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correct the spelling, fixed the grammar
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edit approved Mar 31, 2021 at 22:19
Faizy
edit approved Mar 31, 2021 at 22:19
Faizy
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There are a lot of systems whichthat follow the ancient maxim: "Always two there are; no more, no less. A master and an apprentice."

In reinforcement learning a class of such setups is called Actor-Critic-Method. There you have a master, who'swhose duty it is to create feedback for the actions of the apprentice, who acts in a given environment. This would be comparable to how a human learns some physical activity, like playing table tennis. You basically let your body do it'sits thing, but your consciousness evaluates how good the result is.

The setup of AlphaGo might be even closer to Kahnemann's system 1 and system 2. AlphaGo has two neural networks which provide actions and evaluations (system 1, fast, intuitivintuitive, etc.) and the monte carloMonte Carlo tree search, which uses these actions and evaluations to prune a search tree and make a decision (system 2, deliberate, logical).

In the end, this kind of structure will pop up again and again, because it is often necessary to do some kind of classification or preprocessing on the raw data, before your algorithm can be run on it. You could frame the whole history of gofai as the story of how scientists thought system 1 should be easy and system 2 should be doable in a few decades, where the reality is that we have no idea how difficult system 2 is, because it turned out that system 1 is extremely difficult.

There are a lot of systems which follow the ancient maxim: "Always two there are; no more, no less. A master and an apprentice."

In reinforcement learning a class of such setups is called Actor-Critic-Method. There you have a master, who's duty it is to create feedback for the actions of the apprentice, who acts in a given environment. This would be comparable to how a human learns some physical activity, like playing table tennis. You basically let your body do it's thing, but your consciousness evaluates how good the result is.

The setup of AlphaGo might be even closer to Kahnemann's system 1 and system 2. AlphaGo has two neural networks which provide actions and evaluations (system 1, fast, intuitiv, etc.) and the monte carlo tree search, which uses these actions and evaluations to prune a search tree and make a decision (system 2, deliberate, logical).

In the end this kind of structure will pop up again and again, because it is often necessary to do some kind of classification or preprocessing on the raw data, before your algorithm can be run on it. You could frame the whole history of gofai as the story of how scientists thought system 1 should be easy and system 2 should be doable in a few decades, where the reality is that we have no idea how difficult system 2 is, because it turned out that system 1 is extremely difficult.

There are a lot of systems that follow the ancient maxim: "Always two there are; no more, no less. A master and an apprentice."

In reinforcement learning a class of such setups is called Actor-Critic-Method. There you have a master, whose duty it is to create feedback for the actions of the apprentice, who acts in a given environment. This would be comparable to how a human learns some physical activity, like playing table tennis. You basically let your body do its thing, but your consciousness evaluates how good the result is.

The setup of AlphaGo might be even closer to Kahnemann's system 1 and system 2. AlphaGo has two neural networks which provide actions and evaluations (system 1, fast, intuitive, etc.) and the Monte Carlo tree search, which uses these actions and evaluations to prune a search tree and make a decision (system 2, deliberate, logical).

In the end, this kind of structure will pop up again and again because it is often necessary to do some kind of classification or preprocessing on the raw data before your algorithm can be run on it. You could frame the whole history of gofai as the story of how scientists thought system 1 should be easy and system 2 should be doable in a few decades, where the reality is that we have no idea how difficult system 2 is because it turned out that system 1 is extremely difficult.

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BlindKungFuMaster
BlindKungFuMaster
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There are a lot of systems which follow the ancient maxim: "Always two there are; no more, no less. A master and an apprentice."

In reinforcement learning a class of such setups is called Actor-Critic-Method. There you have a master, who's duty it is to create feedback for the actions of the apprentice, who acts in a given environment. This would be comparable to how a human learns some physical activity, like playing table tennis. You basically let your body do it's thing, but your consciousness evaluates how good the result is.

The setup of AlphaGo might be even closer to Kahnemann's system 1 and system 2. AlphaGo has two neural networks which provide actions and evaluations (system 1, fast, intuitiv, etc.) and the monte carlo tree search, which uses these actions and evaluations to prune a search tree and make a decision (system 2, deliberate, logical).

In the end this kind of structure will pop up again and again, because it is often necessary to do some kind of classification or preprocessing on the raw data, before your algorithm can be run on it. You could frame the whole history of gofai as the story of how scientists thought system 1 should be easy and system 2 should be doable in a few decades, where the reality is that we have no idea how difficult system 2 is, because it turned out that system 1 is extremely difficult.