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Seven replies to the viral Apple reasoning paper and why they fall short

garymarcus.substack.com

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Re: Seven replies to the viral Apple reasoning paper and why they fall short

#131

Post by [deleted] »

aucisson_masque wrote: post #67
Jabrov wrote: post #35

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"…

> It’s patently obvious that LLMs can reason and solve novel problems not in their training data. Would you care to tell us more ? « It’s patently obvious » is not really an argument, I could say just as well that everyone know LLM can’t resonate or think (in the way we living beings do).

[deleted]

Re: Seven replies to the viral Apple reasoning paper and why they fall short

#132

Post by dialup_sounds »

labrador wrote: post #74
aucisson_masque wrote: post #61

Earlier quoted context omitted.

That’s the opposite of reasoning tho. Ai bros want to make people believe LLM are smart but they’re not capable of intelligence and reasoning. Reasoning mean you can take on a problem you’ve never seen before and think of innovative ways to solve it. LLM can only replicate what is in its data, it can in no way think or guess or estimate what will likely be the best solution, it can only output a solution based on a p…

You're assuming we're saying LLMs can't reason. That's not what we're saying. They can execute reasoning-like processes when they've seen similar patterns, but this breaks down when true novel reasoning is required. Most people do the same thing. Some poeple can come up with novel solutions to new problems, but LLMs will choke. Here's an example: Prompt: "Let's try a reasoning test. Estimate how many pianos there are…

How much of that is inability to reason vs. being trained to avoid making things up?

Re: Seven replies to the viral Apple reasoning paper and why they fall short

#133

Post by [deleted] »

kadushka wrote: post #24
foldr wrote: post #18

Earlier quoted context omitted.

I don’t read GM as saying that LLMs “don’t work” in a practical sense. He acknowledges that they have useful applications. Indeed, if they didn’t work at all, why would he be advocating for regulating their use? He just doesn’t think they’re close to AGI.

The funny thing is, if you asked “what is AGI” 5 years ago, most people would describe something like o3.

[deleted]

Re: Seven replies to the viral Apple reasoning paper and why they fall short

#134

Post by autobodie »

serbuvlad wrote: post #124
autobodie wrote: post #104

Earlier quoted context omitted.

Agree. Both sides of the argument are unsatisfying. They seem like quantitative answers to a qualitative question.

"Have we created machines that can do something qualitatevely similar to that part of us that can correlate known information and pattern recognition to produce new ideas and solutions to problems -- that part we call thinking?" I think the answer to this question is certainly "Yes". I think the reason people deny this is because it was just laughably easy in retrospect. In mid-2022 people were like. "Wow this GPT3 t…

> The key is to not become a slave to them. To realize that our worth consists not in our ability to think. And that we are more than that.

I cannot afford to consider whether you are right because I am a slave to capital, and therefore may as well be a slave to capital's LLMs. The same goes for you.

Re: Seven replies to the viral Apple reasoning paper and why they fall short

#135

Post by gjm11 »

lossolo wrote: post #64
labrador wrote: post #54

Earlier quoted context omitted.

"I don't think today's systems can invent, you know, do true invention, true creativity, hypothesize new scientific theories. They're extremely useful, they're impressive, but they have holes." Demis Hassabis On The Future of Work in the Age of AI (@ 2:30 mark) https://www.youtube.com/watch?v=CRraHg4Ks_g

Yes, this one. Thanks

He doesn't say "that LLMs based on transformers can't create anything truly novel". Maybe he thinks that, maybe not, but what he says is that "today's systems" can't do that. He doesn't make any general statement about what transformer-based LLMs can or can't do; he's saying: we've interacted with these specific systems we have right now and they aren't creating genuinely novel things. That's a very different claim, with very different implications.

Again, for all I know maybe he does believe that transformer-based LLMs as such can't be truly creative. Maybe it's true, whether he believes it or not. But that interview doesn't say it.

Re: Seven replies to the viral Apple reasoning paper and why they fall short

#136

Post by viccis »

serbuvlad wrote: post #124
autobodie wrote: post #104

Earlier quoted context omitted.

