I Ain’t Afraid of No-Ghost Theorems

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In honor of Halloween this week, let me say a bit about the spookiest term in physics: ghosts.

In particle physics, we talk about the universe in terms of quantum fields. There is an electron field for electrons, a gluon field for gluons, a Higgs field for Higgs bosons. The simplest fields, for the simplest particles, can be described in terms of just a single number at each point in space and time, a value describing how strong the field is. More complicated fields require more numbers.

Most of the fundamental forces have what we call vector fields. They’re called this because they are often described with vectors, lists of numbers that identify a direction in space and time. But these vectors actually contain too many numbers.

These extra numbers have to be tidied up in some way in order to describe vector fields in the real world, like the electromagnetic field or the gluon field of the strong nuclear force. There are a number of tricks, but the nicest is usually to add some extra particles called ghosts. Ghosts are designed to cancel out the extra numbers in a vector, leaving the right description for a vector field. They’re set up mathematically such that they can never be observed, they’re just a mathematical trick.

Mathematical tricks aren’t all that spooky (unless you’re scared of mathematics itself, anyway). But in physics, ghosts can take on a spookier role as well.

In order to do their job cancelling those numbers, ghosts need to function as a kind of opposite to a normal particle, a sort of undead particle. Normal particles have kinetic energy: as they go faster and faster, they have more and more energy. Said another way, it takes more and more energy to make them go faster. Ghosts have negative kinetic energy: the faster they go, the less energy they have.

If ghosts are just a mathematical trick, that’s fine, they’ll do their job and cancel out what they’re supposed to. But sometimes, physicists accidentally write down a theory where the ghosts aren’t just a trick cancelling something out, but real particles you could detect, without anything to hide them away.

In a theory where ghosts really exist, the universe stops making sense. The universe defaults to the lowest energy it can reach. If making a ghost particle go faster reduces its energy, then the universe will make ghost particles go faster and faster, and make more and more ghost particles, until everything is jam-packed with super-speedy ghosts unto infinity, never-ending because it’s always possible to reduce the energy by adding more ghosts.

The absence of ghosts, then, is a requirement for a sensible theory. People prove theorems showing that their new ideas don’t create ghosts. And if your theory does start seeing ghosts…well, that’s the spookiest omen of all: an omen that your theory is wrong.

Transforming Particles Are Probably Here to Stay

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It can be tempting to imagine the world in terms of lego-like building-blocks. Atoms stick together protons, neutrons, and electrons, and protons and neutrons are made of stuck-together quarks in turn. And while atoms, despite the name, aren’t indivisible, you might think that if you look small enough you’ll find indivisible, unchanging pieces, the smallest building-blocks of reality.

Part of that is true. We might, at some point, find the smallest pieces, the things everything else is made of. (In a sense, it’s quite likely we’ve already found them!) But those pieces don’t behave like lego blocks. They aren’t indivisible and unchanging.

Instead, particles, even the most fundamental particles, transform! The most familiar example is beta decay, a radioactive process where a neutron turns into a proton, emitting an electron and a neutrino. This process can be explained in terms of more fundamental particles: the neutron is made of three quarks, and one of those quarks transforms from a “down quark” to an “up quark”. But the explanation, as far as we can tell, doesn’t go any deeper. Quarks aren’t unchanging, they transform.

Beta decay! Ignore the W, which is important but not for this post.

There’s a suggestion I keep hearing, both from curious amateurs and from dedicated crackpots: why doesn’t this mean that quarks have parts? If a down quark can turn into an up dark, an electron, and a neutrino, then why doesn’t that mean that a down quark contains an up quark, an electron, and a neutrino?

The simplest reason is that this isn’t the only way a quark transforms. You can also have beta-plus decay, where an up quark transforms into a down quark, emitting a neutrino and the positively charged anti-particle of the electron, called a positron.

Also, ignore the directions of the arrows, that’s weird particle physics notation that doesn’t matter here.

So to make your idea work, you’d somehow need each down quark to contain an up quark plus some other particles, and each up quark to contain a down quark plus some other particles.

Can you figure out some complicated scheme that works like that? Maybe. But there’s a deeper reason why this is the wrong path.

Transforming particles are part of a broader phenomenon, called particle production. Reactions in particle physics can produce new particles that weren’t there before. This wasn’t part of the earliest theories of quantum mechanics that described one electron at a time. But if you want to consider the quantum properties of not just electrons, but the electric field as well, then you need a more complete theory, called a quantum field theory. And in those theories, you can produce new particles. It’s as simple as turning on the lights: from a wiggling electron, you make light, which in a fully quantum theory is made up of photons. Those photons weren’t “part of” the electron to start with, they are produced by its motion.

If you want to avoid transforming particles, to describe everything in terms of lego-like building-blocks, then you want to avoid particle production altogether. Can you do this in a quantum field theory?

Actually, yes! But your theory won’t describe the whole of the real world.

In physics, we have examples of theories that don’t have particle production. These example theories have a property called integrability. They are theories we can “solve”, doing calculations that aren’t possible in ordinary theories, named after the fact that the oldest such theories in classical mechanics were solved using integrals.

