4 ms·

If LLMs can come up with formerly truly novel solutions to things, and you have a verification loop to ensure that they are actual proper solutions, I don't und

If LLMs can come up with formerly truly novel solutions to things, and you have a verification loop to ensure that they are actual proper solutions, I don't understand why you think they could never come up with solutions to impressive problems, especially considering the thread we are literally on right now? That seems like a pure assertion at this point that they will always be limited to coming up with truly novel solutions to uninteresting problems.

eru 7mo ago
"Truly novel" is fast becoming a True Scotsman.
- getnormality 7mo ago
  No True Novelty, No True Understanding, etc. The problem with these bromides is not that they're wrong, it's that they're not even wrong. They're predictive nulls. What observable differences can we expect between an entity with True Understanding and an entity without True Understanding? It's a theological question, not a scientific one. I'm not an AI booster by any means, but I do strongly prefer we address the question of AI agent intelligence scientifically rather than theologically.
  - mikkupikku 7mo ago
    Agreed. We should be asking what the machines measurably can or can't do. If it can't be measured, then it doesn't matter from an engineering standpoint. Does it have a soul? Can't measure it, so it doesn't matter.
    - eru 7mo ago
      That's a bit too pessimistic. Often times you can productively find some measurable proxy for the thing you care about but can't measure. Turing's test is a famous example, of that. Sometimes you only have a one-sided proxy. Eg I can't tell you whether Claude has a soul, but I'm fairly sure my dishwasher ain't.
      - thangalin 7mo ago
        Claude has neither a soul nor a warbleflupper.
      - hnfong 7mo ago
        > Turing's test is a famous example Ironically, the Turing test is the OG functionalist approach. The GP's comment basically sums up with the Turing test was designed for.
        eru 7mo ago
        Yes, but I interpret Turing's paper not as saying "souls don't matter", but as "here's a good proxy that we can actually measure". (I don't know what Turing's opinion on souls is, and it doesn't matter for that paper!)
        hnfong 7mo ago
        I think it's generally accepted that Turing thinks soul doesn't matter when we try to determine whether things have intelligence/ability to think.
      - mikkupikku 7mo ago
        When push came to shove, it turns out nobody really cared about the Turing test and immediately found excuses to discount it as soon as machines blew through that goalpost. It's fundamentally theological, but the thing is, it doesn't matter. It has no impact on what the machines can demonstrably do.
  - Marha01 7mo ago
    Well said. That's exactly what has been rubbing me the wrong way with all those "LLMs can never *really* think, ya know" people. Once we pass some level of AI capability (which we perhaps already did?), it essentially turns into an unfalsifiable statement of faith.
  - kelseyfrog 7mo ago
    We've tested this in the small with AI art. When people believe they're viewing human-made art which is later revealed to be AI art, they feel disappointed. The actual content is incidental, the story that supports it is more important than the thing itself. It's the same mechanism behind artisanal food, artist struggles, and luxury goods. It is the metaphysical properties we attach to objects or the frames we use to interpret strips of events. We author all of these and then promptly forget we've done so, instead believing they are simply reality.
    - coldtea 7mo ago
      >The actual content is incidental, the story that supports it is more important than the thing itself. The actual content of a work of art is the expression of lived experience. Not its form.
  - hnfong 7mo ago
    There are already people dealing with AI intelligence scientifically. That's what benchmarks do. It's the "it's just a stochastic parrot!" camp that's doing the theological work. (and maybe also those in the Singularity camp...) That said, I do think there's value in having people understand what "Understanding" means, which is kinda a theological (philosophical :D) question. IMHO, in every-day language there's a functional part (that can be tested with benchmarks), and there's a subjective part (i.e. what does it feel like to understand something?). Most people without the appropriate training simply mix up these two things, and together with whatever insecurities they have with AI taking over the world (which IMHO is inevitable to some extent), they just express their strong opinions about it online...
Yizahi 7mo ago
It probably can, but won't realize that and it won't be efficient in that. LLM can shuffle tokens for an enormous number of tries and eventually come up with something super impressive, though as you yourself have mentioned, we would need to have a mandatory verification loop, to filter slop from good output and how to do it outside of some limited areas is a big question. But assuming we have these verification loops and are running LLMs for years to look for something novel. It's like running an energy grid of small country to change a few dozen of database entries per hour. Yes, we can do that, but it's kinda weird thing to do. But it is novel, no argue about that. Just inefficient. We never had a big demand to define how humans are intelligent or conscious etc, since it is too hard and was relegated to a some frontier researchers. And with LLMs we now do have such demand but the science wasn't ready. So we are all collectively searching in the dark, trying to define if we are different from these programs if not how. I certainly can't do that. I do know that LLMs are useful, but I also suspect that AI (aka AGI nowadays) is not yet reached.
- aspenmartin 7mo ago
  How can people look at - clear generalizability - insane growth rates (go back and look at where we were maybe 2 years ago and then consider the already signed compute infrastructure deals coming online) And still say with a straight face that this is some kind of parlor trick or monkeys with typewriters. we don’t need to run LLMs for years. The point is look at where we are today and consider performance gets 10x cheaper every year. LLMs and agentic systems are clearly not monkeys with typewriters regurgitating training data. And they have and continue to grow in capabilities at extremely fast rates.
  - Yizahi 7mo ago
    I was talking about highest difficulty problems only, in the scope of that comment. Sure at mundane tasks they are useful and we optimizing that constantly. But for super hard tasks, there is no situation when you just dump a few papers for context add a prompt and LLM will spit out correct answer. It's likely that a lead on such project would need to additionally train LLM on their local dataset, then parse through a lot of experimental data, then likely run multiple LLMs for for many iterations homing on the solution, verifying intermediate results, then repeating cycle again and again. And in parallel the same would do other team members. All in all, for such a huge hard task a year of cumulative machine-hours is not something outlandish.

