6 ms·

Is this the infinite monkey Shakespeare trope?

CamperBob2 2y ago
Yeah. Monkeys. Monkeys that write useful C and Python code that needs a bit less revision every time there's a model update. Can we just give the "stochastic parrot" and "monkeys with typewriters" schtick a rest? It made for novel commentary three or four years ago, but at this point, these posts themselves read like the work of parrots. They are no longer interesting, insightful, or (for that matter) true.
- visarga 2y ago
  If you think about it, humans necessarily use abstractions, from the edge detectors in retina to concepts like democracy. But do we really understand? All abstractions leak, and nobody knows the whole stack. For all the poorly grasped abstractions we are using, we are also just parroting. How many times are we doing things because "that is how they are done" never wondering why? Take ML itself, people are saying it's little more than alchemy (stir the pile). Are we just parroting approaches that have worked in practice without real understanding? Is it possible to have centralized understanding, even in principle, or is all understanding distributed among us? My conclusion is that we have a patchwork of partial understanding, stitched together functionally by abstractions. When I go to the doctor, I don't study medicine first, I trust the doctor. Trust takes the place of genuine understanding. So humans, like AI, use distributed and functional understanding, we don't have genuine understanding as meant by philosophers like Searle in the Chinese Room. No single neuron in the brain understands anything, but together they do. Similarly, no single human understands genuinely, but society together manages to function. There is no homunculus, no centralized understander anywhere. We humans are also stochastic parrots of abstractions we don't really grok to the full extent.
  - kaechle 2y ago
    Great points. We're pattern-matching shortcut machines, without a doubt. In most contexts, not even good ones. > When I go to the doctor, I don't study medicine first, I trust the doctor. Trust takes the place of genuine understanding. The ultimate abstraction! Trust is highly irrational by definition. But we do it all day every day, lest we be classified as psychologically unfit for society. Which is to say, mental health is predicated on a not-insignificant amount of rationalizations and self-deceptions. Hallucinations, even.
  - throwaway290 2y ago
    > My conclusion Are you saying you understood something? Was it genuine? Do you think LLM feels the same thing?
    - visarga 2y ago
      Haha, "I doubt therefore I am^W don't understand"
    - exe34 2y ago
      do llms feel?
      - throwaway290 2y ago
        seems like would be the implication if yes
- kaechle 2y ago
  Every time I read "stochastic parrot," my always-deterministic human brain surfaces this quote: > “Most people are other people. Their thoughts are someone else's opinions, their lives a mimicry, their passions a quotation.” - Oscar Wilde, a great ape with a pen
  - OKRainbowKid 2y ago
    Reading this quote makes me wonder why I should believe that I am somehow special or different, and not just another "other".
    - HeatrayEnjoyer 2y ago
      That's just it. We're not unique. We've always been animals running on instinct in reaction to our environment. Our instincts are more complex than other animals but they are not special and they are replicable.
- 93po 2y ago
  AI news article comments bingo card: * Tired ClosedAI joke * Claiming it's predictive text engine that isn't useful for anything * Safety regulations are either good or bad, depending on who's proposing them * Fear mongering about climate impact * Bringing up Elon for no reason * AI will never be able to [some pretty achievable task] * Tired arguments from pro-IP / copyright sympathizers
  - aurareturn 2y ago
    >* Claiming it's predictive text engine that isn't useful for anything This one is very common on HN and it's baffling. Even if it's predictive text, who the hell cares if it achieves its goals? If an LLM is actually a bunch of dolphins typing on a keyboard made for dolphins, I could care less if it does what I need it to do. For people who continue to repeat this on HN, why? I just want to know out of my curiosity. >* AI will never be able to [some pretty achievable task] Also very common on HN. You forgot the "AI will never be able to do what a human can do in the exact way a human does it so AI will never achieve x".
