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Last Updated
17/05/2025
How Is Science Even Possible?
By: Steven Strogatz
Quanta Magazine: 20/06/2024
How are scientists able to crack fundamental questions about nature and
life? How does math make the complex cosmos understandable? In this
episode, the physicist Nigel Goldenfeld and co-host Steven Strogatz
explore the deep foundations of the scientific process.
The universe seems like it should be unfathomably complex. How then is
science able to crack fundamental questions about nature and life?
Scientists and philosophers alike have often commented on the “unreasonable”
success of mathematics at describing the universe. That success has helped
science probe some profound mysteries — but as the physicist Nigel
Goldenfeld points out, it also helps that the “hard” physical sciences,
where this progress is most evident, are in major ways simpler than the
“soft” biological sciences.
In this episode, Goldenfeld speaks with co-host Steven Strogatz about the
scientific importance of asking the right questions at the right time. They
also discuss the mysterious effects of “emergence,” the phenomenon that
allows new properties to arise in systems at different scales, imposing
unexpected order on cosmic complexity.
Transcript
STEVEN STROGATZ: Albert Einstein once wrote, “The eternal mystery of the
world is its comprehensibility.” It really is awesome when you think about
it. The laws of nature, at least in physics, turn out to be amazingly
simple. So simple that we human beings can discover those laws and
understand them and use them to change the world.
But why is nature like this? Why is it so comprehensible? And why is math
so uncannily effective at explaining it, not just in physics, but also in
chemistry, in astronomy, and even in some parts of biology? In short, why is
science even possible?
I’m Steve Strogatz and this is “The Joy of Why,” a podcast from Quanta
Magazine, where my co-host, Janna Levin, and I take turns exploring some of
the biggest mysteries in math and science today. In this episode, we’ll be
speaking with physicist Nigel Goldenfeld about the mystery of nature’s
comprehensibility.
Nigel holds the Chancellor’s Distinguished Professorship in Physics at the
University of California, San Diego, where his research spans condensed
matter theory, the theory of living systems, hydrodynamics and
non-equilibrium statistical mechanics. Previously, he was a professor at the
University of Illinois at Urbana-Champaign, and a founding member of its
Institute for Genomic Biology, where he led the biocomplexity group and
directed the NASA Astrobiology Institute for Universal Biology. In addition
to being a fellow of the American Physical Society, the American Academy of
Arts and Sciences, and the U.S. National Academy of Sciences, Nigel is also
well known for authoring one of the standard — and I have to say, terrific —
graduate textbooks in statistical mechanics.
Nigel, thanks so much for coming on the show.
NIGEL GOLDENFELD: Oh, it’s a pleasure to be here, Steve.
STROGATZ: Yes, it really is a pleasure for me. I am curious where we’re
going to go with this. It’s such a really very profound philosophical
question, this Einstein quote about nature’s comprehensibility, but I wonder
what you think of it? I mean, let’s talk about both parts of it. Is the
world really comprehensible, at least to some degree? And if it is, does
that strike you as mysterious?
GOLDENFELD: So I think it’s a wonderful quote, and certainly one that
inspired me, and I’m sure other people thinking about the research that we
do. And I think the reason it’s important is because we’ve grown physics to
such an extent that it now starts to impinge on other disciplines. You
mentioned biology, but also, you know, I could mention economics and
atmospheric sciences, climate change, all these sorts of things.
And as you start getting into these much more complex and complicated areas
of science, you wonder how were we even able to do anything in physics, let
alone these other things. And in fact, the reason these other fields are
difficult is something that’s also not clear. You know, you could also ask
what is the reason for the unreasonable ineffectiveness of mathematics in
biology.
[STROGATZ laughs]
GOLDENFELD: When you start to think about it, you realize that when we talk
about the effectiveness, we’re talking about problems where we’ve been lucky
to make an impact. And so our sample is skewed.
We have a lot of successes in science. Some of the most accurate things
that we know in science are in physics. You could say, “Well, that’s
because, you know, we only talk about those problems, because those are the
ones that actually worked. All the many other things that we try to do
failed dismally, and we never ask about those. And our sample is somewhat
biased.”
