For robots, simulation is a great teacher for learning long-horizon (multi-step) tasks — especially compared to how long it takes to collect real-world training data.
Imagine you’re tasked with sending a team of football players onto a field to assess the condition of the grass (a likely task for them, of course). If you pick their positions randomly, they might cluster together in some areas while completely neglecting others. But if you give them a strategy, like spreading out uniformly across the field, you might get a far more accurate picture of the grass condition.
In 1994, Florida jewelry designer Diana Duyser discovered what she believed to be the Virgin Mary’s image in a grilled cheese sandwich, which she preserved and later auctioned for $28,000. But how much do we really understand about pareidolia, the phenomenon of seeing faces and patterns in objects when they aren’t really there?
AI systems are increasingly being deployed in safety-critical health care situations. Yet these models sometimes hallucinate incorrect information, make biased predictions, or fail for unexpected reasons, which could have serious consequences for patients and clinicians.
