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Notes on OpenAI's new o1 chain-of-thought models

simonwillison.net

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Re: Notes on OpenAI's new o1 chain-of-thought models

#201

Post by ChicagoDave »

janalsncm wrote: post #157
ChicagoDave wrote: post #137

It’s still just a tool. It does not reason. It has some add-on logic the simulates it. We’re no closer to “AI” today than we were 20 years ago.

> We’re no closer to “AI” today than we were 20 years ago. 20 years ago we had barely figured out how to create superhuman agents to play chess. We have now created a new algorithm to solve Go, which is a much harder game. We then created an algorithm (alpha zero) to teach itself to play any game, and which became the best chess player in the world in hours. We next created a superhuman poker agent. Poker is even mor…

You can point a tool at a solution and certainly get results.

Doesn’t mean it’s intelligent.

Re: Notes on OpenAI's new o1 chain-of-thought models

#202

Post by smokel »

ironhaven wrote: post #133

So is o1 nicknamed “strawberry” because it was designed to solve the “how many many times does the letter R appear in strawberry” problem.

Or is it an obscure reference to the Dutch demogroup "Aardbei", most famous for their 64k intro "please the cookie thing" (2000)?

https://m.youtube.com/watch?v=ycmgjZLU0xQ

Re: Notes on OpenAI's new o1 chain-of-thought models

#204

Post by [deleted] »

techpression wrote: post #170

I just wish we’d stop using words like intelligence or reasoning when talking about LLMs, since they do neither. Reasoning requires you to be able to reconsider every step of the way and continuously take in information, an LLM is dead set in its tracks, it might branch or loop around a bit, but it’s still the same track. As for intelligence, well, there’s clearly none, even if at first the magic trick might fool you…

[deleted]

Re: Notes on OpenAI's new o1 chain-of-thought models

#205

Post by abernard1 »

elicksaur wrote: post #71
helmsb wrote: post #33

I did a few tests and asked it some legal questions. 4o gave me the correct answer immediately. o1 preview gave a much more in depth but completely wrong answer. It took 5 follow ups to get it to recognize that it hallucinated a non-existent law

This is interesting since they claim it does well on STEM questions, which I’d assume would be a similar level of reasoning complexity for a human.

This is an interesting one because math is doing so much of the heavy lifting. And symbolic math has a far smaller representational space than numerical math.

There is one other wonderful thing about symbolic math, the glorious '=' sign. It's structured everywhere from top-to-bottom, left-to-right, which is amenable to the next token prediction behavior and multi-attention heads of transformer based LLMs.

My guess is that problem statement formation into an equation is as difficult of a problem for these as actually running through the equations. However, having taken the Physics GRE, and knowing they try for parity of difficulty between years (even though they normalize it), the problems are fairly standard and have permutations of a problem type between the years.

This is not to diminish how cool this is, just that standardized tests do have an element of predictability to them. I find this result actually neat though; it's an actual qualitative improvement over non-CoT LLMs, even if things like Mathematica can do the steps more reliably post problem formation. I think that judiciously used, this is a valuable feature.

Re: Notes on OpenAI's new o1 chain-of-thought models

#206

Post by ruthmarx »

_sys49152 wrote: post #122
MPSimmons wrote: post #106

Earlier quoted context omitted.

The failure is in how you're using it. I don't mean this as a personal attack, but more to shed light on what's happening. A lot of people use LLMs as a search engine. It makes sense - it's basically a lossy compressed database of everything its ever read, and it generates output that is statistically likely - varying degrees of likeliness depending on the temperature, as well as how many times the particular weights…

ive been doing exactly this for bout a year now. feed it words data, give it a task. get better words back. i sneak in a benchmark opening of data every time i start a new chat - so right off the bat i can see in its response whether this chat session is gonna be on point or if we are going off into wacky world, which saves me time as i can just terminate and try starting another chat. chatgpt is fickle daily. most d…

> i sneak in a benchmark opening of data every time i start a new chat - so right off the bat i can see in its response whether this chat session is gonna be on point or if we are going off into wacky world, which saves me time as i can just terminate and try starting another chat.

I don't think it works like that...

Re: Notes on OpenAI's new o1 chain-of-thought models

#207

Post by _w1tm »

djur wrote: post #200
ramraj07 wrote: post #112

Earlier quoted context omitted.

Many of my PhD and post doc colleagues who emigrated from Korea, China and India who didn’t have English as the medium of instruction would struggle with this question. They only recover when you give them a hint. They’re some of the smartest people in general. If you try to stop stumping these models with trick questions and ask it straightforward reasoning systems it is extremely performant (O1 is definitely a step…

The claim was that "it knows english at or above a level equal to most fluent speakers". If the claim is that it's very good at producing reasonable responses to English text, posing "trick questions" like this would seem to be a fair test.

Does fluency in English make someone good at solving trick questions? I usually don’t even bother trying but mostly because trick questions don’t fit my definition of entertaining.

Re: Notes on OpenAI's new o1 chain-of-thought models

#208

Post by c-fe »

gloosx wrote: post #191
bachback wrote: post #185

Earlier quoted context omitted.

what does divided together mean? maybe your question doesn't have a good answer, because the question is not formulated well enough.

That's a classical school question, word-by-word, except multiplication is replaced by division

With multiplication the question makes sense due to the commutative property but division does not have that so the question becomes ambiguous... And now I see that the model even points this out.

Re: Notes on OpenAI's new o1 chain-of-thought models

#210

Post by latexr »

fragmede wrote: post #151
pedrosorio wrote: post #110

Earlier quoted context omitted.

> It knows english at or above a level equal to most fluent speakers, and it also can produce output that is not just a likely output, but is a logical output This is not an apt description of the system that insists the doctor is the mother of the boy involved in a car accident when elementary understanding of English and very little logic show that answer to be obviously wrong. https://x.com/colin_fraser/status/183…

what's weird is it gets it right when I try it. https://chatgpt.com/share/66e3601f-4bec-8009-ac0c-57bfa4f059...

That’s not weird at all, it’s how LLMs work. They statistically arrive at an answer. You can ask it the same question twice in a row in different windows and get opposite answers. That’s completely normal and expected, and also why you can never be sure if you can trust an answer.
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