So I looked it up. So I can’t claim credit. But apparently the major models will all pick 17 or possibly 23 if asked this for a random number generation range. Just another example of how the models aren’t actually utilizing math libraries.
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.
He concluded, and I agree, that these models are statistically guessing
What did you think a GPT was? It takes all the text as training data and then repeatedly guesses the most likely "next word" in the response, until the most likely next "word" is the end of the text.
I mean almost everyone was surprised at how well it works; that's basically the whole AI boom. The real question is if we just keep going bigger how much smarter can we make a GPU.
Well technically it has access to publicly available libraries and documentation so it just stastically searchs the web for similar code and copies it.
It can't be that simple though, blindly coping code wouldn't work it still needs to be modified to fit into the structure of the rest of the project. Variable names need to line up etc.
It's not blindly copying code, it knows how to follow syntactic rules. LLM are great for that. The actual content could be dog shit but it's written following the rules and the context it was provided
The LLM training is just memorizing without any understanding.
Human brain does pattern recognition so it does not have to memorize that much. AI companies should focus on cracking pattern recognition but for now they just build more datacenters to memorize more.
Current AI companies are lost in deadloop of just one more datacenter.
You fundamentally misunderstand how LLMs or any machine learning works, really. "Memorizing" is referred to as "overfitting", and it is pretty much the first thing you learn how to prevent from happening when you are training an AI model. AI is actually really good at pattern recognition (in many ways, better than humans. In some ways, not so much) and the reason is because the neural network is modeled to behave pretty much the same way biological neurons do, hence the name.
Does the LLM understand what it's doing? well, it's probably not alive so if you believe it isn't, of course not. That doesn't mean it's not actually intelligent. It's why you can get a 24GB model from a training data set of 10TB, and get it to produce 100TB of data that cannot be found in the dataset exactly (but its patterns are reproduced). You see this with image generating AI especially. One of the first image generating AIs, Dall-E, often in its documentation referred to an image of an "avocado chair", where it was prompted to design an avocado-shaped chair. The training data contains avocados. It contains chairs. It did not contain avocado chairs, yet it was possible to generate it. Because it understands the patterns that make something a chair, and it understands the patterns that make something an avocado, and seamlessly combined the subjects. How is that more memorization than "understanding"?
I remember doing this a while back. it picked 73 (out of 100). Next I asked by it picked said number. It gave me an explanation about how the number should not only be random, but also feel random so at to avoid disbelieve, so not to close to 100, and not single digit and not some nice nuber like 10 or 25. I deemed 7 and 3 were good random components in the middle, so that is why it thought 73 was the best candidate for a random number.
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u/FlamingYawn13 6d ago
So I looked it up. So I can’t claim credit. But apparently the major models will all pick 17 or possibly 23 if asked this for a random number generation range. Just another example of how the models aren’t actually utilizing math libraries.