Hello, and welcome to the Physics World Stories
podcast. I'm Andrew Glester. And in this episode,
we're diving into a topic that is revolutionizing
physics itself,
artificial
intelligence.
AI is transforming our world and becoming part
of our day to day, but what about
physics? What happens when we apply machine learning
to the fundamental questions of our universe, and
how is AI accelerating discoveries in particle physics?
Can it help us visualize the unseen through
photography?
Well, and what does the future look like
as AI and physics evolve together?
This episode is inspired by the Institute of
Physics latest white paper on AI in physics,
exploring the challenges,
opportunities,
and ethical questions that come alongside it. We'll
hear from a science photographer at MIT
about AI in her work,
and we'll hear from two people who have
been advising on that white paper, including a
particle physicist
at CERN and the University of Manchester.
But first, Toni Hay formerly of Microsoft,
has spent years at the intersection of computers
and scientific research.
He's one of the consultants on the IOB's
white paper, and he used to be a
particle physicist. I wondered what had made him
move from that into computing. Theoretical particle physics
had essentially
come to a a a a lot of
great crescendo with the gauge theories of quantum
chromodynamics and the standard model. And and you
could read my book, my fifth edition of
my book, gauge series in particle physics, which
is, if you like, an epitaph
of my career in particle physics. So I
was very fortunate to be there at the
critical time. But, yes, I didn't want to
spend my time
doing the same things,
and doing things which are largely irrelevant
to people. Alright? Most people don't care about
particle physics. Most people don't care about,
I would say, they like the pictures from
astrophysics and astronomy, but but, actually, you know,
I'm not sure whether they worry about the
the big bang and the inflationary
period that followed and
the background radiation and stuff like this. So,
I mean, I still find them interesting and
they are fun and and things, but I
do feel that particle physics
evolved into sort of a dead end in
that
it went to
string theory, which is fine,
but
it's sort of metaphysics in that it doesn't
make any predictions that you can check,
and
has excitations at the the Planck level, which
we can never get to.
Oh, and also all the particles I spent
my life caring about, the proton, the pion,
and electron,
they're all zero
mass approximations
because compared to the Planck mass, everything is
zero. So they're there's symmetry breaking effects which
you may get run. Oh, and it's in
the wrong number of dimensions, but maybe maybe
some of them curl up and we get
to four dimensions.
And and really,
it does seem to have evolved into into
sort of
a love of mathematics. And it's very fascinating,
and it's done some wonderful things
in mathematics, but it doesn't have any impact
in particle physics. So I I do I
did feel, yes, increasingly,
particle physics was becoming divorced
from
real life,
and
I got more interested in using computers to
solve them. And then parallel computers,
I was extremely fortunate
to be able to build my team built
and designed
designed and built
a a parallel computer to do physics to
do originally physics, but now I realized there
are other things you can do with it
than physics. And for example,
I in doing e e science,
I really believe things like climate change are
rather more important than particle physics and astronomy.
And,
again, I'm not sure AI
can do a huge amount to accelerate that,
but I think particle
particle physics certainly can't.
So, no, I I welcome the physicists. They're
a wonderful community. They do lots of things,
but but,
I think they're not quite as critical as
they think they are.
Sorry to all our listeners. I mean, that's,
yeah, that's the That's that's that's that's the
way it is. I'm a I I'm a
physicist. I'm giving a talk next week about
Bell's theorem and demonstrating how Bell's theorem demonstrates,
you know, that Einstein's hidden variables were wrong.
And, I still like that, and I'm talking
about difference between John Bell and Einstein and
Bohr. Einstein and Bohr considered correlations at naught
and 90. John Bell, as he used to
delight in saying, because he's Irish, considered correlations
at 37 degrees. Alright? And, then you can
tell the difference. Right? And so, I no.
I I I still think
physics is a wonderful area, and
my hero is still,
Richard Feynman, who
who his wonderful lectures on physics, his wonderful
Cornell lectures he gave, and stuff like that.
And so
and I I worked
with Feynman
to help write up his lectures.
He lectured for the last five years of
his life on computing.
You can find the Feynman lectures on computation
edited by me. You can find them
in a book form, and
they're all about
interesting things and, you know, universality and Turing's
theorem and
and these sort of things. I take it
there's nothing in there about the possibility of
AI?
Yes. Feynman cared about AI.
He called them he didn't like the name
AI. He called them advanced applications, but he
understood, you know, image,
vision, computer vision,
robotics, and things. Yes. He he cared very
much. He had collaborators, one of which on
his original version of the course, he gave
it with two collaborators. One was Carver Mead,
one of the guys who explained why Moore's
Law worked,
and
one was John Hopfield, who won the Nobel
Prize. And he he had a a specific
type of neural network, which is not the
same type as neural network as everybody uses
now. Alright? But but he did win the
Nobel Prize,
and he was originally a physicist.
Hartfield was a physicist. Hinton, on the other
hand, was never a physicist. So,
my computer science friends,
I used to be
have a colleague of famous guy called Jim
Gray, and he was very annoyed about the
physicist claiming engineers
for the guy who invented integrated circuits they
gave a Nobel Prize to. And he was
really, really angry that that he wasn't a
physicist. He was an engineer, and he would
have said the same about
Hinton
and Hopfield,
that they were not physicists. So getting a
Nobel Prize for physics
was a little strange and must have certainly
ruffled a few feathers in the physics community.
And so to
come to the the IOP
document, it's it's a wonderful document, and it's
and it's right to engage,
the attention of physicists because it will become
part of their working life. That's true.
