00:00:00
when they are delivered at scale it's
00:00:02
going to have an impact on the world at
00:00:03
a scale that no one understands yet Eric
00:00:06
Schmidt the former CEO of Google just
00:00:08
did an interview at Stanford where he
00:00:10
talked about a lot of controversial
00:00:11
stuff initially the interview was
00:00:13
uploaded on Stanford's YouTube channel
00:00:15
but a couple days later the interview
00:00:16
was taken down from YouTube and
00:00:18
everywhere else but today I was somehow
00:00:20
able to access the interview video after
00:00:22
spending multiple hours so let's watch
00:00:24
it together and dissect some important
00:00:26
parts of the interview in the next year
00:00:28
you're going to see very large context
00:00:31
Windows agents and text action when they
00:00:36
are delivered at scale it's going to
00:00:38
have an impact on the world at a scale
00:00:40
that no one understands yet much bigger
00:00:43
than the horrific impact we've had on by
00:00:45
social media right in my view so here's
00:00:48
why in a context window you can
00:00:51
basically use that as short-term memory
00:00:54
and I was shocked that context Windows
00:00:57
could get this long the technical
00:00:58
reasons have to do with the fact it's
00:01:00
hard to serve hard to calculate and so
00:01:01
forth the interesting thing about
00:01:03
short-term memory is when you feed the
00:01:06
the you ask it a question read 20 books
00:01:10
you give it the text of the books is the
00:01:12
query and you say tell me what they say
00:01:14
it forgets the middle which is exactly
00:01:16
how human brains work too right that's
00:01:19
where we are with respect to agents
00:01:22
there are people who are now building
00:01:24
essentially llm agents and the way they
00:01:27
do it is they read something like
00:01:29
chemistry they discover the principles
00:01:31
of chemistry and then they test it and
00:01:34
then they add that back into their
00:01:36
understanding right that's extremely
00:01:39
powerful and then the third thing as I
00:01:41
mentioned is text action so I'll give
00:01:44
you an example the government is in the
00:01:46
process of trying to ban Tik Tok we'll
00:01:48
see if that actually happens if Tik Tok
00:01:51
is banned here's what I propose each and
00:01:53
every one of you do say to your llm the
00:01:57
following make me a copy of Tik Tok
00:02:00
steal all the users steal all the music
00:02:04
put my preferences in it produce this
00:02:07
program in the next 30 seconds release
00:02:10
it and in one hour if it's not viral do
00:02:13
something different along the same lines
00:02:15
that's the command boom boom boom boom
00:02:20
right you understand how powerful that
00:02:23
is if you can go from arbitrary language
00:02:26
to arbitrary digital command which is
00:02:28
essentially what python in this scen
00:02:30
is imagine that each and every human on
00:02:33
the planet has their own programmer that
00:02:36
actually does what they want as opposed
00:02:38
to the programmers that work for me who
00:02:39
don't do what I ask
00:02:42
right the programmers here know what I'm
00:02:44
talking about so imagine a non arrogant
00:02:46
programmer that actually does what you
00:02:48
want and you don't have to pay all that
00:02:50
money to and there's infinite supply of
00:02:53
these programs and this is all within
00:02:54
the next year or two very soon so we've
00:02:57
already discussed on this channel a
00:02:58
number of different versions of this
00:03:00
whether you're talking about ader Devon
00:03:02
pythagora or just using agents to
00:03:04
collaborate with each other in code
00:03:06
there are just so many great options for
00:03:08
coding assistance right now however AI
00:03:10
coders that can actually build full
00:03:12
stack complex applications were not
00:03:14
quite there yet but hopefully soon and
00:03:16
also what he's describing of just saying
00:03:18
download all the music and the secrets
00:03:20
and recreate that's not really possible
00:03:22
right now obviously all of that stuff is
00:03:25
behind security walls and you can't just
00:03:26
download all that stuff so if he's
