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Your comment is interesting but seems to conflate two separate though related areas. The need for a body arises from the embodied cognition school of AI which s

Your comment is interesting but seems to conflate two separate though related areas. The need for a body arises from the embodied cognition school of AI which suggests that intelligence is fundamentally embodied, hence the need for a robot equivalent to a body for truly understanding language. However this does not necessarily have to be related to causality, and counterfactual statements about a causal model. The math behind counterfactuals and causality is actually well understood now (see any of Pearl's books). It does not actually require that a system be embodied, just that the system have some suitable (and correct) causal model of the world. It would of course be amazing to have both in one system, but that is not required. An AI system that understood causality and language could be bootstrapped from causal models supplied by humans - or even other AIs :)

mjburgess 6y ago
I'm conflating them because they are deeply connected. Causal analysis can be performed, via Pearl, on datasets collected for causal analysis. You still need some mechanism to collect the data, ie., the scientist. This requires solving the "relevance" (/framing) problem -- which, in my view, cannot be solved under a congitivist (/computational) theory of mind. "Data" which is relevant to a causal hypothesis isn't selected via inference, the body "selects" it. eg., when my hand is on a hot surface, it's temperature isnt "chosen as the relevant casual variable". The body is the primary solution to the relevance problem. So you can't just "shove in causal math" into a computational system and expect it to grasp anything. I also don't think bootstrapping will take you very far: causal models of, eg., dogs are very deep. ie., we understand their 2d, 3d, skeletal, behavioural, color, sound etc. "dimensions". To say, "the dog was well behaved" requires an extraordinarily deep model of "dog". The only way i see this being built is via play, ie., via hypothetical interaction with an environment -- as we do -- with bodies capable of discerning relevance.
- nextos 6y ago
  >Causal analysis can be performed, via Pearl, on datasets collected for causal analysis. There's a lot of past and current work on causal inference on observational data too.

mjburgess 6y ago

I'm conflating them because they are deeply connected. Causal analysis can be performed, via Pearl, on datasets collected for causal analysis. You still need some mechanism to collect the data, ie., the scientist. This requires solving the "relevance" (/framing) problem -- which, in my view, cannot be solved under a congitivist (/computational) theory of mind. "Data" which is relevant to a causal hypothesis isn't selected via inference, the body "selects" it. eg., when my hand is on a hot surface, it's temperature isnt "chosen as the relevant casual variable". The body is the primary solution to the relevance problem. So you can't just "shove in causal math" into a computational system and expect it to grasp anything. I also don't think bootstrapping will take you very far: causal models of, eg., dogs are very deep. ie., we understand their 2d, 3d, skeletal, behavioural, color, sound etc. "dimensions". To say, "the dog was well behaved" requires an extraordinarily deep model of "dog". The only way i see this being built is via play, ie., via hypothetical interaction with an environment -- as we do -- with bodies capable of discerning relevance.

nextos 6y ago
>Causal analysis can be performed, via Pearl, on datasets collected for causal analysis. There's a lot of past and current work on causal inference on observational data too.