Machine Learning’s Limits
By Ed Sperling, Semiconductor Engineering
Experts at the Table, part 1: Why machine learning works in some cases and not in others.
Brinkmann: I agree. It’s a powerful tool in the engineering toolbox of any company that does scientific or technical work. The best applications of this are when you control the space you’re using it in, and when a human is still in the loop to tell the difference between when it behaves reasonably and when it does not. So whenever you optimize your business processes or your test vectors, or something you don’t understand the nature of, it may be a good use of machine learning. But when people have certain expectations, and it hits data it may not have seen before, it starts to fail. That’s where the trouble starts. People believe that machine learning is a universal solution. That’s not the case. We need to make sure people understand the limits of these technologies, while also removing the fear that this technology will take over their jobs. That’s not going to happen for a long time. If you want to use machine learning in applications that are related to safety, like automotive, one key component that’s missing is these systems do not explain themselves. There is no reasoning that you can derive from a network that has been trained about why it does what it does, or when does it fail. There is lots of research going on right now in this area to make these systems more robust and to find a way to verify them. But it has to come with a good understanding of the statistical nature of what you’re dealing with. Applying machine learning is not easy. You need a lot more than a deep learning algorithm. There are other ideas around vision learning and new technologies that make it easier to explain how these things work. This is one of the biggest differences with classical engineering, where you always had an engineer in the loop to explain why something works.