Tag Archives: PublicPerception

Ways Freelance Journalism Is Different From Academic Writing

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A while back, I was surprised when I saw the writer of a well-researched webcomic assume that academics are paid for their articles. I ended up writing a post explaining how academic publishing actually works.

Now that I’m out of academia, I’m noticing some confusion on the other side. I’m doing freelance journalism, and the academics I talk to tend to have some common misunderstandings. So academics, this post is for you: a FAQ of questions I’ve been asked about freelance journalism. Freelance journalism is more varied than academia, and I’ve only been doing it a little while, so all of my answers will be limited to my experience.

Q: What happens first? Do they ask you to write something? Do you write an article and send it to them?

Academics are used to writing an article, then sending it to a journal, which sends it out to reviewers to decide whether to accept it. In freelance journalism in my experience, you almost never write an article before it’s accepted. (I can think of one exception I’ve run into, and that was for an opinion piece.)

Sometimes, an editor reaches out to a freelancer and asks them to take on an assignment to write a particular sort of article. This happens more freelancers that have been working with particular editors for a long time. I’m new to this, so the majority of the time I have to “pitch”. That means I email an editor describing the kind of piece I want to write. I give a short description of the topic and why it’s interesting. If the editor is interested, they’ll ask some follow-up questions, then tell me what they want me to focus on, how long the piece should be, and how much they’ll pay me. (The last two are related, many places pay by the word.) After that, I can write a draft.

Q: Wait, you’re paid by the word? Then why not make your articles super long, like Victor Hugo?

I’m paid per word assigned, not per word in the finished piece. The piece doesn’t have to strictly stick to the word limit, but it should be roughly the right size, and I work with the editor to try to get it there. In practice, places seem to have a few standard size ranges and internal terminology for what they are (“blog”, “essay”, “short news”, “feature”). These aren’t always the same as the categories readers see online. Some places have a web page listing these categories for prospective freelancers, but many don’t, so you have to either infer them from the lengths of articles online or learn them over time from the editors.

Q: Why didn’t you mention this important person or idea?

Because pieces pay more by the word, it’s easier as a freelancer to sell shorter pieces than longer ones. For science news, favoring shorter pieces also makes some pedagogical sense. People usually take away only a few key messages from a piece, if you try to pack in too much you run a serious risk of losing people. After I’ve submitted a draft, I work with the editor to polish it, and usually that means cutting off side-stories and “by-the-ways” to make the key points as vivid as possible.

Q: Do you do those cool illustrations?

Academia has a big focus on individual merit. The expectation is that when you write something, you do almost all of the work yourself, to the extent that more programming-heavy fields like physics and math do their own typesetting.

Industry, including journalism, is more comfortable delegating. Places will generally have someone on-staff to handle illustrations. I suggest diagrams that could be helpful to the piece and do a sketch of what they could look like, but it’s someone else’s job to turn that into nice readable graphic design.

Q: Why is the title like that? Why doesn’t that sound like you?

Editors in journalistic outlets are much more involved than in academic journals. Editors won’t just suggest edits, they’ll change wording directly and even input full sentences of their own. The title and subtitle of a piece in particular can change a lot (in part because they impact SEO), and in some places these can be changed by the editor quite late in the process. I’ve had a few pieces whose title changed after I’d signed off on them, or even after they first appeared.

Q: Are your pieces peer-reviewed?

The news doesn’t have peer review, no. Some places, like Quanta Magazine, do fact-checking. Quanta pays independent fact-checkers for longer pieces, while for shorter pieces it’s the writer’s job to verify key facts, confirming dates and the accuracy of quotes.

Q: Can you show me the piece before it’s published, so I can check it?

That’s almost never an option. Journalists tend to have strict rules about showing a piece before it’s published, related to more political areas where they want to preserve the ability to surprise wrongdoers and the independence to find their own opinions. Science news seems like it shouldn’t require this kind of thing as much, it’s not like we normally write hit pieces. But we’re not publicists either.

