6 ms·

The Illusion of “The Illusion of Thinking”

57 points by jonbaer 1y ago

davedx 1y ago
I stumbled on the paper the article talks about on /r/LocalLlama (this post: https://www.reddit.com/r/LocalLLaMA/comments/1l6ibwg/when_you_figure_out_its_all_just_math/ https://www.reddit.com/r/LocalLLaMA/comments/1l6ibwg/when_yo...) I found this comment to be relevant: "Keep in mind this whitepaper is really just Apple circling the wagons because they have dick for proprietary AI tech." When you question the source, it really does raise eyebrows, especially as an Apple shareholder: that these Apple employees are busy not working on their own AI programme that's now insanely far behind other big tech companies, but are instead spending their time casting shade on the reasoning models developed at other AI labs. What's the motivation here, really? The paper itself isn't particularly insightful or ground-breaking.
- smitty1e 1y ago
  > I found this comment to be relevant: "Keep in mind this whitepaper is really just Apple circling the wagons because they have dick for proprietary AI tech." Now, if we fed the relevant references into an AI model, would the model offer this as a possible motive for the paper in question?
  - K0balt 1y ago
    Probably.
- emp17344 1y ago
  The paper was written by highly accomplished ML researchers who don’t have any stake in Apple’s continued success. Framing this peer-reviewed research written by respected authors as “sour grapes” is intellectually dishonest.
  - reliabilityguy 1y ago
    > Framing this peer-reviewed research How do you know it was peer-reviewed? What venue had accepted this paper for publication?
    - tough 1y ago
      how many papers do apple publish under their own CDN/domains this was certainly a first for me when i saw it pop on hn the other day
      - reliabilityguy 1y ago
        They publish papers pretty frequently https://machinelearning.apple.com/research?domain=Speech%20and%20Natural%20Language%20Processing https://machinelearning.apple.com/research?domain=Speech%20a... Doesn’t mean they are peer reviewed
        tough 1y ago
        got it thanks
  - dandellion 1y ago
    It's easy to argue about the people who write the paper and their incentives. It takes a lot more effort to prove that the data, the procedure or the conclusion in the paper has flaws, and back it up.
  - android521 1y ago
    If they get paid by apple, they have a stake
    - emp17344 1y ago
      This kind of statement isn’t productive. Everyone has a bias. If you don’t believe the paper is valid, I’d like to hear your substantive critique.
- tikhonj 1y ago
  The motivation is doing research to better understand AI? People's time and attention is not fungible—especially in inherently creative pursuits like research—and the mindset in your comment is exactly the sort of superficial administrative reasoning that leads to hype bubbles unconstrained by reality. "Why are you wasting your time trying to understand what we're doing instead of rushing ahead without thinking" is absolutely something I've heard from managers and executives, albeit phrased more politically, and it never ends well in a holistic accounting.
- JimDabell 1y ago
  This is ridiculous. If Apple wanted to make competing AI look bad, getting some researchers to publish a critical paper is hardly going to have any kind of worthwhile outcome.
absummer 1y ago
The painful thing about achieving AGI, is that humans reasoning about AI will seem so dumb.
K0balt 1y ago
[flagged]
- matusp 1y ago
  > LLMs are human culture compiled into code. They will strongly tend to follow patterns of human behavior, to the point of that being their main (only?) feature. Perhaps, but this already disproves the idea of superhuman PhD+ level AI agents. --- I think that the paper has a good motivation. It would be great if we were able to somehow define the complexity of problems AIs are able to tackle. But the Hanoi towers puzzle does not seem like a good match, especially since you can generate the solution mechanically, if you are aware of the very simple algorithm.
  - K0balt 1y ago
    > Perhaps, but this already disproves the idea of superhuman PhD+ level AI agents. The value in AI is not some higher level of understanding, but rather the ability to (potentially) carry on millions of independent and interleaved thought threads/conversations at once, and to work tirelessly at a high throughput. Perhaps with some kind of recursive approach with iterative physical grounding that tests hypothesis against ground truth, AI can transcend human levels of understanding, but for now we need to understand that AI is going to be more of intern-level assistance with occasional episodes of drunk uncle bob.
  - K0balt 1y ago
    Your comment sparked a thought that might be of value. If LLMs are indeed primarily useful for solving simpler tasks and will systematically balk at complex problems, perhaps a variant of the “thinking” approach is of value? Where a task that is high iteration is approached by solving a part of the problem and then identifying the next -small part- of the problem, in an iteration loop. I can also see this easily going off the rails, but perhaps posing each following iteration as -small- and encapsulating only the relevant context while trimming off the context tail could work as an agentic behavior spawned from the main supervisory task? Of course maybe just having the model write and run code would probably be better for many classes of problems. Maybe something to be looked at, if it’s not already being used.
codeflo 1y ago
