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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

#321

Post by staticman2 »

TeMPOraL wrote: post #297
YeGoblynQueenne wrote: post #293

Earlier quoted context omitted.

Hinton and Sutskever are victims of their own success: they can say whatever they like and nobody dares criticise them, or tell them how they're wrong. I recently watched a video of Sutskever speaking to some students, not sure where and I can't dig out the link now. To summarise he told them that the human brain is a biological computer. He repeated this a couple of times then said that this is why we can create a d…

> Two seconds of thought suffice to show how that doesn't work: if a digital computer can do everything the brain can do, because the brain is a biological computer, then how come the brain can't do everything a digital computer can do? Is it possible that two machines can be both computers, and still not equivalent in every sense of the term? Nooooo!!! Biological computers!! AGI!! Another two seconds of thought woul…

Another 2 seconds of thought might have told you only a magic genie can "freely" change hardware and software capability.

Humans have to work within whatever constraints accompany being physical things with physical bodies trying to invent software and hardware in the physical world.

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

#322

Post by mountainriver »

bluefirebrand wrote: post #198
mountainriver wrote: post #93

Earlier quoted context omitted.

Not knowing the most basic things about the subject you are critiquing is utter nonsense. Defending someone who does this is even worse

I think it's pretty fair to be critical of what LLMs are producing and how they fit into the tools without necessarily understanding how they work If you bought a chainsaw that broke when you tried to cut down a tree, then you can criticize the chainsaw without knowing how the motor on it works, right?

Except that he pretends to know how they work, and paints himself as an expert in the ecosystem but knows nothing about how they actually work

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

#323

Post by dzamo_norton »

TeMPOraL wrote: post #290
YeGoblynQueenne wrote: post #260

Earlier quoted context omitted.

>> Ie: a human with no experience of board games cannot reason about chess moves. A human with no math knowledge cannot reason about math problems. Then how did the first humans solve math and chess problems, if there were none around solved to give them examples of how to solve them in the first place?

Incrementally, by tiny steps. Including a lot of doing first, then realizing later this is relevant to some chess/math thing. Also the idea of "problems" like "chess problems" and "math problems" is itself constructed. Chess wasn't created by stacking together enough "chess problems" until they turned into a game - it was invented and tuned as a game for a long time before someone thought about distilling "problems"…

And if we left a population of SOTA LRMs on an island for long time, would we return to find that they had done the same?

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

#324

Post by viccis »

corimaith wrote: post #245
serbuvlad wrote: post #181

Earlier quoted context omitted.

The human mind is an estimator too. The fact that the human mind can think in concepts, images AND words, and then compresses that into words for transmission, wheras LLMs think directly in words, is no object. If you watch someone reach a ledge, your mind will generate, based on past experience, a probabilistic image of that person falling. Then it will tie that to the concept of problem (self-attention) and start g…

Do you think language is sufficient to model reality (not just physical, but abstract) here? I think not, we can get close, but there exists problems and situations beyond that, especially in mathematics and philosophy. And I don't a visual medium or combination of is sufficient either, there's a more fundamental, underlying abstract structure that we use to model reality.

> Do you think language is sufficient to model reality (not just physical, but abstract) here?

After much time trying to accomplish this during the 20th century, the answer was as resounding "no" [1]

[1] https://en.wikipedia.org/wiki/Logical_positivism#Decline_and...

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

#325

Post by viccis »

ac29 wrote: post #266
viccis wrote: post #226

Earlier quoted context omitted.

>When I spill a drink, I don't think "gravity". That's too slow. I think you do, you just don't need to notice it. If you spilled it in the International Space Station, you'd probably respond differently even if you didn't have to stop and contemplate the physics of the situation.

I think they may have been referring to the fact that in the case of a spilled drink there's a shortcut from the sensory input to a motor output. Maybe you reach for the falling cup, maybe you back away to not get spilled on. These don't really require the conscious mind at all.

I don't think that we need to be aware of the reasoning our minds are doing for it to constitute reasoning.

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

#326

Post by sigotirandolas »

thomasahle wrote: post #287
sigotirandolas wrote: post #281

Earlier quoted context omitted.

My thought is that we humans are bad (by computer standards) at arithmetic and memorization because those are not evolutionarily useful on their own. On the other hand general problem solving is, and so far any attempt to replicate it using computer algorithms has more or less failed. So it must be more complex than just some simple heuristics. Perhaps the answer is just "more compute" but the argument that "because…

> On the other hand general problem solving is, and so far any attempt to replicate it using computer algorithms has more or less failed. Well, this is what the whole debate is about isn't it? Can LRMs do "general problem solving"? Can humans? What exactly does it mean?

A lot of it is being able to make reasonable decisions under novel and incomplete information and being able to reflect and refine on their outcome.

LLMs's huge knowledge base covers for their incapacity to reason under incomplete information, but when you find a gap in their knowledge, they are terrible at recovering from it.

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

#327

Post by chongli »

amohn9 wrote: post #310
chongli wrote: post #242

Earlier quoted context omitted.

I work in a mail room sending hard copy letters to customers. If I got my job right only 70% of the time then I’d be causing massive privacy breaches daily by sending the wrong personal information to the wrong customers. Would you trust an AI that gets your banking transactions right only 70% of the time?

No. I also wouldn’t use a hammer to cut a board in half - I’d grab a saw. Knowing how to pick the right tool is a fundamental part of being a good engineer. Sometimes 70% is unacceptable, sometimes it’s exceptional. LLMs are incredible technology, but also just another tool in the toolbox. Use them where they fit, not where they don’t.

Sure, though the marketing around LLMs is aimed at general purpose use. It’s up to the user to decide if it’s actually useful for their use case. Unfortunately, many use cases in business can’t tolerate high error rates.

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

#328

Post by RugnirViking »

neoden wrote: post #320
RugnirViking wrote: post #302

Earlier quoted context omitted.

Why without writing down each step? Would you be able to solve it writing each step required in sequence? Thinking between each one? Pretty sure i could, isn't that closer to an LLM?

I mean I need to offload state of the puzzle being solved from my brain to an external memory device — paper, in this case. Keeping that state in my mind would be much harder. It's like some people can play chess within their minds without a board, but it's obviously not something that everyone can do

But the LLM can think between writing each token, and indeed can factor in it's own previously written tokens into its answer - thats essentially using a piece of paper and writing stuff down and referring back to it. Thats the whole idea behind thinking models, and they are demonstrably better at many tasks than others.

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

#329

Post by sponnath »

thomasahle wrote: post #221
sponnath wrote: post #171

Earlier quoted context omitted.

Didn't they start failing well before they hit token limits? I'm not sure what the point the source you linked to is trying to make.

OP said: > 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)? And that's what the models did. This is a good answer from the model. Has nothing to do with token limits.

I agree that it's the right answer if it truly doesn't know but I don't think that changes the fact that it failed regardless.

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

#330

Post by neoden »

RugnirViking wrote: post #328
neoden wrote: post #320

Earlier quoted context omitted.

I mean I need to offload state of the puzzle being solved from my brain to an external memory device — paper, in this case. Keeping that state in my mind would be much harder. It's like some people can play chess within their minds without a board, but it's obviously not something that everyone can do

But the LLM can think between writing each token, and indeed can factor in it's own previously written tokens into its answer - thats essentially using a piece of paper and writing stuff down and referring back to it. Thats the whole idea behind thinking models, and they are demonstrably better at many tasks than others.

> But the LLM can think between writing each token

Writing a token is the thinking itself. Thinking models just write some tokens behind the scene, that's the whole difference.

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