doing because we designed the algorithms at their heart. So when our computers generate a result, we feel that we intellectually grasp it. The new machine-learning programs are different. Having recognized patterns via deep neural networks, they come up with conclusions, and we have no idea exactly how. When they uncover relationships, we don’t understand it in the same way as if we had deduced those relationships ourselves using an underlying theoretical framework. As data sets become larger, we won’t be able to analyze them ourselves even with the help of computers; rather, we will rely entirely on computers to do the analysis for us. So if someone asks us how we know something, we will simply say it is because the machine analyzed the data and produced the conclusion. One day a computer may well come up with an entirely new result—e.g., a mathematical theorem whose proof, or even whose statement, no human can understand. That is philosophically different from the way we have been doing science. Or at least thought we had; some might argue that we don’t know how our own brains reach conclusions either, and that these new methods are a way of mimicking learning by the human brain. Nevertheless, I find this potential loss of understanding disturbing. Despite the remarkable advances in computing, the hype about AGI—a general- intelligence machine that will think like a human and possibly develop consciousness— smacks of science fiction to me, partly because we don’t understand the brain at that level of detail. Not only do we not understand what consciousness is, we don’t even understand a relatively simple problem like how we remember a phone number. In just that one question, there are all sorts of things to consider. How do we know it is a number? How do we associate it with a person, a name, face, and other characteristics? Even such seemingly trivial questions involve everything from high-level cognition and memory to how a cell stores information and how neu