Normal particle physics theories have conserved charges. Beta decay conserves electric charge: you start out with a neutral particle, and end up with one particle with positive charge and another with negative charge. It also conserves other things, like “electron-number” (the electron has electron-number one, the neutrino that comes out with it has electron-number minus one), energy, and momentum.

Integrable theories have those charges too, but they have more. In fact, they have an infinite number of conserved charges. As a result, you can show that in these theories it is impossible to produce new particles. It’s as if each particle’s existence is its own kind of conserved charge, one that can never be created or destroyed, so that each collision just rearranges the particles, never makes new ones.

But while we can write down these theories, we know they can’t describe the whole of the real world. In an integrable theory, when you build things up from the fundamental building-blocks, their energy follows a pattern. Compare the energy of a bunch of different combinations, and you find a characteristic kind of statistical behavior called a Poisson distribution.

Look at the distribution of energies of nuclei of atoms, and you’ll find a very different kind of behavior. It’s called a Wigner-Dyson distribution, and it indicates the opposite of integrability: chaos. Chaos is behavior that can’t be “solved” like integrable theories, behavior that has to be approached by simulations and approximations.

So if you really want there to be un-changing building-blocks, if you think that’s really essential? Then you should probably start looking at integrable theories. But I wouldn’t hold my breath if I were you: the real world seems pretty clearly chaotic, not integrable. And probably, that means particle production is here to stay.

Lack of Recognition Is a Symptom, Not a Cause

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Science is all about being first. Once a discovery has been made, discovering the same thing again is redundant. At best, you can improve the statistical evidence…but for a theorem or a concept, you don’t even have that. This is why we make such a big deal about priority: the first person to discover something did something very valuable. The second, no matter how much effort and insight went into their work, did not.

Because priority matters, for every big scientific discovery there is a priority dispute. Read about science’s greatest hits, and you’ll find people who were left in the wings despite their accomplishments, people who arguably found key ideas and key discoveries earlier than the people who ended up famous. That’s why the idea Peter Higgs is best known for, the Higgs mechanism,

“is therefore also called the Brout–Englert–Higgs mechanism, or Englert–Brout–Higgs–Guralnik–Hagen–Kibble mechanism, Anderson–Higgs mechanism,Anderson–Higgs–Kibble mechanism, Higgs–Kibble mechanism by Abdus Salam and ABEGHHK’tH mechanism (for Anderson, Brout, Englert, Guralnik, Hagen, Higgs, Kibble, and ‘t Hooft) by Peter Higgs.”

Those who don’t get the fame don’t get the rewards. The scientists who get less recognition than they deserve get fewer grants and worse positions, losing out on the career outcomes that the person famous for the discovery gets, even if the less-recognized scientist made the discovery first.

…at least, that’s the usual story.

You can start to see the problem when you notice a contradiction: if a discovery has already been made, what would bring someone to re-make it?

Sometimes, people actually “steal” discoveries, finding something that isn’t widely known and re-publishing it without acknowledging the author. More often, though, the re-discoverer genuinely didn’t know. That’s because, in the real world, we don’t all know about a discovery as soon as it’s made. It has to be communicated.

At minimum, this means you need enough time to finish ironing out the kinks of your idea, write up a paper, and disseminate it. In the days before the internet, dissemination might involve mailing pre-prints to universities across the ocean. It’s relatively easy, in such a world, for two people to get started discovering the same thing, write it up, and even publish it before they learn about the other person’s work.

Sometimes, though, something gets rediscovered long after the original paper should have been available. In those cases, the problem isn’t time, it’s reach. Maybe the original paper was written in a way that hid its implications. Maybe it was published in a way only accessible to a smaller community: either a smaller part of the world, like papers that were only available to researchers in the USSR, or a smaller research community. Maybe the time hadn’t come yet, and the whole reason why the result mattered had yet to really materialize.

For a result like that, a lack of citations isn’t really the problem. Rather than someone who struggles because their work is overlooked, these are people whose work is overlooked, in a sense, because they are struggling: because their work is having a smaller impact on the work of others. Acknowledging them later can do something, but it can’t change the fact that this was work published for a smaller community, yielding smaller rewards.

And ultimately, it isn’t just priority we care about, but impact. While the first European to make contact with the New World might have been Erik the Red, we don’t call the massive exchange of plants and animals between the Old and New World the “Red Exchange”. Erik the Red being “first” matters much less, historically speaking, than Columbus changing the world. Similarly, in science, being the first to discover something is meaningless if that discovery doesn’t change how other people do science, and the person who manages to cause that change is much more valuable than someone who does the same work but doesn’t manage the same reach.

Am I claiming that it’s fair when scientists get famous for other peoples’ discoveries? No, it’s definitely not fair. It’s not fair because most of the reasons one might have lesser reach aren’t under one’s control. Soviet scientists (for the most part) didn’t choose to be based in the USSR. People who make discoveries before they become relevant don’t choose the time in which they were born. And while you can get better at self-promotion with practice, there’s a limited extent to which often-reclusive scientists should be blamed for their lack of social skills.

