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*Outsourcing to a parrot on steroids which will make mistakes, produce stale ugly ui with 100px border radius, 50px padding and rainbow hipster shadows, write c

*Outsourcing to a parrot on steroids which will make mistakes, produce stale ugly ui with 100px border radius, 50px padding and rainbow hipster shadows, write code biased towards low quality training data and so on. It's the perfect recipe for disaster

xpe 1y ago
Over the top humor duly acknowledged. Disastrous? Quite possibly, but my concerns are based on different concerns. Almost everything changes, so isn’t it better to rephrase these statements as metrics to avoid fixating on one snapshot in an evolving world? As the metrics get better, what happens? Do you still have objections? What objections remain as AI capabilities get better and better without limit? The growth might be slow or irregular, but there are many scenarios where AIs reach the bar where they are better at almost all knowledge work. Stepping back, do you really think of AI systems as stochastic parrots? What does this metaphor buy you? Is it mostly a card you automatically deal out when you pattern match on something? Or does serve as a reusable engine for better understanding the world? We’ve been down this road; there is already much HN commentary on the SP metaphor. (Not that I recommend HN for this kind of thing. This is where I come to see how a subset of tech people are making sense of it, often imperfectly with correspondingly inappropriate overconfidence.) TLDR: smart AI folks don’t anchor on the stochastic parrots metaphor. It is a catchy phrase and helped people’s papers get some attention, but it doesn’t mean what a lot of people think it means. Easily misunderstood, it serves as a convenient semantic stop sign so people don’t have to dig in to the more interesting aspects of modern AI systems. For example: (1) transformers build conceptual models of language that transcend any particular language. (2) They also build world models with spatial reasoning. (3) Many models are quite resilient to low quality training data. And more. To make this very concrete: under the assumption of universal laws of physics, people are just following the laws of physics, and to a first approximation, our brains are just statistical pattern matchers. By this definition, humans would also be “stochastic parrots”. I go all this trouble to show that this metaphor doesn’t cut to the heart of the matter. There are clearer questions to ask: they require getting a lot more specific about various forms and applications of intelligent behavior. For example - under what circumstances does self play lead to superhuman capability in a particular domain? - what limits exist (if any) in the self supervised training paradigm used for sequential data? If the transformer trained in this way can write valid programs then it can create almost any Turing machine; limited only by time and space and energy. What more could you want? (Lots, but I’m genuinely curious as to people’s responses after reflecting on these.)
- hollowturtle 1y ago
  LLMs ARE stochastic parrots, throw whatever chatgpt slop answer but facts are facts
  - xpe 1y ago
    > Comments should get more thoughtful and substantive, not less, as a topic gets more divisive. https://news.ycombinator.com/newsguidelines.html https://news.ycombinator.com/newsguidelines.html
    - hollowturtle 1y ago
      Even then, facts remains facts ;)
- jeremyjh 1y ago
  Until the thing can learn on its own and advance its capabilities to the same degree that a junior developer can, it is not intelligent enough to do that work. It doesn't learn our APIs, it doesn't learn our business domain, it doesn't learn from the countless mistakes I correct it on. What we have now is interesting, it is helping sometimes and wasteful others. It is not intelligent.
  - xpe 1y ago
    > It is not intelligent. Which of the following would you agree to... ? 1. There is no single bar for intelligence. 2. Intelligence is better measured on a scale than with 1 bit (yes/no). 3. Intelligence is better considered as having many components instead of just one. When people talk about intelligence, they often mean different things across domains, such as emotional, social, conceptual, spatial, kinetic, sensory, etc. 4. Many researchers have looked for -- and found -- in humans, at least, some notions of generalized intellectual capability that tends to help across a wide variety of cognitive tasks. If some of these make sense, I suggest it would be wise to conclude: 5. Reasonable people accentuate different aspects and even definitions of intelligence. 6. Expecting a yes/no answer for "is X intelligent?" without considerable explanation is approximately useless. (Unless it is a genuinely curious opener for an in-depth conversation.) 7. Asking "is X intelligent?" tends to be a poorly framed question.

