Michael Nielsen – How science actually progresses

one of the sort of the famous results often presented as this experiment that was done in the 1880s and that helped Einstein come up with the special theory of relativity a little bit later. So sort of changing the way we think about space and time and our fundamental conception of those things. And there's kind of a big gap, I think, between the way Michelson and Morley and other people at the time thought about the experiment and certainly the way in which Einstein thought or did not think about the experiment.

In actual fact, he stated later in his life, he wasn't even sure whether he was aware of the paper at the time. There's a lot of evidence that he probably was aware of the paper at the time, but it actually wasn't dispositive for his thinking at all. Something else completely was going on.

So, what Michelson and Molley thought they were doing was they thought they were testing different theories of what was called the ether. So as you go back to the 1600s, Robert Boyle introduced the idea of the ether, and basically the idea of the is, you know, we know that sound is vibrations in the air, and then Boyle and other people got interested in the question of like is light vibrations in something, and they couldn't figure out what it was. Boyle actually did an experiment where he tested whether or not he could propagate through a vacuum.

He found that you could, you couldn't do it with sound. So he introduced this idea of the ether and then for the next two hundred or so years, people had all these kind of conversations about what the was and what its nature was. And the Michelson and Morley experiment was really an experiment to test different theories of the ether against one another and in particular to find out whether or not there was a so called ether wind.

So the idea was that the earth is passing through maybe this ether wind and if it is passing through the ether wind, sort of this background, and you shoot a light beam sort of parallel to the direction the wind is going in, it'll get accelerated a little bit and if it's being passed back sort of in the opposite direction, it will get slowed down a little bit and you should be able to see this in the results of interference experiments. And what they found, much to their surprise, think was that in fact there was no ether wind And that ruled out some theories of the but not all. And Michelson certainly continued to believe in the ether.

but he continued to believe until, I mean, he died. He died, I think it was like 1929 or so, it was like the late 20s. He was still doing experiments in the 1920s sort of about whether or not the ether existed.

So continued to believe in the ether to the end of his life. I think the last public statement he made is like a year or two before he died and he still believed, basically believed at that point.

Yeah. I mean, it certainly doesn't show that ideas about falsification are wrong, are falsified. But it does show the most naive ideas, things are often much more complicated than you think.

Michaelson did this experiment in 1881, he was a very young man, and then other people, I think Rayleigh was one of them, pointed out that there were some problems with the way he did it so they had to redo it in 1887. And at that point, like a lot of the leading physicists of the day, leading scientists of the day, basically accepted this result that there was no ether wind. But what to do about this?

So yeah, sure, maybe you've falsified some theories of the ether. There are others that you haven't falsified at all at this point and people sort of set to work on developing those. Actually, it is funny, mean people will phrase it as show that there was, you know, that the ether didn't exist and even just the word the there is kind of a misnomer.

Actually had a ton of different theories and a couple of leading contenders. So yeah, there's some version of falsification going on but how you respond to this new experiment is very, very complicated. And most people responded, I mean certainly the leading physicists of the day responded by saying okay, this gives us a lot of information about what the must be but it doesn't tell us that there is no ether.

In fact, Lorentz Yeah.

Yeah, think that's a strong statement. Mean Lorentz introduces this quantity called local time, which he regards as not trying my understanding is he's not trying to give a a physical interpretation of this, but it's what Einstein would later just recognize as time in another inertial reference frame. And he's not trying to attribute much physical meaning to it.

I think Pancrai gets much closer later on to realizing that actually this is the time that's registered by clocks. But if you think about, you go forty odd years later, people start doing these muon experiments where they see basically cosmic rays hit the top of the atmosphere, they produce a shower of muons, and you can look to see at different heights in the atmosphere, you can look to see how many of those muons remain and they decay over time and a very strange thing happens which is that they're decaying way, way, way too slow so you expect actually they shouldn't be able to sort of last the whole way through the atmosphere at all, Their decay rate is too quick if you were in a classical theory. But if in fact their time really has slowed down, it's okay and in fact the measured decay rates in 1940 and then there have since been more accurate experiments done match exactly what you expect from special relativity so you know, that's the kind of thing where, again, if Lorenz had been alive, he'd been dead ten or so years at that point, if he'd been alive, I'm sure he would have tried, well, it seems quite likely that he would have tried to save his theory by patching it up yet again, but it would have been a massive, I mean, that's a real setback.

It starts to just look like, oh no, time is, this thing that Lawrence introduced as a mathematical convenience, no, no, no, that's actually what time is. Right. For the muons, at least.

And then there's a whole bunch of other experiments that show this very similar phenomenon. And when was that experiment done? It was, I think, 1940 or '19 it might have been published in 1941.

So there's clearly some process that human science does which can distinguish different theories. Can I just interrupt? Mean, you used the word process and it's interesting to think about that term.

Process kind of carries connotations of, you know, it's something said in advance, it's something and it's much more complicated in practice. You have people like Lorentz who, I mean, Einstein just absolutely, utterly admired and Poincare, one of the greatest scientists who ever lived, and Michelson, another truly outstanding scientist, never reconciled themselves. So it's not as though there's some standard procedure that we're all using to reconcile these things.

Great scientists can remain wrong for a very long time after the scientific community has broadly changed its opinion.

think you sort of you actually do want to go case by case and try and understand that it's not necessarily clear that they're they're doing the same thing wrong in in all other cases. The Poincare case is amazing. He seems to have understood the principle of relativity, the idea that the laws of physics are the same in all inertial reference frames.

He seems to have understood that the speed of light is the same in all inertial reference frames. Doesn't actually phrase it quite that way, is my understanding, I don't speak French. These the ideas that Einstein uses to deduce special relativity.

Then he also has this additional misunderstanding where he thinks that length contraction is a dynamical effect, that somehow particles are being pushed together by some external force, something is going on dynamically and he doesn't understand that it's purely kinematics, that actually space and time are different than what we thought and you need to fundamentally rethink those things. So it's almost like he knew too much. He had sort of almost too grand a vision in mind and Einstein sort of almost subtracts from that and says no, no, no, space and time are just different than what we thought and here's the correct picture.

And there's a paper in, I think it's 1909, Punkhary, he's got this dynamical picture of what's going on with the length contraction and this is just not necessary. This is a mistake from the modern point of view. So why is he doing this?