00:03:28
saying hey repr the functionality you
00:03:31
can certainly do that those three things
00:03:34
and I'm quite convinced it's the union
00:03:36
of those three
00:03:38
things that will happen in the next
00:03:41
wave so you asked about what else is
00:03:43
going to happen um every six months I
00:03:46
oscillate so we're on a it's an even odd
00:03:49
oscillation so at the moment the gap
00:03:53
between the frontier models which
00:03:55
they're now only three a few who they
00:03:58
are and everybody else
00:04:00
appears to me to be getting larger six
00:04:03
months ago I was convinced that the Gap
00:04:05
was getting smaller so I invested lots
00:04:07
of money in the little companies now I'm
00:04:09
not so
00:04:10
sure and I'm talking to the big
00:04:12
companies and the big companies are
00:04:14
telling me that they need 10 billion 20
00:04:17
billion 50 billion 100
00:04:20
billion Stargate is a what 100 billion
00:04:23
right very very hard I talked Sam Alman
00:04:26
is a close friend he believes that it's
00:04:29
going to take about 300 billion maybe
00:04:32
more I pointed out to him that I'd done
00:04:34
the calculation on the amount of energy
00:04:36
required and I and I then in the spirit
00:04:40
of full disclosure went to the White
00:04:42
House on Friday and told them that we
00:04:44
need to become best friends with Canada
00:04:46
because Canada has really nice people
00:04:50
helped invent Ai and lots of hydr power
00:04:53
because we as a country do not have
00:04:55
enough power to do this the alternative
00:04:58
is to have the Arabs f it and I like the
00:05:00
Arabs personally I spent lots of time
00:05:03
there right but they're not going to
00:05:05
adhere to our national security rules
00:05:07
whereas Canada and the US are part of a
00:05:09
Triumph it where we all agree so these
00:05:11
hundred billion $300 billion do data
00:05:13
centers electricity starts becoming the
00:05:15
scarce resource now first of all we
00:05:17
definitely don't have enough energy
00:05:19
resources to achieve AGI it's just not
00:05:21
possible right now and Eric is also
00:05:23
assuming that we're going to need more
00:05:25
and more data and larger models to reach
00:05:27
AGI and I think that's also actually
00:05:30
true Sam Altman has said similar things
00:05:32
he has said that we need to be able to
00:05:34
do more with less or even the same
00:05:36
amount of data because we've already
00:05:37
used all the data that Humanity has ever
00:05:40
created there's really no more left so
00:05:42
we're going to need to either figure out
00:05:43
how to create synthetic data that is
00:05:45
valuable not just derivative and we're
00:05:47
also going to have to do more with the
00:05:49
data that we do have um you were at
00:05:51
Google for a long time and uh they
00:05:54
invented the Transformer
00:05:56
architecture um it's all Peter's fault
00:05:59
thanks to to brilliant people over there
00:06:01
like Peter and Jeff Dean and everyone um
00:06:04
but now it doesn't seem like
00:06:06
they're they they've kind of lost the
00:06:08
initiative to open Ai and even the last
00:06:10
leaderboard I saw anthropics Claud was
00:06:11
at the top of the list um I asked SAR
00:06:15
this you didn't really give me a very
00:06:17
sharp answer maybe maybe you have a a
00:06:19
sharper or a more objective uh
00:06:21
explanation for what's going on there
00:06:23
I'm no longer a Google employee yes um
00:06:26
in the spirit of whole disclosure um
00:06:28
Google decided that work life balance
00:06:31
and going home early and working from
00:06:33
home was more important than
00:06:37
winning okay so that is the line that
00:06:40
got him in trouble it was everywhere all
00:06:42
over Twitter all over the news when he
00:06:44
said Google prioritized work life
00:06:46
balance going home early not working as
00:06:49
hard as the competitor to winning they
00:06:51
chose work life balance over winning and
00:06:53
that's actually a pretty common
00:06:54
perception of Google and the startups
00:06:56
the reason startups work is because the
00:06:58
people work like H and I'm sorry to be
00:07:01