xpe 1y ago

Over the top humor duly acknowledged. Disastrous? Quite possibly, but my concerns are based on different concerns. Almost everything changes, so isn’t it better to rephrase these statements as metrics to avoid fixating on one snapshot in an evolving world? As the metrics get better, what happens? Do you still have objections? What objections remain as AI capabilities get better and better without limit? The growth might be slow or irregular, but there are many scenarios where AIs reach the bar where they are better at almost all knowledge work. Stepping back, do you really think of AI systems as stochastic parrots? What does this metaphor buy you? Is it mostly a card you automatically deal out when you pattern match on something? Or does serve as a reusable engine for better understanding the world? We’ve been down this road; there is already much HN commentary on the SP metaphor. (Not that I recommend HN for this kind of thing. This is where I come to see how a subset of tech people are making sense of it, often imperfectly with correspondingly inappropriate overconfidence.) TLDR: smart AI folks don’t anchor on the stochastic parrots metaphor. It is a catchy phrase and helped people’s papers get some attention, but it doesn’t mean what a lot of people think it means. Easily misunderstood, it serves as a convenient semantic stop sign so people don’t have to dig in to the more interesting aspects of modern AI systems. For example: (1) transformers build conceptual models of language that transcend any particular language. (2) They also build world models with spatial reasoning. (3) Many models are quite resilient to low quality training data. And more. To make this very concrete: under the assumption of universal laws of physics, people are just following the laws of physics, and to a first approximation, our brains are just statistical pattern matchers. By this definition, humans would also be “stochastic parrots”. I go all this trouble to show that this metaphor doesn’t cut to the heart of the matter. There are clearer questions to ask: they require getting a lot more specific about various forms and applications of intelligent behavior. For example - under what circumstances does self play lead to superhuman capability in a particular domain? - what limits exist (if any) in the self supervised training paradigm used for sequential data? If the transformer trained in this way can write valid programs then it can create almost any Turing machine; limited only by time and space and energy. What more could you want? (Lots, but I’m genuinely curious as to people’s responses after reflecting on these.)

hollowturtle 1y ago
LLMs ARE stochastic parrots, throw whatever chatgpt slop answer but facts are facts
- xpe 1y ago
  > Comments should get more thoughtful and substantive, not less, as a topic gets more divisive. https://news.ycombinator.com/newsguidelines.html https://news.ycombinator.com/newsguidelines.html
  - hollowturtle 1y ago
    Even then, facts remains facts ;)
jeremyjh 1y ago
Until the thing can learn on its own and advance its capabilities to the same degree that a junior developer can, it is not intelligent enough to do that work. It doesn't learn our APIs, it doesn't learn our business domain, it doesn't learn from the countless mistakes I correct it on. What we have now is interesting, it is helping sometimes and wasteful others. It is not intelligent.
- xpe 1y ago
  > It is not intelligent. Which of the following would you agree to... ? 1. There is no single bar for intelligence. 2. Intelligence is better measured on a scale than with 1 bit (yes/no). 3. Intelligence is better considered as having many components instead of just one. When people talk about intelligence, they often mean different things across domains, such as emotional, social, conceptual, spatial, kinetic, sensory, etc. 4. Many researchers have looked for -- and found -- in humans, at least, some notions of generalized intellectual capability that tends to help across a wide variety of cognitive tasks. If some of these make sense, I suggest it would be wise to conclude: 5. Reasonable people accentuate different aspects and even definitions of intelligence. 6. Expecting a yes/no answer for "is X intelligent?" without considerable explanation is approximately useless. (Unless it is a genuinely curious opener for an in-depth conversation.) 7. Asking "is X intelligent?" tends to be a poorly framed question.