What I am claiming is that addressing this isn’t a matter of scrupulously citing the “original” discoverer after the fact. That’s a patch, and a weak one. If we want to get science closer to the ideal, where each discovery only has to be made once, then we need to work to increase reach for everyone. That means finding ways to speed up publication, to let people quickly communicate preliminary ideas with a wide audience and change the incentives so people aren’t penalized when others take up those ideas. It means enabling conversations between different fields and sub-fields, building shared vocabulary and opportunities for dialogue. It means making a community that rewards in-person hand-shaking less and careful online documentation more, so that recognition isn’t limited to the people with the money to go to conferences and the social skills to schmooze their way through them. It means anonymity when possible, and openness when we can get away with it.

Lack of recognition and redundant effort are both bad, and they both stem from the same failures to communicate. Instead of fighting about who deserves fame, we should work to make sure that science is truly global and truly universal. We can aim for a future where no-one’s contribution goes unrecognized, and where anything that is known to one is known to all.

Congratulations to John Hopfield and Geoffrey Hinton!

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The 2024 Physics Nobel Prize was announced this week, awarded to John Hopfield and Geoffrey Hinton for using physics to propose foundational ideas in the artificial neural networks used for machine learning.

If the picture above looks off-center, it’s because this is the first time since 2015 that the Physics Nobel has been given to two, rather than three, people. Since several past prizes bundled together disparate ideas in order to make a full group of three, it’s noteworthy that this year the committee decided that each of these people deserved 1/2 the prize amount, without trying to find one more person to water it down further.

Hopfield was trained as a physicist, working in the broad area known as “condensed matter physics”. Condensed matter physicists use physics to describe materials, from semiconductors to crystals to glass. Over the years, Hopfield started using this training less for the traditional subject matter of the field and more to study the properties of living systems. He moved from a position in the physics department of Princeton to chemistry and biology at Caltech. While at Caltech he started studying neuroscience and proposed what are now known as Hopfield networks as a model for how neurons store memory. Hopfield networks have very similar properties to a more traditional condensed matter system called a “spin glass”, and from what he knew about those systems Hopfield could make predictions for how his networks would behave. Those networks would go on to be a major inspiration for the artificial neural networks used for machine learning today.

Hinton was not trained as a physicist, and in fact has said that he didn’t pursue physics in school because the math was too hard! Instead, he got a bachelor’s degree in psychology, and a PhD in the at the time nascent field of artificial intelligence. In the 1980’s, shortly after Hopfield published his network, Hinton proposed a network inspired by a closely related area of physics, one that describes temperature in terms of the statistics of moving particles. His network, called a Boltzmann machine, would be modified and made more efficient over the years, eventually becoming a key part of how artificial neural networks are “trained”.

These people obviously did something impressive. Was it physics?

In 2014, the Nobel prize in physics was awarded to the people who developed blue LEDs. Some of these people were trained as physicists, some weren’t: Wikipedia describes them as engineers. At the time, I argued that this was fine, because these people were doing “something physicists are good at”, studying the properties of a physical system. Ultimately, the thing that ties together different areas of physics is training: physicists are the people who study under other physicists, and go on to collaborate with other physicists. That can evolve in unexpected directions, from more mathematical research to touching on biology and social science…but as long as the work benefits from being linked to physics departments and physics degrees, it makes sense to say it “counts as physics”.

By that logic, we can probably call Hopfield’s work physics. Hinton is more uncertain: his work was inspired by a physical system, but so are other ideas in computer science, like simulated annealing. Other ideas, like genetic algorithms, are inspired by biological systems: does that mean they count as biology?

Then there’s the question of the Nobel itself. If you want to get a Nobel in physics, it usually isn’t enough to transform the field. Your idea has to actually be tested against nature. Theoretical physics is its own discipline, with several ideas that have had an enormous influence on how people investigate new theories, ideas which have never gotten Nobels because the ideas were not intended, by themselves, to describe the real world. Hopfield networks and Boltzmann machines, similarly, do not exist as physical systems in the real world. They exist as computer simulations, and it is those computer simulations that are useful. But one can simulate many ideas in physics, and that doesn’t tend to be enough by itself to get a Nobel.

Ultimately, though, I don’t think this way of thinking about things is helpful. The Nobel isn’t capable of being “fair”, there’s no objective standard for Nobel-worthiness, and not much reason for there to be. The Nobel doesn’t determine which new research gets funded, nor does it incentivize anyone (except maybe Brian Keating). Instead, I think the best way of thinking about the Nobel these days is a bit like Disney.

When Disney was young, its movies had to stand or fall on their own merits. Now, with so many iconic movies in its history, Disney movies are received in the context of that history. Movies like Frozen or Moana aren’t just trying to be a good movie by themselves, they’re trying to be a Disney movie, with all that entails.

Similarly, when the Nobel was young, it was just another award, trying to reward things that Alfred Nobel might have thought deserved rewarding. Now, though, each Nobel prize is expected to be “Nobel-like”, an analogy between each laureate and the laureates of the past. When new people are given Nobels the committee is on some level consciously telling a story, saying that these people fit into the prize’s history.

