The crystal nights story is pretty good but it stabbed my willing suspension of disbelief right in whatever is its vital organ when the Phites casually and instantly begin manipulating matter in the outside world at the speed of their simulation. Real physical experimentation has a lot of trial-and-error processes as you try to get your parameters to the optimal position—no amount of a priori explicit knowledge will enable you to avoid slowly building up your tacit knowledge.
One man’s a priori is another man’s a posteriori, one might say; there are many places one can acquire informative priors… Learning ‘tacit knowledge’ can be so fast as to look instantaneous. An example here would be OA’s Dactyl hand: it learns robotic hand manipulation in silico, using merely a model simulating physics, with a lot of randomization of settings to teach it to adapt on the fly, to whatever new model it finds itself in. This enables it to, without ever once training on an actual robot hand (only simulated ones), successfully run on an actual robot hand after seconds of adaptation. Another example might be PILCO: it can learn your standard “Cartpole” task within just a few trials by carefully building a Bayesian model and picking maximally informative experiments to run. (Cartpole is quite difficult for a human, incidentally, there’s an installation of one in the SF Exploratorium, and I just had to try it out once I recognized it. My sample-efficiency was not better than PILCO.) Because the Phites have all that computation and observations of the real world, they too can do similar tricks, and who knows what else we haven’t thought of.
The crystal nights story is pretty good but it stabbed my willing suspension of disbelief right in whatever is its vital organ when the Phites casually and instantly begin manipulating matter in the outside world at the speed of their simulation. Real physical experimentation has a lot of trial-and-error processes as you try to get your parameters to the optimal position—no amount of a priori explicit knowledge will enable you to avoid slowly building up your tacit knowledge.
One man’s a priori is another man’s a posteriori, one might say; there are many places one can acquire informative priors… Learning ‘tacit knowledge’ can be so fast as to look instantaneous. An example here would be OA’s Dactyl hand: it learns robotic hand manipulation in silico, using merely a model simulating physics, with a lot of randomization of settings to teach it to adapt on the fly, to whatever new model it finds itself in. This enables it to, without ever once training on an actual robot hand (only simulated ones), successfully run on an actual robot hand after seconds of adaptation. Another example might be PILCO: it can learn your standard “Cartpole” task within just a few trials by carefully building a Bayesian model and picking maximally informative experiments to run. (Cartpole is quite difficult for a human, incidentally, there’s an installation of one in the SF Exploratorium, and I just had to try it out once I recognized it. My sample-efficiency was not better than PILCO.) Because the Phites have all that computation and observations of the real world, they too can do similar tricks, and who knows what else we haven’t thought of.