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Comment by geoduck14 | Hacker News Reader

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 math"

The fact that no one has done this is an indicator that "there are more important things to work on", and it is just a matter of time that someone connects the two together

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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.

Anyone can do higher level math, the problem is that math education is generally done by people who see math as a tool for computation, rather than a study of deep connections bordering on philosophy, and beautiful insights resembling poetry. I've been in arguments before where someone didn't believe me that the underpinnings of modern philosophy are essentially the same as math!

If the teachers don't love math, how can we expect students to?

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 and reinforcement to help sufficiently generalise an understanding of the underlying rules.

Generating solved math problems is trivial, like making AlphaZero play itself in chess. Sparse Data is not the problem. Refusing to use it is.

I don't think it's so much a refusal, as that it's not been a sufficient priority for anyone before. As the article points out there are now a few training sets which includes math problems, and models which do well on them. But the remaining problems seems to be with basics which humans tends to learn to do consistently with a lot of repetition, and it'd be interesting to see those datasets extended to the very simple.

> "convert a word problem into an equation"

Isn't this a huge step? It's not a minor detail remaining to be solved, but possibly the largest step!

What you describe is exactly what state of the art has done. They even lied and said it was "solving math problems" by calling numpy methods.

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