7 ms·

This article really needs a "(2018)" marker. This article predates GPT-3 and GPT-2, it even predates the essay "The Bitter Lesson" <http://www.incompleteideas.

This article really needs a "(2018)" marker. This article predates GPT-3 and GPT-2, it even predates the essay "The Bitter Lesson" <http://www.incompleteideas.net/IncIdeas/BitterLesson.html http://www.incompleteideas.net/IncIdeas/BitterLesson.html>. It might be true long-term, but it's certainly not written with the current advances in mind.

gibsonf1 4y ago
There aren't really any current advances outside of sheer scale of input in the models, and all the engineering and hardware around achieving that scale. And I think the point is no matter how much input data you give the ml/dl system, it will still have no awareness, no understanding of any kind and certainly no causal awareness.
- codeulike 4y ago
  I think LLMs understand things in abstract. e.g. they know that if a 'Cat' is 'Miaowing' then if you 'give' it some 'cat food' it will then be 'happy'. It doesn't know what any of those things _are_, but it knows how X and Y and Z relate. And so in the abstract sense it knows that 'hunger' causes a 'cat' to 'miaow' it will still have no awareness We dont know how awareness works, so we're not in a position to say what has and hasn't got it. Think about the milliseconds in which ChatGPT uses its 4000 token length to analyse approx 2000 words all at same time, in a process encompassing a massive number of GPUs processing a mind boggling number of parameters. How are we to say what it going on for those milliseconds? There could be some sort of abstract analogue to awareness happening there for a burst of a few milliseconds. These LLMs are all about emergent effects from huge scale. Similarly, me and you are made out of 30 trillion microscopic dumb biological robots, none of whom know who we are or care, but nonetheless, we have awareness.
  - breckenedge 4y ago
    The LLMs are predictive text. They only know that a hungry cat wants food because there are enough examples of that scenario on the Web. Related scenarios lead to nonsense like: the cat may want to go on a walk. I’m not making this up, ChatGPT recently suggested that I take my cat on a walk. This is likely bleeding too many concepts together (dog=pet=cat) and represents over fitting IMO. Cosine distance can only do so well, it’s not good at distinguishing nuance. But it’ll gladly regurgitate a rephrased Wikipedia article.
    - darkerside 4y ago
      Seems like something an intelligent child who's never owned a cat might suggest
      - mach1ne 4y ago
        The difference between LLM cognition and human cognition probably lies somewhere in this dichotomy.
    - kilgnad 4y ago
      Wrong. There is science around the fact that chatGPT builds higher level macro structures in the neural network. These structures have been shown to be an actual model of things within the world. This isn't even a debate anymore between two guys on the internet. There is literal science behind this and the difference between us is one person is behind on the science. But forget the science. There is even chatGPT output that literally shows it understood what it was told. It is clear chatGPT does not have perfect understanding of the world. It does create really stupid output. But this is ignoring the fact that there are tons of answers it gives that show unmistakably that it knows what you asked it.
      - deleted 4y ago
        [deleted]
      - Jensson 4y ago
        You don't think that complex structures are required to generate well formed text? Human language is extremely context dependent, and dealing with that is what all those macro structures does. Context dependence is literally the novel thing with transformers, they are context dependent statistical models to generate next words, make it huge and feed it a page of context and it can map those to a page of output to match that context.
        naasking 4y ago
        Context is arguably understanding.
  - Retric 4y ago
    You’re anthropomorphizing these chat bots. They may associate those words in specific contexts, but they don’t have that kind of abstract understanding. The best way to understand them isn’t to look at what they do well but where they fail. A great example is how ChatGPT can initially make a few chess moves that seem reasonable, but very quickly it stops making valid moves. It’s not operating from some model of the game but rather imitating sequences of moves it’s seen before. The best analogy isn’t cognition but someone trying to make a better version of “lorem ipsum” for whatever promo you’re giving it.
    - 2OEH8eoCRo0 4y ago
      > You’re anthropomorphizing these chat bots. Oh? don't anthropomorphize the thing we are supposed to "chat" with? It's basically what they're designed for except when they rudely tell you "I'm just an LLM!"
    - codeulike 4y ago
      GPTs can model simpler games like Othello https://thegradient.pub/othello/ https://thegradient.pub/othello/ https://arxiv.org/abs/2210.13382 https://arxiv.org/abs/2210.13382 with enough training data it could probably model chess - not well enough to win but well enough to make legal moves You think that I am misunderstanding whats going on and anthropomorphising ChatGPT. I know how ChatGPT works, my position is that we might be overestimating our selves, and underestimating the power of emergent phenomenon.
      - kilgnad 4y ago
