78,534 views 2 Nov 2023

Each iteration of ChatGPT has demonstrated remarkable step function capabilities. But what’s next? Ilya Sutskever, Co-Founder & Chief Scientist at OpenAI, joins Sarah Guo and Elad Gil to discuss the origins of OpenAI as a capped profit company, early emergent behaviors of GPT models, the token scarcity issue, next frontiers of AI research, his argument for working on AI safety now, and the premise of Superalignment. Plus, how do we define digital life? Ilya Sutskever is Co-founder and Chief Scientist of OpenAI. He leads research at OpenAI and is one of the architects behind the GPT models. He co-leads OpenAI’s new “Superalignment” project, which tries to solve the alignment of superintelligences in 4 years. Prior to OpenAI, Ilya was co-inventor of AlexNet and Sequence to Sequence Learning. He earned his Ph.D in Computer Science from the University of Toronto. 00:00 – Early Days of AI Research 06:49 – Origins of Open Ai & CapProfit Structure 13:54 – Emergent Behaviors of GPT Models 18:05 – Model Scale Over Time & Reliability 23:51 – Roles & Boundaries of Open Source in the AI Ecosystem 28:38 – Comparing AI Systems to Biological & Human Intelligence 32:56 – Definition of Digital Life 35:11 – Super Alignment & Creating Pro Human AI 41:20 – Accelerating & Decelerating Forces

Early Days of AI Research
0:00[Music] 0:05
open aai a company that we all know now but only a year ago was 100 people is
changing the world their research is leading the charge to AGI since Chachi
captured consumer attention last November they show no signs of slowing down this week elad and I sit down with
ilas Suk co-founder and chief scientist at open aai to discuss the state of AI
research where will hit limit the future of AGI and what it’s going to take to reach super alignment IO welcome to no
priors thank you it’s good to be here let’s start with the beginning pre Alex net nothing in deep learning was really
working and then given that environment you guys took a um a very unique bet
what motivated you to go in this direction indeed in those Dark Ages AI
was not an area where people had hope and people were not accustomed
to any kind of success at all and because there wasn’t there hasn’t been any success there was a lot of debate
and there were different schools of thoughts that had different arguments about how machine learning in AI should
be and you had people who were into knowledge representation from the good old fashioned you had people who were
beian and they liked beian non-parametric methods you had people who like graphical models and you had
the people who like neural networks those people were marginalized because neural networks did
not had the property that you can’t prove math theorems about them if you can’t prove theorems about
something it means that your research isn’t good that’s how it has been but the reason why I gravitated to neural
networks from the beginning is because it felt like those are small little brains and who cares if he can prove any
theorems about them because we are training small little brains and maybe they’ll become maybe they’ll do something one day and the reason that we
were able to do Alex NBD is because a combination of two factors three factors
the first factor is that this was shortly after gpus started to be used in machine learning people kind of had an
intuition that that’s a good thing to do but it wasn’t like today where people exactly knew what the npus is for it was
like oh let’s like play with those cool fast computers and see what we can do with them it was an especially good fit for neural networks so that was a very
that definitely helped us I was very fortunate in that I was able to realize
that the reason neural networks of the time weren’t good is because they were
too small so like if you try to solve a vision task with a neural network which has like a thousand neurons what can it
do it can’t do anything it doesn’t matter how good your learning is and everything else but if you have a much
larger neural network you’ll do something unprecedented what what gave you the intuition to think that that was the case because I think at the time it
was reasonably um contrarian to think that despite to your point you know a lot of the the human brain in some sense
works that way or different you know biological neural circuits but I’m just curious like what gave you that intuition early on to think that this
was a good direction I think yeah looking at the brain and specifically the if you like all those things follow
very easily if you allow yourself if you allow yourself to accept the idea right
now this idea is reasonably well accepted back then people still talked about it they haven’t really accepted it
or internalize the idea that maybe an artificial neuron in some sense is not
that different from a biological neuron so now whatever you imagine animals do with their brains you could perhaps
assemble some artificial neural network of similar size maybe if you train it it
will do something similar so there so that leads to the so that leads you to
start to imagine okay like almost imagine the computation being done by the neural network you can almost think
like if you have a high resolution image and you have like one neuron for like a large group
of pixels what can the neuron do it’s just just not much it can do if you but if you have a lot of neurons then they can actually do something and compute
something so I think it was like our like it was this was it was considerations like this plus a
technical realization the technical realization is that if you have a large
training set that specifies the behavior of the neur Neal Network and the
training set is large enough such that it can constrain the large neural network sufficiently and furthermore if
you have the algorithm to find that neural network because what we do is that we turn the training