I'm seriously fed up with all this fact-free AI hype. Whenever an LLM regurgitates training data, it's heralded as the coming of AGI. Whenever it's shown that they can't solve any novel problem, the research is in bad faith (but please make sure to publish the questions so that the next model version can solve them -- of course completely by chance). Here's a quote from the article: > How many humans can sit down and correctly work out a thousand Tower of Hanoi steps? There are definitely many humans who could do this. But there are also many humans who can’t. Do those humans not have the ability to reason? Of course they do! They just don’t have the conscientiousness and patience required to correctly go through a thousand iterations of the algorithm by hand. (Footnote: I would like to sit down all the people who are smugly tweeting about this with a pen and paper and get them to produce every solution step for ten-disk Tower of Hanoi.) In case someone imagines that fancy recursive reasoning is necessary to solve the Towers of Hanoi, here's the algorithm to move 10 (or any even number of) disks from peg A to peg C: 1. Move one disk from peg A to peg B or vice versa, whichever move is legal. 2. Move one disk from peg A to peg C or vice versa, whichever move is legal. 3. Move one disk from peg B to peg C or vice versa, whichever move is legal. 4. Goto 1. Second-graders can follow that, if motivated enough. There's now constant, nonstop, obnoxious shouting on every channel about how these AI models have solved the Turing test (one wonders just how stupid these "evaluators" were), are at the level of junior devs (LOL), and actually already have "PhD level" reasoning capabilities. I don't know who is supposed to be fooled -- we have access to these things, we can try them. One can easily knock out any latest version of GPT-PhD-level-model-of-the-week with a trivial question. Nothing fundamentally changed about that since GPT-2. The hype and the observable reality are now so far apart that one really has to wonder: Are people this easily gullible? Or do so many people in tech benefit from the hype train that they don't want to rain on the parade?
- akoboldfrying 1y ago
  > obnoxious shouting on every channel about how these AI models have solved the Turing test (one wonders just how stupid these "evaluators" were) Huh? Schoolteachers and university professors complaining about being unable to distinguish ChatGPT-written essay answers from student-written essay answers is literally ChatGPT passing the Turing test in real time.
synthmeat 1y ago
I'm not sure about the paper and claims on the whole but the Hanoi part has received some shade here https://x.com/scaling01/status/1931783050511126954 https://x.com/scaling01/status/1931783050511126954
- sigmoid10 1y ago
  At the end of the day, I fully expect large-n Hanoi and all these things to end up as yet another benchmark. Like all the needles-in-haystack or spelling tests that people used to show shortcomings of LLMs and that were actually just technical implementation artefacts and got solved pretty fast by integrating that kind of problem into training. LLMs will always have to use a slightly different approach to reasoning than humans because of these technical aspects, but that doesn't mean that they are fundamentally inferior or something. It only means we can't rely on human training data forever and have to look more towards stuff like RL.
  - ai-christianson 1y ago
    All I know is I've been getting real world value out of LLMs since ~GPT3 and they've been producing more value with each release.
    - sigmoid10 1y ago
      People also like to forget that from the dawn of modern computing and AI research like 60 years ago all the way to 7 years ago, the best models in the world could barely form a few coherent sentences. If LLMs are this century's transistor, we are barely beyond the point of building-sized computers that are trying to find normal life applications.
talles 1y ago
Someone please reply with the title "The illusion of The illusion of The illusion of Thinking".
deleted 1y ago
[deleted]
deleted 1y ago
[deleted]
KingMob 1y ago
To paraphrase GOB Bluth: "Illusions, Michael! Thinking is something a whore does for money!" ...slow pan to shocked group of staring children... "..or cocaine!"
ksec 1y ago
I am a little lost. >The first issue I have with the paper is that Tower of Hanoi is a worse test case for reasoning than math and coding. If you’re worried that math and coding benchmarks suffer from contamination, why would you pick well-known puzzles for which we know the solutions exist in the training data? Isn't that exactly what is wrong? It is in the training data and it cant complete it. It simply isn't reasoning, it is second guessing a lot of things as though it is reasoning.
deleted 1y ago
[deleted]
crustycoder 1y ago
My favourite example of the underlying probabilistic nature of LLMs is related to a niche hobby of mine, English Change Ringing. Every time someone asks an LLM a question that requires more than a basic definition of what Change Ringing is, the result is hilarious. Not only do the answers suffer from factual hallucinations, they aren't even internally logically consistent. It's literally just probabilistic word soup, and glaringly obviously so. Although there isn't a vast corpus on Method Ringing, there is a fair amount; the "rules" are online (https://framework.cccbr.org.uk/version2/index.html https://framework.cccbr.org.uk/version2/index.html), Change ringing is based on pure maths (Group Theory) and has been linked with CS from when CS first started - it's mentioned in Knuth, and the Steinhaus–Johnson–Trotter algorithm for generating permutations wasn't invented by them in the 1960's, it was known to Change Ringers in the 1650's. Think of it of Towers of Hanoi with knobs on :-) So it would seem a good fit for automated reasoning, indeed such things already exist - https://ropley.com/?page_id=25777 https://ropley.com/?page_id=25777. If I asked a non-ringing human to explain to me how to ring Cambridge Major, they'd say "Sorry, I don't know" and an LLM with insufficient training data would probably say the same. The problem is when LLMs know just enough to be dangerous, but they don't know what they don't know. The more abstruse a topic is, the worse LLMs are going to do at it, and it's precisely those areas where people are most likely to turn to them for answers. They'll get one that's grammatically correct and sounds authoritative - but they almost certainly won't know if it's nonsense. Adding a "reliability" score to LLM output seems eminently feasible, but due to the hype and commercial pressures around the current generation of LLMs, that's never going to happen as the pressure is on to produce plausible sounding output, even if it's bullshit. https://www.lawgazette.co.uk/news/appalling-high-court-judge-alerts-regulators-over-fake-case-authorities/5123200.article https://www.lawgazette.co.uk/news/appalling-high-court-judge...
stephc_int13 1y ago
It is very difficult to formally prove that a given system has reached or is close to reach some limits, and it is even more difficult with neural nets given their black box nature. The paper published by Apple should not be considered as a definitive statement, but more like a hint and a conversation starter. In think that many important actors in the AI field are making predictions about the future and potential of AI with almost nothing to back their claims.