In a few cases, I’ve had people who were worried about something being conveyed incorrectly, or misleadingly. For those, I offer to do more in the fact-checking stage. I can sometimes show you quotes or paraphrase how I’m describing something, to check whether I’m getting something wrong. But under no circumstances can I show you the full text.

Q: What can I do to make it more likely I’ll get quoted?

Pieces are short, and written for a general, if educated, audience. Long quotes are harder to use because they eat into word count, and quotes with technical terms are harder to use because we try to limit the number of terms we ask the reader to remember. Quotes that mention a lot of concepts can be harder to find a place for, too: concepts are introduced gradually over the piece, so a quote that mentions almost everything that comes up will only make sense to the reader at the very end.

In a science news piece, quotes can serve a couple different roles. They can give authority, an expert’s judgement confirming that something is important or real. They can convey excitement, letting the reader see a scientist’s emotions. And sometimes, they can give an explanation. This last only happens when the explanation is very efficient and clear. If the journalist can give a better explanation, they’re likely to use that instead.

So if you want to be quoted, keep that in mind. Try to say things that are short and don’t use a lot of technical jargon or bring in too many concepts at once. Convey judgement, which things are important and why, and convey passion, what drives you and excited you about a topic. I am allowed to edit quotes down, so I can take a piece of a longer quote that’s cleaner or cut a long list of examples from an otherwise compelling statement. I can correct grammar and get rid of filler words and obvious mistakes. But I can’t put words in your mouth, I have to work with what you actually said, and if you don’t say anything I can use then you won’t get quoted.

Government Science Funding Isn’t a Precision Tool

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People sometimes say there is a crisis of trust in science. In controversial subjects, from ecology to health, increasingly many people are rejecting not only mainstream ideas, but the scientists behind them.

I think part of the problem is media literacy, but not in the way you’d think. When we teach media literacy, we talk about biased sources. If a study on cigarettes is funded by the tobacco industry or a study on climate change is funded by an oil company, we tell students to take a step back and consider that the scientists might be biased.

That’s a worthwhile lesson, as far as it goes. But it naturally leads to another idea. Most scientific studies aren’t funded by companies, most studies are funded by the government. If you think the government is biased, does that mean the studies are too?

I’m going to argue here that government science funding is a very different thing than corporations funding individual studies. Governments do have an influence on scientists, and a powerful one, but that influence is diffuse and long-term. They don’t have control over the specific conclusions scientists reach.

If you picture a stereotypical corrupt scientist, you might imagine all sorts of perks. They might get extra pay from corporate consulting fees. Maybe they get invited to fancy dinners, go to corporate-sponsored conferences in exotic locations, and get gifts from the company.

Grants can’t offer any of that, because grants are filtered through a university. When a grant pays a scientist’s salary, the university pays less to compensate, instead reducing their teaching responsibilities or giving them a slightly better chance at future raises. Any dinners or conferences have to obey not only rules from the grant agency (a surprising number of grants these days can’t pay for alcohol) but from the university as well, which can set a maximum on the price of a dinner or require people to travel economy using a specific travel agency. They also have to be applied for: scientists have to write their planned travel and conference budget, and the committee evaluating grants will often ask if that budget is really necessary.

Actual corruption isn’t the only thing we teach news readers to watch out for. By funding research, companies can choose to support people who tend to reach conclusions they agree with, keep in contact through the project, then publicize the result with a team of dedicated communications staff.

Governments can’t follow up on that level of detail. Scientific work is unpredictable, and governments try to fund a wide breadth of scientific work, so they have to accept that studies will not usually go as advertised. Scientists pivot, finding new directions and reaching new opinions, and government grant agencies don’t have the interest or the staff to police them for it. They also can’t select very precisely, with committees that often only know bits and pieces about the work they’re evaluating because they have to cover so many different lines of research. And with the huge number of studies funded, the number that can be meaningfully promoted by their comparatively small communications staff is only a tiny fraction.

In practice, then, governments can’t choose what conclusions scientists come to. If a government grant agency funds a study, that doesn’t tell you very much about whether the conclusion of the study is biased.