Why is he clinging onto this idea? And I don't know, obviously never met the man. It would be fascinating to be able to talk it over and to try and understand.

Expertise seems to be getting in the way. He knows so much, he understands so much, and then he's not able to let go of these things. Actually, a really interesting fact is that a few years prior, so 1890s, Einstein's a teenager, he believes in the ether too, like he knows about this stuff.

But he's not quite as attached obviously as these older people were and maybe they were a little bit prisoner of their own expertise. That's my guess.

Good question and I don't know entirely the answer. I can give you a partial answer that sort of, centuries I, in the future, start to find very compelling. And I'm sure it's sort of part of the historic story at least, which is one of the big shocks for Newton eventually.

He did understand Kepler's laws of motion eventually, so you're able to explain the motions of the planets in sky. But he also, out of the same theory, his theory of gravitation, was able to explain terrestrial motion, so he was able to explain why objects move in parabolas on the Earth, and he was able to explain the tides in terms of the moon and the sun's effect, gravitational effect on water, on the earth. And so you have what seemed like three very different disconnected phenomena all being explained by this one set of ideas, think starts to feel, that's very compelling, at least to me.

And I think most people find that very, very satisfying once they eventually realize it.

he's written an he read an entire book. No. No.

The essay. Yeah. Yeah.

Sure. Yeah. I love I love that.

I mean, this description of him as the last of the magicians is is wonderful. Yeah.

So it's from actually, I believe it was a talk that he gave at Cambridge not not long before he died. He'd acquired Newton's papers somehow and then he gave a lecture I think twice about this or that his brother Geoffrey gave it the other time because he was too ill. There's just this wonderful, wonderful quote in the middle, actually the whole thing is really interesting but I love this particular quote.

Newton was not the first of the age of reason. He was the last of the magicians, the last great mind which looked out on the visible and intellectual world with the same eyes as those who began to build our intellectual inheritance rather less than ten thousand years ago. And this idea that people have that Newton was sort of the first modern scientist is somehow wrong.

There's some truth to it, but he really had this very different way of looking at the world that was part superstitious and part modern. It was a funny hybrid. He's sort of this transitional figure in some sense.

mean, these questions, the point is that where we always get bottlenecked is where the previous processes and heuristics don't apply, right? Like that's almost sort of definitionally what causes the bottlenecks. Because people are smart.

They know what has worked before. They study it, they apply the same kinds of things and so they don't get stuck in the same places as before. They keep getting bottlenecked in different places.

Mean that's over generalizing a bit but I think it's the right, like if you're attempting to reduce science to a process, you're attempting to reduce it to something where there is just a method which you can apply and you turn sort of the crank and out pops Insight, I mean you can do a certain amount of that but you're going to get bottlenecked at the places where your existing method doesn't apply definitionally there's no crank you can turn. You need a lot of people trying different ideas and sort of the more difficult the idea is to have, greater the bottleneck, but then also sort of the greater the triumph. Quantum mechanics is like a great example of this.

It's such a shocking set of ideas. It's such a shocking theory. Actually, the theory of evolution in some sense is also quite a shocking idea.

Not the principle of natural selection but that it can explain so much. That's a shocking idea.

idea must have been known to animal breeders for a long time at some level. Certainly large chunks of the idea were known that artificial selection was a thing. And in some sense, Darwin's genius wasn't in having that idea, it was understanding just how central it was to biology that you can potentially sort of go back and you can explain a tremendous amount about all of the variety of what we see in the world with this as not necessarily the only principle but certainly a core principle.

And so, he writes this wonderful, wonderful book, The Origin of Species. And it's just so much evidence and so many examples and sort of trying to tease this out and see what the implications are and to connect it to as much else as you possibly can, to connect to geology and to connect it to all these other things. So that's sort of hard work that, you know, making the case that it's actually relevant all across the biosphere you know, is what he's doing there.

He's not just having the idea, he's making a compelling case that no, it's intertwined with absolutely everything else. Yeah.

incredibly weird, right, if you think that the origin of life must have been very hard, like that there's a bottleneck there, then it's not so surprising. Yeah.

there's a whole bunch of problems as well.

example of Lael and other geologists, sort of early 1800s, having this idea of deep time, that does seem to have been crucial. I know Darwin was very influenced by Lael, if you don't have at least tens or hundreds of millions of years, evolution just starts to look like a nonstarter. We should be seeing radical change.

In order to make it work on a timescale of say five to ten thousand years or six thousand years, Bishop Usher, you would need to be seeing evolution occurring at a massive rate sort of during human lifetimes and we're just not seeing that. That does seem to have been a blocker. Interesting to your question, what other blockers were there?

Were there any others?

Actually, me just go back and sort of zoom out to your original question. You're talking about sort of the verification loop in and an example I think that should give you pause there is the big signature success so far is certainly AlphaFold. And of course AlphaFold really isn't about AI.

A massive fraction of the success there is the protein data bank. So it's X-ray diffraction, it's NMR, it's CryoM, and several billion dollars that were spent obtaining is 180,000 protein structures. So it's basically the story of we spent many, many decades obtaining protein structure just by going out and looking very hard at the world experimentally and then we fitted a nice model at the end of it and that was like a tiny fraction of the entire investment.

But it's definitely not, that's a story of data acquisition principally. It's not only, mean the AI bit is very, very impressive, it's quite remarkable, but it is only a small part of the total story.

I mean, maybe a really pivotal question. The sense of, if you sort take a very classic point of view, you want these deep explanatory principles, you want sort of as few free parameters as you possibly can, you want very simple models which explain a lot. And AlphaFold doesn't look anything like that.

And so you might just say it's nice, it's maybe helpful as model but it doesn't have, it's not a scientific explanation. So that's like a conservative point of view, that's sort of answer one to the question. I think answer two is to say something like maybe you shouldn't think about AlphaFold as an explanation in the classic sense but maybe it contains lots of little explanations inside it and so maybe part of what you can get out of interpretability work is you can go into AlphaFold and you can start to extract certain things.

Maybe basically by doing sort of archaeology of AlphaFold, we can actually understand a great deal more about these principles. You can start to extract it all, that circuit does this interesting thing and we learn this. So I don't know to what extent that's been done with AlphaFold.

I know it's been done a little bit with something like the chess models. I believe it's AlphaZero. There seem to be some strategies which were certainly borrowed by Magnus Carlsen at least, which he seems to have just taken from AlphaZero.

