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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

#121

Post by ummonk »

FINDarkside wrote: post #99
ummonk wrote: post #40

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 don't think most of the objections are poor at all apart from 3, it's this article that seems to make lots of strawmans. Especially the first objection is often heard because people claim "this paper proves LLMs don't reason". The author moves goalposts and is arguing against about whether LLMs lead to AGI, which is already a strawman for those arguments. And in addition, he even seems to misunderstand AGI, thinkin…

I'm more saying that points 1 and 2 get subsumed under point 5 - to the extent that existing algorithms / logical systems for solving such problems are written by humans, an AGI wouldn't need to match the performance of those algorithms / logical systems - it would merely need to be able to create / use such algorithms and systems itself.

You make a good point though that the question of whether LLMs reason or not should not be conflated with the question of whether they're on the pathway to AGI or not.

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

#123

Post by woopsn »

adamgordonbell wrote: post #33
senko wrote: post #25

Earlier quoted context omitted.

Gary Marcus isn't about "getting real", it's making a name for himself as a contrarian to the popular AI narrative. This article may seem reasonable, but here he's defending a paper that in his previous article he called "A knockout blow for LLMs". Many of his articles seem reasonable (if a bit off) until you read a couple dozen a spot a trend.

This! For all his complaints about llms, his writing could be generated by an llm with a prompt saying: 'write an article responding to this news with an essay saying that you are once again right that this AI stuff is overblown and will never amount to anything.'

Given that the links work, the quotes were actually said, numbers are correct, cited research actually exists etc we can immediately rule that out.

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

#124

Post by serbuvlad »

autobodie wrote: post #104
thomasahle wrote: post #96

> 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…

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 thing generates kind of coherent greentexts"

Since then really only we got: larger models, larger models, search, agents, larger models, chain-of-thought and larger models.

And from a novelty toy we got a set of tools that at the very least massively increase human productivity in a wide range of tasks and certainly pass any Turing test.

Attention really was all you needed.

But of course, if you ask a buddhist monk, he'll tell you we are attention machines, not computation machines.

He'll also tell you, should you listen, that we have a monkey in our mind that is constantly producing new thoughts. This monkey is not who we are, it's an organ. It's thoughts are not our thoughts. It's something we perceive. And that we shouldn't identify with.

Now we have thought-genrating-monkeys with jet engines and adrenaline shots.

This can be good. Thought-genrating-monkeys put us on the moon and wrote Hamlet and the Oddesy.

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.

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

#126

Post by FINDarkside »

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…

What does "choked on it" mean for you? Gemini 2.5 pro gives this, even estimating what amouns of those 3m ships that sank after pianos became common item. Not pasting the full reasoning here since it's rather long.

Combining our estimates:

From Shipwrecks: 12,500 From Dumping: 1,000 From Catastrophes: 500 Total Estimated Pianos at the Bottom of the Sea ≈ 14,000

Also I have to point out that 4o isn't a reasoning model and neither is Sonnet 4, unless thinking mode was enabled.

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

#127

Post by ImageDeeply »

senko wrote: post #84
steamrolled wrote: post #43

Earlier quoted context omitted.

> Gary Marcus isn't about "getting real", it's making a name for himself as a contrarian to the popular AI narrative. That's an odd standard. Not wanting to be wrong is a universal human instinct. By that logic, every person who ever took any position on LLMs is automatically untrustworthy. After all, they made a name for themselves by being pro- or con-. Or maybe a centrist - that's a position too. Either he makes g…

He makes many very good points: For example he continusly calls out AGI hype for what it is, and also showcases dangers of naive use of LLMs (eg. lawyers copy-pasting hallucinated cases into their documents, etc). For this, he has plenty of material! He also makes some very bad points and worse inferences: that LLMs as a technology are useless because they can't lead to AGI, that hallucation makes LLMs useless (but t…

> Compare him with Simon Willison, who also writes about AI a lot, and is vocal about its shortcomings and dangers. Reading Simon, I never get the feeling I'm being sold on a story (either positive or negative), but that I learned something.

Very true!

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

#128

Post by gjm11 »

labrador wrote: post #106
Jabrov wrote: post #88

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.

FWIW I just gave a similar question to Claude Sonnet 4 (I asked about something other than pianos, just in case they're doing some sort of constant fine-tuning on user interactions[1] and to make it less likely that the exact same question is somewhere in its training data[2]) and it gave a very reasonable-looking answer. I haven't tried to double-check any of its specific numbers, some of which don't match my immediate prejudices, but it did the right sort of thing and considered more ways for things to end up on the ocean floor than I instantly thought of. No hints needed or given.

[1] I would bet pretty heavily that they aren't, at least not on the sort of timescale that would be relevant here, but better safe than sorry.

[2] I picked something a bit more obscure than pianos.

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