The future of artificial intelligence needs less data and can tolerate ambiguity.
can program it to perform quick-and-dirty recalls of all possible significations–and to then carry those branching options onto its subsequent tasks, like a human brain that continues reading a poem with multiple potential interpretations held simultaneously in mind. This saves the data intensiveness that traditional machine-learning pours into optimization.
, divergent thinking is far more conservative in its outcomes than human imagination. Although it can spam out gigantic quantities of “new” works, those works are confined to mix-and-matches of earlier models, so what divergent thinking gains in scale it sacrifices in scope. The antifragile alternative is to flip from using data as a source of inspiration to using it as a source of falsification. Falsification is, pointed out that it’s more logical to mobilize facts to knock out ideas than to confirm them. When translated onto AI, this Popperian reframe can invert data’s function from a mass-generator of trivially novel ideas into a mass-destroyer of anything except wildly unprecedented ones.
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