The simple short-term solution is just don't feed all your work into the IP theft machine?
The friction in math right now is you have the fast moving melodic bits racing faster than the base can understand always or keep up with. So you start to need AI for both the left brain part that is executing and the right brain part that is synthesizing. That's just the friction right now. You reduce the friction by using AI to help with understanding AI and getting back to a more normal rhythm of scouting out (with AI) and (what is still developing more and more) synthesizing with AI.
The mathematics itself will only benefit from the discovery of cross links from the human created literature. Think about these LLM as infinitely patient and very long attention experts in what was already done.
So on one side, for a few years at least, it will become normal to publish tens of articles. The article inflation is what scares the present system.
On the other side, after a while, when all the existing mathematical corpus will be mined and most of buried connections will be explored, we shall have a much more better foundation for future mathematics.
It is very naive or misleading to think about mathematics as if it were chess or go.
In few year these (future) tools will be in standard use.
1. People will publish so much frontier mathematics, humans won't be able to understand it all
2. Frontier mathematics will all be kept secret
Fortunately, these seem like they can't both happen at once.
The fact that we don't know why is a clue pointing at some area of math that we haven't discovered yet. The hope is always that it will uncover some hidden fertile valley that will lead to lots of new discoveries. But the proof of the conjecture itself, without understanding, is really not that valuable.
My point is that even if AI discovers many new truths, there's still plenty to do for the mathematical community, in dissecting it and building useful abstractions to understand it, abstractions that can be leveraged for further exploration and uncovering new questions.
That already happened before AI.
> 2. Frontier mathematics will all be kept secret
Gauss kept lots of frontier mathematics in his drawer. In the 20th centuries government spy agencies developed public key cryptography long before that was known to the public. To give just two examples.
It's not the end of the world.
And what do you care, if someone keeps frontier mathematics a secret, if you can ask DeepSeek version 10 in 2030 to prove the Riemann hypothesis for you?
About 4-6 years earlier, depending what you want to count, although the inventors may also have been less clear on its importance or applications compared to the later public inventors.
https://en.wikipedia.org/wiki/Public-key_cryptography#Classi...
I guess that's kind of a long time in computer technology terms.
1. People will publish AI generated frontier math making it difficult to identify frontier mathematicians. 2. Trained frontier mathematicians will become scarce
Which makes total sense unlike the "too much frontier math" nonsense.
The proof must be as succinct as possible so that it's reviewable. It's ok for the same LLM to publish repeatedly, but it's not Ok to drop a bulk 600kb Lean proof.
The proof must build on other works that are similarly bite sized and published before (so that it's incrementally reviewed)
The proof must be published as soon as fully formed, and not in bulk like current LLMs proofs.
It took me 10 years to develop the system, and I intend to ride it out as much as I can. By act 3 of my life sure I will publish the math. But in this economy, I would be a damn fool to publish my equations just to let the LLMs write better music.
Universities can host the llms just like they hosted any other computer lab or web service on campus. This is what universities are supposed to be doing. Universities should be involved in open model research and should offer models that are not datamined.
And I really doubt university ITs would be able to run these, at least in an up to date and stable manner. High performance computing is typically run totally separately from everyday user systems, and tend to be extremely painful to use (hello slurm!).
Maybe we should start crediting and publishing our intermediate results and attempts.
Humans would gain credit for providing a part of the solution to tough problems, LLMs would profit from the resulting data flywheel.
> Maybe we should start crediting and publishing our intermediate results and attempts.
All mathematical results are "intermediate results". What would it mean for something to be a final result?
Just, without the aliens, space travel, or mech-suits.
Got half the other downsides though!
Your joke overestimates the intelligence of real life humans.
There are sources claiming AI is Abominable Intelligence and something completely different from the Machine Spirit. But it seems to me that this is how it feels to be a tech priest.
This now includes the proving of theorems.
He went on to write "Operations of thought are cavalry charges in a battle - they are limited in number, they require fresh horses, and must only be made at decisive moments."
Mechanization also demolished that institution, which had also survived for thousands of years.
That is the main "crisis" of mathematics.
In my opinion there has never been a better time to be a mathematitian, and there has never been a better time to be a software builder.
But there has never been a worst time to have the need to prove your economic value as a mathematitian or software developer alone. Because "understanding" is not something you can prove in one afternoon, its something that you prove with a life.
Prior to LLMs there was some minimal effort required to snipe someone and possibly your reputation was attached otherwise there would have been no point in publishing to begin with.
Now anyone with a few dollars can do it, many who don't have a reputation to worry about.