This year, the Nobel committee clearly wanted to say something about AI. There is no Nobel prize for computer science, or even a Nobel prize for mathematics. (Hinton already has the Turing award, the most prestigious award in computer science.) So to say something about AI, the Nobel committee gave rewards in other fields. In addition to physics, this year’s chemistry award went in part to the people behind AlphaFold2, a machine learning tool to predict what shapes proteins fold into. For both prizes, the committee had a reasonable justification. AlphaFold2 genuinely is an amazing advance in the chemistry of proteins, a research tool like nothing that came before. And the work of Hopfield and Hinton did lead ideas in physics to have an enormous impact on the world, an impact that is worth recognizing. Ultimately, though, whether or not these people should have gotten the Nobel doesn’t depend on that justification. It’s an aesthetic decision, one that (unlike Disney’s baffling decision to make live-action remakes of their most famous movies) doesn’t even need to impress customers. It’s a question of whether the action is “Nobel-ish” enough, according to the tastes of the Nobel committee. The Nobel is essentially expensive fanfiction of itself.

And honestly? That’s fine. I don’t think there’s anything else they could be doing at this point.

At Quanta This Week, With a Piece on Multiple Imputation

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I’ve got another piece in Quanta Magazine this week.

While my past articles in Quanta have been about physics, this time I’m stretching my science journalism muscles in a new direction. I was chatting with a friend who works for a pharmaceutical company, and he told me about a statistical technique that sounded ridiculous. Luckily, he’s a patient person, and after annoying him and a statistician family member for a while I understood that the technique actually made sense. Since I love sharing counterintuitive facts, I thought this would be a great story to share with Quanta’s readers. I then tracked down more statisticians, and annoyed them in a more professional way, finally resulting in the Quanta piece.

The technique is called multiple imputation, and is a way to deal with missing data. By filling in (“imputing”) missing information with good enough guesses, you can treat a dataset with missing data as if it was complete. If you do this imputation multiple times with the help of a source of randomness, you can also model how uncertain those guesses are, so your final statistical estimates are as uncertain as they ought to be. That, in a nutshell, is multiple imputation.

In the piece, I try to cover the key points: how the technique came to be, how it spread, and why people use it. To complement that, in this post I wanted to get a little bit closer to the technical details, and say a bit about why some of the workarounds a naive physicist would come up with don’t actually work.

If you’re anything like me, multiple imputation sounds like a very weird way to deal with missing data. In order to fill in missing data, you have to use statistical techniques to find good guesses. Why can’t you just use the same techniques to analyze the data in the first place? And why do you have to use a random number generator to model your uncertainty, instead of just doing propagation of errors?

It turns out, you can sort of do both of these things. Full Information Maximum Likelihood is a method where you use all the data you have, and only the data you have, without imputing anything or throwing anything out. The catch is that you need a model, one with parameters you can try to find the most likely values for. Physicists usually do have a model like this (for example, the Standard Model), so I assumed everyone would. But for many things you want to measure in social science and medicine, you don’t have any such model, so multiple imputation ends up being more versatile in practice.

(If you want more detail on this, you need to read something written by actual statisticians. The aforementioned statistician family member has a website here that compares and contrasts multiple imputation with full information maximum likelihood.)

What about the randomness? It turns out there is yet another technique, called Fractional Imputation. While multiple imputation randomly chooses different values to impute, fractional imputation gives each value a weight based on the chance for it to come up. This gives the same result…if you can compute the weights, and store all the results. The impression I’ve gotten is that people are working on this, but it isn’t very well-developed.

“Just do propagation of errors”, the thing I wanted to suggest as a physicist, is much less of an option. In many of these datasets, you don’t attribute errors to the base data points to begin with. And on the other hand, if you want to be more sophisticated, then something like propagation of errors is too naive. You have a variety of different variables, correlated with each other in different ways, giving a complicated multivariate distribution. Propagation of errors is already pretty fraught when you go beyond linear relationships (something they don’t tend to tell baby physicists), using it for this would be pushing it rather too far.

The thing I next wanted to suggest, “just carry the distribution through the calculation”, turns out to relate to something I’ve called the “one philosophical problem of my sub-field”. In the area of physics I’ve worked in, a key question is what it means to have “done” an integral. Here, one can ask what it means to do a calculation on a distribution. In both cases, the end goal is to get numbers out: physics predictions on the one hand, statistical estimates on the other. You can get those numbers by “just” doing numerics, using randomness and approximations to estimate the number you’re interested in. And in a way, that’s all you can do. Any time you “just do the integral” or “just carry around the distribution”, the thing you get in the end is some function: it could be a well-understood function like a sine or log, or it could be an exotic function someone defined for that purpose. But whatever function you get, you get numbers out of it the same way. A sine or a log, on a computer, is just an approximation scheme, a program that outputs numbers.

(But we do still care about analytic results, we don’t “just” do numerics. That’s because understanding the analytics helps us do numerics better, we can get more precise numbers faster and more stably. If you’re just carrying around some arbitrarily wiggly distribution, it’s not clear you can do that.)

