Semiconductor Engineering .:. What's Missing From Machine Learning

It's being used to optimize complex chips, balance power and performance inside of data centers, program robots, and to keep expensive electronics updated and operating. What's less obvious, though, is there are no commercially available tools to validate, verify and debug these systems once machines evolve beyond the final specification. The expectation is that devices will continue to work as designed, like a cell phone or a computer that has been updated with over-the-air software patches. But machine learning is different. It involves changing the interaction between the hardware and software and, in some cases, the physical world. In effect, it modifies the rules for how a device operates based upon previous interactions, as well as software updates, setting the stage for much wider and potentially unexpected deviations from that specification. In most instances, these deviations will go unnoticed.