Newton’s Principia is famous for its investigations of the inverse square force law for gravity. But in this book Newton also did something that remained little-known until fairly recently. He figured out what kind of central force exerted upon a particle can rescale its angular velocity by a constant factor without affecting its radial motion. This turns out to be a force obeying an inverse cube law.
Given a particle in Euclidean space, a central force is a force that points toward or away from the origin and depends only on the particle’s distance from the origin. If the particle’s position at time t is \mathbf{r}(t) \in \mathbb{R}^n and its mass is some number m > 0, we have
m ,円 \ddot{\mathbf{r}}(t) = F(r(t)) ,円\hat{\mathbf{r}}(t)
where \hat{\mathbf{r}}(t) is a unit vector pointing outward from the origin at the point \mathbf{r}(t). A particle obeying this equation always moves in a plane through the origin, so we can use polar coordinates and write the particle’s position as \bigl(r(t), \theta(t)\bigr). With some calculation one can show the particle’s distance from the origin, r(t), obeys
\displaystyle{ m \ddot r(t) = F(r(t)) + \frac{L^2}{mr(t)^3} \qquad \qquad \qquad \qquad (1) }
Here L = mr(t)^2 \dot \theta(t), the particle’s angular momentum, is constant in time. The second term in equation (1) says that the particle’s distance from the origin changes as if there were an additional force pushing it outward. This is a "fictitious force", an artifact of working in polar coordinates. It is called the centrifugal force. And it obeys an inverse cube force law!
This explains Newton’s observation. Let us see why. Suppose that we have two particles moving in two different central forces F_1 and F_2, each obeying a version of equation (1), with the same mass m and the same radial motion r(t), but different angular momenta L_1 and L_2. Then we must have
\displaystyle{ F_1(r(t)) + \frac{L_1^2}{mr(t)^3} = F_2(r(t)) + \frac{L_2^2}{mr(t)^3} }
If the particle’s angular velocities are proportional then L_2 = kL_1 for some constant k, so
\displaystyle{ F_2(r_1(t)) - F_1(r(t)) = \frac{(k^2 - 1)L_1^2}{mr(t)^3} }
This says that F_2 equals F_1 plus an additional inverse cube force.
A particle’s motion in an inverse cube force has curious features. First compare Newtonian gravity, which is an attractive inverse square force, say F(r) = -c/r^2 with c > 0. In this case we have
\displaystyle{ m \ddot r(t) = -\frac{c}{r(t)^2} + \frac{L^2}{mr(t)^3 } }
Because 1/r^3 grows faster than 1/r^2 as r \downarrow 0, as long as the angular momentum L is nonzero the repulsion of the centrifugal force will beat the attraction of gravity for sufficiently small r, and the particle will not fall in to the origin. The same is true for any attractive force F(r) = -c/r^p with p < 3. But an attractive inverse cube force can overcome the centrifugal force and make a particle fall in to the origin.
In fact there are three qualitatively different possibilities for the motion of a particle in an attractive inverse cube force F(r) = -c/r^3, depending on the value of c. With work we can solve for 1/r as a function of \theta (which is easier than solving for r). There are three cases depending on the value of
\displaystyle{ \omega^2 = 1 - \frac{cm}{L^2} }
vaguely analogous to the elliptical, parabolic and hyperbolic orbits of a particle in an inverse square force law:
\displaystyle{ \frac{1}{r(\theta)} } = \left\{ \begin{array}{lcl} A \cos(\omega \theta) + B \sin(\omega \theta) & \text{if} & \omega^2 > 0 \\ \\ A + B \theta & \text{if} & \omega = 0 \\ \\ A e^{|\omega| \theta} + B e^{-|\omega| \theta} & \text{if} & \omega^2 < 0 \end{array} \right.
The third case occurs when the attractive inverse cube force is strong enough to overcome the centrifugal force: c > L^2/m. Then the particle can spiral in to its doom, hitting the origin in a finite amount of time after infinitely many orbits, like this:
All three curves above are called Cotes spirals, after Roger Cotes’ work on the inverse cube force law, published posthumously in 1722. Cotes seems to have been the first to compute the derivative of the sine function. After Cotes’ death at the age of 33, Newton supposedly said "If he had lived we would have known something."
The subtlety of the inverse cube force law is greatly heightened when we study it using quantum rather than classical mechanics. Here if c is too large the theory is ill-defined, because there is no reasonable choice of self-adjoint Hamiltonian. If c is smaller the theory is well-behaved. But at a certain borderline point it exhibits a remarkable property: spontaneous breaking of scaling symmetry. I hope to discuss this in my next column.
For more on the inverse cube force law, see:
• N. Grossman, The Sheer Joy of Celestial Mechanics, Birkhäuser, Basel, 1996, p. 34.
For more on Newton’s work involving the inverse cube force law, see:
• Wikipedia, Newton’s theorem of revolving orbits.
• S. Chandrasekhar, Newton’s Principia for the Common Reader, Oxford U. Press, Oxford, 1995, pp. 183–200.
Cotes’ book is
• Roger Cotes, Harmonia Mensuarum, Cambridge, 1722.
Tomorrow I’m headed to Berkeley for the Inkhaven blogging residency, whose participants need to write one blog post per day or get kicked out. I’ll be there to share my "wisdom" as a distinguished elder blogger (note that Shtetl-Optimized is now in its twentieth year). I’m acutely aware of the irony, that I myself can barely muster the willpower these days to put up a post every other week.
And it’s not as if nothing is happening in this blog’s traditional stomping-ground of quantum computing! In fact, the issue is just the opposite: way too much is happening for me to do it any sort of justice. Who do people think I am, Zvi Mowshowitz? The mere thought of being comprehensive, of responsibly staying on top of all the latest QC developments, makes me want to curl up in bed, and either scroll through political Substacks or take a nap.
But then, you know, eventually a post gets written. Let me give you some vignettes about what’s new in QC, any one of which could easily have been its own post if I were twenty years younger.
(1) Google announced verifiable quantum advantage based on Out-of-Time-Order-Correlators (OTOC)—this is actually from back in June, but it’s gotten more and more attention as Google has explained it more thoroughly. See especially this recent 2-page note by King, Kothari, et al., explaining Google’s experiment in theoretical computer science language. Basically, what they do is, starting from the all-|0⟩ state, to apply a random circuit C, then a single gate g, then C-1, then another gate h, then C again, then g again, then C-1, and then measure a qubit. If C is shallow, then the qubit is likely to still be |0⟩. If C is too deep, then the qubit is likely to be in the maximally mixed state, totally uncorrelated with its initial state—the gates g and h having caused a "butterfly effect" that completely ruined all the cancellation between C and C-1. Google claims that, empirically, there’s an intermediate regime where the qubit is neither |0⟩ nor the maximally mixed state, but a third thing—and that this third thing seems hard to determine classically, using tensor network algorithms or anything else they’ve thrown at it, but it can of course be determined by running the quantum computer. Crucially, because we’re just trying to estimate a few parameters here, rather than sample from a probability distribution (as with previous quantum supremacy experiments), the output can be checked by comparing it against the output of a second quantum computer, even though the problem still isn’t in NP. Incidentally, if you’re wondering why they go back and forth between C and C-1 multiple times rather than just once, it’s to be extra confident that there’s not a fast classical simulation. Of course there might turn out to be a fast classical simulation anyway, but if so, it will require a new idea: gauntlet thrown.
(2) Quantinuum, the trapped-ion QC startup in Colorado, announced its Helios processor. Quick summary of the specs: 98 qubits, all-to-all 2-qubit gates with 99.92% fidelity, the ability to choose which gates to apply "just in time" (rather than fixing the whole circuit in advance, as was needed with their previous API), and an "X"-shaped junction for routing qubits one way or the other (the sort of thing that a scalable trapped-ion quantum computer will need many of). This will enable, and is already enabling, more and better demonstrations of quantum advantage.
(3) Quantinuum and JP Morgan Chase announced the demonstration of a substantially improved version of my and Shih-Han-Hung’s protocol for generating cryptographically certified random bits, using quantum supremacy experiments based on random circuit sampling. They did their demo on Quantinuum’s new Helios processor. Compared to the previous demonstration, the new innovation is to send the circuit to the quantum computer one layer at a time, rather than all at once (something that, again, Quantinuum’s new API allows). The idea is that a cheating server, who wanted to spoof the randomness deterministically, now has much less time: using the most competitive known methods (e.g., those based on tensor network contraction), it seems the cheater would need to swing into action only after learning the final layer of gates, so would now have mere milliseconds to spoof rather than seconds, making Internet latency the dominant source of spoofing time in practice. While a complexity-theoretic analysis of the new protocol (or, in general, of "layer-by-layer" quantum supremacy protocols like it) is still lacking, I like the idea a lot.
(4) The startup company BlueQubit announced a candidate demonstration of verifiable quantum supremacy via obfuscated peaked random circuits, again on a Quantinuum trapped-ion processor (though not Helios). In so doing, BlueQubit is following the program that Yuxuan Zhang and I laid out last year: namely, generate a quantum circuit C that hopefully looks random to any efficient classical algorithm, but that conceals a secret high-probability output string x, which pops out if you run C on a quantum computer on the all-0 initial state. To try to hide x, BlueQubit uses at least three different circuit obfuscation techniques, which already tells you that they can’t have complete confidence in any one of them (since if they did, why the other two?). Nevertheless, I’m satisfied that they tried hard to break their own obfuscation, and failed. Now it’s other people’s turn to try.
(5) Deshpande, Fefferman, et al. announced a different theoretical proposal for quantum advantage from peaked quantum circuits, based on error-correcting codes. This seems tempting to try to demonstrate along the way to quantum fault-tolerance.
(6) A big one: John Bostanci, Jonas Haferkamp, Chinmay Nirkhe, and Mark Zhandry announced a proof of a classical oracle separation between the complexity classes QMA and QCMA, something that they’ve been working on for well over a year. Their candidate problem is basically a QMA-ified version of my Forrelation, which Raz and Tal previously used to achieve an oracle separation between BQP and PH. I caution that their paper is 91 pages long and hasn’t yet been vetted by independent experts, and there have been serious failed attempts on this exact problem in this past. If this stands, however, it finally settles a problem that’s been open since 2002 (and which I’ve worked on at various points starting in 2002), and shows a strong sense in which quantum proofs are more powerful than classical proofs. Note that in 2006, Greg Kuperberg and I gave a quantum oracle separation between QMA and QCMA—introducing the concept of quantum oracles for the specific purpose of that result—and since then, there’s been progress on making the oracle steadily "more classical," but the oracle was always still randomized or "in-place" or had restrictions on how it could be queried.
(7) Oxford Ionics (which is now owned by IonQ) announced a 2-qubit gate with 99.99% fidelity: a record, and significantly past the threshold for quantum fault-tolerance. However, as far as I know, it remains to demonstrate this sort of fidelity in a large programmable system with dozens of qubits and hundreds of gates.
(8) Semi-announcement: Quanta reports that "Physicists Take the Imaginary Numbers Out of Quantum Mechanics," and this seems to have gone viral on my social media. The article misses the opportunity to explain that "taking the imaginary numbers out" is as trivial as choosing to call each complex amplitude "just an ordered pair of reals, obeying such-and-such rules, which happen to mimic the rules for complex numbers." Thus, the only interesting question here is whether one can take imaginary numbers out of QM in various more-or-less "natural" ways: a technical debate that the recent papers are pushing forward. For what it’s worth, I don’t expect that anything coming out of this line of work will ever be "natural" enough for me to stop explaining QM in terms of complex numbers in my undergraduate class, for example.
(9) The list of accepted talks for the annual QIP conference, to be held January 24-30 in Riga, Latvia, is now out. Lots of great stuff as always.
(10) There are probably other major recent developments in QC that I should’ve put into this post but forgot about. You can remind me about them in the comments.
(11) Indeed there are! I completely forgot that Phasecraft announced two simulations of fermionic systems that might achieve quantum advantage, one using Google’s Willow superconducting chip and the other using a Quantinuum device.
To summarize three takeaways:
When a word has both an everyday meaning and a technical meaning, it can cause no end of confusion.
I’ve written about this before using one of the most common examples, the word "model", which means something quite different in the phrases "large language model", "animal model for Alzheimer’s" and "model train". And I’ve written about running into this kind of confusion at the beginning of my PhD, with the word "effective".
But there is one example I see crop up again and again, even with otherwise skilled science communicators. It’s the word "discover".
"Discover", in physics, has a technical meaning. It’s a first-ever observation of something, with an associated standard of evidence. In this sense, the LHC discovered the Higgs boson in 2012, and LIGO discovered gravitational waves in 2015. And there are discoveries we can anticipate, like the cosmic neutrino background.
But of course, "discover" has a meaning in everyday English, too.
You probably think I’m going to say that "discover", in everyday English, doesn’t have the same statistical standards it does in physics. That’s true of course, but it’s also pretty obvious, I don’t think it’s confusing anybody.
Rather, there is a much more important difference that physicists often forget: in everyday English, a discovery is a surprise.
"Discover", a word arguably popularized by Columbus’s discovery of the Americas, is used pretty much exclusively to refer to learning about something you did not know about yet. It can be minor, like discovering a stick of gum you forgot, or dramatic, like discovering you’ve been transformed into a giant insect.
Now, as a scientist, you might say that everything that hasn’t yet been observed is unknown, ready for discovery. We didn’t know that the Higgs boson existed before the LHC, and we don’t know yet that there is a cosmic neutrino background.
But just because we don’t know something in a technical sense, doesn’t mean it’s surprising. And if something isn’t surprising at all, then in everyday, colloquial English, people don’t call it a discovery. You don’t "discover" that the store has milk today, even if they sometimes run out. You don’t "discover" that a movie is fun, if you went because you heard reviews claim it would be, even if the reviews might have been wrong. You don’t "discover" something you already expect.
At best, maybe you could "discover" something controversial. If you expect to find a lost city of gold, and everyone says you’re crazy, then fine, you can discover the lost city of gold. But if everyone agrees that there is probably a lost city of gold there? Then in everyday English, it would be very strange to say that you were the one who discovered it.
With this in mind, the way physicists use the word "discover" can cause a lot of confusion. It can make people think, as with gravitational waves, that a "discovery" is something totally new, that we weren’t pretty confident before LIGO that gravitational waves exist. And it can make people get jaded, and think physicists are overhyping, talking about "discovering" this or that particle physics fact because an experiment once again did exactly what it was expected to.
My recommendation? If you’re writing for the general public, use other words. The LHC "decisively detected" the Higgs boson. We expect to see "direct evidence" of the cosmic neutrino background. "Discover" has baggage, and should be used with care.
Next Monday, November 17th at 7pm, I’ll be at the Harvard Bookstore with particle physicist and author Daniel Whiteson. Professor Whiteson and his co-author Andy Warner have a nice new book, for the general science-aware reader, exploring an age-old and unanswered question: how universal is the knowledge and understanding that we call "physics"? How much of modern physics is actually telling us about the universe, and how much of it is created by, or an accident of, the humans who have helped bring it about?
For instance, if we started all over again and reran history from scratch, would the physics (and science more generally) of this re-run culture look much like our own, or might it turn out very differently? If another culture on Earth had had time to develop highly mature science (or something like it) in its own direction, independent of Western Europe’s influence, how different might that science be? (Indeed, would our word "science" even be translatable into their worldview?) Or if we encountered aliens with far greater understanding of the universe than we have, would we be able to recognize, parse, grok, appreciate, comprehend, and/or otherwise make sense of their notions of scientific knowledge?
Whiteson and his co-author, wanting to write a popular book rather than a scholarly one, and desiring nevertheless to take on these serious and challenging intellectual questions, have set their focus mostly on the aliens, accompanied by amusing cartoons and a generous helping of dad jokes (hey, some dad jokes are actually very funny.) They’re looking for a broad audience, and hopefully they will get it. But don’t let the light-hearted title ("Do Aliens Speak Physics?") or the charmingly goofy cover fool you: this book might well make you laugh, but I guarantee it will make you think. Whether you’re just curious about science or you’ve been doing science yourself for years, I suspect that, within the vast array of problems and issues that are raised in this broad-minded book, there will be some you’ve never thought of.
Among scientists and philosophers, there are some who believe that any aliens with the capacity to reach the Earth will obviously "speak physics" — that math and physics float above contingencies of culture and species, and will easily be translated from any intelligent creature to any other. But are they perhaps flying too high? It’s clear that Whiteson and Warner are aiming to poke some holes — lots of holes —- in their hot-air balloon, and to do so in a way that a wide variety of readers can appreciate and enjoy.
I tend to agree with Whiteson on a lot of these issues, but that won’t stop me from asking him some tough questions. You can ask him some tough questions too, if you like — just come to the Harvard Bookstore at 7:00 on Monday and join the conversation!
I have uploaded to the arXiv my paper "New Nikodym set constructions over finite fields". This is a spinoff of my previous project with Bogdan Georgiev, Javier Gómez–Serrano, and Adam Zsolt Wagner that I recently posted about. In that project we experimented with using AlphaEvolve (and other tools, such as DeepThink and AlphaProof) to explore various mathematical problems which were connected somehow to an optimization problem. For one of these — the finite field Nikodym set problem — these experiments led (by a somewhat convoluted process) to an improved asymptotic construction of such sets, the details of which are written up (by myself rather than by AI tools) in this paper.
Let {{\mathbb F}_q} be a finite field of some order {q} (which must be a prime or a power of a prime), and let {d} be a fixed dimension. A Nikodym set in {{\mathbb F}_q^d} is a subset {N} of {{\mathbb F}_q^d} with the property that for every point {x \in {\mathbb F}_q^d}, there exists a line {\ell} passing through {x} such that all points of {\ell} other than {x} lie in {N}. Such sets are close cousins of Kakeya sets (which contain a line in every direction); indeed, roughly speaking, applying a random projective transformation to a Nikodym set will yield (most of) a Kakeya set. As a consequence, any lower bound on Kakeya sets implies a similar bound on Nikodym sets; in particular, one has a lower bound
\displaystyle |N| \geq \frac{q^d}{2^{d-1}} + O(q^{d-1})
on the size of a Nikodym set {N}, coming from a similar bound on Kakeya sets due to Bukh and Chao using the polynomial method.For Kakeya sets, Bukh and Chao showed this bound to be sharp up to the lower order error {O(q^{d-1})}; but for Nikodym sets it is conjectured that in fact such sets should asymptotically have full density, in the sense that
\displaystyle |N| \geq q^d - o(q^d).
This is known in two dimensions thanks to work by Szönyi et al. on blocking sets, and was also established in bounded torsion cases (and in particular for even {q}) by Guo, Kopparty, and Sudan by combining the polynomial method with the theory of linear codes. But in other cases this conjecture remains open in three and higher dimensions.In our experiments we focused on the opposite problem of constructing Nikodym sets of size as small as possible. In the plane {d=2}, constructions of size
\displaystyle |N| = q^2 - q^{3/2} + O(q \log q) \ \ \ \ \ (1)
when {q} is a perfect square were constructed by Blokhuis et al, again using the theory of blocking sets; by taking Cartesian products of such sets, one can also make similar constructions in higher dimensions, again assuming {q} is a perfect square. Apart from this, though, there are few such constructions in the literature.We set AlphaEvolve to try to optimize the three dimensional problem with a variable field size {q} (which we took to be prime for simplicity), with the intent to get this tool to come up with a construction that worked asymptotically for large {q}, rather than just for any fixed value of {q}. After some rounds of evolution, it arrived at a construction which empirically had size about {q^3 - 8q^2}. Inspecting the code, it turned out that AlphaEvolve had constructed a Nikodym set {N} by (mostly) removing eight low-degree algebraic surfaces (all of the form {\{ (x,y,x^i y)\}} for various {i}). We used the tool DeepThink to confirm the Nikodym property and to verify the construction, and then asked it to generalize the method. By removing many more than eight surfaces, and using some heuristic arguments based on the Chebotarev density theorem, DeepThink claimed a construction of size
\displaystyle |N| = q^3 - 2 q^2 \log q + o(q^2 \log q) \ \ \ \ \ (2)
formed by removing several higher degree surfaces, but it acknowledged that the arguments were non-rigorous.The arguments can be sketched here as follows. Let {V} be a random surface of degree {D}, and let {x} be a point in {{\mathbb F}_q^3} which does not lie in {V}. A random line through {x} then meets {V} in a number of points, which is basically the set of zeroes in {{\mathbb F}_q} of a random polynomial of degree {D}. The (function field analogue of the) Chebotarev density theorem predicts that the probability that this polynomial has no roots in {{\mathbb F}_q} is about {\delta_D}, where
\displaystyle \delta_D = 1 - \frac{1}{1!} + \frac{1}{2!} - \dots + \frac{(-1)^D}{D!}
is the proportion of permutations on {D} elements that are derangements (no fixed points). So, if one removes {k} random surfaces of degrees {D_1,\dots,D_k}, the probability that a random line avoids all of these surfaces is about {\delta_{D_1} \dots \delta_{D_k}}. If this product is significantly greater than {1/q^2}, then the law of large numbers (and concentration of measure) then predicts (with high probability) that out of the {\sim q^2} lines through {x}, at least one will avoid the removed surfaces, thus giving (most of) a Nikodym set. The Lang-Weil estimate predicts that each surface has cardinality about {q^2}, so this should give a Nikodym set of size about {q^3 - kq^2}.DeepThink took the degrees {D_1,\dots,D_k} to be large, so that the derangement probabilities {\delta_{D_i}} were close to {1/e}. This led it to predict that {k} could be taken to be as large as {2 \log q}, leading to the claimed bound (2). However, on inspecting this argument we realized that these moderately high degree surfaces were effectively acting as random sets, so one could dramatically simplify DeepThink’s argument by simply taking {N} to be a completely random set of the desired cardinality (2), in which case the verification of the Nikodym set property (with positive probability) could be established by a standard Chernoff bound-type argument (actually, I ended up using Bennett’s inequality rather than Chernoff’s inequality, but this is a minor technical detail).