So at this point, I get it. I’m still curious to see how Fractional Imputation develops, and when I do have an actual model I’d lean to wanting to use Full Information Maximum Likelihood instead. (And there are probably some other caveats I may need to learn at some point!) But I’m comfortable with the idea that Multiple Imputation makes sense for the people using it.

The Mistakes Are the Intelligence

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There’s a lot of hype around large language models, the foundational technology behind services like ChatGPT. Representatives of OpenAI have stated that, in a few years, these models might have “PhD-level intelligence“. On the other hand, at the time, ChatGPT couldn’t count the number of letter “r”s in the word “strawberry”. The model and the setup around it has improved, and GPT-4o1 apparently now gets the correct 3 “r”s…but I’m sure it makes other silly mistakes, mistakes an intelligent human would never make.

The mistakes made by large language models are important, due to the way those models are used. If people are going to use them for customer service, writing transcripts, or editing grammar, they don’t want to introduce obvious screwups. (Maybe this means they shouldn’t use the models this way!)

But the temptation is to go further, to say that these mistakes are proof that these models are, and will always be, dumb, not intelligent. And that’s not the right way to think about intelligence.

When we talk about intelligent people, when we think about measuring things like IQ, we’re looking at a collection of different traits. These traits typically go together in humans: a human who is good at one will usually be good at the others. But from the perspective of computer science, these traits are very different.

Intelligent people tend to be good at following complex instructions. They can remember more, and reason faster. They can hold a lot in their head at once, from positions of objects to vocabulary.

These are all things that computers, inherently, are very good at. When Turing wrote down his abstract description of a computer, he imagined a machine with infinite memory, able to follow any instructions with perfect fidelity. Nothing could live up to that ideal, but modern computers are much closer to it than humans. “Computer” used to be a job, with rooms full of people (often women) hired to do calculations for scientific projects. We don’t do that any more, machines have made that work superfluous.

What’s more, the kind of processing a Turing machine does is probably the only way to reliably answer questions. If you want to make sure you get the correct answer every time, then it seems that you can’t do better than to use a sufficiently powerful computer.

But while computer-the-machine replaced computer-the-job, mathematician-the-job still exists. And that’s because not all intelligence is about answering questions reliably.

Alexander Grothendieck was a famous mathematician, known for his deep insights and powerful ideas. According to legend, when giving a talk referring to prime numbers, someone in the audience asked him to name a specific prime. He named 57.

With a bit of work, any high-school student can figure out that 57, which equals 3 times 19, isn’t a prime number. A computer can easily figure out that 57 is not a prime number. Even ChatGPT knows that 57 is not a prime number.

But this doesn’t mean that Grothendieck was dumber than a high school student, or dumber than ChatGPT. Grothendieck was using a different kind of intelligence, the heuristic kind.

Heuristics are unreliable reasoning. They’re processes that get the right answer some of the time, but not all of the time. Because of that, though, they don’t have the same limits as reliable computer programs. Pick the right situation and the right conditions, and a heuristic can give you an answer faster than you could possibly get by following reliable rules.

Intelligent humans follow instructions well, but they also have good heuristics. They solve problems creatively, sometimes problems that are very hard for computers to address. People like Grothendieck make leaps of mathematical reasoning, guessing at the right argument before they have completely fleshed out a proof. This kind of intelligence is error-prone: rely on it, and you might claim 57 is prime. But at the moment, it’s our only intellectual advantage over machines.

Ultimately, ChatGPT is an advance in language processing, and language is a great example. Sentences don’t have definite meaning, we interpret what we read and hear in context, and sometimes our interpretation is wrong. Sometimes we hear words no-one actually said! It’s impossible, both for current technology and for the human brain, to process general text in a 100% reliable way. So large language models like GPT don’t do it reliably. They use an approximate model, a big complicated pile of rules tweaked over and over again until, enough of the time, they get the next word right in a text.

The kind of heuristic reasoning done by large language models is more effective than many people expected. Being able to predict the next word in a text unreliably also means you can write code unreliably, or count things unreliably, or do math unreliably. You can’t do any of these things as well as an appropriately-chosen human, at least not with current resources.

But in the longer run, heuristic intelligence is precisely the type of intelligence we should aspire to…or fear. Right now, we hire humans to do intellectual work because they have good heuristics. If we could build a machine with equivalent or better heuristics for those tasks, then people would hire a lot fewer humans. And if you’re worried about AI taking over the world, you’re worried about AI coming up with shortcuts to our civilization, tricks we couldn’t anticipate or plan against that destroy everything we care about. Those tricks can’t come from following rules: if they did, we could discover them just as easily. They would have to come from heuristics, sideways solutions that don’t work all the time but happen to work the one time that matters.

So yes, until the latest release, ChatGPT couldn’t tell you how many “r”s are in “strawberry”. Counting “r”s is something computers could already do, because it’s something that can be done by following reliable rules. It’s also something you can do easily, if you follow reliable rules. ChatGPT impresses people because it can do some of the things you do, that can’t be done with reliable rules. If technology like it has any chance of changing the world, those are the kinds of things it will have to be able to do.

