Seven replies to the viral Apple reasoning paper and why they fall short • Hacker News Board

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

#41

Post by krackers » Sat, Jun 14, 2025, 9:33 PM UTC

barrkel wrote: post #16

jakewins wrote: post #9

Earlier quoted context omitted.

I thought this article seemed like well articulated criticism of the hype cycle - can you be more specific what you mean? Are the results in the Apple paper incorrect?

You need to read everything that Gary writes with the particular axe to grind he has in mind: neurosymbolic AI. That's his specialism, and he essentially has a chip in his shoulder about the attention probabilistic approaches like LLMs are getting, and their relative success. You can see this in this article too. The real question you should be asking is if there is a practical limitation in LLMs and LRMs revealed by…

Hasn't the symbolic vs statistical split in AI existed for a long time? With things like Cyc growing out of the former. I'm not too familiar with linguistics but maybe this extends there too, since I think Chomsky was heavy on formal grammars over probabilistic models [1].

Must be some sort of cognitive sunk cost fallacy, after dedicating your life to one sect, it must be emotionally hard to see the other "keep winning". Of course you'd root for them to fall.

[1] https://norvig.com/chomsky.html

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

#42

Post by labrador » Sat, Jun 14, 2025, 9:33 PM UTC

Jabrov wrote: post #35

labrador wrote: post #31

The key insight is that LLMs can 'reason' when they've seen similar solutions in training data, but this breaks down on truly novel problems. This isn't reasoning exactly, but close enough to be useful in many circumstances. Repeating solutions on demand can be handy, just like repeating facts on demand is handy. Marcus gets this right technically but focuses too much on emotional arguments rather than clear explanat…

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.

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

#43

Post by steamrolled » Sat, Jun 14, 2025, 9:34 PM UTC

senko wrote: post #25

bluefirebrand wrote: post #2

I'm glad to read articles like this one, because I think it is important that we pour some water on the hype cycle If we want to get serious about using these new AI tools then we need to come out of the clouds and get real about their capabilities Are they impressive? Sure. Useful? Yes probably in a lot of cases But we cannot continue the hype this way, it doesn't serve anyone except the people who are financially i…

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.

> 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 good points or he doesn't. Unless he has a track record of distorting facts, his ideological leanings should be irrelevant.

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

#44

Post by FranzFerdiNaN » Sat, Jun 14, 2025, 9:34 PM UTC

DiogenesKynikos wrote: post #28

bluefirebrand wrote: post #2

I'm glad to read articles like this one, because I think it is important that we pour some water on the hype cycle If we want to get serious about using these new AI tools then we need to come out of the clouds and get real about their capabilities Are they impressive? Sure. Useful? Yes probably in a lot of cases But we cannot continue the hype this way, it doesn't serve anyone except the people who are financially i…

I don't understand what people mean when they say that AI is being hyped. AI is at the point where you can have a conversation with it about almost anything, and it will answer more intelligently than 90% of people. That's incredibly impressive, and normal people don't need to be sold on it. They're just naturally impressed by it.

I don’t need a tool that’s right maybe 70% of the time (and that’s me being optimistic). It needs to be right all the time or at least tell you when it doesn’t know for sure, instead of just making up something. Comparing it to going out in the streets and asking random people random questions is not a good comparison.

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

#45

Post by lossolo » Sat, Jun 14, 2025, 9:37 PM UTC

Jabrov wrote: post #35

labrador wrote: post #31

The key insight is that LLMs can 'reason' when they've seen similar solutions in training data, but this breaks down on truly novel problems. This isn't reasoning exactly, but close enough to be useful in many circumstances. Repeating solutions on demand can be handy, just like repeating facts on demand is handy. Marcus gets this right technically but focuses too much on emotional arguments rather than clear explanat…

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"…

They can't create anything novel and it's patently obvious if you understand how they're implemented. But I'm just some anonymous guy on HN, so maybe this time I will just cite the opinion of the DeepMind CEO, who said in a recent interview with The Verge (available on YouTube) that LLMs based on transformers can't create anything truly novel.