eru 7mo ago

"Truly novel" is fast becoming a True Scotsman.

getnormality 7mo ago
No True Novelty, No True Understanding, etc. The problem with these bromides is not that they're wrong, it's that they're not even wrong. They're predictive nulls. What observable differences can we expect between an entity with True Understanding and an entity without True Understanding? It's a theological question, not a scientific one. I'm not an AI booster by any means, but I do strongly prefer we address the question of AI agent intelligence scientifically rather than theologically.
- mikkupikku 7mo ago
  Agreed. We should be asking what the machines measurably can or can't do. If it can't be measured, then it doesn't matter from an engineering standpoint. Does it have a soul? Can't measure it, so it doesn't matter.
  - eru 7mo ago
    That's a bit too pessimistic. Often times you can productively find some measurable proxy for the thing you care about but can't measure. Turing's test is a famous example, of that. Sometimes you only have a one-sided proxy. Eg I can't tell you whether Claude has a soul, but I'm fairly sure my dishwasher ain't.
    - thangalin 7mo ago
      Claude has neither a soul nor a warbleflupper.
    - hnfong 7mo ago
      > Turing's test is a famous example Ironically, the Turing test is the OG functionalist approach. The GP's comment basically sums up with the Turing test was designed for.
      - eru 7mo ago
        Yes, but I interpret Turing's paper not as saying "souls don't matter", but as "here's a good proxy that we can actually measure". (I don't know what Turing's opinion on souls is, and it doesn't matter for that paper!)
        hnfong 7mo ago
        I think it's generally accepted that Turing thinks soul doesn't matter when we try to determine whether things have intelligence/ability to think.
    - mikkupikku 7mo ago
      When push came to shove, it turns out nobody really cared about the Turing test and immediately found excuses to discount it as soon as machines blew through that goalpost. It's fundamentally theological, but the thing is, it doesn't matter. It has no impact on what the machines can demonstrably do.
- Marha01 7mo ago
  Well said. That's exactly what has been rubbing me the wrong way with all those "LLMs can never *really* think, ya know" people. Once we pass some level of AI capability (which we perhaps already did?), it essentially turns into an unfalsifiable statement of faith.
- kelseyfrog 7mo ago
  We've tested this in the small with AI art. When people believe they're viewing human-made art which is later revealed to be AI art, they feel disappointed. The actual content is incidental, the story that supports it is more important than the thing itself. It's the same mechanism behind artisanal food, artist struggles, and luxury goods. It is the metaphysical properties we attach to objects or the frames we use to interpret strips of events. We author all of these and then promptly forget we've done so, instead believing they are simply reality.
  - coldtea 7mo ago
    >The actual content is incidental, the story that supports it is more important than the thing itself. The actual content of a work of art is the expression of lived experience. Not its form.
- hnfong 7mo ago
  There are already people dealing with AI intelligence scientifically. That's what benchmarks do. It's the "it's just a stochastic parrot!" camp that's doing the theological work. (and maybe also those in the Singularity camp...) That said, I do think there's value in having people understand what "Understanding" means, which is kinda a theological (philosophical :D) question. IMHO, in every-day language there's a functional part (that can be tested with benchmarks), and there's a subjective part (i.e. what does it feel like to understand something?). Most people without the appropriate training simply mix up these two things, and together with whatever insecurities they have with AI taking over the world (which IMHO is inevitable to some extent), they just express their strong opinions about it online...

Yizahi 7mo ago

It probably can, but won't realize that and it won't be efficient in that. LLM can shuffle tokens for an enormous number of tries and eventually come up with something super impressive, though as you yourself have mentioned, we would need to have a mandatory verification loop, to filter slop from good output and how to do it outside of some limited areas is a big question. But assuming we have these verification loops and are running LLMs for years to look for something novel. It's like running an energy grid of small country to change a few dozen of database entries per hour. Yes, we can do that, but it's kinda weird thing to do. But it is novel, no argue about that. Just inefficient. We never had a big demand to define how humans are intelligent or conscious etc, since it is too hard and was relegated to a some frontier researchers. And with LLMs we now do have such demand but the science wasn't ready. So we are all collectively searching in the dark, trying to define if we are different from these programs if not how. I certainly can't do that. I do know that LLMs are useful, but I also suspect that AI (aka AGI nowadays) is not yet reached.

aspenmartin 7mo ago
How can people look at - clear generalizability - insane growth rates (go back and look at where we were maybe 2 years ago and then consider the already signed compute infrastructure deals coming online) And still say with a straight face that this is some kind of parlor trick or monkeys with typewriters. we don’t need to run LLMs for years. The point is look at where we are today and consider performance gets 10x cheaper every year. LLMs and agentic systems are clearly not monkeys with typewriters regurgitating training data. And they have and continue to grow in capabilities at extremely fast rates.
- Yizahi 7mo ago
  I was talking about highest difficulty problems only, in the scope of that comment. Sure at mundane tasks they are useful and we optimizing that constantly. But for super hard tasks, there is no situation when you just dump a few papers for context add a prompt and LLM will spit out correct answer. It's likely that a lead on such project would need to additionally train LLM on their local dataset, then parse through a lot of experimental data, then likely run multiple LLMs for for many iterations homing on the solution, verifying intermediate results, then repeating cycle again and again. And in parallel the same would do other team members. All in all, for such a huge hard task a year of cumulative machine-hours is not something outlandish.