STROGATZ: Well, that’s great, this point that you’re making that we’re sort
of assuming facts not necessarily in evidence here in saying that the world
is comprehensible. Because as you say, there are these parts of science that
we still have yet to really figure out — economics, parts of atmospheric
science and so on.
So for listeners who aren’t necessarily following what we’re talking about
here, think about the example from the 1850s or ’60s: James Clerk Maxwell
figuring out the equations for how electricity and magnetism work.
It’s just four little equations that nowadays fit on a T-shirt — physics
and math nerds like me and Nigel and maybe even you like those T-shirts.
What’s crazy is that you can really understand almost everything there is to
know about electricity and magnetism with the help of those equations and
some clever math.
For instance, Maxwell himself figured out that a prediction from those
equations is that something called electromagnetic waves could exist. And
today, those are the basis for wireless communication, technology that we
all use every day in our cell phones.
And so the question is: How is it possible that we, with our puny primate
brains, can figure out these four equations that are so marvelous? And is
it, as you suggested, Nigel, just that we’re asking questions whose answers
are likely to be simple and ignoring the really hard ones? Or, I don’t know,
how should we think about this? How is it possible Maxwell could have come
up with these equations?
GOLDENFELD: Let me take another example, which is Einstein’s prediction of
gravitational waves — which has been in the news a lot in the last couple of
years. The story is that Einstein had this idea of thinking about somebody
falling in an elevator. And they realized that the falling in an elevator is
similar to what you get from gravity.
And so they came up with a principle of equivalence. And from that very,
very slender insight, translated into mathematics through Riemannian
geometry and tensor calculus and so on, which Einstein had to learn in order
to do that, he was able to create this amazing mathematical edifice, which
we call the general theory of relativity today, which is actually the theory
of gravitation.
And it explains gravitation to a higher accuracy than Newton’s law of
gravitation, and makes numerous predictions, of which the gravitational
waves are one of the most spectacular. So that’s another fantastic
example.
And it just boggles the mind that somebody could imagine that and create
the science that makes these predictions. And, you know, a hundred years
later using astonishing technology, we’re able to actually observe these
things.
STROGATZ: It is. It seems almost like a miracle. It’s something that the
physicist Eugene Wigner, in a famous essay in 1960, posed [as] “the
unreasonable effectiveness of mathematics in the natural sciences,” and you
already alluded to this phrase of his. What is unreasonable about it?
GOLDENFELD: So you and I have been talking about new qualitative phenomena
that you predict, for example, from Faraday’s law and all these things that
Maxwell had to work with. That’s one thing that’s very important about
science, is that we can predict things that you would otherwise not
expect.
But the unreasonable effectiveness that Wigner is talking about is the
accuracy with which it makes those predictions.
So here’s another example. You look at, say, the quantum mechanics of an
atom interacting with Maxwell’s electromagnetic field. When you take
electromagnetic field, you apply quantum mechanics to the interaction of
that with an atom. You’re able to make predictions to something like 10
decimal places of accuracy. And those agree with experiments to all
significant figures that the experiments, in theory, are applicable for. And
that’s astonishing. And I think Wigner and Einstein wanted to know how could
it be that such very simple mathematics has such great explanatory
power.
And people may say, ”Well, what do you mean it’s simple? You know,
Einstein’s theory of relativity, general theory of relativity, is one of the
most complicated pieces of mathematical physics that you can learn.” And
that’s true.
But the physical insight that goes into it is literally very simple. Just,
acceleration is literally like a gravitational force. And then being able to
turn that into a mathematical equation, which you can then make simple
predictions from, is really where the beauty and the amazingness lies. So, I
think that’s one aspect of it.
There’s another thing, though, that is not talked about very much, which is
that this idea that mathematics and physics is so powerful in its
explanations makes another assumption. That assumption is
reductionism.
This goes back to another quote of another founder of modern physics, Paul
Dirac, who wrote down the relativistic wave equation, an equation that
describes quantum mechanics connected with special relativity — it’s called
the Dirac equation. And he rather arrogantly wrote that his equation
describes most of physics and all of chemistry.
[STROGATZ laughs]
GOLDENFELD: And his idea is that basically — and it’s the same idea that
motivates what today we call high-energy physics, but in an era with more
bravado would be called elementary particle physics.