I I don't think there's any public confirmation of this but some experts have noticed that he changed his game quite radically after some public forensics were released on how AlphaZero worked. So that's kind of an example where I think human beings are starting to extract meaning out of these models and maybe that starts to lead to viewing the models as a potential source of explanations. You need to do more work because they're not very legible upfront but you can extract them potentially and I think that's kind of an interesting intermediate situation where they're not explanations but you can extract interesting explanations out of them, can use them as of a source.

I think the third and the most interesting possibility is they're a new type of object in some sense. They should be taken very seriously as explanations that where in the past we haven't had the ability to really do anything with them Now we're going have interesting new actions which we can do. We can merge them, we can distill them, we can do all these kinds of things and there's going be almost a new, it's a big opportunity in the philosophy of science start to do that.

There's sort of an anticipation of this in some sense I think in the way I know some mathematicians and physicists who historically if you had like a 100 page equation, which is the kind of thing that does come up, there's just nothing you can do if it's nineteen, twenty. There is nothing you can do. At that point you give up on the problem.

And now today with tools like Mathematica, you can just keep going and so that's an object now, that's a thing that you can work with and there are examples where people work with these things that formerly were regarded as too complicated and sometimes they get simple answers out of the end, that's just an intermediate working state.

there, this is sort of to my point that we don't really understand what to do with the models, we don't have the verbs necessarily yet. But it is certainly interesting to think about the question, where you start to apply constraints to the models, sort of essentially saying what's the simplest possible explanation or can you simplify, can you give me sort of the ninetyten explanation? Can you go further and further and further in boiling it down?

going from Newtonian gravity to Einstein's general theory of relativity and these are shockingly different theories and the question is like what causes that flip and as nearly as I understand the history what goes on is Einstein develops special relativity and pretty much straight away he understands, I mean it's a very obvious observation. In special relativity, influences can't propagate faster than the speed of light and in Newtonian gravity, action is at a distance, in fact it's straight away in special relativity could use Newtonian gravity to do faster than light signaling, you could send information backwards in time, you could do all kinds of crazy stuff And so it's not a big leap to realize oh we have a big problem here. And so that's the forcing function there.

You've realized that your old explanation is not sufficient, you need something new and then you're going to start by doing the simplest possible stuff and it just turns out that a lot of that stuff doesn't work very well and so you're sort of forced, in fact it is interesting, he is sort of forced to go through these steps of gradually it gets quite more complicated and it's sort of wrong in a variety of ways and the final theory appears really shockingly simple and beautiful but it's gone through somewhat ugly intermediate stages.

And then the verification loop for that might be quite long, you just need to keep all those research programs alive at the same time. Yeah, I mean, I think there's like, this point that you make about sort of keeping all the different research programs alive, that I think is very important and somehow central. A great example is situations where the same answer has been correct in some circumstances and wrong in other circumstances.

So the planet Uranus was not in quite the right spot and people very famously predicted the existence of Neptune on this basis. Wonderful massive success for Newtonian gravity. The planet Mercury is not in quite the right spot.

You predict the existence of some other distorting planet. Turns out that doesn't exist. Actually the reason Mercury is not in the right spot is because you need general relativity.

And so you've pursued very similar ideas and it's been very successful in one case and it's been completely and utterly unsuccessful in the other case. And I think a priori, you can't tell which of these is the thing to do and you actually need to do both. And so this is certainly very true in the history of science that this kind of diversity where you just have lots of people go off and pursue lots of potentially promising ideas, you just need to support that for a long time.

It's hard to do that for a variety of reasons, but it does seem to be very very very important.

even more powerful telescope or maybe there's some magnetic field which is sort of occluding our measurements. And this happens over and over right? You know there's just so many stories which are exactly like this.

I mean an example I love from in the 1990s, some people noticed that the Pioneer spacecraft weren't quite where they were supposed to be and so you can get very excited about this, oh my goodness, general relativity is wrong, maybe we're going to discover the next theory of gravity. Today, the accepted explanation is that no, actually there's just a slight asymmetry in the spacecraft, it turns out that the thermal radiation is slightly larger in one direction than the other and that's causing a tiny little acceleration towards the sun. And most of the time when there's these apparent exceptions it's just something like that's going on.

It's very much like the Mercury Vulcan case but every once in a while it's not and a priori you can't distinguish these. Science is just full of these. It's funny too, the way we tell the history of science, it sounds so simple, like oh you just focus on the right exception and you realize that you need to throw out the old theory and lo and behold your Nobel Prize awaits.

But in fact these exceptions are all over the place and 99.9% of the time it just turns out to be some effect like this thermal acceleration in the case of the Pioneer spacecraft. Unfortunately there's a lot of selection bias going into those stories.

the difficulty with automating science. Yeah, mean the question is where is the bottleneck at some level and sort of, you know, are we primarily bottlenecked on one thing or one type of thing or we bottlenecked on sort of multiple types of things. Certainly talking to structural biology people, they seem to think that AlphaFold was an enormous advance, it was a shock, so at some level, yes, AI can, it seems certain can help us speed up science.

So it is helping with a certain type of bottleneck. That doesn't mean though, as you're saying, that it's necessarily going to help with all kinds of bottlenecks and I suppose the question you're pointing out is what are the types of bottlenecks that remain and what are the prospects for getting past them. I think even in the case of coding, it's really interesting talking to programmer friends, at the moment they're all in this state of shock and high excitement and they're all over the place actually kind of talking to them.

You do wonder where is the bottleneck going to move to? So certainly one thing that a lot of them seem to be bottlenecked on is now having interesting ideas and in particular having interesting design ideas. So there's not really a verification loop for knowing oh that design idea is very interesting So they are no longer nearly as bottlenecked by their ability to produce code but they are still bottlenecked by this other thing.

Always were, formally they weren't bottlenecked on it because just writing code took so much of their time, could they sort of have lots of ideas, while they were, you know, they'd take three weeks to implement their prototype and then they would implement the next version. You know, they're taking three hours to implement the prototype and they don't have, you know, as good ideas, sort of after that from a design point of view.

I mean, I think probably the idea there that I'm at least somewhat attached to is the idea that sort of the tech tree or the science and tech tree is probably much larger than we realize. I mean we're sort of in this funny situation. People will sometimes talk about a theory of everything as a potential goal for physics and then there is this presumption somehow that physics is done once you get there.