set into a neural network which satisfies a training set neural network
training can almost be seen as solving a neural
equation solving a neural equation where every data point is is an equation and
every parameter is a variable and so it was multiple things
the realization that a bigger neural network could do something unprecedented the realization that if you have a large
data set together with the compute to solve the neural equation
that’s what gradient descent comes in but it’s not gradian descent gradient descent was around for a long time it
was certain technical insights about how to make it work because back then the prevailing belief was well you can’t
train those neuron Nets anything it’s all hopeless so it wasn’t just about the size it was about even if someone did
think gosh it would be cool to try a big neural net they didn’t have the technical ability to turn this idea into
reality you needed not only to code the neural net you need to do a bunch of things right and only then it will work
and then another fortunate thing is that the person with whom I work with Alex kvki he just discovered that he really
loves gpus and he was perhaps one of the the first person who really
mastered writing really like really performant code for for the gpus and that’s why we were able to squeeze a lot
of performance out of two gpus and do something and produce something unprecedented so to sum up it was
multiple things the idea that a big neural network in this case a a vision neural
network a convolutional neural network with many layers one that’s much much bigger than anything that’s ever been
done before could do something very unprecedented because the brain can see and the brain is a large neural network
and we can see quickly so our neurons don’t have a lot of time then the compute needed the technical knowhow
that in fact we can train such neural networks and it was not at all widely distributed most people in machine
learning would not have been able to train such a neural network even if they wanted to did you guys have any um like
Origins of Open Ai & CapProfit Structure
particular goal from a size perspective or was it just as as uh you know and if
that’s biologically inspired or where that number comes from or just as large as we can go definitely as large as we can go because keep in mind I mean we
had a certain amount of compute which we could usefully consume and then what can it do maybe if we think about just like
the origin of open Ai and uh the goals of the organization like what was the
original goal and how’s that evolved over time the goal did not evolve over time the tactic evolved over time
so the goal of open AI from the very beginning has been to make sure that
artificial general intelligence by which we mean autonomous
systems AI that can actually do most of the jobs and activities and tasks that
people do benefits all of humanity that was the goal from the beginning the
initial thinking has been that maybe the best way to do it is by just open
sourcing a lot of Technology we later and we also
attempted to do it as a nonprofit seemed very sensible this is the goal nonprofit is the way to do it what changed some
point at open AI we realized and we were perhaps among among the earlier the
earliest to realize that to make progress in AI for real you need a lot of compute now what does a lot mean the
appetite for compute is truly endless as as now as as now clearly seen but we realize that we will need a
lot and a nonprofit was wouldn’t wouldn’t be the way to to to get there wouldn’t be
able to build a large cluster with a nonprofit that’s why we became we converted into this unusual structure
called CAP profit and to my knowledge we are the only cap profit company in the world the
idea is that investors put in some money but even if the company does incredibly
well they don’t get more than some multiplier on top of their original investment and the reason to do this the
reason why that Mak sense you know there are arguments one could make arguments against it as well
but the argument for it is that if you believe that the technology that we are
building AGI could potentially be so capable as to do every
single task that people do does it mean that it might unemploy
everyone well I don’t know but it’s not impossible and if that’s the case it makes sense it will make a lot of sense
if the company that buil such a technology would not be able to make U infinite would not be incentivized
rather to make infinite profits I don’t know if it will literally play out this way because of competition in AI so
there will be M multiple companies and I think that will have some unforeseen implications
on the argument which I’m making but that was the thing I remember visiting the offices back when you were I think housed at YC or
something or you know cohabited some space there and at the time there was uh a suite of different efforts there was
robotic arms uh that were being manipulated and then there was um you know some video game related work which
was really cutting edge um how did you think about how the research agenda evolved and what really drove it down
this path of Transformer based models and other forms of of learning so our
thinking has been evolving over the years from when we started openi and the first year we indeed did
some of the more conventional machine learning work the conventional machine learning work I mean because the world
has changed so much a lot of things which were known to everyone in 2016 or 2017
are completely and utterly forgotten it’s like the Stone Age almost so in that in that Stone Age the world the
world of machine learning looked very different it was dramatically more
academic the goals values and objectives were much more academic they were about
discovering small bits of knowledge and sharing them with the other researchers and getting scientific recognition as a
result and it’s a very valid goal and it’s very understandable I’ve been doing a for 20 years now more than half of my
