More than 50 million Americans suffer from sleep disorders, and diseases including Parkinson's and Alzheimer's can also disrupt sleep. To make it easier to diagnose and study sleep problems, researchers at MIT and Massachusetts General Hospital have devised a new way to monitor sleep stages without sensors attached to the body. Their device uses an advanced artificial intelligence algorithm to analyze the radio signals around the person and translate those measurements into sleep stages: light, deep, or rapid eye movement (REM). Recent advances in artificial intelligence have made it possible to train computer algorithms known as deep neural networks to extract and analyze information from complex datasets, such as the radio signals obtained from the researchers' sensor.

INTRODUCTION Attempts to write'intelligent' computer programs have commonly involved the choice for attack of some particular aspect of intelligent behaviour, together with the choice of some relevant task, or range of tasks, which the program must perform. The emphasis is sometimes on the generality of the program's ability, sometimes on the importance of the particular task which it can perform. Well-known examples of such programs are Newell, Shaw, and Simon's General Problem Solver (1959; see also Ernst and Newell, 1967), which is applicable to a wide range of simple problems, Samuel's checker (draughts) playing program (1959, 1967), and the program written by Evans (1964), which solves geometric analogy problems. However, there is another approach to the goal of machine intelligence which stresses the relationship of an organism to its environment and which sets out from the start to understand what is involved in this relationship. Long ago Grey Walter (1953) experimented with mechanical'tortoises' which could range over the floor in a lifelike manner. Toda (1962), in a whimsical and illuminating paper, has discussed the problems facing an automaton in a simple artificial environment. Friedman (1967), a psychologist, has described a computer simulation of instinctive behaviour involving an automaton equipped with sensory and motor systems. Sandewall (1967) has gone deeply into an automaton/environment relationship with a rather more formal approach. This list is far from complete. In particular, robots of various kinds are under construction at a number of research centres, notably at the Stanford Research Institute (Nilsson and Raphael, 1967). The reader may find it helpful to meditate on the situation of, say, a rat in a cage, as seen by the rat.