And of course this is not true at all. If you think about computer science, computer science basically got started in the 1930s when Turing and Church and so on just laid down what the theory of everything was. They just said here's how computation works and then we've spent ninety odd years since then just exploring consequences of that and gradually building up more and more interesting ideas.

And those ideas are, to some extent, can just regard as technology but to some extent insofar as they're sort of discovered principles inside that theory of computation, I think they're best regarded as science and in some cases very fundamental science. Ideas like public key cryptography are, I mean they're just incredibly deep, very non obvious ideas which in some sense lay hidden already sort of in the 1930s. And so my expectation is that there will be different ways of exploring this tech tree and we're still relatively low down.

We're still at the point where we're just understanding these basic fundamental theories and we haven't yet explored them. A thing which I think is quite fun is if you look at just the phases of matter. When I was in school we'd get taught that there are three phases of matter or sometimes four phases of matter or five phases of matter depending a little bit on what you included.

And then as an adult, as a physicist, you start to realize, oh, we've been adding to this list. We've got superconductors and superfluids and maybe different types of superconductors and Bose Einstein condensates and the quantum Hall systems and fractional quantum Hall systems and it's starting to turn out it looks like actually there's a lot of phases of matter to discover and we're going to discover a lot more of them. And in fact we're going be able to start to design them in some sense.

We'll still be subject to the laws of physics, but there is this tremendous freedom in there. And this looks to me like, oh, we're down at sort of the bottom of the tech tree. We've barely gotten started there and I expect that to be the case sort of broadly.

Certainly in terms of, I think programming is a very natural place to look. The idea that we've discovered all the deep ideas in programming just seems to me obviously ludicrous. We keep discovering what seems like deep, new fundamental ideas and I mean we're very limited.

We're basically slightly jumped up chimpanzees so we're slow and it's taking us time. But what do we look like sort of another million years in the future in terms of all of the different ideas which people have had around how to manipulate computers, how to manipulate information, I think we're likely to discover that actually there are a lot of very deep ideas still to be discovered. It's a nice, who was it, I think it was Knuth in the preface to the art of computer programming, said something like he started this book back in the 60s and he talked to a mathematician who was a bit contemptuous and said, look, computer science isn't really a thing yet.

Come back to me when there's a thousand deep theorems. And Knuth remarks, and he's writing this now decades later, the preface, there clearly are a thousand deep theorems now. And that means, it's really interesting to sort of think of it, like 's the long term future as you get higher and higher up in the tech tree, like choices about which direction we go and sort of how we choose to explore, I think it's potentially the case that different civilizations or different choices mean that we end up in different parts of that tree.

And in particular just things, mean sort of very basic things about we're very visual creatures, certain other animals are much more orally based, does that bias the types of thoughts that you have and then you extend it to much more exotic kinds of civilizations where maybe just sort of their biases in terms of how they perceive and how they manipulate the world are maybe quite different than ours and that might make some significant changes in terms of how they do that exploration of the tech tree. It's all speculation obviously. This is such an interesting take.

Want to better understand it.

Of all of the intelligences in the Milky Way galaxy, maybe that number is one, actually arguably we've already increased the number. But of all of those, what fraction of the concept of counting? And it does seem very natural.

What fraction have discovered the idea of some kind of decimal place system? Interesting question. And maybe we're missing something really simple and obvious that's actually way better than that.

What fraction got there immediately? What fraction had to go through some other intermediate state? What fraction used linear representations versus a two dimensional or three-dimensional representation.

I think the answers to these questions are just not at all obvious. It's a lot of design freedom.

a very common argument here is sort of the low hanging fruit

And I think it's worth thinking about like why do you expect diminishing returns and how well does that argument actually apply, in practice. An analogy I like, is actually thinking about sort of going to some event, going to a wedding or whatever and you go to the dessert buffet and they've put out 30 desserts. And of course naturally what people do, the best desserts go first.

I mean we don't quite have a well ordered preference there so maybe there is some difference human beings are fairly similar so the best desserts will go first. And this is an argument for why you expect diminishing returns in a lot of different fields if it's relatively easy to see what's available and people have similar preferences then the best stuff goes first and it just gets worse and worse after that. A very static snapshot in time of scientific progress, Maybe there's some truth to that.

But if somebody is sitting behind the dessert table and is replenishing, restocking the desserts and keeps adding new ones in, it may turn out that a little bit later, much better desserts appear so you're going to go and eat those instead. And scientific progress has a little bit of that flavor. We go through these sort of funny time periods, computer science is a great example, where computer science basically arose as sort of a side effect of some pretty abstruse questions in the philosophy of mathematics and logic.

And so you've got these people trying to attack these rather esoteric questions that seem quite high up in some sense in sort of exploration, quite esoteric, and they discover this fundamental new field and all of a sudden there's an explosion there. Sort of the diminishing returns argument just didn't apply there. We just weren't able to see what was there and this has been the case over and over and over again.

New fields arrive and all of a sudden boom, it's actually easy to make progress again, young people flood in because you can be 21 and make major breakthroughs rather than having to spend twenty five years mastering everything that's been done before, it's obviously very attractive. And I don't understand, I'm not sure anybody understands very well sort of the dynamics of that, like how to think about why the structure of knowledge is that way, that these new fields keep opening up, but it does seem empirically at least to be the case.

is notable because it is one big exception to the fact that it's hard to think of other I think that's a consequence of sort of the architecture of attention, right? Like at any given time there's always sort of a most successful thing. Maybe if deep learning wasn't a thing maybe you'd be talking about CRISPR, maybe you'd be talking about whatever it is, maybe we wouldn't think about solving sort of the protein structure prediction problem as a a success of AI, maybe we would have figured out how to doing it with sort of curve fitting like more broadly construed and we'd just be like, oh wow, we took a lot of computing resources but protein structure prediction might be an enormously important thing.

So there is always sort of our biggest thing and I think what you're pointing out is more a consequence of the way in which attention gets centralized. It's basically fashion is sort of what I'm saying. It's not just fashion but there is some dynamic there.

sort of. There's a question of what's actually hard. If it's just the ideas, those spread relatively quickly, it's relatively easy to share ideas.

