21–30 of 201 posts
Language models can generate a Python function that does the math perfectly. I bet you would get better results if you tweaked the prompt to say "Generate a Python program that solves X math problem" and then just ran the resulting Python script. It does not need to be AGI to be useful.
Earlier quoted context omitted.
From my (limited) experience with the advanced ML models, they can "do basic math", but they make amateur mistakes with basic things - which indicates they don't actually know addition , but they are good at looking at patterns in existing language. I would assume that state-of-the-art ML models could "convert a word problem into an equation", then feed that equation into a 30 year-old graphing calculator to "do the…
This seems so much like humans that it makes me think lots of people are learning math with an ML-like approach instead of… whatever the heck people like engineers and mathematicians are doing.
I'm not at all surprised they don't do well at maths, because while there are maths texts online, I doubt there is enough material to give these models the same experience of repetition and reinforcement to help sufficiently generalise an understanding of the underlying rules.
It's a language model; why would we expect it do math or try to somehow shoehorn math into the model? Do the language centers of our brain do math?
If something approximating AGI is going to happen, it's going to be a lot of models tied together with an executive function to recognize and send things to the area that's good at working with them.
Are there any general purpose models that are good at learning math? I mainly know basic feed-forward neural nets, but I don't think they do well outside their training region. Math, of course, has an infinite training region.
From my (limited) experience with the advanced ML models, they can "do basic math", but they make amateur mistakes with basic things - which indicates they don't actually know addition , but they are good at looking at patterns in existing language. I would assume that state-of-the-art ML models could "convert a word problem into an equation", then feed that equation into a 30 year-old graphing calculator to "do the…
Earlier quoted context omitted.
This seems so much like humans that it makes me think lots of people are learning math with an ML-like approach instead of… whatever the heck people like engineers and mathematicians are doing.
I wonder how these language models would do if we tried to teach them maths the way schools do: Feed them explanations first, then endless sequences of toy problems, see which they got wrong and feed them corrected examples back in. I'm not at all surprised they don't do well at maths, because while there are maths texts online, I doubt there is enough material to give these models the same experience of repetition a…
Also Excel is terrible at encoding MP3s. It's a language model; why would we expect it do math or try to somehow shoehorn math into the model? Do the language centers of our brain do math? If something approximating AGI is going to happen, it's going to be a lot of models tied together with an executive function to recognize and send things to the area that's good at working with them.
Math is not irrelevant—and I’m sure it’s a solvable problem with language models.
It's all just passable garbled nonesense that the reader (goes to lengths) to interept based on their prior knowledge, which is not expressed in the syntax of what these systems output.
In the case of mathematics, we're far less willing to "BS away" the interpretive failures. But if we were equally demanding, likewise, all prose generated by these systems isnt AI "getting" anything either.
Pass a film reel thru' a shredder and an art student would still call it a film. Pass math thru' and a mathematician wont. This says more about our ability and inclination to make sense out of nonesense when in apparent communicative situations (since, when speaking to a person, this actually improves our mutual understanding).
So, how much of AI is just hacking people's cognitive failures: (1) people's willingness to attribute intention; (2) people's willingness to impart sense "at all costs" to apparent communication; and (3) "hopeium".
Also Excel is terrible at encoding MP3s. It's a language model; why would we expect it do math or try to somehow shoehorn math into the model? Do the language centers of our brain do math? If something approximating AGI is going to happen, it's going to be a lot of models tied together with an executive function to recognize and send things to the area that's good at working with them.
Also Excel is terrible at encoding MP3s. It's a language model; why would we expect it do math or try to somehow shoehorn math into the model? Do the language centers of our brain do math? If something approximating AGI is going to happen, it's going to be a lot of models tied together with an executive function to recognize and send things to the area that's good at working with them.
The point isn't the math. The point is that, in math and similar disciplines, it's harder to get away with producing mostly undirected gibberish that happens to have some imputed meaning. The point is "use language to do something where it's easy to verify correctness and generating infinite amounts of synthetic data is trivial"
If a language model can't even do high school algebra, then I have a lot less confidence that it will ever be useful for customer service applications or any other number of potential applications outside of propaganda, advertising, and spam.