Teaching Machines to Listen : POMDP : How AI Decides When to Feed a Crying Baby
Teaching Machines to Listen: How AI Decides When to Feed a Crying Baby A surprisingly deep look at how autonomous systems make decisions when they cannot see the full picture — and what a hungry baby has to do with disaster response drones. It is 2am. You hear a sound from the baby's room — or you think you do. You are not sure if the baby is crying or just stirring. You do not know if the baby is hungry. You cannot see into the room from where you are lying. Do you get up? This is not a trick question. It is, in stripped-down form, one of the hardest problems in artificial intelligence: how do you make a good decision when you cannot directly observe the state of the world? Researchers call this a Partially Observable Markov Decision Process — a POMDP. The name is intimidating. The baby problem makes it approachable. And once you understand it through the baby, you start seeing the same structure everywhere: in autonomous drones searching flooded disast...