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Notes on OpenAI's new o1 chain-of-thought models
531–540 of 659 posts
Re: Notes on OpenAI's new o1 chain-of-thought models
#532Earlier quoted context omitted.
I feel like it almost always starts well, given the full picture, but then for non-trivial stuff, gets stuck towards the end. The longer the conversation goes, the more wheel-spinning occurs and before you know it, you have spent an hour chasing that last-mile-connectivity. For complex questions, I now only use it to get the broad picture and once the output is good enough to be a foundation, I build the rest of it m…
This is the way. I've had this experience many times: - hey, can you write me a thing that can do "xyz" - sure, here's how we can do "xyz" (gets some small part of the error handling for xyz slightly wrong) - can you add onto this with "abc" - sure. in order to do "abc" we'll need to add "lmn" to our error handling. this also means that you need "ijk" and "qrs" too, and since "lmn" doesn't support "qrs" out of the bo…
> If you don't hit that kill switch, it just keeps doubling down on absurdly complex/incorrect/hallucinatory stuff.
If you think about it, that's probably the most difficult problem conversational LLMs need to overcome -- balancing sticking to conversational history vs abandoning it.
Humans do this intuitively.
But it seems really difficult to simultaneously (a) stick to previous statements sufficiently to avoid seeming ADD in a conveSQUIRREL and (b) know when to legitimately bail on a previous misstatement or something that was demonstrably false.
What's SOTA in how this is being handled in current models, as conversations go deeper and situations like the one referenced above arise? (false statement, user correction, user expectation of subsequent corrected statement that still follows the rear of the conversational history)
Re: Notes on OpenAI's new o1 chain-of-thought models
#533My 12 YO and I just built a fishing game using o1 preview. Prompt: "make a top down game in pyxel. the play has to pay off a debt to a cat by catching fish. the goal is for the player to catch the giant king fish. To catch the king fish the player needs to sell the fish to the cat and get money to buy better rods, 3 levels of rod, last one can catch the king fish." It nailed the execution. Amazing.
mind sharing the code? I asked o1 preview this verbatim and it produced errors. Just wanted to make sure its not me
Re: Notes on OpenAI's new o1 chain-of-thought models
#534Earlier quoted context omitted.
Being able to do “stuff a human is capable of doing” used to be the definition of “artificial intelligence” and until very recently it was seen as a dream that may never happen. And it hasn't completely happened yet BTW, there are still plenty of trivial stuff LLM can't do just because there's no available training data for that. Also their ability to do “reasoning” or few-shot-learning is overhyped (even if impressi…
> AI isn't going to do “faster than the speed of light” MPEG decoding ever, all physical limits apply to it. This simply isn't a good faith take, because you're straw-manning the implementation of the query that the original poster put forward. They aren't asserting that the AI would need to do supernatural super-real time decoding of MPEG encoded files. What if the AI had already seen them? And was able to encode in…
> This simply isn't a good faith take, because you're straw-manning the implementation of the query that the original poster put forward. They aren't asserting that the AI would need to do supernatural super-real time decoding of MPEG encoded files.
No, they literally said the AI would watch every frame on demand:
> I expect an "AI" to be capable pf obtaining every movie, watching them frame-by-frame, and getting an accurate count.
Talk about bad faith.
> What if the AI had already seen them? And was able to encode in the typically-compressed way LLMs do the information it needs to answer questions like that
LLM are encoding (in a very lossy way) “important” details, that's what allow them to compress their knowledge in little amount of space with respect to the input. But if you're asking completely random questions like this there's no way an LLM will contain such an info, because storing all the random trivia like that is going to be wasting an enormous amount of space.
> There's no moving goalposts. Goal posts have been set in cement since 2014.
Wait until you realize that AI is something much older than 2014… Also, note how the book you're quoting isn't called “artificial intelligence”.
> OpenAI is not worth $150 billion dollars because it purports to be building a human-and-nothing-more in a box.
And yet there are many companies with much higher valuation with goals much more mundane than this. OpenAI has a hundred billion dollar valuation because investors believe it can make money, not matter what it technologically achieves in order to do so.
