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I still think there’s a big difference. If asked to speculate on the architecture of Sharepoint, my response would be “I have no earthly idea.” If pressed furth

I still think there’s a big difference. If asked to speculate on the architecture of Sharepoint, my response would be “I have no earthly idea.” If pressed further for an answer, my response would be “It’s probably some over-complicated mess and I don’t care enough about Sharepoint to spend any further time on this line of questioning.” I have yet to see ChatGPT just admit it doesn’t know, but in this case “I don’t know” is the most trustworthy answer I, as a technical person, could give.

burlesona 4y ago
It doesn't know it doesn't know.
- danaris 4y ago
  It is fundamentally incapable of "knowing" anything. It is a statistical engine, with no internal representation of the world or understanding of the words it emits.
  - Xelynega 4y ago
    They don't know it doesn't know
  - dTal 4y ago
    It clearly does have an internal representation of the world, implicitly encoded in its network weights. Quite an accurate one too, for most general knowledge, and one that can be updated in-context - it handily solves "blocks world[0]" style tasks. "Understanding" and "knowing" aren't helpful words, just focal points for pointless philosophical arguments. [0] https://en.wikipedia.org/wiki/Blocks_world https://en.wikipedia.org/wiki/Blocks_world
    - danaris 4y ago
      That's not a representation of the world. It's simply a lossy encoding of its data. It's not semantically structured in the way that our thoughts largely are—it's merely syntactically structured.
      - EnergyAmy 4y ago
        "That's not a representation of the world. It's simply a [representation of the world as viewed from its input data]" is an... interesting take.
      - dTal 4y ago
        What exactly is the difference? It's clearly managed to abstract the training data to a ludicrously deep degree, such that it's capable of solving semantically non-trivial problems it's never seen before. It can make metaphors, accurately predict the behavior of humans in complex social scenarios, and translate arbitrary passages between syntactically distinct languages while preserving nuance. That last task in particular is pretty much a slam dunk against any argument of the type you are making. "Sufficiently advanced syntax is indistinguishable from semantics."
        danaris 4y ago
        Sufficiently advanced syntax may be superficially indistinguishable from semantics, but we're not talking about output in this subthread: we're talking about an internal representation of the world. Pure syntax, no matter how advanced, is insufficient to represent the world in any meaningful way. By definition, in fact, because pure syntax is divorced from meaning.
        dTal 4y ago
        >Pure syntax, no matter how advanced, is insufficient to represent the world in any meaningful way. By definition, in fact, because pure syntax is divorced from meaning. Then "by definition" LMMs transcend "pure syntax", because of all the examples of semantically interesting tasks they can do which you failed to engage with. It clearly has internal representations of abstract concepts. Your argument seems to be that you intuitively reject the possibility of complex emergent behavior from such networks because they're trained on "just words", and no amount of demonstrably intelligent emergent behavior will convince you otherwise. There's nothing magic about meat brains. Both we and the LMMs learn a world model from a bunch of input data we correlate until it makes sense. There's no "meaning gland" we have that ChatGPT doesn't.

burlesona 4y ago

It doesn't know it doesn't know.

danaris 4y ago
It is fundamentally incapable of "knowing" anything. It is a statistical engine, with no internal representation of the world or understanding of the words it emits.
- Xelynega 4y ago
  They don't know it doesn't know
- dTal 4y ago
  It clearly does have an internal representation of the world, implicitly encoded in its network weights. Quite an accurate one too, for most general knowledge, and one that can be updated in-context - it handily solves "blocks world[0]" style tasks. "Understanding" and "knowing" aren't helpful words, just focal points for pointless philosophical arguments. [0] https://en.wikipedia.org/wiki/Blocks_world https://en.wikipedia.org/wiki/Blocks_world
  - danaris 4y ago
    That's not a representation of the world. It's simply a lossy encoding of its data. It's not semantically structured in the way that our thoughts largely are—it's merely syntactically structured.
    - EnergyAmy 4y ago
      "That's not a representation of the world. It's simply a [representation of the world as viewed from its input data]" is an... interesting take.
    - dTal 4y ago
      What exactly is the difference? It's clearly managed to abstract the training data to a ludicrously deep degree, such that it's capable of solving semantically non-trivial problems it's never seen before. It can make metaphors, accurately predict the behavior of humans in complex social scenarios, and translate arbitrary passages between syntactically distinct languages while preserving nuance. That last task in particular is pretty much a slam dunk against any argument of the type you are making. "Sufficiently advanced syntax is indistinguishable from semantics."
      - danaris 4y ago
        Sufficiently advanced syntax may be superficially indistinguishable from semantics, but we're not talking about output in this subthread: we're talking about an internal representation of the world. Pure syntax, no matter how advanced, is insufficient to represent the world in any meaningful way. By definition, in fact, because pure syntax is divorced from meaning.
        dTal 4y ago
        >Pure syntax, no matter how advanced, is insufficient to represent the world in any meaningful way. By definition, in fact, because pure syntax is divorced from meaning. Then "by definition" LMMs transcend "pure syntax", because of all the examples of semantically interesting tasks they can do which you failed to engage with. It clearly has internal representations of abstract concepts. Your argument seems to be that you intuitively reject the possibility of complex emergent behavior from such networks because they're trained on "just words", and no amount of demonstrably intelligent emergent behavior will convince you otherwise. There's nothing magic about meat brains. Both we and the LMMs learn a world model from a bunch of input data we correlate until it makes sense. There's no "meaning gland" we have that ChatGPT doesn't.