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> The answers are not comming from a thinking mind but a complex pattern-fitting supercomputer hovering over a massive table of precomputed patterns. Are you s

> The answers are not comming from a thinking mind but a complex pattern-fitting supercomputer hovering over a massive table of precomputed patterns. Are you sure you're not also describing the human brain? At some point, after we have sufficiently demystified the workings of the human brain, it will probably also sound something like, "Well, the brain is just a large machine that does X, Y and Z [insert banal-sounding technical jargon from the future] - it doesn't really understand anything." My point here is that understanding ultimately comes down to having an effective internal model of the world, which is capable of taking novel inputs and generating reasonable descriptions of them or reactions to them. It turns out that LLMs are one way of achieving that. They don't function exactly like human brains, but they certainly do exhibit intelligence and understanding. I can ask an LLM a question that it has never seen before, and it will give me a reasonable answer that synthesizes and builds on various facts that it knows. Often the answer is more intelligent than what one would get from most humans. That's understanding.

beardedwizard 3y ago
Human brains form new connections dynamically. Llms are trained on connections human brains have already made. They never make new connections that aren't in training data. Nothing was synthesized, all the data was seen before and related to each other by vector similarity. It can just parrot the collective understanding humans already have and teach it.
- DiogenesKynikos 3y ago
  > It can just parrot the collective understanding humans already have and teach it. The problem with calling an LLM a parrot is that anyone who has actually interacted with an LLM knows that it produces completely novel responses to questions it has never seen before. These answers are usually logical and reasonable, based on both the information you gave the LLM and its previous knowledge of the world. Doing that requires understanding. > They never make new connections that aren't in training data. This is just categorically untrue. They make all sorts of logical connections that are not explicitly contained in the training data. Making logical inferences about subjects one has never heard about - based on the things one does know - is an expression of understanding. LLMs do that.
  - beardedwizard 3y ago
    Isn't this describing temperature induced randomness and ascribing some kind of intelligence to it? This assertion has been made and refuted multiple times on this thread and no solid evidence to the contrary presented. To go back to your first sentence - interacting with an llm is not understanding how it works, building one is. The actual construction of a neural network llm refutes your assertions.
    - DiogenesKynikos 3y ago
      The claim was made that LLMs just parrot back what they've seen in the training data. They clearly go far beyond this and generate completely novel ideas that are not in the training data. I can give ChatGPT extremely specific and weird prompts that have 0% chance of being in its training data, and it will answer intelligently. > The actual construction of a neural network llm refutes your assertions. I don't see how. There's a common view that I see expressed in these discussions, that if the workings of an LLM can be explained in a technical manner, then it doesn't understand. "It just uses temperature induced randomness, etc. etc." Once we understand how the human brain works, it will then be possible to argue, in the exact same way, that humans do not understand. "You see, the brain is just mechanically doing XYZ, leading to the vocal cords moving in this particular pattern."
      - AlexandrB 3y ago
        > They clearly go far beyond this and generate completely novel ideas that are not in the training data. There's a case where this is trivially false. Language. LLMs are bound by language that was invented by humans. They are unable to "conceive" of anything that cannot be described by human language as it exists, whereas humans create new words for new ideas all the time.
        pixl97 3y ago
        Uh, I believe you're really confused on things like ChatGPT versus LLMs in general. You don't have to feed human language to an LLM for them to learn things. You can feed wifi data waveforms for example and they can 'learn' insights from that. Furthermore you're thinking here doesn't even begin to explain multimodal models at all.

beardedwizard 3y ago

Human brains form new connections dynamically. Llms are trained on connections human brains have already made. They never make new connections that aren't in training data. Nothing was synthesized, all the data was seen before and related to each other by vector similarity. It can just parrot the collective understanding humans already have and teach it.

DiogenesKynikos 3y ago
> It can just parrot the collective understanding humans already have and teach it. The problem with calling an LLM a parrot is that anyone who has actually interacted with an LLM knows that it produces completely novel responses to questions it has never seen before. These answers are usually logical and reasonable, based on both the information you gave the LLM and its previous knowledge of the world. Doing that requires understanding. > They never make new connections that aren't in training data. This is just categorically untrue. They make all sorts of logical connections that are not explicitly contained in the training data. Making logical inferences about subjects one has never heard about - based on the things one does know - is an expression of understanding. LLMs do that.
- beardedwizard 3y ago
  Isn't this describing temperature induced randomness and ascribing some kind of intelligence to it? This assertion has been made and refuted multiple times on this thread and no solid evidence to the contrary presented. To go back to your first sentence - interacting with an llm is not understanding how it works, building one is. The actual construction of a neural network llm refutes your assertions.
  - DiogenesKynikos 3y ago
    The claim was made that LLMs just parrot back what they've seen in the training data. They clearly go far beyond this and generate completely novel ideas that are not in the training data. I can give ChatGPT extremely specific and weird prompts that have 0% chance of being in its training data, and it will answer intelligently. > The actual construction of a neural network llm refutes your assertions. I don't see how. There's a common view that I see expressed in these discussions, that if the workings of an LLM can be explained in a technical manner, then it doesn't understand. "It just uses temperature induced randomness, etc. etc." Once we understand how the human brain works, it will then be possible to argue, in the exact same way, that humans do not understand. "You see, the brain is just mechanically doing XYZ, leading to the vocal cords moving in this particular pattern."
    - AlexandrB 3y ago
      > They clearly go far beyond this and generate completely novel ideas that are not in the training data. There's a case where this is trivially false. Language. LLMs are bound by language that was invented by humans. They are unable to "conceive" of anything that cannot be described by human language as it exists, whereas humans create new words for new ideas all the time.
      - pixl97 3y ago
        Uh, I believe you're really confused on things like ChatGPT versus LLMs in general. You don't have to feed human language to an LLM for them to learn things. You can feed wifi data waveforms for example and they can 'learn' insights from that. Furthermore you're thinking here doesn't even begin to explain multimodal models at all.