The Bystander Effect for Reviewers

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I probably came off last week as a bit of an extreme “journal abolitionist”. This week, I wanted to give a couple caveats.

First, as a commenter pointed out, the main journals we use in my field are run by nonprofits. Physical Review Letters, the journal where we publish five-page papers about flashy results, is run by the American Physical Society. The Journal of High-Energy Physics, where we publish almost everything else, is run by SISSA, the International School for Advanced Studies in Trieste. (SISSA does use Springer, a regular for-profit publisher, to do the actual publishing.)

The journals are also funded collectively, something I pointed out here before but might not have been obvious to readers of last week’s post. There is an agreement, SCOAP3, where research institutions band together to pay the journals. Authors don’t have to pay to publish, and individual libraries don’t have to pay for subscriptions.

And this is a lot better than the situation in other fields, yeah! Though I’d love to quantify how much. I haven’t been able to find a detailed breakdown, but SCOAP3 pays around 1200 EUR per article published. What I’d like to do (but not this week) is to compare this to what other fields pay, as well as to publishing that doesn’t have the same sort of trapped audience, and to online-only free journals like SciPost. (For example, publishing actual physical copies of journals at this point is sort of a vanity thing, so maybe we should compare costs to vanity publishers?)

Second, there’s reviewing itself. Even without traditional journals, one might still want to keep peer review.

What I wanted to understand last week was what peer review does right now, in my field. We read papers fresh off the arXiv, before they’ve gone through peer review. Authors aren’t forced to update the arXiv with the journal version of their paper, if they want another version, even if that version was rejected by the reviewers, then they’re free to do so, and most of us wouldn’t notice. And the sort of in-depth review that happens in peer review also happens without it. When we have journal clubs and nominate someone to present a recent paper, or when we try to build on a result or figure out why it contradicts something we thought we knew, we go through the same kind of in-depth reading that (in the best cases) reviewers do.

But I think I’ve hit upon something review does that those kinds of informal things don’t. It gets us to speak up about it.

I presented at a journal club recently. I read through a bombastic new paper, figured out what I thought was wrong with it, and explained it to my colleagues.

But did I reach out to the author? No, of course not, that would be weird.

Psychologists talk about the bystander effect. If someone collapses on the street, and you’re the only person nearby, you’ll help. If you’re one of many, you’ll wait and see if someone else helps instead.

I think there’s a bystander effect for correcting people. If someone makes a mistake and publishes something wrong, we’ll gripe about it to each other. But typically, we won’t feel like it’s our place to tell the author. We might get into a frustrating argument, there wouldn’t be much in it for us, and it might hurt our reputation if the author is well-liked.

(People do speak up when they have something to gain, of course. That’s why when you write a paper, most of the people emailing you won’t be criticizing the science: they’ll be telling you you need to cite them.)

Peer review changes the expectations. Suddenly, you’re expected to criticize, it’s your social role. And you’re typically anonymous, you don’t have to worry about the consequences. It becomes a lot easier to say what you really think.

(It also becomes quite easy to say lazy stupid things, of course. This is why I like setups like SciPost, where reviews are made public even when the reviewers are anonymous. It encourages people to put some effort in, and it means that others can see that a paper was rejected for bad reasons and put less stock in the rejection.)

I think any new structure we put in place should keep this feature. We need to preserve some way to designate someone a critic, to give someone a social role that lets them let loose and explain why someone else is wrong. And having these designated critics around does help my field. The good criticisms get implemented in the papers, the authors put the new versions up on arXiv. Reviewing papers for journals does make our science better…even if none of us read the journal itself.

Why Journals Are Sticky

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An older professor in my field has a quirk: every time he organizes a conference, he publishes all the talks in a conference proceeding.

In some fields, this would be quite normal. In computer science, where progress flows like a torrent, new developments are announced at conferences long before they have the time to be written up carefully as a published paper. Conference proceedings are summaries of what was presented at the conference, published so that anyone can catch up on the new developments.

In my field, this is rarer. A few results at each conference will be genuinely new, never-before-published discoveries. Most, though, are talks on older results, results already available online. Writing them up again in summarized form as a conference proceeding seems like a massive waste of time.

The cynical explanation is that this professor is doing this for the citations. Each conference proceeding one of his students publishes is another publication on their CV, another work that they can demand people cite whenever someone uses their ideas or software, something that puts them above others’ students without actually doing any extra scientific work.

I don’t think that’s how this professor thinks about it, though. He certainly cares about his students’ careers, and will fight for them to get cited as much as possible. But he asks everyone at the conference to publish a proceeding, not just his students. I think he’d argue that proceedings are helpful, that they can summarize papers in new ways and make them more accessible. And if they give everyone involved a bit more glory, if they let them add new entries to their CV and get fancy books on their shelves, so much the better for everyone.

My guess is, he really believes something like that. And I’m fairly sure he’s wrong.

The occasional conference proceeding helps, but only because it makes us more flexible. Sometimes, it’s important to let others know about a new result that hasn’t been published yet, and we let conference proceedings go into less detail than a full published paper, so this can speed things up. Sometimes, an old result can benefit from a new, clearer explanation, which normally couldn’t be published without it being a new result (or lecture notes). It’s good to have the option of a conference proceeding.

