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Deep Science: Introspective, detail-oriented and disaster-chasing AIs

Research papers come out far too frequently for anyone to read them all. That’s especially true in the field of machine learning, which now affects (and produces papers in) practically every industry and company. This column aims to collect some of the most relevant recent discoveries and papers — particularly in, but not limited to, artificial intelligence — and explain why they matter.

It takes an emotionally mature AI to admit its own mistakes, and that’s exactly what this project from the Technical University of Munich aims to create. Maybe not the emotion, exactly, but recognizing and learning from mistakes, specifically in self-driving cars. The researchers propose a system in which the car would look at all the times in the past when it has had to relinquish control to a human driver and thereby learn its own limitations — what they call “introspective failure prediction.”

For instance, if there are a lot of cars ahead, the autonomous vehicle’s brain could use its sensors and logic to make a decision de novo about whether an approach would work or whether none will. But the TUM team says that by simply comparing new situations to old ones, it can reach a decision much faster on whether it will need to disengage. Saving six or seven seconds here could make all the difference for a safe handover.

It’s important for robots and autonomous vehicles of all types to be able to make decisions without phoning home, especially in combat, where decisive and concise movements are necessary. The Army Research Lab is looking into ways in which ground and air vehicles can interact autonomously, allowing, for instance, a mobile landing pad that drones can land on without needing to coordinate, ask permission or rely on precise GPS signals.