I do my Elvis impersonations in private, so no one will ever know.

This gets to some extent into the issue of what we mean by "novel" and admittedly that has some subjectivity to it. One take on what is going on here is that this should be showing us how much that we thought of as creative was just rearranging things. There's a legitimate argument that the AI isn't doing anything creative or clever here, and that if someone says, "Well, by that metric the vast majority of human mathematicians aren't doing anything creative or clever," and while I see that as a pretty hard response, one response is to just say "Yeah, go bite that bullet."

Here? None.

I'm not sure where the line is there. For at least some of these, part of it seems to be the AI importing pieces of techniques used in other areas of math. In general, no human discovery is completely novel. Newton's work relied on prior work of Galileo, Oresme and many others. Einstein's work relied on ideas of hundreds before-hand. At the same time, there's people whose work really does look like it is fundamentally different in many ways than what went before, and both of those are examples. In math, Peter Scholze's perfectoids for example clearly build on existing ideas, but there's a lot of stuff that looks deeply new. How to draw the line here isn't always clear. For what it is worth, Harald Helfgott, who is a much smarter and more knowledgeable mathematician than I am thinks that of what he's looked at it seems like the AI is still producing things in what amounts to almost the "convex hull" of known math or close to it. But his idea of what that looks like may be much bigger than my idea.

His prediction wasn't silly at the time. From a simple mathematical point of view, given the rate of growth at the time and the understanding of the time of the resources available and the carrying capacity of the world, it was a simple calculation to make. In fact at that time many millions of people (probably more like billions) were facing starvation in the coming decades. Particularly on India and other countries in that area. Many experts thought it was a forgon conclusion, though tragic. The so called green revolution started about this time and ultimate more than doubled global food production and saved billions of lives from starvation. This was just starting about the time of this prediction.

And those corrections require atomic clocks.

You think LLMs would write "Slashdot doesn't have table support, so it's hard to do side-by-side well, but:"? You think LLMs like writing sentences that end in conjunctions that express incomplete thoughts? Do you think LLMs write "high frequences", "naively pairs", call H.265 X.265 (I was thinking of what you refer to it as in ffmpeg), imbalanced parens after "lacks motion vectors",etc?

Don't get me wrong, I am a LLM user, I absolutely do use it sometimes for search, fact checking things I'm writing, spelling/grammar checks (clearly not that time, lol), etc. But I write my own posts.

No, not at all. The gender pay gap is in the training data thousands of times, from online comments, studies, news articles, etc. Since AI is a dumb machine replicating what it has in its training data, it replicates that.

I'm pretty sure you can find religious believe, superstitions of all kinds, human fallacies and whatever in AI if you provide it with the right setup.

Slashdot doesn't have table support, so it's hard to do side-by-side well, but:

JPEG XL advantages:

Licensing:

JPEG XL: Fully royalty-free (Open source / Apache 2.0)
HEIC: Proprietary patent minefield (MPEG LA, Advance, Velos; royalties apply)

Legacy JPEG Migration:

JPEG XL: Lossless, reversible bitstream transcoding (~20% smaller); fast enough for real-time web servers
HEIC: Lossy re-encode only (generational quality loss; cannot reconstruct original JPEG)

Fine Detail & Textures

JPEG XL: Retains sharp text, fine lines, subtle textures, and film grain
HEIC: Video-derived coding (HEVC / X.265). Tends to smooth out high frequences and smudge grain.

Software Encoding Speed

JPEG XL: Extremely fast; highly parallelized SIMD architecture
HEIC: Exceptionally slow and computationally expensive in software

Software Decoding Speed

JPEG XL: Fast, lightweight multi-threaded CPU decoding
HEIC: Heavy CPU overhead (without using hardware acceleration)

Progressive Rendering:

JPEG XL: True progressive decode; smart saliency algorithm to focus bandwidth on critical details first.
HEIC: None; full file must be received and decoded before display

Lossless Compression
JPEG XL: Dedicated modular mode; vastly outperforms PNG and WebP
HEIC: Ill-suited; you basically have to try to do lossless compression with an inherently-lossy format

Bit Depth & HDR

JPEG XL: Native HDR; up to 32-bit floating point per channel
HEIC: Typically capped at 10-bit or 12-bit integer

Max Dimensions & Scaling

JPEG XL: Up to 1B x 1B; efficient viewport/crop loading without full decoding
HEIC: v5.2, 4096x2160; v6.2: 8192x4320; a tiling hack allows up to 16384 x 16384