        Exactly. The science is starting to realize the emergent effects of LLMs. These things can literally learn simply by "looking over your shoulder". We thought that you had to explicitly program hierarchical structures of causal reasoning into the network but science is showing that these structures are emergent. People won't believe you if you show actual evidence. You have to throw them a scientific paper written by an "expert" lol. And even then they will find a hard time changing their viewpoint. Its so strange why people are trying to downplay it all when even the science is showing they're wrong. They have to throw accusations around of anthropomorphisation. Seriously? It's very easy to identify the bias of anthropomorphisation. Anyone can easily tiptoe around that bias with a simple argument. Clearly what's going on with chatGPT is much more complex then that. I recommend people stop using that word in this topic. It's akin to accusing someone they have brain damage. Clearly they don't.
- nl 4y ago
  > There aren't really any current advances outside of sheer scale of input in the models That isn't true. https://ai.googleblog.com/2022/11/characterizing-emergent-phenomena-in.html https://ai.googleblog.com/2022/11/characterizing-emergent-ph... While one can correctly argue that this is a consequense of increasing size, no one realized that increasing the size of LLMs would give them reasoning ability in 2018. Indeed, before Minerva[1] came out expert forecasters predicted[2] a 12% improvement in the ability on the MATH benchmark[3]. Minerva improved it by 50% and PaLM improved it even more. [1] https://arxiv.org/abs/2206.14858 https://arxiv.org/abs/2206.14858 [2] https://bounded-regret.ghost.io/ai-forecasting-one-year-in/ https://bounded-regret.ghost.io/ai-forecasting-one-year-in/ [3] https://github.com/hendrycks/math https://github.com/hendrycks/math
  - gibsonf1 4y ago
    You may be mistaking reasoning ability with sheer volume of more patterns to match on. You can only reason when you have awareness within which to reason, which these systems do not.
    - nl 4y ago
      > You may be mistaking reasoning ability with sheer volume of more patterns to match on. It's not my work. And no, they probed this theory. The OCWCourses benchmark was new questions they generated from MIT Open Courseware that didn't exist prior to this benchmark. > You can only reason when you have awareness within which to reason, which these systems do not. I don't understand this objection at all. Reasoning seems roughly the same as logical inference, and logical inference is a computational process.
      - gibsonf1 4y ago
        The LLM has no idea what words mean, so its quite impossible for them to reason about things. They just statistically mash up patterns of words from massive input with no clue whatsoever what those patterns of words actually mean in the real world.
- kilgnad 4y ago
  chatGPT does have causal awareness and even awareness of self and understanding of the things you ask it. There is proof of this in many examples and there is even scientific research that displays this. It is not conscious or "sentient" but it certainly is able to build answers to queries that utilize true understanding of the query it was given. The AI community is only beginning to realize the emergent affect of LLMs. Yes the underlying model is not new, and yes the underlying model just looks like a word predictor. But it is becoming obvious to experts that there are high level macro structures within the network at play here. I don't know why people are constantly downplaying the AI. Yeah it does create stupid output. But if you just focus on that then you'd be completely ignoring the actual science around it.
  - deleted 4y ago
    [deleted]
  - psychoslave 4y ago
    >It is not conscious or "sentient" but it certainly is able to build answers to queries that utilize true understanding of the query it was given. What do you mean with "true understanding" here? Asking a computer to solve say, multiplication of large numbers, and it will provide the correct expected answer, with speed and and accuracy that outperform any human out there. Should we say that such a calculator application "understand" arithmetic? >I don't know why people are constantly downplaying the AI. Maybe that’s in a balance with "everybody seeing the raise to conscious understanding of AI" each time a new credibly-human-like-output is generated? Just like pareidolia: "yes there is obviously something that evokes a visage in that rock" doesn’t have to be interpreted as a sign that Nature loves to purposefully sculpt human faces. From an evolutionary perspective, we see faces everywhere just like we see sign of understanding in surrounding agents because it makes our species more efficient in social cooperation, which to my mind is by a very large margin our best asset. https://science.howstuffworks.com/life/inside-the-mind/human-brain/pareidolia.htm https://science.howstuffworks.com/life/inside-the-mind/human... https://www.amazon.com/SuperCooperators-Altruism-Evolution-Other-Succeed/dp/1451626630 https://www.amazon.com/SuperCooperators-Altruism-Evolution-O...
    - 4y ago
daveguy 4y ago
1 human is the equivalent of several of the most powerful computers in computation. IO is 3 logs less. I'll be worried in about 60 years that we may have the computing power of an artificial human. But only if we understand thought.

