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3 Kommentare
„Axiom’s approach involves combining large language models with a proprietary AI system called AxiomProver that is trained to reason through math problems to reach solutions that are provably correct.
„Even as someone who’s been watching the evolution of AI math tools closely for years, and working with them myself, I find this pretty astounding,” says Scott Kominers, a professor at Harvard Business School who is familiar with Fel’s conjecture as well as with Axiom’s technology. “It’s not just that AxiomProver managed to solve a problem like this fully automated, and instantly verified, which on its own is amazing, but also the elegance and beauty of the math it produced.“
> The techniques being developed by Axiom may prove useful outside the world of advanced math. For example, the same approaches could be used to develop software that is more resilient to certain kinds of cybersecurity attacks. This would involve using AI to verify that code is provably reliable and trustworthy.
The proofs offered here are pretty impressive, ngl. The nice thing about math is that it’s provable – once you’ve got _an_ answer you can verify if it’s correct.
But this paragraph is fantasy. Once you exit the pure world of math, and enter the messy world of… the rest of the world, you stop being able to verify your outputs. This is why you see AI being so good at winning competitions against college math students: it’s the area where you can have a clean scoring function.
The thing is AI could solve insane problems (physics, nuclear fusion, space travel) to change the course of humanity. Instead people are focused on it replacing jobs to improve profits, create garbage memes and fake porn. This is why corporations cannot be the main driver of AI for profit.