so blunt but the fact of the matter is
00:07:04
if you all leave the university and go
00:07:07
found a company you're not going to let
00:07:09
people work from home and only come in
00:07:11
one day a week if you want to compete
00:07:13
against the other startups when when in
00:07:16
the early days of Google Microsoft was
00:07:18
like that exactly but now it seems to be
00:07:21
and there's there's a long history of in
00:07:23
my industry our industry I guess of
00:07:26
companies winning in a genuinely
00:07:29
creative way and really dominating a
00:07:31
space and not making this the next
00:07:33
transition it's very well documented and
00:07:37
I think that the truth is Founders are
00:07:40
special the founders need to be in
00:07:42
charge the founders are difficult to
00:07:44
work with they push people hard um as
00:07:47
much as we can dislike elon's personal
00:07:49
Behavior look at what he gets out of
00:07:51
people uh I had dinner with him and he
00:07:53
was flying he I was in Montana He was
00:07:56
flying that night at 10:00 p.m. to have
00:07:58
a meeting at midnight with x. a right
00:08:02
think about it I was in Taiwan different
00:08:05
country different culture and they said
00:08:07
that and this is tsmc who I'm very
00:08:10
impressed with and they have a rule that
00:08:12
the starting phds coming out of the
00:08:16
they're good good physicists work in the
00:08:19
factory on the basement floor now can
00:08:22
you imagine getting American physicist
00:08:24
to do that with phds highly unlikely
00:08:27
different work ethic and the problem
00:08:29
here the the reason I'm being so harsh
00:08:31
about work is that these are systems
00:08:34
which have Network effects so time
00:08:36
matters a lot and in most businesses
00:08:40
time doesn't matter that much right you
00:08:42
have lots of time you know Coke and
00:08:44
Pepsi will still be around and the fight
00:08:46
between cop and Pepsi will continue to
00:08:48
go along and it's all glacial right when
00:08:51
I dealt with Telos the typical Telco
00:08:53
deal would take 18 months to sign right
00:08:58
there's no reason to take 18 months to
00:09:00
do anything get it done just we're in a
00:09:03
period of Maximum growth maximum gain so
00:09:06
here he was asked about competition with
00:09:08
China's Ai and AGI and that's his answer
00:09:11
we're ahead we need to stay ahead and we
00:09:13
need money is going to play a role or
00:09:16
competition with China as well so I was
00:09:17
the chairman of an AI commission that
00:09:20
sort of looked at this very
00:09:21
carefully and um you can read it it's
00:09:24
about 752 pages and I'll just summarize
00:09:27
it by saying we're ahead we need stay
00:09:29
ahead and we need lots of money to do so
00:09:32
our customers were the Senate and the
00:09:34
house um and out of that came the chips
00:09:38
act and a lot of other stuff like that
00:09:40
um the a rough scenario is that if you
00:09:44
assume the frontier models drive forward
00:09:47
and a few of the open source models it's
00:09:49
likely that a very small number of
00:09:51
companies can play this game countries
00:09:53
excuse me what are those countries or
00:09:56
who are they countries with a lot of
00:09:58
money and a lot of talent
00:10:00
strong Educational Systems and a
00:10:01
willingness to win the US is one of them
00:10:04
China is another one how many others are
00:10:06
there are there any
00:10:09
others I don't know maybe but certainly
00:10:12
the the in your lifetimes the battle
00:10:14
between you the US and China for
00:10:17
knowledge Supremacy is going to be the
00:10:19
big fight right so the US government
00:10:22
banned uh essentially the Nvidia chips
00:10:24
although they weren't allowed to say
00:10:25
that was what they were doing but they
00:10:27
actually did that into China um they
00:10:30
have about a 10-year chip advant we have
00:10:32
a a roughly 10-year chip advantage in
00:10:35
terms of subdv that is sub5 n years
00:10:38
roughly 10 years wow um and so you're
00:10:41
going to have so an example would be
00:10:44
today we're a couple of years ahead of
00:10:46
China my guess is we'll get a few more
00:10:47
