4 ms·

How so?

CamperBob2 2y ago
Wait and see. You're not paying attention now, but it's not too late to start. Go to your favorite programming puzzle site and see how you do against the latest models, for instance. If you can beat o1-pro regularly, for instance, then you're right, you have nothing to worry about and can safely disregard it. Same proposition that was offered to John Henry.
- Joel_Mckay 2y ago
  Please reformulate your argument, and I will check back tomorrow: https://www.youtube.com/watch?v=aNSHZG9blQQ https://www.youtube.com/watch?v=aNSHZG9blQQ LLMs are rules based search engines with higher dimension vector spaces encoding related topics. There is nothing intelligent about these algorithms, except the trick ones play on oneself interpreting well structured nonsense. It is stunting kids development, as students often lack the ability to intuitively reason when they are being misled. "How many R's in 'Strawberry'?" is a classic example exposing the underlying pattern recognition failures. =3
  - CamperBob2 2y ago
    (Shrug) If you're retired or independently wealthy, you can afford that attitude. Hopefully one of those describes you. Otherwise, you're going to spend the rest of your career saying things like, "Well, OK, so the last model couldn't count the number of Rs in 'Strawberry' and the new one can, but..." Personally, I dislike being wrong. So I don't base arguments on points that have a built-in expiration date, or that are based on a fundamental misunderstanding of whatever I'm talking about.
    - Joel_Mckay 2y ago
      Every model is deprecated in time if evidenced Science is done well, and hopefully replaced by something more accurate in time. There is no absolute right/correctness unless you are a naive child under 25 cheating on structured homework. The point was there is nothing intelligent (or AI) about LLMs except the person fooling themselves. In general, most template libraries already implement the best possible algorithms from the 1960s, and tuned for architecture specific optimizations. Knowing when each finite option is appropriate takes a bit of understanding/study, but does give results far quicker than fitting a statistically salient nonsense answer. Several study datum from senior developers is already available, and it proves LLMs provide zero benefit to people that know what they are doing. Note, I am driven by having fun, rather than some bizarre irrational competitiveness. Prove your position, or I will assume you are just a silly person or chat bot. =3
      - mindcrime 2y ago
        I have no position on whether or not CamperBob is a chat-bot, but they are definitely not being silly. Their point, as I take it, is that it's dangerous to look at the state of "AI" as it is today and then ignore the rate of change. To their stated point from above: > Otherwise, you're going to spend the rest of your career saying things like, "Well, OK, so the last model couldn't count the number of Rs in 'Strawberry' and the new one can, but..." That's a very important point. I mean, it's not guaranteed that any form of AI is going to advance to the point that it starts taking jobs from people like us, but when you fail to look forward and project a little bit and imagine what they could do with another year of progress... or two years of progress... or 5 years, etc? I posit that that kind of myopia could leave one very under-prepared for the world one lands in. > The point was there is nothing intelligent (or AI) about LLMs except the person fooling themselves. Sure. The "AI Effect". Irrelevant. It doesn't matter how the machine can do your job, or whether or not it's "really intelligent". It just matters that if it can create more value, more cheaply, than you or I, we are going to wind up like John Henry. Who, btw, for anybody not familiar with that particular bit of folklore "[won the race against the machine] only to die in victory with a hammer in hand as his heart gave out from stress."
        zxvkhkxvdvbdxz 2y ago
        Both you and this chatbot Bob seem to be overly excited by the newfound LLM ability of correctly counting R's in "strawberry". For many, this is not a very exciting development. Mind you, we do follow the progress but your argument of "wait and see" is not deserving serious discussion as your stance has turned into faith.
        Joel_Mckay 2y ago
        The limitations of tokenization does not stop with LLMs it seems for bob. Please don't down-vote the kids karma, as for me it is more important people feel comfortable having conversations (especially when they are almost 99% sure I'm a turnip connected to a USB port.) =3
        mindcrime 2y ago
  - Lerc 2y ago
    I have never understood why the failure to answer the strawberry question has seen as a compelling argument as to the limits of AI. The AIs that suffer from this problem have difficulty counting. That has never been denied. Those AI's also do not see the letters of the words they are processing. Counting the letters in a word is a task that it is quite unsurprising that it fails. I Would say it is more surprising that that they can perform spelling tasks at all. More importantly the models where such weaknesses became apparent are all from the same timeframe where the models advanced so much that those weaknesses were visible only after so many other greater weaknesses had been overcome. People didn't think that planes flying so high that pilots couldn't breathe exposed a fundamental limitation of flight, just that their success had revealed the next hurdle. The assertion that an LLM is X and therefore not intelligent is not a useful claim to make without either proof that it is X and proof that X is insufficient. You could say brains are interconnected cells that send pulses at intervals dictated by a combination of the pulses they sense, and there is nothing intelligent about that. The premises must be true and you have to demonstrate that the conclusion follows from those premises. For the record I think your premises are false and your conclusion doesn't follow. Without a proof you could hypothesise reasons why such a system might not be intelligent and come up with an example of a task that no system that satisfies the premises could accomplish. While that example is unsolved the hypothesis remains unrefuted. What would you suggest as a test that shows a problem that could not be solved by such a machine? It must be solvable by at least one intelligent entity to show that it is solvable by intelligence. It must be undeniable when the problem is solved.
    - tecleandor 2y ago
      The AIs that suffer from this problem have difficulty counting. Nope, its not a counting problem. It's a reasoning problem. Thing is, no matter how much hype they get, the AIs have no reasoning capabilities at all, and they can fail in the silliest ways. Same as with Larry Ellison: Don't fall into the trap of anthropomorphizing the AI.
      - Lerc 2y ago
        Ok, give me an example of what you would consider reasoning.