But there is absolutely no reason to have one for every single talk at a conference.

Between the cynical reason and the explicit reason, there’s the banal one. This guy insists on conference proceedings because they were more useful in the past, because they’re useful in other fields, and because he’s been doing them himself for years. He insists on them because to him, they’re a part of what it means to be a responsible scientist.

And people go along with it. Because they don’t want to get into a fight with this guy, certainly. But also because it’s a bit of extra work that could give a bit of a career boost, so what’s the harm?

I think something similar to this is why academic journals still work the way they do.

In the past, journals were the way physicists heard about new discoveries. They would get each edition in the mail, and read up on new developments. The journal needed to pay professional copyeditors and printers, so they needed money, and they got that money from investors by being part of for-profit companies that sold shares.

Now, though, physicists in my field don’t read journals. We publish our new discoveries online on a non-profit website, formatting them ourselves with software that uses the same programming skills we use in the rest of our professional lives. We then discuss the papers in email threads and journal club meetings. When a paper is wrong, or missing something important, we tell the author, and they fix it.

Oh, and then after that we submit the papers to the same for-profit journals and the same review process that we used to use before we did all this, listing the journals that finally accept the papers on our CVs.

Why do we still do that?

Again, you can be cynical. You can accuse the journals of mafia-ish behavior, you can tie things back to the desperate need to publish in high-ranked journals to get hired. But I think the real answer is a bit more innocent, and human, than that.

Imagine that you’re a senior person in the field. You may remember the time before we had all of these nice web-based publishing options, when journals were the best way to hear about new developments. More importantly than that, though, you’ve worked with these journals. You’ve certainly reviewed papers for them, everyone in the field does that, but you may have also served as an editor, tracking down reviewers and handling communication between the authors and the journal. You’ve seen plenty of cases where the journal mattered, where tracking down the right reviewers caught a mistake or shot down a crackpot’s ambitions, where the editing cleaned something up or made a work more appear more professional. You think of the journals as having high standards, standards you have helped to uphold: when choosing between candidates for a job, you notice that one has several papers in Physical Review Letters, and remember papers you’ve rejected for not meeting what you intuited were that journal’s standards. To you, journals are a key part of being a responsible scientist.

Does any of that make journals worth it, though?

Well, that depends on costs. It depends on alternatives. It depends not merely on what the journals catch, but on how often they do it, and how much would have been caught on its own. It depends on whether the high standards you want to apply to job applicants are already being applied by the people who write their recommendation letters and establish their reputations.

And you’re not in a position to evaluate any of that, of course. Few people are, who don’t spend a ton of time thinking about scientific publishing.

And thus, for the non-senior people, there’s not much reason to push back. One hears a few lofty speeches about Elsevier’s profits, and dreams about the end of the big for-profit journals. But most people aren’t cut out to be crusaders or reformers, especially when they signed up to be scientists. Most people are content not to annoy the most respected people in their field by telling them that something they’ve spent an enormous amount of time on is now pointless. Most people want to be seen as helpful by these people, to not slack off on work like reviewing that they argue needs doing.

And most of us have no reason to think we know that much better, anyway. Again, we’re scientists, not scientific publishing experts.

I don’t think it’s good practice to accuse people of cognitive biases. Everyone thinks they have good reasons to believe what they believe, and the only way to convince them is to address those reasons.

But the way we use journals in physics these days is genuinely baffling. It’s hard to explain, it’s the kind of thing people have been looking quizzically at for years. And this kind of explanation is the only one I’ve found that matches what I’ve seen. Between the cynical explanation and the literal arguments, there’s the basic human desire to do what seems like the responsible thing. That tends to explain a lot.

Grad Students Don’t Have Majors

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A pet peeve of mine:

Suppose you’re writing a story, and one of your characters is studying for a PhD in linguistics. You could call them a grad student or a PhD student, a linguistics student or even just a linguist. But one thing you absolutely shouldn’t call them is a linguistics major.

Graduate degrees, from the PhD to medical doctors to masters degrees, don’t have majors. Majors are a very specific concept, from a very specific system: one that only applies to undergraduate degrees, and even there is uncommon to unheard of in most of the world.

You can think of “major” as short for “major area of study”. In many universities in the US, bachelor’s degree students enter not as students of a particular topic, but as “undecided” students. They then have some amount of time to choose a major. Majors define some of your courses, but not all of them. You can also have “minors”, minor areas of study where you take a few courses from another department, and you typically have to take some number of general courses from other departments as well. Overall, the US system for bachelor’s students is quite flexible. The idea is that students can choose from a wide range of courses offered by different departments at a university, focusing on one department’s program but sampling from many. The major is your major focus, but not your only focus.

Basically no other degree works this way.

In Europe, bachelor’s degree students sign up as students of a specific department. By default, all of their courses will be from that department. If you have to learn more math, or writing skills, then normally your department will have its own math or writing course, focused on the needs of their degree. It can be possible to take courses from other departments, but it’s not common and it’s often not easy, sometimes requiring special permission. You’re supposed to have done your general education as a high school student, and be ready to focus on a particular area.