I I just rather doubt when they say,
you know, large numbers of them know all
about it,
that they do know all about it because,
actually, it requires quite a lot of effort
and investment
in computer science technologies,
and I don't think most
particle physicists do that.
It is true, however, that particle physicists have
used,
you know, things like neural networks and other
types of algorithms, which are part of the
general
AI. But what what drives AI now are
purely these deep neural networks and these now
now
the version of them that these transformer networks
that can do these large language models. From
your perspective looking at at AI in society,
not necessarily in physics, but also in physics,
are there sort of
misconceptions
that you can see about it that
that that are out there, or
are we understanding it as it really is?
Well,
when I came back in 2015
to the Rutherford Lab, I was amazed
that nobody I could find understood
that,
what you needed to do
AI
post 2012
was large amounts of computing power. And
GPUs like Nvidia's GPUs were one solution. And
that was one of the things that was
found out that actually what made the difference
was the scale of the data and the
scale of the computing.
And everybody here were doing AI includes all
sorts of little algorithms, and they're all mentioned
in the report.
And that they were, if you like, pre
deep learning.
And people were thinking, that's fine. I I
do AI, and I can get money from
the government to do no. The whole purpose
was actually to transform,
and the government putting AI in there money
into AI was because of deep learning. And
now these these transform models and large language
models, and I couldn't find anybody,
who was interested.
And so when I I got a grant,
on AI for science, I called it, I
called it AI for science deliberately
because I knew the politicians didn't know about
machine learning
for for science, which is what it was.
Alright?
Machine learning was a nuance they wouldn't appreciate.
And
I managed to persuade,
it was a collaboration with Turing,
and I managed to persuade them to give
me some
GPU computing power so we could Rutherford will
have offer a GPU AI computing service
to Turing
participants.
And that, I think,
is a challenge
still. And, you know, the question is,
you know, Microsoft and and and others, my
old company, Microsoft is is building data centers
at a at a vast rate, which involves
huge amounts of computing power
and,
cost billions of dollars. And there's no way
that Europe can emulate that because we don't
have any, what are these companies are called,
super scalar companies like,
Amazon, Microsoft, Meta,
Google. Yes. That's right. So and possibly Apple.
They're the in The US, they're the only
one. China has some sums that could similarly
do that. We don't have companies in Europe
that can put billions a month into building
these centers. And so
Europe's trying to do something, but it it's
it's a complicated business.
And
I think
given the the promise of AI, I think
there will be some national resources that you
can do these things with, but I think
it's complicated.
And, of course, then you have this result
from China. Deep Seek says, oh, you don't
need large amounts of computing power. I'm slightly
skeptical of that, but we we'll wait and
see what happens on there. Yeah. I was
gonna ask about that. You are skeptical of
it, are you? Slightly. Yes. I am. I
suspect that they've actually learned from what The
US companies and there are open solutions,
what they've done. And,
yes, I think that you will still need
large amounts of computing power. And the Chinese
have that. Right? So,
but but I don't know how open
what they've done and what they copied and
what they haven't because, you know, it's difficult
to tell.
As you mentioned earlier, you have an interest
in climate science.
Right. I think
everybody should have. Right? Everybody.
Yeah. No. Absolutely. But there's, you know,
large amounts of computing power is a large
impact on the climate. Right?
Yes. I'm told. I haven't checked this figure
that the the amount is is less than
used for Bitcoin mining.
Right. Okay.
I haven't checked that, but but but it
seems to me a plausible thing,
especially as Bitcoin
and and its variants are now being used,
and they have they use a large amount
of computing power. But but no. That doesn't
worry me so much.
I think they'll come a natural end that
you won't actually
no. Question is how much training data do
you need? Right? And
one of the projects I'm actually interested in
is related to the Institute of Physics thing
is that
what you can do now, you can pick
up a model
that's being trained by ChatGPT
and and and OpenAI and things like that
and the other companies.
And then you can specialize it to your
domain, but you don't know what the thing
has been trained on. You don't know all
the solutions to be trained on Wikipedia. It's
been trained on Reddit. It's been you know?
And what what what is it being trained
on? You don't know. You don't have control.
So one of the things that my friends
in The US and I'm also interested in
is
is is seeing if you can train
a large language model on a corpus
of scientific
data and literature.
And will that give you different solutions
rather than taking what you've got with all
the sort of
extraneous things it's been trained on and then
specializing it. If you train it on on
on scientific data and and and scientific literature,
does that make a difference? And and That's
really interesting. It is really interesting, and I'm
not sure it is it does, actually. But
but it's certainly, they're beginning to look. See,
I work
one of the things that I still would
detain in The US, I'm on the
advanced scientific computing
computing
advanced scientific computing advisory committee for the US
Department of Energy until they've abolished it, alright,
which I don't think they will. But but
the the the Department of Energy has the
the nuclear weapons, which where they fired all
the people who knew about the nuclear weapons
and then had to rehire them because they
suddenly realized well, the previous
previous Trump administration,
the guy went to the Department of Energy
saying he's gonna close it because it was
all about energy and green energy and stuff
like that. But but, actually, it's about where
the bombs are. And eventually, he realized that's
where the bombs are, and you don't really
necessarily want to close that after this step.
So I
but I work with the the nonsecret
part, and there were three supercomputer labs that
I work with. One is Berkeley, One is
Argonne near Chicago, and the other one is
Oak Ridge in Tennessee.
And and there, they have
very the most powerful computers in The US.
They're gigantic things with large numbers. They have
tens of thousands of GPUs on them, so
they can, in fact, do some serious stuff.
They're built for doing supercomputing,
but they can also be used to AI
because they have large numbers of
of of the GPU chips.