Instead, governments have an enormous influence on the general type of research that gets done. This doesn’t work on the level of conclusions, but on the level of topics, as that’s about the most granular that grant committees can get. Grants work in a direct way, giving scientists more equipment and time to do work of a general type that the grant committees are interested in. It works in terms of incentives, not because researchers get paid more but because they get to do more, hiring more students and temporary researchers if they can brand their work in terms of the more favored type of research. And it works by influencing the future: by creating students and sustaining young researchers who don’t yet have temporary positions, and by encouraging universities to hire people more likely to get grants for their few permanent positions.

So if you’re suspicious the government is biasing science, try to zoom out a bit. Think about the tools they have at their disposal, about how they distribute funding and check up on how it’s used. The way things are set up currently, most governments don’t have detailed control over what gets done. They have to filter that control through grant committees of opinionated scientists, who have to evaluate proposals well outside of their expertise. Any control you suspect they’re using has to survive that.

Which String Theorists Are You Complaining About?

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Do string theorists have an unfair advantage? Do they have an easier time getting hired, for example?

In one of the perennial arguments about this on Twitter, Martin Bauer posted a bar chart of faculty hires in the US by sub-field. The chart was compiled by Erich Poppitz from data in the US particle physics rumor mill, a website where people post information about who gets hired where for the US’s quite small number of permanent theoretical particle physics positions at research universities and national labs. The data covers 1994 to 2017, and shows one year, 1999, when there were more string theorists hired than all other topics put together. The years around then also had many string theorists hired, but the proportion starts falling around the mid 2000’s…around when Lee Smolin wrote a book, The Trouble With Physics, arguing that string theorists had strong-armed their way into academic dominance. After that, the percentage of string theorists falls, oscillating between a tenth and a quarter of total hires.

Judging from that, you get the feeling that string theory’s critics are treating a temporary hiring fad as if it was a permanent fact. The late 1990’s were a time of high-profile developments in string theory that excited a lot of people. Later, other hiring fads dominated, often driven by experiments: I remember when the US decided to prioritize neutrino experiments and neutrino theorists had a much easier time getting hired, and there seem to be similar pushes now with gravitational waves, quantum computing, and AI.

Thinking about the situation in this way, though, ignores what many of the critics have in mind. That’s because the “string” column on that bar chart is not necessarily what people think of when they think of string theory.

If you look at the categories on Poppitz’s bar chart, you’ll notice something odd. “String” its itself a category. Another category, “lattice”, refers to lattice QCD, a method to find the dynamics of quarks numerically. The third category, though, is a combination of three things “ph/th/cosm”.

“Cosm” here refers to cosmology, another sub-field. “Ph” and “th” though aren’t really sub-fields. Instead, they’re arXiv categories, sections of the website arXiv.org where physicists post papers before they submit them to journals. The “ph” category is used for phenomenology, the type of theoretical physics where people try to propose models of the real world and make testable predictions. The “th” category is for “formal theory”, papers where theoretical physicists study the kinds of theories they use in more generality and develop new calculation methods, with insights that over time filter into “ph” work.

“String”, on the other hand, is not an arXiv category. When string theorists write papers, they’ll put them into “th” or “ph” or another relevant category (for example “gr-qc”, for general relativity and quantum cosmology). This means that when Poppitz distinguishes “ph/th/cosm” from “string”, he’s being subjective, using his own judgement to decide who counts as a string theorist.

So who counts as a string theorist? The simplest thing to do would be to check if their work uses strings. Failing that, they could use other tools of string theory and its close relatives, like Calabi-Yau manifolds, M-branes, and holography.

That might be what Poppitz was doing, but if he was, he was probably missing a lot of the people critics of string theory complain about. He even misses many people who describe themselves as string theorists. In an old post of mine I go through the talks at Strings, string theory’s big yearly conference, giving them finer-grained categories. The majority don’t use anything uniquely stringy.

Instead, I think critics of string theory have two kinds of things in mind.

