September 13, 2023 • 20 mins
Open AI’s Sam Altman sits down with Azeem Azhar to give his perspective on the evolution of artificial intelligence and its impact on politics, education and inequality.
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Speaker 1 (00:07):
As artificial intelligence surge is ahead, Can we trust open
ai and its boss Sam Altman. I'm a Zimazar and
in this episode I'm talking to Sam. Welcome to the
Exponentially podcast Now. Sam is a rock and roll star
(00:27):
of AI. He runs open ai and they built chat GPT.
He's raised billions of dollars from Microsoft. His early backers
include Elon Musk and Reid Hoffman. It's been quite the journey,
but the more we know about AI, the more questions
of technology raises. I caught up with Sam at the
beginning of a world tour that would cover twenty countries
in just thirty days. We spoke to each other at
(00:49):
University College London in front of a live audience of
nearly one thousand people. You must be rushed off your feet.
You're in the middle of a enormous world tool. How
are you doing?
Speaker 2 (01:02):
It's been super great and I didn't I wasn't sure
how much fun I was going to have. I really
wanted to do it because I think this sort of
San Francisco echo chamber is not a great thing and
I have never found a replacement for getting my airplanes
and meeting people and the feedback We've gotten about what
people want us to do, how they're thinking about AI,
what they're excited about, what they're nervous about. It's been
(01:23):
even more useful than I expected, and I've had a
great time.
Speaker 1 (01:25):
I've seen you taking notes by hand in a notebook
because you hear it from people as well.
Speaker 2 (01:30):
BE still taking my notes by hand. I do my
to do this by hand.
Speaker 1 (01:33):
There's going to be a lesson in there for many
of us.
Speaker 2 (01:36):
I think there's probably no lesson.
Speaker 1 (01:40):
When you started open ai in twenty fifteen, did you
imagine that within just a few years you would almost
find necessity you have to get on a plane fly
around the world to listen to people from every continent.
Speaker 2 (01:54):
I've always tried to do this when I was running
my combinator, I would try to fly around and meet
people a lot. I think it's really important. I also
like I think most of my important insights come while
traveling in some way, and you get very different perspectives.
And certainly when we started opening I thought it probably
wasn't going to work. But if it did work, then
I thought like it would be an impactful technology and
(02:15):
getting input from the world would be a really important thing.
To do.
Speaker 1 (02:18):
You're in an unprecedented position right now. In many cases,
in the Silicon Valley model that the founder of a
business like this owns a lot of equity, takes a
salary as well, has a financial upsided. You don't have
any of that. You just draw enough for your health insurance.
So what is the inner drive for you given the challenge,
(02:38):
given that the demands are on your time, your energy.
Speaker 2 (02:42):
I can't think of any more exciting thing. I mean,
I hope this is like self explanatory, but I can't
think of anything more exciting to work on. I feel
like extremely privileged to be at this moment in history,
and more than that, like working with this particular team.
I don't know, like how I would possibly rather spend
the days. I was very fortunate. I made a bunch
of money very early in my career, so I don't
think it's some like great noble thing.
Speaker 1 (03:03):
I was thinking about what does it mean to face
these types of exciting challenges? And some of them are
January sort of intellectually and exciting. Some are really hard,
thorny problems. And you are an unprecedented position. Do you
have mentors? Are there people you're learning from?
Speaker 2 (03:19):
I feel like super fortunate to have had great mentors.
But I also think it's important not to try to
learn too much from other people and sort of do
things your own way. One of the magical things about
Silicon Valley is how much people care about mentorship and teaching,
and I've gotten way more of my first shore there.
Speaker 1 (03:37):
If we pick out one or two lessons from the
great mentals you've had, what would they be.
Speaker 2 (03:41):
Paul Graham, who ran my commentrator before it started, ran
it before I did, I think did more to teach
people about how startups work, very heavily from what it
takes to make a high functioning org and then traps
you want to avoid. There. Certainly learning from Elon about
what is possible to do and that you don't need
to accept that part technology is not something to ignore.
(04:03):
That's been super valuable.
Speaker 1 (04:05):
And I can see both of those lessons in open
AI and in what you have shipped and have been
shipping for a few years. When we last spoke a
couple of years ago, you were talking about these large
language models, and we're currently on GPT four, but back
then the state of the art was GPT three, and
you said to me that the gap between gpt E
(04:26):
two and GBT three was just a baby step on
the continue. It's just a little baby step. When you
now look at GPT four, would you say that's another
baby step.
Speaker 2 (04:37):
It'll look like that in retrospect. I think.
