4 ms·

Not really. It's a derivative of that being thrown back at you via statistics.

scotty79 2y ago
People throw "statistics" as if it's something derogatory. All knowledge about how universe works that we ever obtained was through the use of statistics.
- magic_hamster 2y ago
  Statistics play a massive role in our universe, and evidence suggests that even the very fabric of the universe is statistic. I am not putting statistics down, however I do think statistics are orthogonal to a deliberate thought. Which is why I can't see LLMs as a very smart person making deliberate discussion just yet.
  - bdhcuidbebe 2y ago
    > and evidence suggests that even the very fabric of the universe is statistic No, there’s no evidence suggesting that. If you are refering to the mathematical universe, its a fun mental excersise, not a serious idea.
    - jstanley 2y ago
      It's a very serious idea! It's actually the only idea that explains how the universe came to exist with nothing outside it and no creator.
      - scotty79 2y ago
        > how the universe came to exist with nothing outside it and no creator It think that's the most boring and irrelevant question in human culture and I believe humanity is about a million years from tackling it in any competent manner. We have way more to find out about our smallness and irrelevance first.
  - yes_man 2y ago
    What is ”deliberate thought”? And why would human level intelligence require it? Wouldn’t performance of any intelligence be completely separate of how it produces the output? I mean if an AI one day surpasses human performance in every conceivable task, is the argument still going to be ”yeah but I feel like it’s not deliberate thought, it’s just statistics”?
  - Lerc 2y ago
    There's a lot hanging on what you mean by deliberate thought here. I'll just label whatever you mean X I presume you are claiming by saying orthogonal that statistics are neither required nor preclude X. You are using the presence of statistical processes as a claim against ChatGPT with >It's a derivative of that being thrown back at you via statistics. For this to have any weight given the orthogonal claim, you are asserting that LLMs are entirely statistics (the property you assert is orthogonal and thus carries no weight, yet invoking it as a property suggests that it is relevant, presumably because there are no other properties) Then we get into the weeds of what you mean by statistics. It could be broad or narrow. If it is narrow then it is easy to claim neither sufficient nor precluding, but extremely hard to draw any other conclusion because any system can break free of the proof by including the tiniest bit that does not fit within the narrow definition of statistics. If the definition is broader to include everything that happens within ChatGPT, then you are faced with the question of what is not covered by that definition of statistics. How can you show that human minds do not operate within those limitations. I don't think that ChatGPT is conscious in any sense that would be meaningful to a layman, but I also don't think that you can demonstrate it could not be purely based on the fact it uses any particular process without a rigorous proof of the limitations of that process. The only way you could reasonably
- visarga 2y ago
  We are indirectly applying derogatory language to the training set, which is our own culture. Strange position to despise our own past knowledge, took us a long time to compile it. Even if someone thinks LLMs are just stochastic parrots, they parrot our own thoughts. But if we accept LLMs as more than parrots, like an improv jazz musician adapting to the audience and always creating something new recombining the old in novel ways, then it is even less justified to despise LLM outputs. The whole parrot idea is half baked. Yes, in closed book mode, and without a seed prompt that ads anything new to the existing knowledge of the model, it will parrot (adapt) from its training data. But if you add novel data in the prompt, then we push it outside its training distribution, and it learns on the fly. And usually all interactions bring some new elements to the model, new knowledge or skills, or new context. The more elaborate the prompts, the longer the session of messages, the less parroting takes place. The O3 model performance on ARC-AGI is another argument against the reductionist idea of parroting.
j45 2y ago
While it's math, the experience of it is in the same medium it's being interacted with - text.
TeMPOraL 2y ago
It's "statistics" in the same sense your own thoughts are. Or, it's not "statistics" as in they average the shit out and call it a day, like idk computing a census, or a report to your boss about the impact of most recent ad campaign, or something. It's "statistics" in the sense of dealing with probability distributions, systematically teasing out correlations and causal relationships in inputs too fuzzy to handle symbolically. Of which language is literally the prime example. All that talk about grammar and sentence structure and symbolic transformations and Chomsky etc. is just bullshit - it's not how we do it at all, which is obvious to anyone who was forced to learn grammar and sentence breakdown at school, and started wondering how come they already know to speak without knowing all this. Nah, we learn the language the same way LLMs do - by immersion and feedback, until we're able to say what "feels right" and be right. All those formalisms are basically fitting probability distributions with neat symbolic approximations, and are as valid as replacing Navier–Stokes equations with a linear function because that's how it looks like in typical case and we don't know any better. So yeah, it's all "thrown back at you via statistics". So is all of the human thought it's derivative of. Embrace it: we're all "statistical machines".
- magic_hamster 2y ago
  > It's "statistics" in the same sense your own thoughts are. And you know this how? > we learn the language the same way LLMs do Based on? > So yeah, it's all "thrown back at you via statistics". So is all of the human thought it's derivative of You say this based on what? Do you have any real research to support any of this? Because from what I see in the neuroscience community, LLMs are not even remotely an approximation of a real brain, despite being built of "neurons" (which are nothing like real neurons).
  - TeMPOraL 2y ago
    > And you know this how? Probability theory, computability theory. There's no other way. > Based on? High-level description of the learning approach, and approximately the entirety of recorded human history? > Because from what I see in the neuroscience community, LLMs are not even remotely an approximation of a real brain, despite being built of "neurons" (which are nothing like real neurons). So what? Neurons in silica are to neurons in vivo as my Twitter clone in 15 lines of terse Perl is to the actual web-scale monstrosity a Twitter or other modern scalable SaaS is. I.e. it does the same core thing faster and better, but obviously lacks incidental, supporting complexity to work in the same environment as the real thing. Yes, a biological neuron is huge and complex. But a biological neuron is, first and foremost, an incrementally evolved, independent, self-replicating and self-maintaining nanomachine, tuned to be a part of a specialized self-assembling system, filling a role in a large and diverse ensemble of other specialized nanomachines. Incidentally, a neuron also computes - but it's not computing that uses this complexity, it's all the self-replicating self-assembling nanomachine business that needs it. There's absolutely no reason to believe that neurons in silica need to be comparably complex to crack intelligence - not when we're free to strip the problem out of all the Kubernetes/protein nanomachine incidental complexity business, and focus on the core computation.