First, most of the people who made their reputations on string theory are still in academia, and still widely respected. Some of them still work on string theory topics, but many now work on other things. Because they’re still widely respected, their interests have a substantial influence on the field. When one of them starts looking at connections between theories of two-dimensional materials, you get a whole afternoon of talks at Strings about theories of two-dimensional materials. Working on those topics probably makes it a bit easier to get a job, but also, many of the people working on them are students of these highly respected people, who just because of that have an easier time getting a job. If you’re a critic of string theory who thinks the founders of the field led physics astray, then you probably think they’re still leading physics astray even if they aren’t currently working on string theory.

Second, for many other people in physics, string theorists are their colleagues and friends. They’ll make fun of trends that seem overhyped and under-thought, like research on the black hole information paradox or the swampland, or hopes that a slightly tweaked version of supersymmetry will show up soon at the LHC. But they’ll happily use ideas developed in string theory when they prove handy, using supersymmetric theories to test new calculation techniques, string theory’s extra dimensions to inspire and ground new ideas for dark matter, or the math of strings themselves as interesting shortcuts to particle physics calculations. String theory is available as reference to these people in a way that other quantum gravity proposals aren’t. That’s partly due to familiarity and shared language (I remember a talk at Perimeter where string theorists wanted to learn from practitioners from another area and the discussion got bogged down by how they were using the word “dimension”), but partly due to skepticism of the various alternate approaches. Most people have some idea in their heads of deep problems with various proposals: screwing up relativity, making nonsense out of quantum mechanics, or over-interpreting on limited evidence. The most commonly believed criticisms are usually wrong, with objections long-known to practitioners of the alternate approaches, and so those people tend to think they’re being treated unfairly. But the wrong criticisms are often simplified versions of correct criticisms, passed down by the few people who dig deeply into these topics, criticisms that the alternative approaches don’t have good answers to.

The end result is that while string theory itself isn’t dominant, a sort of “string friendliness” is. Most of the jobs aren’t going to string theorists in the literal sense. But the academic world string theorists created keeps turning. People still respect string theorists and the research directions they find interesting, and people are still happy to collaborate and discuss with string theorists. For research communities people are more skeptical of, it must feel very isolating, like the world is still being run by their opponents. But this isn’t the kind of hegemony that can be solved by a revolution. Thinking that string theory is a failed research program, and people focused on it should have a harder time getting hired, is one thing. Thinking that everyone who respects at least one former string theorist should have a harder time getting hired is a very different goal. And if what you’re complaining about is “string friendliness”, not actual string theorists, then that’s what you’re asking for.

A Tale of Two Experiments

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Before I begin, two small announcements:

First: I am now on bluesky! Instead of having a separate link in the top menu for each social media account, I’ve changed the format so now there are social media buttons in the right-hand sidebar, right under the “Follow” button. Currently, they cover tumblr, twitter, and bluesky, but there may be more in future.

Second, I’ve put a bit more technical advice on my “Open Source Grant Proposal” post, so people interested in proposing similar research can have some ideas about how best to pitch it.

Now, on to the post:

Gravitational wave telescopes are possibly the most exciting research program in physics right now. Big, expensive machines with more on the way in the coming decades, gravitational wave telescopes need both precise theoretical predictions and high-quality data analysis. For some, gravitational wave telescopes have the potential to reveal genuinely new physics, to probe deviations from general relativity that might be related to phenomena like dark matter, though so far no such deviations have been conclusively observed. In the meantime, they’re teaching us new consequences of known physics. For example, the unusual population of black holes observed by LIGO has motivated those who model star clusters to consider processes in which the motion of three stars or black holes is related to each other, discovering that these processes are more important than expected.

Particle colliders are probably still exciting to the general public, but for many there is a growing sense of fatigue and disillusionment. Current machines like the LHC are big and expensive, and proposed future colliders would be even costlier and take decades to come online, in addition to requiring a huge amount of effort from the community in terms of precise theoretical predictions and data analysis. Some argue that colliders still might uncover genuinely new physics, deviations from the standard model that might explain phenomena like dark matter, but as no such deviations have yet been conclusively observed people are increasingly skeptical. In the meantime, most people working on collider physics are focused on learning new consequences of known physics. For example, by comparing observed results with theoretical approximations, people have found that certain high-energy processes usually left out of calculations are actually needed to get a good agreement with the data, showing that these processes are more important than expected.

