10 ms·

Open AI was betting on AI, AGI, Super inteligence. Look at the google engineer who thought they had an AI locked up in the basement... https://www.theverge.com

Open AI was betting on AI, AGI, Super inteligence. Look at the google engineer who thought they had an AI locked up in the basement... https://www.theverge.com/2022/6/13/23165535/google-suspends-ai-artificial-intelligence-engineer-sentient https://www.theverge.com/2022/6/13/23165535/google-suspends-... MS paper on sparks of AGI: https://www.microsoft.com/en-us/research/publication/sparks-of-artificial-general-intelligence-early-experiments-with-gpt-4/ https://www.microsoft.com/en-us/research/publication/sparks-... The rumors that OpenAI deal with MS would give them everything till they got to AGI... A perpetual license to all new development. All the "Safety people" have left the OpenAi building. Even musk isnt talking about safety any more. I think the bet was that if you fed an LLM enough, got it big enough it would hit a tipping point, and become AGI, or sentient or sapient. That lines up nicely with the MS terms, and MS's on paper. I think they figured out that the math doesn't work that way (and never was going to). A prediction of the next token being better isnt intelligence any more than weather prediction will become weather.

theGnuMe 2y ago
Weather prediction is pretty on point for 5 days out. Also the state of the art is a graph neural network transformer.
cyberax 2y ago
> A prediction of the next token being better isnt intelligence Why? I think it absolutely can be intelligence.
- RealityVoid 2y ago
  I think it depends on how you define intelligence, but _I_ mostly agree with Francois Collet's stance that intelligence is the ability to find novel solutions and adaptability to new challenges. He feels that memorisation is an important facet, but it is not enough for true intelligence ant that these systems excel at type2 thinking but gave huge gaps at type1. The alternative I'm considering is that It might just be that it's just a dataset problem, feeding these llms on words makes the lack a huge facet of embodied axistance that is needed to get context. I am a nobody though, so who knows....
  - cyberax 2y ago
    And who says that LLMs won't be able to adapt to new conditions? Right now they are limited by the context, but that's probably a temporary limitation.
  - smus 2y ago
    *Chollet
  - ben_w 2y ago
    I agree, LLM are interesting to me only to the extent that they are doing more than memorisation. They do seem to do generalisation, to at least some degree. If it was literal memorisation, we do literally have internet search already.
- mdp2021 2y ago
  Intelligence implies a critical evaluation of the statement under examination, before stating it, on considerations over content. ("You think before you speak". That thinking of course does not stop at "sounding" proper - it has to be proper in content...)
  - cyberax 2y ago
    > Intelligence implies a critical evaluation of the statement under examination, before stating it, on considerations over content. And who says LLM are not able to do that (eventually)?
    - mdp2021 2y ago
      The poster wrote that prediction of the next token seems like intelligence to him. He was replied that consideration over content is required. You are now stating that it is not proven it will not happen. But the point was that prediction of the next token is not the intelligence sought, and if and when the intelligence sought will happen, that will be a new stage - the current stage we do not call intelligence.
      - cyberax 2y ago
        I have some experience with LLM, and they definitely do consider the question. They even seem to do simple logical inference. They are not _good_ at it right now, and they are totally bad at making generalizations. But who says it's not just an artifact of the limited context?
  - roenxi 2y ago
    A LLM has to do an accurate simulation of someone critically evaluating their statement in order to predict a next word. If an LLM can predict the next word without doing a critical evaluation, then it raises the question of what the intelligent people are doing. They might not be doing a critical evaluation at all.
    - mdp2021 2y ago
garretraziel 2y ago
When my friends talked about how AGI is just creating huge enough neural network & feeding it enough data, I have always compared it to: imagine locking a mouse in a library with all the knowledge in the world & expecting it to come out super intelligent.
- ben_w 2y ago
  GPT-3 is about the same complexity as a mouse brain; it did better than I expected… … but it's still a brain the size of a mouse's.
  - spyhi 2y ago
    To be fair, it’s the horsepower of a mouse, but all devoted to a single task, so not 100% comparable to the capabilities of a mouse, and language is too distributed to make a good comparison of what milestone is human-like. But it’s indeed surprising how much that little bit of horsepower can do.
  - dgb23 2y ago
    Mice are fare more impressive though.
    - surfingdino 2y ago
      And they can cook. I know it, I have seen it in a movie /s
      - varelse 2y ago
        [dead]
    - ben_w 2y ago
      I've yet to see a mouse write even mediocre python, let alone a rap song about life in ancient Athens written in Latin. Don't get me wrong, organic brains learn from far fewer examples than AI, there's a lot organic brains can do that AI don't (yet), but I don't really find the intellectual capacity of mice to be particularly interesting. On the other hand, the question of if mice have qualia, that is something I find interesting.
aa-b 2y ago
The "next token" thing is literally true, but it might turn out to be a red herring, because emergence is a real phenomenon. Like how with enough NAND-gates daisy-chained together you can build any logic function you like. Gradually, as these LLM next-token predictors are set up recursively, constructively, dynamically, and with the right inputs and feedback loops, the limitations of the fundamental building blocks become less important. Might take a long time, though.
- wizzwizz4 2y ago
  > Like how with enough NAND-gates daisy-chained together you can build any logic function you like. The version of emergence that AI hypists cling to isn't real, though, in the same way that adding more NAND gates won't magically make the logic function you're thinking about. How you add the NAND gates matters, to such a degree that people who know what they're doing don't even think about the NAND gates.
  - RaftPeople 2y ago
    Exactly this, a thousand times. There are infinitely more wrong and useless circuits than there are the ones that provide the function you want/need.
    - lcnPylGDnU4H9OF 2y ago
      But isn't that what the training algorithm does? (Genuinely asking since I'm not very familiar with this.) I thought it tries anything, including wrong things, as it gradually finds better results from the right things.
      - wizzwizz4 2y ago
        Better results, yes, but that doesn't mean good results. It can only find local optima in a predetermined state space. Training a neural network involves (1) finding the right state space, and (2) choosing a suitable gradient function. If the Correct Solution isn't in the state space, or isn't reachable via gradual improvement, the neural network will never find it. An algorithm that can reason about the meaning of text probably isn't in the state space of GPT. Thanks to the https://en.wikipedia.org/wiki/Universal_approximation_theorem https://en.wikipedia.org/wiki/Universal_approximation_theore..., we can get something that looks pretty close when interpolating, but that doesn't mean it can extrapolate sensibly. (See https://xkcd.com/2048/ https://xkcd.com/2048/, bottom right.) As they say, neural networks "want" to work, but that doesn't mean they can. That's the hard part of machine learning. Your average algorithm will fail obviously, if you've implemented it wrong. A neural network will just not perform as well as you expect it to (a problem that usually goes away if you stir it enough https://xkcd.com/1838/ https://xkcd.com/1838/), without a nice failure that points you at the problem. For example, Evan Miller reckons that there's an off-by-one error in everyone's transformers. https://www.evanmiller.org/attention-is-off-by-one.html https://www.evanmiller.org/attention-is-off-by-one.html If you add enough redundant dimensions, the global optimum of a real-world gradient function seems to become the local optimum (most of the time), so it's often useful to train a larger model than you theoretically need, then produce a smaller model from that.