Speaker 1 (04:42):
That's a nature of the exponential in retrospect. But when
we're living through it, it.
Speaker 2 (04:46):
Felt like a big jump for a little while, and
now people are it's very much like, what have you've
done for me lately? Where's GPT five? And that's fine.
That's the way of the world. That's how it's supposed
to go. People get used to anything. We established new
baselines very quick.
Speaker 1 (05:00):
I'm curious what were the insights that you gained in
developing GPT for and in the months following its release
that were different to the ones from the previous models
that you released.
Speaker 2 (05:11):
We finished training GPT for like eight ish months before
you released it, I think, and that was by far
the longest we've ever spent on a model pre release.
One of the things that we had learned with GPD
three was all of the ways these things break down
once you put them out in the wild. We think
it is really important to deploy models incrementally, to give
the world time to adapt, understand what we think is
(05:33):
going to happen, what might happen, to give people time
to figure out what the risks are, what the benefits are,
what the rules should be. We don't want to put
out a model that we know has a bunch of problems.
So we spent more time applying the lessons from the
earlier versions of GPT three to this one, and it's
been nice to see it's behaving as advertised most of
the time, much more of a time than before. So
that was a lesson, which is that if we really
(05:54):
spent a lot of time on alignment, auditing, testing our
whole safety system, we can make a lot of progress.
Speaker 1 (06:00):
So you build this model, it's an incredibly complicated machine.
GPT three, the precursor, had one hundred and seventy five
billion parameters, which I think of as sliders on a
graphic equalizer. It's a lot of configuration, and GPT four
is larger still, although you haven't formally said how much larger.
How do you take that machine and get it to
(06:24):
do what we want it to do and not do
what we don't want to do. That's the alignment problem,
and that's where you've spent this eight months.
Speaker 2 (06:32):
Yeah, so I want to be clear on this. Just
because we're able to align GPT four does not mean
we're like out of the woods, not even close, as
I hope is obvious. We have a huge amount of
work to do to figure out how to we're going
to align superintelligence and much more powerful systems than what
we have now. And I worry that when we say, hey,
we can align GPT four pretty well, people think we
(06:53):
think we've solved the problem. We don't. But it is
I think remarkable that we can take the base model
of GPT four, which if you use it, you'd be like,
this is not very impressive, or it's the least extremely
difficult to use, and with so little effort we can
do URLHF and get the model to be so usable
and so aligned.
Speaker 1 (07:10):
And URLHF is reinforcement learning with human feedback, which I
think is the way that you get people to answer
questions from GPT four and tell it when it's been
good and when it's not met expectations, and.
Speaker 2 (07:22):
It turns and it's very tiny amounts of feedback, and
it's very unsophisticated too. It's really just like thumbs up
thumbs down, and the fact that this works, I think
is quite remarkable.
Speaker 1 (07:31):
You've said you're not training GPT five right now, and
I was curious about why that was. Was it that
there's not enough data. Was it work that there aren't
enough computer chips to train it on. Was it that
you saw things going on when you were making GPT
four happen that you thought you need to figure out
how to tackle these before we build the next.
Speaker 2 (07:53):
These models are very difficult to build, Like the time
between GPT three and four was almost three years. It
just takes a while. There's a lot of research to
go do. There's also a lot of other stuff we
want to do with GPT four, not that it's done.
We want to study post training a lot, We want
to expand it in all sorts of ways. The fact
that they can ship an iPhone every year is incredible to me.
But we're just going to be on a longer than
(08:13):
one year cadence.
Speaker 1 (08:14):
You said that there's more research to be done, and
there are a number of very well storied AI researchers
who have said that large language models are limited. They
will not get us to the next performance increase that
you can't build artificial general intelligence with llms. Do you
agree with that?
Speaker 2 (08:33):
I mean, first of all, I think most of those
commentators have been horribly wrong about what lllms are going
to be able to do, and a lot of them
have now switched to saying, well, it's not that lllms
aren't going to work, it's that they work too well
and they're too dangerous and we've got to stop them.
Or others have just said, well, you know, it's all
still like a parlor trick and this is not any
(08:54):
real learning. Some of the more sophisticated ones say, Okay,
lllms work better than expect, but they're not going to
get all the way to AGI in the current paradigm,
and that we agree with. So I think we absolutely
should push as far as we can in the current paradigm,
but we're hard at work trying to figure out the
next paradigm. The thing I'm personally most excited about, maybe
of the whole AGI world, is that these models at
(09:17):
some point are going to help us discover new science
fast and in really meaningful ways. But I think the
fastest way to get there is to go beyond the
GPT paradigm models that can generate new knowledge, models that
can come up with new ideas, models that can sort
of just figure things out that they haven't seen before,
and that's going to require new work.