…ok, you see what I did there, right? Was that fair?

There are a few key differences, with implications to keep in mind:

First, collider physics is significantly more expensive than gravitational wave physics. LIGO took about $300 million to build and spends about $50 million a year. The LHC took about $5 billion to build and costs $1 billion a year to run. That cost still puts both well below several other government expenses that you probably consider frivolous (please don’t start arguing about which ones in the comments!), but it does mean collider physics demands a bit of a stronger argument.

Second, the theoretical motivation to expect new fundamental physics out of LIGO is generally considered much weaker than for colliders. A large part of the theoretical physics community thought that they had a good argument why they should see something new at the LHC. In contrast, most theorists have been skeptical of the kinds of modified gravity theories that have dramatic enough effects that one could measure them with gravitational wave telescopes, with many of these theories having other pathologies or inconsistencies that made people wary.

Third, the general public finds astrophysics cooler than particle physics. Somehow, telling people “pairs of black holes collide more often than we thought because sometimes a third star in the neighborhood nudges them together” gets people much more excited than “pairs of quarks collide more often than we thought because we need to re-sum large logarithms differently”, even though I don’t think there’s a real “principled” difference between them. Neither reveals new laws of nature, both are upgrades to our ability to model how real physical objects behave, neither is useful to know for anybody living on Earth in the present day.

With all this in mind, my advice to gravitational wave physicists is to try, as much as possible, not to lean on stories about dark matter and modified gravity. You might learn something, and it’s worth occasionally mentioning that. But if you don’t, you run a serious risk of disappointing people. And you have such a big PR advantage if you just lean on new consequences of bog standard GR, that those guys really should get the bulk of the news coverage if you want to keep the public on your side.

The “That’s Neat” Level

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Everything we do, we do for someone.

The simplest things we do for ourselves. We grab that chocolate bar on the table and eat it, and it makes us happier.

Unless the chocolate bar is homemade, we probably paid money for it. We do other things, working for a living, to get the money to get those chocolate bars for ourselves.

(We also get chocolate bars for our loved ones, or for people we care about. Whether this is not in a sense also getting a chocolate bar for yourself is left as an exercise to the reader.)

What we do for the money, in turn, is driven by what would make someone else happier. Sometimes this is direct: you cut someone’s hair, they enjoy the breeze, they pay you, you enjoy the chocolate.

Other times, this gets mediated. You work in HR at a haircut chain. The shareholders want more money, to buy things like chocolate bars, so they vote for a board who wants to do what the shareholders want so as not to be in breach of contract and get fewer chocolate bars, so the board tells you to do things they believe will achieve that, and you do them because that’s how you get your chocolate bars. Every so often, the shareholders take a look at how many chocolate bars they can afford and adjust.

Compared to all this, academia is weirdly un-mediated.

It gets the closest to this model with students. Students want to learn certain things because they will allow them to provide other people with better services in future, which they can use to buy chocolate bars, and other things for the sheer pleasure, a neat experience almost comparable to a chocolate bar. People running universities want more money from students so they can spend it on things like giant statues of chocolate bars, so they instruct people working in the university to teach more of the things students want. (Typically in a very indirect way, for example funding a department in the US based on number of majors rather than number of students.)

But there’s a big chunk of academics whose performance is mostly judged not by their teaching, but by their research. They are paid salaries by departments based on the past quality of their research, or paid out of grants awarded based on the expected future quality of their research. (Or to combine them, paid salaries by departments based on the expected size of their grants.)

And in principle, that introduces many layers of mediation. The research universities and grant agencies are funded by governments, which pool money together in the expectation that someday by doing so they will bring about a world where more people can eat chocolate bars.

But the potential to bring about a world of increased chocolate bars isn’t like maximizing shareholder value. Nobody can check, one year later, how much closer you are to the science-fueled chocolate bar utopia.

