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Why I'm still bearish on LLMs after Navier-Stokes

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Re: Why I'm still bearish on LLMs after Navier-Stokes

#171

Post by willmarch »

skydhash wrote: post #152
willmarch wrote: post #112

Earlier quoted context omitted.

Yes, but my point is that humans can’t even do the thing that the above comments are claiming humans can do (read a book or two and be decent at chess), and then they complain that LLMs can’t do the same thing (that humans can’t do either). We seem to be moving goalposts to the point that humans don’t even live up to the expectations of the AI critics. The only way you get better at chess is by playing a lot of games…

> The only way you get better at chess is by playing a lot of games and learning from mistakes How can you play without being aware of the rules and how can you learn from your mistakes without knowing they are mistakes? That’s what I said about reading a book of two. It is to kickstart the process. Then mastery is gained over time through practice. This kickstarting then gradual refinement is how most people learn.…

Reading can kickstart the process, but you can also make random moves guided by some sort of system (such as a computer GUI) or learn by watching other players play. The overall point is that you learn through observation and lots of trial and error (whether you are a human or a computer). And beginners in chess often make illegal moves even after learning the rules, it's fairly common.

It feels like you're trying to say that humans never make illegal moves while learning chess, which doesn't match with my experience. I'm trying to understand your overall point.

Re: Why I'm still bearish on LLMs after Navier-Stokes

#172

Post by fhe »

bravoetch wrote: post #28
againstapples wrote: post #14

> the models generalize well only on tasks within a small neighborhood of the specific tasks they've been trained on, and even then with severe caveats. the frontier labs have developed a general recipe to teach models almost any specific task enjoying clearly defined levels of task performance; many tasks are covered in the training data Is this really any different to how humans learn, it takes a lot of training on…

I was a young child when I learned chess by reading a short book, then practicing with a friend. That is not how LLMs learn. I'm no expert on LLMs, but if you showed a human all chess games and books in all history and then said 'play chess' and they still kept making illegal moves, they would have to have a brain injury.

but maybe... the said human has also read every other piece of text ever written, including ones about other (similar?) board games, which in aggregate vastly dwarfs whatever he has read on chess, that non-chess reading could have corrupted whatever he's learned about chess?

Re: Why I'm still bearish on LLMs after Navier-Stokes

#173

Post by fhe »

yunwal wrote: post #95

> those who need done a small set of narrowly defined tasks with existing clear guardrails: repetitive physical labor in a controlled environment, call center and customer service chat work, etc. I have no idea how people can so confidently say that call center work is a “controlled environment” or “repetitive”. It’s almost by definition not repetitive or controlled. Customer support is what I go to when the controll…

came here to say exactly this. in fact, this is probably why we are not seeing a lot of AI application on customer service use case, and when we see one, it's almost always frustrating.

Re: Why I'm still bearish on LLMs after Navier-Stokes

#174

Post by willy_k »

camd32 wrote: post #109

> current frontier models need laborious oversight and guardrails on even the simplest tasks. This is only true if you are concerned about the intermediate steps of the model as opposed to the outcome. The huggingface hack was a perfect example of the model doing whatever it takes to accomplish the goal of maximizing its score.

So, if you are concerned about what the model does? Yeah.

Re: Why I'm still bearish on LLMs after Navier-Stokes

#175

Post by Wazzymandias »

woeirua wrote: post #84
knuppar wrote: post #19

Short and to the point! Open and cheap models will undercut the big labs continuously. The blast radius won't be pretty once spending commitments knock the door.

Open models wont be open for long. No one is going to release an open model capable of chaining zero-days. Even the Chinese aren't that reckless because it will just be turned around and used against them.

depends on the blast radius of zero-days, it's not like there's a continuous immediate release process for these models; they can eval internally before releasing publicly

Re: Why I'm still bearish on LLMs after Navier-Stokes

#176

Post by janalsncm »

hackinthebochs wrote: post #150
janalsncm wrote: post #137

Earlier quoted context omitted.

If something has general intelligence it should be able to read the rules of a game and follow them. Therefore an artificial general intelligence (AGI) should be able to do this. So we have a situation where very powerful and influential people are saying we will have AGI in 6 months (if we don’t already), yet the facts on the ground are so clearly pointing in the opposite direction.

I would bet a lot of money that Astra can follow the rules of chess (perhaps if repeated within the context window). Also, this is a different argument than what I responded to.

I would definitely take you up on that.

Re: Why I'm still bearish on LLMs after Navier-Stokes

#177

Post by fittingopposite »

keeda wrote: post #73

The premise in the very first point seems off: > the frontier labs are priced according to the narrative that they have produced or will in the very near future produce a fully automated drop-in replacement for most knowledge workers... Even assuming this is how the AI companies are being valued (they're not), the numbers are off. The "value" of most knowledge workers -- based on what enterprises currently pay for th…

> It's reasonable to assume that if AI drop-in-replaced all those knowledge workers, AI companies could credibly charge somewhere in that order of magnitude, because that's what the market is already bearing.

Future supply and demand will set the price - not what is paid today. If supply by open models is vast and cheap, I can't see that the entire knowledge industry can hold the current size. It'll rather collapse to a fraction of its current value.

Re: Why I'm still bearish on LLMs after Navier-Stokes

#179

Post by bluegatty »

krapp wrote: post #170
bluegatty wrote: post #146

Earlier quoted context omitted.

I think that the AI people believe in their own nonsense a bit. Like - the guy on TV talking about 'AI will destroy everything' ... I don't think he's lying. I think they are like we here on HN and Reddit and a bit caught up in our own thoughts. If AI were unleashed, in raw form today, it could cause havoc. Bad. Maybe very bad but I think we'd get over it. It would probably trigger a recession (because we are in a bu…

>If AI were unleashed, in raw form today, it could cause havoc. What is "raw form?"

The SOTA models are heavily 'guardrailed' today; they won't let you do all sorts of things.

Re: Why I'm still bearish on LLMs after Navier-Stokes

#180

Post by qarl »

thesmtsolver2 wrote: post #54
consensus1 wrote: post #42

Earlier quoted context omitted.

This isn't how intelligence works. The LLM may not be able to play chess directly through inference, but it can write a program to do it and execute that program. Same as how human intelligence works. We can't fly, but we can build planes.

Human beings can play chess directly without coding up a tool.

If they wanted to train an LLM to play chess they could easily do so.

But nobody wants that.

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