Speaker 1 (09:35):
I've been using GPT four obsessively. I'm happy to hear
the last few months. It's quite something, and I do
feel that it's sometimes coming up with new knowledge. I
haven't done a robust test that I'm sitting here as
somebody who works in research, and I'm thinking I have
learned something new here. So what's going on?
Speaker 2 (09:51):
Yeah, I mean there's like glimpses of it, right, and
it can do small things, but it can't self correct
and stay on the rails enough where you can just say, hey,
GPT four, please go cure cancer. That's not going to happen, right,
but it would be nice if we had a system
that could do that.
Speaker 1 (10:11):
Obviously, we're talking about how powerful these technologies are and
there will also be downsides, and let's start with one
that is quite approximate today. So GPT for these other
large language models are very very good at producing text
human sounding text, and so it opens up that risk
of misinformation and disinformation in particular, as we head in
(10:33):
towards important elections in the United States. How serious a
risk do you see that.
Speaker 2 (10:40):
I do think disinformation is becoming a bigger challenge in
the world. And also I think it's a somewhat fraught category.
You know, we've labeled things as disinformation as a society
that turned out to be true, right, We've kicked people
off platforms for saying things that turned out to be true.
And we're going to have to find a balance where
we preserve the ability to be wrong in exchange for
(11:03):
sometimes exposing important information and without saying everything is intentional
disinformation used to manipulate, but people that are intentionally being
wrong in order to manipulate, I think is a real problem,
and we've seen more of that with technology.
Speaker 1 (11:19):
I mean three point five in particular is really quite good.
So if there was going to be a disinformation wave,
wouldn't it have come?
Speaker 2 (11:26):
So I was going to get there. I think humans
are already good at making disinformation, and maybe the GPT
models make it easier, but that's not the thing I'm
afraid of. Also, I think it's tempting to compare AI
and social media, but they're super different. Like you can
generate all the disinformation you want with GPT four, but
if it's just for yourself and it's not being spread,
it's not going to do much. I think what is
worth considering is what's going to be different with AI
(11:49):
and where is it going to plug into channels that
could help it spread. And I think one thing that
will be different is the interactive, personalized persuasive ability of
these systems.
Speaker 1 (12:00):
That I might get a robocall on my phone, I
pick it up, and then the messaging in there is
really attuned to me, so it's emotionally resonant, really realistic,
and read out by machinery.
Speaker 2 (12:12):
That's what I think the new challenge will be, and
there's a lot to do there. We can build refusals
into the models, we can build monitoring systems, so people
can't do that at scale, But we're going to have
powerful open source models out in the world, and those
the open eye techniques of what we can do on
our own systems won't work the same.
Speaker 1 (12:31):
Right, So just to clarify that point, right, because with
open AI, you have an API and you have a
named customer, so if you see bad behavior, you can
turn that person off, whereas an open source model could
be run by anyone on their desktop computer at some point,
and it's actually much harder. There's a proliferation problem. Yeah,
but solving this can't just be open eyes reams, right.
(12:53):
You must be asking for help.
Speaker 2 (12:55):
There's regulatory things that we can do that will help some.
The real solution here is to you educate people about
what's happening. We've been through this before. When photoshop first
became popular. There was a brief period of time where
people like seeing as believing it's got to be real,
and then people learn quickly that it's not. And some
people still fall for this stuff. But on the whole,
if you've see an image, you know it might be
(13:15):
digitally manipulated. Well understood. The same thing will happen with
these new technologies. But the sooner we can educate people
about it, because the emotional resonance is going to be
so much higher, I think the better.
Speaker 1 (13:33):
Let's turn to education. We're at a global university here,
and of course education is closely connected to the job market.
When we previous times, we've seen powerful new technologies emerge.
They have really impacted power dynamics between workers and employers.
I think back to the late eighteenth century there was
Engels Pause, the point in time in England where GDP
(13:57):
went up the worker wages were stagnant. Looking at Ai,
we might see something similar, and neither you nor I,
I think want historians of the future to be describing
Altman's pause, when wages suffered under a point of wage
pressure because of the new technology. What are the interventions
that are needed to make sure that there is a
(14:17):
equitable sharing of the gains from the technology.
Speaker 2 (14:20):
Well, first of all, we just need gains. We need growth.
I think one of the problems in the developed the
world right now is we don't have enough sustainable growth,
and that's causing all sorts of problems. So I'm excited
that this technology can bring the missing productivity gains. In
the last few decades back, some technologies are reduced inequality
by nature, and some enhance it. I'm not totally sure
(14:42):
this one's going to go, but I think this is
a technology that the shape of which is to reduce inequality.
