Machine Learning

"Yes, Machine Learning is a textbook and I would call it the textbook for machine learning and artificial intelligence . Machine learning is just the math of teaching a machine how to solve a problem on its own, because you’re not going to be able to be there to solve it for the machine. It can be any kind of problem: it could be a robot that needs to figure out how to get from point A to point B or it could be a supply inventory algorithm for trying to figure out how many products it should order for Walmart. What’s great about this book is that, first of all, it’s not intimidating. It’s really slim and it covers the full range of artificial intelligence algorithms for really solving any problem. You can think of it this way – if you’re a wrestler facing a problem, this has every wrestling move that you’re going to need, in order to knock out any problem that you see. And that is really, really empowering. Because after you read a book like this, you look at any problem that is out there, and you think to yourself, ‘Oh I can build a machine that can figure this out better than a human, with better accuracy, and more quickly.’ They say that once you have a hammer everything begins to look like a nail. That’s absolutely true, and Machine Learning is one hell of a hammer. It’s a textbook, so they update it. The author, Tom Mitchell, is the chair of the Machine Learning Department at Carnegie Mellon. In layman’s terms he’s a total badass – a very, very accomplished guy. I disagree. I think machine learning has actually pretty much ripened and matured. Machine learning arguably started in the 1950s, and the term artificial intelligence was coined by John McCarthy in 1956. Back then we didn’t know anything – but scientists were really convinced that they had this thing nipped in the bud, that pretty soon they were going to replace all humans. This was because whenever you are teaching machines to think, the lowest hanging fruit is to give them problems that are very constrained. For example, the rules of a board game. So if you have a certain number of rules and you can have a perfect model of your whole world and you know how everything works within this game, well, yes, a machine is going to kick the crap out of people at chess. What those scientists didn’t realise is how complicated and unpredictable and full of noise the real world is. That’s what mathematicians and artificial intelligence researchers have been working on since then. And we’re getting really good at it. In terms of applications, they’re solving things like speech recognition, face recognition, motion recognition, gesture recognition, all of this kind of stuff. So we’re getting there, the field is maturing."