And so places where I think that's about
to happen and it hasn't happened yet.
Um, one of them is interpretability. I
think interpretability is both the key
to steering and making safe AI systems
um and and we're about to understand and
interpretability contains insights about
intelligent optimization problems and
about how the human brain works. Um,
I've said and I I'm really not joking.
Chris Ola is going to be a future Nobel
Medicine Laurette. I'm serious. I'm
serious because a lot of these I used to
be a neuroscientist. A lot of these
mental illnesses, the ones we haven't
figured out, right? Schizophrenia or the
mood disorders, I suspect there's some
higher level system thing going on and
that it's hard to make sense of those
with brains because brains are so mushy
and hard to hard to open up and interact
with. Neural nets are not like this.
They're not a perfect analogy, but as
time goes on, they will be a better
analogy. In AI, it's really something
where like in three three to six months,
you can really learn a lot about how
these systems work. It's not not so
complex. And I think uh I think it's
probably worth learning if you think
it's plausible that these systems are
going to become that powerful because I
think we need our best and brightest to
kind of I don't know have their game
face on or something for uh the
challenges and the opportunities that
that the technology might bring. where I
had like my personal runway. I I ran out
and so I was like, "Okay, I think that
I'm going to need six months of stealth
study to have a shot at getting a job."
At that point, it was Deep Mind or
Google Brain were the two places to do
work there. Or Merie Merie was the third
one that I was like looking at. So, I
was like, if I want to help out with
that, those are the three places to look
at. I don't have any of the skills yet.
I need six months of self-study to feel
like I would not be a drag on them and
like actually be helping instead. Can
you um maybe explain a bit what was that
cell study like because I'm sure there's
a lot of software engineers right now in
their 20s are looking tool to become AI
researchers. What was what was that 6
months like even though as you said you
had uh gotten a B minus in linear
algebra which is like core
>> might have been C plus I'm checking
going to give
>> that's pretty impressive where where you
got to.
>> Yeah. Yeah. It turned out okay. First I
did a contract actually with Twitch um
and like earned like enough to have that
six months of runway. So I did like
three month contract with Twitch and
then I made a a plan to self-study. I
don't think it's the right plan now for
people too because at least the 2015.
What did it look like? It was like take
a Corsera course on machine learning,
try to solve some Kaggle projects, read
linear algebra done right and uh I had a
statistics textbook. I think I had YC
alumni credits and so I bought like a
GPU
and I would like SSH into the GPU to
like work through my courses for it.
>> And this is right after Yeah, it was
already after Alex Knack, right?
>> This after Alex Net. Yeah. So I was
mostly doing image image classification
stuff that I was trying to learn was
like the thing that all the courses
would teach you to do.
>> How did you get the open AI job? Because
you were one of the few engineers. it
was mostly researchers and they had
pretty stacked team of researchers.
>> I messaged Greg um as soon as OpenAI was
announced and I was like, I'd love to
help out in some way. I got a B minus in
my linear algebra, but I know some
engineering. I've done a bit of
distributed systems work. If you guys
need help, I'm like happy to mop floors
if if