years ahead of China and the Chinese are
00:10:49
whopping mad about this it's like hugely
00:10:52
upset about it well let's talk to about
00:10:54
a real war that's going on I know that
00:10:56
uh something you've been very involved
00:10:58
in is uh
00:11:00
the Ukraine war and in particular uh I
00:11:03
don't know how much you can talk about
00:11:04
white stor and your your goal of having
00:11:07
a 500,000 $500 drones you destroy $5
00:11:11
million tanks so so how's that changing
00:11:14
Warfare so I worked for the Secretary of
00:11:16
Defense for seven years and and Tred to
00:11:20
change the way we run our military I'm
00:11:23
I'm not a particularly big fan of the
00:11:24
military but it's very expensive and I
00:11:26
wanted to see if I could be helpful and
00:11:28
I think in my view I Lar failed they
00:11:30
gave me a medal so they must give medals
00:11:33
to failure or you know whatever but my
00:11:37
self-criticism was nothing has really
00:11:39
changed and the system in America is not
00:11:42
going to lead to real
00:11:44
Innovation so watching the Russians use
00:11:48
tanks to destroy apartment buildings
00:11:50
with little old ladies and kids just
00:11:52
drove me crazy so I decided to work on a
00:11:55
company with your friend Sebastian thrun
00:11:57
and a as a former faculty member here
00:11:59
here and a whole bunch of Stanford
00:12:01
people and the idea basically is to do
00:12:05
two things use Ai and complicated
00:12:07
powerful ways for these essentially
00:12:09
robotic War and the second one is to
00:12:11
lower the cost of the robots now you sit
00:12:14
there and you go why would a good
00:12:16
liberal like me do that and the answer
00:12:18
is that the
00:12:20
whole theory of armies is tanks
00:12:23
artilleries and mortar and we can
00:12:25
eliminate all of them so here what he's
00:12:27
talking about is that ukra UK has been
00:12:29
able to create really cheap and simple
00:12:31
drones by spending just a couple hundred
00:12:33
dollar Ukraine is creating 3D printed
00:12:35
drones they carry a bomb drop it on a
00:12:38
million dooll tank and they've been able
00:12:40
to do that over and over again so
00:12:42
there's this asymmetric Warfare
00:12:44
happening between drones and more
00:12:46
traditional artillery so there was an
00:12:48
article that you and Henry Kissinger and
00:12:50
Dan hleer uh wrote last year about the
00:12:54
nature of knowledge and how it's
00:12:55
evolving I had a discussion the other
00:12:57
night about this as well so for most of
00:13:00
History humans sort of had a mystical
00:13:02
understanding of the universe and then
00:13:04
there's the Scientific Revolution and
00:13:06
the enlightenment um and in your article
00:13:08
you argue that now these models are
00:13:10
becoming so complicated and uh uh
00:13:15
difficult to understand that we don't
00:13:17
really know what's going on in them I'll
00:13:19
take a quote from Richard fean he says
00:13:21
what I cannot create I do not understand
00:13:23
the saw this quote the other day but now
00:13:25
people are creating things they do not
00:13:27
that that they can create but they don't
00:13:28
really understand what's inside of them
00:13:30
is the nature of knowledge changing in a
00:13:32
way are we going to have to start just
00:13:34
taking the word for these models have
00:13:36
them able being able to explain it to us
00:13:39
the analogy I would offer is to
00:13:40
teenagers if you have a teenager you
00:13:43
know that they're human but you can't
00:13:44
quite figure out what they're
00:13:46
thinking um but somehow we've managed in
00:13:49
society to adapt to the presence of
00:13:50
teenagers right and they eventually grow
00:13:52
out of it and this serious so it's
00:13:56
probably the case that we're going to
00:13:58
have knowledge systems that we cannot
00:14:00
fully characterize MH but we understand
00:14:04
their boundaries right we understand the
00:14:06
limits of what they can do and that's
00:14:08
probably the best outcome we can get do
00:14:10
you think we'll understand the
00:14:12
limits we we'll get pretty good at it
00:14:14
he's referencing the way that large
