Comments URL: https://news.ycombinator.com/item?id=50000570

Points: 3

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"Strong agree. I know that there is rivalry between the labs but it's important that we learn to work together given what's coming. " -- Noam Brown, an OpenAI researcher [2]

We all should heed the implied warnings of these top researchers about what's coming. The world is far from ready and everyone who can should pitch in.

[1] https://x.com/_sholtodouglas/status/2097224624274911368 [2] https://x.com/polynoamial/status/2097225279366414541

Comments URL: https://news.ycombinator.com/item?id=48924083

Points: 2

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EDIT: Fable 5 turned up some relevant references:

[1] Richardson, D. (1968). "Some Undecidable Problems Involving Elementary Functions of a Real Variable." Journal of Symbolic Logic, 33(4). — Differentiation is a simple recursive algorithm; deciding integrability in elementary terms is undecidable in general.

[2] Risch, R. H. (1969). "The Problem of Integration in Finite Terms." Transactions of the American Mathematical Society, 139. — The partial recovery: a (semi-)decision procedure for a restricted class.

[3] Guilford, J. P. (1967). The Nature of Human Intelligence. McGraw-Hill. — The divergent vs. convergent thinking distinction, the classic psychometric cousin of synthesis vs. analysis.

[4] Anderson, L. W., & Krathwohl, D. R. (Eds.) (2001). A Taxonomy for Learning, Teaching, and Assessing (revision of Bloom's taxonomy). Longman. — Moved "Create" (synthesis) to the top of the cognitive hierarchy, above "Analyze."

[5] Aaronson, S. (2011). "Why Philosophers Should Care About Computational Complexity." arXiv:1108.1791. — Argues complexity asymmetries (verification vs. generation, P vs. NP) bear directly on questions about cognition.

Comments URL: https://news.ycombinator.com/item?id=48640897

Points: 2

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The level of discourse here has dropped so much.

At least I should stop replying to people hiding under throwaway accounts.

Just a taste of what's to come: https://openai.com/index/model-disproves-discrete-geometry-c...

Anthropic's latest model also solved this 80-year-old problem that eluded many expert mathematicians, in a different way, according to one of its employees.

Remember we still have 4 years until 2030.

I think [AGI] will be an enormous transformative technology, it's going to effectively be a new human era...

We can feel this year, I would say, even though I've been working towards this for 30 years, I think this year with the way the agents are working and tool use, it started to become really useful, still early days of it, but genuinely useful in people's workflows...

And it's not any one thing, it's several different technologies, several use cases, several things that I thought were maybe a bit further out, turned out to be now, that are coming together that make me feel that in aggregate.

I think society needs to hear that because we don't have long to prepare for what that means." -- Demi Hassabis, CEO of Google DeepMind & Nobel laureate

Source with interview clip: https://x.com/deredleritt3r/status/2062223035940139253

EDIT: Ten years ago, most of the skeptics in these threads would have said the success of AlphaFold 3 was impossible.

I think [AGI] will be an enormous transformative technology, it's going to effectively be a new human era...

We can feel this year, I would say, even though I've been working towards this for 30 years, I think this year with the way the agents are working and tool use, it started to become really useful, still early days of it, but genuinely useful in people's workflows...

And it's not any one thing, it's several different technologies, several use cases, several things that I thought were maybe a bit further out, turned out to be now, that are coming together that make me feel that in aggregate.

I think society needs to hear that because we don't have long to prepare for what that means." -- Demi Hassabis, CEO of Google DeepMind & Nobel laureate

https://x.com/deredleritt3r/status/2062223035940139253

Should we start preparing for something that could be world-changing in the next 10-20 years?

For software, however, a rapid turn is often a possibility. See: AI for coding over the last 3-4 years.

AI autocomplete --> AI coding assistants --> vibe coding --> agent orchestration

Coders can now accomplish work that used to take a week or longer in a couple of hours, with the right tools and skills.

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A key issue the article implies is that the real world increasingly runs on software.

He did say a few months later in an interview in India that AI will eventually take over most of SWE tasks.

—-

My statement on startups is largely about automating coding by SWEs. My startup also uses AI to automate part of technical specifications and code review but I am not sure how widespread that is.

Moreover, there's no reason to believe the progress of LLMs, which couldn't reliably solve high-school math problems just 3–4 years ago, will stop anytime soon.

You might want to track the progress of these models on the CritPt benchmark, which is built on *unpublished, research-level* physics problems:

https://critpt.com/

Frontier models are still nowhere near solving it, but progress has been rapid.

* o3 (high) <1.5 years ago was at 1.4%

* GPT 5.4 (xhigh), 23.4%

* GPT-5.5 (xhigh), 27.1%

* GPT-5.5 Pro (xhigh) 30.6%.

https://artificialanalysis.ai/evaluations/critpt.

Economics will likely sort itself out through optimizations, which are also highly plausible.