time that I spent in AI was in that framework and so what do you do you
write papers you share your small discoveries two realizations the first realization is just at a high level it
doesn’t seem like it’s the way to go to for a dramatic impact and why is that
because if you imagine how an AGI should look like it
has to be some kind of a big engineering project that’s using a lot of compute right even if you don’t know how
to build it what that should look like you know that this is the ideal you want to strive towards so you want to somehow move towards larger projects as opposed
to small projects so while we attempted a first large project where we
trained the neural network to play a real real time strategy game as well as as well as the best humans it’s the Dota
2 project and it was it was driven by two people um yakob botski and Greg
Brockman they they really dropped this project and make it made it a success and this was our first attempt at a
large project but it wasn’t quite the right formula for us because that the
neural networks were a little bit too small it was just an arrow domain just a game I mean it’s cool to play a game
they kept looking and at some point we realized that hey if you train a large neural network a very very large
Transformer to predict text better and better something very surprising will happen this realization also arrived a
little bit gradually we were exploring generative models we were exploring
ideas around next word prediction those are ideas also related to compression we were exploring them
Transformer came out we got really excited we like this is this is the greatest thing we’re going to do Transformers now it’s clearly Superior
than anything else before it we started doing Transformers we did gpt1 gpt1 started to show very interesting signs
of life and that led us to doing gpt2 and then ultimately gpt3 gpt3 really
opened everyone else’s eyes as well to hey this thing has a lot of traction there is one specific formula right now
that everyone is doing and this formula is train a larger and larger Transformer
on more and more data I mean for me the big wake up moment to your point was gpt2 to gpt3 transition where you saw
such a big step function and capabilities and then obviously with four um open AI published some really
interesting uh uh research around some of the different domains of knowledge or
domains of expertise or Chain of Thought or other things that the models can suddenly do in an emergent form what was
the most surprising thing for you in terms of emergent behavior in these models over time you know it’s very hard
to answer that question it’s very hard to answer because I’m too close and I’ve seen it progress every step of the
Emergent Behaviors of GPT Models
way so as much as I’d like I find it very hard to answer that question I
think if I had to pick one I think maybe the the most surprising thing for me is
the whole thing works at all
you know it’s hard it’s and I’m not sure I I know
how to convey this what what I have in mind here because if you see a lot of
neural networks do amazing things well obviously neural networks is the thing that works but I have witnessed
personally what it’s like to be in a world for many years where the neural networks not work at all and then to
contrast that to where we are today just the fact that they work and they do these amazing things I think maybe the
most surprising the most surprising if I had to pick one it would be the fact that when I speak to it I feel
yeah there’s a there’s a really good um saying from I’m trying to remember maybe it’s Arthur Clark or one
of the Sci-Fi authors which is effectively it says advanced technology is sometimes indistinguishable from
Magic yeah I’m I’m fully in this Camp yeah yeah it definitely feels like there’s some magical moments with with
uh some of these models now is there a way that you guys decide internally uh
given all of the different capabilities you could pursue how to continually
choose the set of big projects you’ve sort of described that centralization and committing to certain research
directions at scale is really important to open AI success given the breath of opportunity now like what’s the process
for deciding what’s worth working on I mean I think there is some combination of bottom up and top down where we have
some top down ideas that we believe should work but we not 100% sure so we
still we need to have good top- down ideas and there is a lot of bottomup exploration Guided by those top down
ideas as well and their combination is what informs us as to what to do
next and uh if you think about those bottom I mean either Direction top down
or bottom up ideas like clearly we have this dominant continue to scale
Transformers Direction um do you explore additional like architectural directions
or is that just not relevant it’s certainly possible that various improvements can be
found I think I think improvements can be found in all kinds of places both small improvements and large
improvements I think the way to think about it is that while the current thing that’s being
done keeps getting better as you keep on increasing the amount of compute and
data that you put into it so we have that property the bigger you make it the better it
gets it is also the property that different things get better by different
amount as you keep on improving as you keep on scaling them up so not only you want to of course scale up what we doing

we also want to SC keep scaling up the best thing possible what is uh a I mean you you
probably don’t need to predict because you can see internally what do you think is um improving most from a capability
perspective in the current generation of scale the best way for me to answer this question would be to point out the to
point to the models that are publicly available and you can see how they