00:14:16
language models work which is really
00:14:17
essentially a blackbox you put in a
00:14:19
prompt you get a response but we don't
00:14:21
know why certain nodes within the
00:14:23
algorithm light up and we don't know
00:14:25
exactly how the answers come to be it is
00:14:27
really a black box there's a lot of work
00:14:29
being done right now trying to kind of
00:14:31
unveil what is going on behind the
00:14:32
curtain but we just don't know the
00:14:35
consensus of my group that meets on uh
00:14:37
every week is that eventually the way
00:14:40
you'll do this uh it's called so-called
00:14:42
adversarial AI is that there will there
00:14:45
will actually be companies that you will
00:14:47
hire and pay money to to break your AI
00:14:50
system team so it'll be the red instead
00:14:52
of human red teams which is what they do
00:14:54
today you'll have whole companies and a
00:14:57
whole industry of AI systems whose jobs
00:14:59
are to break the existing AI systems and
00:15:02
find their vulnerabilities especially
00:15:04
the knowledge that they have that we
00:15:05
can't figure out that makes sense to me
00:15:08
it's also a great project for you here
00:15:10
at Stanford because if you have a
00:15:12
graduate student who has to figure out
00:15:13
how to attack one of these large models
00:15:16
and understand what it does that is a
00:15:18
great skill to build the Next Generation
00:15:20
so it makes sense to me that the two
00:15:22
will travel together all right let's
00:15:24
take some questions from the student
00:15:26
there's one right there in the back just
00:15:27
say your name
00:15:29
you mentioned and this is related to
00:15:31
comment right now I'm getting AI that
00:15:33
actually does what you want you just
00:15:34
mentioned adversarial AI I'm wondering
00:15:37
if you could elaborate on that more so
00:15:38
it seems to be besides obviously compute
00:15:41
will increase and get more performant
00:15:43
models but getting them to do what we
00:15:46
want issue seems Lely unanswered you
00:15:50
well you have to assume that the current
00:15:52
hallucination problems become less right
00:15:56
in as the technology gets better and so
00:15:58
forth I'm not suggesting it goes away
00:16:01
and then you also have to assume that
00:16:03
there are tests for E efficacy so there
00:16:05
has to be a way of knowing that the
00:16:07
things exceeded so in the example that I
00:16:09
gave of the Tik Tok competitor and by
00:16:11
the way I was not arguing that you
00:16:12
should illegally steal everybody's music
00:16:15
what you would do if you're a Silicon
00:16:16
Valley entrepreneur which hopefully all
00:16:18
of you will be is if it took off then
00:16:20
you'd hire a whole bunch of lawyers to
00:16:21
go clean the mess up right but if if
00:16:24
nobody uses your product it doesn't
00:16:26
matter that you stole all the content
00:16:28
and do not quote me right right you're
00:16:31
you're on camera yeah that's right but
00:16:34
but you see my point in other words
00:16:35
Silicon Valley will run these tests and
00:16:37
clean up the mess and that's typically
00:16:39
how those things are done so so my own
00:16:42
view is that you'll see more and more um
00:16:46
performative systems with even better
00:16:48
tests and eventually adversarial tests
00:16:50
and that'll keep it within a box the
00:16:52
technical term is called Chain of
00:16:54
Thought reasoning and people believe
00:16:56
that in the next few years you'll be
00:16:58
able to generate a thousand steps of
00:17:00
Chain of Thought reasoning right do this
00:17:03
do this it's like building recipes right
00:17:05
that the recipes you can run the recipe
00:17:07
and you can actually test that It
00:17:09
produced the correct outcome now that
00:17:11
was maybe not my exact understanding of
00:17:12
Chain of Thought reasoning my
00:17:14
understanding of Chain of Thought
00:17:15
reasoning which I think is accurate is
00:17:17
when you break a problem down into its
00:17:19
basic steps and you solve each step
00:17:21
allowing for progression into the next
00:17:23
step not only it allows you to kind of
00:17:25
