Dispatches from the possibly last days of human relevance | Hacker News Reader

Dispatches from the possibly last days of human relevance

scottaaronson.blog

32 pointsgone3523 comments

It remains to be seen if LLMs would do any good in the "theory-building" heavy fields of math. They have certainly proven themselves in branches of math where the progress is verifiable, but fields like AG commonly have papers that do not concretely solve a problem but provide a new perspective/framework. This is iterated upon if other mathematicians find the construction rich and interesting enough, which eventually leads to breakthroughs.

LLMs have yet to show that they can meaningfully make such helpful abstractions. Not saying that it can't be done, but I wouldn't write such doomer posts just as yet.

This crystallised a take.

Unsolved Erdosh problems were touted as a cheap way to generate new perspectives. outcome has been slightly disappointing. overlooked frameworks were all that has been needed so far. not new ones. Could change as LLMs are pointed at other kinds of inexplicables

He's embedded in a social and professional world that has every incentive to believe the current state of AI progress is real and important and should be hyped to the stars. I am unsurprised to read such frothing soothsaying as a result.

> I feel like at this point, both the prophets of AI utopia like Ray Kurzweil, and of AI doom like Eliezer Yudkowsky, could be forgiven for asking: dude, will you listen to us YET?

What did Kurzweil or Yudkowsky predict that actually came to pass?

I assign this Scott no points for bringing up Penrose as a straw man. That’s a very old canard.

Luckily, humans will always remain relevant to humans.

Reports of our demise are greatly exaggerated.

Someone close to me is about to embark on a maths PhD. I'm curious about what advice people here would have for people in that position.

Learn a trade that involves using your hands.

At least until robotic dexterity catches up.

So don't bother with higher education at all?

Most academic math is still done with pencil and paper, so there is that.

The academic market in the US is very bad and in the EU only marginally better. China seems to prefer domestic talent now.

Outside of academia there are only a few niche industries still hiring. Mag7 is drying up. The semi-private research institutes want seniors with grants or customers in pocket, not fresh phds with no connections.

Probably only getting worse in the near term.

There are a few specific applications that are still good. Medical imaging seems okay for now. Advanced signal processing is still a viable route. Consumer robotics, possibly.

I think it depends what they’re hoping to get out of it.

I think they'll do okay considering there's another story in the front page about a university in California just now realizing after six years (without the requirement) that standardized math testing should be pre-requisite for...wait for it...STEM candidates.

Short-term: you better love it. Long-term: just focus on being one of the ones that ensures humans retain their ability to understand math. Do a YouTube channel on the side where you make math legible to the masses in a fun, engaging way. Use the cash from that to fund your research/genuine passions.

Whatever you do: please don't submit to the machine or throw away your genuine curiosity as a sacrifice on the Altar of Commerce.

I wouldn't particularly worry. Big picture: don't base your future on the fears of the present. AI is a tool for humans, so be curious about it and use it if it can help you. Otherwise, ignore the noise.

Here's a research mathematician replying to pretty much exactly this question, and I think it's worth reading: https://davidbessis.substack.com/p/letter-to-a-phd-student

It's also relevant for non-AI reasons. The upshot is: Do it if you want to. You're at an age where you're supposed to be mostly learning, but over time, start transitioning into whyever you did this (which doesn't have to mean staying in academia, and in fact can mean teaching or popularisation or consulting or...).

bwaaaaaa! ha ha ha bwaaaaa! wheeeeeeuew! is the title what happens from too much ketamine? or is the hype machine being tasked with streeeetching things out for one more quarter? bills, elections, push back,lack of relevance, that sort of stuff.

Different Aaronson

Soon we will all just be human cattle owner by billionaires who own all the technology used to keep us poor and indoors.

Oh, no - that's actually now.

the billionaires currently see the problem as how to properly read the ownership tag.

I think that's temporarily. The trend is to reduce the population, see Europe and China.

Aaronson has worked for OpenAI and probably has stock options. Gowers is funded by the "AI for math" fund by XTX markets.

The students who have been alarmed outside his office should take a course in marketing and journalistic ethics to assess the situation more rationally and figure out that the problem might sit in the office.

It is interesting by the way that Google does use Lean in a loop to refine proofs. This was called heresy by AI boosters earlier who said that Lean was never used in proofs.

> Aaronson has worked for OpenAI and probably has stock options.

IIRC he explicitly stated that he doesn't.

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