compare from this year to last year and the difference is quite significant I’m not talking about the difference between
not only the difference between let’s you can look at the difference between gpt3 and GPT 3.5 and then chat GPT chat
GPT 4 chat GPT 4 with vision and you can just see for yourself it’s easy to forget where things used to be but
certainly the big way in which things are changing is that these models become more and more
reliable before they were very they were only very partly there right now they
are mostly there but there are still gaps and in the future perhaps these models will be there even more you could
trust their answers they’ll be more reliable they’ll be able to do more tasks in general across the board and
Model Scale Over Time & Reliability
then another thing that they will do is that they’ll have deeper insight as we train them they gain more and more
insight into the true nature of the human world and their Insight will
continue to deepen I I was just going to ask about how that relates to sort of um model scale over time because a lot of
people are really stricken by the capabilities of the very large scale models and emergent behavior in terms of
understanding of the world and then in parallel as people incorporate some of these things into products which is a
very different type of path they often start worrying about inference costs going up with the scale of the model and
therefore they’re looking for smaller models that are fine-tuned but then of course you may lose some of the capabilities around some of the insights
and ability to to reason and so I was curious in your thinking in terms of how all this evolves over the coming years I
would actually point out that the main thing that’s lost when you switch to the smaller models is reli ability I would
argue that at this point it is reliability that’s the biggest bottleneck to these models being truly
useful how you defining reliability so it’s like when you ask a question that’s not much harder than other questions
that the model succeeds at then you’ll have very high degree of confidence that
it will continue to succeed so I’ll give you an example let’s suppose that I want to learn about some historical thing and
I can ask what tell me what is the prevailing opinion about this and about that and I can keep asking questions and
let’s suppose it answered 20 of my questions correctly I really don’t want the 21st question to have a gross gross
mistake that’s what I mean by by reliability or like let’s suppose I upload some documents some financial
documents suppose they say something I want you to do some analysis and to make some conclusion and I want to take action on this basis on this conclusion
and it’s like it’s not a super hard task and the model these models clearly succeed on this task most of the time
but because they don’t succeed all the time and if it’s a consequential decision I actually can’t trust the model any of those times and I have to
verify the answer somehow so that’s how I Define reliability it’s very similar to the self-driving situation right if
you have a self-driving car and it’s like does things mostly well that’s not
good enough situation is not as Extreme as with a cell driving car but that’s what I mean by reliability my perception
reliability is that a um to your point it goes up with model scale but also it goes up in if you tune for specific in
uh use cases or instances or data sets and so there is that trade-off in terms of size
versus uh you know specialized fine-tuning versus reliability so
certainly people who care about some specific application have every incentive to get the smallest model
working well enough I think that’s true it’s undeniable I think anyone who cares
about a specific application will want the smallest model for it that’s self-evident I do think though that as
models continue to get larger and better then they will unlock new and
unprecedentedly valuable applications so yeah the small models will have their Niche for the less interesting
applications which are still very useful and then the bigger models will be delivering on applications okay let’s
let’s pick an example consider the task of producing good legal advice it’s
really valuable if you can really trust the answer maybe you need a much bigger model for it but it justifies the cost
there’s been a lot of investment this year uh at the 7B in particular but 7B
13B 34b sizes do you do you think continued research at those scales is
wasted no of course not I mean I think that in the kind of Med like medium term
medium term by I time scale anyway there will be an ecosystem there will be
different uses for different model sizes there will be plenty of people who are very excited for whom it’s the best 7B
model is good enough they’ll be very happy with it and then there’ll be very
plenty of very very exciting and amazing applications for which it won’t be enough I think that’s all I mean I think
the big models will will be better than the small models but not all applications will justify the cost of a
of a large model what do you think the role of Open Source is in this ecosystem
well open source is complicated I’ll describe to you my mental picture I think that in the near term open source
is just helping companies produce useful like let’s see why would one want
to have an open source to use an open source model instead of a Clos Source model that’s hosted by some other
company I mean I think it’s very valid to want to be the final decider
on the exact way in which you want your model to be used and for you to make the
decision of exactly how you want the model to be used in which use case you wish to support and I think there’s
going to be a lot of demand for open source models and I think there will be quite a few companies that will use them
and I’d imagine that will be the case in the near term I would say in the long run I think the situation with open