theGnuMe 2y ago

Weather prediction is pretty on point for 5 days out. Also the state of the art is a graph neural network transformer.

cyberax 2y ago

> A prediction of the next token being better isnt intelligence Why? I think it absolutely can be intelligence.

RealityVoid 2y ago
I think it depends on how you define intelligence, but _I_ mostly agree with Francois Collet's stance that intelligence is the ability to find novel solutions and adaptability to new challenges. He feels that memorisation is an important facet, but it is not enough for true intelligence ant that these systems excel at type2 thinking but gave huge gaps at type1. The alternative I'm considering is that It might just be that it's just a dataset problem, feeding these llms on words makes the lack a huge facet of embodied axistance that is needed to get context. I am a nobody though, so who knows....
- cyberax 2y ago
  And who says that LLMs won't be able to adapt to new conditions? Right now they are limited by the context, but that's probably a temporary limitation.
- smus 2y ago
  *Chollet
- ben_w 2y ago
  I agree, LLM are interesting to me only to the extent that they are doing more than memorisation. They do seem to do generalisation, to at least some degree. If it was literal memorisation, we do literally have internet search already.
mdp2021 2y ago
Intelligence implies a critical evaluation of the statement under examination, before stating it, on considerations over content. ("You think before you speak". That thinking of course does not stop at "sounding" proper - it has to be proper in content...)
- cyberax 2y ago
  > Intelligence implies a critical evaluation of the statement under examination, before stating it, on considerations over content. And who says LLM are not able to do that (eventually)?
  - mdp2021 2y ago
    The poster wrote that prediction of the next token seems like intelligence to him. He was replied that consideration over content is required. You are now stating that it is not proven it will not happen. But the point was that prediction of the next token is not the intelligence sought, and if and when the intelligence sought will happen, that will be a new stage - the current stage we do not call intelligence.
    - cyberax 2y ago
      I have some experience with LLM, and they definitely do consider the question. They even seem to do simple logical inference. They are not _good_ at it right now, and they are totally bad at making generalizations. But who says it's not just an artifact of the limited context?
- roenxi 2y ago
  A LLM has to do an accurate simulation of someone critically evaluating their statement in order to predict a next word. If an LLM can predict the next word without doing a critical evaluation, then it raises the question of what the intelligent people are doing. They might not be doing a critical evaluation at all.
  - mdp2021 2y ago