And there was the idea that you can just find the elementary building
blocks of matter. And then once you’ve got those, all you have to do is put
them together and you’ve explained everything in the world. And we know that
that’s not true. And that’s the sort of fundamental insight that came out of
physics around about 1950 or so. Led to the birth of what’s known as
condensed matter physics, and is certainly operative on steroids when you
look at biological phenomena, where just knowing the basic forces between
atoms doesn’t explain, you know, why you can think.
So when we talk about the effectiveness, we’re talking about the
effectiveness on very simple problems.
STROGATZ: Hmm, interesting distinction. So just to review some of these
examples again to make sure I’m with you. With Maxwell, his equations, not
so simple unless you know vector calculus or something equivalent. But then
once you know that math, as I tried to emphasize, it’s just four little
equations that can fit on a T-shirt. So simple in that way, and simple
principles going into them.
But then your point seems to be, yes, but you can only predict simple
phenomena like a propagating wave through a vacuum, whereas really
complicated stuff, say, predicting patterns of thought in a human mind — I
mean, this is the tricky part.
In principle, do we believe that it is actually somehow in the physics, but
we just can’t figure out how to do the math to show phenomena like
consciousness and emotion and all that? Or is there something else than what
the physical laws imply?
GOLDENFELD: Well, I think there is. And this goes back to the question of
why it is that we can do science at all.
If you truly believe that to understand, say, the phenomenon that we see in
biology, you can get all of that, say, from Dirac’s equation or, you know,
quantum mechanics and so on, then every time you try to understand something
quantitatively — in biology or solid state physics, for example — you know,
you’d have to worry about the radiative corrections to the mass of the top
quark. And none of us think that all of those things that happen at such
small scales inside a nucleon at very high energies have anything to do
with, you know, why a bird can fly or stuff like that.
The fact that we can do science tells us that somehow these scales get
separated through something which we typically call emergence. The great
benefit of that is that we don’t have to solve everything all the way down
in order to understand something.
STROGATZ: Interesting. So you’re saying worrying about quarks isn’t going
to tell us anything about the behavior of the stock market tomorrow. We can
somehow… It’s like, as if different scales in nature are insulated from each
other, or something like that. What’s the language you would use? You spoke
of separation.
GOLDENFELD: I talked of separation and I talked about emergence. And I’d
like to give you another example of that which is very different from the
one that people like Einstein and Wigner and Dirac and so on would’ve used,
and they wouldn’t even have known about it.
So there’s a phenomenon in nature called a phase transition. The simple
example is, you take a lump of ice and heat it up, and eventually it’ll melt
into liquid. So it’ll go from the solid phase into the liquid phase. And
then from there, if you heat it up further, it’ll go into the gas
phase.
And another example would be if I took a magnet and I heated it up. It
turns out that above a certain temperature, a magnet will stop being
magnetic. There is a theory of that transition, the magnetic transition, and
other transitions which are like it, such as how materials become
superconducting and very exotic things like that. But the most interesting
thing about the transition is that we can understand it using a branch of
physics called “renormalization group theory.” And I’m not going to go into
the technicalities of it, but what the theory predicts is that if you
measure how magnetic something is very close to the temperature where it
first becomes a magnet, whilst also applying a magnetic field, you get a
certain magnetization that you can measure as a function of temperature and
external magnetic field.
And you can do this for any magnet that you’d like. But it doesn’t really
matter what the atoms are. And if you take the data and process it in a
certain way, what you find is the results are the same for every single
magnetic material. It doesn’t matter what it is. As you go just below the
temperature where it first becomes magnetic, you find that it obeys a
certain equation. And that equation is exactly the same for every material.
And not just exactly the same: All the data from all the different magnetic
materials, they all lie on one curve. And physicists call this universality.
We completely understand that.
Now, the other thing is amazing, though, is that we can make a theoretical
prediction about what that curve should be if you process the data in the
way that the theory tells you to do it. And when you take the data and you
take the theoretical curve, it falls exactly on the experimental data.
OK, so that’s fantastic. This model of what a phase transition is is very
successful and obviously extremely accurate. Not only does it predict this
universality, but it also predicts exactly not just a number, but a whole
function.