Channels & Color Spaces:

JPEG XL: Arbitrary color spaces (hyperspectral); unlimited extra channels (alpha, depth, thermal, masks, CMYK, etc)
HEIC: Rigid container; limited auxiliary channels and standard video color spaces

HEIC advantages:

Rollout / acceleration:

JPEG XL: no dedicated hardware acceleration (thankfully, it's not as important because it's so much more efficient). Software adoption still rolling out.
HEIC: Hardware silicon (ASICs). Default capture format on modern iOS/Android; native capture in Sony, Canon, and Nikon cameras

Video:

JPEG XL: Supports animations (GIF/APNG replacement), with some optimizations** (it's not just a series of stills), but lacks the full set of optimizations that a proper video codec has.
HEIC: Container naively pairs full HEVC video tracks and audio with video.

** - JPEG XL can store up to 4 reference frames in a buffer, with multiple blending modes from the references (add, replace, multiply, etc); has subframe bounding boxes ("dirty rectangles") for when only part of a frame changes; invisible frames; modular deltas (differences between frames); etc. However, it lacks motion vectors (e.g. detecting a feature drifting across a scene and simply having to encode "move it" (followed by any needed deltas). So it's great for "GIFs", but if you wanted to encode a full movie, it'd be significantly larger than e.g. HEVC.

Look, I use HEIC, but JPEG-XL is just better.

* Royalty-free
* Lossless transcoding of legacy JPEGs to immediately reduce their sizes by ~20% (fast enough that you can have webservers do it in realtime)
* Better fine detail / high frequency data representation for the same bitrate. HEIC was designed for video and simply is not as good at stills.
* Faster decoding
* MUCH faster compression
* True progressive decoding - and not old-school blocky progressive decoding, but using a smart algorithm that restores coarse detail (particularly focusing on parts of the image that the eyes immediately focus on) first, then progressively adding in finer detail that the eyes take more time to notice later.
* Vastly better lossless compression
* Better colour depth and native HDR (up to 32 bit per channel)
* Up to 1B x 1B pixel images (again, useful for scientific and engineering applications), with efficient loading (don't have to load and decode the whole 1B x 1B image at full resolution to view it)
* Arbitrary colour spaces - you can even use it for scientific hyperspectral imaging. You can include e.g. alpha, depth maps, thermal data, selection masks, etc etc.

It's just a better format for stills. It's annoying that it's taken this long to get support to take off (because it takes ages for most people to update their browsers), but I'm really glad that it's starting to.

No one saw quasi-RH coming or many of the others here. So that is unlikely. And the last batch of 10 problems they released a few weeks ago which were impressive enough were all novel results. There have been issues with them using techniques or ideas that should be better credited to where some of those approaches are coming from. But that's the sort of thing that often happens at a preprint stage. When I referee a paper, if this happens, you just request they cite the relevant papers and move on. This isn't at all stealing things.

This is really not accurate. There's Fields Medal level work here with quasi-RH for example. And for Hadwiger-Nelson it took an approach that doesn't seem to be in in the literature. These things really are doing novel math. I've personally seen this is in a bunch of situations. Here's a personal example, much smaller than anything like these problems. I have a recent preprint with a student here https://arxiv.org/abs/2609.36068 (about 90% of this was done by her. She's very good.) But part of this came from when I ran a version of Proposition 13 in that paper through Claude just to clean up the draft of that bit before I sent it to her. Claude informed me (essentially unprompted) that the argument had a whole in it (in addition to pointing out grammar errors, unbalanced parentheses and some other embarrassing minor mistakes). I then fixed the hole, and gave it back to Claude. Claude thought for a few minutes, and then informed me that I had *not* fixed the hole, and the reason was that my version of the proposition was missing an entire infinite family which it constructed. The literature on this problem is small, and I'm very familiar with it. The family it produced is straightforward (see Prop 8), but definitely was not in the existing literature. So yes, these systems really can do novel math, and can even do so in an essentially minimally prompted fashion, in this case, explaining to the meat mathematician why his proof is wrong. My student and I then generalized Claude's family to Theorem 9 in that paper, but the AI definitely had an impact.

At some level this was actually not a good thing. I'm trying to encourage students to *not* rely on the AI for research so they develop basic research skills. So if the AI had not volunteered the family I wouldn't have had to tell her that the AI had discovered it, but honestly required noting it. So I had a conflict between intellectual honesty and being a good role model.

