Artificial intelligence is rapidly evolving, moving beyond simple automation to become a personalized assistant, a powerful diagnostic tool, and an individualized tutor, with profound implications for work, education, and healthcare, necessitating careful consideration of its societal impact and regulation.
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When we last chatted, I think it was
November or maybe October, you were two
weeks into the university giving you a
personal assistant. Can you share more what it's
been like having that and we may be
able to extrapolate to everyone having the equivalent
of that? So fairly recently, I woke up
earlier than usual. And up until that point,
I've been thinking, maybe I don't need the
personal assistant anymore because when I look in
my mailbox, there's only about sort of 30
things to be dealt with. But the morning
I woke up early, I discovered there were
hundreds of things to be dealt with because
my personal assistant was just dealing with them.
That was kind of essential. And when you
look at how she has learned about you
and the way you might answer questions or
how you would assess a situation, what has
it been like if she's gotten to know
you personally better? It's been good. She's getting
much better at knowing which questions I want
to answer myself, which talks I might be
interested in giving, and which talks I'm definitely
not interested in giving. She can pretty much
recognize my former students. To begin with, one
of my former students would send me mail,
and they'd get a very polite answer saying
I was busy. And I remember talking to
students. I got this answer from you. Didn't
sound like you. And so now I tell
my students, if you ever you get a
really polite answer, that's not me. Don't have
time to write the full polite answer. And
so in some ways, she's acting as your
proxy. She's learned how you see the world
and is placing that as a first filter.
How would AI develop that ability to do
the proxies to help people navigate how work
and life might change as AI is able
to automate more things? Do you see a
world where people will have many different specialized
assistants or just one that knows them? Any
thoughts on that? It's a very good question.
Why do you need a train one being
neural net to do everything? Because that's more
efficient in the long run, because you can
share what different tasks have in common. So
there's always this tension between, having a small
neural net specialized to one thing, which doesn't
have much training data. If you got enough
training data, that's a sensible thing to do.
And so we have huge amounts of training
data. It's quite sensible to have many small
neural nets, each of which is only trained
on a tiny fraction of the training data,
and a manager who decides which neural net
should answer each question. If you don't have
that much training data, it's typically better to
have one neural net that's learned on all
the training data. And then maybe after you
trained on all the training data, you might
fine tune it to be a specialist in
different domains, and that seems to be a
good compromise. Train one neural net on everything,
and then in particular domains fine tune it
for that domain. I mean, it sounds like
if I look through the history, people said,
you know, it might do this, but it
won't do a, b, c. And I think
your your answer sounds like it could be
just a matter of time and scale, maybe
data. Go back ten years and take take
anything that people said it couldn't do. It's
now doing it. And so if we fast
forward now ten years in the future, obviously,
the implications for society are huge. But on
the positive use cases, health care is one.
Tell us a little bit about how why
that is is so personally important to you
and how that could evolve over the next,
let's say, five years. What a family doctor
does, the sort of first line. The family
doctor knows quite a bit about you, maybe
knows something about your family, maybe even knows
a few things about your genetics. But she's
only seen a few thousand patients. I mean,
almost certainly, she's seen less than a hundred
thousand patients in her life. There just isn't
time. An AI doctor could have seen the
data on millions of patients, hundreds of millions
of patients, and so and also could know
about a lot about your genome, a lot
about how you integrate information from the genome
with information from tests. So you're gonna get
much better family doctors with AI. And we're
gonna get all sorts of things like that
for CAT scans and MRI scans where AI
can see all sorts of things that current
doctors don't know how to see. I brought
up that example to a a doctor who
had looked at the interaction between radiologists and
AI, and there were a few different scenarios.
So one is, you know, AI is confident
and the doctor is confident. Same diagnosis, obviously
easy. But not, I would trust the
AI. So they were doing a study on
this. But but what was interesting is if
a doctor is, you know, confident it's it's
x and and AI is confident it's y, the
doctor chooses to go with their own diagnosis.
Fair enough. Now if the doctor is not
confident and the AI is also not confident,
the doctor chooses the AI solution with the
sort of human thinking of, like, well, if
I'm not sure, I'll blame it on the
AI for being wrong. I just thought the
human nature of that feels so real and
dangerous at the same time. Yeah. I think
that's telling us more about human nature than
about what the optimal strategy is. Absolutely. And
and ways that we might misuse AI in
the human-AI interaction. The thing I know
a bit more about from a paper that's
more than a year ago now is you
take a bunch of cases that are difficult
to diagnose. So this isn't scans. This is,
you're given the description of the patient, and
the test results. And on these difficult cases,
doctors, get 40% of them right, an AI
system gets 50% of them right, and the
combination of the doctor and the AI system
gets 60% right. And if I remember right,
the main interaction is that, the doctor would
often make mistakes by not thinking about a
particular possibility, and the AI system will raise
that possibility. It'll have a list of possibilities.
