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Despite their enormous size and power, today's artificial intelligence systems routinely fail to distinguish between hallucination and reality. Autonomous driving systems can fail to perceive pedestrians and emergency vehicles right in front of them, with fatal consequences. Conversational AI systems confidently make up facts and, after training via reinforcement learning, often fail to give accurate estimates of their own uncertainty.
Working together, researchers from MIT and the University of California at Berkeley have developed a new method for building sophisticated AI inference algorithms that simultaneously generate collections of probable explanations for data, and accurately estimate the quality of these explanations.
The new method is based on a mathematical approach called sequential Monte Carlo (SMC). SMC algorithms are an established set of algorithms that have been widely used for uncertainty-calibrated AI, by proposing probable explanations of data and tracking how likely or unlikely the proposed explanations seem whenever given more information. But SMC is too simplistic for complex tasks. The main issue is that one of the central steps in the algorithm — the step of actually coming up with guesses for probable explanations (before the other step of tracking how likely different hypotheses seem relative to one another) — had to be very simple. In complicated application areas, looking at data and coming up with plausible guesses of what’s going on can be a challenging problem in its own right. In self driving, for example, this requires looking at the video data from a self-driving car’s cameras, identifying cars and pedestrians on the road, and guessing probable motion paths of pedestrians currently hidden from view. Making plausible guesses from raw data can require sophisticated algorithms that regular SMC can’t support.