They're not necessarily optimally designed for that, but
nonetheless, they're very useful. So that those those
are the community. I think that's a very
valuable community if it isn't destroyed by the
present administration,
but but they're they're they're doing some very
interesting things. If you're not concerned too much
about the climate impact, are there other impacts
of AI that you are more concerned about?
Yes. I'm not concerned about,
you know,
the the the most scary things that taking
over the world and things like this. I
don't actually subscribe to that, that I'm worried
about
the great things also for disinformation,
dis deep fakes, and putting out
all sorts of,
generating all sorts of evil stuff. Yes. I
do. So I my my view on on
that, I hope The UK
has
the search into unethical AI
because, for sure, North Korea,
Iran,
China, Russia are all looking at attacks on
us
with whatever they can do. And so I
don't think they're too concerned about ethical AI.
So it's great to see that we're concerned
about it, but so long as some parts
of our establishment
are actually looking at how you counter
unethical
AI. So that's my question, and I and
I'm sure that
some parts of GCHQ or elsewhere are are
doing some stuff like that. I hope Now
regular listeners will know I'm something of a
fan of particle physics, so let's hear from
a particle physicist. Here is Caterina Dolioni. I'm
a professor of particle physics. I work mainly,
at the ATLAS experiment at the Large Hadron
Collider
and where I do data acquisition, real time
analysis, and searches for
dark matter, try to produce it in the
lab.
I'm also very interested in,
software and open science and the environmental impacts
of the research that we do. You say
you're looking for dark matter. Have you found
any? No. Not yet. I mean, we've not
we don't think we've produced it yet. Or
maybe we have produced it, but it's too
rare to be distinguished from the backgrounds
yet. So we don't know. We keep looking.
Okay. Can can you give me a sense
of what that would look like? I I
know it's quite a hard thing to picture,
but what what would it look like in
the data if you saw that? The easiest
answer is you'd see nothing.
There's a because that's the main signature for
what we in in what we're doing of
a dark matter candidate.
And, when I say candidate, it's it's important
to, like, keep that in mind. It's not
guaranteed that that particle that we're going to
see is dark matter. We did many other
experiments to confirm it. But assume that you're
producing dark matter at the Large Hadron Collider
and an experiment detects it. What you're actually
detecting is the missing
transverse energy that is left by particles exiting
your detector without any trace.
And we use more or less conservation of
energy. It's actually conservation of transfer from momentum,
where you have
two visible particles coming in, colliding,
and then a lot of debris coming out,
most of it, you'll be able to detect
with a detector. But the dark matter particles,
you're not going to be able to detect
them. They're dark. They don't interact very much,
so they're just going to escape.
So you're going to see if you sum
the energy that you had at the beginning
and the energy that you had at the
end, you'll see that something is missing. And
this missing
is the signature of potential dark matter. Okay.
It's also the signature of neutrinos.
So those particles exist. Yes. So that's the
main problem. You have a lot of background,
that you need to distinguish.
You need to distinguish what is signal and
what is background. And a lot of the
time, you can only do it with the
analyzing a lot of data and accumulating a
lot of data.
So that that's one of the ways in
which dark matter could appear. I'm not looking
specifically for that. At the moment, I'm, looking
for,
a sort of a sister theory of,
the quantum chromodynamics theory that is called dark
quantum chromodynamics.
So imagine we have a copy of
our plentiful particles and beautiful particles from the
standard model that we all know and love.
There's a copy of that, and it's
dark. We don't see it because it's only
connected
to the Standard Model particles via very weak
interactions or very,
rare particles or very heavy particles.
So we call that a a complete dark
sector somewhere else.
And within this dark sector, that might be
dark matter particles.
It's one of them or a combination of
this dark sector particles makes it makes it
for a a dark matter candidate.
How does AI come into this research that
you're doing here? It comes in quite a
lot because with the amount of data that
we have, we have to be smart
on how we analyze it. And we could
possibly,
do most of the things that we're doing.
Maybe not not all, but most, I would
say, in classical ways.
The thing is that it would take us
much, much longer. It's the same kind of
revolution that we had in particle physics when
people were looking at slides that taking pictures
of,
a collision or taking picture of a certain
process in bubble chambers
and then going from there to computers.
You have something some algorithm, something that really
accelerates
your the speed at which you can gain
insight from the data.
So that's where machine learning is coming from.
This is only one way in which the
field of physics uses machine learning for data
analysis. So in this case, we might not
be the proponents of new algorithms. We're mostly
users,
but it is still having a huge impact.
But there's other people that are trying to
put physics inside into machine learning. So that's
another kind of crosstalk. I don't do that
specifically. I'm more of a someone who is
using machine learning to
get things done.
I mean, the big question
that seems to come up all the time
with this sort of thing is,
does that mean you won't need your PhD
students? I mean, would you
I would not. I mean,
who's going to do any data analysis if
I do teaching all the time?
No. Anyway, jokes aside, it's,
there's a lot of, experience,
I think, that
it's not something that,
even the best large language model is going
to be able to to inject into
this kind of endeavor.
So I'm not worried that anyone's
knowledge based job is going to be in
our field. At least it's gonna be taken
away
by machine learning
simply because we have the tools, but we
need to know how to use it. We
have Copilot for programming, but we still have
to know how to design the software.
So this is the kind of input that
we still need to need to have. Sure.
If you will go for a general artificial
intelligence or something that is bigger and not
necessarily my my field of expertise, then maybe
you can think about
a longer term future and and see what
what that brings. But at the moment, I
think we do need a lot of human
input in the design
and the use of the correct,
algorithm or correct tool for the problem that
you have at hand. You know, five years
ago,
if you told me that AI was gonna
be as big in society as it is
now, I would have said, no. It's not.