gibsonf1 4y ago

There aren't really any current advances outside of sheer scale of input in the models, and all the engineering and hardware around achieving that scale. And I think the point is no matter how much input data you give the ml/dl system, it will still have no awareness, no understanding of any kind and certainly no causal awareness.

codeulike 4y ago
I think LLMs understand things in abstract. e.g. they know that if a 'Cat' is 'Miaowing' then if you 'give' it some 'cat food' it will then be 'happy'. It doesn't know what any of those things _are_, but it knows how X and Y and Z relate. And so in the abstract sense it knows that 'hunger' causes a 'cat' to 'miaow' it will still have no awareness We dont know how awareness works, so we're not in a position to say what has and hasn't got it. Think about the milliseconds in which ChatGPT uses its 4000 token length to analyse approx 2000 words all at same time, in a process encompassing a massive number of GPUs processing a mind boggling number of parameters. How are we to say what it going on for those milliseconds? There could be some sort of abstract analogue to awareness happening there for a burst of a few milliseconds. These LLMs are all about emergent effects from huge scale. Similarly, me and you are made out of 30 trillion microscopic dumb biological robots, none of whom know who we are or care, but nonetheless, we have awareness.
- breckenedge 4y ago
  The LLMs are predictive text. They only know that a hungry cat wants food because there are enough examples of that scenario on the Web. Related scenarios lead to nonsense like: the cat may want to go on a walk. I’m not making this up, ChatGPT recently suggested that I take my cat on a walk. This is likely bleeding too many concepts together (dog=pet=cat) and represents over fitting IMO. Cosine distance can only do so well, it’s not good at distinguishing nuance. But it’ll gladly regurgitate a rephrased Wikipedia article.
  - darkerside 4y ago
    Seems like something an intelligent child who's never owned a cat might suggest
    - mach1ne 4y ago
      The difference between LLM cognition and human cognition probably lies somewhere in this dichotomy.
  - kilgnad 4y ago
    Wrong. There is science around the fact that chatGPT builds higher level macro structures in the neural network. These structures have been shown to be an actual model of things within the world. This isn't even a debate anymore between two guys on the internet. There is literal science behind this and the difference between us is one person is behind on the science. But forget the science. There is even chatGPT output that literally shows it understood what it was told. It is clear chatGPT does not have perfect understanding of the world. It does create really stupid output. But this is ignoring the fact that there are tons of answers it gives that show unmistakably that it knows what you asked it.
    - deleted 4y ago
      [deleted]
    - Jensson 4y ago
      You don't think that complex structures are required to generate well formed text? Human language is extremely context dependent, and dealing with that is what all those macro structures does. Context dependence is literally the novel thing with transformers, they are context dependent statistical models to generate next words, make it huge and feed it a page of context and it can map those to a page of output to match that context.
      - naasking 4y ago
        Context is arguably understanding.
- Retric 4y ago
  You’re anthropomorphizing these chat bots. They may associate those words in specific contexts, but they don’t have that kind of abstract understanding. The best way to understand them isn’t to look at what they do well but where they fail. A great example is how ChatGPT can initially make a few chess moves that seem reasonable, but very quickly it stops making valid moves. It’s not operating from some model of the game but rather imitating sequences of moves it’s seen before. The best analogy isn’t cognition but someone trying to make a better version of “lorem ipsum” for whatever promo you’re giving it.
  - 2OEH8eoCRo0 4y ago
    > You’re anthropomorphizing these chat bots. Oh? don't anthropomorphize the thing we are supposed to "chat" with? It's basically what they're designed for except when they rudely tell you "I'm just an LLM!"
  - codeulike 4y ago
    GPTs can model simpler games like Othello https://thegradient.pub/othello/ https://thegradient.pub/othello/ https://arxiv.org/abs/2210.13382 https://arxiv.org/abs/2210.13382 with enough training data it could probably model chess - not well enough to win but well enough to make legal moves You think that I am misunderstanding whats going on and anthropomorphising ChatGPT. I know how ChatGPT works, my position is that we might be overestimating our selves, and underestimating the power of emergent phenomenon.
    - kilgnad 4y ago