Agree. Both sides of the argument are unsatisfying. They seem like quantitative answers to a qualitative question.

"Have we created machines that can do something qualitatevely similar to that part of us that can correlate known information and pattern recognition to produce new ideas and solutions to problems -- that part we call thinking?" I think the answer to this question is certainly "Yes". I think the reason people deny this is because it was just laughably easy in retrospect. In mid-2022 people were like. "Wow this GPT3 t…

>I think the answer to this question is certainly "Yes".

It is unequivocally "No". A good joint distribution estimator is always by definition a posteriori and completely incapable of synthetic a priori thought.

Re: Seven replies to the viral Apple reasoning paper and why they fall short

#137

Post by FINDarkside »

simonw wrote: post #116
FINDarkside wrote: post #108

Earlier quoted context omitted.

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.

That very much depends on which AGI definition you are using. I imagine there are a dozen or so variants out there. See also "AI" and "agents" and (apparently) "vibe coding" and pretty much every other piece of jargon in this field.

I think it's very widely accepted definition and there's really no competing definitions either as far as I know. While some people might think AGI means superintelligence, it's only because they've heard the term but never bothered to look up what it means.

Re: Seven replies to the viral Apple reasoning paper and why they fall short

#138

Post by kgeist »

labrador wrote: post #74
aucisson_masque wrote: post #61

Earlier quoted context omitted.

That’s the opposite of reasoning tho. Ai bros want to make people believe LLM are smart but they’re not capable of intelligence and reasoning. Reasoning mean you can take on a problem you’ve never seen before and think of innovative ways to solve it. LLM can only replicate what is in its data, it can in no way think or guess or estimate what will likely be the best solution, it can only output a solution based on a p…

You're assuming we're saying LLMs can't reason. That's not what we're saying. They can execute reasoning-like processes when they've seen similar patterns, but this breaks down when true novel reasoning is required. Most people do the same thing. Some poeple can come up with novel solutions to new problems, but LLMs will choke. Here's an example: Prompt: "Let's try a reasoning test. Estimate how many pianos there are…

GPT4o isn't considered an "advanced" LLM at this point. It doesn't use reasoning.

I gave your prompt to o3 pro, and this is what I got without any hints:

  Historic shipwrecks (1850 → 1970)
  • ~20 000 deep water wrecks recorded since the age of steam and steel  
  • 10 % were passenger or mail ships likely to carry a cabin class or saloon piano   
  • 1 piano per such vessel 20 000 × 10 % × 1 ≈ 2 000

  Modern container losses (1970 → today)
  • ~1 500 shipping containers lost at sea each year  
  • 1 in 2 000 containers carries a piano or electric piano   
  • Each piano container holds ≈ 5 units   
  • 50 year window 1 500 × 50 / 2 000 × 5 ≈ 190

  Coastal disasters (hurricanes, tsunamis, floods)
  • Major coastal disasters each decade destroy ~50 000 houses  
  • 1 house in 50 owns a piano   
  • 25 % of those pianos are swept far enough offshore to sink and remain (50 000 / 50) × 25 % × 5 decades ≈ 1 250

  Add a little margin for isolated one offs (yachts, barges, deliberate dumping): ≈ 300

  Best guess range: 3 000 – 5 000 pianos are probably resting on the seafloor worldwide.

Re: Seven replies to the viral Apple reasoning paper and why they fall short

#139

Post by jjaksic »

labrador wrote: post #42
Jabrov wrote: post #35

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.

"Solving novel problems" does not mean "solving world problems that even humans are unable to solve", it simply means solving problems that are "novel" compared to what's in the training data.

Can you reason? Yes? Then why haven't you cured cancer? Let's not have double standards.

Re: Seven replies to the viral Apple reasoning paper and why they fall short

#140

Post by starchild3001 »

We built planes—critics said they weren't birds. We built submarines—critics said they weren't fish. Progress moves forward regardless.

You have a choice: master these transformative tools and harness their potential, or risk being left behind by those who do.

Pro tip: Endless negativity from the same voices won't help you adapt to what's coming—learning will.

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