111–120 of 331 posts
Earlier quoted context omitted.
If the student could reference notes a fraction of the size of the LLM then I would not be convinced.
I suspect human memory consists of a lot more bits than LLMs encode.
Earlier quoted context omitted.
That seems like a totally reasonable response ... ?
I think you missed the part where I had to give them hinits to solve it. All 3 initially couldn't or refused saying it was not a real problem on their first try.
https://chatgpt.com/share/684e02de-03f0-800a-bfd6-cbf9341f71...
The key insight is that LLMs can 'reason' when they've seen similar solutions in training data, but this breaks down on truly novel problems. This isn't reasoning exactly, but close enough to be useful in many circumstances. Repeating solutions on demand can be handy, just like repeating facts on demand is handy. Marcus gets this right technically but focuses too much on emotional arguments rather than clear explanat…
Earlier quoted context omitted.
I don't understand what people mean when they say that AI is being hyped. AI is at the point where you can have a conversation with it about almost anything, and it will answer more intelligently than 90% of people. That's incredibly impressive, and normal people don't need to be sold on it. They're just naturally impressed by it.
I don’t need a tool that’s right maybe 70% of the time (and that’s me being optimistic). It needs to be right all the time or at least tell you when it doesn’t know for sure, instead of just making up something. Comparing it to going out in the streets and asking random people random questions is not a good comparison.
> 1. Humans have trouble with complex problems and memory demands. True! But incomplete. We have every right to expect machines to do things we can’t. [...] If we want to get to AGI, we will have to better. I don't get this argument. The paper is about "whether RLLMs can think". If we grant "humans make these mistakes too", but also "we still require this ability in our definition of thinking", aren't we saying "thin…
Agreed. But also his point about AGI is incorrect. AI that will perform on the level of average human in every task is AGI by definition.
Earlier quoted context omitted.
I’m so tired of hearing this be repeated, like the whole “LLMs are _just_ parrots” thing. It’s patently obvious to me that LLMs can reason and solve novel problems not in their training data. You can test this out in so many ways, and there’s so many examples out there. ______________ Edit for responders, instead of replying to each: We obviously have to define what we mean by "reasoning" and "solving novel problems"…
I've done this excercise dozens of times because people keep saying it, but I can't find an example where this is true. I wish it was. I'd be solving world problems with novel solutions right now. People make a common mistake by conflating "solving problems with novel surface features" with "reasoning outside training data." This is exactly the kind of binary thinking I mentioned earlier.
Earlier quoted context omitted.
The funny thing is, if you asked “what is AGI” 5 years ago, most people would describe something like o3.
Even Sam Altman thinks we’re not at AGI yet (although of course it’s coming “soon”).
> 1. Humans have trouble with complex problems and memory demands. True! But incomplete. We have every right to expect machines to do things we can’t. [...] If we want to get to AGI, we will have to better. I don't get this argument. The paper is about "whether RLLMs can think". If we grant "humans make these mistakes too", but also "we still require this ability in our definition of thinking", aren't we saying "thin…
Agreed. But also his point about AGI is incorrect. AI that will perform on the level of average human in every task is AGI by definition.
Most of the objections and their counterarguments seem like either poor objections (e.g. ad hominem against the first listed author) or seem to be subsumed under point 5. It’s annoying that most of this post focuses so much effort on discussing most of the other objections when the important discussion is the one to be had in point 5: I.e. to what extent are LLMs able to reliably make use of writing code or using log…
> I’d expect a smart human to just say “that’s too much” or “that’s beyond my abilities” rather than do a best effort faulty answer)? That's what the models did. They gave the first 100 steps, then explained how it was too much to output all of it, and gave the steps one would follow to complete it. They were graded as "wrong answer" for this. --- Source: https://x.com/scaling01/status/1931783050511126954?t=ZfmpSxH..…
>lead them to paradise
>intelligence is inherently about scaling
>be kind to us AGI
Who even is this guy? He seems like just another r/singularity-style tech bro.