And it’s a whole relationship that you can measure experimentally. So
that’s true. And I like to say that it’s not really true that the model has
given a precise prediction in agreement with experiment. It’s really a model
of a model of a model of a model.
STROGATZ: [laughs] OK, what?
GOLDENFELD: Yeah. OK.
STROGATZ: You better explain that.
GOLDENFELD: Yes. A model of a model of a model of a model. So, so why is
that? Well, so suppose you said to a scientist, OK, make a theory for me of
a magnet. So they’d say, well, a magnet is made out of atoms. So in order to
understand atoms and how they interact and become magnetic, I need to worry
about the electrons on the atoms. I need to worry about the magnetic moments
of those atoms. And so I make a model of the material based on quantum
chemistry.
But that model is unimaginably complicated, and it gives you no hint that
there could be something that doesn’t depend on atoms in it. Because the
model itself is very specific to the particular atoms.
So then you say, well, really, that’s way too hard. Maybe a quantum chemist
could simulate this and make a prediction. And if they did that, they would
see that the prediction did agree with what you see experimentally, and does
agree with what the theory predicts. But that’s a very huge computer
calculation.
So then you say, let’s simplify it. Let’s just not worry about the atoms
too much. Let’s just worry about how the electrons move around in the
material. So you go ahead and do that, and you find you’ve got a complicated
model of electronic structure.
STROGATZ: Sorry let me interrupt for a second, just to make sure that this
whole model of a model thing is clear. So there was the real magnet, then
there was the quantum chemistry model of the magnet, then there was the
electronic structure model of the quantum chemistry model.
GOLDENFELD: Yes, well, now we’re going to go to the quantum Heisenberg
model of the magnetic moments of the electrons inside the electronic
structure, which came from the quantum chemistry. And that model is too
hard.
So you say, OK, well, let’s throw away quantum mechanics. We’ll just make
it classical. So you do that, and the model is still too complicated. So
then you say, well, let’s take the thermodynamics, which is what everything
depends upon in any case, and let’s do some kind of expansion of that. And
that’s a model where you can finally do a calculation.
As you said, you’ve got one, two, three, four, five models of a model of a
model of a model of a model of this material. And at each step along the
way, you have made an approximation that would be rejected from every
physics journal. Because everybody would say, “That’s approximation you
can’t justify. There’s no small quantity. No idea what you’re talking about.
How can that possibly work?”
STROGATZ: I must also say that here in the math department, you know,
people would be hysterical
GOLDENFELD: Oh, yes. Oh, yes. They would be horrified. But the joke’s on
them. Because, at the end of the day, you do this whole procedure, and then
you find you make a prediction with no adjustable parameters, and it agrees
precisely with experiment.
STROGATZ: Dun, dun, dun. [laughs]
GOLDENFELD: Dun, dun, dun. Every step along the way, the approximations
you’re making are not systematic and not justifiable, at least ahead of
time. And that, I think, is a fantastic way to articulate this mystery that
you’re alluding to.
STROGATZ: Hmm, that is a marvelous exposition. I didn’t imagine this ahead
of time while preparing for this interview, but I love it. And I think
you’re really capturing the mystery. It’s like we have no right for this to
work as well as it does. It’s as if nature is somehow acting in a very
forgiving or convenient or cooperative manner for us. Like it’s helping us
get lucky or something.
GOLDENFELD: Well, that’s the thing. This happens only under special
circumstances. In this particular case, very close to a phase transition. So
we understand how it works there. But these different levels of description
that I alluded to, you know, all of these are different ways of describing
something at different length and time and space scales. And as you go to
each level, you kind of absorb all the complications of the level lower down
into some parameter that is in the description that you’re talking about.
And then once you’ve done that, you don’t need to worry about what happened
below.
That, I think, is why we can solve this particular problem and why it works
so accurately.
STROGATZ: We’re going to take a short break and we’ll be right back.
STROGATZ: Alright, welcome back. I’m speaking with Nigel Goldenfeld about
how we can model complex phenomena — and how we manage to do it so
accurately.
GOLDENFELD: So, when we talk about how we can do science at all, here is an
example which says the only reason you can do this sort of calculation is
because there’s these separations of scales and energy and time and
space.
