4 ms·

Agree. Both sides of the argument are unsatisfying. They seem like quantitative answers to a qualitative question.

serbuvlad 1y ago
"Have we created machines that can do something qualitatevely similar to that part of us that can correlate known information and pattern recognition to produce new ideas and solutions to problems -- that part we call thinking?" I think the answer to this question is certainly "Yes". I think the reason people deny this is because it was just laughably easy in retrospect. In mid-2022 people were like. "Wow this GPT3 thing generates kind of coherent greentexts" Since then really only we got: larger models, larger models, search, agents, larger models, chain-of-thought and larger models. And from a novelty toy we got a set of tools that at the very least massively increase human productivity in a wide range of tasks and certainly pass any Turing test. Attention really was all you needed. But of course, if you ask a buddhist monk, he'll tell you we are attention machines, not computation machines. He'll also tell you, should you listen, that we have a monkey in our mind that is constantly producing new thoughts. This monkey is not who we are, it's an organ. It's thoughts are not our thoughts. It's something we perceive. And that we shouldn't identify with. Now we have thought-genrating-monkeys with jet engines and adrenaline shots. This can be good. Thought-genrating-monkeys put us on the moon and wrote Hamlet and the Oddesy. The key is to not become a slave to them. To realize that our worth consists not in our ability to think. And that we are more than that.
- autobodie 1y ago
  > The key is to not become a slave to them. To realize that our worth consists not in our ability to think. And that we are more than that. I cannot afford to consider whether you are right because I am a slave to capital, and therefore may as well be a slave to capital's LLMs. The same goes for you.
  - serbuvlad 1y ago
    I am not a slave to capital. I am a slave to the harsh nature of the world. I get too hot in summer and too cold in winter. I die of hunger. I am harassed by critters of all sorts. And when my bed breaks, to keep my fragile spine from straining at night, I _want_ some trees to be cut, some mattresses to be provisioned, some designers to be provisioned etc. And capital is what gets me that, from people I will never meet, who wouldn't blink once if I died tomorrow.
    - LinXitoW 1y ago
      Considering capitalism is a very new phenomenon in human history, how do you think people survived and thrived for the other 248000 years? It's as ludicrous to believe that capitalism is some kind of force of nature as it is to believe kings were chosen by god.
      - serbuvlad 1y ago
        That depends on how you define your terms. A pro-capital laissez-faire policy is new, sure. But the first civilizations in the world around 3000BC had trade, money, banking, capital accumulation, divison of labour etc.
      - TeMPOraL 1y ago
        > how do you think people survived and thrived for the other 248000 years? In small tribes, where everyone knew everyone intimately because they lived together, and everything was managed by feels. Things like rules, laws, money, banking, hierarchies, well-defined private vs. public ownership, are all things that came with scale, because interpersonal relationships fail to keep group cohesion once it reaches more than ~100 people.
- viccis 1y ago
  >I think the answer to this question is certainly "Yes". It is unequivocally "No". A good joint distribution estimator is always by definition a posteriori and completely incapable of synthetic a priori thought.
  - nerdponx 1y ago
    That doesn't seem true to me at all. Let's say you fit y=c+bx+ax^2 on the domain -10,10 with 1000 data points uniformly distributed along x and with no more than 1% noise in observed y. Your model will be pretty damn good and absolutely will be able to generate "synthetic a priori" y outputs for any given x within the domain. Now let's say you didn't know the true function and had to use a neural network instead. You would probably still get a great result in the sense of generating "new" outputs that are not observed in the training data, as long as they are within or reasonably close to the original domain. LLMs are that. With enough data and enough parameters and the right inductive bias and the right RLHF procedure etc, they are getting increasingly good at estimating a conditional next token distribution given the context. If by "synthetic" you mean that an LLM can never generate a truly new idea that was not in it's training data, then that becomes the question of what the "domain" of the data really is. I'm not convinced that LLMs are strictly limited to ideas that they have "learned" in their data. Before LLMs, I don't think people realized just how much pattern and structure there was in human thought, and how exposed it was through text. Given the advances of the last couple of years, I'm starting to come around to the idea that text contains enough instances of reasoning and thinking that these models might develop some kind of ability to do something like reasoning and thinking simply because they would have to in order to continue decreasing validation loss. I want to be clear that I am not at all an AI maximalist, and the fact that these things are built largely on copyright infringement continues to disgust me, as do the growing economic and environmental externalities and other problems surrounding their use and abuse. But I don't think it does any good to pretend these things are dumber than they are, or to assume that the next AI winter is right around the corner.
    - viccis 1y ago
      >Your model will be pretty damn good and absolutely will be able to generate "synthetic a priori" y outputs for any given x within the domain. You don't seem to understand what synthetic a priori means. The fact that you're asking a model to generate outputs based on inputs means it's by definition a posteriori. >You would probably still get a great result in the sense of generating "new" outputs that are not observed in the training data, as long as they are within or reasonably close to the original domain. That's not cognition and has no epistemological grounds. You're making the assumption that better prediction of semiotic structure (of language, images, etc.) results in better ability to produce knowledge. You can't model knowledge with language alone, the logical positivists found that out to their disappointment a century or so ago. For example, I don't think you adequately proved this statement to be true: >they would have to in order to continue decreasing validation loss This works if and only if the structure of knowledge lies latently beneath the structure of semiotics. In other words, if you can start identifying the "shape" of the distribution of language, you can perturb it slightly to get a new question and expect to get a new correct answer.