On the other hand, the derangement probabilities {\delta_D} oscillate around {1/e}, and in fact are as large as {1/2} when {D=2}. This suggested that one could do better than the purely random construction if one only removed quadratic surfaces instead of higher degree surfaces, and heuristically predicted the improvement
\displaystyle |N| = q^3 - \frac{2}{\log 2} q^2 \log q + o(q^2 \log q). \ \ \ \ \ (3)
However, our experiments with both AlphaEvolve and DeepThink to try to make this idea work either empirically, heuristically, or rigorously were all unsuccessful! Eventually Deepthink discovered the problem: random quadratic polynomials often had two or zero roots (depending on whether the discriminant was a non-zero quadratic residue, or a nonresidue), but would only very rarely have just one root (the discriminant would have to vanish). As a consequence, if {x} happened to lie on one of the removed quadratic surfaces {V}, it was extremely likely that most lines through {x} would intersect {V} in a further point; only the small minority of lines that were tangent to {V} and {x} would avoid this. None of the AI tools we tried were able to overcome this obstacle.However, I realized that one could repair the construction by adding back a small random portion of the removed quadratic surfaces, to allow for a non-zero number of lines through {x} to stay inside the putative Nikodym set even when {x} was in one of the surfaces {V}, and the line was not tangent to {V}. Pursuing this idea, and performing various standard probabilistic calculations and projective changes of variable, the problem essentially reduced to the following: given {k} random quadratic polynomials in the plane {{\mathbb F}_q^2}, is it true that these polynomials simultaneously take quadratic residue values for {\gg 2^{-k}} of the points in that plane? Heuristically this should be true even for {2^{-k}} close to {1/q^2}. However, it proved difficult to accurately control this simultaneous quadratic residue event; standard algebraic geometry tools such as the Weil conjectures seemed to require some vanishing of étale cohomology groups in order to obtain adequate error terms, and this was not something I was eager to try to work out. However, by exploiting projective symmetry (and the {2}-transitive nature of the projective linear group), I could get satisfactory control of such intersections as long as {2^{-k}} was a little bit larger than {1/q} rather than {1/q^2}. This gave an intermediate construction of size
\displaystyle |N| = q^3 - (\frac{1}{\log 2} + 1) q^2 \log q + o(q^2 \log q),
which still beat the purely random construction, but fell short of heuristic predictions. This argument (generalized to higher dimensions) is what is contained in the paper. I pose the question of locating a construction with the improved bound (3) (perhaps by some modification of the strategy of removing quadratic varieties) as an open question.We also looked at the two-dimensional case to see how well AlphaEvolve could recover known results, in the case that {q} was a perfect square. It was able to come up with a construction that was slightly worse than the best known construction, in which one removed a large number of parabolas from the plane; after manually optimizing the construction we were able to recover the known bound (1). This final construction is somewhat similar to existing constructions (it has a strong resemblance to a standard construction formed by taking the complement of a Hermitian unital), but is still technically a new construction, so we have also added it to this paper.
Physics is really bizarre and wonderful. Here I start explaining why the Standard Model has U(1) ×ばつ SU(2) ×ばつ SU(3) as its symmetry group. But I don’t assume you know anything about groups or quantum mechanics! So I have to start at the beginning: how the electromagnetic, weak, and strong force are connected to the numbers 1, 2, and 3. It’s all about quunits, qubits and qutrits.
You’ve heard of bits, which describe a binary alternative, like 0 and 1. You’ve probably heard about qubits, which are the quantum version of bits. The weak force is connected to qubits where the 2 choices are called "isospin up" and "isospin down". The most familiar example is the choice between a proton and a neutron. A better example is the choice between an up quark and a down quark.
The strong force is connected to qutrits—the quantum version of a choice between 3 alternatives. In physics these are whimsically called "red", "green" and "blue". Quarks come in 3 colors like this.
The electromagnetic force is connected to "quunits" – the quantum version of a choice between just one alternative. It may seem like that’s no choice at all! But quantum mechanics is weird: there’s just one choice, but you can still rotate that choice.
Yes, I know this stuff sounds crazy. But this is how the world actually works. I start explaining it here, and I’ll keep on until it’s all laid out quite precisely.
The 2026 APS Oliver E. Buckley Prize in condensed matter physics was announced this week, and it's a really interesting combination of topics that, to a lay person, may seem to be completely unrelated.
The key idea here is the role of vortices. Superfluidity in helium is described by an order parameter that looks like a wavefunction - it has an amplitude, \(\Psi_{0}\), and a phase \(\phi\), so that \(\Psi(\mathbf{r}) = \Psi_{0} \exp(i \phi)\). That order parameter is supposed to be single-valued, meaning if you go around a closed loop of some kind, that phase will either remain the same or ramp by some integer multiple of \(2\pi\). The gradient of the phase is related to the velocity of the superfluid, so if the phase winds by \(2\pi\), that implies there is a circulation of flow and orbital angular momentum that has to be an integer multiple of \(\hbar\). In the BKT theory, the demise of the superfluid phase as the system is warmed happens through the creation and unbinding of vortex-antivortex pairs.
On the other hand, the other recipients of the Buckley Prize were Gwendal Fève and Mike Manfra for their work (experiments here and here) regarding the braiding statistics of anyons in fractional quantum Hall systems. I'd written about anyons here. For electrons in 2D, the wavefunctions of excitations of the fractional quantum Hall system look like vortices. The phase of the electronic wavefunction can wind due to circulation, and because electrons are charged, the phase can also wind due to magnetic flux attached to the little whirlpool. It's the combination of these phase effects that can lead to those excitations acting like anyons (so that when two are physically swapped or braided around one another, the wavefunction picks up a phase factor that is not just the \(+1\) of bosons or the \(-1\) of fermions).
As my friend Dan Arovas pointed out, there was a hope back in the early 1980s that perhaps vortices in superfluid helium would also act like anyons and have fractional statistics. However, this paper by Haldane and Wu disproved that possibility.
Perhaps it is fitting that I am posting this on the 85th anniversary of the Tacoma Narrows bridge collapse. That classic civil engineering failure was caused by vortex shedding by the bridge coupling to its torsional resonance frequency. Vortices can have big consequences!
I did manage to get home last night from Providence, despite widespread cancellations and delays caused by the government shutdown de-staffing air traffic control. United was speedy, flexible, and informative under rapidly changing conditions, their employees, while clearly stressed out, stayed professional. I made it onto my 7:35 connecting flight from O’Hare to Madison with about 10 minutes to spare. The plane was only half full, because so many people were coming in late, and United ended up holding the flight for 45 minutes to let as many people get on as were able to get to O’Hare. I’m sure there were still a lot of people who didn’t get home. As I write this, the average delay departing O’Hare is 24 minutes; out of Newark, 53 minutes. There are already 263 announced cancellations for Monday. And there’s apparently a United flight in the air right now from Newark to Savannah that’s flying at 10,000 feet the whole way because of air traffic control shortages, burning much more fuel and keeping the passengers in their seats the whole flight. Good on United for figuring out a way to get people where they were going, but still, what a mess.
I miss the days when you looked to the federal government to protect Americans from big corporations, instead of the other way around.
I thought the book had some interesting virtues but was also quite badly overwritten, and very confusing in its treatment of the mathematics. Here’s my review.
For length reasons I wasn’t able to fit in all the material I wrote, so here’s a section addressing the confusingness, which didn’t make it into the final piece. Only here at Quomodocumque do you get the director’s cut!
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This is not the book to read if you want a precise understanding of the mathematics Hilbert, Brouwer, and Russell were going to war over. Bardi’s over-the-top style is confusing as often as it’s entertaining. "Contemplate a never-ending, constantly evolving universe of single-grain galaxies. Think about an eternity of revelation in every single grain for every subsecond of existence. That’s starting to get at the difference between large and small infinity—an infinite number of sand buckets versus the infinite variation inside every single bucket. Big infinity is much bigger. But even then, you’re not close to defining the continuum." Even for someone like me who knows the definition of the continuum, this is hard to parse. I can’t imagine what sense it makes to everybody else. Later, Bardi writes:
"The continuum hypothesis says cardinality of an infinite set is like the Highlander – there can be only one!"
This is not, in fact, what the continuum hypothesis says, and indeed it is false; the whole point of Cantor’s theory, as Bardi explains perfectly well elsewhere in the book, is that there are many cardinalities (roughly, sizes) of infinite sets. Bardi sacrifices clarity of exposition here for the sake of a verbal flourish, and not even much of one – a movie catchphrase reference that only a small age band of readers, including Bardi and me, is likely to recognize.
Scientists have different goals when they communicate, leading to different styles, or registers, of communication. If you don’t notice what register a scientist is using, you might think they’re saying something they’re not. And if you notice someone using the wrong register for a situation, they may not actually be a scientist.
Sometimes, a scientist is trying to explain an idea to the general public. The point of these explanations is to give you appreciation and intuition for the science, not to understand it in detail. This register makes heavy use of metaphors, and sometimes also slogans. It should almost never be taken literally, and a contradiction between two different scientist explanations usually just means they are using incompatible metaphors for the same concept. Sometimes, scientists who do this a lot will comment on other metaphors you might have heard, referencing other slogans to help explain what those explanations miss. They do this knowing that they do, in the end, agree on the actual science: they’re just trying to give you another metaphor, with a deeper intuition for a neglected part of the story.
Other times, scientists are trying to teach a student to be able to do something. Teaching can use metaphors or slogans as introductions, but quickly moves past them, because it wants to show the students something they can use: an equation, a diagram, a classification. If a scientist shows you any of these equations/diagrams/classifications without explaining what they mean, then you’re not the student they had in mind: they had designed their lesson for someone who already knew those things. Teaching may convey the kinds of appreciation and intuition that explanations for the general public do, but that goal gets much less emphasis. The main goal is for students with the appropriate background to learn to do something new.
Finally, sometimes scientists are trying to advocate for a scientific point. In this register, and only in this register, are they trying to convince people who don’t already trust them. This kind of communication can include metaphors and slogans as decoration, but the bulk will be filled with details, and those details should constitute evidence: they should be a structured argument, one that lays out, scientifically, why others should come to the same conclusion.
A piece that tries to address multiple audiences can move between registers in a clean way. But if the register jumps back and forth, or if the wrong register is being used for a task, that usually means trouble. That trouble can be simple boredom, like a scientist’s typical conference talk that can’t decide whether it just wants other scientists to appreciate the work, whether it wants to teach them enough to actually use it, or whether it needs to convince any skeptics. It can also be more sinister: a lot of crackpots write pieces that are ostensibly aimed at convincing other scientists, but are almost entirely metaphors and slogans, pieces good at tugging on the general public’s intuition without actually giving scientists anything meaningful to engage with.
If you’re writing, or speaking, know what register you need to use to do what you’re trying to do! And if you run into a piece that doesn’t make sense, consider that it might be in a different register than you thought.
Bogdan Georgiev, Javier Gómez-Serrano, Adam Zsolt Wagner, and I have uploaded to the arXiv our paper "Mathematical exploration and discovery at scale". This is a longer report on the experiments we did in collaboration with Google Deepmind with their AlphaEvolve tool, which is in the process of being made available for broader use. Some of our experiments were already reported on in a previous white paper, but the current paper provides more details, as well as a link to a repository with various relevant data such as the prompts used and the evolution of the tool outputs.
AlphaEvolve is a variant of more traditional optimization tools that are designed to extremize some given score function over a high-dimensional space of possible inputs. A traditional optimization algorithm might evolve one or more trial inputs over time by various methods, such as stochastic gradient descent, that are intended to locate increasingly good solutions while trying to avoid getting stuck at local extrema. By contrast, AlphaEvolve does not evolve the score function inputs directly, but uses an LLM to evolve computer code (often written in a standard language such as Python) which will in turn be run to generate the inputs that one tests the score function on. This reflects the belief that in many cases, the extremizing inputs will not simply be an arbitrary-looking string of numbers, but will often have some structure that can be efficiently described, or at least approximated, by a relatively short piece of code. The tool then works with a population of relatively successful such pieces of code, with the code from one generation of the population being modified and combined by the LLM based on their performance to produce the next generation. The stochastic nature of the LLM can actually work in one’s favor in such an evolutionary environment: many "hallucinations" will simply end up being pruned out of the pool of solutions being evolved due to poor performance, but a small number of such mutations can add enough diversity to the pool that one can break out of local extrema and discover new classes of viable solutions. The LLM can also accept user-supplied "hints" as part of the context of the prompt; in some cases, even just uploading PDFs of relevant literature has led to improved performance by the tool. Since the initial release of AlphaEvolve, similar tools have been developed by others, including OpenEvolve, ShinkaEvolve and DeepEvolve.
We tested this tool on a large number (67) of different mathematics problems (both solved and unsolved) in analysis, combinatorics, and geometry that we gathered from the literature, and reported our outcomes (both positive and negative) in this paper. In many cases, AlphaEvolve achieves similar results to what an expert user of a traditional optimization software tool might accomplish, for instance in finding more efficient schemes for packing geometric shapes, or locating better candidate functions for some calculus of variations problem, than what was previously known in the literature. But one advantage this tool seems to offer over such custom tools is that of scale, particularly when when studying variants of a problem that we had already tested this tool on, as many of the prompts and verification tools used for one problem could be adapted to also attack similar problems; several examples of this will be discussed below. The following graphic illustrates the performance of AlphaEvolve on this body of problems:
Another advantage of AlphaEvolve was (削除) robustness (削除ここまで) adaptability: it was relatively easy to set up AlphaEvolve to work on a broad array of problems, without extensive need to call on domain knowledge of the specific task in order to tune hyperparameters. In some cases, we found that making such hyperparameters part of the data that AlphaEvolve was prompted to output was better than trying to work out their value in advance, although a small amount of such initial theoretical analysis was helpful. For instance, in calculus of variation problems, one is often faced with the need to specify various discretization parameters in order to estimate a continuous integral, which cannot be computed exactly, by a discretized sum (such as a Riemann sum), which can be evaluated by computer to some desired precision. We found that simply asking AlphaEvolve to specify its own discretization parameters worked quite well (provided we designed the score function to be conservative with regards to the possible impact of the discretization error); see for instance this experiment in locating the best constant in functional inequalities such as the Hausdorff-Young inequality.
A third advantage of AlphaEvolve over traditional optimization methods was the interpretability of many of the solutions provided. For instance, in one of our experiments we sought to find an extremum to a functional inequality such as the Gagliardo–Nirenberg inequality (a variant of the Sobolev inequality). This is a relatively well-behaved optimization problem, and many standard methods can be deployed to obtain near-optimizers that are presented in some numerical format, such as a vector of values on some discretized mesh of the domain. However, when we applied AlphaEvolve to this problem, the tool was able to discover the exact solution (in this case, a Talenti function), and create code that sampled from that function on a discretized mesh to provide the required input for the scoring function we provided (which only accepted discretized inputs, due to the need to compute the score numerically). This code could be inspected by humans to gain more insight as to the nature of the optimizer. (Though in some cases, AlphaEvolve’s code would contain some brute force search, or a call to some existing optimization subroutine in one of the libraries it was given access to, instead of any more elegant description of its output.)
For problems that were sufficiently well-known to be in the training data of the LLM, the LLM component of AlphaEvolve often came up almost immediately with optimal (or near-optimal) solutions. For instance, for variational problems where the gaussian was known to be the extremizer, AlphaEvolve would frequently guess a gaussian candidate during one of the early evolutions, and we would have to obfuscate the problem significantly to try to conceal the connection to the literature in order for AlphaEvolve to experiment with other candidates. AlphaEvolve would also propose similar guesses for other problems for which the extremizer was not known. For instance, we tested this tool on the sum-difference exponents of relevance to the arithmetic Kakeya conjecture, which can be formulated as a variational entropy inequality concerning certain two-dimensional discrete random variables. AlphaEvolve initially proposed some candidates for such variables based on discrete gaussians, which actually worked rather well even if they were not the exact extremizer, and already generated some slight improvements to previous lower bounds on such exponents in the literature. Inspired by this, I was later able to rigorously obtain some theoretical results on the asymptotic behavior on such exponents in the regime where the number of slopes was fixed, but the "rational complexity" of the slopes went to infinity; this will be reported on in a separate paper.
Perhaps unsurprisingly, AlphaEvolve was extremely good at locating "exploits" in the verification code we provided, for instance using degenerate solutions or overly forgiving scoring of approximate solutions to come up with proposed inputs that technically achieved a high score under our provided code, but were not in the spirit of the actual problem. For instance, when we asked it (link under construction) to find configurations to extremal geometry problems such as locating polygons with each vertex having four equidistant other vertices, we initially coded the verifier to accept distances that were equal only up to some high numerical precision, at which point AlphaEvolve promptly placed many of the points in virtually the same location so that the distances they determined were indistinguishable. Because of this, a non-trivial amount of human effort needs to go into designing a non-exploitable verifier, for instance by working with exact arithmetic (or interval arithmetic) instead of floating point arithmetic, and taking conservative worst-case bounds in the presence of uncertanties in measurement to determine the score. For instance, in testing AlphaEvolve against the "moving sofa" problem and its variants, we designed a conservative scoring function that only counted those portions of the sofa that we could definitively prove to stay inside the corridor at all times (not merely the discrete set of times provided by AlphaEvolve to describe the sofa trajectory) to prevent it from exploiting "clipping" type artefacts. Once we did so, it performed quite well, for instance rediscovering the optimal "Gerver sofa" for the original sofa problem, and also discovering new sofa designs for other problem variants, such as a 3D sofa problem.
For well-known open conjectures (e.g., Sidorenko’s conjecture, Sendov’s conjecture, Crouzeix’s conjecture, the ovals problem, etc.), AlphaEvolve generally was able to locate the previously known candidates for optimizers (that are conjectured to be optimal), but did not locate any stronger counterexamples: thus, we did not disprove any major open conjecture. Of course, one obvious possible explanation for this is that these conjectures are in fact true; outside of a few situations where there is a matching "dual" optimization problem, AlphaEvolve can only provide one-sided bounds on such problems and so cannot definitively determine if the conjectural optimizers are in fact the true optimizers. Another potential explanation is that AlphaEvolve essentially tried all the "obvious" constructions that previous researchers working on these problems had also privately experimented with, but did not report due to the negative findings. However, I think there is at least value in using these tools to systematically record negative results (roughly speaking, that a search for "obvious" counterexamples to a conjecture did not disprove the claim), which currently only exist as "folklore" results at best. This seems analogous to the role LLM Deep Research tools could play by systematically recording the results (both positive and negative) of automated literature searches, as a supplement to human literature review which usually reports positive results only. Furthermore, when we shifted attention to less well studied variants of famous conjectures, we were able to find some modest new observations. For instance, while AlphaEvolve only found the standard conjectural extremizer {z^n-1} to Sendov’s conjecture, as well as for variants such as Borcea’s conjecture, Schmeisser’s conjecture, or Smale’s conjecture it did reveal some potential two-parameter extensions to a conjecture of de Bruin and Sharma that had not previously been stated in the literature. (For this problem, we were not directly optimizing some variational scalar quantity, but rather a two-dimensional range of possible values, which we could adapt the AlphaEvolve framework to treat). In the future, I can imagine such tools being a useful "sanity check" when proposing any new conjecture, in that it will become common practice to run one of these tools against such a conjecture to make sure there are no "obvious" counterexamples (while keeping in mind that this is still far from conclusive evidence in favor of such a conjecture).
