That sounds very different from what Uber is doing here, which is basically accelerating training with synthetic data to accelerate otherwise standard neural architecture search tools. The focus is on the data synthesis network.
Also, the system you describe sounds impractical for any of the complex learning tasks you suggest, especially if it hasn't even done much simpler things yet. Why would machine code be the right level of abstraction for a vision or robotics problem?
> Why would machine code be the right level of abstraction for a vision or robotics problem?
That code would be used to calculate the output function and the transition function of the automaton. At first, as the automaton tries some action and receives a reaction, those functions are constructed accordingly in plain movs and cmps with jmps (suppose x86 ISA here). Then a whole machine code of all actions-reactions is optimized by arithmetic rules, loop rolling and unrolling, etc, so its size is reduced. That optimization may include some hypotheses about Don't Care values of the functions too, which will be corrected in future passes, if they turn out to be wrong... Imagine that code running on something like Thomas Sohmers' Neo processor or Sunway SW26010.
Yeah, it is completely different to Neural Nets. I posted it here because I feel the urge to popularize the idea : ) I am a dilettante in machine learning actually.
Also, the system you describe sounds impractical for any of the complex learning tasks you suggest, especially if it hasn't even done much simpler things yet. Why would machine code be the right level of abstraction for a vision or robotics problem?