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This is just chinese room. But also, I disagree that the languages would not share properties. A novel is too small. Consider a network trained on GPT-3 scale d

This is just chinese room. But also, I disagree that the languages would not share properties. A novel is too small. Consider a network trained on GPT-3 scale datasets in 1000 alien languages. The shared structures behind the sentences will be the same, even if the grammar is completely different. Stars will be stars, moons will be moons. I'd bet you that the model would be able to translate shared concepts between those languages even if it had not been trained on the same novels, same as GPT can translate terms that it has not seen in dictionary pairs. (I have no idea if it can! But I'm confident enough that I'm willing to just say it can, at risk of being proven wrong. If GPT can't do that, I'm fundamentally misunderstanding how it works - that is, I don't have a paper offhand showing that GPT shares concept neurons between languages, but I'm willing to bet I could find one if I went looking.) In other words, if you co-trained GPT-4 on Earth Internet and Alien Internet, there's a good chance it'd end up able to translate English to Alienese, purely as an emergent ability, if it had the concept of translation at all. Intelligence is compression. With sufficient abstraction (layers) and sufficient volume (dataset), any description of the same reality will assume the same structure. And no learning algo worth its salt will keep two identical structures around.

mjburgess 2y ago
Almost every predicate in natural languages is an arbitrary association of properties in the world. There isn't any property a person has "bald", nor are there "tree"s in the world. What bundles of properties the vast majority of language names are a product of historical and contingent associations we've made for practical reasons. Likewise, languages do not have the same distributional structure. There is no reason an alien language would use discrete tokens to name properties, nor co-locate tokens by linear position in a 'sentence'. Historical human languages did not; using, eg., the full 2D structure of the clay tablet. To suppose that it is the glyphs and their colocation which somehow bare meaning is a nonesensical superstition. The world is what our words mean, and it is we, in that world, who provide them their meaning. We can do so with arbitrary linguistic structures.
- FeepingCreature 2y ago
  Right, but neural networks at a certain level of scale begin to compress the perception described by the text rather than the text per se. The lexical features are abstracted away and the network begins to understand meaning directly. Of course, you'd still have differentiation by different perceptions and focus on different parts of reality, but I see no reason why that should not likewise abstract out at even higher levels. Ultimately, the whole point of language is that it's about reality, and there is only one reality no matter how many different cultures you feed in. The network isn't doing magic - it's just doing exactly the same thing that we're doing with language. The reason why language is useful is the same reason it's learnable.
  - mjburgess 2y ago
    If you want to understand how statistical AI systems work, then you should understand the basics of formalism. They are curve-fitting algorithms that approximate the probability distribution of a hisotrical dataset. That's all they are. By analogising to any animal or human mind, you aren't describing anythign that acutally exists. A neutral network algorithm isnt neural and it isnt a network. It's a statistical curve-fitting algorith. One oughtnt study trees to understand a decision tree either. This language is entirely metaphorical. There are no neurones in an NN, there are just summation entires in a matrix. This matrix comprises weights, which define the orientation and scale of the line pieces which form the curve being fit to the data.
    - FeepingCreature 2y ago
      They're, in particular, multi-level curve-fitting algorithms. A formalism which, I'm sure you are aware, can express any bounded computation. In particular, depending on how you set up the tape, neural networks are comfortably Turing complete. We're not talking about 2-layer perceptrons anymore here. In my opinion, you should look less at the formalism and more at the empirically demonstrated performance.
      - mjburgess 2y ago

mjburgess 2y ago

Almost every predicate in natural languages is an arbitrary association of properties in the world. There isn't any property a person has "bald", nor are there "tree"s in the world. What bundles of properties the vast majority of language names are a product of historical and contingent associations we've made for practical reasons. Likewise, languages do not have the same distributional structure. There is no reason an alien language would use discrete tokens to name properties, nor co-locate tokens by linear position in a 'sentence'. Historical human languages did not; using, eg., the full 2D structure of the clay tablet. To suppose that it is the glyphs and their colocation which somehow bare meaning is a nonesensical superstition. The world is what our words mean, and it is we, in that world, who provide them their meaning. We can do so with arbitrary linguistic structures.

FeepingCreature 2y ago
Right, but neural networks at a certain level of scale begin to compress the perception described by the text rather than the text per se. The lexical features are abstracted away and the network begins to understand meaning directly. Of course, you'd still have differentiation by different perceptions and focus on different parts of reality, but I see no reason why that should not likewise abstract out at even higher levels. Ultimately, the whole point of language is that it's about reality, and there is only one reality no matter how many different cultures you feed in. The network isn't doing magic - it's just doing exactly the same thing that we're doing with language. The reason why language is useful is the same reason it's learnable.
- mjburgess 2y ago
  If you want to understand how statistical AI systems work, then you should understand the basics of formalism. They are curve-fitting algorithms that approximate the probability distribution of a hisotrical dataset. That's all they are. By analogising to any animal or human mind, you aren't describing anythign that acutally exists. A neutral network algorithm isnt neural and it isnt a network. It's a statistical curve-fitting algorith. One oughtnt study trees to understand a decision tree either. This language is entirely metaphorical. There are no neurones in an NN, there are just summation entires in a matrix. This matrix comprises weights, which define the orientation and scale of the line pieces which form the curve being fit to the data.
  - FeepingCreature 2y ago
    They're, in particular, multi-level curve-fitting algorithms. A formalism which, I'm sure you are aware, can express any bounded computation. In particular, depending on how you set up the tape, neural networks are comfortably Turing complete. We're not talking about 2-layer perceptrons anymore here. In my opinion, you should look less at the formalism and more at the empirically demonstrated performance.
    - mjburgess 2y ago