And when the doctor sees that possibilities, the
doctor will say, oh, yeah. The AI system
is right there. I didn't think about that.
That's one way in which the combination works
much better. The AI system, doesn't fail to
notice things in the same way a doctor
often does. But there, it's already the case
that, and this was more than a year
ago, with the combination of AI system and
doctor, it's much better doing diagnosis than the
doctor alone. And the, what it sounds like
the AI is doing is is generating a
scenario-specific checklist. Here are a range of
different things, and it could do that very
quickly, and a doctor can just look at
that and go, no. No. No. Oh, maybe
this. And it sort of allows it to
do a little more system one intuition on
those and then pay more attention to the
ones that it thinks is important versus difficult
system two thinking across every possibility. Yeah. So
that's certainly one of the things that's going
on. The other thing that's going on, of
course, is you get the ensemble effect. If
you have two experts who work very differently
and you average what they say, you'll do
better then. Anything that's processing vast amounts of
data, finding patterns and similarities, and then identifying
sort of promising candidates, for humans in that
sort of collaborative model you mentioned, that's gonna
power things. Part of that leads to my
next topic, which is around personalization. And so
we're in a, we'll be in a world
where your biology is different from mine, it's
different from someone else's. And so that intervention
on the medical side can be more tailored
to each of us. Is there research currently
going on around, how that might, you know,
how that might change sort of health health
outcomes? I believe there is. I don't know
as much as I should about this. But
for example, in cancer, you'd like to use
your own immune system to fight it, and
you'd like to sort of help your immune
system recognize the cancer cells. And there's many
ways of doing that. I think AI is
already being used to choose which which things
to mess with. Are most likely to work
for your particular area. So that would be
individual therapy based on AI. And then, obviously,
in education, AI is gonna be very useful.
And, again, it's gonna be individual therapy for
misunderstandings. Tell us, an AI system that's seen
thousands or millions of people learning about something
and there are different ways in which different
people misunderstand, that will be very good at
recognizing for an individual person, oh, they're misunderstanding
in this way. It's what a really good
teacher can do. They're misunderstanding this way, and
here's an example that will make it clear
to them what they're misunderstanding. AI is gonna
be very good at that, and we're gonna
get much better tutors. We're not there yet,
but we're beginning to get there. And I
I'm now happy to predict that in the
next ten years, we'll have really good AI
tutors. I may be wrong by a factor
of two, but it's gonna, it's coming. You
mentioned on the AI tutor side of things
for students. I think there was a study
that you referenced about how much better the
outcome is when people get individualized tutors. Yeah.
I can't, I don't have the citation for
it, but the number I remember quite well,
and I've seen it quoted elsewhere too, which
is you learn about twice as fast with
a tutor as in a classroom. And it's
kind of obvious why. First of all, you
don't, your attention doesn't lapse. You're interacting with
somebody, so your attention stays on it. You
don't just stare out the window and wait
'til the lesson ends. I spent a lot
of my time at school doing that. Secondly,
the person's attending to you and can see
what you're getting wrong and give you, correct
it. And in a classroom, you can't do
that. So it's sort of obvious why human
tutor is gonna be much more efficient than
a classroom. An AI tutor should be better
than a human tutor eventually. Right now, it's
probably worse, but getting there. And so my
guess is it will be three or four
times as efficient once we have really good
AI tutors because they would have seen so
much more data. There's probably another element too,
I would guess, which is around motivation. And
what we found is if, you know, I'm
sure for you and many students, if
it was an interesting topic, if it was
framed in such a way that captured our
curiosity, we'd pay more attention. I guess AI
tutoring will be able to do that at
mass scale. Yeah. So for most of us,
interacting with other people is the most important
thing there is and the most motivating thing
there is. And I think AI tutors, it'll
be pretty motivating. Even though they're not people,
you'll get the same kind of effect of
someone paying attention to you and telling you
interesting things. It will be very motivating. And
30 kids in a class might have 30
different things that are quote, unquote interesting to
them, and AI tutoring will be able to
to tailor it to them. Yeah. So as
you know, what we're doing at Valence is
we're building a, an AI leadership coach. And
so the goal is to help personalize that
learning and guidance at work. One of the
things we're talking to an education company, and
they said it's such a shame that everything
we've learned in education about how to help
people learn concepts seems to fly out the
door the moment they step into the work
world, and they're just left mostly on their
own to learn. So we're excited about that.