AlphaEvolve did not perform equally well across different areas of mathematics. When testing the tool on analytic number theory problems, such as that of designing sieve weights for elementary approximations to the prime number theorem, it struggled to take advantage of the number theoretic structure in the problem, even when given suitable expert hints (although such hints have proven useful for other problems). This could potentially be a prompting issue on our end, or perhaps the landscape of number-theoretic optimization problems is less amenable to this sort of LLM-based evolutionary approach. On the other hand, AlphaEvolve does seem to do well when the constructions have some algebraic structure, such as with the finite field Kakeya and Nikodym set problems, which we will turn to shortly.
For many of our experiments we worked with fixed-dimensional problems, such as trying to optimally pack {n} shapes in a larger shape for a fixed value of {n}. However, we found in some cases that if we asked AlphaEvolve to give code that took parameters such as {n} as input, and tested the output of that code for a suitably sampled set of values of {n} of various sizes, then it could sometimes generalize the constructions it found for small values of this parameter to larger ones; for instance, in the infamous sixth problem of this year’s IMO, it could use this technique to discover the optimal arrangement of tiles, which none of the frontier models could do at the time (although AlphaEvolve has no capability to demonstrate that this arrangement was, in fact, optimal). Another productive use case of this technique was for finding finite field Kakeya and Nikodym sets of small size in low-dimensional vector spaces over finite fields of various sizes. For Kakeya sets in {{\mathbf F}_q^d}, it located the known optimal construction based on quadratic residues in two dimensions, and very slightly beat (by an error term of size {O(q)}) the best construction in three dimensions; this was an algebraic construction (still involving quadratic residues) discovered empirically that we could then prove to be correct by first using Gemini’s "Deep Think" tool to locate an informal proof, which we could then convert into a formalized Lean proof by using Google Deepmind’s "AlphaProof" tool. At one point we thought it had found a construction in four dimensions which achieved a more noticeable improvement (of order {O(q^3)}) of what we thought was the best known construction, but we subsequently discovered that essentially the same construction had appeared already in a paper of Bukh and Chao, although it still led to a more precise calculation of the error term (to accuracy {O(q^{3/2})} rather than {O(q^2)}, where the error term now involves the Lang-Weil inequality and is unlikely to have a closed form). Perhaps AlphaEvolve had somehow absorbed the Bukh-Chao construction within its training data to accomplish this. However, when we tested the tool on Nikodym sets (which are expected to have asymptotic density {1}, although this remains unproven), it did find some genuinely new constructions of such sets in three dimensions, based on removing quadratic varieties from the entire space. After using "Deep Think" again to analyze these constructions, we found that they were inferior to a purely random construction (which in retrospect was an obvious thing to try); however, they did inspire a hybrid construction in which one removed random quadratic varieties and performed some additional cleanup, which ends up outperforming both the purely algebraic and purely random constructions. This result (with completely human-generated proofs) will appear in a subsequent paper.
Update (Nov. 6): I’ve closed the comments, as they crossed the threshold from "sometimes worthwhile" to "purely abusive." As for Mamdani’s victory: as I like to say in such cases (and said, e.g., after George W. Bush’s and Trump’s victories), the silver lining to which I cling is that either I’ll be pleasantly surprised, and things won’t be quite as terrible as I expect, or else I’ll be vindicated.
This Halloween, I didn’t need anything special to frighten me. I walked all day around in a haze of fear and depression, unable to concentrate on my research or anything else. I saw people smiling, dressed up in costumes, and I thought: how?
The president of the Heritage Foundation, the most important right-wing think tank in the United States, has now explicitly aligned himself with Tucker Carlson, even as the latter has become a full-on Holocaust-denying Hitler-loving antisemite, who nods in agreement with the openly neo-Nazi Nick Fuentes. Meanwhile, Vice President J.D. Vance—i.e., plausibly the next President of the United States—pointedly did nothing whatsoever to distance himself from the MAGA movement’s lunatic antisemites, in response to their lunatic antisemitic questions at the Turning Point USA conference. (Vance thus dishonored the memory of Charlie Kirk, who for all my many disagreements with him, was a firmly committed Zionist.) It’s become undeniable that, once Trump himself leaves the stage, this is the future of MAGA, and hence of the Republican Party itself. Exactly as I warned would happen a decade ago, thisis what’s crawled out from underneath the rock that Trump gleefully overturned.
While the Republican Party is being swallowed by a movement that holds that Jews like me have no place in America, the Democratic Party is being swallowed by a movement that holds that Jews have no place in Israel. If these two movements ever merged, the obvious "compromise" would be the belief, popular throughout history, that Jews have no place anywhere on earth.
Barring a miracle, New York City—home to the world’s second-largest Jewish community—is about to be led by a man for whom eradicating the Jewish state is his deepest, most fundamental moral imperative, besides of course the proletariat seizing the means of production. And to their eternal shame, something like 29% of New York’s Jews are actually going to vote for this man, believing that their own collaboration with evil will somehow protect them personally—in breathtaking ignorance of the millennia of Jewish history testifying to the opposite.
Despite what you might think, I try really, really hard not to hyperventilate or overreact. I know that, even if I lived in literal Warsaw in 1939, it would still be incumbent on me to assess the situation calmly and figure out the best response.
So for whatever it’s worth: no, I don’t expect that American Jews, even pro-Zionist Jews in New York City, will need to flee their homes just yet. But it does seem to me that they (to say nothing of British and Canadian and French Jews) might, so to speak, want to keep their suitcases packed by the door, as Jews have through the centuries in analogous situations. As Tevye says near the end of Fiddler on the Roof, when the Jews are given three days to evacuate Anatevka: "maybe this is why we always keep our hats on." Diaspora Jews like me might also want to brush up on Hebrew. We can thank Hashem or the Born Rule that, this time around, at least the State of Israel exists (despite the bloodthirsty wish of half the world that it cease to exist), and we can reflect that these contingencies are precisely why Israel was created.
Let me make something clear: I don’t focus so much on antisemitism only because of parochial concern for the survival of my own kids, although I freely admit to having as much such concern as the next person. Instead, I do so because I hold with David Deutsch that, in Western civilization, antisemitism has for millennia been the inevitable endpoint toward which every bad idea ultimately tends. It’s the universal bad idea. It’s bad-idea-complete. Antisemitism is the purest possible expression of the worldview of the pitchfork-wielding peasant, who blames shadowy elites for his own failures in life, and who dreams in his resentment and rage of reversing the moral and scientific progress of humanity by slaughtering all those responsible for it. Hatred of high-achieving Chinese and Indian immigrants, and of gifted programs and standardized testing, are other expressions of the same worldview.
As far as I know, in 3,000 years, there hasn’t been a single example—not one—of an antisemitic regime of which one could honestly say: "fine, but once you look past what they did to the Jews, they were great for everyone else!" Philosemitism is no guarantee of general goodness (as we see for example with Trump), but antisemitism pretty much does guarantee general awfulness. That’s because antisemitism is not merely a hatred, but an entire false theory of how the world works—not just a but the conspiracy theory—and as such, it necessarily prevents its believers from figuring out true explanations for society’s problems.
I’d better end a post like this on a note of optimism. Yes, every single time I check my phone, I’m assaulted with twenty fresh examples of once-respected people and institutions, all across the political spectrum, who’ve now fallen to the brain virus, and started blaming all the world’s problems on "bloodsucking globalists" or George Soros or Jeffrey Epstein or AIPAC or some other suspicious stand-in du jour. (The deepest cuts come from the new Jew-haters who I myself once knew, or admired, or had some friendly correspondence with.)
But also, every time I venture out into the real world, I meet twenty people of all backgrounds whose brains still seem perfectly healthy, and who respond to events in a normal human way. Even in the dark world behind the screen, I can find dozens of righteous condemnations of Zohran Mamdani and Tucker Carlson and the Heritage Foundation and the others who’ve chosen to play footsie with those seeking a new Final Solution to the Jewish Question. So I reflect that, for all the battering it’s taken in this age of TikTok and idiocracy—even then, our Enlightenment civilization still has a few antibodies that are able to put up a fight.
In their beautiful book Abundance , Ezra Klein and Derek Thompson set out an ambitious agenda by which the Democratic Party could reinvent itself and defeat MAGA, not by indulging conspiracy theories but by creating actual broad prosperity. Their agenda is full of items like: legalizing the construction of more housing where people actually want to live; repealing the laws that let random busybodies block the construction of mass transit; building out renewable energy and nuclear; investing in science and technology ... basically, doing all the things that anyone with any ounce of economic literacy knows to be good. The abundance agenda isn’t only righteous and smart: for all I know, it might even turn out to be popular. It’s clearly worth a try.
Last week I was amused to see Kate Willett and Briahna Joy Gray, two of the loudest voices of the conspiratorial far left, denounce the abundance agenda as ... wait for it ... a cover for Zionism. As far as they’re concerned, the only reason why anyone would talk about affordable housing or high-speed rail is to distract the masses from the evil Zionists murdering Palestinian babies in order to harvest their organs.
The more I thought about this, the more I realized that Willett and Gray actually have a point. Yes, solving America’s problems with reason and hard work and creativity, like the abundance agenda says to do, is the diametric opposite of blaming all the problems on the perfidy of Jews or some other scapegoat. The two approaches really are the logical endpoints of two directly competing visions of reality.
Naturally I have a preference between those visions. So I’ve been on a bit of a spending spree lately, in support of sane, moderate, pro-abundance, anti-MAGA, liberal Enlightenment forces retaking America. I donated 1000ドル to Alex Bores, who’s running for Congress in NYC, and who besides being a moderate Democrat who favors all the usual good things, is also a leader in AI safety legislation. (For more, see this by Eric Neyman of Alignment Research Center, or this from Scott Alexander himself—the AI alignment community has been pretty wowed.) I also donated 1000ドル to Scott Wiener, who’s running for Nancy Pelosi’s seat in California, has a nuanced pro-two-states, anti-Netanyahu position that causes him to get heckled as a genocidal Zionist, and authored the excellent SB1047 AI safety bill, which Gavin Newsom unfortunately vetoed for short-term political reasons. And I donated 1000ドル to Vikki Goodwin, a sane Democrat who’s running to unseat Lieutenant Governor Dan Patrick in my own state of Texas. Any other American office-seeker who resonates with this post, and who’d like a donation, can feel free to contact me as well.
My bag is packed ... but for now, only for a brief trip to give the physics colloquium at Harvard, after which I’ll return back home to Austin. Until it becomes impossible, I call on my thousands of thoughtful, empathetic American readers to stay right where you are, and simply do your best to fight the brain-eaten zombies of both left and right. If you are one of the zombies, of course, then my calling you one doesn’t even begin to express my contempt: may you be remembered by history alongside the willing dupes of Hitler, Stalin, and Mao. May the good guys prevail.
Oh, and speaking of zombies, Happy Halloween everyone! Boooooooo!
A month ago William Inboden, the provost of UT Austin (where I work), invited me to join a university-wide "Faculty Working Group on Academic Integrity." The name made me think that it would be about students cheating on exams and the like. I didn’t relish the prospect but I said sure.
Shortly afterward, Jim Davis, the president of UT Austin, sent out an email listing me among 21 faculty who had agreed to serve on an important working group to decide UT Austin’s position on academic free speech and the responsibilities of professors in the classroom (!). Immediately I started getting emails from my colleagues, thanking me for my "service" and sharing their thoughts about what this panel needed to say in response to the Trump administration’s Compact on Higher Education. For context: the Compact would involve universities agreeing to do all sorts of things that the Trump administration wants—capping international student enrollment, "institutional neutrality," freezing tuition, etc. etc.—in exchange for preferential funding. UT Austin was one of nine universities originally invited to join the Compact, along with MIT, Penn, Brown, Dartmouth, and more, and is the only one that hasn’t yet rejected it. It hasn’t accepted it either.
Formally, it was explained to me, UT’s Working Group on Academic Integrity had nothing to do with Trump’s Compact, and no mandate to either accept or reject it. But it quickly became obvious to me that my faculty colleagues would see everything we did exclusively in light of the Compact, and of other efforts by the Trump administration and the State of Texas to impose conservative values on universities. While not addressing current events directly, what we could do would be to take a strong stand for academic freedom, and more generally, for the role of intellectually independent universities in a free society.
So, led by Provost Inboden, over two meetings and a bunch of emails we hashed out a document. You can now read the Texas Statement on Academic Integrity, and I’d encourage you to do so. The document takes a pretty strong swing for academic freedom:
Academic freedom lies at the core of the academic enterprise. It is foundational to the excellence of the American higher education system, and is non-negotiable. In the words of the U.S. Supreme Court, academic freedom is "a special concern of the First Amendment." The world’s finest universities are in free societies, and free societies honor academic freedom.
The statement also reaffirms UT Austin’s previous commitments to the Chicago Principles of Free Expression, and the 1940 and 1967 academic freedom statements of the American Association of University Professors.
Without revealing too much about my role in the deliberations, I’ll say that I was especially pleased by the inclusion of the word "non-negotiable." I thought that that word might acquire particular importance, and this was confirmed by the headline in yesterday’s Chronicle of Higher Education: As Trump’s Compact Looms, UT-Austin Affirms ‘Non-Negotiable’ Commitment to Academic Freedom (warning: paywall).
At the same time, the document also talks about the responsibility of a public university to maintain the trust of society, and about the responsibilities of professors in the classroom:
Academic integrity obligates the instructor to protect every student’s academic freedom and right to learn in an environment of open inquiry. This includes the responsibilities:
- to foster classroom cultures of trust in which all students feel free to voice their questions and beliefs, especially when those perspectives might conflict with those of the instructor or other students;
- to fairly present differing views and scholarly evidence on reasonably disputed matters and unsettled issues;
- to equip students to assess competing theories and claims, and to use reason and appropriate evidence to form their own conclusions about course material; and
- to eschew topics and controversies that are not germane to the course.
All stuff that I’ve instinctively followed, in nearly 20 years of classroom teaching, without the need for any statement telling me to. Whatever opinions I might get goaded into expressing on this blog about Trump, feminism, or Israel/Palestine, I’ve always regarded the classroom as a sacred space. (I have hosted a few fierce classroom debates about the interpretation of quantum mechanics, but even there, I try not to tip my own hand!)
I’m sure that there are commenters, on both ends of the political spectrum, who will condemn me for my participation in the faculty working group, and for putting my name on the statement. At this point in this blog’s history, commenters on both ends of the political spectrum would condemn me for saying that freshly baked chocolate chip cookies are delicious. But I like the statement, and find nothing in it that any reasonable person should disagree with. Overall, my participation in this process increased my confidence that UT Austin will be able to navigate this contentious time for the state, country, and world while maintaining its fundamental values. It made me proud to be a professor here.
Here’s a draft of my next column for the Notices of the American Mathematical Society. It’s about the inverse cube force law in classical mechanics.
Newton’s Principia is famous for his investigations of the inverse square force law for gravity. But in this book Newton also did something that was rarely discussed until the 1990s. He figured out what kind of central force exerted upon a particle can rescale its angular velocity by a constant factor without affecting its radial motion. This turns out to be a force obeying an inverse cube law.
Given a particle in Euclidean space, a central force is a force that points toward or away from the origin and depends only on the particle’s distance from the origin. If the particle’s position at time is and its mass is some number we have
where is a unit vector pointing outward from the origin at the point A particle obeying this equation always moves in a plane through the origin, so we can use polar coordinates and write the particle’s position as With some calculation one can show the particle’s distance from the origin, obeys
Here , the particle’s angular momentum, is constant in time. The second term in the equation above says that the particle’s distance from the origin changes as if there were an additional force pushing it outward. This is a "fictitious force", an artifact of working in polar coordinates. It is called the centrifugal force. And it obeys an inverse cube force law!
This explains Newton’s observation. Let us see why. Suppose we have two particles moving in two different central forces and each obeying a version of equation (1), with the same mass and the same radial motion but different angular momenta and Then we must have
If the particle’s angular velocities are proportional we must have for some constant so
This says that equals plus an additional inverse cube force.
There are other interesting things about the inverse cube force law. Newtonian gravity is an attractive inverse square force, say with so in this case we have
Because grows faster than as as long as the angular momentum is nonzero the repulsion of the centrifugal force will beat the attraction of gravity for sufficiently small and the particle will not fall in to the origin. The same is true for any attractive force with But an attractive inverse cube force can overcome the centrifugal force and make a particle fall in to the origin.
In fact there are three qualitatively different possibilities for the motion of a particle in an attractive inverse cube force depending on the value of . With work we can solve for as a function of (which is easier than solving for ). There are three cases depending on the value of
They are vaguely analogous to the elliptical, parabolic and hyperbolic orbits of a particle in an inverse square force law:
The third case occurs when the attractive inverse cube force is strong enough to overcome the centrifugal force: Then the particle can spiral in to its doom, hitting the origin in a finite amount of time after infinitely many orbits, like this:
All three curves in the equation above are called Cotes spirals, after Roger Cotes’ work on the inverse cube force law, published posthumously in 1722. Cotes seems to have been the first to compute the derivative of the sine function. After Cotes’ death at the age of 33, Newton supposedly said "If he had lived we would have known something."
The subtlety of the inverse cube force law is vastly heightened when we study it using quantum rather than classical mechanics. Here if is too large the theory is ill-defined, because there is no reasonable choice of self-adjoint Hamiltonian. If is smaller the theory is well-behaved. But at a certain borderline point it exhibits a remarkable property: spontaneous breaking of scaling symmetry. I hope to discuss this in my next column.
For more on the inverse cube force law, see:
For more on Newton’s work involving the inverse cube force law, see:
Wikipedia, Newton’s theorem of revolving orbits.
S. Chandrasekhar, Newton’s Principia for the Common Reader, Oxford U. Press, Oxford, 1995, pp. 183–200.
Cotes’ book is
In ordinary quantum mechanics, in the special case where observables are described as self-adjoint complex matrices, we can describe time evolution of an observable using Heisenberg’s equation
where is a fixed self-adjoint matrix called the Hamiltonian. This framework is great when we want to focus on observables rather than states. But Heisenberg’s equation doesn’t make sense in a general Jordan algebra. In this stripped-down framework, all we can do is raise observables to powers and take real linear combinations of them. This lets us define a ‘Jordan product’ of observables:
but not commutators and not multiplication by . What do we do then?
I wrote a long paper about this:
My starting-point was that self-adjoint complex matrices form not only a Jordan algebra with product
but also a Lie algebra with bracket
See, the commutator of two self-adjoint matrices is skew-adjoint, but we can multiply it by , or more conventionally , to get something self-adjoint. That’s what is going on in Heisenberg’s equation. But this trick doesn’t work for other Jordan algebras, at least not automatically—so there was a lot to say.
I just bumped into a nice paper on this issue that I hadn’t seen before:
The idea here is pretty wild: you can replace the commutator in Heisenberg’s equation by an associator:
This is well-defined whenever our observables are elements in a Jordan algebra. Jordan algebras are always commutative, but rarely associative!
Here’s the trick. Let be the Jordan algebra of self-adjoint complex matrices, and let’s start with Heisenberg’s equation
where . Suppose we can write
for some . In this case we can use a really cool identity to express the commutator in Heisenberg’s equation in terms of an associator:
This holds in any associative algebra if you define , and . It’s easy to check: just expand out both sides and compare them!
Using this identity, we get
Now we’re describing dynamics using only operations that are available in any Jordan algebra!
This raises the question of when a self-adjoint complex matrix can be written as for self-adjoint matrices . This is true whenever is traceless, since is a compact simple real Lie algebra, and every element of such a Lie algebra is a commutator (as shown by Akhieser).
But any self-adjoint complex matrix is of the form where is traceless, so writing we have
so we can rewrite Heisenberg’s equation as
Moreover, in any Jordan algebra, any pair of elements determines a derivation : see Section I.7 of Jacobson’s Structure and Representations of Jordan Algebras. In the finite-dimensional case there is no difficulty with exponentiating any derivation to obtain a one-parameter group of automorphisms. Thus, for any elements of a finite-dimensional Jordan algebra, the solution of the above equation always determines a one-parameter group of Jordan algebra automorphisms! And this is just what we’d want for describing how observables change with time.
The are two obvious next questions: one mathematical and one more philosophical.
