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Actually, it's not as inflated as you might imagine. For example, extend Deep Minds work on video games to a fully simulated 3D environment of a factory. No
by blazespin 11y ago
Actually, it's not as inflated as you might imagine. For example, extend Deep Minds work on video games to a fully simulated 3D environment of a factory. Now, have Deep Mind learn to 'play the factory' to produce goods (starting off with the current process as the baseline). Suddenly, the potential gets pretty exciting.
Assuming of course, the AI doesn't get control of the Factory by accident and does something terrible in the name of improving productivity.
- tsunamifury 11y agoYou comment is already forecasting, overvaluing, and then presently justifying yourself using guesses based on a technology you likely don't fully understand.
- emcq 11y agoReinforcement learning isn't magic pixie dust that suddenly solves AI. One thing we often do is assume for some compact subproblem that AI will offer massive optimizations relative to our current human baseline. However it often turns out that engineers have spent enough time to do a good enough job that the relative improvement is either not existant or small when focused on optimizing subcomponents of a system. With your video game reference there are some games that q learning does better than humans and others that does worse. For the factory floor I know data scientists and industrial engineers run optimizations to increase productivity and reduce costs. With their constraints it becomes hard or impossible for any algorithm to find a much more optimal solution especially when their problem is convex where the human generated solution is provably globally optimal.