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So this is to repeat hume and mistake causal analysis as a kind of induction or inference. It isnt. Our bodies are the primary site of "causal analysis". Eg.,

So this is to repeat hume and mistake causal analysis as a kind of induction or inference. It isnt. Our bodies are the primary site of "causal analysis". Eg., when I touch the hot surface of a stove I do not infer that temperature is a cause of my hand being hot. Such an inference, as the basis for our models of the world, would be -- as you/hume/etc. say -- deeply insufficient. The operation of the world on our bodies is already laden with causal information. A hand striking a face does not just create a "painful sense impression"... rather the body encodes it as caused by the object you also saw. It is the action of the world upon the body that is the bedrock of our causal model building. Scientific/inferential processes sit on top of that to disambiguate between possible distal causes. A machine, as a person, given "mere data" cannot hope to do much.

hackinthebochs 6y ago
None of this explains how modelling time sequences of events does not sufficiently approximate the kind of causal knowledge you mention.
- mjburgess 6y ago
  Suppose I place a pot on a stove and the water boils. Now I feed into the machine what data? Here, it gets this: all gravitational, electomagnetic, etc data within 1km; all geometric information about all objects within 1km (, and all of their properties, etc.) Now, machine, what caused the pot to boil? It has no clue. There are an infinite number of antecedent temporal events. The problem isn't the mathematics of causal inference. The problem is relevance. That isn't an inferential problem.
  - hackinthebochs 6y ago
    >It has no clue. There are an infinite number of antecedent temporal events. Right, determining the cause of the pot boiling given only local information about this one event is impossible. But that's not a good representation of learning from a real-world data set. A real training corpus might also have an example of a pot boiling in a campfire, a water heater heating up water using fire, someone touching an open flame and going "ouch!", fire burning down a house, etc. All these examples taken together can reasonably lead one to infer that fire causes things to become hot. This is the kinds of regularity found in real world datasets and a good learning algorithm will extract this regularity in the course of predicting the dataset.
    - mjburgess 6y ago
      Who prepares the dataset? You are just shifting relevance problem to the human to solve. That's my point. The "regularity" isn't very hard to find when the data which exposes it is already selected.
      - hackinthebochs 6y ago
        No, I don't see your point. No one "prepared" common crawl to contain multiple instances of relevant examples for GPT-3 to learn from. It's just that a sufficiently large training corpus will naturally have this sort of regularity in it. My point is that a general learning algorithm trained on a sufficiently large and representative corpus will capture causal regularity without the sort of fine-tuning of the algorithm or the training data you are suggesting. You haven't given any reason to think otherwise.
        mjburgess 6y ago
        identifying causes cannot be done statistically, as a fact of statistics events A then B then C do not imply C is caused by B is caused by A This problem is worse the more data you have, as with my example above of giving the machine /every/ event within a 1km radius of a pot boiling. Identifying a cause is a dynamic experimental process. A machine can only accept a highly prepared dataset which has already been chosen because the associations are known to be causal.
        hackinthebochs 6y ago
        But this problem isn't specific to machines. The fact that my hand hurts after touching an open flame stove doesn't entail the flame caused my pain. No amount of first-hand experience with flames can entail that flames cause pain. All we can do is increase the likelihood of this model. Experimental processes are in the same boat, except that the statistical power is greater. But our lived experiences with flames and pain and all other stimuli is a sort of low power ongoing experiment. But with enough of these poor experimental runs we can converge on an approximately true model. The same goes for a statistical learning algorithm and a large training corpus.

hackinthebochs 6y ago

None of this explains how modelling time sequences of events does not sufficiently approximate the kind of causal knowledge you mention.

mjburgess 6y ago
Suppose I place a pot on a stove and the water boils. Now I feed into the machine what data? Here, it gets this: all gravitational, electomagnetic, etc data within 1km; all geometric information about all objects within 1km (, and all of their properties, etc.) Now, machine, what caused the pot to boil? It has no clue. There are an infinite number of antecedent temporal events. The problem isn't the mathematics of causal inference. The problem is relevance. That isn't an inferential problem.
- hackinthebochs 6y ago
  >It has no clue. There are an infinite number of antecedent temporal events. Right, determining the cause of the pot boiling given only local information about this one event is impossible. But that's not a good representation of learning from a real-world data set. A real training corpus might also have an example of a pot boiling in a campfire, a water heater heating up water using fire, someone touching an open flame and going "ouch!", fire burning down a house, etc. All these examples taken together can reasonably lead one to infer that fire causes things to become hot. This is the kinds of regularity found in real world datasets and a good learning algorithm will extract this regularity in the course of predicting the dataset.
  - mjburgess 6y ago
    Who prepares the dataset? You are just shifting relevance problem to the human to solve. That's my point. The "regularity" isn't very hard to find when the data which exposes it is already selected.
    - hackinthebochs 6y ago
      No, I don't see your point. No one "prepared" common crawl to contain multiple instances of relevant examples for GPT-3 to learn from. It's just that a sufficiently large training corpus will naturally have this sort of regularity in it. My point is that a general learning algorithm trained on a sufficiently large and representative corpus will capture causal regularity without the sort of fine-tuning of the algorithm or the training data you are suggesting. You haven't given any reason to think otherwise.
      - mjburgess 6y ago
        identifying causes cannot be done statistically, as a fact of statistics events A then B then C do not imply C is caused by B is caused by A This problem is worse the more data you have, as with my example above of giving the machine /every/ event within a 1km radius of a pot boiling. Identifying a cause is a dynamic experimental process. A machine can only accept a highly prepared dataset which has already been chosen because the associations are known to be causal.
        hackinthebochs 6y ago
        But this problem isn't specific to machines. The fact that my hand hurts after touching an open flame stove doesn't entail the flame caused my pain. No amount of first-hand experience with flames can entail that flames cause pain. All we can do is increase the likelihood of this model. Experimental processes are in the same boat, except that the statistical power is greater. But our lived experiences with flames and pain and all other stimuli is a sort of low power ongoing experiment. But with enough of these poor experimental runs we can converge on an approximately true model. The same goes for a statistical learning algorithm and a large training corpus.