A new training scheme could remind artificial intelligence programs that they aren’t know-it-alls.
AI programs that run robots, self-driving cars and other autonomous machines often train in simulated environments before making real-world debuts (SN: 12/8/18, p. 14). But situations that an AI doesn’t encounter in virtual reality can become blind spots in its real-life decision making. For instance, a delivery bot trained in a virtual cityscape with no emergency vehicles may not know that it should pause before entering a crosswalk if it hears sirens.
To create machines that err on the side of caution, computer scientist Ramya Ramakrishnan of MIT and colleagues developed a post-simulation training program in which a human demonstrator helps the AI identify gaps in its education. “This allows the [AI] to safely act in the real world,” says Ramakrishnan, whose work is being presented January 31 at the AAAI Conference on Artificial Intelligence. Engineers could also use information on AI blind spots to design better simulations in the future.
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