The Soul of a New Machine

"This is a highly acclaimed work by Tracy Kidder. He was embedded in a team at a computer company called Data General (which no longer exists). It was 1979, and they were building a mini-computer. He was introducing to the world this group of people that later became known as ‘nerds’ — though I call them the numerati. These were computer scientists and electrical engineers, and they were putting together this machine that for most of us was utterly foreign back then. We knew NASA used computers; we knew they were important; but they weren’t part of our lives. Kidder was introducing that engineering culture to us — the way they thought, the way they analysed things. It’s a marvelously detailed book, and I found it very inspiring, as I tried to write a book that, in a way, had a similar goal, as I followed a team at IBM that was building a machine. It’s also an interesting way of looking at how computing has utterly changed in the last 30 years. They were building a machine, but all it could do, from our perspective, was count things and put things into columns and calculate stuff. Now these machines are dealing with music and images and ideas and facts. They’ve invaded our world. If you look at Watson, it made some foolish mistakes and it struggles to understand English. They’ve made remarkable progress in that area, but there is a whole lot of refining to do. Yes, the language in understanding the clue, and also in the information that it is searching through. So finding meaning and then figuring out not only that it has an answer, but also that it has enough confidence in that answer to bet on it. That’s the remarkable thing. There are all sorts of critics out there who pooh-pooh Watson and say, ‘Oh, it’s not that smart — it’s just looking up facts.’ But this in itself is a very hard thing to do, and Watson only does it well enough to win a game of Jeopardy! , which means it gets about 85% of questions right. So Watson has a lot of learning to do. It has to get a lot smarter and also a lot cheaper, so that we can all have access to this kind of information technology. And we all will, because we need it. We are all swimming in vast oceans of data that we ourselves are creating. All of that data has little bits of intelligence in it, but we can’t find them. Let’s say you’re a neuroscientist. Every day, neuroscientists are doing brain scans and creating massive amounts of data and studies about how the brain and the nervous system work. And they’re publishing their findings. Last year alone, there were some 50,000 academic studies published on neuroscience. If you’re a neuroscientist, how could you possibly be on top of your field? You need machines to help you with that. Sometimes a machine like this, if you ask it a question, will come back with 10 possible answers, and confidence in each one. A number of those might be stupid. One or two might even be outlandish. But if it comes back with some good ideas, then the humans, with our superior brains, can pick out the ones that are worth pursuing."