Hacker News Board Hacker News, browsed like a classic message board
Live data from Hacker News

Learning to Reason with LLMs

openai.com

Post reply on HN
Print view View original

301–310 of 1001 posts

Re: Learning to Reason with LLMs

#301

Post by 0x_rs »

seydor wrote: post #241
wmf wrote: post #184

Earlier quoted context omitted.

People have been celebrating the fact that tokens got 100x cheaper and now here's a new system that will use 100x more tokens.

If it 's reasoning correctly, it shouldnt need a lot of tokens because you don't need to correct it. You only need to ask it to solve nuclear fusion once.

AlphaFold simulated the structure of over 200 million proteins. Among those, there could be revolutionary ones that could change the medical scientific field forever, or they could all be useless. The reasoning is sound, but that's as far as any such tool can get, and you won't know it until you attempt to implement it in real life. As long as those models are unable to perfectly recreate the laws of the universe to the maximum resolution imaginable and follow them, you won't see an AI model, let alone a LLM, provide anything of the sort.

Re: Learning to Reason with LLMs

#302

Post by Szpadel »

sbochins wrote: post #212
apsec112 wrote: post #152

Earlier quoted context omitted.

This one [o1/Strawberry] is available. I have it, though it's limited to 30 messages/week in ChatGPT Plus.

How do you get access? I don’t have it and am a ChatGPT plus subscriber.

I'm plus subscriber and I have o1-preview and o1-mini available

Re: Learning to Reason with LLMs

#304

Post by imiric »

thelastparadise wrote: post #41
dinobones wrote: post #4

Generating more "think out loud" tokens and hiding them from the user... Idk if I'm "feeling the AGI" if I'm being honest. Also... telling that they choose to benchmark against CodeForces rather than SWE-bench.

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?

Except that these aren't thoughts. These techniques are improvements to how the model breaks down input data, and how it evaluates its responses to arrive at a result that most closely approximates patterns it was previously rewarded for. Calling this "thinking" is anthropomorphizing what's really happening. "AI" companies love to throw these phrases around, since it obviously creates hype and pumps up their valuation.

Human thinking is much more nuanced than this mechanical process. We rely on actually understanding the meaning of what the text represents. We use deduction, intuition and reasoning that involves semantic relationships between ideas. Our understanding of the world doesn't require "reinforcement learning" and being trained on all the text that's ever been written.

Of course, this isn't to say that machine learning methods can't be useful, or that we can't keep improving them to yield better results. But these are still methods that mimic human intelligence, and I think it's disingenuous to label them as such.

Re: Learning to Reason with LLMs

#307

Post by exe34 »

oblio wrote: post #292
bjornsing wrote: post #276

Earlier quoted context omitted.

So now it’s a question of how fast the AGI will run? :)

It's fine, it will only need to be powered by a black hole to run.

the first one anyway. after that it will find more efficient ways. we did, afterall.

Re: Learning to Reason with LLMs

#308

Post by oblio »

randomdata wrote: post #211
paxys wrote: post #96

2018 - gpt1 2019 - gpt2 2020 - gpt3 2022 - gpt3.5 2023 - gpt4 2023 - gpt4-turbo 2024 - gpt-4o 2024 - o1 Did OpenAI hire Google's product marketing team in recent years?

They partnered with Microsoft, remember? 1985 – Windows 1.0 1987 – Windows 2.0 1990 – Windows 3.0 1992 – Windows 3.1 1995 – Windows 95 1998 – Windows 98 2000 – Windows ME (Millennium Edition) 2001 – Windows XP 2006 – Windows Vista 2009 – Windows 7 2012 – Windows 8 2013 – Windows 8.1 2015 – Windows 10 2021 – Windows 11

Why did you have to pick on Windows? :-(

If you want real atrocities, look at Xbox.

Re: Learning to Reason with LLMs

#309

Post by mewpmewp2 »

not_pleased wrote: post #226
mewpmewp2 wrote: post #199

Earlier quoted context omitted.

It seems opposite to me. Imagine all the amazing technological advancements, etc. If there wasn't something like that what would you be looking forward to? Everything would be what it has already been for years. If this evolves it helps us open so many secrets of the universe.

>If there wasn't something like that what would you be looking forward to? First of all, I don't want to be poor. I know many of you are thinking something along the lines of "I am smart, I was doing fine before, so I will definitely continue to in the future". That's the unearned hubris I was referring to. We got very lucky as programmers, and now the gravy train seems to be coming to an end. And not just for progra…

I'm not sure what you are saying exactly? Are you saying we live for the work?

Re: Learning to Reason with LLMs

#310

Post by crazygringo »

Hansenq wrote: post #87

Reading through the Chain of Thought for the provided Cipher example (go to the example, click "Show Chain of Thought") is kind of crazy...it literally spells out every thinking step that someone would go through mentally in their head to figure out the cipher (even useless ones like "Hmm"!). It really seems like slowing down and writing down the logic it's using and reasoning over that makes it better at logic, simi…

Seriously. I actually feel as impressed by the chain of thought, as I was when ChatGPT first came out.

This isn't "just" autocompletion anymore, this is actual step-by-step reasoning full of ideas and dead ends and refinement, just like humans do when solving problems. Even if it is still ultimately being powered by "autocompletion".

But then it makes me wonder about human reasoning, and what if it's similar? Just following basic patterns of "thinking steps" that ultimately aren't any different from "English language grammar steps"?

This is truly making me wonder if LLM's are actually far more powerful than we thought at first, and if it's just a matter of figuring out how to plug them together in the right configurations, like "making them think".

Post reply on HN