Can you share how you can see that
thread of learning continuing throughout someone's career and
not just ending when when school's over? So
I would relate this to the longer-term
development of AI. AI is gonna be used
everywhere, and it's gonna get to be very
intelligent. If we can reach a situation where
we get a symbiosis between people and AI,
AI is gonna make the world much more
interesting for people. Mundane things will just be
done by AI, and this symbiotic relationship will
allow people to learn much faster, have much
more interesting lives. That's the good scenario, and
I'm hoping we can get there. How should
policy makers and CEOs be thinking about and paying
attention to the wide range of outcomes that
could emerge? This very quickly gets you into
politics because what's gonna happen is mundane intellectual
work is gonna be done by AI, and
that's gonna replace a lot of jobs. In
some areas, that's fine. In health care, for
example, if you could make doctors and nurses
more efficient, we could just all get more
health care. There's a kind of more or
less endless capacity for absorbing health care. We'd
all like to have a doctor on the
side who you can ask questions about all sorts
of minor things you wouldn't bother your own
doctor with, but you're quite interested to know
why does your finger hurt today and stuff
like that. Health care is great because it's
elastic. You can absorb huge amounts of it,
so it's not gonna lead to joblessness there.
But there's other things where, there's just only
so much of it you need, and it's
gonna lead to joblessness there, I believe. Some
people think it won't. Some people think it'll
create new jobs. I'm not convinced. I think
it's gonna be more like, people used to
dig ditches with spades, and now people who
can dig big holes in the ground with
spades aren't in much demand because there's better
ways of doing it. The worry is you'll
get a big increase in productivity, which should
be good, but the increase in goods and
services that you can get from that big
increase in productivity won't go to most people.
Many people will get unemployed, and a few
people will get very rich. That's not so
much a problem with AI. It's a problem
with AI being developed in the kind of
society we have now. So what would you
say to the techno optimists? Because I think
that everyone can see a scenario in which
AI can make, you know, take the mundane
off your plate, give you personalized learning, personalized
tutoring, support you as you navigate this transition.
And it seems like our social and political
setup is not going to lead to that
outcome. So how would you square that circle?
What advice would you give to people who
just say it's it's gonna work out? Yeah.
My first piece of advice would be, do
you believe that because it's convenient for you
to believe that, or do you really believe
it? Now, people are very good at believing
whatever is convenient for them. I've seen a
lot of that recently. I just think they're
being very shortsighted. And if someone was self
aware enough to say, okay. I recognize that
this might be convenient for me, and I'm
willing to ask myself a question or two.
What question would you want them to ponder?
One big question is, should AI be regulated?
And I think regulation is gonna be essential,
if we're gonna avoid some of the really
bad outcomes. If you think of the media,
what's one, if you had a magic wand,
what's one change you would make to how
they portray or cover AI? It's interesting. I
haven't thought about that because I don't have
a magic wand. But I wish they'd go
into more depth so that people would understand
what AI is. People have used ChatGPT and
Gemini and Claude, and so they sort of
have some sense of what it can do,
but they understand very little about how it
actually works. And so they still think that
it's very different from us. And I think
it's very important for people to understand it's
actually very like us. So our best model
of how we understand language is these large
language models. People, linguists linguists will tell you,
no, that's not how we understand language at
all. They have their own theory that never
worked. They never could produce things that understood
language using their theory. They basically don't have
a good theory of meaning. And these neural
nets use large feature vectors to represent things.
It's a much better theory of meaning. So
I wish the media would go into more
depth to give people an understanding. If people
did understand that, how do you think it
would adjust the lens through which they view
AI and the policy importance of regulating it?
I think they'd be much more concerned and
much more active in telling their representatives we've
got to regulate this stuff and soon. And
in fact, people have talked a lot about
will AI be able to regulate AI? I
think that's wishful thinking. I think that's about
as hopeful as having the police regulate the
police. We've talked to some scientists who've been
part of trials who have AI generates concepts
and scientists evaluate which ones seem to be
the most promising. And it seems like it's
a more effective way of making progress. Right
now, yes. Right now, having AI suggest things
and people make the final decision seems pretty
sensible. I don't think it'll stay like that.
Then it will continue to just go up
the ladder and just get better capabilities. And
what does superintelligence explain that to a layperson?