source models will become more complicated and I’m not sure what the right answer is there right now it’s a
little bit difficult to imagine so we need to put our future hat maybe futurist hat it’s not too hard
to get into sci-fi into a Sci-Fi mode when you remember that we are talking to computers and they understand us but so
far these computers these models actually not very competent they can’t do tasks at
all I do think that there will come a day where the level of capability of models
will be very high like in the end of the day intelligence is power right right
Roles & Boundaries of Open Source in the AI Ecosystem
now these models their main impact I would say at least least popular impact is primarily around entertainment and
like simple question answer so you talk to a model about this is so cool you produce some images you had a
conversation maybe you had some question you could answer it but it’s very different from completing some large and
complicated task like what about if you had a model which could autonomously start and build a
large tech company I think if these models were open source they would have a difficult
to predict consequence like we are quite far from these models right now and by quite far I mean by by it time scale but
still like this is not what you’re talking about but the day will come when you have models which can do science
autonomously like be deliver on big science projects and it becomes more complicated
as to whether it is desirable that models of such power should be open
I think the argument there is a lot less clearcut a lot less straightforward compared to the current
level models which are very useful and I think it’s fantastic that the current level models have been built so
like that is maybe maybe I answered a slightly bigger question rather than what is the role of Open Source models
like what’s the deal with open source and the deal is up to a certain capability it’s great but not difficult
to imagine model sufficiently powerful which will be built where it becomes a lot less obvious to the benefits of
their open source
is there signal for you that we’ve reached that level or
that we’re approaching it like what’s the what’s the boundary so I think figuring out this boundary very well is
an urgent research Pro research project I think one of the things that help is
that the closed Source models are more capable than open source models so the
Clos Source models could be studied and so on and so you’d have some experience with the generation of close Source
model and then then you know like oh these models capabilities it’s fine there’s no big deal there then in a in
like couple years the open source models catch up maybe a day will come when we going to say w like these close Source
models they’re getting a little too a little too drastic and then some other approaches needed if we have our you
know future hat on maybe let’s like think about like a several year timeline
um what are the limits you see if any in the in the near- term in scaling is it
like data token scarcity cost of compute architectural issues so the most
near-term limit to scaling is obviously data this is well known and some
research is required to address it without going into the details I’ll just say that the data limit can be
overcome and progress will continue one question I’ve heard people debate a
little bit is the degree to which the Transformer based models can be applied to sort of the full set of
areas that you’d need for AGI and if you look at the human brain for example you do have reasonably specialized systems
or allal networks be specialized systems for the visual cortex versus you know um areas of higher thought areas for
empathy or other sort of aspects of everything from personality to processing do you think that the
Transformer architectures are the main thing that will just keep going and get us there or do you think we’ll need other architectures over time so I have
to I understand precisely what you’re saying and have two answers to this question the first is that in my opinion
the best way to think about the question of Architecture is not in terms of a binary is it enough but how much effort
how what will be the cost of using this particular architecture like at this
point I don’t think anyone doubts that the Transformer architecture can do amazing things but maybe something else
maybe some modification could have have some computer efficiency benefits so so
better to think about it in terms of computer efficiency rather than in terms of can it get there at all I think at
this point the answer is obviously yes to the question about well what about the human brain then with its brain
regions I actually think that the situation there is subtle
and deceptive for the following reasons so what I believe you alluded to is the fact that the human brain has known
regions it has like it has a speech perception region it has a speech production region it has an image region
it has a face region has like all these regions and it looks like it’s specialized but you know what’s
Comparing AI Systems to Biological & Human Intelligence
interesting sometimes there are cases where very young children have severe cases of epilepsy at a young age and the
only way they figure out how to treat such children is by removing half of their
brain because it happened at such a young age these children grow grow up to
be pretty functional adults and they have all the same brain regions but they are somehow compressed onto one
hemisphere so maybe some you know information processing efficiency is
lost it’s a very traumatic thing to experience but somehow all these brain regions rearrange themselves there is
another experiment where that which was done maybe 30 or 40 years ago on ferrets
so the ferret is a small animal it’s a pretty mean experiment they took the optic nerve of the feret which comes
from its eye and attached it to its auditory cortex