My basic model of the world is that the cost
of intelligence and the cost of energy are the two
limiting inputs, and if you can make those dramatically cheaper,
dramatically more accessible, that does more to help poor people
than rich people. Frankly, all thought to help everyone a lot.
(15:04):
This technology will lift all of the world up. Most
people in this room, if they need some sort of
intellectual cognitive labor, they can afford it. Most people in
the world often can't. And if we can commoditize that,
I think that is an equalizing force and an important one.
Can I say one, YEA cool? I think there will
be way more jobs on the other side of this
technological revolution. I'm not a believer that this is the
(15:25):
end of work at all. I think like we will
look back at the mundane jobs many of us do
today and be like, that was really bad. This is
much better and more interesting now. I still think we'll
have to think about distribution of wealth differently than we
do today, and that's fine. We actually think about that
somewhat differently after every technological revolution. I also think, given
the shape of this particular one, the way that access
(15:48):
to these systems is distributed fairly is going to be
a very challenging question.
Speaker 1 (15:52):
Right, And you know, I think access is so important.
And in those previous revolutions, the technology revolutions, the thing
that drew us together was political structures I mean it
was trade unionism and labor collectives in the late nineteenth century.
When we look at something like AI, can you imagine
the types of structures that would be needed for recognizing
and redistributing the gains from unpaid or low paid work
(16:16):
that's often not recognized, for example, the work that women
are doing around the world.
Speaker 2 (16:21):
I think there will be an important and overdo shift
in the kinds of work that we value, and providing
human connection to people will all of a sudden be
as I think should be one of the most valued
types of work, happening all kinds of different ways.
Speaker 1 (16:37):
So when you reflect on how AI has progressed to
this point, what lessons, if any, can we draw about
the journey towards artificial superintelligence and how that might emerge.
This is the idea of having an artificial intelligence that
is more capable than humans in every and all domains.
Speaker 2 (16:57):
It's hard to give a short answer this question, but
you've got I think there's a lot of things that
we've learned so far, but one of them is that
a we have an algorithm that can genuinely truly learn
and be It gets predictably better with skill, and these
are two remarkable facts put together, and I think even
though we think about that every day, I suspect we
(17:19):
don't quite feel how important that is. One observation is
that it's just going to keep going. Another observation is
that we will have these discontinuous increases occasionally where we
figure out something new. And a third is that I
think the way that I used to think about heading
towards superintelligence is we were going to build this one
(17:42):
extremely capable system, and there were a bunch of challenge
safety challenges with that, and it was sort of a
world that was going to feel quite unstable. But I
think we now see a path where we very much
build these tools, not creatures, tools that get more and
more powerful, and they're there's billions of copies, trillions of
copies being used in the world, helping individual people just
(18:06):
be way more effective, capable of doing way more The
amount of output that one person can have can dramatically increase.
And where the super intelligence emerges is not just the
capability of our biggest single neural network, but all of
the new science we're discovering, all of the new things
we're creating.
Speaker 1 (18:24):
And the interactions between these billions and trillions of other systems.
Speaker 2 (18:28):
The society we build up, which is AI assisted humans
using these tools to build up this society, and the
knowledge and the technology and the institutions and the norms
that we have, and that vision of living with superintelligence
seems to me way better all around and a way
more exciting future for me, for all of you. I hope,
hope you agree on this than that. Kind of like
(18:49):
one super briin, you.
Speaker 1 (18:50):
Really brought a visionary picture to us today. Thank you
so much, thank you, thank you. Reflecting on this conversation
with Sam, I'm struck by how willing he is to
engage with the profound risks that AI could pose. Maybe
this is because the technology is evolving so quickly that
it's hard even for someone in his position to figure
(19:13):
out what comes next. One thing remains true. I believe
it isn't just down to the tech bosses to work
out how this technology can help us. Instead, this is
a process we should all have a saying. Thanks for
listening to the exponentially podcast. If you enjoy the show,
(19:35):
please leave a review or rating. It really does help
others find us. The exponentially podcast is presented by me
Azeem as are. The sound designer is Will Horricks. The
research was led by Chloe Ippaer and music composed by
Emily Green and John Zarcone. The show is produced by
Frederick Cassella, Maria Garrilov and me Azeem As are special
(19:57):
thanks to Sage Bauman, Jeff Grocott and Magnus Henrikson. The
executive producers are Andrew Barden, Adam Camiski and Kyle Kramer.