If it's something more, it's almost sort of a Dan Wang kind of an idea where it's actually sort of, there's some notion of capacity, you need all the right techs, you need all of the right manufacturing capacity and so on. And so, you know, Civilization A has very different kind of manufacturing capacity and it's just not so easy to build in civilization B even if civilization B is kind of ahead, I think that becomes true. There is actually comparative advantage which is really worth, I mean it's going to provide massive benefits to trade in both directions.

Eventually you're going to expect some diffusion of innovation. It is funny to think about what the barriers are there. A thought experiment I like to think about is of GitHub but for aliens.

So somebody presents you with all of the code from some alien civilization and I don't even know what code means there but sort of their specification of algorithms. It would have many interesting new ideas in there and it would take forever for human beings to dig through and to try and extract all of those. One reason, I mean the origin of this for me was actually thinking about proteins in nature.

We've been gifted just this incredible variety of machines which we don't understand really at all and we just have to go and sort of try and understand them on a one by one basis. We're still understanding hemoglobin and insulin and things like this and no doubt and there's hundreds of millions of proteins known. So it is a little bit like that.

We've been gifted by biology just this immense library of machines, no doubt containing an enormous number of very interesting ideas and we're just at the very, very, very beginning of understanding it. So actually that's I suppose kind of your point actually is I need to relabel your argument slightly but you sort of think of that as a gift from an alien civilization which obviously it isn't but you think of it that way and it's like oh my goodness there's so much in there and we're going to study it and goodness knows how long we could continue to study it. There's tens of thousands of papers about the hemoglobin and things like that and we still don't understand them and yet we're getting so much out of it.

Think about insulin alone, it's such an important thing.

name it, right? About Think about Chenissen walking along, and we know almost nothing about these proteins and yet the tiny few facts we do know are just incredible. The ribosome, another example, this miraculous engineer, sort of device, little factory.

a very important observation. Yeah. I hadn't thought about that at all.

That is a very interesting observation. It is funny, I mean, comparative advantage is something that people love to invoke and it's a very beautiful idea, obviously. There are limits to it.

It's a special limited model. Chimpanzees can do interesting things. We don't trade with them.

And I think it's sort of interesting to think about the reasons why. Part of it is just power, think. Once there's a sufficiently large power imbalance, very often, always, but very often groups of people seem to shift into this other mode where they just seek to dominate.

Maybe there's something special about human beings but maybe it's also sort of a more general sort of a thing.

That subsistence is gonna be worth the benefit you get out of the horse. I I do think it's interesting like that that just the the sheer fact that, my expectation and my intuition obviously differs a great deal from yours on this is that most parts of the tech tree are never going to be explored. There's just too many interesting ways of combining things, there's too many sort of deep ideas waiting to be discovered and not only we but nobody ever is going to discover most of them.

So choices about how to do the exploration actually matter quite a bit. Interesting. It's something I really dislike about sort of technological determinist arguments.

I'm willing to buy it sort of low enough down when progress is relatively simple but higher up you start to get to shape the way in which you do the exploration and we are starting to shape it in interesting ways. I mean there's various technologies that have been essentially banned, think about DDT, you think about chlorofluorocarbons, you think about restrictions on the use of nuclear weapons, the Nuclear Non Proliferation Treaty. Those kinds of things are, they weren't done before the fact but it's starting to get pretty close in some cases where we just sort of preemptively decide we're not going to go down that path.

So that starts to look like a set of institutions where we are actually influencing how we explore the tech tree.

get sort of very uniform and get really commoditized like three d printers have been the next big thing for at least twenty years now. Why do they still not work all that well? Why are they still not actually at the center of manufacturing and sort of what comes after that.

It is funny to look at say the ribosome by contrast, it really is at the center of biology in a whole lot of really interesting ways and whether or not that's the future of manufacturing is something very simple, sort of where everything goes sort of as throughput through, I don't know, maybe it's a bioreactor or something like that so you send the information and then you grow stuff or you have some three d printer that actually works. If they're good enough, then actually it does become much more a pure information problem and some of this process knowledge becomes much less important.

I don't know. I mean, know, All I have here is speculation and sort of instinct. My instinct is we keep finding very fundamental new things.

It was very, I mean for me anyway, quite formative to understand, as I say, I gave the example before, these wonderful ideas of Church and Turing and these other people, ideas about universal programmable devices and then you understand later, oh, this also contains within it the ideas of public key cryptography and then you understand later, oh, that also contains within it the ideas, mean people refer to it as cryptocurrency or whatever but there's a very deep set of ideas there about the ability to collectively maintain an agreed upon ledger which is built upon this and there's probably many deep ideas to sort of it actually took whatever, it's taken many years really to figure out the right canonical form of those. And so just this fact that you keep finding what seem like deep new fundamental primitives, I find very, for me that has been a very important intuition bump and it's across, I mean I've given that particular example but I think you see that same pattern in a lot of different areas.

Mean, of all, all of their examples are narrow, They pick a particular thing and then they look at some particular metric. Nowhere in that shows up, like GPUs don't show up there, right? Like in the sense of all of a sudden you get this ability to parallelize and that's really interesting.

So there's sort of a lot of external consequences that are just delighted from basically they have these simple quantitative measures, they look at it in agricultural productivity, they look at it in a whole lot of different ways, but you do have to focus narrowly and I suppose I'm certainly interested, as I say, in this fact that just new types of progress keep becoming possible. But there is still, I think even there, there does seem to be some phenomenon of diminishing returns. Is that intrinsic?

Is that something about the structure of the world? What is it? Well, one thing which hasn't changed that much is sort of the individual minds which are doing this kind of work and maybe those should be sort of being improved as well or some sort of feedback process going on there.

And maybe that changes the nature of things. Suppose I look at scientific progress up until let's say 1700, something like that, and it was very slow and also it was very irregular. Had the Ionians back sort of five centuries before Christ doing these quite remarkable things and so much knowledge would get lost and then it would be rediscovered and then it would be lost again and you'd have to say that progress was very slow.

There it's partially just bound up with the fact that there were some very good ideas that we just didn't have. Even once you've had the ideas, then you need to build institutions around them, actually need to solve a whole lot of different problems about training, about allocation of capital, about all these kinds of things, just about basic sort of security for researchers so they're not worried about the inquisition or things like that. So there's all these kind of complicated problems.

