“There’s a perception of mobster behavior” from leading AI companies, one mathematician tells WIRED as OpenAI prepares to release more than 100 new solutions to unsolved problems.
I don’t have an English source, but on this trustworthy French public service radio scientific podcast, 2 mathematicians (from reputed organizations, including one Henri Poincaré Prize) are discussing the Navier-Stokes AI solution. https://www.radiofrance.fr/franceculture/podcasts/la-science-cqfd/ia-et-mathematiques-quand-la-solution-pose-probleme-5497880
They say that the results are legitimate and proven through mathematical proof software. It did build on recent progress by humans, but it would still have taken years for humans to get there, because AI could explore so many paths in a much shorter time than a few human specialists can.
They explain the help from AI is technically remarkable, and people who don’t recognize it are in denial. They think mathematics research without AI will not make any sense soon. There’s also some hope that it will allow focusing on new interesting problems that the current AI cannot solve yet. They also discuss the problem of AI being owned by foreign mega corporations and that’s a risk for public research produced for the common good.
I’m probably going to get downvoted for this unpopular opinion here, just know that I also hate the social and environmental impact of AI, but denying its effectiveness is clearly irrational now.
Ok but actual practical real world use case exists for this? So we know that in theory an imaginary thing that doesn’t exist could create a singularity event?
Honestly seems like it’s not useful but I’m not a mathematician so I don’t really know.
I am more skeptical. Even with Navier Stokes, I never heard anyone actually internalize the result and have trust in it. I just heard vague lean-dih waving.
Supposedly it’s 26000 subtheorems of goobledigook, feels untameable.
Mathematicians seemingly: Axioms are correct, we trust lean => Result correct.
As a physics, CS thinker: AI probably found a bug in our axioms. And/or usual self-referential math issue. And/or connected to Goedel’s incompleteness. Also I wonder why I even trust lean as much, havent even read its code. Most software works great until somebody starts poking at its limits.
How the robot proves these statement is by writing LEAN code that represents the math. If the lean compiles, the math was valid. If it doesn’t, it tries again.
It will do this forever until it finds a solution.
Goedel predicted this in 1933, btw. From his work on formal systems, he ascertained that since every mathematical statement is trivially true following from the rigorous application of the axioms of mathematics or entirely false, theorems could be checked and proven automatically.
These systems are able to prove statements at a scale beyond humans, because they have effectively infinite time to exhaustively explore applying every rule.
Unfortunately, this isn’t all that helpful for the various fields of math as a whole. Effectively they have a list of statements and a second column with ‘true’ or ‘false’, but the actual route taken by the models to get that answer is often incredibly indirect and therefore difficult to gain insight from and apply to new areas. The approach taken to produce an answer is often more valuable to the field than the answer itself.
The field moves forward when the people in it understand the new results and techniques sufficiently to begin asking new questions, simply giving the answer isn’t all that useful. The robots cannot (yet) do this. Someone has to, at a minimum, pose the question in formal mathematical language. There are an infinite number of true statements in mathematics ( trivial example 1=1, 2=2, …) asking the interesting questions is the important part. The field will still only advance as fast as the people in it are able to digest results.
Further, results in pure math do not generalize to the more applied fields that directly affect our ability to do things. Most engineering formulae are NOT rigorously true - they are weak approximations with envelopes of applicability. The same is true even in base physics. So while a robot can crank through ten million lines of lean to prove an obscure theorem, it cannot think about physics for a week and come up with a theory of quantum gravity.
Assume all that is true, AI can really do well in these fields, but there are some other fields it can’t do well in.
Now who is going to work on those fields? Who is going to take the risk of studying for it, practicing for years, being an expert in the field that takes maybe a decade, and the AI can suddenly solve your field too, and you don’t get anything.
Because the AI contribution was so huge was surely the reason openAI did not want to give any credit to the researches that worked on the foundation for that breakthrough.
I don’t have an English source, but on this trustworthy French public service radio scientific podcast, 2 mathematicians (from reputed organizations, including one Henri Poincaré Prize) are discussing the Navier-Stokes AI solution. https://www.radiofrance.fr/franceculture/podcasts/la-science-cqfd/ia-et-mathematiques-quand-la-solution-pose-probleme-5497880
They say that the results are legitimate and proven through mathematical proof software. It did build on recent progress by humans, but it would still have taken years for humans to get there, because AI could explore so many paths in a much shorter time than a few human specialists can.
They explain the help from AI is technically remarkable, and people who don’t recognize it are in denial. They think mathematics research without AI will not make any sense soon. There’s also some hope that it will allow focusing on new interesting problems that the current AI cannot solve yet. They also discuss the problem of AI being owned by foreign mega corporations and that’s a risk for public research produced for the common good.
I’m probably going to get downvoted for this unpopular opinion here, just know that I also hate the social and environmental impact of AI, but denying its effectiveness is clearly irrational now.
Ok but actual practical real world use case exists for this? So we know that in theory an imaginary thing that doesn’t exist could create a singularity event?
Honestly seems like it’s not useful but I’m not a mathematician so I don’t really know.
I am more skeptical. Even with Navier Stokes, I never heard anyone actually internalize the result and have trust in it. I just heard vague lean-dih waving.
Supposedly it’s 26000 subtheorems of goobledigook, feels untameable.
Mathematicians seemingly: Axioms are correct, we trust lean => Result correct.
As a physics, CS thinker: AI probably found a bug in our axioms. And/or usual self-referential math issue. And/or connected to Goedel’s incompleteness. Also I wonder why I even trust lean as much, havent even read its code. Most software works great until somebody starts poking at its limits.
How the robot proves these statement is by writing LEAN code that represents the math. If the lean compiles, the math was valid. If it doesn’t, it tries again.
It will do this forever until it finds a solution.
Goedel predicted this in 1933, btw. From his work on formal systems, he ascertained that since every mathematical statement is trivially true following from the rigorous application of the axioms of mathematics or entirely false, theorems could be checked and proven automatically.
These systems are able to prove statements at a scale beyond humans, because they have effectively infinite time to exhaustively explore applying every rule.
Unfortunately, this isn’t all that helpful for the various fields of math as a whole. Effectively they have a list of statements and a second column with ‘true’ or ‘false’, but the actual route taken by the models to get that answer is often incredibly indirect and therefore difficult to gain insight from and apply to new areas. The approach taken to produce an answer is often more valuable to the field than the answer itself.
The field moves forward when the people in it understand the new results and techniques sufficiently to begin asking new questions, simply giving the answer isn’t all that useful. The robots cannot (yet) do this. Someone has to, at a minimum, pose the question in formal mathematical language. There are an infinite number of true statements in mathematics ( trivial example 1=1, 2=2, …) asking the interesting questions is the important part. The field will still only advance as fast as the people in it are able to digest results.
Further, results in pure math do not generalize to the more applied fields that directly affect our ability to do things. Most engineering formulae are NOT rigorously true - they are weak approximations with envelopes of applicability. The same is true even in base physics. So while a robot can crank through ten million lines of lean to prove an obscure theorem, it cannot think about physics for a week and come up with a theory of quantum gravity.
Assume all that is true, AI can really do well in these fields, but there are some other fields it can’t do well in.
Now who is going to work on those fields? Who is going to take the risk of studying for it, practicing for years, being an expert in the field that takes maybe a decade, and the AI can suddenly solve your field too, and you don’t get anything.
Because the AI contribution was so huge was surely the reason openAI did not want to give any credit to the researches that worked on the foundation for that breakthrough.
My server contribution is huge too, hosting shit all the time. Given as much, I wonder if I can void the licenses of the software I use.
Depends. Are you an AI company?