When you start talking about, you know, physics being successful, and
biology or economics or social interactions and things like that. Can we
expect to be able to do those things if there isn’t any obvious way that one
can separate scales, and make sure that what happens at very small scales
doesn’t affect what happens at large scales? And it may be that there’s some
areas of science where that is not true. And then you may not be able to be
successful in those things.
STROGATZ: Hmm. That’s an interesting point. I may be going off the rails
here, but I’m thinking of something like economics, which you might want to
think of as the byproduct of hundreds or thousands or millions of people and
firms interacting through markets and so on. That it’s a kind of complex
system, economics, where the smaller scale, the molecules or the atoms or
the quarks are people making individual decisions that then aggregate into
an economy or a market.
In your example, where the fussy behavior of the top quark doesn’t affect
what’s happening to the birds flying overhead, here we might not have that
separation. Like, individual decision makers can have an outsized impact on
the economy? Is that the issue that makes economics so difficult or one of
the issues?
GOLDENFELD: Yeah, I don’t know about economics per se, but I’ve given this
some thought in terms in finance. So, finance is a very interesting example
to think about emergence. So remember in finance, we have data. We know
every single transaction that occurred. We know when it occurred, how much.
We have every piece of information like that. And now the question is, can
you make predictions based on it?
So let me give you an example. First of all, of course, we know that you
can’t predict things very well, and not only can you not predict things into
the future, you can’t even predict things into the past.
[STROGATZ laughs]
GOLDENFELD: So, there was a wonderful example of this, which was an event
called the Flash Crash. Do you remember what that is?
STROGATZ: You should remind us. I’m not sure I remember when and what
happened.
GOLDENFELD: On May the 6th, 2010, there was a trillion-dollar crash of the
U.S. stock market. The Dow Jones plunged like a thousand points within a few
minutes. And eventually it came back up again. And this was an unexpected
event, and to this day, people aren’t really 100% sure what triggered that.
It certainly wasn’t something that people expected at the time.
What actually happened, I believe, is that you have a cooperative
phenomenon where a lot of people are doing algorithmic trading, they’re all
more or less using the same signals to trigger their computer guided trades
and I think the whole system just synchronized and crashed and eventually
people had to stop the thing happening by pulling from the network and
things like this. So this is an example of extreme sensitivity cascading
through the system because of collective properties of the whole financial
system, properties that nobody even knew were there.
STROGATZ: Hmm. Yeah, it’s interesting to hear you use the word “cascade,”
because that comes up in connection with the power grid, where sometimes
you’ll have an event like a lightning storm somewhere and then because, as
you say, there’s this connectivity, in this case, through high voltage
transmission lines in the power grid, you can get propagating
failures.
So this does seem to be another example where a small-scale event can
propagate and have consequences at a much broader scale. So is this the idea
why maybe the hard sciences are the easiest?
GOLDENFELD: Oh, I always say that the hard sciences are the easiest. Yes,
the reason physics is so successful is because we only ask very simple
questions.
STROGATZ: So the supposed soft sciences are, in a certain sense, you would
say then, the hardest?
GOLDENFELD: So you have to ask a question. You know, what is the purpose of
science? What do we want to be able to predict? So, let’s go back to my
example about the phase transition. I talked about this example of how you
can look at the behavior of a magnet very close to the temperature where it
becomes a magnet. And there’s a universal phenomena there, and we understand
it exquisitely, and it’s wonderful and it’s amazing. So, the listener might
get the impression that we understand everything about this and there’s
nothing mysterious about it at all. But there is still one thing that I
didn’t tell you. And that is this.
There is a temperature where every material becomes a magnet. But that
temperature is different for each of the materials. And we don’t know how to
predict that number very accurately. That number is not something that is
universal, unlike the curve that I alluded to that tells you the response of
a magnet.
That number depends on everything. All of the levels of description that I
swept under the rug in order to explain what happens near a phase
transition. All of those things come back to bite you when you want to know
what is that actual critical temperature where the material first becomes a
magnet.
STROGATZ: Hmm, interesting.
