The Simple Economics of Machine Intelligence
When the cost of any input falls so precipitously, there are two other well-established economic implications. First, we will start using prediction to perform tasks where we previously didn’t. Second, the value of other things that complement prediction will rise.
.. Once prediction became cheap, innovators reframed driving as a prediction problem. Rather than programing endless if-then-else statements, they instead simply asked the AI to predict: “What would a human driver do?” They outfitted vehicles with a variety of sensors – cameras, lidar, radar, etc. – and then collected millions of miles of human driving data. By linking the incoming environmental data from sensors on the outside of the car to the driving decisions made by the human inside the car (steering, braking, accelerating), the AI learned to predict how humans would react to each second of incoming data about their environment. Thus, prediction is now a major component of the solution to a problem that was previously not considered a prediction problem.
.. However, this does not spell doom for human jobs, as many experts suggest. That’s because the value of human judgment skills will increase. Using the language of economics, judgment is a complement to prediction and therefore when the cost of prediction falls demand for judgment rises. We’ll want more human judgment.
.. when prediction is cheap, diagnosis will be more frequent and convenient, and thus we’ll detect many more early-stage, treatable conditions.