"Have we created machines that can do something qualitatevely similar to that part of us that can correlate known information and pattern recognition to produce new ideas and solutions to problems -- that part we call thinking?" I think the answer to this question is certainly "Yes". I think the reason people deny this is because it was just laughably easy in retrospect. In mid-2022 people were like. "Wow this GPT3 thing generates kind of coherent greentexts" Since then really only we got: larger models, larger models, search, agents, larger models, chain-of-thought and larger models. And from a novelty toy we got a set of tools that at the very least massively increase human productivity in a wide range of tasks and certainly pass any Turing test. Attention really was all you needed. But of course, if you ask a buddhist monk, he'll tell you we are attention machines, not computation machines. He'll also tell you, should you listen, that we have a monkey in our mind that is constantly producing new thoughts. This monkey is not who we are, it's an organ. It's thoughts are not our thoughts. It's something we perceive. And that we shouldn't identify with. Now we have thought-genrating-monkeys with jet engines and adrenaline shots. This can be good. Thought-genrating-monkeys put us on the moon and wrote Hamlet and the Oddesy. The key is to not become a slave to them. To realize that our worth consists not in our ability to think. And that we are more than that.

autobodie 1y ago
> The key is to not become a slave to them. To realize that our worth consists not in our ability to think. And that we are more than that. I cannot afford to consider whether you are right because I am a slave to capital, and therefore may as well be a slave to capital's LLMs. The same goes for you.
- serbuvlad 1y ago
  I am not a slave to capital. I am a slave to the harsh nature of the world. I get too hot in summer and too cold in winter. I die of hunger. I am harassed by critters of all sorts. And when my bed breaks, to keep my fragile spine from straining at night, I _want_ some trees to be cut, some mattresses to be provisioned, some designers to be provisioned etc. And capital is what gets me that, from people I will never meet, who wouldn't blink once if I died tomorrow.
  - LinXitoW 1y ago
    Considering capitalism is a very new phenomenon in human history, how do you think people survived and thrived for the other 248000 years? It's as ludicrous to believe that capitalism is some kind of force of nature as it is to believe kings were chosen by god.
    - serbuvlad 1y ago
      That depends on how you define your terms. A pro-capital laissez-faire policy is new, sure. But the first civilizations in the world around 3000BC had trade, money, banking, capital accumulation, divison of labour etc.
    - TeMPOraL 1y ago
      > how do you think people survived and thrived for the other 248000 years? In small tribes, where everyone knew everyone intimately because they lived together, and everything was managed by feels. Things like rules, laws, money, banking, hierarchies, well-defined private vs. public ownership, are all things that came with scale, because interpersonal relationships fail to keep group cohesion once it reaches more than ~100 people.
viccis 1y ago
>I think the answer to this question is certainly "Yes". It is unequivocally "No". A good joint distribution estimator is always by definition a posteriori and completely incapable of synthetic a priori thought.
- nerdponx 1y ago
  That doesn't seem true to me at all. Let's say you fit y=c+bx+ax^2 on the domain -10,10 with 1000 data points uniformly distributed along x and with no more than 1% noise in observed y. Your model will be pretty damn good and absolutely will be able to generate "synthetic a priori" y outputs for any given x within the domain. Now let's say you didn't know the true function and had to use a neural network instead. You would probably still get a great result in the sense of generating "new" outputs that are not observed in the training data, as long as they are within or reasonably close to the original domain. LLMs are that. With enough data and enough parameters and the right inductive bias and the right RLHF procedure etc, they are getting increasingly good at estimating a conditional next token distribution given the context. If by "synthetic" you mean that an LLM can never generate a truly new idea that was not in it's training data, then that becomes the question of what the "domain" of the data really is. I'm not convinced that LLMs are strictly limited to ideas that they have "learned" in their data. Before LLMs, I don't think people realized just how much pattern and structure there was in human thought, and how exposed it was through text. Given the advances of the last couple of years, I'm starting to come around to the idea that text contains enough instances of reasoning and thinking that these models might develop some kind of ability to do something like reasoning and thinking simply because they would have to in order to continue decreasing validation loss. I want to be clear that I am not at all an AI maximalist, and the fact that these things are built largely on copyright infringement continues to disgust me, as do the growing economic and environmental externalities and other problems surrounding their use and abuse. But I don't think it does any good to pretend these things are dumber than they are, or to assume that the next AI winter is right around the corner.
  - viccis 1y ago
    >Your model will be pretty damn good and absolutely will be able to generate "synthetic a priori" y outputs for any given x within the domain. You don't seem to understand what synthetic a priori means. The fact that you're asking a model to generate outputs based on inputs means it's by definition a posteriori. >You would probably still get a great result in the sense of generating "new" outputs that are not observed in the training data, as long as they are within or reasonably close to the original domain. That's not cognition and has no epistemological grounds. You're making the assumption that better prediction of semiotic structure (of language, images, etc.) results in better ability to produce knowledge. You can't model knowledge with language alone, the logical positivists found that out to their disappointment a century or so ago. For example, I don't think you adequately proved this statement to be true: >they would have to in order to continue decreasing validation loss This works if and only if the structure of knowledge lies latently beneath the structure of semiotics. In other words, if you can start identifying the "shape" of the distribution of language, you can perturb it slightly to get a new question and expect to get a new correct answer.