Most of the practical AI success stories in recent years have involved what computer scientists call supervised machine learning: the use of labeled datasets to train algorithms to automate what had been a human activity. For example, take a dataset of symptoms and test results of thousands of patients, along with their eventual diagnosis by doctors, and train an algorithm to learn the patterns in the dataset—that is, which symptoms and clinical markers predict which diseases. Similarly, take a dataset of labeled images and train an algorithm to recognize people’s faces. These successes show that machine learning can, with the right training data, approximate tacit human knowledge. But is it possible for AI to extract knowledge unknown even to experts? Can we automate something like scientific discovery?
One potential approach, which I discuss in my recently published book A Human’s Guide to Machine Intelligence, came from the late Don Swanson, ...