David Ravella is the managing editor. Exponentially was created by
Frederick Cassella and is an Eat the Pie I plus
one limited production in association with Bloomberg LLC.
As artificial intelligence surge is ahead, Can we trust open
ai and its boss Sam Altman. I'm a Zimazar and
in this episode I'm talking to Sam. Welcome to the
Exponentially podcast Now. Sam is a rock and roll star
(00:27):
of AI. He runs open ai and they built chat GPT.
He's raised billions of dollars from Microsoft. His early backers
include Elon Musk and Reid Hoffman. It's been quite the journey,
but the more we know about AI, the more questions
of technology raises. I caught up with Sam at the
beginning of a world tour that would cover twenty countries
in just thirty days. We spoke to each other at
(00:49):
University College London in front of a live audience of
nearly one thousand people. You must be rushed off your feet.
You're in the middle of a enormous world tool. How
are you doing?
Speaker 2 (01:02):
It's been super great and I didn't I wasn't sure
how much fun I was going to have. I really
wanted to do it because I think this sort of
San Francisco echo chamber is not a great thing and
I have never found a replacement for getting my airplanes
and meeting people and the feedback We've gotten about what
people want us to do, how they're thinking about AI,
what they're excited about, what they're nervous about. It's been
(01:23):
even more useful than I expected, and I've had a
great time.
Speaker 1 (01:25):
I've seen you taking notes by hand in a notebook
because you hear it from people as well.
Speaker 2 (01:30):
BE still taking my notes by hand. I do my
to do this by hand.
Speaker 1 (01:33):
There's going to be a lesson in there for many
of us.
Speaker 2 (01:36):
I think there's probably no lesson.
Speaker 1 (01:40):
When you started open ai in twenty fifteen, did you
imagine that within just a few years you would almost
find necessity you have to get on a plane fly
around the world to listen to people from every continent.
Speaker 2 (01:54):
I've always tried to do this when I was running
my combinator, I would try to fly around and meet
people a lot. I think it's really important. I also
like I think most of my important insights come while
traveling in some way, and you get very different perspectives.
And certainly when we started opening I thought it probably
wasn't going to work. But if it did work, then
I thought like it would be an impactful technology and
(02:15):
getting input from the world would be a really important thing.
To do.
Speaker 1 (02:18):
You're in an unprecedented position right now. In many cases,
in the Silicon Valley model that the founder of a
business like this owns a lot of equity, takes a
salary as well, has a financial upsided. You don't have
any of that. You just draw enough for your health insurance.
So what is the inner drive for you given the challenge,
(02:38):
given that the demands are on your time, your energy.
Speaker 2 (02:42):
I can't think of any more exciting thing. I mean,
I hope this is like self explanatory, but I can't
think of anything more exciting to work on. I feel
like extremely privileged to be at this moment in history,
and more than that, like working with this particular team.
I don't know, like how I would possibly rather spend
the days. I was very fortunate. I made a bunch
of money very early in my career, so I don't
think it's some like great noble thing.
Speaker 1 (03:03):
I was thinking about what does it mean to face
these types of exciting challenges? And some of them are
January sort of intellectually and exciting. Some are really hard,
thorny problems. And you are an unprecedented position. Do you
have mentors? Are there people you're learning from?
Speaker 2 (03:19):
I feel like super fortunate to have had great mentors.
But I also think it's important not to try to
learn too much from other people and sort of do
things your own way. One of the magical things about
Silicon Valley is how much people care about mentorship and teaching,
and I've gotten way more of my first shore there.
Speaker 1 (03:37):
If we pick out one or two lessons from the
great mentals you've had, what would they be.
Speaker 2 (03:41):
Paul Graham, who ran my commentrator before it started, ran
it before I did, I think did more to teach
people about how startups work, very heavily from what it
takes to make a high functioning org and then traps
you want to avoid. There. Certainly learning from Elon about
what is possible to do and that you don't need
to accept that part technology is not something to ignore.
(04:03):
That's been super valuable.
Speaker 1 (04:05):
And I can see both of those lessons in open
AI and in what you have shipped and have been
shipping for a few years. When we last spoke a
couple of years ago, you were talking about these large
language models, and we're currently on GPT four, but back
then the state of the art was GPT three, and
you said to me that the gap between gpt E
(04:26):
two and GBT three was just a baby step on
the continue. It's just a little baby step. When you
now look at GPT four, would you say that's another
baby step.
Speaker 2 (04:37):
It'll look like that in retrospect. I think.
Speaker 1 (04:42):
That's a nature of the exponential in retrospect. But when
we're living through it, it.