More or less everything intellectually is just better
than us. If you have a debate with
it about something, you'll lose. And what about
creativity? What about those things that we consider
essentially human, just as good at us? A
thousand Picassos? Maybe it'll, that'll come a bit
later. Many people have suggested that because it's
not mortal and they have a different view
of things, the idea that it's not creative,
I think, is silly. I think it is
creative. It's already very creative. It's seeing all
these analogies, and a lot of creativity comes
from seeing weird analogies. Is the LLM or
the AI that we have today conscious? I
would rather answer a different question. I know
this sounds like being a politician, but there's
three things people typically talk about. Is it
sentient? Is it conscious? Does it have subjective
experience? They're all obviously related. There are a
lot of people who say very confidently, it's
not sentient. And then you say, what do
you mean by sentient? And they say, I
don't know, but it's not sentient. That seems
to be a silly position to hold. I
would rather talk about subjective experience because I
think it's clear there that almost all of
us have a wrong model of what subjective
experience is. When I, suppose I have a
lot to drink and I say, I have
the subjective experience of little pink elephants floating
in front of me. Most people think the
words subjective experience of work like photograph of.
And if I have a photograph of a
little pink elephant floating in front of me,
you can ask where is the photograph and
what's it made of? So if you think
subjective experience of works like photograph of, then
you can ask, well, where is this subjective
experience and what's it made of? And a
philosopher will tell you, it's in your mind,
which is a kind of theater that only
you can see and an inner theater. So
let me give you an alternative model of
what the word subjective experience of me. I
believe my perceptual system is lying to me.
So I say to you, my perceptual system
is lying to me, but, what it's telling
me would be true if there were little
pink elephants floating in front of me. Okay.
So I just said the same thing without
using the word subjective experience. And what I'm
doing is trying to tell you how my
perceptual system is lying to me. We think
there's this inner theater. There is no inner
theater. The inner theater is as wrong a
view of how the, what the mind is
as the view that the Earth was made
six thousand years ago is of how the
real world works. Almost everybody has this wrong
view. They think that there's an inner theater
with funny stuff in it that only I
can see. That's just rubbish. And once you
see that, you see that these chatbots, a
multimodal chatbot already has subjective experience. So I'll
give you an example. Suppose I have a
chatbot that can see and has a robot
arm and can talk, and I train it
up, and I put an object in front
of it. Let's say point at the object,
points at the object. Then I put a
prism in front of its camera when it's
not looking. And I put an object in
front of it and say point at the
object, it points off to one side. And
I say, no. The object's not there. It's
straight in front of you, but I put
a prism in front of your lens. And
the chatbot says, oh, I see. The prism
bent the light rays. So the object's actually
straight in front of me, but I had
the subjective experience. It was over there. Fascinating.
That is the chatbot using the word subjective
experience in exactly the way we use them.
It's saying, my perceptual system was lying to
me because of the prism. But if it
hadn't been lying, the object would be over
there. If you had a a Manhattan-style
project to try to address some of the
challenges, either socially or from a research or
regulatory perspective on artificial intelligence. What would that
Manhattan Project be? Oh, I think too there's
one really essential question we need to figure
out in the long run. There's lots of
short-term things we need to do, but
in the long run, we need to figure
out, can we build things smarter than us
that never have the desire to take over
from us? We don't know how to do
that, and we should be focusing a lot
of resources on that. You know, alignment is
a core a core of the sort of
Manhattan Project. Is there any KPI? I know
that's gonna sound sort of, you know, mundane,
but any KPI that we could track to
say, are we making progress on these alignment
questions that you have? Well, my worry, my
main worry about alignment is how do you
draw a line that's parallel to two lines
at right angles? It's kinda tricky. And humans
don't align with each other. Is there a
concept that is really important for people to
grasp that is hard for you to explain
in a way that a layperson can viscerally
understand it? I think often it's to do
with probability distributions. The whole idea of a
probability distribution people find hard to understand, think
of it as a thing. And so in
a large language model, you give it a
context and it's trying to predict the next
word, and it has a probability distribution over
words. And people find that hard to grasp.
And crucial because that's. And it's perfectly straightforward
if you understand probability. That science. But unless
you understand the idea of a probability distribution
and changing what you're doing when you change
the weights within the neural, the connection strengths is
changing the probabilities that will assign to all
the various words or word fragments. That's a
concept ordinary people find difficult to grasp. What
would you say is the most overused buzzword
in AI right now? Well, the most overused
buzzword by critics of AI is definitely hype.
So for years and years, we've we've we've
been saying AI is overhyped, and my view
has always been that it's underhyped. I think
that's a, that is a very important message
to get out to people. I've I've seen
that same thing. Oh, there's hallucinations. AI is
never gonna catch up. Exactly. And there's, we've
talked about sort of the rough edges of
technology. There's always rough edges, but you have
to look at the central sort of engine
of it and the the possibilities are so
so powerful there. Really appreciate the conversation. It's
enlightening. I enjoy it so much and I
know that our our our viewers and listeners
and watchers will as well. So thank you.
It was a lot of fun.
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