You solve all those complicated problems and then all of a sudden boom, there's a massive sort of burst of scientific progress. If you're not changing it, if there's some kind of stagnation there, if you're not changing those external sort of circumstances, yes, you may start to get sort of diminishing returns again. But that doesn't mean there's anything intrinsic about the situation.

Maybe something external needs to change again. Obviously a lot of people think AI is potentially going to be a driver. I mean, it certainly will at some level, fact to the extent you can think of a lot of modern scientific instrumentation as really, I mean, some level kind of robots.

What is the James Webb Space Telescope? Well, it's unconventional maybe to describe it as a robot but it's not completely unreasonable either. It is an example of a highly automated, very sophisticated system with electronically mediated sensors and actuators where machine learning in fact is being used to process the data.

So in that sense we're already starting to sort of see that transition, we've been seeing it for decades.

mean obviously we can't prove this, but it certainly seems quite plausible. I mean part of the issue of course is just the substrate we have available to conceive seems all wrong. You can't speculate with a bunch of chimpanzees about what it would be like to have language.

Just to sort of pick a major transition in the past, the transition itself is the thing and it seems likely. If we're talking about taking a puff kind of thoughts, I'm certainly amused by the idea that there's going to be some transition involving artificial general intelligence using classical computers, but actually there'll be an interesting transition with quantum computers as well. They're probably capable of a strictly larger class of potentially interesting computations.

So maybe actually the character of AQGI or whatever it should be called is actually qualitatively different. So maybe there's sort of a brief period between those two things. Interesting.

Shor's algorithms do. Yeah, mean we've only been thinking about it for thirty years or whatever, forty or so years, not for very long and we sort of haven't in some sense thought that hard about it as a civilisation. Does it turn out that it's very narrow?

Maybe. Does it turn out that it's very broad? That's also like a really radical expansion.

That seems distinctly possible. Keep in mind as well, we've been doing it without the benefit of having the devices. Right, like that's a pretty big bottleneck to have.

bright but it is a pretty hard situation.

for Feynman to Deutsch to everybody else who came along? Yeah so I mean let's just focus on the question about what actually changed. So why was quantum computing not a thing in the 1950s?

Somebody like, I don't know, John von Neumann, good example, absolutely pioneering computation, also wrote a very important book about quantum mechanics and was deeply interested in quantum mechanics. Like he could have invented quantum computing at that time and I think there were quite a number of people who potentially could have. So why do we have these papers by people like Feynman and Deutsch in the 80s?

Those are, I think, fairly regarded as the foundation of the field. There are some partial anticipations a little bit earlier, but they were nowhere near as comprehensive and nowhere near as deep. And well, you should ask David.

You can ask Feynman unfortunately, but he'll know much better than I do. A couple of things that I think are interesting, one is that of course computation became far more salient sort of late 70s, early 80s. It just became a thing which many more people were interested in, partially for very banal reasons.

You could go and buy a PC, you could buy an Apple II, you could buy a Commodore 64, you could buy all these kinds of things. It became apparent to people that these were very powerful devices, very interesting to think about. At the same time, in the quantum case, that was also the time of the ball trap and the ability to trap single ions and so on and up to that point we hadn't really had the ability to manipulate single quantum states.

So you kind of got these two separate things that just for historically contingent reasons had both matured around, let's say, 1980 or so and somebody like Von Dymen could have had the idea earlier but it is I think quite an interesting fact. I remember the story about Richard Feynman. He went and got one of the first PCs around 1980, 1981 and he was apparently just so excited with this device.

He actually tripped and hurt himself quite badly carrying his brand new computing device. That's a very historically contingent coincidence but having somebody who is very, very talented and understanding of quantum mechanics also just very excited about these new machines. It's not so surprising perhaps that he's thinking then.

What similar story could you have told ten years earlier? Like there is just no, the conditions don't exist for it. So I think that's, I mean it's quite a banal story.

Ried Feynman writes this great paper in 1982, David Deutsch writes an absolutely fantastic paper in 1985, sort of sketching out a lot of the fundamental ideas of quantum computing. So I'm 11 in 1985, I'm not thinking about this, I'm playing soccer and doing whatever. But in 1992 I took a class on quantum mechanics that was really terrific given by Jared Milburn and I just went and asked Jared one day after the fifth lecture or something, I said, you have anything, sort of papers or whatever that you could give me?

And he said come by my office in a couple of days' time and I did and he presented me with a giant stack of papers which included the Deutsch paper, included the Feynman paper and included a whole bunch of other very fundamental papers about quantum computing and quantum information at a time when essentially nobody in the world was working on it. He was, he'd actually, I think he wrote the very first paper that proposed I mean, sort of a practical approach to quantum computing. It wasn't very practical but it was actually in a real system and so in some sense, you know, I'm benefiting from the taste of this other person.

But as soon as I read the papers or take a look at the papers, these are exciting papers. They're asking very fundamental questions and you're sort of like, oh, I can make progress here. These are things that one could potentially work on.

Deutsch has this sort of conjecture that basically, yeah, there should be, I don't know what the right term for it is, thesis or what you would call it, that a universal model quantum Turing machine should be capable of efficiently simulating any system, any physical system at all. This is a very provocative idea. I think in that paper, he more or less claims that he's proved it.

I'm not sure that necessarily everybody would agree with that. There's questions about whether or not you can simulate quantum field theory effectively and that kind of question is I think very interesting and very exciting there. It's obviously a fundamental question about the universe.

You know, here's some wonderful ideas in there about sort of quantum algorithms and where they come from and what they mean and what they relate to the meaning of the wave function and questions like this which is still not, it's not agreed upon amongst physicists. So yeah, there's just some sense of, oh, I am in contact with something which is A, deeply important and B, we as a civilization don't have this. And so of course you start to focus your attention a little bit there.

Maybe I misunderstood the question.

mean, so to some extent you're sort of saying, okay, I wanted to get into this game of contributing to humanity's understanding of the universe and you are applying this low hanging fruit algorithm, you're like relative to my particular set of interests and abilities, where should I pick up my shovel and start digging? There it was like oh, this looks like quite a good place to start digging. You know, and different people of course, you know, chose very differently.

Was a very unusual choice at the time. It was 1992.

the set of ideas about open science, I mean, it's interesting. You didn't stop and define open science there, which I think twenty years ago you would have had to do. People recognize the frights, people have some set of associations with it.

