4 ms·

Othellogpt is fed only moves and builds a model of the current board state in its activations. It never sees a board. > It's like saying you can use a lego mod

• mjburgess 3y ago
  The "board" is abstract. Any game of this sort is defined by a series of conditional probabilities: {P(Pawn_on_sqare_blah|previous_moves) ... etc.} What all statistical learning algorithms model is sets of conditional probabilities. So any stat alg is a model of a set of these rules... that's the "clay" of these models. The problem is the physical world isn't anything like this. The reason I say, "I liked that TV show" is because I had a series of mental states caused by the TV show over time (and so on). This isnt representable as a set of conditional probs in the same way. You could imagine, at the end of history, there being a total set of all possible conditional probabilities: P(I liked show|my_mental_states, time, person, location, etc.) -- this would be uncomputable, but it could be supposed. If you had that dataset then yes, NNs would learn the entire structure of the world, because that's the dataset. The problem is that the world cannot be represented in this fashion, not that NNs could model it if it could be. A decision tree could. P(I liked the TV show) doesnt follow from any dataset ever collected. It follows from my mental states. So no NN can ever model it. They can model frequency associations of these phrases in historical text documents: this isnt a model of hte world
  • IanCal 3y ago
    > Any game of this sort is defined by a series of conditional probabilities: {P(Pawn_on_sqare_blah|previous_moves) ... etc.} That would always be 1 or 0, but also that data is not fed into othellogpt. That is not the dataset. It is not fed in board states at all. It learns it, but it is not the dataset.
    • mjburgess 3y ago
      It is the dataset. When you're dealing with abstract objects (ie., mathematical spaces), they are all isomorphic. It doesnt matter if you "feed in" 1+1+1+1 or 2+2 or sqrt(16). The rules of chess are encoded either explicit rules or by contrast classes of valid/invalid games. These are equivalent formulations. When you're dealing with text tokens it does matter if "Hot" is frequently after "The Sun is..." because reality isnt an abstract space, and text tokens arent measures of it.
      • IanCal 3y ago
        > It is the dataset. No. A series of moves alone provides strictly less information than a board state or state + list of rules.
        • mjburgess 3y ago
          If the NN learns the game, that is itself an existence proof of the opposite, (by obvious information-theoretic arguments). Training is supervised, so you don't need bare sets of moves to encode the rules; you just need a way of subsetting the space into contrast classes of valid/invalid. It's a lie to say the "data" is the moves, the data is the full outcome space: ({legal moves}, {illegal moves}) where the moves are indexed by the board structure (necessarily, since moves are defined by the board structure -- its an abstract game). So there's two deceptions here: (1) supervision structures the training space; and (2) the individual training rows have sequential structure which maps to board structure. Complete information about the game is provided to the NN. But let's be clear, the othellogpt still generates illegal moves -- showing that it does not learn the binary conditional structure of the actual game. The deceptiveness of training a NN on a game whose rules are conditional probability structures and then claiming the very-good-quality conditional probability structures it finds are "World Models" is... maddening. This is all just fraud to me; frauds dressing up other frauds in transparent clothing. LLMs trained on the internet are being sold as approximating the actual world, not 8x8 boardgames. I have nothing polite to say about any of this
          • 3y ago