161–170 of 1001 posts
Pricing page updated for O1 API costs. https://openai.com/api/pricing/ $15.00 / 1M input tokens $60.00 / 1M output tokens For o1 preview Approx 3x the price of gpt4o. o1-mini $3.00 / 1M input tokens $12.00 / 1M output tokens About 60% of the cost of gpt4o. Much more expensive than gpt4o-mini. Curious on the performance/tokens per second for these new massive models.
Interesting sequence from the Cipher CoT: Third pair: 'dn' to 'i' 'd'=4, 'n'=14 Sum:4+14=18 Average:18/2=9 9 corresponds to 'i'(9='i') But 'i' is 9, so that seems off by 1. So perhaps we need to think carefully about letters. Wait, 18/2=9, 9 corresponds to 'I' So this works. ----- This looks like recovery from a hallucination. Is it realistic to expect CoT to be able to recover from hallucinations this quickly?
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Why not? Isn't that basically what humans do? Sit there and think for a while before answering, going down different branches/chains of thought?
This new approach is showing: 1) The "bitter lesson" may not be true, and there is a fundamental limit to transformer intelligence. 2) The "bitter lesson" is true, and there just isn't enough data/compute/energy to train AGI. All the cognition should be happening inside the transformer. Attention is all you need. The possible cognition and reasoning occurring "inside" in high dimensions is much more advanced than any…
This chain of thought / reflection method allows you to make better use of the hardware as the hardware itself scales. If a given transformer is N billion parameters, and to solve a harder problem we estimate we need 10N billion parameters, one way to do it is to build a GPU cluster 10x larger.
This method shows that there might be another way: instead train the N billion model differently so that we can use 10x of it at inference time. Say hardware gets 2x better in 2 years -- then this method will be 20x better than now!
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Is it? They talk about 10k attempts to reach gold medal status in the mathematics olympiad, but zero shot performance doesn't even place it in the upper 50th percentile. Maybe I'm confused but 10k attempts on the same problem set would make anyone an expert in that topic? It's also weird that zero shot performance is so bad, but over a lot of attempts it seems to get correct answers? Or is it learning from previous a…
The correct metaphor is that 10,000 attempts would allow anyone to cherry pick a successful attempt. You’re conflating cherry picking with online learning. This is like if an entire school of students randomized their answers on a multiple choice test, and then you point to someone who scored 100% and claim it is proof of the school’s expertise.
https://openai.com/index/introducing-openai-o1-preview/ > ChatGPT Plus and Team users will be able to access o1 models in ChatGPT starting today. Both o1-preview and o1-mini can be selected manually in the model picker, and at launch, weekly rate limits will be 30 messages for o1-preview and 50 for o1-mini. We are working to increase those rates and enable ChatGPT to automatically choose the right model for a given p…
A lot of skepticism here, but these are astonishing results! People should realize we’re reaching the point where LLMs are surpassing humans in any task limited in scope enough to be a “benchmark”. And as anyone who’s spent time using Claude 3.5 Sonnet / GPT-4o can attest, these things really are useful and smart! (And, if these results hold up, O1 is much, much smarter.) This is a nerve-wracking time to be a knowled…
I have spent significant time with GPT-4o, and I disagree. LLMs are as useful as a random forum dweller who recognises your question as something they read somewhere at some point but are too lazy to check so they just say the first thing which comes to mind.
Here’s a recent example I shared before: I asked GPT-4o which Monty Python members have been knighted (not a trick question, I wanted to know). It answered Michael Palin and Terry Gilliam, and that they had been knighted for X, Y, and Z (I don’t recall the exact reasons). Then I verified the answer on the BBC, Wikipedia, and a few others, and determined only Michael Palin has been knighted, and those weren’t even the reasons.
Just for kicks, I then said I didn’t think Michael Palin had been knighted. It promptly apologised, told me I was right, and that only Terry Gilliam had been knighted. Worse than useless.
Coding-wise, it’s been hit or miss with way more misses. It can be half-right if you ask it uninteresting boilerplate crap everyone has done hundreds of times, but for anything even remotely interesting it falls flatter than a pancake under a steam roller.
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Why not? Isn't that basically what humans do? Sit there and think for a while before answering, going down different branches/chains of thought?
This new approach is showing: 1) The "bitter lesson" may not be true, and there is a fundamental limit to transformer intelligence. 2) The "bitter lesson" is true, and there just isn't enough data/compute/energy to train AGI. All the cognition should be happening inside the transformer. Attention is all you need. The possible cognition and reasoning occurring "inside" in high dimensions is much more advanced than any…
Congrats to OpenAI for yet another product that has nothing to do with the word "open"
My first interpretation of this is that it's jazzed-up Chain-Of-Thought. The results look pretty promising, but i'm most interested in this: > Therefore, after weighing multiple factors including user experience, competitive advantage, and the option to pursue the chain of thought monitoring, we have decided not to show the raw chains of thought to users. Mentioning competitive advantage here signals to me that OpenA…
This will make it harder for things like DSPy to work, which rely using "good" CoT examples as few-shot examples.