57 points by jonbaer 1y ago

I stumbled on the paper the article talks about on /r/LocalLlama (this post: https://www.reddit.com/r/LocalLLaMA/comments/1l6ibwg/when_you_figure_out_its_all_just_math/ https://www.reddit.com/r/LocalLLaMA/comments/1l6ibwg/when_yo...) I found this comment to be relevant: "Keep in mind this whitepaper is really just Apple circling the wagons because they have dick for proprietary AI tech." When you question the source, it really does raise eyebrows, especially as an Apple shareholder: that these Apple employees are busy not working on their own AI programme that's now insanely far behind other big tech companies, but are instead spending their time casting shade on the reasoning models developed at other AI labs. What's the motivation here, really? The paper itself isn't particularly insightful or ground-breaking.

smitty1e 1y ago
> I found this comment to be relevant: "Keep in mind this whitepaper is really just Apple circling the wagons because they have dick for proprietary AI tech." Now, if we fed the relevant references into an AI model, would the model offer this as a possible motive for the paper in question?
- K0balt 1y ago
  Probably.
emp17344 1y ago
The paper was written by highly accomplished ML researchers who don’t have any stake in Apple’s continued success. Framing this peer-reviewed research written by respected authors as “sour grapes” is intellectually dishonest.
- reliabilityguy 1y ago
  > Framing this peer-reviewed research How do you know it was peer-reviewed? What venue had accepted this paper for publication?
  - tough 1y ago
    how many papers do apple publish under their own CDN/domains this was certainly a first for me when i saw it pop on hn the other day
    - reliabilityguy 1y ago
      They publish papers pretty frequently https://machinelearning.apple.com/research?domain=Speech%20and%20Natural%20Language%20Processing https://machinelearning.apple.com/research?domain=Speech%20a... Doesn’t mean they are peer reviewed
      - tough 1y ago
        got it thanks
- dandellion 1y ago
  It's easy to argue about the people who write the paper and their incentives. It takes a lot more effort to prove that the data, the procedure or the conclusion in the paper has flaws, and back it up.
- android521 1y ago
  If they get paid by apple, they have a stake
  - emp17344 1y ago
    This kind of statement isn’t productive. Everyone has a bias. If you don’t believe the paper is valid, I’d like to hear your substantive critique.
tikhonj 1y ago
The motivation is doing research to better understand AI? People's time and attention is not fungible—especially in inherently creative pursuits like research—and the mindset in your comment is exactly the sort of superficial administrative reasoning that leads to hype bubbles unconstrained by reality. "Why are you wasting your time trying to understand what we're doing instead of rushing ahead without thinking" is absolutely something I've heard from managers and executives, albeit phrased more politically, and it never ends well in a holistic accounting.
JimDabell 1y ago
This is ridiculous. If Apple wanted to make competing AI look bad, getting some researchers to publish a critical paper is hardly going to have any kind of worthwhile outcome.