Re: Notes on OpenAI's new o1 chain-of-thought models
#535Earlier quoted context omitted.
LLMs are not for expanding the sphere of human knowledge, but for speeding up auto-correct of higher order processing to help you more quickly reach the shell of the sphere and make progress with your own mind :)
Definitely. When we talk about being skilled in a T shape LLMs are all about spreading your top of T and not making the bottom go deeper.
I prefer reading some book. Maybe the LLM was trained on some piece of knowledge not available on the net, but I much prefer the reliability and consistency of a book.
Re: Notes on OpenAI's new o1 chain-of-thought models
#536Near the end, the quote from OpenAI researcher Jason Wei seems damning to me: > Results on AIME and GPQA are really strong, but that doesn’t necessarily translate to something that a user can feel. Even as someone working in science, it’s not easy to find the slice of prompts where GPT-4o fails, o1 does well, and I can grade the answer. But when you do find such prompts, o1 feels totally magical. We all need to find…
He's speaking about his objective to make ever stronger LLMs: so for this his secondary objective is to measure their real performance. The human preference is not that good of a proxy measurement: for instance, it can be gamed by making the model more assertive, causing the human error-spotting ability to decrease a lot [0]. So what he's really saying is that non-rigorous human vibe checks (like those LMSys Chatbot…
It's trivial to come up with prompts that 4o fails. If it's hard to come up with prompts that 1o succeeds on but 4o fails, that implies the delta is not that great.
Re: Notes on OpenAI's new o1 chain-of-thought models
#537My 12 YO and I just built a fishing game using o1 preview. Prompt: "make a top down game in pyxel. the play has to pay off a debt to a cat by catching fish. the goal is for the player to catch the giant king fish. To catch the king fish the player needs to sell the fish to the cat and get money to buy better rods, 3 levels of rod, last one can catch the king fish." It nailed the execution. Amazing.
My first few attempts at getting it to work with an existing codebase have not been impressive. Perhaps 1o is best suited to difficult problems that can be stated in only a few sentences.
Re: Notes on OpenAI's new o1 chain-of-thought models
#538Re: Notes on OpenAI's new o1 chain-of-thought models
#539I tried a problem I was looking at recently, to refactor a small rust crate to use one datatype instead of an enum, to help me understand the code better. I found o1-mini made a decent attempt, but couldn't provide error free code. o1-preview was able to provide code that compiled and passed all but the test that is expected to fail, given the change I asked it to make. This is the prompt I gave: simplify this rust l…
I've been having a weird timezone issue in my Rails application that I've had a hard time getting my head around. I tried giving o1-preview the relevant code and context it needed to know and it gave answers that seemed to make sense but it still wasn't able to resolve the bug and explain exactly what was going on. So, it seems like anything that requires some actual thought and problem-solving is tough for it to ans…
I'm still not convinced that it's not going through approximate reasoning chain retrieval and that's self-triggered to get more reasoning chains that will maximize it's goal. I'm seeing a lot of comments from other SWEs using it for non-trivial tasks in which it fails at but is just trying harder to look like it's problem solving. Even with more context and documentation, it fails to realize details an experienced SWE would pick up quickly.
Re: Notes on OpenAI's new o1 chain-of-thought models
#540Earlier quoted context omitted.
Order doesn't matter with multiplication (eg: (20 * 5) * 2 == (5 * 2) * 20) but it obviously does with division ((20/5)/2 != (2/5)/20) so the question doesn't make sense. It's you making grade-school level mistakes here.
The question makes perfect sense. Here it is written in logical language. I'm curious at which point does it stop making sense for you? numbers divided together ↓----------↓ ((a / b / c) = a + b + c) ← numbers added together | ((a / c / b) = a + b + c) | ((b / a / c) = a + b + c) | ((b / c / a) = a + b + c) | ((c / a / b) = a + b + c) | ((c / b / a) = a + b + c) | ((a / (b / c)) = a + b + c) | ((a / (c / b)) = a + b…
So you want it to solve 12 simultaneous equations? LLMs are not good at that.
Is there in fact an answer? ChatGPT says no.
https://chatgpt.com/share/66e482cc-331c-8013-98ca-999d7d3f3e...