Not not in five years. So how quickly
is that future gonna come around where we
do need to think about it replacing people's
jobs and that sort of thing? Have a
crystal ball at the moment. No. Not from
my field. I think anything that we find
boring can possibly be something that is
taken over
by some algorithm, but that's normal. Like, when
you feel like you have everything done,
like, you have everything that is,
well established, and then you you get a
robot or, you know, something
machine like doing it for you. It has
happened before and that hasn't really
made the so it has made changes, but
it hasn't made changes
for the worse
if handled correctly from the the workers' perspective.
So you can't say, oh, well, bye. I'm
not meeting you anymore. That that's not a
nice thing to say to someone who was
doing the same job as,
you know, a robot or or something. You
need to find parts
in which these people can be appreciated, can
be recognized. So that, I think, my concern
maybe is more about that than about
the replacement
of its
there might be a way to do it,
but we have to do it in a
in a way that's sustainable for people,
not just maximizing,
scientific profit, whatever you want to call it.
Yeah. Yeah. Yeah. Yeah.
So but I'm sorry. I will get on
to the more positive things in a minute,
obviously. But but so you're
doing teaching as well. Does
does students' use of AI concern you?
Yes.
In not in a way that, I mean,
I encourage my students to use I teach
a programming course. So it's something that, if
you don't use AI, you use Stack Overflow.
If you don't use Stack Overflow, use Google.
If you don't use Google, you ask your
friend. So there's always been a case of
I need to find this information, this punctual
information of something that is happening to me
and I don't know how to solve. Where
do I go?
And this is it's fine to go to,
I think large language models,
or
you know, it it's it's replacing it's it's
just evolving something that was happening before.
What I'm not entirely sure I understand at
the moment is how,
the perspective has shifted between,
I want to learn something for myself
because I want to have that experience. I
want to be able to apply that experience,
and I want to reach a goal with
a minimum
amount of effort possible
because that is the trick, I think. That
this is the tricky part.
When we're tired and,
we have a lot of things to do,
we want to just find the easiest way
to get something done. But if you make
your entire course or your entire programming course,
work like that, what have you learned at
the end?
So one thing that we're trying to to
encourage students about is,
share your prompts. Ask the right questions. If
you use this kind of tools,
you have to use them responsibly. You can't
just ask do the assignment for me because
we found,
hallucinations in our in our assignments.
There is that that that is the immediate,
the immediate problem, but also very complicated solutions
for problems that if you just had thought
five minutes, you would have found yourself.
So I'm not saying that this is going
to be overcome. There's going to be better
versions of, Charge GPTs or Copilots that will
do this better.
But it somehow,
doesn't stimulate
learning for oneself. And I think this is
an important part of,
at least a physics degree.
Because we're not I mean, so maybe we
do it we do it also for the
money. We're gonna do it exclusively for the
money. We do it also for our pleasure,
for our,
will to understand the world. And if we're
just asking someone else to understand the world
for us, then where does it leave us?
But I think there's also good things about
the
the the use of
artificial intelligence for
for teaching, for solving teaching problems, for solving
learning problems. It democratizes knowledge quite a lot.
A lot of people have similar access to
knowledge that they might not have had before.
And then there's all the,
the I'm not gonna fall into that hole
at the moment because I would talk about
it for hours.
Environmental sustainability of these kind of tools. So
what are we doing? We're just continuing to
use resources without thinking because it's good for
us because it eases
our understanding. It accelerates our our knowledge.
How do we use it responsibly? My wondering
is do we get to a point in
physics where
it doesn't actually matter if AI is doing
all the discoveries?
Because and
there's a black box though, isn't there, of
we don't know how it did it.
Well, I I think there's a science fiction
story about that. Don't remember the title or
the author at the moment, but I I
brought it up before. And that is where,
in the far future, AI is doing all
discoveries, and there are a few months just
to reverse engineer those.
Figure out how they've done
it. Yeah. So imagine it could be AI,
could be an alien that comes and brings
you up an amazing technology, and then you're
like, okay. How can I use it? Why
how is it there? How is it working?
You know, it's it's kind of reverse
the scientific process because you're not making a
discovery yourself. You're just figuring out how it's
done. But it wouldn't be too different from
reverse engineering a piece of code for someone
who has not left documentation.
You still have to figure things out. So
Can you see that as a possible future
with AI? I mean, I can see a
lot of possible futures for AI, and I'm
worried about different things in the world apart
like, that are not that thing before.
But,
I think it
it it could happen, but I'm still thinking
that
the human brain is the best possible kind
of AI that we got. And the collaborations
of humans is something that is that brings
in serendipitous,
discoveries as well. Not to say that maybe
at some point, we're not going to be
able to simulate that, but I think the
power in the
individuals and collaborations
of of humans is not going to be
something that is easily
matched
by AI. So we'll still
so if the future is
that it's not too near
because we we're not going to be able
to be I I have faith in
in our in in the human aspect of
of science and research, doing a lot of
collaborative science. I think that there's things there
that cannot be replaced by by AI. I
think most people listening will have had some
experience now of using something like chat GPT
or the other,
generative AI word based things, maybe even images.
But how do you actually use AI in
the work that you're doing? How what what
do you actually do with it? So we're
not using,
generative AI
too much. We're figuring out how to do
it, and, there are some
uses. There's, this thing is called ATLAS GPT.
So it's an it's an experiment in within
our experiment
that is trolling the, knowledge base of the
experiment.
And, you ask a question, you get an
answer.