It's the same problem as YouTube AI slop, AI-generated music, and everything else. Don't you dare tweet or blog about a video idea or hum a few bars from a song - within an hour 27 people will have posted AI slop rip-offs. There's something uniquely depressing about being beaten to the punch by a thief that doesn't deliver the same soul-crushing impact as having someone copy you after the fact.
See this amusing reddit post. https://www.reddit.com/r/IndieDev/comments/1wfl64l/game_went...
I'm sure there's a named concept for this: the idea of the game is its selling point, and has to be revealed to consumers to market itself. But the market has low friction (via Steam distribution, and by copies being cheap to make w/ LLMs), so you can't reliably establish your product before masses take over, especially if you don't have reputation built up from previous games.
You have to be bringing in something else for this not to happen, trade secrets not present to the consumer being one such factor.
It's a screenshot of a list of games on Steam. Half of them are called "Needle in a Haystack" and the rest are variations. The title says the game went viral a week ago and the original creator had to put "(original)" in the title.
Take a good look at the textbooks that teach mathematics and tell me I am wrong.
Part of the difficulty is artificial.
Whether intentional or not, it's there. I have yet to see a textbook that really taught learners as they deserve to be taught. Some are good in some aspects, but a perfect textbook does not exist.
"Here are some theorems and some proofs and here are some ideas left as exercise to the student". smh.
3B1Br is great but only for a 'hobbyist'. There's no systematic progression to teach from A to Z. Also, you can't learn mathematics just by watching videos.
What we need to do is make mathematics about understanding, rather than churning out results. People should be rewarded for reaching an ability to explain mathematics without the aid of computers, to teach people for the sake of their learning.
I do think AI also needs to be eradicated because of its destructive properties, but I think that at the same time it also is a manifestation of the sickness in our society to go after the wrong incentives that are detrimental in the long run.
This is all but guaranteed now.
Mathematicians/Scientists/Researchers need to stop sharing freely with "AI Companies" and have explicit clauses in place in their publications about not using their research without their explicit consent.
There should be a clear legal distinction between using research data for AI model-training vs. another researcher using it.
Come up with a legal framework, establish procedures for sharing and using others work and have a single scientific body in charge of enforcing it.
The frontier LLM vendors do sell enterprise licenses which contractually guarantee that your prompts won't be used for training. (Maybe they'll secretly violate the agreement but in principle it's legally enforceable.) Scholars and universities who care about credit and attribution will either have to purchase those licenses or run their own private open-weight LLM instances.
I hate this but I don't think there's any going back now that LLMs exist.
Though maybe if there's a flurry of math-optimized agents coming up, like there are small coding agents, those might be feasible to host personally.
At the same time, mathematicians should be using sophisticated, specialized LLM tools in much more sophisticated ways than lay people. There should be no way lay people can compete. There are new tools to master and if you use your slide rule, you won't keep up. It's a chance for mathematics productivity to boom.
With apologies to Baudelaire: The greatest trick exploitative powers ever played was convincing the people that it was impossible to imagine anything else.
Sometimes blinkering people so that they never ask the question "why is this being done to me" or "why is justice not possible" is much easier than finding an answer that will get them to go away.
Analogously with software development, it's always been good practice to write things down, but since the start of this year it's become dramatically more important for everyday work.
> Maybe mathematicians are smart enough to never ever buy such piece of shit on higher principle
They are not. Mathematicians are as human as most of the rest of us here.
I wonder how life was while the Butlerian Jihad was raging through the universe.
Jihadis were fighting opponents with thinking machines. Did they win because the machines were not capable enough?
The idea that this genie is going back in the bottle is, imo, very wishful thinking. The thinking machines can rip off your software ideas completely, in days.
"Eradicated" is a pretty strong word. It's also completely unfeasible.
Wrong incentives can maybe be adjusted for. Shutting down the pursuit of one of the most astonishing things we've ever created is simply not going to happen short of a cataclysm, and I'm not a big fan of those.
I wonder how it revealed it... oh, right, because AI labs rushed to churn out new results for marketing purposes. Mathematicians didn't make them do it. So I'm not sure it's really an indictment of the field.
In fact, how often does mathematics feature in university press releases, how often do mathematicians compete for multi-million grants, how many of them are interviewed on TV?... This discipline is less afflicted by weird incentives than most other fields. It's mostly just a small clique of nerds publishing abstract "open source" work.
Muddling through with markets, democracy and welfare.