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

#46

Post by 2muchcoffeeman » Sat, Jun 14, 2025, 9:38 PM UTC

senko wrote: post #25

bluefirebrand wrote: post #2

I'm glad to read articles like this one, because I think it is important that we pour some water on the hype cycle If we want to get serious about using these new AI tools then we need to come out of the clouds and get real about their capabilities Are they impressive? Sure. Useful? Yes probably in a lot of cases But we cannot continue the hype this way, it doesn't serve anyone except the people who are financially i…

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.

What’s the argument here that he’s not considering all the information regarding GenAI?

That there’s a trend to his opinion?

If I consider all the evidence regarding gravity, all my papers will be “gravity is real”.

In what ways is he only choosing what he wants to hear?

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

#47

Post by anonthrowawy » Sat, Jun 14, 2025, 9:38 PM UTC

bowsamic wrote: post #23

This doesn’t address the primary issue: that they had no methodology for choosing puzzles that weren’t in the training set and indeed while they claimed to have chosen puzzles that aren’t they didn’t explain why they think that. The whole point of the paper was to test LLM reasoning in untrained cases but there’s no reason to expect such puzzles to not part of the training set, and if you don’t have any way of tellin…

how could you prove that?

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

#48

Post by bfung » Sat, Jun 14, 2025, 9:38 PM UTC

Jabrov wrote: post #35

labrador wrote: post #31

The key insight is that LLMs can 'reason' when they've seen similar solutions in training data, but this breaks down on truly novel problems. This isn't reasoning exactly, but close enough to be useful in many circumstances. Repeating solutions on demand can be handy, just like repeating facts on demand is handy. Marcus gets this right technically but focuses too much on emotional arguments rather than clear explanat…

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"…

Any links or examples available? Curious to try it out

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

#49

Post by 2muchcoffeeman » Sat, Jun 14, 2025, 9:40 PM UTC

bandrami wrote: post #39

bluefirebrand wrote: post #2

I'm glad to read articles like this one, because I think it is important that we pour some water on the hype cycle If we want to get serious about using these new AI tools then we need to come out of the clouds and get real about their capabilities Are they impressive? Sure. Useful? Yes probably in a lot of cases But we cannot continue the hype this way, it doesn't serve anyone except the people who are financially i…

How actually useful are they though? We've had more than a year now of saying these things 10X knowledge workers and creatives, so.... where is the output? Is there a new office suite I can try? 10 times as many mobile apps? A huge new library of ebooks? Is this actually in practice producing things beyond Ghibli memes and RETVRN nostalgia slop?

I think it largely depends on what you’re writing. I’ve had it reply to corporate emails which is good since I need to sound professional not human.

If I’m coding it still needs a lot of baby sitting and sometimes I’m much faster than it.

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

#50

Post by spookie » Sat, Jun 14, 2025, 9:45 PM UTC

fhd2 wrote: post #4

bluefirebrand wrote: post #2

I'm glad to read articles like this one, because I think it is important that we pour some water on the hype cycle If we want to get serious about using these new AI tools then we need to come out of the clouds and get real about their capabilities Are they impressive? Sure. Useful? Yes probably in a lot of cases But we cannot continue the hype this way, it doesn't serve anyone except the people who are financially i…

Even of the people invested in these tools, hype only benefits those attempting a pump and dump scheme, or those selling training, consulting or similar services around AI. People who try to make genuine progress, while there's more money in it now, might just have to deal with another AI winter soon at this rate.

Think the same thing, we need more breakthroughs. Until then, it is still risky to rely on AI for most applications.

The sad thing is that most would take this comment the wrong way. Assuming it is just another doomer take. No, there is still a lot to do, and promissing the world too soon will only lead to disappointment.