garretraziel 2y ago

When my friends talked about how AGI is just creating huge enough neural network & feeding it enough data, I have always compared it to: imagine locking a mouse in a library with all the knowledge in the world & expecting it to come out super intelligent.

ben_w 2y ago
GPT-3 is about the same complexity as a mouse brain; it did better than I expected… … but it's still a brain the size of a mouse's.
- spyhi 2y ago
  To be fair, it’s the horsepower of a mouse, but all devoted to a single task, so not 100% comparable to the capabilities of a mouse, and language is too distributed to make a good comparison of what milestone is human-like. But it’s indeed surprising how much that little bit of horsepower can do.
- dgb23 2y ago
  Mice are fare more impressive though.
  - surfingdino 2y ago
    And they can cook. I know it, I have seen it in a movie /s
    - varelse 2y ago
      [dead]
  - ben_w 2y ago
    I've yet to see a mouse write even mediocre python, let alone a rap song about life in ancient Athens written in Latin. Don't get me wrong, organic brains learn from far fewer examples than AI, there's a lot organic brains can do that AI don't (yet), but I don't really find the intellectual capacity of mice to be particularly interesting. On the other hand, the question of if mice have qualia, that is something I find interesting.

aa-b 2y ago

The "next token" thing is literally true, but it might turn out to be a red herring, because emergence is a real phenomenon. Like how with enough NAND-gates daisy-chained together you can build any logic function you like. Gradually, as these LLM next-token predictors are set up recursively, constructively, dynamically, and with the right inputs and feedback loops, the limitations of the fundamental building blocks become less important. Might take a long time, though.

wizzwizz4 2y ago
> Like how with enough NAND-gates daisy-chained together you can build any logic function you like. The version of emergence that AI hypists cling to isn't real, though, in the same way that adding more NAND gates won't magically make the logic function you're thinking about. How you add the NAND gates matters, to such a degree that people who know what they're doing don't even think about the NAND gates.
- RaftPeople 2y ago
  Exactly this, a thousand times. There are infinitely more wrong and useless circuits than there are the ones that provide the function you want/need.
  - lcnPylGDnU4H9OF 2y ago
    But isn't that what the training algorithm does? (Genuinely asking since I'm not very familiar with this.) I thought it tries anything, including wrong things, as it gradually finds better results from the right things.
    - wizzwizz4 2y ago
      Better results, yes, but that doesn't mean good results. It can only find local optima in a predetermined state space. Training a neural network involves (1) finding the right state space, and (2) choosing a suitable gradient function. If the Correct Solution isn't in the state space, or isn't reachable via gradual improvement, the neural network will never find it. An algorithm that can reason about the meaning of text probably isn't in the state space of GPT. Thanks to the https://en.wikipedia.org/wiki/Universal_approximation_theorem https://en.wikipedia.org/wiki/Universal_approximation_theore..., we can get something that looks pretty close when interpolating, but that doesn't mean it can extrapolate sensibly. (See https://xkcd.com/2048/ https://xkcd.com/2048/, bottom right.) As they say, neural networks "want" to work, but that doesn't mean they can. That's the hard part of machine learning. Your average algorithm will fail obviously, if you've implemented it wrong. A neural network will just not perform as well as you expect it to (a problem that usually goes away if you stir it enough https://xkcd.com/1838/ https://xkcd.com/1838/), without a nice failure that points you at the problem. For example, Evan Miller reckons that there's an off-by-one error in everyone's transformers. https://www.evanmiller.org/attention-is-off-by-one.html https://www.evanmiller.org/attention-is-off-by-one.html If you add enough redundant dimensions, the global optimum of a real-world gradient function seems to become the local optimum (most of the time), so it's often useful to train a larger model than you theoretically need, then produce a smaller model from that.