People throw "statistics" as if it's something derogatory. All knowledge about how universe works that we ever obtained was through the use of statistics.

magic_hamster 2y ago
Statistics play a massive role in our universe, and evidence suggests that even the very fabric of the universe is statistic. I am not putting statistics down, however I do think statistics are orthogonal to a deliberate thought. Which is why I can't see LLMs as a very smart person making deliberate discussion just yet.
- bdhcuidbebe 2y ago
  > and evidence suggests that even the very fabric of the universe is statistic No, there’s no evidence suggesting that. If you are refering to the mathematical universe, its a fun mental excersise, not a serious idea.
  - jstanley 2y ago
    It's a very serious idea! It's actually the only idea that explains how the universe came to exist with nothing outside it and no creator.
    - scotty79 2y ago
      > how the universe came to exist with nothing outside it and no creator It think that's the most boring and irrelevant question in human culture and I believe humanity is about a million years from tackling it in any competent manner. We have way more to find out about our smallness and irrelevance first.
- yes_man 2y ago
  What is ”deliberate thought”? And why would human level intelligence require it? Wouldn’t performance of any intelligence be completely separate of how it produces the output? I mean if an AI one day surpasses human performance in every conceivable task, is the argument still going to be ”yeah but I feel like it’s not deliberate thought, it’s just statistics”?
- Lerc 2y ago
  There's a lot hanging on what you mean by deliberate thought here. I'll just label whatever you mean X I presume you are claiming by saying orthogonal that statistics are neither required nor preclude X. You are using the presence of statistical processes as a claim against ChatGPT with >It's a derivative of that being thrown back at you via statistics. For this to have any weight given the orthogonal claim, you are asserting that LLMs are entirely statistics (the property you assert is orthogonal and thus carries no weight, yet invoking it as a property suggests that it is relevant, presumably because there are no other properties) Then we get into the weeds of what you mean by statistics. It could be broad or narrow. If it is narrow then it is easy to claim neither sufficient nor precluding, but extremely hard to draw any other conclusion because any system can break free of the proof by including the tiniest bit that does not fit within the narrow definition of statistics. If the definition is broader to include everything that happens within ChatGPT, then you are faced with the question of what is not covered by that definition of statistics. How can you show that human minds do not operate within those limitations. I don't think that ChatGPT is conscious in any sense that would be meaningful to a layman, but I also don't think that you can demonstrate it could not be purely based on the fact it uses any particular process without a rigorous proof of the limitations of that process. The only way you could reasonably
visarga 2y ago
We are indirectly applying derogatory language to the training set, which is our own culture. Strange position to despise our own past knowledge, took us a long time to compile it. Even if someone thinks LLMs are just stochastic parrots, they parrot our own thoughts. But if we accept LLMs as more than parrots, like an improv jazz musician adapting to the audience and always creating something new recombining the old in novel ways, then it is even less justified to despise LLM outputs. The whole parrot idea is half baked. Yes, in closed book mode, and without a seed prompt that ads anything new to the existing knowledge of the model, it will parrot (adapt) from its training data. But if you add novel data in the prompt, then we push it outside its training distribution, and it learns on the fly. And usually all interactions bring some new elements to the model, new knowledge or skills, or new context. The more elaborate the prompts, the longer the session of messages, the less parroting takes place. The O3 model performance on ARC-AGI is another argument against the reductionist idea of parroting.

