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ChatGPT In Trouble: OpenAI may go bankrupt by 2024, AI bot costs company $700,000 every day
(www.firstpost.com)
this post was submitted on 13 Aug 2023
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That's not true. These models aren't just regurgitating text that they were trained on. They learn the patterns and concepts in that text, and they're able to use those to infer things that weren't explicitly present in the training data.
I read recently about some researchers who were experimenting with ChatGPT's ability to do basic arithmetic. It's not great at it, but it's definitely figured out some techniques that allow it to answer math problems that were not in its training set. It gets them wrong sometimes, but it's like a human doing math in its head rather than a calculator using rigorous algorithms so that's to be expected.
they learn statistical correlations between words. given the last 5000 (or however large the context is) words, and absolutely no other information besides that, what is the most likely word to appear next? It's a glorified order 5000 markov chain.
The reason it can "do" some math is that there are tons of examples in the training set using small numbers usually used as examples. it can do basic arithmetic because it has seen "2+2=4" and other examples with simple numbers like that. The studies used test basic arithmetic. The same things that it had millions of pre-worked examples of. And it still gets those wrong, with astonishing frequency. those studies aren't talking about asking it "what is the square root of pi" or stuff like that. but stuff such as "is 7 greater than 4?", "what is 10 + 3?", "is 97 prime?" stuff it has most definitely seen the answers to. ask it about some large prime, and it'll nay no, and be probably right, because most numbers are composite
No, they very explicitly checked to see whether the training set contains the literal math problem that they asked it for the answer to. ChatGPT is able to answer math questions that it has never seen before. I believe this is the article (though I had to go searching, it's been a while).
When people dismiss LLMs as "just prediction engines" they're really missing the point. Of course they're prediction engines, that's not in dispute. The question is about how they go about making those predictions. When I show you the string "18 + 10 =" you can predict what comes next, yes? Well, how did you predict it? Did you memorize that particular specific string, or have you developed heuristics for how to do simple addition problems when you see them?
These things are currently infamously bad at math, though.
I won't argue that it'll never get there. I'm confident it will, - though with a lot more perl hacks than elegant emergence.
But today, these things have an astonishingly high 'appearance of intelligence' to 'incredible stupidity' ratio.
Humans are also not particularly well known for their math skills. Ask a random stranger to do simple arithmetic in their head, with only a few seconds to think and no outside help, and I wouldn't expect particularly reliable results.
however, people are not notoriously bad at the types of basic arithmetic they test for. every time I pay something with cash, I work out how much change I'm owed mentally, and so does the seller. I can count on one hand the number of times I've actually been given incorrect change throughout my entire lifetime. And when I did get wrong change, it was usually "oh, I thought you gave me €10 ínstead of €20". Meaning that they actually still did the math correctly.
No sane person will ever tell you 4 is bigger than 7. Yet llms sometimes get even this type of question wrong. They learn patterns, but not concepts. This is even simpler than basic arithmetic.
Haha. Fair point.
It gives me the giggles that folks speculating are getting more upvotes than your post that demonstrates actual understanding of the implementation details.
If I were the type to sell sizzle hype and snake oil, now would be the time to do it. The venture capitalists may have learned their lesson, but the general public haven't.