Now an interesting thing, about the training of
that, and I think it's it's part of
the the reason why there's still a lot
of human input needed in this this machinery,
is that if the documentation that it's,
crawling is,
that it's been trained on is obsolete, it's
gonna give you obsolete answers.
And so how do you make sure that
the training data is
proper?
A human has to go there and clean
up the pages of the documentation.
So you still are feeding some human information
into this machine. It's just digesting it and
giving it to you in a different form.
So this is just a simple
example of why I think we still need
human in in the loop.
But so this is not the main use
that we have in our field for for
AI.
We mostly use algorithms that analyze large amount
large number of features that give us,
some insight in the data that we would
not have had otherwise.
So this is
we have different stages in, in our experiments.
One stage is to reconstruct
from the signals that the detector gives
you. Some
there there is a particle with this energy.
So that process we call it reconstruction.
And here you can have a lot of,
neural networks
of different sorts
to do this kind of reconstructions. Imagine you
have a lot of points in space in
your detector because it's like a big digital
camera, and you want to reconstruct tracks of
particles. This is a gigantic combinatorics problem.
Graph neural networks can do it well and
give you an answer of where what tracks
you got with fewer fakes than other algorithms,
for example.
Or you can, use it for clustering problems.
That's one of the other classic uses of
AI. So if you have
a a detector that gives you, spray of
particles and the spray of particles represented by
a bunch of energy deposits,
then you can use, AI to cluster this
energy deposits in a more efficient way, for
example. So that's one thing that,
they can do.
Tagging, we call it identifying
particle a from particle b. That's another really
big,
problem that we use for we use AI
for. So is this particle a quark,
derived from a quark of type b?
Then this is the beauty core. It has
the
this party will have the tendency to fly
a little away from the interaction vertex. So
you find
a neural network that can
distinguish what is a probe particle from a
particle that's long lived.
So this is something that we use quite
a lot for this
well, maybe basic task, but it's not quite
data analysis. Data analysis, I have
10 b's and five electrons. What is the
process that gave it this output to me?
I mean, very, very simple. Right?
We we have many more particles in our
events.
And then you can use all of this
for data analysis because you can you have,
the the problem there is to distinguish signal
for background,
and you have a variety of supervised and
unsupervised
methods
to to make this,
distinction.
One thing that I've been dabbling with, but,
is unsupervised
learning,
what, people call the anomaly detection or the
outlier detection methods.
It's the the kind of algorithm that ping
your credit card when you're
abroad. So it's the same thing that we
use. And the advantage is that we don't
know what to expect from the the new
physics.
Ideally, we just we know very well. We
know standard model very well, and we have
plenty of standard model data. Most of our
collision and most of our collisions that we
analyze are standard model processes.
So can we get an algorithm to learn
how this looks like
and then tell us if there's any difference
or deviations
from the
the data that the, that we have. Now
it's conceptually, this is very easy, and it's
beautiful. It works super well.
In practice,
there's a lot
of pitfalls and the things that one needs
to understand before calling an anomaly
seen by an algorithm,
new physics.
For example,
detector noise that you weren't expecting
or,
you know, clustering of a specific process that
was too rare to be seen in your
previous data analysis, but now you found that
out and if not your physics.
So trying to understand
what the
known unknowns are and what the unknown unknowns
are, that the that's the key that's the
key problem there, not necessarily what I gotta
be using for your anomaly detection.
So I I I'm very interested in that,
that side of things because it also has,
an impact on
more basic data position things like data compression.
So if you don't, if you want to
compress using machine compress data using machine learning,
are you going to just,
wipe out any anomalies because the compression will
reconstruct will compress them badly?
It's only the anomalies, and then you'll just
put them back into the bulk, and then
you've lost your signal.
And so this is all interesting interplay that,
you have to think about it when you're
using AI and physics. Yeah. Yeah. But so
you do need your students to be able
to understand things
Yeah. Without the AI, the use of AI.
They need to understand how to use it
Yeah. But also how to do it if
it wasn't there. I think there's also another
aspect that the communities,
has realized that you need to have,
reproducible
science. This is generally a pillar of what
we're doing.
You can also go into the open science.
So you want someone else, not you, maybe
the general public even, to reproduce, you know,
reasonable manner what you're doing.
AI doesn't make this easy,
mostly because of the complexity and the
the complexity of the networks, but also the
computational resources needed for running large algorithms.
So how do you make,
AI based analysis
accessible to others,
understandable by others, reproducible by others?
And
sometimes the easy answer is, well, you don't.
You give them a version of the analysis
that sees the same thing
that is not using AI.
So you still need someone to do that
part. And, usually, when you want to convince
someone that you've made the discovery, you will
be asked, I think, over the field, can
you please now
do something that is,
that at least indicates in the right direction
that you have
something
solid with your AI algorithm.
I think what we're missing is also the,
some more crosstalk with people that are working
on,
explainable AI and,
because we don't we don't want to treat
our data analysis as a black box.
We can't. That's not scientific method.
And,
this kind of, theoretical advances,
when we try to to work with people
that work on this, this topic,
are still very, very far from our,
understanding.
People are doing
marvelous work, and, it all makes sense if
you think about it in from afar and
when they explain to you. But how do
we get that into the physics field? How
do we how do we make sure that
we're using the state of the art? Mhmm.
Caterina was also involved in the IOP's white
paper. It came out of a workshop that
wants to take the temperature off
how AI is impacting physics and where are
we going with that. Because I think the
IOP is a very,
good, very strong stakeholder, but also can influence
where we're going also because of the impact
on teaching, undergraduate teaching.