so now the inputs from the eye starts to map to the speech processing area of the brain and then they recorded different
neurons after it had a few days of learning to C and they found neurons in the auditory cortex which were very
similar to the visual cortex or vice versa it was either they mapped the eye to the ear to the auditory cortex or the
ear to the visual cortex but something like this has happened these are fairly well-known ideas in AI that the cortex
of humans and animals are extremely uniform and so that further supports the a like you just need one big uniform
architecture so yeah in general it seems like every biological system is reasonably lazy in terms of taking one
system and then reproducing it and then reusing it in different ways and that’s true of everything from DNA in coding you know there’s 20 amino acids and
protein sequences and so everything is made out of the same 20 amino acids on through to uh to your point sort of how
you think about tissue architectures so it’s remarkable that that carries over into the digital world as well depending on the you use I mean the way I see it
is that this is an indication that from a technological point of view we are very much on the right track because you
have all these interesting analogies between human intelligence and biological intelligence and artificial
intelligence we’ve got artificial neurons biological neurons unified brain architecture for
biological intelligence unified neural network architecture for artificial intelligence at what point do you think
we should start thinking about these systems in digital life I can answer that question I think that will happen
when those systems become reliable in such a way as to be very autonomous
right now those systems are clearly not autonomous they’re inching there but they’re not and that makes them a lot
less useful too because you can’t ask it hey like do my homework or do my taxes or you see what I mean so the usefulness
is greatly limited as the usefulness increases they will indeed become more like artificial life which is also makes
it more I would argue um trepidacious right like if you imagine
actual artificial life with brains that are smarter than humans go gosh that’s
like that seems pretty Monumental why is your uh definition based on autonomy
because you know if you often look at the definition of biological life it has to do with reproductive
capability plus I guess some form of autonomy right like a virus isn’t really necessarily considered alive much of the
time right but a bacteria is and you could imagine situations where you have
um a symbiotic relation a ships or other things where something can’t really quite function autonomously but it’s still considered a life form so I’m a
little bit curious about autonomy being the definition versus some of these other aspects well I mean definitions
are chosen for our convenience and it’s a matter of debate in my opinion
technology already has the reproduction the reproductive function right and if you look at for examp I don’t know if
you seen those images of the evolution of cell phones and then smartphones over the past 25 years you got this like what
almost looks like an evolutionary tree or the evolution of cars over the past Century so technology is already reproducing using the minds of people
who copy ideas from previous generation of technology so I claim that the reproduction is already there the
autonomy piece I claim is not and indeed I also agree that there is no autonomous
reproduction but that would be like can you imagine if you have like autonomously reproducing AIS I actually
think that that is pretty dramatic and I would say quite a scary thing
if you
have an autonomous reproducing AI if it’s is also very capable should we talk about uh super alignment yeah very much
Definition of Digital Life
so can you um just sort of Define it and then you know we were talking about what the boundary is for we when we when you
feel we need to begin to worry about uh these capabilities being in in open source like what is super alignment and
like why invest in it now the answer to your question
really depends to where you think AI is headed you just try to imagine look into
the future which is of course a very difficult thing to do but let’s make let’s let’s try to do it anyway where do
we think things will be in five years or in 10 years mean progress has been really stunning over the past few years
maybe it will be a little bit slower but still if you if you extrapolate this kind of progress you’ll be in a very
very different place in 5 years L Al on 10 years it doesn’t seem implausible it
doesn’t doesn’t seem at all implausible that we will have computers data centers that are much
smarter than people and by smarter I don’t mean just have more memory or have more knowledge but I also have mean have
deeper insight into the same subjects that we people are studying and looking
into it means learn even faster than people like what could such AIS do I
don’t know certainly if such an AI were the basis of some artificial life it would be well how do you even think
about it if you have some very powerful data center that’s also alive in a sense
that’s what you’re talking about and when I imagine this world I my reaction is Gush this is very unpredictable
what’s going to happen very unpredictable but the bare minimum but there is a bare minimum which we can
articulate that if such super if such very very
intelligent super intelligent data centers buil being built at all we want those data centers to have warm and
positive feelings towards people towards Humanity because those this is going to
be nonhuman life in a sense potentially it could be potentially be that and so I
Super Alignment & Creating Pro Human AI

would want that any instance of such super intelligence to have warm
feelings towards humanity and so this is what we doing with the super alignment project we saying hey if if you just
