The most interesting unconventional FPGA application I ever came across was Adrian Thompson's foray into evolutionary algorithms in the 90's. Implementing a "s

The most interesting unconventional FPGA application I ever came across was Adrian Thompson's foray into evolutionary algorithms in the 90's. Implementing a "survival of the fittest" approach he taught a board to discern the difference between a 1kHz and 10KHz tone. What's fascinating is the final generation of the circuit was more compact than anything a human engineer would ever come up with (a mere 37 logic gates), and utilized all kinds of physical nuances specific to the chip it evolved on - including feedback loops, and EMI effects between unconnected logic units. Article: https://www.damninteresting.com/on-the-origin-of-circuits/ https://www.damninteresting.com/on-the-origin-of-circuits/ Paper: https://www.researchgate.net/publication/2737441_An_Evolved_Circuit_Intrinsic_in_Silicon_Entwined_With_Physics https://www.researchgate.net/publication/2737441_An_Evolved_... Reddit: https://www.reddit.com/r/MachineLearning/comments/2t5ozk/what_ever_happened_with_the_evolutionary/ https://www.reddit.com/r/MachineLearning/comments/2t5ozk/wha... If you think about how intelligence evolved on earth, we had a rich environment of physics for variety of causes and effects to interact in all kinds of diverse ways (not just boolean 1's and 0's or a limited set of instructions). Makes me wonder if to achieve true AGI you need to offer evolution a fairly rich set of primitives on which to flourish. Edit: Just realized harwoodr beat me to this example.