    - peterhadlaw 2y ago
      I think it's less about what it is, but what it claims to be. "Artificial Intelligence"... It's not. Dolphin keyboard squad (DKS), then sure. The "just fancy autocomplete" is in response, but a criticism
      - aurareturn 2y ago
        What's wrong with the phrase "artificial intelligence"? To me, it doesn't imply that it's human-like. It's just human created intelligence to me.
        danparsonson 2y ago
        Partly because "artificial intelligence" is a loaded phrase which brings implications of AGI along for the ride, partly because "intelligence" is not a well defined term, so an artificial version of it could be argued to be almost anything, and partly because even if you lean on the colloquial understanding of what "intelligence" is, ChatGPT (and its friends) still isn't it. It's a Chinese Room - or a stochastic parrot.
- ffsm8 2y ago
  > novel commentary three or four years ago, Chatgpt was released November 2022. That's one year and 10 months ago. Their marketing started in the summer of the same year, still far of from 3-4 years.
  - Banou 2y ago
    But chatgpt wasnt the first, openai had coding playground with gpt2, and you could already code even before that, around 2020 already, so I'd say it has been 3-4years
  - deleted 2y ago
    [deleted]
  - killerstorm 2y ago
    GPT-3 paper announcement got 200 comments on HN back in 2020. It doesn't matter when marketing started, people were already discussing it in 2019-2020. Stochastic parrot: The term was coined by Emily M. Bender[2][3] in the 2021 artificial intelligence research paper "On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? " by Bender, Timnit Gebru, Angelina McMillan-Major, and Margaret Mitchell.[4]
    - ffsm8 2y ago
      Confusing to read your comment. So the term was coined 3 yrs ago, but it's been 4 years out of date? Seems legit It could be that the term no longer applies, but there is no way you could honestly make that claim pre gpt4, and that's not 3-4yrs ago
      - killerstorm 2y ago
        Text generated by GPT-3 usually makes more sense than your comments.
        ffsm8 2y ago
        Arw, did I hurt your feelings by pointing out how nonsensical you were? Poor boy <3
- hegFdH 2y ago
  The infinite monkey post was in response to this claim, which, like the universal approximation theorem, is useless in practice: "We have mathematically proven that transformers can solve any problem, provided they are allowed to generate as many intermediate reasoning tokens as needed. Remarkably, constant depth is sufficient." Like an LLM, you omit the context and browbeat people with the "truth" you want to propagate. Together with many political forbidden terms since 2020, let us now also ban "stochastic parrot" in order to have a goodbellyfeel newspeak.
  - chaosist 2y ago
    There is also a problem of "stochastic parrot" being constantly used in a pejorative sense as opposed to a neutral term to keep grounded and skeptical. Of course, it is an overly broad stroke that doesn't quite capture all the nuance of the model but the alternative of "come on guys, just admit the model is thinking" is much worse and has much less to do with reality.
throwup238 2y ago
More like the universal approximation theorem extended to computation rather than network complexity: https://en.wikipedia.org/wiki/Universal_approximation_theorem https://en.wikipedia.org/wiki/Universal_approximation_theore...
- immibis 2y ago
  The universal approximation theorem is good to know because says there's no theoretical upper bound to a function-approximating NN's accuracy. In practice it says nothing about what can be realistically achieved, though.
nopinsight 2y ago
A key difference is that the way LMMs (Large Multimodal Models) generate output is far from random. These models can imitate/blend existing information or imitate/probably blend known reasoning methods in the training data. The latter is a key distinguishing feature of the new OpenAI o1 models. Thus, the signal-to-noise ratio of their output is generally way better than infinite monkeys. Arguably, humans rely on similar modes of "thinking" most of the time as well.

