Nvidia chip shows what AI thinks is important
Nvidia’s deep learning chip technology is able to show how its algorithms are “thinking” by highlighting the areas of images that are contributing most to deep neural network behaviour. The network determines what parts of images are the most pertinent by itself, making it useful for applications such as guiding autonomous vehicles. The innovation could also prove important when applied to data such as medical, military or financial, by giving insights into which factors the network decides are relevant in reaching a decision.
Nvidia provides chips that are ideal for deep learning, an especially powerful machine-learning technique (see “10 Breakthrough Technologies 2013: Deep Learning”).

Nvidia's neural network software highlights the areas it's focusing on as it makes driving decisions.

The chip maker has also been developing systems that demonstrate how an automaker might apply deep learning to autonomous driving. This includes a car that is controlled entirely by a deep-learning algorithm. Amazingly, the vehicle’s computer isn’t given any rules to follow—it simply matches input from several video cameras to the behavior of a human driver, and figures out for itself how it should drive. The only catch is that the system is so complex that it’s difficult to untangle how it actually works.

But Nvidia is working to open this black box. It has developed a way to visually highlight what the system is paying attention to. As explained in a recently published paper, the neural network architecture developed by Nvidia’s researchers is designed so that it can highlight the areas of a video picture that contribute most strongly to the behavior of the car’s deep neural network. Remarkably, the results show that the network is focusing on the edges of roads, lane markings, and parked cars—just the sort of things that a good human driver would want to pay attention to.

“What’s revolutionary about this is that we never directly told the network to care about these things,” Urs Muller, Nvidia’s chief architect for self-driving cars, wrote in a blog post.

It isn’t a complete explanation of how the neural network reasons, but it’s a good start. As Muller says: “I can’t explain everything I need the car to do, but I can show it, and now it can show me what it learned.”

This sort of approach could become increasingly important as deep learning is applied to just about any problem involving large quantities of data, including critical areas like medicine, finance, and military intelligence.

A handful of academic researchers are exploring the issue as well. For example, Jeff Clune at the University of Wyoming and Carlos Guestrin at the University of Washington (and Apple) have found ways of highlighting the parts of images that classification systems are picking up on. And Tommi Jaakola and Regina Barzilay at MIT are developing ways to provide snippets of text that help explain a conclusion drawn from large quantities of written data.

The Defense Advanced Projects Research Agency (DARPA), which does long-term research for the U.S. military, is funding several similar research efforts through a program it calls Explainable Artificial Intelligence (XAI).

Beyond the technical specifics, though, it’s fascinating to consider how this compares to human intelligence. We do all sorts of things we can’t explain fully, and the explanations we concoct are often only approximations, or “stories” about what’s going on. Given the opacity of today’s increasingly complex machine-learning methods, we may someday be forced to accept such explanations from AI, too.

(Sources: Nvidia, “The Dark Secret at the Heart of AI”, “The U.S. Military Wants Its Autonomous Machines to Explain Themselves”)