Mathematician here specializing in number theory with a side-order of graph theory. I've only had time to start looking at two of them. First, is the Hadwiger-Nelson/chromatic number of the plane https://en.wikipedia.org/wiki/Hadwiger%E2%80%93Nelson_problem proof. As far as I'm aware (and I could be wrong) the technique it uses here is not in the literature, so this is a genuine construction of a new technique. Second is the Erdos Egyptian fraction bound, and for that one it looks like the techniques are about what I'd expect, but I'm definitely still digesting both of these.

For the quasi-Riemann Hypothesis the striking thing is almost the opposite direction. It looks like the AI used standard complex analytic techniques to get the result. But many mathematicians have often thought for years that those techniques would not likely be strong enough to get this sort of result.

I know less about the Unique Games Conjecture https://en.wikipedia.org/wiki/Unique_games_conjecture but having talked with some of the people there it looks like it took a somewhat standard set of ideas and then combined them with multiple just weird stuff and sort of took a hard left turn at one point for no clear reason and ended up at the result.

It is also worth noting that while many of these have Lean code confirming their correctness (quasi-RH for example) others do not. The three I mentioned above have all also been looked at at this point by human mathematicians who have not found issues; that's likely true for others, but those three I'm aware at least of people doing so. Not all the claims have Lean code though; a bit under half. One of the non-formalized problems also has been withdrawn due to what essentially amounts to a sign error https://github.com/openai/math/blob/main/preprints/Algebraicity-of-Weil-classes-on-split-abelian-eightfolds-September-18-2026/paper.pdf. It is likely others will be withdrawn also by the end, but I'd be surprised if more than 10 are. And even if everything single one without Lean code turned out to be wrong (which seems very unlikely), this would still be an amazing set of math. I commented elsewhere that if a human mathematician had made the quasi-RH result they'd be likely a shoe-in for the Fields Medal, and another mathematician replied saying "delete likely."

Now a more editorial comment: There are legitimate concerns about what this is doing to mathematics. This sort of thing is very cool. But it also is part of a trend that may make it much harder to train young mathematicians or get them to exist at all. If the AIs are limited in how genuinely novel their ideas can be, then we may end up in a situation where we get a massive burst in math over the next few years, and then math stalls out because we don't have enough good really high caliber mathematicians (Not the mathematicians like me, but people like Serre, Tao, Scholze, Clausen,etc.) to come up with deeply new ideas that the AIs can build on.

This. No good game was ever created from a single prompt. Not to an AI, not a human development team. Every single person writing those damn "look which game my AI built from a single prompt and $x in tokens!" postings is a newbie and almost certainly has never actually shipped a single game.

Game development can be summed up as endless iterations. Every system you build, every visual you create, every sound, text, button, movement, weapon, skill, powerup needs polishing, refinement and changes. It is never, never good enough the first time.

No it is not, but it IS a very successful platform for a specific subclass of games. If I were in it for the money, I could probably AI refactor some of my earliest games and publish them on Roblox and make a quick buck. If your thing is the kind of games you played on your C64 back in 1793, then Roblox got you covered. Well, if you ignore the scams and money-grabbing.

*sigh* I wish Unity would actually complete some of their dozens half-finished sub-systems instead of constantly chasing the latest trend. It's become really, really annoying. The engine used to be really good. These days, it falls apart if you look at it the wrong way, and at least from the responses to my bug reports it's clear that fixing issues is way down on the agenda.

Maybe this is their attempt to regain grounds in the indie game market - the very market they pushed away by focussing on AAA requirements for years and ignoring the single devs and small teams. If so, I can already tell them that it won't work. We collectively laugh about all the "look what my AI built for me from a single prompt" postings on reddit, where people proudly show off some AI slop that would get them last place at a game jam.

Their AI efforts so far: Tried to build and sell their own AI tools, to a collective yawn from the audience. Built a barely-working MCP server, then deprecated it before it was even finished in order to push a CLI interface to the editor instead, which only works if it's run as the same user as the editor on the same machine. And completely ignores every single advise on workflows and good development practices you can think of.

Unity, I love your engine, I despise your management. Please return to when you were actually good and stop chasing butterflies.

The need for them came from relativity, but the atomic clocks needed to be able to handle such fine corrections are based on the hyperfine transition of 133Cs. You're not getting very far on atomic clocks without particle physics