Most often they have a relatively simple set of associations. It means maybe something about making scientific papers open access. Very often they have some set of notions about maybe it means also making code openly available, maybe it means making data openly available.

But already those are I think very large successes of the open science movement, which is to make those salient issues. Those are issues on which people have opinions and then there are relatively common arguments. An argument like, so this is of the meme version, you know, publicly funded science should be open science.

That's a distillation of a set of ideas which you might be able to contest, but if you can get people actually sort of thinking about it and engaged with that kind of argument, that's a very fundamental kind of an issue to be considering in the whole political economy of science. If you go back, say three centuries, there was a very similar kind of an argument prosecuted which is the question, do we publicly disclose our scientific results or not. So if you look at people like Galileo and Kepler and so on, the extent to which they publicly disclosed, like it was done in a very odd kind of a way.

Sometimes they did bizarre things where famously they published some of their results as anagrams so basically they'd find some discovery, they would write down the result in sort of a sentence like the discovery of the I'm trying to think of an example, I think the moons of Mars I think was one such example. I'm getting it wrong, was it Hooke's Law? Anyway, it doesn't matter.

The point was they wrote it down but then they'd scramble it, publish that, and then if somebody else later made the same discovery they would unscramble the anagram and say, oh, I actually did it first. This is not an ideal way, this is not an ideal foundation for a discovery system. And then it took, I mean, a very long time, over a century I think to obtain more or less modern ideals in which what you do is you disclose the knowledge in the form of a paper.

There is then an expectation of attribution and so there is a kind of reputation economy which gets built and so basically, oh such and such did this work so they deserve the credit for that and that's then the basis for their careers. So this is sort of the underlying political economy of science and that made a lot of sense when what you've got is a printing press and the ability to do scientific journals. Then you transition to this modern situation where in fact you can start to share a lot more.

You can start to share your code, you can start to share your data, you can start to share in progress ideas but there's no direct credit associated to those. It's not at all obvious sort of how much reputation should be associated to them. That's all constructed socially and so making it a live issue is I think a very important thing to have done and that's, I view anyway, one of the main positive outcomes of work on open science.

Shelley, I'll give you a really practical sort of example to illustrate the problem. For a long time in physics, there was a preprint culture in which people would upload preprints to the preprint archive and in biology, this didn't happen. There was no preprint culture, that's changing now but for a long time this was the case.

And I used to of amuse myself by asking physicists and biologists why this was the case and what I would hear sometimes from biologists was they would say, well biology is so much more competitive than physics that we need to protect our priority and so we can't possibly upload to the archive, we have to just publish in journals. And then we sometimes hear from physicists, physics is so much more competitive than biology that we need to establish our priority by uploading as rapidly as possible to the preprint archive. We can't possibly wait to do it with the journals.

And I think this emphasizes the extent to which this kind of attribution economy is just something we construct, is just something which we do by sort of agreement and so any attempt to sort of change that economy results in a different system by which we construct knowledge so there is sort of this very fundamental set of problems around the political economy of science. We've got this collective project and how we mediate it depends upon the economy we have around ideas.

of such a discovery? I mean, I'm not sure I have a well ordering of them to give you a best, but I mean, I think an example that I think is very interesting is the LHC where it's just this immensely complicated object. Years ago, I snuck into an accelerator physics conference.

I didn't know anything at all about accelerator physics but I was just kind of curious to see what they were talking about and this particular group of people were experts on numerical methods, in particular on inverse methods and so basically it turns out, you know, inside these accelerators you have these cascades so a particle will be massively accelerated, maybe it will be collided and then you'll get a shower of particles which decays and decays and decays and there's just this incredible sort of consequential shower which is ultimately what you see at the detector and then you have to retroactively figure out what produced it. And so there's these very, very complicated sort of inverse problems need be solved. You've got this final data but you need to figure out what produced it and that's how you look for sort of signatures of these.

And what many of these people were was they were incredibly deep experts on simulation methods for sort of following particle tracks. And this was really deep and difficult stuff and I'm like wow, you could spend a lifetime just learning sort of how to do this and how to solve some of these inverse problems and you would know nothing about or you would know very little about quantum field theory, you would know very little about detector physics, would know very little about vacuum physics, all these other things that are absolutely, very little about data processing, very little about all these things that are absolutely essential to understanding the Higgs boson. And I don't think it's possible for one person to understand everything in-depth.

Lots of people understand broadly a lot of these ideas but they don't understand sort of everything in the depth that is actually utilized. That's why there's these papers with well over a thousand authors and those people can, yeah, they can talk to one another at a high level but they don't understand each other's specialties. Interesting.

Things like, as I say, detective physics, vacuum physics, these kinds of solving of inverse problems, this stuff is incredibly different from each other and to understand it in real detail is serious work.

mean it's something I stress about a lot, sometimes I feel like I'm too slow. Actually it's funny though, I mean the Darwin example is really interesting, like Prolific at what? God knows how many letters he wrote.

It must have been an enormous number. So he was certainly very active. There's also like there's two types of work that tends to be involved in any kind of creative project.

There's routine stuff and there you just want to avoid procrastination, you just want to like how do I get good at this or how do I outsource it and how do I do it as rapidly as possible and just avoid getting into a situation where you are prolonging it. And then there is high variance stuff where you actually, you need to be willing to take a lot of time, you need to be willing to go to the different places and talk to the different people where in any given instance most of it is just not going to be an input. Somehow sort of balancing those two things, I think a lot of people are very good at doing one or the other but it's hard to, it's almost like a personality trait, sort of which one you prefer and people tend to end up doing a lot of one and not enough of the other.

So I certainly try and balance those two things. Mean Einstein is such an interesting example, I mean 1905 is just this extraordinary year, like you can delete spectral relativity entirely and it's an extraordinary year. You can delete spectral relativity and you can delete the photoelectric effect for which he won the Nobel Prize, and it's still an extraordinary year, plausibly a multi Nobel Prize winning year.

So what's he doing? Maybe the answer is just he's smarter than the rest of us. And there's a lot of luck as well.

Certainly But for myself anyway, to identify those things that are routine that I should get good at and then just try and do as quickly as possible, think that's yielded a certain amount of returns, but also being willing to bet a little bit more on myself on sort of the variance side has also been very, very, very helpful. That's really hard because intrinsically you're putting yourself in situations where you don't know what the outcome is going to be. And so if you're very driven to be productive and whatever and actually mostly it's not working over there, you're like let's reduce this, like it doesn't feel right.

