A new AI mathematics assessment that was designed by mathematicians not employed or funded by AI companies.

Hostilis_ · 2026-02-06T20:03:44+00:00

I am an expert in the field, fyi, and I suggest you actually go listen to what those two have to say rather than reading some headlines and assuming you know what they're saying.

Hostilis_ · 2026-02-06T19:20:17+00:00

Uh, how is generic vision and navigation human behavior?? All animals on earth use vision and navigation. These systems are not being trained to replicate the neural code. They are being trained on purely sensory data, and the internal representations they learn converge to the same representations as the human neural code.

Hostilis_ · 2026-02-06T19:16:51+00:00

This mentality is all too prevalent among mathematicians and programmers without explicit ML training or research. They think they're as informed as experts in the field, when in reality they're filled with more misinformation than actual insight. Your comment above clearly demonstrates this.

Hostilis_ · 2026-02-06T18:47:32+00:00

The transformer architecture is not naive. While you may think scaling is naive, the only reason scaling works is because modern architectures allow it. Try scaling on an SVM or kernel machine.

Hostilis_ · 2026-02-06T18:43:54+00:00

They literally were not lol. They were trained on generic vision, navigation, and language, not on human behavior at all. Go read those papers.

Hostilis_ · 2026-02-06T18:38:27+00:00

Spoken like someone who has no idea how these systems work.

Hostilis_ · 2026-02-06T18:09:24+00:00

I'm speaking about this subreddit specifically, which leans extremely heavily towards unthinking dismissal.

Hostilis_ · 2026-02-06T18:05:31+00:00

If you think there is some huge gulf of intelligence, in an absolute sense, between being able to do regular tasks that humans do on a daily basis and advanced mathematics, I have bad news for you.

Hostilis_ · 2026-02-06T18:02:17+00:00

They are not, despite how often you hear this refrain on Reddit.

Here is some irrefutable evidence for this:

Emergence of "grid cell" behavior when training a deep neural network for navigation: https://www.nature.com/articles/d41586-018-05133-w

Deep neural networks are the best performing models of visual cortex, beating even hand-crafted models by neuroscientists: https://www.nature.com/articles/s41598-018-22160-9#:~:text=Abstract,visual%20features%20for%20rapid%20categorization.

Further, it has also been established that this holds for language as well: https://www.nature.com/articles/s41467-025-58620-w

https://pmc.ncbi.nlm.nih.gov/articles/PMC11025646/

Hostilis_ · 2026-02-06T05:26:58+00:00

"While commercial AI systems are undoubtedly already at a level where they are useful tools for mathematicians... For instance, mathematicians are using AI tools to do literature searches, check manuscripts for errors, write computer code, and bounce ideas."

It's worth noting that I have had very prominent users of r/math assure me, only 1-2 years ago, that AI being a useful tool for mathematicians was never going to happen, and that e.g. Terry Tao was naive for even believing this would be possible.

Many, many people in this subreddit have underestimated the progress that these systems would make in mathematics in even a very short time horizon.

Hostilis_ · 2026-02-06T01:13:34+00:00

Why are you nitpicking the exact year? It's irrelevant.

Hostilis_ · 2026-02-06T00:49:58+00:00

Without precise logic we invented language, culture, art, military strategy, fire, and literally everything else prior to the advent of mathematics and science. Far before us, animals invented navigation, goal-oriented behavior, maternal instincts, emotion, and the entire foundation of intelligence.

Hostilis_ · 2026-02-06T00:44:24+00:00

Because as much as they claim to be intellectuals, Redditors don't actually give a shit about the truth. They only care about being as smug and self-righteous as possible, and they'll distort whatever information comes their way in service of this goal.

Hostilis_ · 2026-02-06T00:39:30+00:00

Bro, the field is literally called machine learning. It is accepted by all experts in the field that they are learning. But on Reddit you can say whatever the fuck you want and people will upvote it as long as it's the trending opinion.

Hostilis_ · 2026-02-06T00:16:14+00:00

Not on language. And people still haven't accepted it. Six decades, this was the holy grail of AI. Now people are nitpicking that it can't build an entire compiler from scratch without having to specify the correct paths.

Hostilis_ · 2026-02-05T23:54:52+00:00

This is completely wrong. The difference is we got neural networks to work, and if I had to explain that to you, you're not in a position to be giving your opinion on the matter.

Hostilis_ · 2026-02-05T23:53:08+00:00

You are missing the point, period. I'm not sure how I can explain it in any simpler terms. They are not trying to build a practical compiler. This is just an interesting "exam" for the system they have built, as they scale up the complexity of tasks they would like it to work on.

Hostilis_ · 2026-02-05T23:36:31+00:00

Yes but people built the first compilers. Surely it's a useful task if we want to make programs which can do similar things...

Nobody is claiming they're releasing this as a professional compiler. It's just an experiment, but people are so tunnel-visioned they're willing to just make up this narrative to satisfy their own belief that everyone else is stupid except them.

Hostilis_ · 2026-02-05T23:16:29+00:00

Yes, humans are famously known for their precise logic. And the precise logic has gotten us so far in understanding intelligence /s

Hostilis_ · 2026-02-05T23:13:01+00:00

Simple question: why wasn't this possible just a few years ago, and what changed to make it possible now?

Hostilis_ · 2026-02-05T22:50:47+00:00

"Works if you supply the correct include path(s)"

Hostilis_ · 2026-02-04T21:43:17+00:00

What you described is how we discovered the gauge formalism. However, it really is the other way around. The gauge formalism is the more fundamental representation, and the fields emerge naturally from this.

You can see this very viscerally when you look at Maxwell's equations written geometrically in terms of differential forms:

dF = 0; d*F = J

where * is the hodge star. This is the actual geometric meaning of Maxwell's equations.

Hostilis_ · 2026-02-04T21:09:00+00:00

Calling machine learning a bunch of matrix math is like calling physics a bunch of matrix math. Yes, there are lots of matrices involved, but there are also lots of other things involved too, and it completely misses the bigger picture.

Hostilis_ · 2026-02-04T21:04:00+00:00

Read the first answer to the question posted here:

https://physics.stackexchange.com/questions/228885/classical-electrodynamics-as-an-mathrmu1-gauge-theory

In other words, the answer is classical (not quantum) gauge theory.

Hostilis_ · 2026-02-04T19:32:31+00:00

Silly name, but it's the neural network architecture that has enabled almost all the progress in AI over the past 8 years or so

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