Yeah. Monkeys. Monkeys that write useful C and Python code that needs a bit less revision every time there's a model update. Can we just give the "stochastic parrot" and "monkeys with typewriters" schtick a rest? It made for novel commentary three or four years ago, but at this point, these posts themselves read like the work of parrots. They are no longer interesting, insightful, or (for that matter) true.

visarga 2y ago
If you think about it, humans necessarily use abstractions, from the edge detectors in retina to concepts like democracy. But do we really understand? All abstractions leak, and nobody knows the whole stack. For all the poorly grasped abstractions we are using, we are also just parroting. How many times are we doing things because "that is how they are done" never wondering why? Take ML itself, people are saying it's little more than alchemy (stir the pile). Are we just parroting approaches that have worked in practice without real understanding? Is it possible to have centralized understanding, even in principle, or is all understanding distributed among us? My conclusion is that we have a patchwork of partial understanding, stitched together functionally by abstractions. When I go to the doctor, I don't study medicine first, I trust the doctor. Trust takes the place of genuine understanding. So humans, like AI, use distributed and functional understanding, we don't have genuine understanding as meant by philosophers like Searle in the Chinese Room. No single neuron in the brain understands anything, but together they do. Similarly, no single human understands genuinely, but society together manages to function. There is no homunculus, no centralized understander anywhere. We humans are also stochastic parrots of abstractions we don't really grok to the full extent.
- kaechle 2y ago
  Great points. We're pattern-matching shortcut machines, without a doubt. In most contexts, not even good ones. > When I go to the doctor, I don't study medicine first, I trust the doctor. Trust takes the place of genuine understanding. The ultimate abstraction! Trust is highly irrational by definition. But we do it all day every day, lest we be classified as psychologically unfit for society. Which is to say, mental health is predicated on a not-insignificant amount of rationalizations and self-deceptions. Hallucinations, even.
- throwaway290 2y ago
  > My conclusion Are you saying you understood something? Was it genuine? Do you think LLM feels the same thing?
  - visarga 2y ago
    Haha, "I doubt therefore I am^W don't understand"
  - exe34 2y ago
    do llms feel?
    - throwaway290 2y ago
      seems like would be the implication if yes
kaechle 2y ago
Every time I read "stochastic parrot," my always-deterministic human brain surfaces this quote: > “Most people are other people. Their thoughts are someone else's opinions, their lives a mimicry, their passions a quotation.” - Oscar Wilde, a great ape with a pen
- OKRainbowKid 2y ago
  Reading this quote makes me wonder why I should believe that I am somehow special or different, and not just another "other".
  - HeatrayEnjoyer 2y ago
    That's just it. We're not unique. We've always been animals running on instinct in reaction to our environment. Our instincts are more complex than other animals but they are not special and they are replicable.
93po 2y ago
AI news article comments bingo card: * Tired ClosedAI joke * Claiming it's predictive text engine that isn't useful for anything * Safety regulations are either good or bad, depending on who's proposing them * Fear mongering about climate impact * Bringing up Elon for no reason * AI will never be able to [some pretty achievable task] * Tired arguments from pro-IP / copyright sympathizers
- aurareturn 2y ago
  >* Claiming it's predictive text engine that isn't useful for anything This one is very common on HN and it's baffling. Even if it's predictive text, who the hell cares if it achieves its goals? If an LLM is actually a bunch of dolphins typing on a keyboard made for dolphins, I could care less if it does what I need it to do. For people who continue to repeat this on HN, why? I just want to know out of my curiosity. >* AI will never be able to [some pretty achievable task] Also very common on HN. You forgot the "AI will never be able to do what a human can do in the exact way a human does it so AI will never achieve x".
  - peterhadlaw 2y ago
    I think it's less about what it is, but what it claims to be. "Artificial Intelligence"... It's not. Dolphin keyboard squad (DKS), then sure. The "just fancy autocomplete" is in response, but a criticism
    - aurareturn 2y ago
      What's wrong with the phrase "artificial intelligence"? To me, it doesn't imply that it's human-like. It's just human created intelligence to me.
      - danparsonson 2y ago
        Partly because "artificial intelligence" is a loaded phrase which brings implications of AGI along for the ride, partly because "intelligence" is not a well defined term, so an artificial version of it could be argued to be almost anything, and partly because even if you lean on the colloquial understanding of what "intelligence" is, ChatGPT (and its friends) still isn't it. It's a Chinese Room - or a stochastic parrot.
ffsm8 2y ago
> novel commentary three or four years ago, Chatgpt was released November 2022. That's one year and 10 months ago. Their marketing started in the summer of the same year, still far of from 3-4 years.
- Banou 2y ago
  But chatgpt wasnt the first, openai had coding playground with gpt2, and you could already code even before that, around 2020 already, so I'd say it has been 3-4years
- deleted 2y ago
  [deleted]
- killerstorm 2y ago
  GPT-3 paper announcement got 200 comments on HN back in 2020. It doesn't matter when marketing started, people were already discussing it in 2019-2020. Stochastic parrot: The term was coined by Emily M. Bender[2][3] in the 2021 artificial intelligence research paper "On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? " by Bender, Timnit Gebru, Angelina McMillan-Major, and Margaret Mitchell.[4]
  - ffsm8 2y ago
    Confusing to read your comment. So the term was coined 3 yrs ago, but it's been 4 years out of date? Seems legit It could be that the term no longer applies, but there is no way you could honestly make that claim pre gpt4, and that's not 3-4yrs ago
    - killerstorm 2y ago
      Text generated by GPT-3 usually makes more sense than your comments.
      - ffsm8 2y ago
        Arw, did I hurt your feelings by pointing out how nonsensical you were? Poor boy <3
hegFdH 2y ago
The infinite monkey post was in response to this claim, which, like the universal approximation theorem, is useless in practice: "We have mathematically proven that transformers can solve any problem, provided they are allowed to generate as many intermediate reasoning tokens as needed. Remarkably, constant depth is sufficient." Like an LLM, you omit the context and browbeat people with the "truth" you want to propagate. Together with many political forbidden terms since 2020, let us now also ban "stochastic parrot" in order to have a goodbellyfeel newspeak.
- chaosist 2y ago
  There is also a problem of "stochastic parrot" being constantly used in a pejorative sense as opposed to a neutral term to keep grounded and skeptical. Of course, it is an overly broad stroke that doesn't quite capture all the nuance of the model but the alternative of "come on guys, just admit the model is thinking" is much worse and has much less to do with reality.

throwup238 2y ago

More like the universal approximation theorem extended to computation rather than network complexity: https://en.wikipedia.org/wiki/Universal_approximation_theorem https://en.wikipedia.org/wiki/Universal_approximation_theore...

immibis 2y ago
The universal approximation theorem is good to know because says there's no theoretical upper bound to a function-approximating NN's accuracy. In practice it says nothing about what can be realistically achieved, though.

nopinsight 2y ago

A key difference is that the way LMMs (Large Multimodal Models) generate output is far from random. These models can imitate/blend existing information or imitate/probably blend known reasoning methods in the training data. The latter is a key distinguishing feature of the new OpenAI o1 models. Thus, the signal-to-noise ratio of their output is generally way better than infinite monkeys. Arguably, humans rely on similar modes of "thinking" most of the time as well.