Speaker 2 (04:46):
Felt like a big jump for a little while, and
now people are it's very much like, what have you've
done for me lately? Where's GPT five? And that's fine.
That's the way of the world. That's how it's supposed
to go. People get used to anything. We established new
baselines very quick.
Speaker 1 (05:00):
I'm curious what were the insights that you gained in
developing GPT for and in the months following its release
that were different to the ones from the previous models
that you released.
Speaker 2 (05:11):
We finished training GPT for like eight ish months before
you released it, I think, and that was by far
the longest we've ever spent on a model pre release.
One of the things that we had learned with GPD
three was all of the ways these things break down
once you put them out in the wild. We think
it is really important to deploy models incrementally, to give
the world time to adapt, understand what we think is
(05:33):
going to happen, what might happen, to give people time
to figure out what the risks are, what the benefits are,
what the rules should be. We don't want to put
out a model that we know has a bunch of problems.
So we spent more time applying the lessons from the
earlier versions of GPT three to this one, and it's
been nice to see it's behaving as advertised most of
the time, much more of a time than before. So
that was a lesson, which is that if we really
(05:54):
spent a lot of time on alignment, auditing, testing our
whole safety system, we can make a lot of progress.
Speaker 1 (06:00):
So you build this model, it's an incredibly complicated machine.
GPT three, the precursor, had one hundred and seventy five
billion parameters, which I think of as sliders on a
graphic equalizer. It's a lot of configuration, and GPT four
is larger still, although you haven't formally said how much larger.
How do you take that machine and get it to
(06:24):
do what we want it to do and not do
what we don't want to do. That's the alignment problem,
and that's where you've spent this eight months.
Speaker 2 (06:32):
Yeah, so I want to be clear on this. Just
because we're able to align GPT four does not mean
we're like out of the woods, not even close, as
I hope is obvious. We have a huge amount of
work to do to figure out how to we're going
to align superintelligence and much more powerful systems than what
we have now. And I worry that when we say, hey,
we can align GPT four pretty well, people think we
(06:53):
think we've solved the problem. We don't. But it is
I think remarkable that we can take the base model
of GPT four, which if you use it, you'd be like,
this is not very impressive, or it's the least extremely
difficult to use, and with so little effort we can
do URLHF and get the model to be so usable
and so aligned.
Speaker 1 (07:10):
And URLHF is reinforcement learning with human feedback, which I
think is the way that you get people to answer
questions from GPT four and tell it when it's been
good and when it's not met expectations, and.
Speaker 2 (07:22):
It turns and it's very tiny amounts of feedback, and
it's very unsophisticated too. It's really just like thumbs up
thumbs down, and the fact that this works, I think
is quite remarkable.
Speaker 1 (07:31):
You've said you're not training GPT five right now, and
I was curious about why that was. Was it that
there's not enough data. Was it work that there aren't
enough computer chips to train it on. Was it that
you saw things going on when you were making GPT
four happen that you thought you need to figure out
how to tackle these before we build the next.
Speaker 2 (07:53):
These models are very difficult to build, Like the time
between GPT three and four was almost three years. It
just takes a while. There's a lot of research to
go do. There's also a lot of other stuff we
want to do with GPT four, not that it's done.
We want to study post training a lot, We want
to expand it in all sorts of ways. The fact
that they can ship an iPhone every year is incredible to me.
But we're just going to be on a longer than
(08:13):
one year cadence.
Speaker 1 (08:14):
You said that there's more research to be done, and
there are a number of very well storied AI researchers
who have said that large language models are limited. They
will not get us to the next performance increase that
you can't build artificial general intelligence with llms. Do you
agree with that?
Speaker 2 (08:33):
I mean, first of all, I think most of those
commentators have been horribly wrong about what lllms are going
to be able to do, and a lot of them
have now switched to saying, well, it's not that lllms
aren't going to work, it's that they work too well
and they're too dangerous and we've got to stop them.
Or others have just said, well, you know, it's all
still like a parlor trick and this is not any
(08:54):
real learning. Some of the more sophisticated ones say, Okay,
lllms work better than expect, but they're not going to
get all the way to AGI in the current paradigm,
and that we agree with. So I think we absolutely
should push as far as we can in the current paradigm,
but we're hard at work trying to figure out the
next paradigm. The thing I'm personally most excited about, maybe
of the whole AGI world, is that these models at
(09:17):
some point are going to help us discover new science
fast and in really meaningful ways. But I think the
fastest way to get there is to go beyond the
GPT paradigm models that can generate new knowledge, models that
can come up with new ideas, models that can sort
of just figure things out that they haven't seen before,
and that's going to require new work.
