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Sam Altman says GPT-5 is superhuman at knowledge, pattern recognition, and recall -- but still struggles with long-term thinking it can now solve Olympiad-level math problems that take 90 minutes, but proving a new Math theorem, which takes 1,000 hours? "we're not close"

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345,910 views • 10 months ago •via X (Twitter)

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📁 Demis Hassabis says that one of the main reasons we are not yet at AGI is the lack of consistency. He explains that current systems can solve International Math Olympiad level problems but still fail at basic logic tasks or even chess. They are uneven intelligences, exceptional in some areas and surprisingly weak in others, which falls short of true AGI.
1:01

📁 Demis Hassabis says that one of the main reasons we are not yet at AGI is the lack of consistency. He explains that current systems can solve International Math Olympiad level problems but still fail at basic logic tasks or even chess. They are uneven intelligences, exceptional in some areas and surprisingly weak in others, which falls short of true AGI.

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Sam Altman says GPT-5 is not finished yet, but he expects it to be a "significant leap forward" and better at a wider variety of tasks than GPT-4
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Sam Altman says GPT-5 is not finished yet, but he expects it to be a "significant leap forward" and better at a wider variety of tasks than GPT-4

Tsarathustra

111,151 views • 2 years ago

Terence Tao thinks AI is already very good at using existing, well-understood math techniques to solve problems. An important question is how many open problems in math could be solved this way, without developing any new ideas. An extreme case of a proof like this is the four-color theorem, which was proved by checking a thousand examples one-by-one. If there are lots of problems that are vulnerable to just mechanically applying existing techniques, AI could start to produce new math results incredibly rapidly. But these proofs might not help advance our civilization’s knowledge the way math has in the past.
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Terence Tao thinks AI is already very good at using existing, well-understood math techniques to solve problems. An important question is how many open problems in math could be solved this way, without developing any new ideas. An extreme case of a proof like this is the four-color theorem, which was proved by checking a thousand examples one-by-one. If there are lots of problems that are vulnerable to just mechanically applying existing techniques, AI could start to produce new math results incredibly rapidly. But these proofs might not help advance our civilization’s knowledge the way math has in the past.

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64,547 views • 2 months ago

Sam Altman on GPT 6 • The GDPv eval will shift some of the way we do post training • overall we are keeping the same strategy going into GPT 6 Sam Altman on AGI: Sam Altman about AGI: "we are finally in the moment": "As the closer we get to it, the fuzzier the Agi concept becomes. But the one thing that I care the most about and, to my great surprise, we're finally in the moment; where it's starting to happen is, when it can do novel discovery, when It can expand the total human knowledge base.“ I have seen some examples from credible professors, that novel math problems have been solved. Such as GPT 5 Pro reportedly solving Yu Tsumura’s 554th problem, an IMO level group theory challenge that an August preprint said no LLM had solved, so the new claim is being actively contested. Sébastien Bubeck also said GPT 5 Pro produced a new proof that tightened a bound in smooth convex optimization.
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Sam Altman on GPT 6 • The GDPv eval will shift some of the way we do post training • overall we are keeping the same strategy going into GPT 6 Sam Altman on AGI: Sam Altman about AGI: "we are finally in the moment": "As the closer we get to it, the fuzzier the Agi concept becomes. But the one thing that I care the most about and, to my great surprise, we're finally in the moment; where it's starting to happen is, when it can do novel discovery, when It can expand the total human knowledge base.“ I have seen some examples from credible professors, that novel math problems have been solved. Such as GPT 5 Pro reportedly solving Yu Tsumura’s 554th problem, an IMO level group theory challenge that an August preprint said no LLM had solved, so the new claim is being actively contested. Sébastien Bubeck also said GPT 5 Pro produced a new proof that tightened a bound in smooth convex optimization.

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221,717 views • 8 months ago

Sam Altman says AI reasoning is still at the GPT-2 stage but the improvement curve is steep and the new o1 model represents a new paradigm of AI development which will enable rapid progress in capabilities
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Sam Altman says AI reasoning is still at the GPT-2 stage but the improvement curve is steep and the new o1 model represents a new paradigm of AI development which will enable rapid progress in capabilities

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419,003 views • 1 year ago

Sam Altman on GPT 6: “There will be a chance that it will be a GPT 3-4 style leap” in terms of science problems, where with GPT 5 it has these tiny glimmers and “GPT 6 it can really do it”
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Sam Altman on GPT 6: “There will be a chance that it will be a GPT 3-4 style leap” in terms of science problems, where with GPT 5 it has these tiny glimmers and “GPT 6 it can really do it”

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42,684 views • 7 months ago

Sam Altman says the perfect AI is “a very tiny model with superhuman reasoning, 1 trillion tokens of context, and access to every tool you can imagine.” It doesn't need to contain the knowledge - just the ability to think, search, simulate, and solve anything.
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Sam Altman says the perfect AI is “a very tiny model with superhuman reasoning, 1 trillion tokens of context, and access to every tool you can imagine.” It doesn't need to contain the knowledge - just the ability to think, search, simulate, and solve anything.

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775,717 views • 1 year ago

At a keynote conversation during Snowflake Summit in San Francisco, OpenAI CEO Sam Altman predicted that by next year, AI systems will begin helping businesses solve complex problems and even uncover new knowledge
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At a keynote conversation during Snowflake Summit in San Francisco, OpenAI CEO Sam Altman predicted that by next year, AI systems will begin helping businesses solve complex problems and even uncover new knowledge

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37,586 views • 1 year ago

📁 Sam Altman says that even though building software is now dramatically easier, building a real business is still just as hard. AI removes technical barriers, but it does not solve the scarcest resource, human attention. Even in a world of radical abundance, distribution and focus remain the bottleneck. The edge will not be speed, but creativity and building things that truly matter.
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📁 Sam Altman says that even though building software is now dramatically easier, building a real business is still just as hard. AI removes technical barriers, but it does not solve the scarcest resource, human attention. Even in a world of radical abundance, distribution and focus remain the bottleneck. The edge will not be speed, but creativity and building things that truly matter.

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📁 Sam Altman says that a truly intelligent AI will filter information, understand your context and manage your life with clarity and calm. He explains that with trust and long term use, it can create a peaceful and distraction free experience. He adds that this level of digital serenity is impossible with pre AI technology.
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33,285 views • 6 months ago

Bianca STILL has a problem with bayley but is happy to dance with iyo... The math is not mathing
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Sam Altman says the day is approaching when we can ask an AI model to solve all of physics and it can actually do that
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Sam Altman says the day is approaching when we can ask an AI model to solve all of physics and it can actually do that

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Sam Altman says superintelligence may not unlock all science just by thinking harder about existing data Big breakthroughs will still need new instruments, experiments, and machines. "the real world is slow and messy" Progress will come -- but building the tools will take time
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Demis Hassabis explains that tasks like coding and math are easier to automate because they offer clear, verifiable outputs. However, scientific discovery like creating new hypotheses or theories is much harder for AI. It’s not just about solving known problems, but inventing new questions, which requires a higher level of creativity. He believes we're still missing key capabilities needed for that level of AI. While progress is rapid, reaching true scientific innovation without human input remains uncertain.
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