allow yourself if you just accept that the progress that we’ve seen maybe it will be slower but it will
continue if you allow yourself that then can you can start doing
productive work today to build the science so that we will be able to handle the problem of
controlling such Future Super intelligence of imprinting onto them a
strong desire desire to be nice and kind to people because those data centers
right they’ll be they’ll be really quite powerful you know there’ll probably be many of them they will be the world will
be very complicated but somehow to the extent that they are autonomous to the extent that they are
agents to the extent they are beings I want them to be to be pro-social Pro Human Social
that’s the goal
what do you think is the likelihood of that coal I mean some of
it it feels like a a outcome you can hopefully affect right but uh are we are
we likely to have pro-social AIS that we are friends with individually or you
know as a species well I mean friends be I think that that part is not
necessary the the the Friendship piece I think is optional but I do think that we want to have very Pro social AI I think
it’s I think it’s possible I don’t think it’s guaranteed but I think it’s possible possible I think it’s going to be possible and the possibility of that
will increase in so far as more and more people allow themselves to look into the
future into the five to 10 year future and just ask yourself
what what do you expect AI to be able to do then how capable do you expect it to
be then and I think that with each passing year if indeed AI continues to improve
and as people get to experience because right now you’re talking making arguments but if you actually get to
experience oh gosh the AI from last year which was really helpful this year puts the previous one
to shame and you go okay and then one year later and one starting to do
science the AI software engineer is starting to get really quite good let’s say I think that you create a lot more
desire in people for what you just described for the
future super intelligence to indeed be very pro-social you know I think there going to be a lot of disagreement it’s
going to be a lot of political questions but I think that as people see AI actually getting better as people
experience it the desire for the pro-social super intelligence the
humanity loving super intelligence you know as much as this is as as much as it can be done will increase and on the
scientific problem you know I think right now it’s still being an area where not that many people were working
on are AI are getting powerful enough you can really start studying it productively we’ll have some very
exciting research to to share soon but I would say that’s the big
picture situation here just really it really boils down to look at what you’ve experienced with AI up until now ask
yourself like is it slowing down will it slow down next year like we will see and
we’ll experience it again and again and I think it will keep keep and what needs to be done will keep becoming clear do you think we’re just on an
accelerative path because I think fundamentally if you look at certain technology waves they tend to inflect
and then accelerate versus decelerate and so it really feels like we’re in an acceleration phase right now
versus the deceleration phase yeah I mean we are right now it is indeed the
case that we are in an acceleration phase you know it’s hard to say you know multiple forces will come
in to play some forces are accelerating forces and some forces are decelerating so for example the cost and scale are a
decelerating force the fact that our data is finite is a decelerating force
to some to some degree at least I don’t want to overstate yeah it’s kind of a within an ASM toote right like at some point you hit it but one it’s the
standard S curve right or sigal well with the data in particular I just think it won’t be it just won’t be an issue
because we’ll figure out some something else but then you might might argue like the size of the engineering project is a
accelerating Force just the complexity of management on the other hand the amount of investment is an accelerating
Force the amount of interest from people from Engineers scientists is an accelerating force and I think there is
one other accelerating force and that is the fact that biological evolution has
been able to figure it out and the fact that up until now progress in AI has had
up until this point this weird property that it’s kind of been you know it’s been very hard to execute
on but in some sense it’s also been more straightforward than one would have
expected perhaps like in some sense I don’t know much physics but my
understanding is that if you want to make progress in quantum physics or something you need to be really
intelligent and spend many years in grad school studying how these things work
whereas with AI you have people come in get up to speed quickly start making contributions quickly it has the flavor
is somehow different somehow it’s very there is some kind of there’s a lot of give to this particular area of research
and I think this is also an accelerating Force how will it all play out remains to be seen like it may be that somehow
the scale required the engineering complexity will start to make it so that the rate of progress will start to slow
down it will still continue but maybe not as quick as we had before or maybe the forces which are coming together to
Accelerating & Decelerating Forces
push it will be such that it will be as fast for maybe a few more years before it will start to slow down if at all
that’s that would be my articulation here Ilia this has been a great conversation thanks for joining us thank
you so much for the conversation I really enjoyed it find us on Twitter at no prior pod subscribe to our YouTube
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