4 ms·

The model doesn't "understand its plot". So I am not sure this is a good analogy.

by kxrm 1y ago

ilikehurdles 1y ago
To what extent connections in a neural network are analogous to connections between neurons in your brain is open to interpretation and study, but the point of the analogy is that in neither case is a copy being made.
- jamiek88 1y ago
  Yeah but a copy IS made. A human just reads. The machine copies the full text then compresses a lossy copy in its weights. You keep dodging that with tortuous analogies of a human learning. I’m sure all these ‘clever’ questions would be useful if this trial was about humans but it’s not.
  - Workaccount2 1y ago
    Model training works roughly by feeding the model a text excerpt and then hiding the last word in the excerpt. The model is then asked to "guess" what the final word is. It will then move around it's weights until the guess sufficiently matches the actual token. Then the process repeats. The training material is used to play this guessing game to dial in it's weights. The training data is picked up, used as reference material for the game, and then discarded. It's hard to place this far from what humans do when reading, because both are using the information to mold their respective "brains" and both are doing an acquire, analyze, discard process. At no point is training data actually copied into the model itself, it's just run past the "eyes" of the model to play the training game.
    - __loam 1y ago
      This trial has nothing to with how the brain works and even if they did work the same, humans obviously have different legal rights than a computer.
- amlib 1y ago
  I can arrange a series of bricks in many ways to try and build a wall but that doesn't mean I will automatically get a good result if my process (like a ML training algorithm) doesn't precisely arrange then in a manner that produces a rigid wall with the desired characteristics. In the same vein you can have a fancy neural network arranged by some fancy LLM training algorithm with gobs of data about a subject but current methods likely won't produce anything with the depth of "understanding" that a human can do. It's a crumbly wall that falls once you do any real inspection or put any real load into it.

The model doesn't "understand its plot". So I am not sure this is a good analogy.

by kxrm 1y ago

The model doesn't "understand its plot". So I am not sure this is a good analogy.

To what extent connections in a neural network are analogous to connections between neurons in your brain is open to interpretation and study, but the point of the analogy is that in neither case is a copy being made.

jamiek88 1y ago
Yeah but a copy IS made. A human just reads. The machine copies the full text then compresses a lossy copy in its weights. You keep dodging that with tortuous analogies of a human learning. I’m sure all these ‘clever’ questions would be useful if this trial was about humans but it’s not.
- Workaccount2 1y ago
  Model training works roughly by feeding the model a text excerpt and then hiding the last word in the excerpt. The model is then asked to "guess" what the final word is. It will then move around it's weights until the guess sufficiently matches the actual token. Then the process repeats. The training material is used to play this guessing game to dial in it's weights. The training data is picked up, used as reference material for the game, and then discarded. It's hard to place this far from what humans do when reading, because both are using the information to mold their respective "brains" and both are doing an acquire, analyze, discard process. At no point is training data actually copied into the model itself, it's just run past the "eyes" of the model to play the training game.
  - __loam 1y ago
    This trial has nothing to with how the brain works and even if they did work the same, humans obviously have different legal rights than a computer.
amlib 1y ago
I can arrange a series of bricks in many ways to try and build a wall but that doesn't mean I will automatically get a good result if my process (like a ML training algorithm) doesn't precisely arrange then in a manner that produces a rigid wall with the desired characteristics. In the same vein you can have a fancy neural network arranged by some fancy LLM training algorithm with gobs of data about a subject but current methods likely won't produce anything with the depth of "understanding" that a human can do. It's a crumbly wall that falls once you do any real inspection or put any real load into it.