nopinsight
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Comment · 1 month ago
"It is extremely sad that this didn't end up as an example of how the labs could cooperate/coordinate, because the stakes will be so much higher in the future." -- Sholto Douglas, an Anthropic researcher [1]
"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. <quote tweet [1] above>" -- 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
Comment · 3 months ago
I suspect an AI, possibly a successor to current LLMs, will achieve that by the early 2030s. It might help illuminate many mysteries in math and beyond for us all.
Comment · 4 months ago
As someone who's always loved synthesizing ideas and having lightbulb moments, I find the headline flattering. I wonder, though, if there's any more rigorous and general analysis (hah!) of the complexity of these two modes of thought.
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.
Comment · 4 months ago
Someone also input lots of pre-GPT texts to Pangram and they were all clear. The point is Kramnik’s claims are much weaker and not comparable.
Comment · 4 months ago
To my knowledge, Pangram has a very low false positive rate approaching zero. (False negative is another matter.) I’m not sure that’s what you want to use as the analogy here? (I don’t know much about this Kramnik situation.)
Comment · 4 months ago
Quite frustrating to see all these cynical, borderline-irrational comments on HN. Maybe I should do what pg and other ex-HN contributors have done--avoid taking part in the discussions here.
The level of discourse here has dropped so much.
At least I should stop replying to people hiding under throwaway accounts.
Comment · 4 months ago
Demis' bar is high and he stated clearly multiple times: AGI should be capable of inventing truly novel things. Examples he gave included the Theory of General Relativity and the game of Go.
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.
Comment · 4 months ago
No one rational ever said software engineers were going to be replaced in 6 months. Some people said AI will automate 90% of coding in 6 months and they were not far off (and accurate in some contexts, e.g. startups).
Comment · 4 months ago
Ten years ago, most of the skeptics in these threads would have said the success of AlphaFold 3 was impossible.
Comment · 4 months ago
"Ten years from now, I think we will realize that we were standing in the foothills of the singularity now... I believe that we're only a few years away from [AGI], maybe 2030 plus or minus a year...
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.
Comment · 4 months ago
"Ten years from now, I think we will realize that we were standing in the foothills of the singularity now... I believe that we're only a few years away from [AGI], maybe 2030 plus or minus a year...
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
Working on:
agentic systems, natural language understanding, machine learning
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