https://www.youtube.com/watch?v=Ucp0TTmvqOE&t=7358
A "brute-force" algorithm (an exhaustive search, in other words) is the easiest way to find an answer to almost any engineering problem. But it often must be optimized before being computed. The optimization may be done by an AI agent based on Neural Nets, or on a Learning Mealy Machine.
A Learning Mealy Machine is an finite automaton in which training data stream is remembered by constructing disjunctive normal forms of the output function of the automaton and the transition function between its states. Then those functions are optimized (compressed with losses by logic transformations like De Morgan's Laws, arithmetic rules, loop unrolling/rolling, etc.) into some generalized forms. That introduces random hypotheses into the automaton's functions, so it can be used in inference. The optimizer for automaton's functions may be another AI agent, or any heuristic algorithm, which you like...
Some interesting engineering (and scientific) problems are: - finding a machine code for a controller of a car, which makes it able to drive autonomously; - finding a machine code for a controller of a bipedal robot, which makes it able to work in warehouses and factories; - finding a CAD file, which describes the design of a spheromak working with a guiding center drift generator (hypothetical device, idk!); - finding a CAD file, which describes some kind of working Smoluchowski’s trapdoor (in some specific conditions, of course); - finding a file, which describes an automaton working in accordance to the data of a scientific experiment; - finding a file, which describes manufacturing steps to produce the first molecular nanofactory in the world.
Related work by Embecosm is here: superoptimization.org Though it seems people have superoptimized only tiny programs so far as you can see from the ICRL 2017 paper (App. D): arxiv.org/abs/1611.01787 And loops can also be rolled, not just unrolled. That kind of loop optimization seems to be absent here: en.wikipedia.org/wiki/Loop_optimization
If you have any questions, ask me here: https://www.facebook.com/eugene.zavidovsky
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?
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.
Arguably, this is still a new field — but IMO will eventually become standard practice. Imo you can completely separate humans from data and still do machine learning (likely analytics). This would dramatically limit data breaches if implemented properly.
PS: I'm the author of the GTN paper. Feel free to ask any questions
I tried implementing it, and the samples generated by the Teacher seem to suffer from mode collapse (as if the generator is ignoring the random vector z but not the label condition). Do you recall having that issue at some point?
I have to say I'm using a simpler generator than the one in the paper, and I'm not changing the learner architechture at each batch, only its weights.
Thanks!
Hi, thanks for the invitation!
How can you be sure that the synthetic data you generate does not bias the architecture search away from the optimal solution for real data in a way similar to how early truncation of learning biases architecture search towards quick learners, and possibly away from peak performers?
