David Schmidt is protecting the environment, but not in the way he first intended. In engineering graduate school, his interest was nuclear fusion. A persuasive Ph.D. advisor guided him toward the physics of fuel injection, a process central to both inertial confinement fusion reactors and internal combustion engines, the advisor's other line of research. While electric cars may seem to be taking over, internal combustion engines (ICEs) will remain on the roads, seas, and tarmacs for decades to come. Schmidt's work makes them cleaner and more efficient.

Then Peetak Mitra devised a new way to prune networks, removing unimportant nodes and connections. Pruning reduced network size by 90%, making it 10 times faster--while simultaneously increasing accuracy. That's because large networks adapt to any information in the system, which makes them good at generalizing to many scenarios, but they can learn from the noise in the system. If you're applying machine learning to a regular environment--similar types of cylinders--you can afford to shrink the network, thereby filtering out the noise. The team also used a process called quantization, which reduces the excessive precision of the network's values.