So there was a workshop in October, I
believe, that,
gathered
information from,
the participants
in a variety of of forms and
the then summary this white paper summarizes
the the findings of this workshop.
And here, it's the AI is really broad,
so it's
machine learning,
like the traditional machine learning, but then there's
also other
all other kinds of things. So it's, the
participants were also from different fields.
But in general, it's, it came out that,
of course, AI is useful.
It's
and physics can play and is already playing
a a special role in AI and can
also be
highlight that can also be highlighted
highlighted further.
And and at the moment, we can say
that I think physics needs AI
because of this, data,
reconstruction, data processing, data analysis.
Where we have a lot of datasets, a
lot of features, then AI is really making
a difference. Sorry, Scott. Just to go to
the environmental thing, is it the case that
it was it will speed things up to
such a degree that
that will mean it's using less resources
because we're not Hopefully. Okay. I think this
was a a recent, UN,
report that,
where it can use AI to improve the
the environment.
Right? So it's it's kind of coming full
circle.
But I think we don't know enough or
at least it's not transparent enough on how
we run our resource and how how much
our resources
cost.
So,
there's a number of
groups that are,
trying to do that. We also have a
UKIF. It's the
European Coalition for AI and Fundamental Sciences, and
we have a group on on, environmental sustainability.
It's not yet taken off, but it hopefully
will.
That are just informing people. That that's the
main the main point.
Computing is not free in general. We think
that it's free because we are not paying
the bill. We're not paying the electricity bill,
but the electricity bill
translates into
a,
environmental
bill, servers, and the there's a life cycle
assessment of all of that stuff. So it's
trying to make the wisest choices to obtain
the results that we want. It doesn't mean
turning everything off because it consumes less or
not using the rate latest and greatest AI
algorithm because it consumes too much. Just trying
to,
map the problem
to the algorithm in a way that also
includes environmental sustainability as one of the axis,
not just speed or
that 1% more or,
you know, efficiency.
Felice Frankel is a science photographer
and research scientist at the Massachusetts
Institute of Technology
or MIT. In a recent daily briefing on
nature.com,
Felice wrote about what science photos do that
AI generated images can't.
Most of the work I do is in
science photography. That is I make pictures
of the science,
or I create
images
that,
that are metaphoric in as far as describing
what the research is about.
And, of course, everybody is thinking about AI,
and I'm I'm now looking at what
AI can do photographically.
Not not in the sciences, but I'm seeing
in the, you know, in the world around
us this
extraordinary
tool that can create
an image from
nothing,
basically.
And so
I started thinking, oh, boy. You know? Maybe
I'll be out of a job at one
point because
AI someday
will be able
to
depict,
research
art completely
artificially.
That is to say,
not
creating an image from the thing,
but will develop an image from pixels
out of out of the large language
model. So
it's
it was it started about a year ago
when I when I when I was thinking,
am I in trouble?
And, you know, I'm the fact is
I have to be realistic. I'm next month,
I'm turning 80, I will have you know.
I can't believe that I'm that old. Right?
And so I think I'm thinking about my
future,
but it but it's it was it's an
interesting
thing to think about,
if in fact,
I will be taken over even if I
were young
by AI. And and so the article was
is investigating
that idea.
And where,
I did some experiments,
I I have a a photograph that I
made of
science that, in fact,
Munji Gewendi's science
got a Nobel Prize last year for this
work.
It it's an image that you're looking at
nanocrystals
in in vials, different colors,
fluorescent different wavelengths. Let's leave it at that.
And so I asked AI to with my
prompt
to create x, y, and z. And very
I used various models, and they were terrible.
They were cartoon like. They were silly,
but
I could see it happening.
At some point,
AI will be able to create an image
that looks
and represents
like the real science.
The key is how are we going to
create a system
to judge whether an image for
submission
is AI or not.
And so at the very end
of the article, I list some ideas of
when you submit an image,
you you ask the researcher to say, is
this an AI image?
If so, what model you used? What prompt
did you use? You know, a number of
questions.
But in the end,
the key is that we should not permit
any AI image to
be presented
as a record of the science.
Yes. AI
will be very good at creating images that
are explanatory.
Conceptually,
even structurally,
it sort of looks like this,
but never
accept it as the record
of the science. And, unfortunately,
we have to trust
the submission.
If AI
keeps
improving,
you can never see an occasion where it
gets to the point where we can accept
it for that. Yeah. Because it it's the
intention.
That is the that's the key to the
submission.
If your intention
is to explain the science,
fine. If your intention is to say this
is a record of the science,
that's verboten for me and for anybody, really.
Now I don't know the answer of how
to
teach honesty.
I mean, we we've seen manipulated
images. We there's a whole list of images
that are we're looking at that have been
manipulated
to, in fact,
say what the researcher wanted to say
because the science wasn't there. So that's that's
the manipulation of images is something that we've
been around that's been around for years. Even,
for example,
as I say in the article,
these glorious
images that we see of of the universe
from the James Webb and the Hubble,
those are all highly manipulated
images, those colors. People think the universe looks
like that. It doesn't. But we but we'd
say that these have been falsely colored.
At least, we should be saying that. And
but,
so changing an image
is something that we've been doing for
for a while, but it's starting from scratch
to create an image that
literally never existed
to represent
the thing
is, in fact, the problem. Because you have
this
photography interest. Right? You have the science interest
and the photography interest.
Does looking at an AI photograph, a photograph
that's been generated, not an image, but a
photograph that's been generated by AI, AI. You
know, something to look like a photograph.
Does that offend your sort of artistic
sensibilities
as well?
Let me first say I'm not an artist,
and that's kind of important.
Artistically,
I'm very I'm blown away by what is
what people are able to do with AI.