First, how many one-parameter groups of Jordan algebra automorphisms do we actually get out of solutions to
In the case of , we get them all, since it’s already known that we get them all from Heisenberg’s equation
What about and ? I’m actually more interested in the exceptional Jordan algebra , and here it seems we get them all! This was shown in a paper that’s fairly hard to find even though it’s available for free online:
It starts on page 214 of the PDF file.
(The editor of this journal has some crazy ideas, which has put off some people I’m talking to about this paper. But you can’t judge a paper by the journal it appeared in. Truini and Biedenharn are good — in fact Biedenharn is famous for helping discover an identity, the Biedenharn–Elliott identity, that amounts to the pentagon identity for the category of representations of ! And the paper looks fine, as far as I can tell.)
Second, the more philosophical question: what does it mean to describe dynamics using not one observable, the Hamiltonian, but two? Perhaps the best way to tackle this is to try doing it, and seeing how it works. Note that this method is not just good for dynamics, but for any Lie group of symmetries.
Check out my video on the big ideas that go into the Standard Model of particle physics!
In the late 1800s physics had 3 main pillars: classical mechanics, statistical mechanics and electromagnetism. But they contradict each other! That was actually good – because resolving the contradictions helped lead us to special relativity and quantum mechanics.
I explain how this worked, or more precisely how it could have worked: the actual history is far more messy. For example, Planck and Einstein weren’t really thinking about the ultraviolet catastrophe when they came up with the idea that the energy of light comes in discrete packets:
• Helge Kragh, Max Planck: the reluctant revolutionary, Physics World, 1 December 2000.
Then, I sketch out how deeper thoughts on electromagnetism led us to the concept of ‘gauge theory’, which is the basis for the Standard Model.
This is a very quick intro, just to map out the territory. I’ll go into more detail later.
By the way, if you prefer to avoid YouTube, you can watch my videos at the University of Edinburgh:
The poet Blake wrote that you can see a world in a grain of sand. But even better, you can see a universe in an atom!
Bound states of hydrogen atom correspond to states of a massless quantum particle moving at the speed of light around the Einstein universe — a closed, static universe where space is a 3-sphere. We need to use a spin-1⁄2 particle to account for the spin of the electron. The states of the massless spin-1⁄2 particle where it forms a standing wave then correspond to the orbitals of the hydrogen atom. This explains the secret 4-dimensional rotation symmetry of the hydrogen atom.
In fact, you can develop this idea to the point of getting the periodic table of elements from a quantum field theory on the Einstein universe! I worked that out here:
but you can see a more gentle explanation in the following series of blog articles.
Part 1: a quick overview of Kepler’s work on atoms and the solar system, and more modern developments.
Part 2: why the eccentricity vector is conserved for a particle in an inverse square force, and what it means.
Part 3: why the momentum of a particle in an inverse square force moves around in a circle.
Part 4: why the 4d rotation group acts on bound states of a particle in an attractive inverse square force.
Part 5: quantizing the bound states of a particle in an attractive inverse square force, and getting the Hilbert space for bound states of a hydrogen atom, neglecting the electron’s spin.
Part 6: how the Duflo isomorphism explains quantum corrections to the hydrogen atom Hamiltonian.
Part 7: why the Hilbert space of bound states for a hydrogen atom including the electron’s spin is
Part 8: why is also the Hilbert space for a massless spin-1/2 particle in the Einstein universe.
Part 9: a quaternionic description of the hydrogen atom’s bound states (a digression not needed for later parts).
Part 10: changing the complex structure on to eliminate negative-energy states of the massless spin-1/2 particle, as often done.
Part 11: second quantizing the massless spin-1/2 particle and getting a quantum field theory on the Einstein universe, or alternatively a theory of collections of electrons orbiting a nucleus.
Part 12: obtaining the periodic table of elements from a quantum field theory on the Einstein universe.
I explain how the Hamiltonian of this quantum field theory has to be tweaked a bit to give the ‘Madelung rules’ for the order in which electrons get added as we go along the periodic table:
This continues to be a very busy time, but I wanted to point out two preprints that caught my eye this week. Their subjects are completely disparate, but they stand out as essentially reviews written in a much more conversational tone than the usual literature.
The first is this preprint about chirality-induced spin selectivity, a subject that I've mentioned before on this blog. There is now an extensive body of evidence (of varying quality) that there is a connection between structural chirality of molecules and their interactions with the spin angular momentum of electrons. This includes monolayers of chiral molecules leading to net spin polarization of photoemitted electrons (here), a lot of electronic transport experiments involving chiral molecules and magnetic electrodes that seem to show spin-dependent transmission that is absent with achiral molecules, and even a chirality dependence of molecular adsorption kinetics on magnetic surfaces (here). The preprint is a provocative discussion of the topic and possible mechanisms, and the importance of precision in the description of the various phenomena.
On a completely different topic, this preprint is a fun discussion about quantum gravity (!) and how condensed matter ideas of "the vacuum" can lead to insights about how quantum mechanics and gravity might need to play together. One fun bit early on is a discussion of something I like to point out to my undergrad stat mech students: A single hydrogen atom in a very very large box will apparently (if the usual stat mech formalism of partition functions is valid) be spontaneously ionized, even when the box (which presumably functions as a reservoir at temperature \(T\)) and atom are at temperatures faaaaaar below the energy scale for ionization. This is discussed nicely in this 1966 article in the Journal of Chemical Education. Anyway, I thought this was an interesting discussion from three condensed matter theorists.
In 2016 I wrote that the Orioles were setting "a fewest-triples record that may never be broken" by hitting only 6 triples in a full major league season. It was broken the next year by the Blue Jays, who hit only 5 triples. We are actually in a golden age of not hitting triples. Nine teams in the modern era of MLB have finished the season with ten triples or fewer. Of those nine teams, six have been since 2022, with three this year alone. The 2025 Mariners stole 161 bases. Not a slow team! But they only hit nine triples.
Philip Roth, in The Great American Novel, gives one of his characters a multipage monologue on the greatness of triples. He would be mad now, about a lot of things probably. Speaking of old novelists named Philip, I discovered today that Philip Pullman has, after six years of silence, finally released the final book in the Lyra Silvertongue series. They have been getting longer and less wonderful. And slower, like a team that never hits triples. But I’m glad this exists. The Golden Compass remains the only truly great contemporary work of fantasy I’ve ever read.
I turned 54 yesterday, a number sometimes called "triple chai." Chai, or חַי, is the Hebrew word for "life," and the two letters that make it up are chet (the 8th letter) and yud (the 10th), giving the word a total numerical value of 18; and that’s why you often see Jewish people giving charity or gifts in the amount of 72,ドル or 180,ドル or some other multiple of chai, like 54ドル. Triple life!
I do think I have a kind of triple life. I’m a mathematician, a teacher (in which I include writer), and a father. Those are three pretty good lives to have! I feel no need for a fourth, but ask me again when I turn 72.
Happy Halloween! I’ve got a yearly tradition on this blog of talking about the spooky side of physics. This year, we’ll think about what happens...when you turn off the lights.
Over history, astronomy has given us larger and larger views of the universe. We started out thinking the planets, Sun, and Moon were human-like, just a short distance away. Measuring distances, we started to understand the size of the Earth, then the Sun, then realized how much farther still the stars were from us. Gradually, we came to understand that some of the stars were much farther away than others. Thinkers like Immanuel Kant speculated that "nebulae" were clouds of stars like our own Milky Way, and in the early 20th century better distance measurements confirmed it, showing that Andromeda was not a nearby cloud, but an entirely different galaxy. By the 1960’s, scientists had observed the universe’s cosmic microwave background, seeing as far out as it was possible to see.
But what if we stopped halfway?
Since the 1920’s, we’ve known the universe is expanding. Since the 1990’s, we’ve thought that that expansion is speeding up: faraway galaxies are getting farther and farther away from us. Space itself is expanding, carrying the galaxies apart...faster than light.
That ever-increasing speed has a consequence. It means that, eventually, each galaxy will fly beyond our view. One by one, the other galaxies will disappear, so far away that light will not have had enough time to reach us.
From our perspective, it will be as if the lights, one by one, started to turn out. Each faraway light, each cloudy blur that hides a whirl of worlds, will wink out. The sky will get darker and darker, until to an astronomer from a distant future, the universe will appear a strangely limited place:
A single whirl of stars, in a deep, dark, void.
At this point it's old hat to decry the problems facing traditional news media. Still, it is abundantly clear in our late stage capitalist society that there has been a collective business decision over the last 20+ years that, like local newspapers and television news, real science journalism is not a money maker. Just a few examples: Seventeen years ago, CNN cut its entire science, technology and environment reporting team. In 2022, Popular Science ceased publication. In 2023, National Geographic laid off their staff writers. Last week, the Wall Street Journal laid off their science and health reporters.
I have it on good authority that there is now only one science reporter left at the WSJ. One, at a time when science and technology are more critically important to our rapidly changing society than ever, and there is enormous tumult in the US and elsewhere about how science is or is not supported and is or is not factoring into policy decisions. All of this is happening at a time when public trust in science is falling. (Check out this from Science Friday.)
(updated for context) Leaving aside professional science outlets (the news sections of Science , Nature , and society publications like Physics Today , C&EN , Physics World, Chemistry World ), there are some good publications out there, like Quanta and Nautilus (both founded by nonprofits). There are outstanding public writers of science, like Philip Ball, Helen Czerski, Katie Mack, Ethan Siegel, and many others (apologies for the incompleteness of this list). There are some excellent freelance journalists. The internet also means that there are many opportunities for great engagement. For example, the videos from 3blue1brown are uniformly outstanding. However, there are no filters, and the temptation to be click-baity or sensationalistic is problematic.
I have no solutions to offer, except that I encourage you to support good science journalism and reporting when you see it. It's important.
The next annual conference on applied category theory is in Estonia!
• Applied Category Theory 2026, Tallinn, Estonia, 6–10 July, 2026. Preceded by the Adjoint School Research Week, 29 June — 3 July.
The conference particularly encourages participation from underrepresented groups. The organizers are committed to non-discrimination, equity, and inclusion. The code of conduct for the conference is available here.
Deadlines
• Registration: TBA
• Abstracts Due: 23 March 2026
• Full Papers Due: 30 March 2026
• Author Notification: 11 May 2026
• Adjoint School: 29 June — 3 July 2026
• Conference: 6 — 10 July 2026
• Final versions of papers for proceedings due: TBA
Submissions
ACT2026 accepts submissions in English, in the following three tracks:
Software demonstrations
Teaching and communication
The detailed Call for Papers is available here.
Extended abstracts and conference papers should be prepared with LaTeX. For conference papers please use the EPTCS style files available here. The submission link is here.
Reviewing is single-blind, and we are not making public the reviews, reviewer names, the discussions nor the list of under-review submissions. This is the same as previous instances of ACT.
Program Committee Chairs
• Geoffrey Cruttwell, Mount Allison University, Sackville
• Priyaa Varshinee Srinivasan, Tallinn University of Technology, Estonia
Program Committee
• Alexis Toumi, Planting Space
• Bryce Clarke, Tallinn University of Technology
• Barbara König, University of Duisburg-Essen
• Bojana Femic, Serbian Academy of Sciences and Arts
• Chris Heunen, The University of Edinburgh
• Daniel Cicala, Southern Connecticut State University
• Dusko Pavlovic, University of Hawaii
• Evan Patterson, Topos Institute
• Fosco Loregian, Tallinn University of Technology
• Gabriele Lobbia, Università di Bologna
• Georgios Bakirtzis, Institut Polytechnique de Paris
• Jade Master, University of Strathclyde
• James Fairbanks, University of Florida
• Jonathan Gallagher, Hummingbird Biosciences
• Joe Moeller, Caltech
• Jules Hedges, University of Strathclyde
• Julie Bergner, University of Virginia
• Kohei Kishida, University of Illinois, Urbana-Champaign
• Maria Manuel Clementino, CMUC, Universidade de Coimbra
• Mario Román, University of Oxford
• Marti Karvonen, University College London
• Martina Rovelli, UMass Amherst
• Masahito Hasegawa, Kyoto University
• Matteo Capucci, University of Strathclyde
• Michael Shulman, University of San Diego
• Nick Gurski, Case Western Reserve University
• Niels Voorneveld, Cybernetica
• Paolo Perrone, University of Oxford
• Peter Selinger, Dalhousie University
• Paul Wilson, University of Southampton
• Robin Cockett, University of Calgary
• Robin Piedeleu, University College London
• Rory Lucyshyn-Wright, Brandon University
• Rose Kudzman-Blais, University of Ottawa
• Ryan Wisnesky, Conexus AI
• Sam Staton, University of Oxford
• Shin-Ya Katsumata, Kyoto Sangyo University
• Simon Willerton, University of Sheffield
• Spencer Breiner, National Institute of Standards and Technology
• Tai Danae Bradley, SandboxAQ
• Titouan Carette, École Polytechnique
• Tom Leinster, The University of Edinburgh
• Walter Tholen, York University
Teaching & Communication
• Selma Dündar-Coecke, University College London, Institute of Education
• Ted Theodosopoulos, Nueva School
Organizing Committee
• Pawel Sobocinski, Tallinn University of Technology
• Priyaa Varshinee Srinivasan, Tallinn University of Technology
• Sofiya Taskova, Tallinn University of Technology
• Kristi Ainen, Tallinn University of Technology
Steering Committee
• John Baez, University of California, Riverside
• Bob Coecke, University of Oxford
• Dorette Pronk, Dalhousie University
• David Spivak, Topos Institute
• Michael Johnson, Macquarie University
• Simona Paoli, University of Aberdeen
This year for STOC, we decided to run an experiment to explore the use of Large Language Models in the theoretical computer science community, and we’re inviting the entire community to participate.
We—a team from the STOC PC—are offering authors the chance to get automated pre-submission feedback from an advanced, Gemini-based LLM tool that’s been optimized for checking mathematical rigor. The goal is simple: to provide constructive suggestions and, potentially, help find technical mistakes before the paper goes to the PC. Some important points:
This tool is specifically optimized for checking a paper’s mathematical rigor. It’s a hopefully useful way to check the correctness of your arguments. Note that sometimes it does not possess external, area-specific knowledge (like "folklore" results). This means it may flag sections that rely on unstated assumptions, or it might find simple omissions or typos.
Nevertheless, we hope you’ll find this feedback valuable for improving the paper’s overall clarity and completeness.
If you’re submitting to STOC, we encourage you to opt-in. You’ll get (we hope) useful feedback, and you’ll be providing invaluable data as we assess this tool for future theory conferences.
The deadline to opt-in on the HotCRP submission form is November 1 (5pm EST).
You can read the full "Terms of Participation" (including all privacy and confidentiality details) at the link below.
This experiment is being run by PC members David Woodruff (CMU) and Rajesh Jayaram (Google), as well as Vincent Cohen-Addad (Google) and Jon Schneider (Google).
We’re excited to offer this resource to the community.
Please see the STOC Call for Papers here and specific details on the experiment here.
The next annual conference on applied category theory is in Estonia!
For more details, read on!
The conference particularly encourages participation from underrepresented groups. The organizers are committed to non-discrimination, equity, and inclusion. The code of conduct for the conference is available here.
Deadlines
Submissions
ACT2026 accepts submissions in English, in the following three tracks:
Research
Software demonstrations
Teaching and communication
The detailed Call for Papers is available here.
Extended abstracts and conference papers should be prepared with LaTeX. For conference papers please use the EPTCS style files available here. The submission link is here.
Reviewing is single-blind, and we are not making public the reviews, reviewer names, the discussions nor the list of under-review submissions. This is the same as previous instances of ACT.
Program Committee Chairs
Program Committee
Teaching & Communication
Organizing Committee
Steering Committee
A white hole is a purely hypothetical time-reversed black hole. What does general relativity say about them? Would they repel you? Could you fall into a white hole—or only fall out? Could the universe be a white hole?
Philip Gibbs answers all these questions and more in my first interview for Edinburgh Explorations—a series put out by the School of Mathematics and the School of Physics and Astronomy at the University of Edinburgh.
I met Philip online on sci.physics in the early 1990s, and together with a bunch of other folks we created the Physics FAQ, which answers a lot of the most fascinating questions about physics. At first I didn’t believe everything he said about white holes, but eventually I realized he was right!
What do I mean by "right"? Be careful: white holes are just solutions of the equations of general relativity, not things we’ve actually seen. But you can work out what general relativity predicts about them: that’s the game here, and that determines what’s "right". It doesn’t mean white holes actually exist and do these things.
For the physics FAQ, much of it created by Philip Gibbs, go here.
The cover picture, showing the maximally extended Schwarzschild solution containing both a black hole and white hole, was made by Isaak Neutelings.
This October, fantasy readers are devouring a sequel: the final installment in Philip Pullman’s trilogy The Book of Dust. The series follows student Lyra Silvertongue as she journeys from Oxford to the far east. Her story features alternate worlds, souls that materialize as talking animals, and a whiff of steampunk. We first met Lyra in the His Dark Materials trilogy, which Pullman began publishing in 1995. So some readers have been awaiting the final Book of Dust volume for 30 years.
Another sequel debuts this fall. It won’t spur tens of thousands of sales; nor will Michael Sheen narrate an audiobook version of it. Nevertheless, the sequel should provoke as much thought as Pullman’s: the sequel to the Maryland Quantum-Thermodynamics Hub’s first three years.
The Maryland Quantum-Thermodynamics Hub debuted in 2022, courtesy of a grant from the John F. Templeton Foundation. Six theorists, three based in Maryland, have formed the hub’s core. Our mission has included three prongs: research, community building, and outreach. During the preceding decade, quantum thermodynamics had exploded, but mostly outside North America. We aimed to provide a lodestone for the continent’s quantum-thermodynamics researchers and visitors.
Also, we aimed to identify the thermodynamics of how everyday, classical physics emerges from quantum physics. Quantum physics is reversible (doesn’t distinguish the past from the future), is delicate (measuring a quantum system can disturb it), and features counterintuitive phenomena such as entanglement. In contrast, our everyday experiences include irreversibility (time has an arrow), objectivity (if you and I read this article, we should agree about its contents), and no entanglement. How does quantum physics give rise to classical physics at large energy and length scales? Thermodynamics has traditionally described macroscopic, emergent properties. So quantum thermodynamics should inform our understanding of classical reality’s emergence from quantum mechanics.
Our team has approached this opportunity from three perspectives. One perspective centers on quantum Darwinism, a framework for quantifying how interactions spread information about an observed quantum system. Another perspective highlights decoherence, the contamination of a quantum system by its environment. The third perspective features incompatible exchanged quantities, described in an earlier blog post. Or two. Or at least seven.
Each perspective led us to discover a tension, or apparent contradiction, that needs resolving. One might complain that we failed to clinch a quantum-thermodynamic theory of the emergence of classical reality. But physicists adore apparent contradictions as publishers love splashing "New York Times bestseller" on their book covers. So we aim to resolve the tensions over the next three years.
I’ll illustrate the tensions with incompatible exchanged quantities, of course. Physicists often imagine a small system, such as a quantum computer, interacting with a large environment, such as the surrounding air and the table on which the quantum computer sits. The system and environment may exchange energy, particles, electric charge, etc. Typically, the small system thermalizes, or reaches a state mostly independent of its initial conditions. For example, after exchanging enough energy with its environment, the system ends up at the environment’s temperature, mostly regardless of the system’s initial temperature.
For decades, physicists implicitly assumed that the exchanged quantities are compatible: one can measure them simultaneously. But one can’t measure all of a quantum system’s properties simultaneously. Position and momentum form the most famous examples. Incompatibility epitomizes quantum physics, underlying Heisenberg’s uncertainty relation, quantum error correction, and more. So collaborators and I ask how exchanged quantities’ incompatibility alters thermalization, which helps account for time’s arrow.
Our community has discovered that such incompatibility can hinder certain facets of thermalization—in a sense, stave off certain aspects of certain quantum systems’ experience of time. But incompatible exchanged quantities enhance other features of thermalization. How shall we reconcile the hindrances with the enhancements? Does one of the two effects win out? I hope to report back in three years. For now, I’m rooting for Team Hindrance.
In addition to resolving apparent conflicts, we’re adding a fourth perspective to our quiver—a gravitational one. In our everyday experiences, space-time appears smooth; unlike Lyra’s companion Will in The Subtle Knife, we don’t find windows onto other worlds. But quantum physics, combined with general relativity, suggests that you’d find spikes and dips upon probing space-time over extremely short length scales. How does smooth space-time emerge from its quantum underpinnings? Again, quantum thermodynamics should help us understand.
To address these challenges, we’re expanding the hub’s cast of characters. The initial cast included six theorists. Two more are joining the crew, together with the hub’s first two experimentalists. So is our first creative-writing instructor, who works at the University of Maryland (UMD) Jiménez-Porter Writers’ House.
