Raik Brinkmann, president and CEO of OneSpin Solutions, sat down with Semiconductor Engineering to talk about AI changes and challenges, new opportunities for using existing technology to improve AI, and vice versa. What follows are excerpts of that conversation. Brinkmann: There are a couple of big changes underway. One involves AI in functional safety, where you use context to prove the system is doing something good and that it's not going to fail. Basically, it's making sure that the data you use for training represents the scenarios you need to worry about. When you have many vectors of input it's difficult to cover all the relevant cases.

Imagine you're on a cross-country road trip and you aren't sure how to get to your final destination. You have a paper map in the passenger seat, but you keep getting lost because you have to continuously pull over to study this very large and confusing map. Eventually, you arrive at your destination, albeit a bit frustrated, but getting there wasn't enjoyable, and it certainly wasn't efficient. Now imagine you're on the same road trip, but instead you're driving a car with a built-in GPS navigation system guiding you the entire time. The journey to your final destination will take considerably less time and make for a much more pleasant experience.

Semiconductor Engineering sat down with Rob Aitken, an Arm fellow; Raik Brinkmann, CEO of OneSpin Solutions; Patrick Soheili, vice president of business and corporate development at eSilicon; and Chris Rowen, CEO of Babblelabs. What follows are excerpts of that conversation. To view part one, click here. SE: Is the industry's knowledge of machine learning keeping up with the pace of development? Rowen: It's clear that more theories will help us understand what is really possible and some things about what kinds of network designs will be better than others. At the same time, many of our biggest technological advancements have been when deployments got well ahead of theories.

Semiconductor Engineering sat down with Rob Aitken, an Arm fellow; Raik Brinkmann, CEO of OneSpin Solutions; Patrick Soheili, vice president of business and corporate development at eSilicon; and Chris Rowen, CEO of Babblelabs. What follows are excerpts of that conversation. SE: Where are we with machine learning? What problems still have to be resolved? Aitken: We're in a state where things are changing so rapidly that it's really hard to keep up with where we are at any given instance. We've seen that machine learning has been able to take some of the things we used to think were very complicated and rendered them simple to do.

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.

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.