— From The Master Algorithm – How the Quest for the ultimate learning machine will remake our world by Pedro Domingos P. 7
The power of machine learning is perhaps best explained by a low-tech analogy: farming. In an industrial society, goods are made in factories, which means the engineers have to figure out exactly how to assemble them from their parts, how to make those parts, and so on — all the way to raw materials. It’s a lot of work. Computer are the most complex goods ever invented, and designing them, is a ton of work. But there’s another, much older way in which we can get some of the things we need: by letting nature make them. In farming, we plant the seeds, make sure they have enough water and nutrients, and reap the grown crops. Why can’t technology be more like that? It can, and that’s the promise of machine learning. Learning algorithms are the seed, data is the soil, and the learned programs are the grown plants. The machine-learning expert is like a farmer, sowing the seeds, irrigating and fertilizing the soil, and keeping an eye on the health of the crop but otherwise staying out of the way.
Once we look at machine learning this way, two things immediately jump out. The first is that the more data we have, the more we can learn. No data? Nothing to learn. Big data? Lots to learn. That’s why machine learning has been turning up everywhere, driven by exponentially growing mountains of data. If machine learning was something you bought in the supermarket, its carton would say: ‘Just add data.’
The second thing is that machine learning is a sword with which to slay the complexity monster. Given enough data, a learning program that’s only a few hundred lines long can easily generate a program with millions of lines, and it can do this again and again for different problems. The reduction in complexity for the programmer is phenomenal. Of course, like the Hydra, the complexity monster sprouts new heads as soon as we cut off the old ones, but they start off smaller and take a while to grow, so we still get a big leg up.”