absummer 1y ago

The painful thing about achieving AGI, is that humans reasoning about AI will seem so dumb.

K0balt 1y ago

[flagged]

matusp 1y ago
> LLMs are human culture compiled into code. They will strongly tend to follow patterns of human behavior, to the point of that being their main (only?) feature. Perhaps, but this already disproves the idea of superhuman PhD+ level AI agents. --- I think that the paper has a good motivation. It would be great if we were able to somehow define the complexity of problems AIs are able to tackle. But the Hanoi towers puzzle does not seem like a good match, especially since you can generate the solution mechanically, if you are aware of the very simple algorithm.
- K0balt 1y ago
  > Perhaps, but this already disproves the idea of superhuman PhD+ level AI agents. The value in AI is not some higher level of understanding, but rather the ability to (potentially) carry on millions of independent and interleaved thought threads/conversations at once, and to work tirelessly at a high throughput. Perhaps with some kind of recursive approach with iterative physical grounding that tests hypothesis against ground truth, AI can transcend human levels of understanding, but for now we need to understand that AI is going to be more of intern-level assistance with occasional episodes of drunk uncle bob.
- K0balt 1y ago
  Your comment sparked a thought that might be of value. If LLMs are indeed primarily useful for solving simpler tasks and will systematically balk at complex problems, perhaps a variant of the “thinking” approach is of value? Where a task that is high iteration is approached by solving a part of the problem and then identifying the next -small part- of the problem, in an iteration loop. I can also see this easily going off the rails, but perhaps posing each following iteration as -small- and encapsulating only the relevant context while trimming off the context tail could work as an agentic behavior spawned from the main supervisory task? Of course maybe just having the model write and run code would probably be better for many classes of problems. Maybe something to be looked at, if it’s not already being used.

codeflo 1y ago

I'm seriously fed up with all this fact-free AI hype. Whenever an LLM regurgitates training data, it's heralded as the coming of AGI. Whenever it's shown that they can't solve any novel problem, the research is in bad faith (but please make sure to publish the questions so that the next model version can solve them -- of course completely by chance). Here's a quote from the article: > How many humans can sit down and correctly work out a thousand Tower of Hanoi steps? There are definitely many humans who could do this. But there are also many humans who can’t. Do those humans not have the ability to reason? Of course they do! They just don’t have the conscientiousness and patience required to correctly go through a thousand iterations of the algorithm by hand. (Footnote: I would like to sit down all the people who are smugly tweeting about this with a pen and paper and get them to produce every solution step for ten-disk Tower of Hanoi.) In case someone imagines that fancy recursive reasoning is necessary to solve the Towers of Hanoi, here's the algorithm to move 10 (or any even number of) disks from peg A to peg C: 1. Move one disk from peg A to peg B or vice versa, whichever move is legal. 2. Move one disk from peg A to peg C or vice versa, whichever move is legal. 3. Move one disk from peg B to peg C or vice versa, whichever move is legal. 4. Goto 1. Second-graders can follow that, if motivated enough. There's now constant, nonstop, obnoxious shouting on every channel about how these AI models have solved the Turing test (one wonders just how stupid these "evaluators" were), are at the level of junior devs (LOL), and actually already have "PhD level" reasoning capabilities. I don't know who is supposed to be fooled -- we have access to these things, we can try them. One can easily knock out any latest version of GPT-PhD-level-model-of-the-week with a trivial question. Nothing fundamentally changed about that since GPT-2. The hype and the observable reality are now so far apart that one really has to wonder: Are people this easily gullible? Or do so many people in tech benefit from the hype train that they don't want to rain on the parade?