Speaker 1 (09:35):
I've been using GPT four obsessively. I'm happy to hear
the last few months. It's quite something, and I do
feel that it's sometimes coming up with new knowledge. I
haven't done a robust test that I'm sitting here as
somebody who works in research, and I'm thinking I have
learned something new here. So what's going on?
Speaker 2 (09:51):
Yeah, I mean there's like glimpses of it, right, and
it can do small things, but it can't self correct
and stay on the rails enough where you can just say, hey,
GPT four, please go cure cancer. That's not going to happen, right,
but it would be nice if we had a system
that could do that.
Speaker 1 (10:11):
Obviously, we're talking about how powerful these technologies are and
there will also be downsides, and let's start with one
that is quite approximate today. So GPT for these other
large language models are very very good at producing text
human sounding text, and so it opens up that risk
of misinformation and disinformation in particular, as we head in
(10:33):
towards important elections in the United States. How serious a
risk do you see that.
Speaker 2 (10:40):
I do think disinformation is becoming a bigger challenge in
the world. And also I think it's a somewhat fraught category.
You know, we've labeled things as disinformation as a society
that turned out to be true, right, We've kicked people
off platforms for saying things that turned out to be true.
And we're going to have to find a balance where
we preserve the ability to be wrong in exchange for
(11:03):
sometimes exposing important information and without saying everything is intentional
disinformation used to manipulate, but people that are intentionally being
wrong in order to manipulate, I think is a real problem,
and we've seen more of that with technology.
Speaker 1 (11:19):
I mean three point five in particular is really quite good.
So if there was going to be a disinformation wave,
wouldn't it have come?
Speaker 2 (11:26):
So I was going to get there. I think humans
are already good at making disinformation, and maybe the GPT
models make it easier, but that's not the thing I'm
afraid of. Also, I think it's tempting to compare AI
and social media, but they're super different. Like you can
generate all the disinformation you want with GPT four, but
if it's just for yourself and it's not being spread,
it's not going to do much. I think what is
worth considering is what's going to be different with AI
(11:49):
and where is it going to plug into channels that
could help it spread. And I think one thing that
will be different is the interactive, personalized persuasive ability of
these systems.
Speaker 1 (12:00):
That I might get a robocall on my phone, I
pick it up, and then the messaging in there is
really attuned to me, so it's emotionally resonant, really realistic,
and read out by machinery.
Speaker 2 (12:12):
That's what I think the new challenge will be, and
there's a lot to do there. We can build refusals
into the models, we can build monitoring systems, so people
can't do that at scale, But we're going to have
powerful open source models out in the world, and those
the open eye techniques of what we can do on
our own systems won't work the same.
Speaker 1 (12:31):
Right, So just to clarify that point, right, because with
open AI, you have an API and you have a
named customer, so if you see bad behavior, you can
turn that person off, whereas an open source model could
be run by anyone on their desktop computer at some point,
and it's actually much harder. There's a proliferation problem. Yeah,
but solving this can't just be open eyes reams, right.
(12:53):
You must be asking for help.
Speaker 2 (12:55):
There's regulatory things that we can do that will help some.
The real solution here is to you educate people about
what's happening. We've been through this before. When photoshop first
became popular. There was a brief period of time where
people like seeing as believing it's got to be real,
and then people learn quickly that it's not. And some
people still fall for this stuff. But on the whole,
if you've see an image, you know it might be
(13:15):
digitally manipulated. Well understood. The same thing will happen with
these new technologies. But the sooner we can educate people
about it, because the emotional resonance is going to be
so much higher, I think the better.
Speaker 1 (13:33):
Let's turn to education. We're at a global university here,
and of course education is closely connected to the job market.
When we previous times, we've seen powerful new technologies emerge.
They have really impacted power dynamics between workers and employers.
I think back to the late eighteenth century there was
Engels Pause, the point in time in England where GDP
(13:57):
went up the worker wages were stagnant. Looking at Ai,
we might see something similar, and neither you nor I,
I think want historians of the future to be describing
Altman's pause, when wages suffered under a point of wage
pressure because of the new technology. What are the interventions
that are needed to make sure that there is a
(14:17):
equitable sharing of the gains from the technology.
Speaker 2 (14:20):
Well, first of all, we just need gains. We need growth.
I think one of the problems in the developed the
world right now is we don't have enough sustainable growth,
and that's causing all sorts of problems. So I'm excited
that this technology can bring the missing productivity gains. In
the last few decades back, some technologies are reduced inequality
by nature, and some enhance it. I'm not totally sure
(14:42):
this one's going to go, but I think this is
a technology that the shape of which is to reduce inequality.
