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That is why DNNs will not solve the general AI problem. They do not cover the full decision space, they do not cover the true feasible region, they make huge (a
by whatever1 5y ago
That is why DNNs will not solve the general AI problem. They do not cover the full decision space, they do not cover the true feasible region, they make huge (and unpredictably wrong) assumptions about interpolation and extrapolation.
In controlled environments with well-known responses they can probably work (not sure why not go with traditional control approaches though), but really don’t see DNN working outside that.
- visarga 5y agoAgents that are embodied and can do interventions in their environment have a chance to go beyond correlation learning. They can formulate hypothesis and try them out in the real world, like a scientist.
- whatever1 5y agoThing is that you dont have to formulate hypotheses for things we have figured out. We, humans have already figured out for example that momentum balance holds, so when we make trajectory predictions, this (differential) equality is a hard constraint in our calculations. A DNN cannot guarantee that its predictions respect momentum balance. By proper training you can just only reduce the probability of violating it. It is plainly a wrong tool for the task.