OpenAI spends about $700,000 a day, just to keep ChatGPT going. The cost does not include other AI products like GPT-4 and DALL-E2. Right now, it is pulling through only because of Microsoft's $10 billion funding
If we know, but there’s no public text about it, it doesn’t know either.
it is trained off of stuff that has already been written, and trained to emulate the statistical properties of those words. It cannot and will not tell us anything new
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
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.
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?
if we don’t know, it doesn’t know.
If we know, but there’s no public text about it, it doesn’t know either.
it is trained off of stuff that has already been written, and trained to emulate the statistical properties of those words. It cannot and will not tell us anything new
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?