As the hub has grown, so has the continent’s quantum-thermodynamics community. We aim to continue expanding that community and strengthening its ties to counterparts abroad. As Lyra learned in Pullman’s previous novel, partnering with Welsh miners and Czech book sellers and Smyrnan princesses can further one’s quest. I don’t expect the Maryland Quantum-Thermodynamics Hub to attract Smyrnan princesses, but a girl can dream. The hub is already partnering with the John F. Templeton Foundation, Normal Computing, the Fidelity Center for Applied Technology, the National Quantum Laboratory, Maryland’s Capital of Quantum team, and more. We aim to integrate quantum thermodynamics into North America’s scientific infrastructure, so that the field thrives here even after our new grant terminates. Reach out if you’d like to partner with us.
To unite our community, the hub will host a gathering—a symposium or conference—each year. One conference will feature quantum thermodynamics and quantum-steampunk creative writing. Scientists and authors will present. We hope that both groups will inspire each other, as physicist David Deutsch’s work on the many-worlds formulation of quantum theory inspired Pullman.
That conference will follow a quantum-steampunk creative-writing course to take place at UMD during spring 2026. I’ll co-teach the course with creative-writing instructor Edward Daschle. Students will study quantum thermodynamics, read published science-fiction stories, write quantum-steampunk stories, and critique each other’s writing. Five departments have cross-listed the course: physics, arts and humanities, computer science, chemistry, and mechanical engineering. If you’re a UMD student, you can sign up in a few weeks. Do so early; seats are limited! We welcome graduate students and undergrads, the latter of whom can earn a GSSP general-education credit.1 Through the course, the hub will spread quantum thermodynamics into Pullman’s world—into literature.
Pullman has entitled his latest novel The Rose Field. The final word refers to an object studied by physicists. A field, such as an electric or gravitational field, is a physical influence spread across space. Hence fiction is mirroring physics—and physics can take its cue from literature. As ardently as Lyra pursues the mysterious particle called Dust, the Maryland Quantum-Thermodynamics Hub is pursuing a thermodynamic understanding of the classical world’s emergence from quantum physics. And I think our mission sounds as enthralling as Lyra’s. So keep an eye on the hub for physics, community activities, and stories. The telling of Lyra’s tale may end this month, but the telling of the hub’s doesn’t.
1Just don’t ask me what GSSP stands for.
I don’t usually do obituaries here, but sometimes I have something worth saying.
Chen Ning Yang, a towering figure in particle physics, died last week.
I never met him. By the time I started my PhD at Stony Brook, Yang was long-retired, and hadn’t visited the Yang Institute for Theoretical Physics in quite some time.
(Though there was still an office door, tucked behind the institute’s admin staff, that bore his name.)
The Nobel Prize doesn’t always honor the most important theoretical physicists. In order to get a Nobel Prize, you need to discover something that gets confirmed by experiment. Generally, it has to be a very crisp, clear statement about reality. New calculation methods and broader new understandings are on shakier ground, and theorists who propose them tend to be left out, or at best combined together into lists of partial prizes long after the fact.
Yang was lucky. With T. D. Lee, he had made that crisp, clear statement. He claimed that the laws of physics, counter to everyone’s expectations, are not the same when reflected in a mirror. In 1956, Wu confirmed the prediction, and Lee and Yang got the prize the year after.
That’s a huge, fundamental discovery about the natural world. But as a theorist, I don’t think that was Yang’s greatest accomplishment.
Yang contributed to other fields. Practicing theorists have seen his name strewn across concepts, formalisms, and theorems. I didn’t have space to talk about him in my article on integrability for Quanta Magazine, but only just barely: another paragraph or two, and he would have been there.
But his most influential contribution is something even more fundamental. And long-time readers of this blog should already know what it is.
Yang, along with Robert Mills, proposed Yang-Mills Theory.
There isn’t a Nobel prize for Yang-Mills theory. In 1953, when Yang and Mills proposed the theory, it was obviously wrong, a theory that couldn’t explain anything in the natural world, mercilessly mocked by famous bullshit opponent Wolfgang Pauli. Not even an ambitious idea that seemed outlandish (like plate tectonics), it was a theory with such an obvious missing piece that, for someone who prioritized experiment like the Nobel committee does, it seemed pointless to consider.
All it had going for it was that it was a clear generalization, an obvious next step. If there are forces like electromagnetism, with one type of charge going from plus to minus, why not a theory with multiple, interacting types of charge?
Nothing about Yang-Mills theory was impossible, or contradictory. Mathematically, it was fine. It obeyed all the rules of quantum mechanics. It simply didn’t appear to match anything in the real world.
But, as theorists learn, nature doesn’t let a good idea go to waste.
Of the four fundamental forces of nature, as it would happen, half are Yang-Mills theories. Gravity is different, electromagnetism is simpler, and could be understood without Yang and Mills’ insights. But the weak nuclear force, that’s a Yang-Mills theory. It wasn’t obvious in 1953 because it wasn’t clear how the massless, photon-like particles in Yang-Mills theory could have mass, and it wouldn’t become clear until the work of Peter Higgs over a decade later. And the strong nuclear force, that’s also a Yang-Mills theory, missed because of the ability of such a strong force to "confine" charges, hiding them away.
So Yang got a Nobel, not for understanding half of nature’s forces before anyone else had, but from a quirky question of symmetry.
In practice, Yang was known for all of this, and more. He was enormously influential. I’ve heard it claimed that he personally kept China from investing in a new particle collider, the strength of his reputation the most powerful force on that side of the debate, as he argued that a developing country like China should be investing in science with more short-term industrial impact, like condensed matter and atomic physics. I wonder if the debate will shift with his death, and what commitments the next Chinese five-year plan will make.
Ultimately, Yang is an example of what a theorist can be, a mix of solid work, counterintuitive realizations, and the thought-through generalizations that nature always seems to make use of in the end. If you’re not clear on what a theoretical physicist is, or what one can do, let Yang’s story be your guide.
Given a threshold {y>1}, a {y}-smooth number (or {y}-friable number) is a natural number {n} whose prime factors are all at most {y}. We use {\Psi(x,y)} to denote the number of {y}-smooth numbers up to {x}. In studying the asymptotic behavior of {\Psi(x,y)}, it is customary to write {y} as {x^{1/u}} (or {x} as {y^u}) for some {u>0}. For small values of {u}, the behavior is straightforward: for instance if {0 < u < 1}, then all numbers up to {x} are automatically {y}-smooth, so
\displaystyle \Psi(x,y) \sim x
in this case. If {1 < u < 2}, the only numbers up to {x} that are not {y}-smooth are the multiples of primes {p} between {y} and {x}, so\displaystyle \Psi(x,y) \sim x - \sum_{y < p \leq x} (\frac{x}{p} + O(1))
\displaystyle \sim x - x (\log\log x - \log\log y)
\displaystyle \sim x (1 - \log u)
where we have employed Mertens’ second theorem. For {2 < u < 3}, there is an additional correction coming from multiples of two primes between {y} and {x}; a straightforward inclusion-exclusion argument (which we omit here) eventually gives\displaystyle \Psi(x,y) \sim x (1 - \log u + \int_2^u \frac{\log(t-1)}{t} dt)
in this case.More generally, for any fixed {u>0}, de Bruijn showed that
\displaystyle \Psi(x, y) \sim \rho(u) x
where {\rho(u)} is the Dickman function. This function is a piecewise smooth, decreasing function of {u}, defined by the delay differential equation\displaystyle u \rho'(u) + \rho(u-1) = 0
with initial condition {\rho(u) = 1} for {0 \leq u \leq 1}.The asymptotic behavior of {\rho(u)} as {u \rightarrow \infty} is rather complicated. Very roughly speaking, it has inverse factorial behavior; there is a general upper bound {\rho(u) \leq 1/\Gamma(u+1)}, and a crude asymptotic
\displaystyle \rho(u) = u^{-u+o(u)} = \exp( - u \log u + o(u \log u)).
With a more careful analysis one can refine this to\displaystyle \rho(u) = \exp( - u \log u - u \log\log u + u + o(u)); \ \ \ \ \ (1)
and with a very careful application of the Laplace inversion formula one can in fact show that\displaystyle \rho(u) \sim \sqrt{\frac{\xi'(u)}{2\pi}} \exp( \gamma - u \xi(u) + \int_0^{\xi(u)} \frac{e^s - 1}{s} ds) \ \ \ \ \ (2)
where {\gamma} is the Euler-Mascheroni constant and {\xi(u)} is defined implicitly by the equation\displaystyle e^{\xi(u)} - 1 = u \xi(u). \ \ \ \ \ (3)
One cannot write {\xi(u)} in closed form using elementary functions, but one can express it in terms of the Lambert {W} function as {\xi(u) = -W(-\frac{1}{u} e^{-1/u}) - 1/u}. This is not a particularly enlightening expression, though. A more productive approach is to work with approximations. It is not hard to get the initial approximation {\xi(u) \approx \log u} for large {u}, which can then be re-inserted back into (3) to obtain the more accurate approximation\displaystyle \xi(u) = \log u + \log\log u + O(1)
and inserted once again to obtain the refinement\displaystyle \xi(u) = \log u + \log\log u + O(\frac{\log\log u}{\log u}).
We can now see that (2) is consistent with previous asymptotics such as (1), after comparing the integral {\int_0^{\xi(u)} \frac{e^s - 1}{s} ds} to\displaystyle \int_0^{\xi(u)} \frac{e^s - 1}{\xi(u)} ds = u - 1.
For more details of these results, one can see for instance this survey by Granville.This asymptotic (2) is quite complicated, and so one does not expect there to be any simple argument that could recover it without extensive computation. However, it turns out that one can use a "maximum entropy" analysis to get a reasonably good heuristic approximation to (2), that at least reveals the role of the mysterious function {\xi(u)}. The purpose of this blog post is to give this heuristic.
Viewing {x = y^u}, the task is to try to count the number of {y}-smooth numbers of magnitude {y^u}. We will propose a probabilistic model to generate {y}-smooth numbers as follows: for each prime {p \leq y}, select the prime {p} with an independent probability {c_p/p} for some coefficient {c_p}, and then multiply all the selected primes together. This will clearly generate a random {y}-smooth number {n}, and by the law of large numbers, the (log-)magnitude of this number should be approximately
\displaystyle \log n \approx \sum_{p \leq y} \frac{c_p}{p} \log p, \ \ \ \ \ (4)
(where we will be vague about what "{\approx}" means here), so to obtain a number of magnitude about {y^u}, we should impose the constraint\displaystyle \sum_{p \leq y} \frac{c_p}{p} \log p = u \log y. \ \ \ \ \ (5)
The indicator {1_{p|n}} of the event that {p} divides this number is a Bernoulli random variable with mean {c_p/p}, so the Shannon entropy of this random variable is
\displaystyle \mathbf{H}(1_{p|n}) = - \frac{c_p}{p} \log(\frac{c_p}{p}) - (1 - \frac{c_p}{p}) \log(1 - \frac{c_p}{p}).
If {c_p} is not too large, then Taylor expansion gives the approximation\displaystyle \mathbf{H}(1_{p|n}) \approx \frac{c_p}{p} \log p - \frac{c_p}{p} \log c_p + \frac{c_p}{p}.
Because of independence, the total entropy of this random variable {n} is\displaystyle \mathbf{H}(n) = \sum_{p \leq y} \mathbf{H}(1_{p|n});
inserting the previous approximation as well as (5), we obtain the heuristic approximation\displaystyle \mathbf{H}(n) \approx u \log y - \sum_{p \leq y} \frac{c_p}{p} (\log c_p - 1).
The asymptotic equipartition property of entropy, relating entropy to microstates, then suggests that the set of numbers {n} that are typically generated by this random process should have cardinality approximately\displaystyle \exp(\mathbf{H}(n)) \approx \exp\left(u \log y - \sum_{p \leq y} \frac{c_p}{p} (\log c_p - 1)\right)
\displaystyle = \exp\left(- \sum_{p \leq y} \frac{c_p \log c_p - c_p}{p}\right) x.
Using the principle of maximum entropy, one is now led to the approximation\displaystyle \rho(u) \approx \exp\left(- \sum_{p \leq y} \frac{c_p \log c_p - c_p}{p}\right). \ \ \ \ \ (6)
where the weights {c_p} are chosen to maximize the right-hand side subject to the constraint (5).One could solve this constrained optimization problem directly using Lagrange multipliers, but we simplify things a bit by passing to a continuous limit. We take a continuous ansatz {c_p = f(\log p / \log y)}, where {f: [0,1] \rightarrow {\bf R}} is a smooth function. Using Mertens’ theorem, the constraint (5) then heuristically becomes
\displaystyle \int_0^1 f(t)\ dt = u \ \ \ \ \ (7)
and the expression (6) simplifies to\displaystyle \rho(u) \approx \exp( - \int_0^1 \frac{f(t) \log f(t) - f(t)}{t}\ dt). \ \ \ \ \ (8)
So the entropy maximization problem has now been reduced to the problem of minimizing the functional {\int_0^1 \frac{f(t) \log f(t) - f(t)}{t}\ dt} subject to the constraint (7). The astute reader may notice that the integral in (8) might diverge at {t=0}, but we shall ignore this technicality for the sake of the heuristic arguments.This is a standard calculus of variations problem. The Euler-Lagrange equation for this problem can be easily worked out to be
\displaystyle \frac{\log f(t)}{t} = \lambda
for some Lagrange multiplier {\lambda}; in other words, the optimal {f} should have an exponential form {f(t)= e^{\lambda t}}. The constraint (7) then becomes\displaystyle \frac{e^\lambda - 1}{\lambda} = u
and so the Lagrange multiplier {\lambda} is precisely the mysterious quantity {\xi(u)} appearing in (2)! The formula (8) can now be evaluated as\displaystyle \rho(u) \approx \exp\left( - \int_0^1 \frac{e^{\xi(u) t} \xi(u) t - e^{\xi(u) t}}{t}\ dt \right)
\displaystyle \approx \exp\left( - e^{\xi(u)} + 1 + \int_0^1 \frac{e^{\xi(u) t} - 1}{t}\ dt + \int_0^1 \frac{1}{t}\ dt \right)
\displaystyle \approx \exp\left( - u \xi(u) + \int_0^{\xi(u)} \frac{e^s - 1}{s}\ ds + C\right)
where {C} is the divergent constant\displaystyle C = \int_0^1 \frac{1}{t}\ dt.
This recovers a large fraction of (2)! It is not completely accurate for multiple reasons. One is that the hypothesis of joint independence on the events {p|n} is unrealistic when trying to confine {n} to a single scale {x}; this comes down ultimately to the subtle differences between the Poisson and Poisson-Dirichlet processes, as discussed in this previous blog post, and is also responsible for the otherwise mysterious {e^\gamma} factor in Mertens’ third theorem; it also morally explains the presence of the same {e^\gamma} factor in (2). A related issue is that the law of large numbers (4) is not exact, but admits gaussian fluctuations as per the central limit theorem; morally, this is the main cause of the {\sqrt{\frac{\xi'(u)}{2\pi}}} prefactor in (2).Nevertheless, this demonstrates that the maximum entropy method can achieve a reasonably good heuristic understanding of smooth numbers. In fact we also gain some insight into the "anatomy of integers" of such numbers: the above analysis suggests that a typical {y}-smooth number {n} will be divisible by a given prime {p \sim y^t} with probability about {e^{\xi(u) t}/p}. Thus, for {t = 1 - O(1/\log u)}, the probability of being divisible by {p} is elevated by a factor of about {\asymp e^{\xi(u)} \asymp u \log u} over the baseline probability {1/p} of an arbitrary (non-smooth) number being divisible by {p}; so (by Mertens’ theorem) a typical {y}-smooth number is actually largely comprised of something like {\asymp u} prime factors all of size about {y^{1-O(1/\log u)}}, with the smaller primes contributing a lower order factor. This is in marked contrast with the anatomy of a typical (non-smooth) number {n}, which typically has {O(1)} prime factors in each hyperdyadic scale {[\exp\exp(k), \exp\exp(k+1)]} in {[1,n]}, as per Mertens’ theorem.
Last week, I gave the Patrick Suppes Lecture in the Columbia University Philosophy Department. Patrick Suppes was a distinguished philosopher at Stanford who (among many other things) pioneered remote gifted education through the EPGY program, and who I was privileged to spend some time with back in 2007, when he was in his eighties.
My talk at Columbia was entitled "Computational Complexity and Explanations in Physics." Here are the PowerPoint slides, and here’s the abstract:
The fact, or conjecture, of certain computational problems being intractable (that is, needing astronomical amounts of time to solve) clearly affects our ability to learn about physics. But could computational intractability also play a direct role in physical explanations themselves? I’ll consider this question by examining three possibilities:
(1) If quantum computers really take exponential time to simulate using classical computers, does that militate toward the many-worlds interpretation of quantum mechanics, as David Deutsch famously proposed?
(2) Are certain speculative physical ideas (e.g., time travel to the past or nonlinearities in quantum mechanics) disfavored, over and above any other reasons to disfavor them, because they would lead to "absurd computational superpowers"?
(3) Do certain effective descriptions in physics work only because of the computational intractability of violating those descriptions — as for example with Harlow and Hayden’s resolution of the "firewall paradox" in black hole thermodynamics, or perhaps even the Second Law of Thermodynamics itself?
I’m grateful to David Albert and Lydia Goehr of Columbia’s Philosophy Department, who invited me and organized the talk, as well as string theorist Brian Greene, who came and contributed to the discussion afterward. I also spent a day in Columbia’s CS department, gave a talk about my recent results on quantum oracles, and saw many new friends and old there, including my and my wife’s amazing former student Henry Yuen. Thanks to everyone.
This was my first visit to Columbia University for more than a decade, and certainly my first since the upheavals following the October 7 massacre. Of course I was eager to see the situation for myself, having written about it on this blog. Basically, if you’re a visitor like me, you now need both a QR code and an ID to get into the campus, which is undeniably annoying. On the other hand, once you’re in, everything is pleasant and beautiful. Just from wandering around, I’d have no idea that this campus had recently been Ground Zero for the pro-intifada protests, and then for the reactions against those protests (indeed, the use of the protests as a pretext to try to destroy academia entirely) that rocked the entire country, filling my world and my social media feed.
When I asked friends and colleagues about the situation, I heard a range of perspectives: some were clearly exasperated with the security measures; others, while sharing in the annoyance, suggested the measures seem to be needed, since every time the university has tried to relax them, the "intifada" has returned, with non-university agitators once again disrupting research and teaching. Of course we can all pray that the current ceasefire will hold, for many reasons, the least of which is that perhaps then the obsession of the world’s young and virtuous to destroy the world’s only Jewish state will cool down a bit, and they’ll find another target for their rage. That would also help life at Columbia and other universities return to how it was before.
Before anyone asks: no, Columbia’s Peter Woit never showed up to disrupt my talk with rotten vegetables or a bullhorn—indeed, I didn’t see him at all on his trip, nor did I seek him out. Given that Peter chose to use his platform, one of the world’s best-known science blogs, to call me a mentally ill genocidal fascist week after week, it meant an enormous amount to me to see how many friends and supporters I have right in his own backyard.
All in all, I had a wonderful time at Columbia, and based on what I saw, I won’t hesitate to come back, nor will I hesitate to recommend Jewish or Israeli or pro-Zionist students to study there.
Since it resonated with the audience, I’ll recap my main argument against AGI here. ‘General intelligence’ is like phlogiston, or the aether. It’s an outmoded scientific concept that does not refer to anything real. Any explanatory work it did can be done better by a richer scientific frame. 1/3
— Shannon Vallor (@shannonvallor.bsky.social) 2025年10月02日T22:09:06.610Z
I ran into this Bluesky post, and while a lot of the argument resonated with me, I think the author is missing something important.
Shannon Vallor is a philosopher of technology at the University of Edinburgh. She spoke recently at a meeting honoring the 75th anniversary of the Turing Test. The core of her argument, recapped in the Bluesky post, is that artificial general intelligence, or AGI, represents an outdated scientific concept, like phlogiston. While some researchers in the past thought of humans as having a kind of "general" intelligence that a machine would need to replicate, scientists today break down intelligence into a range of capabilities that can be present in different ways. From that perspective, searching for artificial general intelligence doesn’t make much sense: instead, researchers should focus on the particular capabilities they’re interested in.
