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First, there’s the matter of what architecture of neural net one should use for a particular task. One might have thought that for every particular kind of task one would need a different architecture of neural net. OK, so let’s say one’s settled on a certain neural net architecture.
Given a defined “goal”, an AI can automatically work towards achieving it. Most of our existing intuition about “machinery” and “automation” comes from a kind of “clockwork” view of engineering—in which we specifically build systems component by component to achieve objectives we want. And that’s where we humans come in.
In other words, we’re concerned more with what computational results are obtained, with what computational resources, rather than on the details of the program constructed to achieve this. And this is where our pieces of “falsifiable naturalscience” come in. Why can’t one human consciousness “get inside” another?
In early 1984 I visited MIT to use the machine to try to do what amounted to naturalscience, systematically studying 2D cellular automata. I quickly determined that there were no such rules with 2 colors and blocks of sizes 2 or 3 that achieved any kind of randomization. mode, often accompanied by rapid physical rewiring.
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