Earlier quoted context omitted.
No, this is just how Microsoft names things.
We'll know the Microsoft takeover is complete when OpenAI release Ai.net.
GPT# forthcoming. You heard it here first.
Learning to Reason with LLMs
381–390 of 1001 posts
Re: Learning to Reason with LLMs
#382The "safety" example in the "chain-of-thought" widget/preview in the middle of the article is absolutely ridiculous. Take a step back and look at what OpenAI is saying here "an LLM giving detailed instructions on the synthesis of strychnine is unacceptable, here is what was previously generated vs our preferred, neutered content " What's this obsession with "safety" when it comes to LLMs? "This knowledge is perfectly…
tl;dr You can easily ask an LLM to return JSON results, and now working code, on your exact query and plug those to another system for automation.
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LLMs are usually accessible through easy-to-use API which can be used in an automated system without human in the loop. Larger scale and parallel actions with this method become far more plausible than traditional means.
Text-to-action capabilities are powerful and getting increasingly more so as models improve and more people learn to use them to the their full potential.
Re: Learning to Reason with LLMs
#383This is incredible. In April I used the standard GPT-4 model via ChatGPT to help me reverse engineer the binary bluetooth protocol used by my kitchen fan to integrate it into Home Assistant. It was helpful in a rubber duck way, but could not determine the pattern used to transmit the remaining runtime of the fan in a certain mode. Initial prompt here [0] I pasted the same prompt into o1-preview and o1-mini and both c…
Wow, that is impressive! How were you able to use o1-preview? I pay for ChatGPT, but on chatgpt.com in the model selector I only see 4o, 4o-mini, and 4. Is o1 in that list for you, or is it somewhere else?
Re: Learning to Reason with LLMs
#384Very interesting. I guess this is the strawberry model that was rumoured. I am a bit surprised that this does not beat GPT-4o for personal writing tasks. My expectations would be that a model that is better at one thing is better across the board. But I suppose writing is not a task that generally requires "reasoning steps", and may also be difficult to evaluate objectively.
> My expectations would be that a model that is better at one thing is better across the board.
No, it's the opposite. This is simply a function of resources applied during training.
Re: Learning to Reason with LLMs
#385Interesting to note, as an outside observer only keeping track of this stuff as a hobby, that it seems like most of OpenAI’s efforts to drive down compute costs per token and scale up context windows is likely being done in service of enabling larger and larger chains of thought and reasoning before the model predicts its final output tokens. The benefits of lower costs and larger contexts to API consumers and applic…
I think you can slice it whichever direction you prefer e.g. OpenAI needs more than "we ran it on 10x as much hardware" to end up with a really useful AI model, it needs to get efficient and smarter just as proportionally as it gets larger. As a side effect hardware sizes (and prices) needed for a certain size and intelligence of model go down too.
In the end, however you slice it, the goal has to be "make it do more with less because we can't get infinitely more hardware" regardless of which "why" you give.
Re: Learning to Reason with LLMs
#386The "safety" example in the "chain-of-thought" widget/preview in the middle of the article is absolutely ridiculous. Take a step back and look at what OpenAI is saying here "an LLM giving detailed instructions on the synthesis of strychnine is unacceptable, here is what was previously generated vs our preferred, neutered content " What's this obsession with "safety" when it comes to LLMs? "This knowledge is perfectly…
"This knowledge is perfectly fine to disseminate via traditional means, but God forbid an LLM share it!"
Re: Learning to Reason with LLMs
#387> THERE ARE THREE R’S IN STRAWBERRY
It finally got it!!!
Re: Learning to Reason with LLMs
#388Earlier quoted context omitted.
Given the significant chain of thought tokens being generated, it also feels a bit odd to hide it from a cost fairness perspective. How do we believe they aren't inflating it for profit?
That sounds like the GPU labor theory of value that was debunked a century ago.
Not sure why you didn’t bother to check their pricing page (1) before dismissing my point. They are charging significantly more for both input (3x) and output (4x) tokens when using o1.
Per 1M in/out tokens:
GPT4o - 5$/15$
O1-preview - 15$/60$
Re: Learning to Reason with LLMs
#389One thing that makes me skeptical is the lack of specific labels on the first two accuracy graphs. They just say it's a "log scale", without giving even a ballpark on the amount of time it took. Did the 80% accuracy test results take 10 seconds of compute? 10 minutes? 10 hours? 10 days? It's impossible to say with the data they've given us. The coding section indicates "ten hours to solve six challenging algorithmic…
So now it’s a question of how fast the AGI will run? :)
It's not AGI - it's tree of thoughts, driven by some RL-derived heuristics.
I suppose what this type of approach provides is better prediction/planning by using more of what the model learnt during training, but it doesn't address the model being able to learn anything new.
It'll be interesting to see how this feels/behaves in practice.
Re: Learning to Reason with LLMs
#390One thing that makes me skeptical is the lack of specific labels on the first two accuracy graphs. They just say it's a "log scale", without giving even a ballpark on the amount of time it took. Did the 80% accuracy test results take 10 seconds of compute? 10 minutes? 10 hours? 10 days? It's impossible to say with the data they've given us. The coding section indicates "ten hours to solve six challenging algorithmic…
I don't think it's hard to compute the following: - At the high end, there is a likely nonlinear relationship between answer quality and compute. - We've gotten used to a flat-price model. With AGI-level models, we might have to pay more for more difficult and more important queries. Such is the inherent complexity involved. - All this stuff will get better and cheaper over time, within reason. I'd say let's start by…
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