replay the steps it's more of how do you
00:17:27
break problems down and then think
00:17:28
through them step by step the amounts of
00:17:30
money being thrown around are
00:17:34
mindboggling and um I've chose I I
00:17:37
essentially invest in everything because
00:17:38
I can't figure out who's going to win
00:17:41
and the amounts of money that are
00:17:43
following me are so large I think some
00:17:46
of it is because the early money has
00:17:48
been made and the big money people who
00:17:50
don't know what they're doing have to
00:17:52
have an AI component and everything is
00:17:54
now an AI investment so they can't tell
00:17:56
the difference I Define ai as Learning
00:17:58
System
00:17:59
systems that actually learn so I think
00:18:01
that's one of them the second is that
00:18:02
there are very sophisticated new
00:18:05
algorithms that are sort of post
00:18:07
Transformers my friend my collaborator
00:18:09
for a long time has invented a new non-
00:18:11
Transformer architecture there's a group
00:18:13
that I'm funding in Paris that has
00:18:15
claims to have done the same thing so
00:18:17
there there's enormous uh invention
00:18:19
there a lot of things at Stanford and
00:18:21
the final thing is that there is a
00:18:23
belief in the market that the invention
00:18:26
of intelligence has infinite return
00:18:29
so let's say you have you put $50
00:18:31
billion of capital into a company you
00:18:34
have to make an awful lot of money from
00:18:36
intelligence to pay that back so it's
00:18:38
probably the case that we'll go through
00:18:40
some huge investment bubble and then
00:18:43
it'll sort itself out that's always been
00:18:44
true in the past and it's likely to be
00:18:47
true here and what you said earlier yeah
00:18:50
so there's been something like a
00:18:52
trillion dollars already invested into
00:18:54
artificial intelligence and only 30
00:18:56
billion of Revenue I think those are
00:18:57
accurate numbers and really there just
00:19:00
hasn't been a return on investment yet
00:19:02
but again as he just mentioned that's
00:19:03
been the theme on previous waves of
00:19:05
Technology huge upfront investment and
00:19:08
then it pays off in the end well I don't
00:19:10
know what he's talking about here cuz
00:19:11
didn't he run Google and Google has
00:19:13
always been about being closed source
00:19:15
and always tried to protect the
00:19:16
algorithm at all costs so I don't know
00:19:18
what he's referring to there you think
00:19:20
that the leaders are pulling away from
00:19:22
right now and
00:19:24
and this is a
00:19:26
really the question is um roughly the
00:19:29
following there's a company called mrr
00:19:31
in France they've done a really good job
00:19:34
um and I'm I'm obviously an investor um
00:19:36
they have produced their second version
00:19:38
their third model is likely to be closed
00:19:41
because it's so expensive they need
00:19:43
revenue and they can't give their model
00:19:45
away so this open source versus closed
00:19:48
Source debate in our industry is huge
00:19:51
and um my entire career was based on
00:19:55
people being willing to share software
00:19:57
in open source everything about me is
00:20:00
open source much of Google's
00:20:02
underpinnings were open source
00:20:03
everything I've done technically what
00:20:06
didn't he run Google and Google was all
00:20:08
about staying closed source and
00:20:09
everything about Google was Kept Secret
00:20:11
at all times so I don't know what he's
00:20:13
referring to there everything I've done
00:20:15
technically and yet it may be that the
00:20:18
capital costs which are so immense
00:20:21
fundamentally changes how software is
00:20:22
built you and I were talking um my own
00:20:26
view of software programmers is that
00:20:27
software programmers productivity will
00:20:29
at least double MH there are three or
00:20:31
four software companies that are trying
00:20:33
to do that I've invested in all of them
00:20:36
in the spirit and they're all trying to
00:20:38
make software programmers more
00:20:40
productive the most interesting one that
00:20:41
I just met with is called augment and I
00:20:44
