The problem with #machinelearning in a nutshell, via extensional vs intensional logic:
A: {2, 4, 6, 8, 10, ... X}
B: {2x: x ∈ N}
There is no way for a statistical, asymbolic machine to arrive at B from A, no matter how large you choose X @GaryMarcus @filippie509 #ai
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Properly understood,
#MachineLearning is the study of the relationship of biases to what can be learned with each (and how to implement them computationally). It's bias, all the way down.
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Gary, are you really arguing that anything you can't get your ML algorithm to do, you can just build in and declare it to be "innate". It is totally ad hoc science to rely on innateness to solve your machine learning problems.
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Indeed. All these folks need to internalize the old story: In the days when Sussman was a novice, Minsky once came to him as he sat hacking at the PDP-6. “What are you doing?”, asked Minsky. “I am training a randomly wired neural net to play Tic-Tac-Toe” Sussman replied. 1/n
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“Why is the net wired randomly?”, asked Minsky. “I do not want it to have any preconceptions of how to play”, Sussman said. Minsky then shut his eyes. “Why do you close your eyes?”, Sussman asked his teacher. “So that the room will be empty.” 2/3
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