When I worked in San Francisco, actually a practice I used to have each day was instead of taking the fifteen minute walk to work, I would take the more beautiful thirty minute walk to work, partially just because it was beautiful but partially also as just a reminder that there are real benefits to not being efficient. But it's not an answer to your question. Mean really I think all I'm saying is I struggle a lot with the question.

I mean there are these, Dean Keith Sivington, I forgot his exact name. Yeah. Yeah.

I know who you mean.

of course there's kind of examples, know, Godel publishing almost nothing. Broadly speaking, you need a very good reason to be avoiding it, basically to not do that. It's funny, I've met a lot of people over the years who you talk to, they're clearly brilliant and they're just obsessed that they are going to work on the great project that makes them famous and they never do anything.

And that seems connected like it's a type of aversiveness. Think very often they just don't want public judgment. Something that I would love to see, there's an awful lot of biographies and memoirs and histories of people who achieve a lot.

I wish there was like a very large of biographies of people who are fantastically talented who just missed. Like I've known people who won gold medals at IMOs and things like that who then tried to become mathematicians and failed. What happened?

What was the reason? I suspect in many cases that's actually more informative and incredibly interesting than anything else.

complicated and rich question. Mean it does seem like sort of the question is like how do you make it a higher growth context, how do you make it a more demanding context and sort of you can do that in relatively small ways but that might have a yield compounding returns or you can do something that is maybe more radical, maybe it means actually starting sort of a parallel project in which you do something that is actually quite a bit different. There is something I think really interesting about like how being very demanding can simply change your response to something, something that I would sometimes do students and sometimes with myself was really aimed more at myself was they would say some week I'm gonna try and do this work over the coming week and then the next week would come by and they hadn't solved the problem or whatever.

You sort of like, if a million dollars had been at stake, would you have put the same effort in? And the answer is no, sort of invariably. They've tried but they haven't really tried.

I think that's a very familiar feeling for all of us. Often you could do a lot more if you had just the right sort of demanding taskmaster standing by you and saying look you're barely operating here. And so I do sort of wonder a little bit about like what's the demanding task master, what can they ask you that is going to make your preparation way more intense?

that you do this exercise and if you do it you will understand. Yeah, so I mean really what you're sort of saying is you can do a good job at podcasting without actually attaining this kind of moment and that's the problem from your You point of want to sort of change your job description so that you are internalizing these chunks and just getting this kind of integration each time And it seems to me like what that means is you actually want to change the structure of the work output at some level. Mean lots of people think, there's this terrible idea people have that they should be in flow all of the time.

And of course as far as I can tell, high performers just don't believe this at all. They are in flow some of the time, you certainly see this with athletes. When they are actually out there playing basketball or tennis or whatever, ideally they are in flow much of the time but when they're training they're not, they're stuck a lot of the time or they're doing things badly and I suppose I wonder what that looks like for you.

Ada Palmer, like what wonderful to talk to, incredibly interesting but for you personally like what changed?

clamp what you have learned. Have you tried doing that with somebody?

Maybe I should just hire different ones for different topics. Maybe or there's something about like, I mean, what problem are you solving sort of for each episode? As far as I can tell, that's the only way I really understand anything is that I get interested in something, at first I don't even have a problem but there's just some sense of there's some contribution to make here and gradually you home in and there's a problem and then Funnily enough, spending time stuck is incredibly important and that used to just be annoying, now it seems like this is actually maybe even the most important part of the whole process.

But that very hard oneness of it means that I internalize it afterwards. I often find actually if I, I've written sometimes 10,000 word essays in a couple of days and I've written them in three months or six months, I feel like I didn't learn very much from the ones only took a couple of days. I understand.

things, there's always some creative artifact. Sometimes it's a class, sometimes it's engagement with a group of people who, there's some collective creative artifact that you're working on together. I mean you might not even be aware of it but you're acting as an input to their creative ends in some way.

And sometimes it's just an essay or a book or whatever. It's one of the reasons why I often quite enjoy doing podcasts. Mean particularly, I said yes to come here partially because I know you ask unusually demanding questions and so that's an attempt to get this sort of perspective from a different kind of forcing function.

appropriately humbled. How do how do you make it as jugular as possible? Right?

Like, the higher you can raise the stakes, the better. I mean the interview is in some sense high stakes but also it doesn't necessarily test deep understanding. Yeah but I don't think the interview is that high stakes, right?

You're not writing a book about special relativity and you're not trying to write a book that replaces the current, whatever the existing standard textbook is, that's a really high, really high, phrase that I sort of find particularly difficult and it's a funny one. People will talk about going deep on a subject and it turns out different people have different ideas of what this means. Some people it means they read a couple of blog posts, some people it means they read a book about it, some people it means they wrote a book about it.

Think what your standard is, the standard you hold yourself to determines a lot about your ability to integrate knowledge in this way.

it's somewhat valuable, it's not, I mean that's part of the seductiveness of course, like it's not actually useless, but yeah, it can sort of substitute for actually doing the thing that maybe you should be doing. It's interesting that, like the extent to which, to what extent should you be outsourcing that kind of stuff and to what extent, there's some sort of interesting judgment call about actually, there is a whole bunch of routine work that you want done and in fact it's low value for you so you may as well get, you can get a chatbot to do it, may as well. Somebody interviewed the pioneering computer scientist Alan Kay years ago and he was asked what he thought about basically Linux and if I remember his answer correctly, basically said look, it doesn't have anything to do with computer science, it's just a great big ball of mud.

There's a few interesting ideas in there which are worth understanding but mostly all you're learning is stuff about Linux, like you're not actually learning anything which is transferable. There's a certain kind of seductiveness to some things where it's sort of a Rib Goldberg machine, you can just sort of learn about all the bits and it feels kind of entertaining, but if you step back and think about the question, what am I actually doing here, it might not actually be meeting your objectives. Maybe you want to become a sysadmin and learning Linux is a great use of your time, there's no harm in that at all, but if your objective is to understand the fundamentals of computing, it's much less clear that that's a good use of your time.

I think that was certainly an answer I've thought a lot about where you actually need to, for a certain type of mind, there is a seductiveness in just learning systems and confusing that with understanding.