YHK - yet another hacker news clone

I just had a chance to flinch first.

Also, it's impossible to really know how you're going to react to something until it happens to you. When ChatGPT came out, I already thought "in the long run they're going to automate almost everything humans love to do". Despite having 4 years to brood about it, I did not really know what it would feel like until it happened to me directly.

> Today, the mathematicians are weeping, because the machine smashed their tower first. But tomorrow, it may be the physicists, the economists, the illustrators, or the writers who weep over their fallen blocks.

Isn't it extraordinary that these models still can't write well? I mean, they write passably well, but they all have a highly characteristic style that you can spot from a mile away. These language models can crack the toughest open problems in math, but they haven't yet cracked human-like writing, especially long-form writing, without exposing themselves. This is very very odd, IMO.

Either these mathematicians thought it would never come for their jobs, because all of these other jobs were simpler or they've been living under a rock.

Either way, I don't feel for them any more than I do for all of humanity.

How do you know this?

Still disgusted?

The bean counters laying off software engineers ought to take notes from the frontier labs' own hiring practices. They're still hiring lots of humans for business and engineering roles even when they would be better positioned than anyone to automate away those jobs with their world-class models (https://openai.com/careers/search/).

Neither sits right with me. Can't we just all collectively acknowledge that we have to grapple with this new piece of technology that either is already or at least seems to be going to be hurtful to a lot of individuals?

Instead of looking at the in comparison limited group of people currently pushing this technology and asking ourselves whether we should be on board with their doing, we look to one another and are happy that someone else is now suffering just the same as we are. What a sad state of affairs.

I don't personally grieve for the likes of Strogatz or Tao, it seems likely both of them will be fine however this pans out. But many more mathematicians have been toiling away in precarity and obscurity and without tenure for years and years. And of course many mathematicians went into private sector roles as software engineers, actuaries, analysts, ML engineers and similar, and have been displaced there.

Visible blog posts written by famous mathematicians closely aligned with the news and hype cycles getting frontpaged on HN doesn't mean that mathematicians are disproportionately whiney, or that software engineers aren't writing about it or ending up on the front page

This is said by a lot of CEOs trying to make layoffs look good, but I haven’t seen anything that suggests that it’s true.

> Yet I see far more of these blogs about the plight of mathematicians, not enough about the plight of software engineers.

Really?!? We’ve been living through 3 years of blog posts, LinkedIn hot takes, and podcasts talking about the plight of software engineers in the era of AI. There isn’t a week that goes by without an HN post about it.

I can feel empathy for the practitioners in a field that may have been profoundly changed, the same way I can feel empathy toward software engineers who are struggling with the changes in their own field.

In the end, we’re all trying to figure out where this leads. And how this will affect our lives and our work.

Sure, there are people who overstate things. But that isn't an excuse to keep your head in the sand.

How will it pan out, no one really knows. The leading frontier labs are continuing to hire aggressively. But who knows for how long or for long or near-term goals.

The "moderation by less", as well as the guidelines, are both farcical in nature. They've given way to ruination of an otherwise decent tech discussion forum (or news aggregator, or YC propaganda machine or whatever you want to call it).

If you want actual discussion around things, lobsters, awful or even 4chan are better than HN.

But many people perceive this very differently, see link below. And perception is reality after all.

https://news.ycombinator.com/item?id=50093180

And this is only the beginning.

But from my perspective proofs have never been the most interesting part of maths. What really interests me is the intuition that leads to new applications, and to new questions. Newton, for example, gets more credit for calculus than Leibniz because he's the one who demonstrated how darned useful it is. Erdos is arguably more famous for asking interesting questions than for answering them.

I still don't see LLMs seriously contributing to that facet of mathematics any time soon. Every so often I ask one to suggest a good binomial confidence interval for a particular application, and it still tends to just suggest one of the subpar options that everyone learns in a first year stats course.

Yes, outsiders (investors, journalists) are given a false impression while insiders (software engineers) are supposed to give up. Double win for the agitprop machine.

Is it like Chess and Go? There are still plenty of people playing for fun, but the machines are better at it than we are.

Jokes aside, its like working for a company, individually you can lift mountains and someone else will take credit. When you use LLMs for a single bit, all credit goes to LLMs lol

https://en.wikipedia.org/wiki/IBM_and_World_War_II

the above, a "sorting" machine is the primary usage of hyperscalers currently, they're just trying to find new uses to boot for extra money/legitimation.

the browser industry the phone industry the CAs JavaScript

it's all paid for by those wanting to sort humanity into whatever bucket the can fear monger the peasantry into for aweek or however long it takes the classification system to sort out.

gentrification works, but we chose forcible displacement now we have to live with the forever consequences.

would you give guns and bombs to the displaced native americans who taste blood over their beads and trinkets?

we've been inside the event horizon of the singularity for years, the tax bill will only get worse unless we recognize it for the tick that it is.

The participant is not an LLM, the participant is a company. The company learns. It adapts. It has a trillion dollars of skin in the game.

Seeing AI being lauded for solving problems like this makes me feel... uncomfortable.

We are feeding all works of the giants that we have all stood on into a huge vat, in the hope to generate something meaningful.

If we can't understand the output, internalize it, or build from the time and experience that a human would have gained from solving the same problem, it feels like a dead end.

When all the problems have been "solved" by AI, what's next? If humans no longer struggle to solve the problems that the previous generation have left behind, how do we progress?

At worst its time is past, but even that is unlikely.

If it was about understanding interesting proofs that some AI came up with, and extracting their essence, I don't think the situation would be so bad.

I see the much larger problem in the now existing career prospects of non-tenured researchers in mathematics. Everybody know how precarious the career prospects in academia and how precarious often the non-tenured positions in academia already are. With the new AI breakthroughs in math, these have in my opinion even worsened a lot.

UPDATE: For a related point, see also pixl97's post: https://news.ycombinator.com/item?id=50104145

> You are a helpless onlooker. Before you a channel through which humans have accessed the ineffable and sacred for thousands of years is being sealed for eternity.

https://kirwinhampshire.substack.com/p/the-dark-night-of-mat...

Yes, both of these + an infinite token/compute budget and SOTA+ models. It's not like some hobbyist out in the woods with a GPU running Qwen 27B can do this.

Lots of mathematics entails recognizing that a tool/techniques that works in one area can be adapted to a completely different problem. For a human to do this requires having a robust mental model of the technique and the problem itself, and probably having just the "right" mental model to see how they fit together. And we are talking about extremely hard/sophisticated techniques and extremely hard/sophisticated problems, so the ability to deeply appreciate either can require years of study. And on top of that, even if the mathematician has sufficient understanding to develop the intuition that the technique might work, they will need profound fortitude, skill, and patience to see the solution through.

Unlike human mathematicians, who will develop a deep understanding of only a relative handful of techniques and problems in their lifetime, LLMs have ALL written techniques, problems, solutions, etc stored in their internal representations. So when they ably match a problem to a technique, we can imagine the operation as a supercharged autocomplete, or even an extremely proficient search engine. Their stroke of genius is really a consequence of capacity, moreso than cleverness.

heck, we have the resources to that now but we don't do it.

It is mostly impossible to figure out what we find when we solve these problems. But occasionally they'll lead to real world efficiency in algorithms we use and it changes the world.