3 ms·

Cooperation is more than an i/o loop. Layering and pooling models is nice though. Perhaps "conceptualization" is the indicator here.

ben_w 3y ago
Perhaps I'm up too late, but I can't think what else is there to cooperation besides two or more agents doing things in alignment with some goal? (Regardless of who or what sets that goal). Also I don't know what you mean by "conceptualization".
- hackable_sand 3y ago
  It's fuzzy because intelligence is relative right. I mean "being able to conceive an idea". As humans, two or more of us can reason our way to a conclusion without domain knowledge. There is an upper limit where the idea is incomplete (assuming respectful ignorance), but it's generative nonetheless. With an LLM I have to prompt engineer to guide it. I would rather have it generate novel concepts to push domain boundaries. They work great as knowledge bases though.
  - ben_w 2y ago
    > As humans, two or more of us can reason our way to a conclusion without domain knowledge That sounds like step-by-step thinking? > With an LLM I have to prompt engineer to guide it. I generally have to in humans, too. I mean, you and I are prompting each other, aren't we? For me the difference between prompting a human and prompting an AI is that I can reset the AI, I can't make a human forget a previous analogy that had only confused them. (And likewise, I don't expect that I fully forget bad analogies which confuse me, though I do try). > They work great as knowledge bases though. IMO, that's their weakest part. We had knowledge bases before — where each claim can be easily localised within the model, corrected when it needs to be, verified in advance, and which give predictable output — LLMs are none of those things. LLMs are much better at understanding the question (constant time for a fixed-length output, even when the query is phrased badly and relatively complex), and being able to synthesise things in the form of "${x} won't work, try ${y}".
    - hackable_sand 2y ago
      Huh. Do you think integrating the Semantic Web metadata and ontologies in LLM training can help us bootstrap conceptual modeling using natural language?

ben_w 3y ago

Perhaps I'm up too late, but I can't think what else is there to cooperation besides two or more agents doing things in alignment with some goal? (Regardless of who or what sets that goal). Also I don't know what you mean by "conceptualization".

hackable_sand 3y ago
It's fuzzy because intelligence is relative right. I mean "being able to conceive an idea". As humans, two or more of us can reason our way to a conclusion without domain knowledge. There is an upper limit where the idea is incomplete (assuming respectful ignorance), but it's generative nonetheless. With an LLM I have to prompt engineer to guide it. I would rather have it generate novel concepts to push domain boundaries. They work great as knowledge bases though.
- ben_w 2y ago
  > As humans, two or more of us can reason our way to a conclusion without domain knowledge That sounds like step-by-step thinking? > With an LLM I have to prompt engineer to guide it. I generally have to in humans, too. I mean, you and I are prompting each other, aren't we? For me the difference between prompting a human and prompting an AI is that I can reset the AI, I can't make a human forget a previous analogy that had only confused them. (And likewise, I don't expect that I fully forget bad analogies which confuse me, though I do try). > They work great as knowledge bases though. IMO, that's their weakest part. We had knowledge bases before — where each claim can be easily localised within the model, corrected when it needs to be, verified in advance, and which give predictable output — LLMs are none of those things. LLMs are much better at understanding the question (constant time for a fixed-length output, even when the query is phrased badly and relatively complex), and being able to synthesise things in the form of "${x} won't work, try ${y}".
  - hackable_sand 2y ago
    Huh. Do you think integrating the Semantic Web metadata and ontologies in LLM training can help us bootstrap conceptual modeling using natural language?