And so in practice, in science, people fund you because they think what you’re doing is neat. Because it scratches the chocolate-bar-shaped hole in their brains. They might have some narrative about how your work could lead to the chocolate bar utopia the government is asking for, but it’s not like they’re calculating the expected distribution of chocolate bars if they fund your project versus another. You have to convince a human being, not that you are doing something instrumentally and measurably useful…but that you are doing something cool.

And that makes us very weird people! Halfway between haircuts and HR, selling a chocolate bar that promises to be something more.

Replacing Space-Time With the Space in Your Eyes

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Nima Arkani-Hamed thinks space-time is doomed.

That doesn’t mean he thinks it’s about to be destroyed by a supervillain. Rather, Nima, like many physicists, thinks that space and time are just approximations to a deeper reality. In order to make sense of gravity in a quantum world, seemingly fundamental ideas, like that particles move through particular places at particular times, will probably need to become more flexible.

But while most people who think space-time is doomed research quantum gravity, Nima’s path is different. Nima has been studying scattering amplitudes, formulas used by particle physicists to predict how likely particles are to collide in particular ways. He has been trying to find ways to calculate these scattering amplitudes without referring directly to particles traveling through space and time. In the long run, the hope is that knowing how to do these calculations will help suggest new theories beyond particle physics, theories that can’t be described with space and time at all.

Ten years ago, Nima figured out how to do this in a particular theory, one that doesn’t describe the real world. For that theory he was able to find a new picture of how to calculate scattering amplitudes based on a combinatorical, geometric space with no reference to particles traveling through space-time. He gave this space the catchy name “the amplituhedron“. In the years since, he found a few other “hedra” describing different theories.

Now, he’s got a new approach. The new approach doesn’t have the same kind of catchy name: people sometimes call it surfaceology, or curve integral formalism. Like the amplituhedron, it involves concepts from combinatorics and geometry. It isn’t quite as “pure” as the amplituhedron: it uses a bit more from ordinary particle physics, and while it avoids specific paths in space-time it does care about the shape of those paths. Still, it has one big advantage: unlike the amplituhedron, Nima’s new approach looks like it can work for at least a few of the theories that actually describe the real world.

The amplituhedron was mysterious. Instead of space and time, it described the world in terms of a geometric space whose meaning was unclear. Nima’s new approach also describes the world in terms of a geometric space, but this space’s meaning is a lot more clear.

The space is called “kinematic space”. That probably still sounds mysterious. “Kinematic” in physics refers to motion. In the beginning of a physics class when you study velocity and acceleration before you’ve introduced a single force, you’re studying kinematics. In particle physics, kinematic refers to the motion of the particles you detect. If you see an electron going up and to the right at a tenth the speed of light, those are its kinematics.

Kinematic space, then, is the space of observations. By saying that his approach is based on ideas in kinematic space, what Nima is saying is that it describes colliding particles not based on what they might be doing before they’re detected, but on mathematics that asks questions only about facts about the particles that can be observed.

(For the experts: this isn’t quite true, because he still needs a concept of loop momenta. He’s getting the actual integrands from his approach, rather than the dual definition he got from the amplituhedron. But he does still have to integrate one way or another.)

Quantum mechanics famously has many interpretations. In my experience, Nima’s favorite interpretation is the one known as “shut up and calculate”. Instead of arguing about the nature of an indeterminately philosophical “real world”, Nima thinks quantum physics is a tool to calculate things people can observe in experiments, and that’s the part we should care about.

From a practical perspective, I agree with him. And I think if you have this perspective, then ultimately, kinematic space is where your theories have to live. Kinematic space is nothing more or less than the space of observations, the space defined by where things land in your detectors, or if you’re a human and not a collider, in your eyes. If you want to strip away all the speculation about the nature of reality, this is all that is left over. Any theory, of any reality, will have to be described in this way. So if you think reality might need a totally new weird theory, it makes sense to approach things like Nima does, and start with the one thing that will always remain: observations.

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.

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.