Graduate degrees in the US also don’t work this way. A student in medical school or law school isn’t a medicine major or a law major, they’re a med student or a law student. They typically don’t take courses from the rest of the university at that point, just from the med school or the law school. A student studying for an MBA (Master’s in Business Administration) is similarly a business student, not the business major they might have been during their bachelor’s studies. And a student studying for a PhD is a PhD student, a student of a specific department. They might still have the option of taking classes outside of that department (for example, I took classes in science communication). But these are special exceptions. A linguistics PhD student will take almost all of their classes from the linguistics department, a physics PhD student will take almost all of their classes from the physics department. They don’t have majors.

So the next time you write a story with people with advanced degrees, keep this in mind. Majors are a thing for US bachelor’s degrees, and a few similar systems. Anything else, don’t call it a major!

The Machine Learning for Physics Recipe

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Last week, I went to a conference on machine learning for physics. Machine learning covers a huge variety of methods and ideas, several of which were on full display. But again and again, I noticed a pattern. The people who seemed to be making the best use of machine learning, the ones who were the most confident in their conclusions and getting the most impressive results, the ones who felt like they had a whole assembly line instead of just a prototype, all of them were doing essentially the same thing.

This post is about that thing. If you want to do machine learning in physics, these are the situations where you’re most likely to see a benefit. You can do other things, and they may work too. But this recipe seems to work over and over again.

First, you need simulations, and you need an experiment.

Your experiment gives you data, and that data isn’t easy to interpret. Maybe you’ve embedded a bunch of cameras in the antarctic ice, and your data tells you when they trigger and how bright the light is. Maybe you’ve surrounded a particle collision with layers silicon, and your data tells you how much electric charge the different layers absorb. Maybe you’ve got an array of telescopes focused on a black hole far far away, and your data are pixels gathered from each telescope.

You want to infer, from your data, what happened physically. Your cameras in the ice saw signs of a neutrino, you want to know how much energy it had and where it was coming from. Your silicon is absorbing particles, what kind are they and what processes did they come from? The black hole might have the rings predicted by general relativity, but it might have weirder rings from a variant theory.

In each case, you can’t just calculate the answer you need. The neutrino streams past, interacting with the ice and camera positions in unpredictable ways. People can write down clean approximations for particles in the highest-energy part of a collision, but once they start cooling down the process becomes so messy that no straightforward formula describes them. Your array of telescopes fuzz and pixellate and have to be assembled together in a complicated way, so that there is no one guaranteed answer you can find to establish what they saw.

In each case, though, you can use simulations. If you specify in advance the energy and path of the neutrino, you can use a computer to predict how much light your cameras should see. If you know what particles you started with, you can run sophisticated particle physics code to see what “showers” of particles you eventually find. If you have the original black hole image, you can fuzz and pixellate and take it apart to match what your array of telescopes will do.

The problem is, for the experiments, you can’t anticipate, and you don’t know in advance. And simulations, while cheaper than experiments, aren’t cheap. You can’t run a simulation for every possible input and then check them against the experiments. You need to fill in the gaps, run some simulations and then use some theory, some statistical method or human-tweaked guess, to figure out how to interpret your experiments.

Or, you can use Machine Learning. You train a machine learning model, one well-suited the task (anything from the old standby of boosted decision trees to an old fad of normalizing flows to the latest hotness of graph neural networks). You run a bunch of simulations, as many as you can reasonably afford, and you use that data for training, making a program that matches the input data you want to find with its simulated results. This program will be less reliable than your simulations, but it will run much faster. If it’s reliable enough, you can use it instead of the old human-made guesses and tweaks. You now have an efficient, reliable way to go from your raw experiment data to the physical questions you actually care about.

Crucially, each of the elements in this recipe is essential.

You need a simulation. If you just have an experiment with no simulation, then you don’t have a way to interpret the results, and training a machine to reproduce the experiment won’t tell you anything new.

You need an experiment. If you just have simulations, training a machine to reproduce them also doesn’t tell you anything new. You need some reason to want to predict the results of the simulations, beyond just seeing what happens in between which the machine can’t tell you.

And you need to not have anything better than the simulation. If you have a theory where you can write out formulas for what happens then you don’t need machine learning, you can interpret the experiments more easily without it. This applies if you’ve carefully designed your experiment to measure something easy to interpret, like the ratio of rates of two processes that should be exactly the same.

These aren’t the only things you need. You also need to do the whole thing carefully enough that you understand well your uncertainties, not just what the machine predicts but how often it gets it wrong, and whether it’s likely to do something strange when you use it on the actual experiment. But if you can do that, you have a reliable recipe, one many people have followed successfully before. You have a good chance of making things work.

This isn’t the only way physicists can use machine learning. There are people looking into something more akin to what’s called unsupervised learning, where you look for strange events in your data as clues for what to investigate further. And there are people like me, trying to use machine learning on the mathematical side, to guess new formulas and new heuristics. There is likely promise in many of these approaches. But for now, they aren’t a recipe.