How so?

Wait and see. You're not paying attention now, but it's not too late to start. Go to your favorite programming puzzle site and see how you do against the latest models, for instance. If you can beat o1-pro regularly, for instance, then you're right, you have nothing to worry about and can safely disregard it. Same proposition that was offered to John Henry.

Joel_Mckay 2y ago
Please reformulate your argument, and I will check back tomorrow: https://www.youtube.com/watch?v=aNSHZG9blQQ https://www.youtube.com/watch?v=aNSHZG9blQQ LLMs are rules based search engines with higher dimension vector spaces encoding related topics. There is nothing intelligent about these algorithms, except the trick ones play on oneself interpreting well structured nonsense. It is stunting kids development, as students often lack the ability to intuitively reason when they are being misled. "How many R's in 'Strawberry'?" is a classic example exposing the underlying pattern recognition failures. =3
- CamperBob2 2y ago
  (Shrug) If you're retired or independently wealthy, you can afford that attitude. Hopefully one of those describes you. Otherwise, you're going to spend the rest of your career saying things like, "Well, OK, so the last model couldn't count the number of Rs in 'Strawberry' and the new one can, but..." Personally, I dislike being wrong. So I don't base arguments on points that have a built-in expiration date, or that are based on a fundamental misunderstanding of whatever I'm talking about.
  - Joel_Mckay 2y ago
    Every model is deprecated in time if evidenced Science is done well, and hopefully replaced by something more accurate in time. There is no absolute right/correctness unless you are a naive child under 25 cheating on structured homework. The point was there is nothing intelligent (or AI) about LLMs except the person fooling themselves. In general, most template libraries already implement the best possible algorithms from the 1960s, and tuned for architecture specific optimizations. Knowing when each finite option is appropriate takes a bit of understanding/study, but does give results far quicker than fitting a statistically salient nonsense answer. Several study datum from senior developers is already available, and it proves LLMs provide zero benefit to people that know what they are doing. Note, I am driven by having fun, rather than some bizarre irrational competitiveness. Prove your position, or I will assume you are just a silly person or chat bot. =3
    - mindcrime 2y ago
      I have no position on whether or not CamperBob is a chat-bot, but they are definitely not being silly. Their point, as I take it, is that it's dangerous to look at the state of "AI" as it is today and then ignore the rate of change. To their stated point from above: > Otherwise, you're going to spend the rest of your career saying things like, "Well, OK, so the last model couldn't count the number of Rs in 'Strawberry' and the new one can, but..." That's a very important point. I mean, it's not guaranteed that any form of AI is going to advance to the point that it starts taking jobs from people like us, but when you fail to look forward and project a little bit and imagine what they could do with another year of progress... or two years of progress... or 5 years, etc? I posit that that kind of myopia could leave one very under-prepared for the world one lands in. > The point was there is nothing intelligent (or AI) about LLMs except the person fooling themselves. Sure. The "AI Effect". Irrelevant. It doesn't matter how the machine can do your job, or whether or not it's "really intelligent". It just matters that if it can create more value, more cheaply, than you or I, we are going to wind up like John Henry. Who, btw, for anybody not familiar with that particular bit of folklore "[won the race against the machine] only to die in victory with a hammer in hand as his heart gave out from stress."
      - zxvkhkxvdvbdxz 2y ago
        Both you and this chatbot Bob seem to be overly excited by the newfound LLM ability of correctly counting R's in "strawberry". For many, this is not a very exciting development. Mind you, we do follow the progress but your argument of "wait and see" is not deserving serious discussion as your stance has turned into faith.
        Joel_Mckay 2y ago
        The limitations of tokenization does not stop with LLMs it seems for bob. Please don't down-vote the kids karma, as for me it is more important people feel comfortable having conversations (especially when they are almost 99% sure I'm a turnip connected to a USB port.) =3
        mindcrime 2y ago
- Lerc 2y ago
  I have never understood why the failure to answer the strawberry question has seen as a compelling argument as to the limits of AI. The AIs that suffer from this problem have difficulty counting. That has never been denied. Those AI's also do not see the letters of the words they are processing. Counting the letters in a word is a task that it is quite unsurprising that it fails. I Would say it is more surprising that that they can perform spelling tasks at all. More importantly the models where such weaknesses became apparent are all from the same timeframe where the models advanced so much that those weaknesses were visible only after so many other greater weaknesses had been overcome. People didn't think that planes flying so high that pilots couldn't breathe exposed a fundamental limitation of flight, just that their success had revealed the next hurdle. The assertion that an LLM is X and therefore not intelligent is not a useful claim to make without either proof that it is X and proof that X is insufficient. You could say brains are interconnected cells that send pulses at intervals dictated by a combination of the pulses they sense, and there is nothing intelligent about that. The premises must be true and you have to demonstrate that the conclusion follows from those premises. For the record I think your premises are false and your conclusion doesn't follow. Without a proof you could hypothesise reasons why such a system might not be intelligent and come up with an example of a task that no system that satisfies the premises could accomplish. While that example is unsolved the hypothesis remains unrefuted. What would you suggest as a test that shows a problem that could not be solved by such a machine? It must be solvable by at least one intelligent entity to show that it is solvable by intelligence. It must be undeniable when the problem is solved.
  - tecleandor 2y ago
    The AIs that suffer from this problem have difficulty counting. Nope, its not a counting problem. It's a reasoning problem. Thing is, no matter how much hype they get, the AIs have no reasoning capabilities at all, and they can fail in the silliest ways. Same as with Larry Ellison: Don't fall into the trap of anthropomorphizing the AI.
    - Lerc 2y ago
      Ok, give me an example of what you would consider reasoning.