It's just remarkable.
But as a scientist,
I I'm worried
because
it's about truth
as as
as what I'm trying to do when I
make an image. Remember,
when I make a photograph an image,
it is a representation
of the work.
It is not the work.
Is is it a it's a representation.
So there's always
in my picture some sort of manipulation.
The very nature of making an image is
a manipulation
of reality.
You know, I'm not, you're not showing everything.
I'm I'm framing it,
and so that's my initial
manipulation.
But I I
I'm not doing anything
to the data.
That's that's the bottom line.
If I start manipulating the data,
then then I I I'm making a terrible
mistake, and that's I try very hard. And
when if I do anything,
like remove a dust particle, for example,
I always indicate that I've done so. Always.
In sort of captions and that sort of
thing. Yes. Exact yeah. Absolute for example, I
have a book out,
a series of books called the visual elements,
communicating science and engineering, and the first element
is photography.
And I taught it's very it's a handbook,
and it's I've I'm told it's very good.
And so I say at the very beginning,
all of these images
have been digitally
enhanced
because
I I wanted you to pay attention
to the process, not necessarily the the the
distraction.
And so at the very beginning, I and
and when I say enhanced,
I'm talking about removing dust particles and which
really
but but you have to know that I've
done that.
So I always indicate if I if I
do anything like that. Okay. That's good. But
so it because you've you've touched on this
that there's
a concern about honesty, right, and and how
we ensure honesty.
At times, when you look around the world,
it feels like it's a runaway train
of dishonesty.
And
how we hold on to it, as you
say, it's difficult. But could you give us
some suggestions of how we Yeah. As as
the people who are who do care about
it, what do we do? Yeah. I I
think
now this might be
naive on my part.
But I think the more people understand
our process,
like, for example, how I make a particular
image,
the more we will engage
people to understand
what can be or what cannot be,
if
if if they
if they see if they understand, for example,
that
how how NASA
or the the James Webb people color, if
they actually see that,
then it's a new part of your thinking.
So for example, I'll give you a personal
experience. I used to be a a choral
singer. My voice is gone at this point.
That's very disturbing.
But I used to be a very serious
auditioned
choral singer.
Because
I know the music,
because I've sung this particular song piece,
when I hear another chorus sing it,
I'm engaged
more with it, and, actually, I can
sort of tell when something is not quite
right. It's because I know more about it.
Same with cooking, for example.
I'm I'm a very good cook, and I
could read a recipe
and know that, uh-uh, I'm not gonna do
this part because I'm experienced with it. I
maintain that engaging people in our process,
especially the, yeah, the next generation of researchers,
even if they're not making pictures, but engaging
them in the process,
I believe,
might push all of us into
understanding
what is not
right. What what do you think? Do you
think that's correct? I mean, no. I don't.
I I mean, it I'm a lecturer in
science communication.
Right? Oh. Yeah. It's my thing. So
you're preaching to the converted, but I I'm
you know, there's people listening who might not
agree. I'm always teaching about communicating the scientific
process and helping people to understand that process
is a really big part of science communication.
It's not about
scientific facts. You know, science at school quite
often is about learning facts, and there's a
bit about the process
because but, you know, the scientific process of
vaccines, if people understood how that was
those new vaccines
in inverted commas that came along during the
the pandemic, if people know the process knew
the process that had gone into producing those,
there wouldn't be as much fear, I think.
As much. I'm not saying it's it's it's
an all or nothing thing, but they're they're
engaged in thinking. This is the
ongoing issue that I did talk to colleagues
about.
People are not interested in thinking.
It's hard.
You know? And and they want quick answers.
I don't know how to engage people in
thinking.
There should be a way to make it
rewarding
to think. I mean, as scientists, we are,
and, you know, we're rewarded in the thinking
process.
But for the most part,
most people just want to be told
what to to choose either a or b
and not why.
My hope is if we start,
for example,
on a on a simple level, if we
start creating visuals
that are engaging
and gives permission
to people
who to ask questions.
You people are not frightened of images.
It's a means of engaging them to ask
questions.
And once you get them,
frankly, it's a seduction
to ask a question. My thinking is that
there's a next step
so that, for example, I'm coming out with
a young adult's book in
in the fall.
It's for teenagers.
It's called Phenomenal Moments.
And the idea is that everything around us
is science,
period. That's it. Everything we look at, everything
we touch is about science.
So the whole book is about everyday
phenomena,
and the pictures, I'd like to think, are
beautiful, but you can't you don't know what
they are. It's sort of a guessing game.
And
the my hope is that
they the kids will look at the picture.
They'll see the caption about what it is
that it is.
And then when they start walking through the
park one day, they're gonna see something like
what they just saw in the book.
The picture I'm making is engaging them
to
remember,
perhaps, when they see it again,
they're gonna know what that what is
because the picture
is a means
of getting them interested.
It's very simple. I mean, I'm not doing
anything brilliant here, but
but I think that with pictures,
we can we can get more people to
start looking and thinking about.
Absolutely. And but in that sort of use,
would you see AI being a useful tool
in in generating Yeah. Well yeah. Oh, boy.
Yes.
Yes. I think it can be
as long as we it is indicated that
this image was
done with AI.
That's the primary.
And whether or not
we could get the AI image maker to
do that is a whole other thing. I
don't I don't know. I don't know how
to do that. Yeah. I think it's a
brilliant example, the NASA images.
You know, how if we can understand how
that works, then there's there's an there's another
level of interest in that for me. I'm
sort of okay. So how have they colored
those images? And there's there's a there's a
level of intrigue about AI, which I think
disappears in a few years when everything's it's
just gonna be a thing. But, there's a
there's a sort of, oh, that one's created
by AI. That's interesting.
