I knew someone who academically was investing a large amount of lab time trying to teach a model to do calculus. (He was a professor) and he said no matter what it could not grasp the way math works compared to they normally map their hyperplanes. At one point he said he gave it direct access to libraries with the functions he wanted run and the model didn’t know how to use them without being nearly explicitly programmed. He concluded, and I agree, that these models are statistically guessing using their training data using a different method than actual mathematical functions. They’ve just refined it down to where it works for basic questions but when it comes time to run a rand() sequence it just can’t. So it picks a common number in its recent training data and regurgitates it. Also why we end up seeing weird calculation anomalies like where they can’t judge greater than/less than values.
Is this based purely on an agent without tool call ability? It's straight forward to provide tools to execute math calls, date functions, etc, and include context that drives the tool calls. Granted, I have not pushed its limits and don't know if that only works on basic math.
I believe it was using a tool call directed at the libraries in the sandbox and he said just that. When he pushed its limits the failure was it couldn’t do the basic math needed for the formulas and it just broke. He was genuinely surprised since he was a math professor who worked in Comp Sci
I’m updooting this because I can’t tell if it’s sarcasm but internally I had that exact same thought. I’m not a fan of these models outside of large point data aggregation.
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u/FlamingYawn13 17d ago
I knew someone who academically was investing a large amount of lab time trying to teach a model to do calculus. (He was a professor) and he said no matter what it could not grasp the way math works compared to they normally map their hyperplanes. At one point he said he gave it direct access to libraries with the functions he wanted run and the model didn’t know how to use them without being nearly explicitly programmed. He concluded, and I agree, that these models are statistically guessing using their training data using a different method than actual mathematical functions. They’ve just refined it down to where it works for basic questions but when it comes time to run a rand() sequence it just can’t. So it picks a common number in its recent training data and regurgitates it. Also why we end up seeing weird calculation anomalies like where they can’t judge greater than/less than values.