GOLDENFELD: So you have to ask the questions that you ask in science with
an eye to saying, “First of all, let me ask the easy questions, the ones
that don’t depend on too much. First I understand those things, and then
later on we’ll get to the other ones.” And maybe never, but there’s a sort
of rational order in which you, would ask questions. And so science in some
sense, has to be realistic in what its goals are.
STROGATZ: Hmm, so then the resolution to our earlier question about why is
science even possible, if I’m hearing you right, you’re suggesting that some
things in nature could be described by the adage that you hear people say
all the time, “Everything depends on everything else.” And some things in
nature are not like that; not everything depends on everything else.
Am I on the right track there? That the ones where everything does depend
on everything else are really going to be hard.
GOLDENFELD: Yeah. Yeah. And there’s no shortcut. And there’s other things
where, if you ask the question in the right way, you can get an interesting
answer, which is useful and it helps your understanding of the phenomena and
so on. But if you want to know, you know, what the actual number is in
degrees Fahrenheit, well, it’s not going to tell you that.
STROGATZ: Hmm. So then it seems like we’re coming to what some people might
view as a disappointing cop-out of an answer, which is that science is
possible because we restrict ourselves to the questions that have this kind
of separation or an insulation that lets us do calculations where what’s
happening here doesn’t depend on what’s happening out at Alpha
Centauri.
And so it’s like we can answer the things that are easy in this sense, that
they’re well separated. The others are just going to be hopeless forever? Is
that the idea?
GOLDENFELD: Well, I don’t think it’s a cop-out. I think it’s a great
advance to be able to say, “This question here, that’s an example of one of
those things that you shouldn’t ask. And this question here is an example of
one that you should.” So about 15 years or so ago, we came up with a theory
that explains why there is one genetic code. It’s a general theory about the
ability to express genes and make proteins and that’s what the genetic code
is for, and also, by the way, it explains how life could have evolved so
rapidly early on. So, it’s quite an interesting theory. And so often I’ll go
and give a talk about this work, and people will ask me, “Well, why are
there 20 amino acids of life?” OK? And I’ll say, “I haven’t a clue.”
[STROGATZ laughs]
GOLDENFELD: And so I think that’s an example of one of those questions that
you shouldn’t ask. And I’ve got another reason for saying that. So the
genetic code is literally a code book that goes from DNA — or actually,
messenger RNA to amino acid, that then gets linked into a protein.
So it’s a kind of grammar, a language of molecular biology. So Francis
Crick, who of course had with [James] Watson discovered the structure of
DNA, wanted to try to understand why there are 20 amino acids in life. And
he came up with an amazing and beautiful theory, which is mathematical. Can
I tell you what the theory is? I don’t know if you know about it.
STROGATZ: So 20 amino acids, and there’s a theory for why 20?
GOLDENFELD: Yeah. So the theory is very simple. If you have a sequence of
letters in threes — ACG, TAC, whatever, these correspond to certain
nucleotides — you don’t know where the sequence starts. So really, whenever
you read the genome, you should put commas in to tell you where the words
start. So, Crick asked the question, ”Well, can you make a code so that if
I’ve got four nucleotide bases, what is the largest number of amino acids
you can code for so that every string in this code can make sense without
you having to put in the commas?”
STROGATZ: It’s a very natural question, a beautiful question.
GOLDENFELD: It’s a beautiful question, and he came up with an answer. And
the answer was that the largest number of amino acids you can get is 20.
Hence, 20 amino acids of life. So then you can enumerate all of these codes
without commas that Francis Crick had postulated. You can enumerate them.
And when the actual genetic code was discovered by [Marshall] Nirenberg and
others five or six years later, the actual code is not one of the ones that
he had predicted. It’s completely wrong.
So, this is, if you like, the reasonable ineffectiveness of mathematics in
biology. Because, in fact, the real code is a product of evolution. And
there’s nothing special about the number 20.
So, this is an example of, you’ve got to ask the right question. You
thought you could do science, biology in this case, using the same sort of
elegant mathematical principles that are so powerful in physics, but you
completely get egg on your face when you try them, without really
understanding more about the scientific phenomena that are relevant in
biology.