j45 2y ago

While it's math, the experience of it is in the same medium it's being interacted with - text.

TeMPOraL 2y ago

It's "statistics" in the same sense your own thoughts are. Or, it's not "statistics" as in they average the shit out and call it a day, like idk computing a census, or a report to your boss about the impact of most recent ad campaign, or something. It's "statistics" in the sense of dealing with probability distributions, systematically teasing out correlations and causal relationships in inputs too fuzzy to handle symbolically. Of which language is literally the prime example. All that talk about grammar and sentence structure and symbolic transformations and Chomsky etc. is just bullshit - it's not how we do it at all, which is obvious to anyone who was forced to learn grammar and sentence breakdown at school, and started wondering how come they already know to speak without knowing all this. Nah, we learn the language the same way LLMs do - by immersion and feedback, until we're able to say what "feels right" and be right. All those formalisms are basically fitting probability distributions with neat symbolic approximations, and are as valid as replacing Navier–Stokes equations with a linear function because that's how it looks like in typical case and we don't know any better. So yeah, it's all "thrown back at you via statistics". So is all of the human thought it's derivative of. Embrace it: we're all "statistical machines".

magic_hamster 2y ago
> It's "statistics" in the same sense your own thoughts are. And you know this how? > we learn the language the same way LLMs do Based on? > So yeah, it's all "thrown back at you via statistics". So is all of the human thought it's derivative of You say this based on what? Do you have any real research to support any of this? Because from what I see in the neuroscience community, LLMs are not even remotely an approximation of a real brain, despite being built of "neurons" (which are nothing like real neurons).
- TeMPOraL 2y ago
  > And you know this how? Probability theory, computability theory. There's no other way. > Based on? High-level description of the learning approach, and approximately the entirety of recorded human history? > Because from what I see in the neuroscience community, LLMs are not even remotely an approximation of a real brain, despite being built of "neurons" (which are nothing like real neurons). So what? Neurons in silica are to neurons in vivo as my Twitter clone in 15 lines of terse Perl is to the actual web-scale monstrosity a Twitter or other modern scalable SaaS is. I.e. it does the same core thing faster and better, but obviously lacks incidental, supporting complexity to work in the same environment as the real thing. Yes, a biological neuron is huge and complex. But a biological neuron is, first and foremost, an incrementally evolved, independent, self-replicating and self-maintaining nanomachine, tuned to be a part of a specialized self-assembling system, filling a role in a large and diverse ensemble of other specialized nanomachines. Incidentally, a neuron also computes - but it's not computing that uses this complexity, it's all the self-replicating self-assembling nanomachine business that needs it. There's absolutely no reason to believe that neurons in silica need to be comparably complex to crack intelligence - not when we're free to strip the problem out of all the Kubernetes/protein nanomachine incidental complexity business, and focus on the core computation.