My basic model of the world is that the cost
of intelligence and the cost of energy are the two
limiting inputs, and if you can make those dramatically cheaper,
dramatically more accessible, that does more to help poor people
than rich people. Frankly, all thought to help everyone a lot.
(15:04):
This technology will lift all of the world up. Most
people in this room, if they need some sort of
intellectual cognitive labor, they can afford it. Most people in
the world often can't. And if we can commoditize that,
I think that is an equalizing force and an important one.
Can I say one, YEA cool? I think there will
be way more jobs on the other side of this
technological revolution. I'm not a believer that this is the
(15:25):
end of work at all. I think like we will
look back at the mundane jobs many of us do
today and be like, that was really bad. This is
much better and more interesting now. I still think we'll
have to think about distribution of wealth differently than we
do today, and that's fine. We actually think about that
somewhat differently after every technological revolution. I also think, given
the shape of this particular one, the way that access
(15:48):
to these systems is distributed fairly is going to be
a very challenging question.
Speaker 1 (15:52):
Right, And you know, I think access is so important.
And in those previous revolutions, the technology revolutions, the thing
that drew us together was political structures I mean it
was trade unionism and labor collectives in the late nineteenth century.
When we look at something like AI, can you imagine
the types of structures that would be needed for recognizing
and redistributing the gains from unpaid or low paid work
(16:16):
that's often not recognized, for example, the work that women
are doing around the world.
Speaker 2 (16:21):
I think there will be an important and overdo shift
in the kinds of work that we value, and providing
human connection to people will all of a sudden be
as I think should be one of the most valued
types of work, happening all kinds of different ways.
Speaker 1 (16:37):
So when you reflect on how AI has progressed to
this point, what lessons, if any, can we draw about
the journey towards artificial superintelligence and how that might emerge.
This is the idea of having an artificial intelligence that
is more capable than humans in every and all domains.
Speaker 2 (16:57):
It's hard to give a short answer this question, but
you've got I think there's a lot of things that
we've learned so far, but one of them is that
a we have an algorithm that can genuinely truly learn
and be It gets predictably better with skill, and these
are two remarkable facts put together, and I think even
though we think about that every day, I suspect we
(17:19):
don't quite feel how important that is. One observation is
that it's just going to keep going. Another observation is
that we will have these discontinuous increases occasionally where we
figure out something new. And a third is that I
think the way that I used to think about heading
towards superintelligence is we were going to build this one
(17:42):
extremely capable system, and there were a bunch of challenge
safety challenges with that, and it was sort of a
world that was going to feel quite unstable. But I
think we now see a path where we very much
build these tools, not creatures, tools that get more and
more powerful, and they're there's billions of copies, trillions of
copies being used in the world, helping individual people just
(18:06):
be way more effective, capable of doing way more The
amount of output that one person can have can dramatically increase.
And where the super intelligence emerges is not just the
capability of our biggest single neural network, but all of
the new science we're discovering, all of the new things
we're creating.
Speaker 1 (18:24):
And the interactions between these billions and trillions of other systems.
Speaker 2 (18:28):
The society we build up, which is AI assisted humans
using these tools to build up this society, and the
knowledge and the technology and the institutions and the norms
that we have, and that vision of living with superintelligence
seems to me way better all around and a way
more exciting future for me, for all of you. I hope,
hope you agree on this than that. Kind of like
(18:49):
one super briin, you.
Speaker 1 (18:50):
Really brought a visionary picture to us today. Thank you
so much, thank you, thank you. Reflecting on this conversation
with Sam, I'm struck by how willing he is to
engage with the profound risks that AI could pose. Maybe
this is because the technology is evolving so quickly that
it's hard even for someone in his position to figure
(19:13):
out what comes next. One thing remains true. I believe
it isn't just down to the tech bosses to work
out how this technology can help us. Instead, this is
a process we should all have a saying. Thanks for
listening to the exponentially podcast. If you enjoy the show,
(19:35):
please leave a review or rating. It really does help
others find us. The exponentially podcast is presented by me
Azeem as are. The sound designer is Will Horricks. The
research was led by Chloe Ippaer and music composed by
Emily Green and John Zarcone. The show is produced by
Frederick Cassella, Maria Garrilov and me Azeem As are special
(19:57):
thanks to Sage Bauman, Jeff Grocott and Magnus Henrikson. The
executive producers are Andrew Barden, Adam Camiski and Kyle Kramer.
David Ravella is the managing editor. Exponentially was created by
Frederick Cassella and is an Eat the Pie I plus
one limited production in association with Bloomberg LLC.
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