I have a lot of sympathy for Vallor’s argument, though perhaps from a different direction than what she had in mind. I don’t know enough about intelligence in a biological context to comment there. But from a computer science perspective, intelligence obviously is composed of distinct capabilities. Something that computes, like a human or a machine, can have different amounts of memory, different processing speeds, different input and output rates. In terms of ability to execute algorithms, it can be a Turing machine, or something less than a Turing machine. In terms of the actual algorithms it runs, they can have different scaling for large inputs, and different overhead for small inputs. In terms of learning, one can have better data, or priors that are closer to the ground truth.
These days, all of these Turing machine algorithm capabilities are in some sense obviously not what the people interested in AGI are after. We already have them in currently-existing computers, after all. Instead, people who pursue AGI, and AI researchers more generally, are interested in heuristics. Humans do certain things without reliable algorithms, instead we do them faster, but unreliably. And while some human heuristics seem pretty general, it’s widely understood that in the heuristics world there is no free lunch. No heuristic is good for everything, and no heuristic is bad for everything.
So is "general intelligence" a mirage, like phlogiston?
If you think about it as a scientific goal, sure. But as a product, not so much.
Consider a word processor.
Obviously, from a scientific perspective, there are lots of capabilities that involve processing words. Some were things machines could do well before the advent of modern computers: consider typewriters, for instance. Others still are out of reach, after all, we do still pay people to write. (I myself am such person!)
But at the same time, if I say that a computer program is a word processor, you have a pretty good idea of what that means. There was a time when processing words involved an enormous amount of labor, work done by a large number of specialized people (mostly women). Look at a workplace documentary from the 1960’s, and compare it to a workplace today, and you’ll see that word processor technology has radically changed what tasks people do.
AGI may not make sense as a scientific goal, but it’s perfectly coherent in these terms.
Right now, a lot of tasks are done by what one could broadly call human intelligence. Some of these tasks have already fallen to technology, others will fall one by one. But it’s not unreasonable to think of a package deal, a technology that covers enough of such tasks that human intelligence stops being economically viable. That’s not because there will be some scientific general intelligence that the technology would then have, but because a decent number of intellectual tasks do seem to come bundled together. And you don’t need to cover 100% of human capabilities to radically change workplaces, any more than you needed to cover 100% of the work of a 1960’s secretary with a word processor for modern secretarial work to have a dramatically different scope and role.
It’s worth keeping in mind what is and isn’t scientifically coherent, to be aware that you can’t just extrapolate the idea of general intelligence to any future machine. (For one, it constrains what "superintelligence" could look like.) But that doesn’t mean we should be complacent, and assume that AGI is impossible in principle. AGI, like a word processor, would be a machine that covers a set of tasks well enough that people use it instead of hiring people to do the work by hand. It’s just a broader set of tasks.
I started a tradition a little while back where every year we have a special departmental colloquium entitled "The Nobel Prize in Physics: Who/What/Why". This year my job in finding speakers was made easier by having 2/3 of this years newly-minted Nobel Prize winners in physics in the Department! (Michel Devoret and John Martinis.) So our room was a bit more well-attended than normal...(hundreds and hundreds rather than dozens and dozens). Here is a recording of the event, which I was delighted to host, and there's a celebration afterwards too. (Pls share widely!)
[...] Click to continue reading this post →
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I have nothing to say about this, I just think it’s cool that Ohio State is hosting an International Conference on Ancient Magic this weekend. Open to the public! Go to this if you’re in Columbus and feeling eldritch!
The Rice Advanced Materials Research Institute is having its 2025-2026 competition for prestigious postdoctoral fellowships - see here: https://rami.rice.edu/rami-postdoctoral-fellowship-program .
If you are interested and meet the criteria, I'd be happy to talk. I have some ideas that lean into the materials for electronics direction, and other possibilities are welcome.
Dubois-Violette and Todorov noticed that the Standard Model gauge group is the intersection of two maximal subgroups of . I’m trying to understand these subgroups better.
Very roughly speaking, is the symmetry group of an octonionic qutrit. Of the two subgroups I’m talking about, one preserves a chosen octonionic qubit, while the other preserves a chosen complex qutrit.
A precise statement is here:
Over on Mathstodon I’m working with Paul Schwahn to improve this statement. He made a lot of progress on characterizing the first subgroup. is really the group of automorphisms of the Jordan algebra of self-adjoint octonion matrices, . He showed the first subgroup, the one I said "preserves a chosen octonionic qubit", is really the subgroup that preserves any chosen Jordan subalgebra isomorphic to .
Now we want to show the second subgroup, the one I said "preserves a chosen complex qutrit", is really the subgroup that preserves any chosen Jordan subalgebra isomorphic to .
I want to sketch out a proof strategy. So, I’ll often say "I hope" for a step that needs to be filled in.
Choose an inclusion of algebras . All such choices are related by an automorphism of the octonions, so it won’t matter which one we choose.
There is then an obvious copy of sitting inside . I’ll call this the standard copy. To prove the desired result, it’s enough to show:
The subgroup of preserving the standard copy of in is a maximal subgroup of , namely .
All Jordan subalgebras of isomorphic to are related to the standard copy by an transformation.
Part 1. should be the easier one to show, but I don’t even know if this one is true! is a maximal subgroup of , and Yokota shows it preserves the standard copy of in . But he shows it also preserves more, seemingly: it preserves a complex structure on the orthogonal complement of that standard copy. Is this really ‘more’ or does it hold automatically for any element of that preserves the standard copy of ? I don’t know.
But I want to focus on part 2). Here’s what we’re trying to show: any Jordan subalgebra of isomorphic to can be obtained from the standard copy of by applying some element of .
So, pick a Jordan subalgebra A of isomorphic to . Pick an isomorphism A ≅ .
Consider the idempotents
in . Using our isomorphism they give idempotents in , which I’ll call . Since these are also idempotents in .
Hope 1: I hope there is an element of mapping to ⊂ .
Hope 2: Then I hope there is an element of that fixes and maps the subalgebra to the standard copy of in .
If so, we’re done: maps to the standard copy of .
Hope 1 seems to be known. The idempotents form a so-called ‘Jordan frame’ for , and so do . Faraut and Korányi say that "in the irreducible case, the group acts transitively on the set of all Jordan frames", and I think that implies Hope 1.
As for Hope 2, I know the subgroup of that fixes contains . I bet it’s exactly . But to prove Hope 2 it may be enough to use .
Let me say a bit more about how we might realize Hope 2. It suffices to consider a Jordan subalgebra of that is isomorphic to and contains
and prove that there is an element of that fixes and maps the subalgebra to the standard copy of in . (In case you’re wondering, this is what I was calling .)
Hope 3: I hope that we can show consists of matrices
where are arbitrary real numbers and range over 2-dimensional subspaces of . This would already make it look fairly similar to the standard copy of , where the subspaces are all our chosen copy of in .
If Hope 3 is true, the subspaces don’t need to be the same, but I believe they do need to obey and cyclic permutations thereof, simply because is closed under the Jordan product.
So, we naturally want to know if such a triple of 2d subspaces of must be related to the ‘standard’ one (where they are all ) by an element of , where acts on the three copies of by the vector, left-handed spinor, and right-handed spinor representations, respectively — since this is how naturally acts on while fixing all the diagonal matrices.
This is a nice algebra question for those who have thought about triality, and more general ‘trialities’.
So, that’s where I am now: a bunch of hopes which might add up to a clarification of what I mean by "the subgroup of symmetries of an octonionic qutrit that preserve a complex qutrit".
Recently I had to update Mathematica on my laptop and after having solved the challenges of the license manager that keeps looking different every time I have to use it, I learned that Mathematica 14 can now officially work with finite fields.
This reminded me that for a while I wanted to revive an old project that had vanished together with the hard drive of some old computer: Holosplit. So, over the last two days and with the help of said version of Mathematica I did a complete rewrite which you can now find on Github.
It consists of two C programs "holosplit" and "holojoin". To the first you give a positive integer \(N\) and a file and it spits out a new file ("fragment") that is roughly \(1/N\) of the size. Every time you do that you obtain a new random fragment.
The later you give any collection of \(N\) of these fragments and it reproduces the original file. So you can for example distribute a file over 10 people such that when any 3 of them work together, they can recover the original.
How does it work? I uses the finite field \(F\) of \(2^3=256\) elements (in the Github repository, there is also a header file that implements arithmetic in \(F\) and matrix operations like product and inverse over it). Each time, it is invoked, it picks a random vector \(v\in F^N\) and writes it to the output. Then it reads \(N\) bytes from the file at a time which it also interprets as a vector \(d\in F^N\). It then outputs the byte that corresponds to the scalar product \(v\cdot d\).
To reassemble the file, holojoin takes the \(N\) files with its random vectors \(v_1,\ldots,v_N\) and interprets those as the rows of a \(N\times N\) matrix \(A\). With probability
$$\frac{\prod_{k=1}^N \left(256^N-k\right)}{(256)^{N^2}}$$
which exponentially in \(N\) approaches 1 this matrix is invertible (homework: why?). So we can read one byte from each file, assemble those into yet another vector \(e\in F^N\) and recover
$$d=A^{-1}e.$$
Besides the mathematics, it also poses philosophical/legal questions: Consider for example the original file is copyrighted, for example an mp3 or a video. The fragments are clearly derived works. But individually, they do not contain the original work, without sufficiently many other fragments they are useless (although not in a cryptographic sense). So by publishing one fragment, I do not provide access to the original work. What if others publish other fragments? Then my fragment could be the last remaining one that was missing. If there are more, any individual fragment is redundant so publishing it strictly speaking does not provide new information.
Peter’s photos: https://www.icloud.com/sharedalbum/#B275oqs3qKSZvQ
Screenshots: https://www.icloud.com/sharedalbum/#B27532ODWjIQb9
Climbing book launch: https://www.icloud.com/sharedalbum/#B27GWZuqDGnuOyN
Salisbury waters: https://www.icloud.com/sharedalbum/#B275qXGF1JQFkx
Christmas with Ash: https://www.icloud.com/sharedalbum/#B27G6XBubAhoT6
Hosin BBQ duck: https://www.icloud.com/sharedalbum/#B27GY8gBYG3b5mD
Hawks Nest to Smiths Lake: https://www.icloud.com/sharedalbum/#B2759UlCqSH5bE
Europe & Alps: https://www.icloud.com/sharedalbum/#B275ON9t3W0lu
Point Perpendicular: https://www.icloud.com/sharedalbum/#B27GqkRUiGivXD2
Newnes canyoning: https://www.icloud.com/sharedalbum/#B27GfnH8tgHSmX
Coffs Harbour to Yamba: https://www.icloud.com/sharedalbum/#B27J0DiRHJKuuWr
Wendy Bruere Christmas (2020): https://www.icloud.com/sharedalbum/#B27G4TcsmGoHysj
Six Foot Track: https://www.icloud.com/sharedalbum/#B2753qWtHZA9EX
Kosciusko to Kiandra: https://www.icloud.com/sharedalbum/#B27GgZLKuGaewVm
Camping food: https://www.icloud.com/sharedalbum/#B27GtnIORgbmHu
The Aardvark: https://www.icloud.com/sharedalbum/#B275VaUrzvmAiT
Kangaroo Valley kayaking: https://www.icloud.com/sharedalbum/#B27JEsNWnJrCpi0
Claustral canyon: https://www.icloud.com/sharedalbum/#B2755Z2WMOTpsk
Budawang: https://www.icloud.com/sharedalbum/#B27GDdyTvGvpINL
Mother’s Day panoramas (2021): https://www.icloud.com/sharedalbum/#B27GFssfGG9WmJP
Point Perpendicular & Nowra: https://www.icloud.com/sharedalbum/#B27GRMtznGPdeuZ
Blood moon: https://www.icloud.com/sharedalbum/#B27GdIshaG8NgGX
La Perouse to Coogee: https://www.icloud.com/sharedalbum/#B275aVbMK4h7qo
Canberra ASPI launch: https://www.icloud.com/sharedalbum/#B27GQOeMmGj4Zcv
Edible foraging: https://www.icloud.com/sharedalbum/#B275ejO179Si0N
Sydney to Wollongong: https://www.icloud.com/sharedalbum/#B275M7GFPUasMe
Album for Dad, Father’s Day (2021): https://www.icloud.com/sharedalbum/#B2752plgjnnkUe
Vaucluse (with Cheryl, Nestor & Wendy): https://www.icloud.com/sharedalbum/#B275CmvAS4uA0Z
Bouddi National Park: https://www.icloud.com/sharedalbum/#B27GdPblXG8WdOo
Tom Thumb (the 2nd): https://www.icloud.com/sharedalbum/#B275aDWbr4CN2w
Eden to Victoria: https://www.icloud.com/sharedalbum/#B27GJDfWGArX8l
Wendy’s book launch (the 2nd): https://www.icloud.com/sharedalbum/#B27GIcgc2G7h08y
Mark & Pat Bruere visit Sydney: https://www.icloud.com/sharedalbum/#B27G0ehgLbyWyg
New Years Eve climb (2021): https://www.icloud.com/sharedalbum/#B27Ju8EH6JOZxmU
Newnes Canyoning (2022): https://www.icloud.com/sharedalbum/#B275BydzFU0GZ8
Royal National Park (2022): https://www.icloud.com/sharedalbum/#B27GlxzuqGVI5nE
Peter & Wendy: https://www.icloud.com/sharedalbum/#B27Gf693ZG52tfd
Book photo shoots: too rude...
Wendy & Peter’s mushroom trip: https://www.icloud.com/sharedalbum/#B27GrhkPxG27So8
Post-mushroom hike: https://www.icloud.com/sharedalbum/#B27GdFryYG8i3Ur
Wendy Kalymnos favourites: https://www.icloud.com/sharedalbum/#B27JqstnBJEXkH2
Wendy Frenchmans screenshots: https://www.icloud.com/sharedalbum/#B27Jr1PPdJpd7Dq
Instagram: https://www.icloud.com/sharedalbum/#B27GzFCC1Gb4tqr
Haute route: https://www.icloud.com/sharedalbum/#B27J8GySPJtWoQ1
Kim’s KKKalendar: https://www.icloud.com/sharedalbum/#B275fk75vIL0sH
Frenchmans Cap Wild: https://www.icloud.com/sharedalbum/#B27G4VTwGGoFBkz
Photoshoot with Zixin: https://www.icloud.com/sharedalbum/#B27GPCdxkGKPkM4
Wendy birthday hike (2023): https://www.icloud.com/sharedalbum/#B27GWBC59GnHpQW
Bateman’s Bay to Bawley Point: https://www.icloud.com/sharedalbum/#B27JsHvHoJ8bxWf
Stockton Sand dunes (2023): https://www.icloud.com/sharedalbum/#B27GVfZ2vGloFZV
Wendy book launch (2023): https://www.icloud.com/sharedalbum/#B27J058xyJR4IBM
Dolomites (2023): https://www.icloud.com/sharedalbum/#B0Z5kuVsbGJUzKO
Mount Arapiles: https://www.icloud.com/sharedalbum/#B275GH8Mq8Uh2X
Mount Solitary loop: https://www.icloud.com/sharedalbum/#B275nhQST2mETE
Klaus Hanz Franz Rohde Kunst: https://www.icloud.com/sharedalbum/#B27GqQrCLGiY3vb
Klaus Rohde funeral slideshow: https://www.icloud.com/sharedalbum/#B27GDZLe8GXP58K
Dad (old, B&W): https://www.icloud.com/sharedalbum/#B27GLLXGLJ5mbT2
Klaus & Ursula wedding: https://www.icloud.com/sharedalbum/#B275cLqfN7154g
Test Greece: https://www.icloud.com/sharedalbum/#B27Jq4WnLJ6JMNd
From Will Skea (Alps): https://www.icloud.com/sharedalbum/#B27JHciePJFwacG
From Will Skea (Frenchmans Cap): https://www.icloud.com/sharedalbum/#B275ZhN2v3EVq6
From Will Skea (Arapiles): https://www.icloud.com/sharedalbum/#B27JPrgBGJu3BTD
Coffs Harbour to Yamba (2): https://www.icloud.com/sharedalbum/#B27GFqhgJG9LHgT
Mark magic show (2021): https://www.icloud.com/sharedalbum/#B27G60dj6ARCvd
Wendy Christmas present (2020): https://www.icloud.com/sharedalbum/#B275FrPQ6GxvRu
AHS 25 year reunion: https://www.icloud.com/sharedalbum/#B275O3DjHUvSv
WhatsApp: https://www.icloud.com/sharedalbum/#B275tzEA5fX1nc
Armidale High School: https://www.icloud.com/sharedalbum/#B27GnbeumG4PnAF
Book photos for Mum & Dad: https://www.icloud.com/sharedalbum/#B27Gtec4XQkASe
Miscellaneous: https://www.icloud.com/sharedalbum/#B27Gq6kMgGKn7GR
Three Capes Trail (2022): https://www.icloud.com/sharedalbum/#B27G7HOIlGrDUGZ
Childhood computer programming: https://www.icloud.com/sharedalbum/#B275fu2MutDU8N
Magic with Mark in Maroubra: https://www.icloud.com/sharedalbum/#B27Gv6DhEGD9U3G
Photoshoot with Zixin (2024): https://www.icloud.com/sharedalbum/#B27GCATCnJGoRfW
Butt Crack (2021): https://www.icloud.com/sharedalbum/#B275VtHQfMv0zw
Greece photos new (edited to remove photos from wrong album): https://www.icloud.com/sharedalbum/#B27GY3uThGoBcGj
Singapore (all combined): https://www.icloud.com/sharedalbum/#B275qsTcwJKJjl
Hong Kong (transit): https://www.icloud.com/sharedalbum/#B2759v1AbS8Hve
Taiwan: https://www.icloud.com/sharedalbum/#B27GQD2D7Gw0hAp
India (combined): https://www.icloud.com/sharedalbum/#B27Gtue8VQy83g
Freycinet: https://www.icloud.com/sharedalbum/#B27G5VpecGE5Tbg
Triglav: https://www.icloud.com/sharedalbum/#B275MbK9Vy8erz
Shared with me: https://www.icloud.com/sharedalbum/#B27GGXqixzPOrm
Mount Wellington climbing: https://www.icloud.com/sharedalbum/#B27Gd59qiG8Kjy4
New Zealand combined (2004): https://www.icloud.com/sharedalbum/#B27GIZ8BIGNN5jy
New Zealand combined (2005): https://www.icloud.com/sharedalbum/#B27GcuRfIGFVIcL
Yea: https://www.icloud.com/sharedalbum/#B27GZYbYHGhFIir
Mount Pleasant: https://www.icloud.com/sharedalbum/#B275Iy2hC0JTTL
D’Aguilar: https://www.icloud.com/sharedalbum/#B27Gh7fzTGZBosS
Bali (2001): https://www.icloud.com/sharedalbum/#B27G1qNHBGOTbIr
Samba Ninjas: https://www.icloud.com/sharedalbum/#B27GG34bAzqQ0v
Armidale (misc): https://www.icloud.com/sharedalbum/#B27GSkLVwGyobbX
Emma’s party (2008): https://www.icloud.com/sharedalbum/#B275S2ms99Zyby
Goettingen (2011): https://www.icloud.com/sharedalbum/#B27JIrbT3Jsgxhd
South Coast track: https://www.icloud.com/sharedalbum/#B27G58NWBG6QyN7
Minsk (2006): https://www.icloud.com/sharedalbum/#B27G3JpSBGX1UkQ
Baden-Baden (2019): https://www.icloud.com/sharedalbum/#B27595X5HTVzJr
Berlin (combined): https://www.icloud.com/sharedalbum/#B27JqWzChJ6qizD
Switzerland (combined): https://www.icloud.com/sharedalbum/#B275zXwoYGJ6HMF
Italy highlights: https://www.icloud.com/sharedalbum/#B27G47PHQGoJium
Germany (misc): https://www.icloud.com/sharedalbum/#B275hPMfYGu5xVJ
Garmisch (2022): https://www.icloud.com/sharedalbum/#B27GFsbvlG9Xrr6
Germany (2019): https://www.icloud.com/sharedalbum/#B27G6Mn98G56Ncb
Garmisch (2006): https://www.icloud.com/sharedalbum/#B27J5lIdKGLC9KG
Baden-Baden (2005): https://www.icloud.com/sharedalbum/#B275sWRpHHQkt9
Berlin (2005): https://www.icloud.com/sharedalbum/#B27GgOQtrGjQrpH
Zugspitze (2005): https://www.icloud.com/sharedalbum/#B27G81mNdGcApGt
Amsterdam, Bristol (2006): https://www.icloud.com/sharedalbum/#B275B9SRzyBjlH
Baden-Baden (2006): https://www.icloud.com/sharedalbum/#B275eD9V79I2XR
Berlin (2006): https://www.icloud.com/sharedalbum/#B275toRf1fH8MD
Berlin, Jena (2007): https://www.icloud.com/sharedalbum/#B27GTI3fvGVgNit
Erlangen (2006): https://www.icloud.com/sharedalbum/#B27JrotZ2JpMb0i
Garmisch (2010): https://www.icloud.com/sharedalbum/#B27JPJPSiJurzNg
Germany (2010): https://www.icloud.com/sharedalbum/#B275FhYPQP650
Stuttgart (2006): https://www.icloud.com/sharedalbum/#B27GmitydGVVaZh
Changi (2019): https://www.icloud.com/sharedalbum/#B27GnmlKoG4JHpX
Japan (2007): https://www.icloud.com/sharedalbum/#B275AerZbG6FxVL
Japan (2012): https://www.icloud.com/sharedalbum/#B27GjBjobGg6PUa
Miscellaneous (including Japan 2013): https://www.icloud.com/sharedalbum/#B27GTpbybGySbE8
Currumbin & Tugin (2021): https://www.icloud.com/sharedalbum/#B275vBKZ4xH9X6
Brisbane (2021): https://www.icloud.com/sharedalbum/#B275YHsSjxQnm0
Weed in Byron (26/6/2025): https://www.icloud.com/sharedalbum/#B275Q2ydoGsQ4O5
Weed in Byron 2: https://www.icloud.com/sharedalbum/#B27GQDYhLGwsuY4
It’s been over three years since my last post on this blog and I have sometimes been asked, understandably, whether the project I announced in my previous post was actually happening. The answer is yes — the grant I received from the Astera Institute has funded several PhD students and a couple of postdocs, and we have been busy. In my previous post I suggested that I would be open to remote collaboration, but that has happened much less, partly because a Polymath-style approach would have been difficult to manage while also ensuring that my PhD students would have work that they could call their own to put in their theses.