Or this one's created using that piece of
software. That's interesting. I don't I didn't know
about that. And it adds a it adds
a level to it. But I
I I can totally see that. What I
struggle with, and this is slightly other to
this, you know, I can't imagine logging into
Netflix
and going,
which one of these were created by AI?
That's the one I'm gonna sit down and
watch tonight. You know, I want like, you're
talking about music,
individual
differences between choirs.
And then there's, you know, the the nuances
of actors. There's the nuances of
photographers. There's the nuances
of filmmakers. There's the nuances of artists,
individual artists. And
and I I I worry that
if we
allow AI in image creation at any level,
then we we end up.
Yeah.
You're right. I mean,
the question is, will there be a time
when we will see that AI really
is missing
that creativity
that only a human can bring?
It let me quickly go back to the
image that I was talking about,
the AI image
that,
Dali created
of these vials.
As I said, it was very cartoon like.
There were all kinds of mistakes. But,
interestingly,
the the model created little dots,
in the vials.
And the model put a couple of the
dots
on the surface of the table.
That was an aesthetic decision
that AI
decided to do.
It was stupid.
It was silly.
Maybe
the machine will never be able
to be as creative
as we. I I actually don't know. I
mean, I don't know enough about that world.
And maybe, you know, what I should do
is talk to people who do know. Well,
I did.
I did before I wrote the article, and
they all agree we have to have some
kind of guardrails.
That's that's a that's a done deal. But
the question that you're asking, which is a
very important question, is will AI
be as creative as a human,
and can we discern that difference?
I think at at least at this point,
I don't think it come it will come
I think
there's always something that the human can do
that AI now can't do. But will it
happen in the future?
Probably, yes.
So there goes the answer to that.
We spoke to Tony Hay earlier in the
podcast, and I wonder who he thought should
read the IOP's
white paper
on AI and physics. Oh, I think it's
got a a a lot of things that
I absolutely agree with.
It is very physics oriented. It says, you
know, physics is the only field with large,
well curated datasets and theories. Well, there are
things like chemistry, possibly biology, material science, a
few other things who might object to that
statement. But but but,
no. It's it's it's it's a good thing
to
to to galvanize the community that,
AI is going to be in their future,
and I I do see that
that that you could make an AI assistant,
which was really very effective
in advising you in your day to day
job and in your work,
as a as a physicist.
So I I I think that with the
large language models, you will find that there
are AI assistants
for physics, and that will be part of
many people's lives. Alright?
And I think people understanding the strengths and
weaknesses of it
and the fact that you need to do
skills. And so the the things it recommends
are very sensible things, and it's good that
the the physics community is aware
of the potential.
But I did, for example,
very much approve of the chemistry Nobel Prize
this year,
which
was awarded for for
protein folding
and gave it to DeepMind
two people from DeepMind,
and also a colleague of mine from University
of Washington where I used to have a
joint position,
David Baker, who's been doing it for years.
And I think that was a good thing,
and
and will actually have ramifications,
huge ramifications in all sorts of omics type
stuff.
So so I see that there's really exciting
applications in that area, drugs and cures the
diseases and things like that. Physics is less
clear. I mean,
material science is probably the major hope that
you actually will find something really exciting,
and that would be
a good thing to do.
If you did
just
program,
inform
the larger language model
with physics information. Right? All it knew was
the physics information.
Would that make a really good physicist, or
does it do you need other things as
well? That those no. That that's that's the
sort of question that's interesting me. And and
you see,
we we use deep learning on to help
analyze some of the data at the lab,
which is usually
in in images. And if for images, it's
it's ideal, except that you have to train
it on ground truth. And once you've trained
it on ground truth where you know the
answer,
it can then go and see data it
hasn't seen before and make decisions.
If you take something like quantum computing, right,
which is something I care about. And
at this point, I advertise my lectures with
Feynman,
the Feynman lectures on computation. The question is
we do simulations
at all different
scales, but we don't do a fully quantum
mechanical simulation
with
with everything in in terms of the Hilbert
space just grows exponentially,
and and that computers don't have enough memory
to handle more than the, you know, small
number of electrons and things like that, a
handful. But quantum computing can do it very
easily, and it grows linearly, and it's it's
it's much easier to do a big system
there. Question is, what would be different
by doing it that? Because we've done
a whole range
of modeling at various levels, which are, yes,
approximations,
but they're are we going to find really
some new things by doing that? And that's
an interesting question. But but
and people talk about, oh, this wonderful stuff
on
quantum machine learning. Now I don't know
of any machine,
quantum
computer
that can really do serious calculations yet. I
think it's getting close,
closer than I thought it would.
And
I don't know
whether quantum machine learning or quantum AI, if
you like,
is a real real thing. But that's that's
something that that, you
know, smart people should look at and young
kids could could find really that an interesting
thing to do.
And
and then you could also
that's starting with the physics model.
And then the question is,
does it make any difference whether you've trained
it on,
you know, Wikipedia and Reddit or scientific literature?
Again, I won't make. Yeah.
Yeah. Okay. Well, maybe we'll find out in
a future episode of the podcast at some
point. But No. That would that would be
nice. Yes. Yeah. There's no it's an exciting
time.
I'd like to thank Tony Hay, Felice Frankel,
and Caterina Dolioni for talking to me for
this episode of the Physics World Stories podcast.
You can find links to their work and,
of course, the IOP's white paper on AI
and physics on physicsworld.com.
We'll be back soon with something else from
this wonderful world of physics, and thank you
very much for listening.