STROGATZ: And so would you generalize, then, to say that the role of
history or evolution or contingency, those kinds of things, are another
ingredient for why we might expect certain subjects to be difficult, or
maybe not amenable to the elegance of math? Is that the issue?
GOLDENFELD: Well, it is, but it’s not completely hopeless. I mean, we did
make a theory for the evolution of the genetic code, which did explain, you
know, how is it that the world started 4.6 billion years ago? The last
universal common ancestor of all life on Earth today was around 3.8 billion
years ago.
So that means that in less than a billion years, life went from nothing to
the architectural complexity of the modern cell. And then after that hardly
evolved at all.
OK, so that is staggering. I mean, I don’t know, of course, the ultimate
reason of how life evolved and so on, but at least when we made our theory
of this, it explained why it evolves so rapidly, and it explained why the
genetic code is so accurate and why there’s only one of them. So it explains
some things, but not the other.
So we definitely understood — advanced in our understanding of basic
science, but we were able to do that because we fully recognized, “Here’s a
question that’s not going to be a good one to go after. Here’s a question
that we might be able to do.” And I think one of the jobs of the scientist
is to really ask the right questions in the right way. And that’s harder
than it looks.
STROGATZ: Oh, that’s a very, very marvelous stopping point for us in a way,
that part of the secret of science is the art of asking the right questions.
There’s even a book with that name, isn’t there? Isn’t that Peter Medawar’s
book, The Art of the Soluble?
GOLDENFELD: That’s right, but yes, I mean all science starts with asking
questions. And if you don’t know how to ask questions, you can’t do science.
Science is not the technology, the techniques of doing science. Of course,
that’s how we’re able to do it, but fundamentally, it comes from asking
questions.
STROGATZ: Probably a lot of our listeners are thinking: What about
everything that’s going on today with machine learning, artificial
intelligence, the possible existence of quantum computers that’s supposed to
solve all kinds of problems once they really start to get serious?
Do you think that those kinds of technologies will help us deal with these
intricately interwoven kinds of problems where everything or many things
depend on each other, and we don’t have a good separation.
GOLDENFELD: Yes. Well, there’s two things I want to say about that. I’m
really glad you raised that issue. So one of the things is the phenomenon of
emergence. I mean, when people started building, you know, things like
ChatGPT and so on, what those things are, are basically machines that can
predict the next word. And nobody expected that those machines could pass
the bar exam or medical exams or help people with their homework or help
people write computer programs and so on. The range of applications has been
staggering and a surprise even to the people who built these machines. And
in fact, nobody really knows how they work.
In fact, if you look at the effectiveness of AI in solving problems, it
also exhibits parallel relationships very much like the ones that you see in
phase transitions. So one of the things I think is a great frontier for
science is trying to understand how these machines are able to do so much
more than what they were designed to do.
The other thing, where I think it’s important, is that what AI is very good
at is discerning patterns in data, which are so complex that we don’t
perceive as well as these machines.
So I think there’s great opportunity to use them to solve problems, which
are very, very hard. The problem that I think is an ambitious problem I
think could only be solved by using AI is trying to understand the origin of
instinct. So how is it that instincts are coded in biological organisms? OK?
We understand the genetic code and we understand how the proteins that go
into living organisms, how they’re coded and so on.
But going from that level of description to the complexity of an organism
like a fish that knows where to swim to in order to go to its breeding
ground and seagulls and things like this. Clearly, we’ve somehow managed to
encode very, very complex behavior. So this is something that reaches across
all scales of living systems. And it’s hard for me to see, in principle, how
something as complicated as instinct can be coded, but I think that AI would
be able to perhaps be a tool that we could use to help us make a scientific
discovery and not just, you know, build amazing technological
machines.
STROGATZ: Hmm. Well, that is fascinating, Nigel. I knew it would be
provocative and stimulating to talk to you and you’ve just, I think,
demonstrated how, the art of science is asking good questions with that
question you’ve left us with. So thank you. We’ve been speaking with
physicist Nigel Goldenfeld. It has been a really great pleasure to talk to
you today. Thank you.
GOLDENFELD: Thank you.
STROGATZ: Thanks for listening. If you’re enjoying “The Joy of Why” and
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“The Joy of Why” is produced by PRX Productions; the production team is
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