I do wonder what this implies. The boring answers are obvious – our conjectures suck, AI stronk, more mafs to be done etc. But when I see mathematical truths that are strictly useless in our impoverished, tiny physics, I am compelled to ask: is there another?

Announcing three major scientific discoveries, one each in mathematics (harmonic analysis), computer science (complexity theory), and physics (quantum field theory). AI starting to help us improve our understanding of the universe! openai.com/index/building-sc…

We tested leading video world models on their ability to predict simple physics and found that most fail within a few frames. Today we're releasing MC-PhysBench, a new kind of benchmark that measures how long a model's output stays physically valid. 15+ models. 9 scenes. 6,000+ videos scored. 7 failure checks.

I said "auto-regressive LLMs, in and of themselves, will not lead human-level AI" That statement is still totally true. First, the reasoning abilities of current AI systems are based non-auto-regressive search (which is what I have always advocated for). But AFAICT, they do it in token space, which is limited and inefficient. I have claimed that human-like reasoning must be a search in continuous representation space. It looks like the industry is moving towards that. Second, the self-improvement methods, as currently practiced, only work for domains where the quality of outputs can be scored without human intervention, such as mathematics, code, and scenarios that can be simulated accurately. Not anything else. Humans and animals learn new skills way more efficiently than current RL methods. Third, the multimodal capabilities of current AI assistants generally use separately-trained encoders (that are not LLMs). This is also what I've been advocating. Except that I think the best way to do this is with JEPA trained with self-supervised learning. The research community is clearly moving towards that (3000 papers on JEPA in just 4 years). Fourth, if LLMs were a path to human-level AI, we would have domestic robots and Level-4 or Level-5 self-driving cars for consumers by now. And we don't. We certainly don't have cars that can learn to drive in 20 hours or practice like any teenager. We're still missing something pretty huge to claim human-level intelligence (let alone superhuman). Sure, we now have computer systems that are impressive, very useful, and whose performance is superhuman in an increasing number of domains (coding being one of them). But that's true of the entire history of progress in computer technology. Lastly, there is a basic confusion about what intelligence actually is. It is not the mere accumulation and regurgitation of existing declarative knowledge (which is essentially what LLMs do). As Jean Piaget famously said, "intelligence is not what you know, it is what you do when you don't know." It is your ability to solve new problem without any prior training, to act in previously-unknown scenarios, and to adapt very quickly to new situations with minimal training. We're still far from that.

in about a month we’ll all transition to being like oh yeah it’s obvious that models can communicate by manipulating the cosmological constant, it’s just a matter of doing proper network security