Machine learning going from merely unnerving to scary.

It seems like you can't go a day with any exposure to media without hearing about machine learning, or developing software which isn't designed to do anything in particular but is capable of teaching itself to carry out tasks tasks and make educated predictions based upon its training and data already available to it. If you've ever had to deal with a speech recognition system, bought something off of Amazon that you didn't know existed (but seemed really interesting at the time), or used a search engine you've interacted with a machine learning system of some kind. That said, here's a roundup of some fascinating stuff being done with machine learning systems at this time. First, let's talk about the chess. As board games go it's a tricky one to write software for due to the number of potential moves every turn. Pretty much every chess engine out there, from IBM's Deep Blue to Colossus Chess back in 1984 use more or less the same general technique, which is brute forcing the set of all possible moves for that board configuration, deleting the moves that obviously won't work (i.e., illegal moves) with varying degrees of cleverness, and winnowing down the remaining possible positions to extract the best possible move for that moment. Well engineered systems can run several hundred million possible moves in a few seconds before settling on a move; conversely, human chess players are observed using the fusiform face areas of their brains to evaluate five or six moves per second before picking a move, which is obviously much slower but history has borne out just how efficient a means of playing chess wetware is. Enter Giraffe by Matthew Lai at the Imperial College of London. Giraffe is implemented as a very sophisticated machine learning system which makes use of multiple layers of neural networks, each of which analyzes chess boards in a different way. One layer looks at the state of the game board as a whole, another analyzes the location of each piece relative to others on the board, and another considers the squares each piece can move to as well as the game effects of each possible move. Giraffe started out knowing nothing about the game of chess becasue it was an unformatted, unprogrammed neural network construct. Lai then began feeding into Giraffe carefully selected parts of databases of chess games, where each game is documented move-by-move and annotated every step of the way. This is, incidentally, the important bit about training AI software. Whatever data sets you train them with have to be annotated in some natively machine readable way so that the software has a "native language" to attach ideas to, just as you or I would think in our native languages and mentally translate into a second language learned later in life. All told, it took Giraffe about 72 hours continously to assimilate the information needed to play chess. At the end of the traning process Giraffe was benchmarked against human chess players, and it was discovered that Giraffe ranks as a FIDE International Chess Master. If you're curious, here's the paper Lai wrote about building and training Giraffe.(Disclaimer: I'm not a parent.) If you've ever watched an infant explore its environment, what we would ordinarily consider play is actually a concerted effort to learn how to interact with objects driven by pure curiosity. Babies don't seem to know how to use their limbs or understand what things are or how they can be moved around because they are all novel experiences, so they're training themselves. AI software is the same way: When it's first started up it has no inherent knowledge of what it's supposed to do, unless it has a saved state file to read in from storage. For many years it's been difficult at best to train robots to carry out tasks: Either a human operator had to grasp a waldo's end effector and walk it through the task multiple times until the robot's software can carry it out unassisted or a teach pendant is used to do the same thing. Le