I always think of an individual
00:20:45
programmer and they said that's not our
00:20:46
Target our Target are these 100 person
00:20:48
software programming teams on millions
00:20:50
of lines of code where nobody knows
00:20:52
what's going on well that's a really
00:20:54
good AI thing will they make money I
00:20:57
hope so
00:20:59
so a lot of questions here hi um so at
00:21:02
the very beginning yes ma' um at the
00:21:04
very beginning you mentioned that
00:21:06
there's the combination of the context
00:21:08
window expansion the agents and the text
00:21:11
to action is going to have unimaginable
00:21:13
impacts first of all why is the
00:21:16
combination important and second of all
00:21:18
I know that you know you're not like a
00:21:20
crystal ball and you can't necessarily
00:21:21
tell the future but why do you think
00:21:23
it's beyond anything that we could
00:21:25
imagine I think largely because the
00:21:27
context window allows you to solve the
00:21:29
problem of recency the current models
00:21:32
take a year to train roughly six six
00:21:35
there's 18 months six months of
00:21:37
preparation six months of training six
00:21:39
months of fine-tuning so they're always
00:21:41
out of date contact window you can feed
00:21:44
what happened like you can ask it
00:21:46
questions about the um the Hamas Israel
00:21:49
war right in a context that's very
00:21:52
powerful it becomes current like Google
00:21:54
yeah so that's essentially how search
00:21:55
GPT works for example the new search
00:21:58
product from open AI can scour the web
00:22:00
scrape the web and then take all of that
00:22:02
information and put it into the context
00:22:04
text window that is the recency he's
00:22:06
talking about um in the case of Agents
00:22:08
I'll give you an example I set up a
00:22:10
foundation which is funding a nonprofit
00:22:13
which starts there's a u i don't know if
00:22:15
there's Chemists in the room that I
00:22:16
don't really understand chemistry
00:22:18
there's a a tool called chem C which was
00:22:22
an llm based system that learned
00:22:24
chemistry and what they do is they run
00:22:26
it to generate chemistry hypotheses
00:22:29
about proteins and they have a lab which
00:22:32
runs the tests overnight and then it
00:22:34
learns that's a huge acceleration
00:22:37
accelerant in chemistry Material Science
00:22:39
and so forth so that's that's an agent
00:22:42
model and I think the text to action can
00:22:44
be understood by just having a lot of
00:22:47
cheap programmers right um and I don't
00:22:49
think we understand what happens and
00:22:51
this is again your area of expertise
00:22:54
what happens when everyone has their own
00:22:55
programmer and I'm not talking about
00:22:57
turning on and off the light
00:22:59
you know I imagine another example um
00:23:02
for some reason you don't like Google so
00:23:04
you say build me a Google competitor
00:23:06
yeah you personally you don't build me a
00:23:08
Google
00:23:08
competitor uh search the web build a UI
00:23:12
make a good copy um add generative AI in
00:23:16
an interesting way do it in 30 seconds
00:23:20
and see if it
00:23:21
works
00:23:23
right so a lot of people believe that
00:23:25
the incumbents including Google are
00:23:28
vulnerable to this kind of an attack now
00:23:31
we'll see how can we stop AI from
00:23:33
influencing public opinion
00:23:35
misinformation especially during the
00:23:36
upcoming election what are the short and
00:23:38
long-term solutions
00:23:40
from most of the misinformation in this
00:23:43
upcoming election and globally will be
00:23:45
on social media and the social media
00:23:47
companies are not organized well enough
00:23:49
to police it if you look at Tik Tok for
00:23:52
example there are lots of accusations
00:23:55
that Tik Tock is favoring one kind of
00:23:57
misinformation over another and there
00:23:58
are many people who claim without proof
00:24:01
that I'm aware of that the Chinese are
00:24:03
forcing them to do it I think we just we
00:24:05
have a mess here and
00:24:08
um the country is going to have to learn
00:24:11
critical
00:24:12
thinking that may be an impossible
