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I see a different path forward based on the success of AlphaGo. This looks like a clever example of supervised learning. But supervised learning doesn't get yo
by jostmey 5y ago
I see a different path forward based on the success of AlphaGo.
This looks like a clever example of supervised learning. But supervised learning doesn't get you cause and effect, it is just pattern matching.
To get at cause and effect, you need reinforcement learning, like AlphaGo. You can imagine an AI writing code that is then scored for performing correctly. Overtime the AI will learn to write code that performs as intended. I think coding can be used as a "playground" for AI to rapidly improve itself, like how AlphaGo could play Go over and over again
- deleted 5y ago[deleted]
- Jeff_Brown 5y agoImparting a sense of objective to the AI is surely important, and an architecture like AlphaGo might be useful for the general problem of helping a coder. I'm not seeing it, however, for this particular autocomplete-flavored idiom. AlphaGo learns a game with fixed, well-defined, measurable objectives, by trying it a bazillion times. In this autocomplete idiom the AI's objective is constantly shifting, and conveyed by extremely partial information. But you could imagine a different arrangement, where the coder expresses the problem in a more structured way -- hopefully involving dependent types, probably involving tests. That deeper encoding would enable a deeper AI understanding (if I can responsibly use that word). The human-provided spec would have to be extremely good, because AlphaGo needs to run a bazillion times, so you can't go the autocomplete route of expecting the human to actually read the code and determine what works.