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Which current sub field of ML do you find most promising for generality? My intuition would be Reinforcement Learning and Genetic Algorithms, because they don’t require human labeled data to build. But they do suffer acutely from task specificity as well..
Flexible and broad AI will come from a combination of techniques, including pattern recognition. I see program synthesis and in particular genetic programming as holding potential. But really, we should explore more.
Program synthesis seems so far from biology that it's hard to believe it would be a huge help, but then again helicopters have rotors
Marcos -- our blog and the attached paper "cognitive programs" has cognitive science and neuroscience references that might be of interest to you. https://www.vicarious.com/2019/01/18/a-thought-is-a-program/ …
Hey @fchollet, I'm really interested in program synthesis, do you know of any research groups working on this?
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