Global life expectancy was 34 in 1913. Today it's 70.
https://ourworldindata.org/life-expectancy
Global GDP was less than 1Trn until about 1800. In 1950 it was 11.7Trn . Today it's 157 Trn. In a life time it's gone up by more than 10 fold.
https://ourworldindata.org/grapher/global-gdp-over-the-long-...
People are living longer, richer lives all over the world.
The world's largest country grew at 7.8% last year.
https://www.reuters.com/world/india/why-indias-strong-gdp-gr...
Things are getting better. Look at the data, work at what you do and ignore the doom sayers.
I know lot of folks on Hacker News hates patents but that was the whole point of them: the patent holder got exclusive rights to the invention for 20 years. But the trade off was that specific details about the invention, how it worked and how it's made were entered openly for anyone to look up. The patent holder would basically benefit it for the majority of his or her life but then after that it was permanently part of the public domain.
AFAIK it was the reason why the patent system was invented, otherwise the best option jealous guarding of trade secrets that ideally (for the secret holder) died with them.
The primary goal should be democratic management of the means of production.
In a sane society it should be easy to argue against that claim, but looking around in the US at least, it turns out you’re correct: that doesn’t seem to be the norm under US-style capitalism.
Luckily we have other countries to provide an example here.
Some methods are more realistic than others, but I don't see a requirement for any outlandish ideas.
https://warhammer40k.fandom.com/wiki/Tech-Priest:
> Despite the never-ending thirst for knowledge of all branches of the order, most Tech-priests of the Adeptus Mechanicus have lost the ability to innovate or carry out basic scientific research.
> No longer the master of its creations, the Cult Mechanicus is enslaved to the past. It maintains the glories of yesteryear with rite, dogma and edict instead of true discernment and comprehension.
> For instance, even the theoretically simple process of activating a vehicle's engine is preceded by the application of ritual oils, the burning of sacred resins and the chanting of long and complex hymns.
— Mitch Hedberg
Though it appears to have gained popularity from 2017-2021, it dipped in '22 and that's as far as ngram goes.
Two people can keep a secret if one of them is dead.
https://en.wikipedia.org/wiki/Information_wants_to_be_free#H...
Automated spreadsheets and PPTs won't get people fired.
Which groups are those?
If you are good at it, end result is that minority can keep majority of the seats and power. So, as there are two parties, the groups are "likely to voted republicans" and "likely to vote democrats".
According to you, there is no discourse about politicians using gerrymandering to give themselves safer seats.
" The National Security Agency is the largest employer of mathematicians in the U.S. "
https://www.scientificamerican.com/article/mathematicians-an...
Tell me you have no clue about quant finance without telling me you have no clue about quant finance.
You probably think it’s abstract topology and Ito calculus, when in reality it’s linear regression and PCA. If you’re lucky, maybe you’ll see a sigmoid function.
If someone in finance is using LLMs, it’s for marketing purposes (recruiting new grads or impressing investors). Jane Street and DE Shaw are notorious for this.
Nope.
Let me guess: you listened to The Man Who Solved the Market and think RenTech uses/used unpublished frontier mathematics to predict the market.
There are plenty of firms with both higher annual % returns (consistently over 15-20 years) and higher absolute $ returns than RenTech/Medallion fund, especially post-2010. And they’re not using “frontier mathematics”.
A lot of alpha comes from how well you know the VP at the exchange. Not math. Not tech. But good ol’ politics.
Don’t like a market participant? Tell the exchange to issue violations to that participant. Get them banned for a few months.
Fat fingered a trade with wrong price or quantity? The exchange can and does undo a trade after the fact. All in the name of “orderly markets”. Similar to when governments use “national security” as an excuse.
There are very-very few cases where you need truly advanced math in finance. The problems that need solving are generally vastly more pedestrian.
You need to deal with terrible data quality, terrible formats, noise in every aspect of your work, disruptions, lack of standards, inability to generate new data (and repeat experiments), conflicting and often opaque incentives, technical problems ranging from shitty APIs to having to squeeze nanoseconds out of your network stack, etc.
These are all difficult problems, but they are crucially not frontier math problems (by and large).
No, more like, some differential geometry and gauge theory. The topics that Jim Simons worked on.
risk of prescient theory is more important than dismissive ablation.
edit: to wit, facebook google and anything else not e2e.
it's not like the ai is homomorphic.
But you can copyright the artistic aspects of your game, like you can have the copyright to MONOPOLY and trademark the logo.
If you could patent gameplay there would be only 1 pong, which is a pretty basic idea. There are probably hundreds of pongs though.
"government intelligence agencies" ARE 'AI'.