akoboldfrying 1y ago
> obnoxious shouting on every channel about how these AI models have solved the Turing test (one wonders just how stupid these "evaluators" were) Huh? Schoolteachers and university professors complaining about being unable to distinguish ChatGPT-written essay answers from student-written essay answers is literally ChatGPT passing the Turing test in real time.

synthmeat 1y ago

I'm not sure about the paper and claims on the whole but the Hanoi part has received some shade here https://x.com/scaling01/status/1931783050511126954 https://x.com/scaling01/status/1931783050511126954

sigmoid10 1y ago
At the end of the day, I fully expect large-n Hanoi and all these things to end up as yet another benchmark. Like all the needles-in-haystack or spelling tests that people used to show shortcomings of LLMs and that were actually just technical implementation artefacts and got solved pretty fast by integrating that kind of problem into training. LLMs will always have to use a slightly different approach to reasoning than humans because of these technical aspects, but that doesn't mean that they are fundamentally inferior or something. It only means we can't rely on human training data forever and have to look more towards stuff like RL.
- ai-christianson 1y ago
  All I know is I've been getting real world value out of LLMs since ~GPT3 and they've been producing more value with each release.
  - sigmoid10 1y ago
    People also like to forget that from the dawn of modern computing and AI research like 60 years ago all the way to 7 years ago, the best models in the world could barely form a few coherent sentences. If LLMs are this century's transistor, we are barely beyond the point of building-sized computers that are trying to find normal life applications.

talles 1y ago

Someone please reply with the title "The illusion of The illusion of The illusion of Thinking".

deleted 1y ago

[deleted]

deleted 1y ago

[deleted]

KingMob 1y ago

To paraphrase GOB Bluth: "Illusions, Michael! Thinking is something a whore does for money!" ...slow pan to shocked group of staring children... "..or cocaine!"

ksec 1y ago

I am a little lost. >The first issue I have with the paper is that Tower of Hanoi is a worse test case for reasoning than math and coding. If you’re worried that math and coding benchmarks suffer from contamination, why would you pick well-known puzzles for which we know the solutions exist in the training data? Isn't that exactly what is wrong? It is in the training data and it cant complete it. It simply isn't reasoning, it is second guessing a lot of things as though it is reasoning.

deleted 1y ago

[deleted]

crustycoder 1y ago

My favourite example of the underlying probabilistic nature of LLMs is related to a niche hobby of mine, English Change Ringing. Every time someone asks an LLM a question that requires more than a basic definition of what Change Ringing is, the result is hilarious. Not only do the answers suffer from factual hallucinations, they aren't even internally logically consistent. It's literally just probabilistic word soup, and glaringly obviously so. Although there isn't a vast corpus on Method Ringing, there is a fair amount; the "rules" are online (https://framework.cccbr.org.uk/version2/index.html https://framework.cccbr.org.uk/version2/index.html), Change ringing is based on pure maths (Group Theory) and has been linked with CS from when CS first started - it's mentioned in Knuth, and the Steinhaus–Johnson–Trotter algorithm for generating permutations wasn't invented by them in the 1960's, it was known to Change Ringers in the 1650's. Think of it of Towers of Hanoi with knobs on :-) So it would seem a good fit for automated reasoning, indeed such things already exist - https://ropley.com/?page_id=25777 https://ropley.com/?page_id=25777. If I asked a non-ringing human to explain to me how to ring Cambridge Major, they'd say "Sorry, I don't know" and an LLM with insufficient training data would probably say the same. The problem is when LLMs know just enough to be dangerous, but they don't know what they don't know. The more abstruse a topic is, the worse LLMs are going to do at it, and it's precisely those areas where people are most likely to turn to them for answers. They'll get one that's grammatically correct and sounds authoritative - but they almost certainly won't know if it's nonsense. Adding a "reliability" score to LLM output seems eminently feasible, but due to the hype and commercial pressures around the current generation of LLMs, that's never going to happen as the pressure is on to produce plausible sounding output, even if it's bullshit. https://www.lawgazette.co.uk/news/appalling-high-court-judge-alerts-regulators-over-fake-case-authorities/5123200.article https://www.lawgazette.co.uk/news/appalling-high-court-judge...

stephc_int13 1y ago

It is very difficult to formally prove that a given system has reached or is close to reach some limits, and it is even more difficult with neural nets given their black box nature. The paper published by Apple should not be considered as a definitive statement, but more like a hint and a conversation starter. In think that many important actors in the AI field are making predictions about the future and potential of AI with almost nothing to back their claims.