      Exactly. The science is starting to realize the emergent effects of LLMs. These things can literally learn simply by "looking over your shoulder". We thought that you had to explicitly program hierarchical structures of causal reasoning into the network but science is showing that these structures are emergent. People won't believe you if you show actual evidence. You have to throw them a scientific paper written by an "expert" lol. And even then they will find a hard time changing their viewpoint. Its so strange why people are trying to downplay it all when even the science is showing they're wrong. They have to throw accusations around of anthropomorphisation. Seriously? It's very easy to identify the bias of anthropomorphisation. Anyone can easily tiptoe around that bias with a simple argument. Clearly what's going on with chatGPT is much more complex then that. I recommend people stop using that word in this topic. It's akin to accusing someone they have brain damage. Clearly they don't.
nl 4y ago
> There aren't really any current advances outside of sheer scale of input in the models That isn't true. https://ai.googleblog.com/2022/11/characterizing-emergent-phenomena-in.html https://ai.googleblog.com/2022/11/characterizing-emergent-ph... While one can correctly argue that this is a consequense of increasing size, no one realized that increasing the size of LLMs would give them reasoning ability in 2018. Indeed, before Minerva[1] came out expert forecasters predicted[2] a 12% improvement in the ability on the MATH benchmark[3]. Minerva improved it by 50% and PaLM improved it even more. [1] https://arxiv.org/abs/2206.14858 https://arxiv.org/abs/2206.14858 [2] https://bounded-regret.ghost.io/ai-forecasting-one-year-in/ https://bounded-regret.ghost.io/ai-forecasting-one-year-in/ [3] https://github.com/hendrycks/math https://github.com/hendrycks/math
- gibsonf1 4y ago
  You may be mistaking reasoning ability with sheer volume of more patterns to match on. You can only reason when you have awareness within which to reason, which these systems do not.
  - nl 4y ago
    > You may be mistaking reasoning ability with sheer volume of more patterns to match on. It's not my work. And no, they probed this theory. The OCWCourses benchmark was new questions they generated from MIT Open Courseware that didn't exist prior to this benchmark. > You can only reason when you have awareness within which to reason, which these systems do not. I don't understand this objection at all. Reasoning seems roughly the same as logical inference, and logical inference is a computational process.
    - gibsonf1 4y ago
      The LLM has no idea what words mean, so its quite impossible for them to reason about things. They just statistically mash up patterns of words from massive input with no clue whatsoever what those patterns of words actually mean in the real world.
kilgnad 4y ago
chatGPT does have causal awareness and even awareness of self and understanding of the things you ask it. There is proof of this in many examples and there is even scientific research that displays this. It is not conscious or "sentient" but it certainly is able to build answers to queries that utilize true understanding of the query it was given. The AI community is only beginning to realize the emergent affect of LLMs. Yes the underlying model is not new, and yes the underlying model just looks like a word predictor. But it is becoming obvious to experts that there are high level macro structures within the network at play here. I don't know why people are constantly downplaying the AI. Yeah it does create stupid output. But if you just focus on that then you'd be completely ignoring the actual science around it.
- deleted 4y ago
  [deleted]
- psychoslave 4y ago
  >It is not conscious or "sentient" but it certainly is able to build answers to queries that utilize true understanding of the query it was given. What do you mean with "true understanding" here? Asking a computer to solve say, multiplication of large numbers, and it will provide the correct expected answer, with speed and and accuracy that outperform any human out there. Should we say that such a calculator application "understand" arithmetic? >I don't know why people are constantly downplaying the AI. Maybe that’s in a balance with "everybody seeing the raise to conscious understanding of AI" each time a new credibly-human-like-output is generated? Just like pareidolia: "yes there is obviously something that evokes a visage in that rock" doesn’t have to be interpreted as a sign that Nature loves to purposefully sculpt human faces. From an evolutionary perspective, we see faces everywhere just like we see sign of understanding in surrounding agents because it makes our species more efficient in social cooperation, which to my mind is by a very large margin our best asset. https://science.howstuffworks.com/life/inside-the-mind/human-brain/pareidolia.htm https://science.howstuffworks.com/life/inside-the-mind/human... https://www.amazon.com/SuperCooperators-Altruism-Evolution-Other-Succeed/dp/1451626630 https://www.amazon.com/SuperCooperators-Altruism-Evolution-O...
  - 4y ago

daveguy 4y ago

1 human is the equivalent of several of the most powerful computers in computation. IO is 3 logs less. I'll be worried in about 60 years that we may have the computing power of an artificial human. But only if we understand thought.