In general I don’t see a satisfactory solution to that problem, but in this post I want to mention a subproject of the main project that is very much intended to be a large public collaboration. A few months ago, a call came out from Renaissance Philanthropies saying that they were launching a 9ドルm AI for Math Fund to spend on projects in the general sphere of AI and mathematics, and inviting proposals. One of the categories that they specifically mentioned was creating new databases, and my group submitted a proposal to create a database of what we call "structured motivated proofs," a piece of terminology that I will explain a bit more later in just a moment. I am happy to report that our proposal was one of the 29 successful ones. Since a good outcome to the project will depend on collaboration from many people outside the group, we need to publicize it, which is precisely the purpose of this post. Below I will be more specific about the kind of help we are looking for.
The underlying thought behind this project is that AI for mathematics is being held back not so much by an insufficient quantity of data as by the wrong kind of data. All mathematicians know, and some of us enjoy complaining about it, that it is common practice when presenting a proof in a mathematics paper, or even textbook, to hide the thought processes that led to the proof. Often this does not matter too much, because the thought processes may be standard ones that do not need to be spelt out to the intended audience. But when proofs start to get longer and more difficult, they can be hard to read because one has to absorb definitions and lemma statements that are not obviously useful, are presented as if they appeared from nowhere, and demonstrate their utility only much later in the argument.
A sign that this is a problem for AI is the behaviour one observes after asking an LLM to prove a statement that is too difficult for it. Very often, instead of admitting defeat, it will imitate the style of a typical mathematics paper and produce rabbits out of hats, together with arguments later on that those rabbits do the required job. The problem is that, unlike with a correct mathematics paper, one finds when one scrutinizes the arguments carefully that they are wrong. However, it is hard to find superficial features that distinguish between an incorrect rabbit with an incorrect argument justifying that rabbit (especially if the argument does not go into full detail) and a correct one, so the kinds of statistical methods used by LLMs do not have an easy way to penalize the incorrectness.
Of course, that does not mean that LLMs cannot do mathematics at all — they are remarkably good at it, at least compared with what I would have expected three years ago. How can that be, given the problem I have discussed in the previous paragraph?
The way I see it (which could change — things move so fast in this sphere), the data that is currently available to train LLMs and other systems is very suitable for a certain way of doing mathematics that I call guess and check. When trying to solve a maths problem, you will normally write down the routine parts of an argument without any fuss (and an LLM can do them too because it has seen plenty of similar examples), but if the problem as a whole is not routine, then at some point you have to stop and think, often because you need to construct an object that has certain properties (I mean this in a rather general way — the "object" might be a lemma that will split up the proof in a nice way) and it is not obvious how to do so. The guess-and-check approach to such moments is what it says: you make as intelligent a guess as you can and then see whether it has the properties you wanted. If it doesn’t, you make another guess, and you keep going until you get lucky.
The reason an LLM might be tempted to use this kind of approach is that the style of mathematical writing I described above makes it look as though that is what we as mathematicians do. Of course, we don’t actually do that, but we tend not to mention all the failed guesses we made and how we carefully examined why they failed, modifying them in appropriate ways in response, until we finally converged on an object that worked. We also don’t mention the reasoning that often takes place before we make the guess, saying to ourselves things like "Clearly an Abelian group can’t have that property, so I need to look for a non-Abelian group."
Intelligent guess and check works well a lot of the time, particularly when carried out by an LLM that has seen many proofs of many theorems. I have often been surprised when I have asked an LLM a problem of the form \exists x\in X \ P(x), where P is some property that is hard to satisfy, and the LLM has had no trouble answering it. But somehow when this happens, the flavour of the answer given by the LLM leaves me with the impression that the technique it has used to construct x is one that it has seen before and regards as standard.
If the above picture of what LLMs can do is correct (the considerations for reinforcement-learning-based systems such as AlphaProof are not identical but I think that much of what I say in this post applies to them too for slightly different reasons), then the likely consequence is that if we pursue current approaches, then we will reach a plateau: broadly speaking they will be very good at answering a question if it is the kind of question that a mathematician with the right domain expertise and good instincts would find reasonably straightforward, but will struggle with anything that is not of that kind. In particular, they will struggle with research-level problems, which are, almost by definition, problems that experts in the area do not find straightforward. (Of course, there would probably be cases where an LLM spots relatively easy arguments that the experts had missed, but that wouldn’t fundamentally alter the fact that they weren’t really capable of doing research-level mathematics.)
But what if we had a database of theorems and proofs that did not hide the thought processes that lay behind the non-obvious details of the proofs? If we could train AI on a database of accounts of proof discoveries and if, having done so, we then asked it to provide similar accounts, then it would no longer resort to guess-and-check when it got stuck, because the proof-discovery accounts it had been trained on would not be resorting to it. There could be a problem getting it to unlearn its bad habits, but I don’t think that difficulty would be impossible to surmount.
The next question is what such a database might look like. One could just invite people to send in stream-of-consciousness accounts of how they themselves found certain proofs, but that option is unsatisfactory for several reasons.
To deal with these kinds of difficulties, we plan to introduce a notion of a structured motivated proof, by which we mean a proof that is generated in a very particular way that I will partially describe below. A major part of the project, and part of the reason we needed funding for it, is to create a platform that will make it convenient to input structured motivated proofs and difficult to insert the kinds of rabbits out of hats that make a proof mysterious and unmotivated. In this way we hope to gamify the task of creating the database, challenging people to input into our system proofs of certain theorems that appear to rely on "magic" ideas, and perhaps even offering prizes for proofs that contain steps that appear in advance to be particularly hard to motivate. (An example: the solution by Ellenberg and Gijswijt of the cap-set problem uses polynomials in a magic-seeming way. The idea of using polynomials came from an earlier paper of Croot, Lev and Pach that proved a closely related theorem, but in that paper it just appears in the statement of their Lemma 1, with no prior discussion apart from the words "in the present paper we use the polynomial method" in the introduction.)
I wrote about motivated proofs in my previous post, but thanks to many discussions with other members of the group, my ideas have developed quite a lot since then. Here are two ways we like to think about the concept.
I will not go into full detail about what I mean by this, but will do so in a future post when we have created the platform that we would like people to use in order to input proofs into the database. But the basic idea is that at any one moment one is in a certain state, which we call a proof-discovery state, and there will be a set of possible moves that can take one from the current proof-discovery state to a new one.
A proof-discovery state is supposed to be a more formal representation of the state one is in when in the middle of solving a problem. Typically, if the problem is difficult, one will have asked a number of questions, and will be aware of logical relationships between them: for example, one might know that a positive answer to Q1 could be used to create a counterexample to Q2, or that Q3 is a special case of Q4, and so on. One will also have proved some results connected with the original question, and again these results will be related to each other and to the original problem in various ways that might be quite complicated: for example P1 might be a special case of Q2, which, if true would reduce Q3 to Q4, where Q3 is a generalization of the statement we are trying to prove.
Typically we will be focusing on one of the questions, and typically that question will take the form of some hypotheses and a target (the question being whether the hypotheses imply the target). One kind of move we might make is a standard logical move such as forwards or backwards reasoning: for example, if we have hypotheses of the form P(x) and \forall u\ P(u)\implies Q(u), then we might decide to deduce Q(x). But things get more interesting when we consider slightly less basic actions we might take. Here are three examples.
This is a surprisingly useful way to conceive of what we are talking about, especially as it relates closely to what I was talking about earlier: imposing a standard form on motivated proofs (which is why we call them "structured" motivated proofs) and gamifying the process of producing them.
The idea is that a structured motivated proof is one that can be generated using an interface (which we are in the process of creating — at the moment we have a very basic prototype that has a few of the features we will need, but not yet the more interesting ones) that has one essential property: the user cannot type in data. So what can they do? They can select text that is on their screen (typically mathematical expressions or subexpressions), they can click buttons, choose items from drop-down menus, and accept or reject "obvious" suggestions made to them by the interface.
If, for example, the current goal is an existential statement \exists x\ P(x), then typing in a formula that defines a suitable x is not possible, so instead one must select text or generate new text by clicking buttons, choosing from short drop-down menus, and so on. This forces the user to generate x, which is our proxy for showing where the idea of using x came from.
Broadly speaking, the way the prototype works is to get an LLM to read a JSON object that describes the variables, hypotheses and goals involved in the proof state in a structured format, and to describe (by means of a fairly long prompt) the various moves it might be called upon to do. Thus, the proofs generated by the system are not formally verified, but that is not an issue that concerns us in practice since there will be a human in the loop throughout to catch any mistakes that the LLM might make, and this flexibility may even work to our advantage to better capture the fluidity of natural-language mathematics.
There is obviously a lot more to say about what the proof-generating moves are, or (approximately equivalently) what the options provided by a point-and-click system will be. I plan to discuss that in much more detail when we are closer to having an interface ready, the target for which is the end of this calendar year. But the aim of the project is to create a database of examples of proofs that have been successfully generated using the interface, which can then be used to train AI to play the generate-structured-motivated-proof game.
There are several tasks that will need doing once the project gets properly under way. Here are some of the likely ones.
If you think you might be interested in any of these roles, please feel free to get in touch. Probably the hardest recruitment task for us will be identifying the right people with the right mixture of mathematical knowledge and software engineering skills to help us turn the platform into a well-designed web-based one that is convenient and pleasurable to use. If you think you might be such a person, or if you have a good idea for how we should go about finding one, we would be particularly interested to hear from you.
In a future post, I will say more about the kinds of moves that our platform will allow, and will give examples of non-motivated proofs together with how motivated versions of those proofs can be found and entered using the platform (which may involve a certain amount of speculation about what the platform will end up looking like).
In one way, our "moves" can be regarded as tactics of a kind. However, some of the moves we will need are difficult to implement in conventional proof assistants such as Lean. In parallel with the work described above, we hope to create an interface to Lean that would allow one to carry out proof-discovery moves of the kind discussed above but with the proof-discovery states being collections of Lean proof states. Members of my group have already been working on this and have made some very interesting progress, but there is some way to go. However, we hope that at some point (and this is also part of the project pitched to the AI for Math Fund) we will have created another interface that will have Lean working in the background, so that it will be possible to generate motivated proofs that will be (or perhaps it is better to say include) proofs in Lean at the same time.
Another possibility that we are also considering is to use the output of the first platform (which, as mentioned above, will be fairly formal, but not in the strict sense of a language such as Lean) to create a kind of blueprint that can then be autoformalized automatically. Then we would have a platform that would in principle allow mathematicians to search for proofs while working on their computers without having to learn a formal language, with their thoughts being formalized as they go.
A preamble
Subnuclear physics obeys the laws of quantum mechanics, which are quite a far cry from those of classical mechanics we are accustomed to. For that reason, one might be inclined to believe that analogies based on everyday life cannot come close to explaining the behavior of elementary particles. But that is not true – in fact, many properties of elementary particles are understandable in analogy with the behavior of classical systems, without the need to delve into the intricacies of the quantum world. And if you have been reading this blog for a while, you know what I think – the analogy is a powerful didactical instrument, and it is indeed at the very core of our learning processes.
I judge a bookstore by the number of Diana Wynne Jones novels it stocks. The late British author wrote some of the twentieth century’s most widely lauded science-fiction and fantasy (SFF). She clinched more honors than I should list, including two World Fantasy Awards. Neil Gaiman, author of American Gods, called her "the best children’s writer of the last forty years" in 2010—and her books suit children of all ages.1 But Wynne Jones passed away as I was finishing college, and her books have been disappearing from American bookshops. The typical shop stocks, at best, a book in the series she began with Howl’s Moving Castle, which Hayao Miyazaki adapted into an animated film.
I don’t recall the last time I glimpsed Deep Secret in a bookshop, but it ranks amongst my favorite Wynne Jones books—and favorite books, full-stop. So I relished living part of that book this spring.
Deep Secret centers on video-game programmer Rupert Venables. Outside of his day job, he works as a Magid, a magic user who helps secure peace and progress across the multiple worlds. Another Magid has passed away, and Rupert must find a replacement for him. How does Rupert track down and interview his candidates? By consolidating their fate lines so that the candidates converge on an SFF convention. Of course.
My fate line drew me to an SFF convention this May. Balticon takes place annually in Baltimore, Maryland. It features not only authors, agents, and publishers, but also science lecturers. I received an invitation to lecture about quantum steampunk—not video-game content,2 but technology-oriented like Rupert’s work. I’d never attended an SFF convention,3 so I reread Deep Secret as though studying for an exam.
Rupert, too, is attending his first SFF convention. A man as starched as his name sounds, Rupert packs suits, slacks, and a polo-neck sweater for the weekend—to the horror of a denim-wearing participant. I didn’t bring suits, in my defense. But I did dress business-casual, despite having anticipated that jeans, T-shirts, and capes would surround me.
I checked into a Renaissance Hotel for Memorial Day weekend, just as Rupert checks into the Hotel Babylon for Easter weekend. Like him, I had to walk an inordinately long distance from the elevators to my room. But Rupert owes his trek to whoever’s disrupted the magical node centered on his hotel. My hotel’s architects simply should have installed more elevator banks.
Balticon shared much of its anatomy with Rupert’s con, despite taking place in a different century and country (not to mention world). Participants congregated downstairs at breakfast (continental at Balticon, waitered at Rupert’s hotel). Lectures and panels filled most of each day. A masquerade took place one night. (I slept through Balticon’s; impromptu veterinary surgery occupies Rupert during his con’s.) Participants vied for artwork at an auction. Booksellers and craftspeople hawked their wares in a dealer’s room. (None of Balticon’s craftspeople knew their otherworldly subject matter as intimately as Rupert’s Magid colleague Zinka Fearon does, I trust. Zinka paints her off-world experiences when in need of cash.)
In our hotel room, I read out bits of Deep Secret to my husband, who confirmed the uncanniness with which they echoed our experiences. Both cons featured floor-length robes, Batman costumes, and the occasional slinky dress. Some men sported long-enough locks, and some enough facial hair, to do a Merovingian king proud. Rupert registers "a towering papier-mâché and plastic alien" one night; on Sunday morning, a colossal blow-up unicorn startled my husband and me. We were riding the elevator downstairs to breakfast, pausing at floor after floor. Hotel guests packed the elevator like Star Wars fans at a Lucasfilm debut. Then, the elevator halted again. The doors opened on a bespectacled man, 40-something years old by my estimate, dressed as a blue-and-white unicorn. The costume billowed out around him; the golden horn towered multiple feet above his head. He gazed at our sardine can, and we gazed at him, without speaking. The elevator doors shut, and we continued toward breakfast.
Despite having read Deep Secret multiple times, I savored it again. I even laughed out loud. Wynne Jones paints the SFF community with the humor, exasperation, and affection one might expect of a middle-school teacher contemplating her students. I empathize, belonging to a community—the physics world—nearly as idiosyncratic as the SFF community.4 Wynne Jones’s warmth for her people suffuses Deep Secret; introvert Rupert surprises himself by enjoying a dinner with con-goers and wishing to spend more time with them. The con-goers at my talk exhibited as much warmth as any audience I’ve spoken to, laughing, applauding, and asking questions. I appreciated sojourning in their community for a weekend.5
This year, my community is fêting the physicists who founded quantum theory a century ago. Wynne Jones sparked imaginations two decades ago. Let’s not let her memory slip from our fingertips like a paperback over which we’re falling asleep. After all, we aren’t forgetting Louis de Broglie, Paul Dirac, and their colleagues. So check out a Wynne Jones novel the next time you visit a library, or order a novel of hers to your neighborhood bookstore. Deep Secret shouldn’t be an actual secret.
With thanks to Balticon’s organizers, especially Miriam Winder Kelly, for inviting me and for fussing over their speakers’ comfort like hens over chicks.
1Wynne Jones dedicated her novel Hexwood to Gaiman, who expressed his delight in a poem. I fancy the comparison of Gaiman, a master of phantasmagoria and darkness, to a kitten.
2Yet?
3I’d attended a steampunk convention, and spoken at a Boston SFF convention, virtually. But as far as such conventions go, attending virtually is to attending in person as my drawings are to a Hayao Miyazaki film.
4But sporting fewer wizard hats.
5And I wonder what the Diana Wynne Jones Conference–Festival is like.
Sunflowers are blooming, stores are trumpeting back-to-school sales, and professors are scrambling to chart out the courses they planned to develop in July. If you’re applying for an academic job this fall, now is the time to get your application ducks in a row. Seeking a postdoctoral or faculty position? Your applications will center on research statements. Often, a research statement describes your accomplishments and sketches your research plans. What do evaluators look for in such documents? Here’s my advice, which targets postdoctoral fellowships and faculty positions, especially for theoretical physicists.
The 2025 Quantum Leadership Awards were announced at the Quantum World Congress on 18 September 2025. Upon receiving the Academic Pioneer in Quantum Award, John Preskill made these remarks.
I’m enormously excited and honored to receive this Quantum Leadership Award, and especially thrilled to receive it during this, the International Year of Quantum. The 100th anniversary of the discovery of quantum mechanics is a cause for celebration because that theory provides our deepest and most accurate description of how the universe works, and because that deeper understanding has incalculable value to humanity. What we have learned about electrons, photons, atoms, and molecules in the past century has already transformed our lives in many ways, but what lies ahead, as we learn to build and precisely control more and more complex quantum systems, will be even more astonishing.
As a professor at a great university, I have been lucky in many ways. Lucky to have the freedom to pursue the scientific challenges that I find most compelling and promising. Lucky to be surrounded by remarkable, supportive colleagues. Lucky to have had many collaborators who enabled me to do things I could never have done on my own. And lucky to have the opportunity to teach and mentor young scientists who have a passion for advancing the frontiers of science. What I’m most proud of is the quantum community we’ve built at Caltech, and the many dozens of young people who imbibed the interdisciplinary spirit of Caltech and then moved onward to become leaders in quantum science at universities, labs, and companies all over the world.
Right now is a thrilling time for quantum science and technology, a time of rapid progress, but these are still the early days in a nascent second quantum revolution. In quantum computing, we face two fundamental questions: How can we scale up to quantum machines that can solve very hard computational problems? And once we do so, what will be the most important applications for science and for industry? We don’t have fully satisfying answers yet to either question and we won’t find the answers all at once – they will unfold gradually as our knowledge and technology advance. But 10 years from now we’ll have much better answers than we have today.
Companies are now pursuing ambitious plans to build the world’s most powerful quantum computers. Let’s not forget how we got to this point. It was by allowing some of the world’s most brilliant people to follow their curiosity and dream about what the future could bring. To fulfill the potential of quantum technology, we need that spirit of bold adventure now more than ever before. This award honors one scientist, and I’m profoundly grateful for this recognition. But more importantly it serves as a reminder of the vital ongoing need to support the fundamental research that will build foundations for the science and technology of the future. Thank you very much!
