. Can
you um maybe explain a bit what was that

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cell study like because I'm sure there's
a lot of software engineers right now in

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their 20s are looking tool to become
AI researchers. What was what was that 6

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months like even though as you said
you had uh gotten a B minus in linear

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algebra which is like core >> might have
been C plus I'm checking going to give

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>> that's pretty impressive
where where you got to.

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>> Yeah. Yeah. It turned out okay. First
I did a contract actually with Twitch um

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and like earned like enough to have
that six months of runway. So I did like

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three month contract with Twitch
and then I made a a plan to self-study. I

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don't think it's the right plan now
for people too because at least the 2015.

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What did it look like? It was like take

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a Corsera course on machine learning,

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try to solve some Kaggle projects, read

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linear algebra done right and uh I had a

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statistics textbook. I think I had YC

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alumni credits and so I bought like a

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GPU and I would like SSH into the GPU to

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like work through my courses for it.

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>> And this is right after Yeah,
it was already after Alex Knack, right?

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>> This after Alex Net. Yeah. So I was
mostly doing image image classification

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stuff that I was trying to learn
was like the thing that all the courses

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would teach you to do. >> How did
you get the open AI job? Because

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you were one of the few engineers.
it was mostly researchers and they had

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pretty stacked team of researchers.

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>> I messaged Greg um
as soon as OpenAI was

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announced and I was like, I'd love to help
out in some way. I got a B minus in

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my linear algebra, but I know some
engineering. I've done a bit of

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distributed systems work. If you guys need
help, I'm like happy to mop floors if if