61–70 of 177 posts
What happens to science when the most talented kids won't be able to compete in ~2 years tops? Would our civilization reach plateau or start downward trajectory as there will be no incentive to torture oneself to become the best in the world? Will it all fade away like chess once computers started beating grandmasters?
It was interesting. One of the reviewers noted that one of the generated proofs didn’t seem that elegant in his opinion. Given enough compute, I wonder how much this would be improved by having it find as many solutions as possible within the allotted time and proposing the simplest one.
The human proof style instead chunks the parts of a solution into human-meaningful "lemmas" (helper theorems) and builds bodies of theory into well-defined and widely used abstractions like complex numbers or derivatives, with a corpus of accepted results.
Some human proofs of theorems also have a bit of this flavor of inscrutable lists of highly technical steps, especially the first time something is proven. Over time, the most important theorems are often recast in terms of a more suitable grounding theory, in which they can be proven with a few obvious statements or sometimes a clever trick or two.
If I read their paper right, this is legit work (much more legit than DeepMind's AI math paper last month falsely advertised as solving an open math research problem) but it's still pretty striking how far away the structure of it is from the usual idea of automated reasoning/intelligence. A transformer is trained on millions of elementary geometry theorems and used as brute search for a proof, which because of the e…
>A transformer is trained on millions of elementary geometry theorems and used as brute search for a proof, which because of the elementary geometry context has both a necessarily elementary structure and can be easily symbolically judged as true or false. When the brute search fails, an extra geometric construction is randomly added (like adding a midpoint of a certain line) to see if brute search using that extra r…
Google DeepMind keeps publishing these things while having missed the AI-for-the-people train, that I'm getting more and more the feeling that they have such a huge arsenal of AI-tech piling up in their secret rooms, as if they're already simulating entire AI-based societies. As if they're stuck in a loop of "let's improve this a bit more until it is even better", while AGI is already a solved problem for them. Or th…
With all the bickering about copyright, could something similar be used for coding llms? Would kill the ip issues, at least for coding
If I read their paper right, this is legit work (much more legit than DeepMind's AI math paper last month falsely advertised as solving an open math research problem) but it's still pretty striking how far away the structure of it is from the usual idea of automated reasoning/intelligence. A transformer is trained on millions of elementary geometry theorems and used as brute search for a proof, which because of the e…
How so? Reasoning is fundamentally a search problem.
The process you described is exactly the process humans use: i.e. make a guess about what's useful, try to work out details mechanically. If get stuck, make another guess, etc. So the process is like searching through a tree.
People figured out this process back in 1955 (and made a working prototype which can prove theorems): https://en.wikipedia.org/wiki/Logic_Theorist but it all hinges on using good 'heuristics'. Neural networks are relevant here as they can extract heuristics from data.
What do you think is "the usual idea of automated reasoning"? Some magic device which can solve any problem using a single linear pass?
If I read their paper right, this is legit work (much more legit than DeepMind's AI math paper last month falsely advertised as solving an open math research problem) but it's still pretty striking how far away the structure of it is from the usual idea of automated reasoning/intelligence. A transformer is trained on millions of elementary geometry theorems and used as brute search for a proof, which because of the e…
> it's still pretty striking how far away the structure of it is from the usual idea of automated reasoning/intelligence. How so? Reasoning is fundamentally a search problem. The process you described is exactly the process humans use: i.e. make a guess about what's useful, try to work out details mechanically. If get stuck, make another guess, etc. So the process is like searching through a tree. People figured out…
>To train AlphaGeometry's language model, the researchers had to create their own training data to compensate for the scarcity of existing geometric data. They generated nearly half a billion random geometric diagrams and fed them to the symbolic engine. This engine analyzed each diagram and produced statements about their properties. These statements were organized into 100 million synthetic proofs to train the lang…