Thomas Bloom’s erdosproblems.com site hosts nearly a thousand questions that originated, or were communicated by, Paul Erdős, as well as the current status of these questions (about a third of which are currently solved). The site is now a couple years old, and has been steadily adding features, the most recent of which has been a discussion forum for each individual question. For instance, a discussion I had with Stijn Cambie and Vjeko Kovac on one of these problems recently led to it being solved (and even formalized in Lean!).
A significantly older site is the On-line Encyclopedia of Integer Sequences (OEIS), which records hundreds of thousands of integer sequences that have some mathematician has encountered at some point. It is a highly useful resource, enabling researchers to discover relevant literature for a given problem so long as they can calculate enough of some integer sequence that is "canonically" attached to that problem that they can search for it in the OEIS.
A large fraction of problems in the Erdos problem webpage involve (either explicitly or implicitly) some sort of integer sequence – typically the largest or smallest size {f(n)} of some {n}-dependent structure (such as a graph of {n} vertices, or a subset of {\{1,\dots,n\}}) that obeys a certain property. In some cases, the sequence is already in the OEIS, and is noted in the Erdos problem web page. But in a large number of cases, the sequence either has not yet been entered into the OEIS, or it does appear but has not yet been noted on the Erdos web page.
Thomas Bloom and I are therefore proposing a crowdsourced project to systematically compute the hundreds of sequences associated to the Erdos problems and cross-check them against the OEIS. We have created a github repository to coordinate this process; as a by-product, this repository will also be tracking other relevant statistics about the Erdos problem website, such as the current status of formalizing the statements of these problems in the Formal Conjectures Repository.
The main feature of our repository is a large table recording the current status of each Erdos problem. For instance, Erdos problem #3 is currently listed as open, and additionally has the status of linkage with the OEIS listed as "possible". This means that there are one or more sequences attached to this problem which *might* already be in the OEIS, or would be suitable for submission to the OEIS. Specifically, if one reads the commentary for that problem, one finds mention of the functions {r_k(N)} for {k=3,4,\dots}, defined as the size of the largest subset of {\{1,\dots,N\}} without a {k}-term progression. It is likely that several of the sequences {r_3(N)}, {r_4(N)}, etc. are in the OEIS, but it is a matter of locating them, either by searching for key words, or by calculating the first few values of these sequences and then looking for a match. (EDIT: a contributor has noted that the first foursequences appear as A003002, A003003, A003004, and A003005 in the OEIS, and the table has been updated accordingly.)
We have set things up so that new contributions (such as the addition of an OEIS number to the table) can be made by a Github pull request, specifically to modify this YAML file. Alternatively, one can create a Github issue for such changes, or simply leave a comment either on the appropriate Erdos problem forum page, or here on this blog.
Many of the sequences do not require advanced mathematical training to compute, and so we hope that this will be a good "citizen mathematics" project that can bring in the broader math-adjacent community to contribute to research-level mathematics problems, by providing experimental data, and potentially locating relevant references or connections that would otherwise be overlooked. This may also be a use case for AI assistance in mathematics through generating code to calculate the sequences in question, although of course one should always stay mindful of potential bugs or hallucinations in any AI-generated code, and find ways to independently verify the output. (But if the AI-generated sequence leads to a match with an existing sequence in the OEIS that is clearly relevant to the problem, then the task has been successfully accomplished, and no AI output needs to be directly incorporated into the database in such cases.)
This is an experimental project, and we may need to adjust the workflow as the project progresses, but we hope that it will be successful and lead to further progress on some fraction of these problems. The comment section of this blog can be used as a general discussion forum for the project, while the github issue page and the erdosproblems.com forum pages can be used for more specialized discussions of specific problems.
The Simons-Laufer Mathematical Sciences institute, or SLMath (formerly the Mathematical Sciences Research Institute, or MSRI) has recently restructured its program formats, and is now announcing three new research initiatives, whose applications open on Sep 1 2025:
(Disclosure: I am vice-chair of the board of trustees at SLMath.)
Let it be known to all governments and systems of power:
This is the natural order that guarantees our survival and gifts this world to our children. This world belongs to them and where we fail to serve them we condemn ourselves. And where government has failed to uphold this, we will not obey them as they have no right to exist.
We do not have to ask for these things, they are required, and if not given we shall simply take them.
Where the truth has not been told it shall be told.
If we fail to do so we condemn our children ourselves.
In a previous post, we discussed a phase transition that occurred in the piping above you on a game show. In the scenario, you are led on stage in front of a large audience. After a brief time, the audience votes on how "likeable" you are. The catch is that it doesn’t simply tally the votes, but turns spigots on a lattice of piping above your head. Water is then released and if enough people like you, it closes off the passage, keeping you dry. This exciting game show1 was described in that post:
Each "like" turns a spigot off, stopping water from flowing through one pipe in a grid overhead. Once voting ends, water is dumped into the system. If it can find a path to the bottom, you get soaked. [Emphasis added] The better your "likeability," the less likely spigots open a path for water to flow and the drier you stay. That’s your prize for this game show (and hey, you also get the knowledge that people out there like you).
This system models a type of phase transition known as percolation.
The full post is here:
I highlighted above a key phrase "If it can find a path to the bottom, you get soaked." What I didn’t say, but should have is that the water was being forced through the pipes, not just dropping down due to gravity. This is a very important point since our phases and phase transition changes dramatically if we just let gravity do the work. In the case of the water being "forced," it can travel back up pipes if it helps it find its way out and onto your head, but in the case when only gravity is present, it falls down the pipes. To facilitate gravity, we’ll turn the pipes 45 degrees, and if we insert water at a single point on top, it could look like this:
This setup is a different problem called directed percolation. It also has a phase transition, but one that is different in some fundamental ways from regular percolation.
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Before we explore its stranger properties, we can ask, "At what likability threshold do you remain dry?" Well, this happens to have a transition chance of 35.53%!2 This system is a lot more generous, keeping you dry even when a majority of people dislike you. This number comes from numerical computations which have been done rather precisely, and we can even compute it ourselves. In fact, you can see this clearly with this plot
Notice that as we make the system bigger and bigger, the chance of getting soaked less than 35.53% increases and above it, it decreases. This is the same kind of hallmark of a phase transition as we saw in our previous case.
We can also look at the water as it flows down the system to see the clusters that make it from top to bottom
There is still a fractal-looking pattern at the transition point. With all of these similarities with the regular percolation problem from the last post, what is different? And why is that plot so long and skinny? If gravity wants to pull you down, is that somehow altering the motion down, making it distinct from the motion left or right?
Well, if you go back to the two plots above, you’ll notice a few things that really make them differ from the percolation plots. In the fine print of the first, I’ve noted that the vertical distance is L1.58, so for a horizontal size of 40, the vertical size is roughly 340! That is definitely not a square. And in the second plot, there appears to be more vertical distance than horizontal distance. What is special about this 1.58 number3? It turns out, it’s a critical exponent in this problem, a universal aspect of directed percolation, that distinguishes it from regular percolation. We will call it z = 1.58 the dynamical critical exponent since it is revealed as water flows down in time (dynamically). This dynamical exponent z can reveal itself by looking at these "long and skinny" setups, but be masked by the square setup.
One thing we took away in the previous post was that we lose any sense of scale at this type of phase transition4. But whenever we have "only" thousands of pipes, the size of the system provides a scale! This is the main reason why we begin to see smooth curves and not sharp jumps in quantities. If the system of pipes were infinite (and we had infinite time for the water to go down the pipes), the probability you get soaked would be 100% less than the 35.53% likeability and 0% more than 35.53% likeability. For physical systems, the finite size is often not a huge issue since the scale is closer to the 1023 atoms present in macroscopic systems, and so even things that are technically smooth curves look very sharp.
The problem of size becomes more severe with directed percolation because horizontal and vertical distances start behaving differently thanks to gravity. In this case, if we lay out our nice grid of 10 ×ばつ 10, 20 ×ばつ 20, or 30 ×ばつ 30, we start to notice that the likeability threshold where you stop getting soaked, seems to depend on the size of the system more than before. In actuality it doesn’t, but for these small sizes, you are definitely getting soaked well into the so-called "Dry Phase" we previously labeled. This is seen in the red curves here where each bigger square has a curve underneath the last:
Gravity has done something to the system. Flowing down is different from flowing left or right. In fact, if we flow down by some amount h and over to the right by some distance w, then at the directed percolation transition point
The amount water flows down is related to how far it flows to the right or left by this weird, fractional power of w. This 1.58 is z, our new dynamical critical exponent, which is a universal feature of directed percolation5. It tells us that if we make a system 30 pipes wide, it should extend roughly 301.58 ≈ 216 pipes in height to begin picking out the phase transition effectively. The blue curves in the above plot show this and notice how they all converge on one point; that point is the phase transition. It is revealed by small sizes! To understand why, just think about how the curves are changing as we make the system bigger and bigger.
The red curves will still converge to the phase transition, but it takes larger system sizes for it to reveal itself. This is related to the property that at the phase transition there is no longer a sense of scale, but away from the transition, the vertical scale of clusters could be so large that our puny 60-by-60 grid cannot even begin to reveal it. So if we sit at say a likeability of 0.4 in the 60-by-60 grid, we can say that the vertical size of a typical cluster is most likely more than 60.
This "gravity mode" for our game show we may call "easy mode" since it requires less of the audience to like you, but the implications here are wide. This type of phase transition has been seen in many kinds of local dynamics where there is a preferred configuration or state. These called an absorbing state phase transitions, and they are a property of certain random dynamical systems. Gravity has provided the distinction here, but more generically, causality and time itself provide that direction, leading to dynamics that obey the same universality as directed percolation.
Trademark pending.
Usually, you’ll see 0.6447 quoted instead, but that’s just 1−0.3553, which counts open pipes instead of closed as we’re doing.
I should note that we have this number to much higher precision than the two decimal points presented here, see the Wikipedia entry where
This is a second-order or continuous phase transition. Most transitions in the water phase diagram are first-order transitions which still retain a scale.
To drive this point home: Even if we change the lattice, this power law will remain intact. Sometimes it shows up in completely different scenarios too, like in absorbing state phase transitions.
There’s a lot of joyful knife-work in my future. #bolognese #summersalad –cvj
The post Harvest appeared first on Asymptotia.
Further insipired by yesterday's post about binary fitting, I worked today on the treatment of nuisance parameters that have known distributions. These can be treated as noise sometimes. Let me explain:
If I had to cartoon inference (or measurement) in the face of nuisance parameters, I would say that frequentists profile (optimize) over the nuisances and Bayesians marginalize (integrate) over the nuisances. In general frequentists cannot integrate over anything, because there is no measure in any of the parameter spaces. But sometimes there is a measure. In particular, when there is a compact symmetry:
We know (or very strongly believe) that all possible orientations of a binary-star orbit are equally likely. In this model (or under this normal assumption) we have a distribution over two angles (theta and phi for that orbit pole, say); it is the distribution set by the compact group SO(2). Thus we can treat the orientation as a noise source with known distribution and integrate over it, just like we would any other noise source. So, in this case (and many cases like it) we can integrate (marginalize) even as frequentists. That is, there are frequentism-safe marginalizations possible in binary-star orbit fitting. This should drop the 12-parameter fits (for ESA Gaia data) down to 8-parameter, if I have done my math right.
On Friday, Kareem El-Badry (Caltech) gave a seminar about looking for (and finding!) stars in binary orbits around dark or much darker companions, like black holes, neutron stars, and white dwarfs. He showed results that involve ESA Gaia astrometry, where he noted that the Gaia Mission has no sensitivity to periods right at (or within an inverse mission-length frequency difference of) one-year periods (inverse year frequencies). After the talk I objected that these are not exactly degenerate; El-Badry said that the inferences blow up there.
I spent some time on the weekend thinking about this point, and I now understand it: There is a particular one-year orbit that a star can have (around a darker companion) such that the photocenter of the system makes a motion that is identical to the apparent parallax motion. Thus there is an exact degeneracy between the parallax and a certain one-year orbit.
Does that mean that we can't measure orbits at one year (or, for that matter, parallaxes)? No, it does not. After all, the parallax ellipse has a particular celestial (angular) shape and phase. But it might require some kind of reparameterization of orbits near one-year periods. I think I know how to do that. Should we find the missing binaries? (Oh and by the way, this degeneracy means that, in a strict frequentist sense, Gaia can't measure parallaxes at all without additional information.)
A common saying goes, you should never meet your heroes, because they’ll disappoint you. But you shouldn’t trust every common saying; some heroes impress you more, the better you know them. Ray Laflamme was such a hero.
I first heard of Ray in my undergraduate quantum-computation course. The instructor assigned two textbooks: the physics-centric "Schumacher and Westmoreland" and "Kaye, Laflamme, and Mosca," suited to computer scientists. Back then—in 2011—experimentalists were toiling over single quantum logic gates, implemented on pairs and trios of qubits. Some of today’s most advanced quantum-computing platforms, such as ultracold atoms, resembled the scrawnier of the horses at a racetrack. My class studied a stepping stone to those contenders: linear quantum optics (quantum light). Laflamme, as I knew him then, had helped design the implementation.
Imagine my awe upon meeting Ray the following year, as a master’s student at the Perimeter Institute for Theoretical Physics. He belonged to Perimeter’s faculty and served as a co-director of the nearby Institute for Quantum Computing (IQC). Ray was slim, had thinning hair of a color similar to mine, and wore rectangular glasses frames. He often wore a smile, too. I can hear his French-Canadian accent in my memory, but not without hearing him smile at the ends of most sentences.
My master’s program entailed a research project, which I wanted to center on quantum information theory, one of Ray’s specialties. He met with me and suggested a project, and I began reading relevant papers. I then decided to pursue research with another faculty member and a postdoc, eliminating my academic claim on Ray’s time. But he agreed to keep meeting with me. Heaven knows how he managed; institute directorships devour one’s schedule like ravens dining on a battlefield. Still, we talked approximately every other week.
My master’s program intimidated me, I confessed. It crammed graduate-level courses, which deserved a semester each, into weeks. My class raced through Quantum Field Theory I and Quantum Field Theory II—a year’s worth of material—in part of an autumn. General relativity, condensed matter, and statistical physics swept over us during the same season. I preferred to learn thoroughly, deeply, and using strategies I’d honed over two decades. But I didn’t have time, despite arriving at Perimeter’s library at 8:40 every morning and leaving around 9:30 PM.
In response, Ray confessed that his master’s program had intimidated him. Upon completing his undergraduate degree, Ray viewed himself as a nobody from nowhere. He chafed in the legendary, if idiosyncratically named, program he attended afterward: Part III of the Mathematical Tripos at the University of Cambridge. A Cambridge undergraduate can earn a master’s degree in three steps (tripos) at the Department of Applied Mathematics and Theoretical Physics. Other students, upon completing bachelor’s degrees elsewhere, undertake the third step to earn their master’s. Ray tackled this step, Part III.
He worked his rear off, delving more deeply into course material than lecturers did. Ray would labor over every premise in a theorem’s proof, including when nobody could explain the trickiest step to him.1 A friend and classmate helped him survive. The two studied together, as I studied with a few fellow Perimeter students; and Ray took walks with his friend on Sundays, as I planned lunches with other students on weekends.
Yet the program’s competitiveness appalled Ray. All students’ exam scores appeared on the same piece of paper, posted where everyone could read it. The department would retain the highest scorers in its PhD program; the other students would have to continue their studies elsewhere. Hearing about Ray’s program, I appreciated more than ever the collaboration characteristic of mine.
Ray addressed that trickiest proof step better than he’d feared, come springtime: his name appeared near the top of the exam list. Once he saw the grades, a faculty member notified him that his PhD advisor was waiting upstairs. Ray didn’t recall climbing those stairs, but he found Stephen Hawking at the top.
As one should expect of a Hawking student, Ray studied quantum gravity during his PhD. But by the time I met him, Ray had helped co-found quantum computation. He’d also extended his physics expertise as far from 1980s quantum gravity as one can, by becoming an experimentalist. The nobody from nowhere had earned his wings—then invented novel wings that nobody had dreamed of. But he descended from the heights every other week, to tell stories to a nobody of a master’s student.
Seven and a half years later, I advertised openings in the research group I was establishing in Maryland. A student emailed from the IQC, whose co-directorship Ray had relinquished in 2017. The student had seen me present a talk, it had inspired him to switch fields into quantum thermodynamics, and he asked me to co-supervise his PhD. His IQC supervisor had blessed the request: Ray Laflamme.
The student was Shayan Majidy, now a postdoc at Harvard. Co-supervising him with Ray Laflamme reminded me of cooking in the same kitchen as Julia Child. I still wonder how I, green behind the ears, landed such a gig. Shayan delighted in describing the difference between his supervisors’ advising styles. An energetic young researcher,2 I’d respond to emails as early as 6:00 AM. I’d press Shayan about literature he’d read, walk him through what he hadn’t grasped, and toss a paper draft back and forth with him multiple times per day. Ray, who’d mellowed during his career, mostly poured out support and warmth like hollandaise sauce.
Once, Shayan emailed Ray and me to ask if he could take a vacation. I responded first, as laconically as my PhD advisor would have: "Have fun!" Ray replied a few days later. He elaborated on his pleasure at Shayan’s plans and on how much Shayan deserved the break.
This June, an illness took Ray earlier than expected. We physicists lost an intellectual explorer, a co-founder of the quantum-computing community, and a scientist of my favorite type: a wonderful physicist who was a wonderful human being. Days after he passed, I was holed up in a New York hotel room, wincing over a web search. I was checking whether a quantum system satisfies certain tenets of quantum error correction, and we call those tenets the Knill–Laflamme conditions. Our community will keep checking the Knill–Laflamme conditions, keep studying quantum gates implementable with linear optics, and more. Part of Ray won’t leave us anytime soon—the way he wouldn’t leave a nobody of a master’s student who needed a conversation.
1For the record, some of the most rigorous researchers I know work in Cambridge’s Department of Applied Mathematics and Theoretical Physics today. I’ve even blogged about some.
2As I still am, thank you very much.
Well, I can now officially mention that I've been part of the filmmaking team (in a way) working hard to bring you an enjoyable and interesting Fantastic Four movie! I think it has been about two and a half years (?) since this all began. This was a nearly perfect model of how science consulting can work in film. I worked with everyone, wherever I was needed, with the director, writers, producers, director of photography, VFX teams, set design, and so on. They made me feel welcome and part of whatever creative team I was talking to, which was great. They were open to lots of ideas right from when they were starting out thinking about tone, story ideas, and so forth, right through to final (key) tweaks right at the end of the process as recently as mere weeks ago.
It began early on with with having great conversations Matt Shakman and his writing team about the fact that Reed Richards is first and foremost a curiosity-driven physicist (and so quite different from the engineer we have in Tony Stark that we see RdJ bring out so well), and how things like his dedication to his work (and his outlook on things that comes from such work) might play out in terms of family dynamic, personal relationships, etc., - Without it turning into the tedious cliches about scientists somehow not being able to navigate the world of human relationships. Obviously, I could speak to this as a physicist who works on precisely the things Reed works on, as well as a family man, and as well as someone who remembers that it's still all about telling a story. And there are so many stories to tell at that intersection... Anyway, I think these early conversations (as well as suggestions I made in many sets of notes along the way) helped inform (even if only a little bit? who knows?) what Pedro Pascal brought to the character. This aspect of the film is one of the things I'm most pleased about seeing up on screen.
Beyond that, you'll see lots of things I gave them that I'm also delighted to see made it to the film, in many scenes. This includes (but not limited to!): [...] Click to continue reading this post →
The post Fantastic Collaboration! appeared first on Asymptotia.
So imagine that you have a unique data set Y, and in that data set Y you measure a bunch of parameters θ by a bunch of different methods. Then you find, in your favorite analysis, your estimate of one particular parameter is way out of line: All of physics must be wrong! How do you figure out the significance of your result?
If you only ever have data Y, you can't answer this question very satisfactorily: You searched Y for an anomaly, and now you want to test the significance. That's why so many a posteriori anomaly results end up going away: That search probably tested way more hypotheses than you think it did, so any significances should be reduced accordingly.
The best approach is to use only part of your data (somehow) to search, and then use a found anomaly to propose a hypothesis test, and then test that test in the held-out or new data. But that often isn't possible, or it is already too late. But if you can do this, then there is usually a likelihood ratio that is decisive about the significance of the anomaly!
I discussed all these issues today with Kate Storey-Fisher (Stanford) and Abby Williams (Chicago) today, as we are trying to finish a paper on the anomalous amplitude of the kinematic dipole in quasar samples.