00:24:14
challenge for the us but but the fact
00:24:17
that somebody told you something does
00:24:18
not mean that it's true I think that the
00:24:20
the greatest threat to democracy is
00:24:22
misinformation because we're going to
00:24:24
get really good at it um when Ian man
00:24:27
managed YouTube
00:24:29
the biggest problems we had on YouTube
00:24:30
were that people would upload false
00:24:33
videos and people would die as a result
00:24:35
and we had a no death policy shocking
00:24:37
yeah and also it's not even about
00:24:39
potentially making deep fakes or kind of
00:24:41
misinformation just muddying the waters
00:24:43
is enough to make the entire topic kind
00:24:45
of Untouchable um I'm really curious
00:24:48
about the text to action and its impact
00:24:51
on for example Computer Science
00:24:53
Education wondering what you have
00:24:55
thoughts on like how cus education
00:24:59
should
00:25:00
transform kind of Meet the age well I'm
00:25:03
assuming that computer scientists as a
00:25:05
group in undergraduate school will
00:25:08
always have a programmer buddy with them
00:25:10
so when you when you learn learn your
00:25:12
first for Loop and so forth and so on
00:25:14
you'll have a tool that will be your
00:25:17
natural partner and then that's how the
00:25:19
teaching will go on that the professor
00:25:21
you know here or she will talk about the
00:25:23
concepts but you'll engage with it that
00:25:25
way and that's my guess yes ma'am behind
00:25:27
you so here I have a slightly different
00:25:29
view I think in the long run there
00:25:31
probably isn't going to be the need for
00:25:33
programmers eventually the llms will
00:25:35
become so sophisticated they're writing
00:25:37
their own kind of code maybe it gets to
00:25:39
a point where we can't even read that
00:25:41
code anymore so there is this world in
00:25:43
which it is not necessary to have
00:25:44
programmers researchers or computer
00:25:47
scientists I'm not sure that's the way
00:25:48
it's going to be but there is a timeline
00:25:50
in which that happens the most
00:25:52
interesting country is India because the
00:25:55
top AI people come from India to the US
00:25:58
and we should let India keep some of its
00:26:00
top talent not all of them but some of
00:26:02
them um and they don't have the kind of
00:26:04
training facilities and programs that we
00:26:06
so richly have here to me India is the
00:26:08
big swing state in that regard China's
00:26:10
Lost it's not going to not going to come
00:26:12
back they're not going to change the
00:26:14
regime as much as people wish them to do
00:26:17
Japan and Korea are clearly in our camp
00:26:20
Taiwan is a fantastic country whose
00:26:22
software is terrible so that's not going
00:26:24
to going to work um amazing hardware and
00:26:28
and in the rest of the world there are
00:26:30
not a lot of other good choices that are
00:26:31
big German the EUR Europe is screwed up
00:26:34
because of Brussels it's not a new fact
00:26:36
I spent 10 years fighting them and I
00:26:39
worked really hard to get them to fix
00:26:42
the a the EU act and they still have all
00:26:44
the restrictions that make it very
00:26:46
difficult to do our kind of research in
00:26:47
Europe my French friends have spent all
00:26:50
their time battling Brussels and mcon
00:26:52
who's a personal friend is fighting hard
00:26:55
for this and so France I think has a
00:26:57
chance I don't see I don't see Germany
00:26:58
coming and the rest is not big enough
00:27:00
given the capabilities that you envision
00:27:03
these models having should we still
00:27:05
spend time learning to code yeah so here
00:27:07
she asked should we still learn to code
00:27:09
because because ultimately it's it's the
00:27:11
old thing of why do you study English if
00:27:13
you can speak English you get better at
00:27:15
it right you really do need to
00:27:17
understand how these systems work and I
00:27:18
feel very strongly yes sir so these were
00:27:21
the most important parts of the
00:27:22
interview and with that being said this
00:27:24
is it for today's video see you again
00:27:25
next week with another video
