AI engines utilizing deep learning neural networks provide excellent tools for analyzing traditional board games. Here we are interested in gaining new insights into the ancient game of Go. For that purpose, we need to define new numerical measures based on the raw output of the engines. In this paper, we develop a numerical tool for automated move-by-move performance evaluation in a context-sensitive manner and for recognizing game features. We measure the urgency of a move by the cost of passing, which is the score value difference between the current configuration of stones and after a hypothetical pass in the same board position. Here we investigate the properties of this measure and describe some applications.

In 1901, on an excavation trip to Crete, British archaeologist Arthur Evans unearthed items he believed belonged to a royal game dating back millennia: a board fashioned out of ivory, gold, silver, and rock crystals, and four conical pieces nearby, assumed to be the tokens. Playing it, however, stumped Evans, and many others after him who took a stab at it. There was no rulebook, no hints, and no other copies have ever been found. Games need instructions for players to follow. Without any, the Greek board's function remained unresolved--that is, until recently. Enter artificial intelligence, and a group of researchers from Maastricht University in the Netherlands.

A new version of the ancient Chinese board game Go that uses quantum entanglement to add an element of randomness could make it a tougher test for artificial intelligences than regular board games. "Board games have long been good test beds for AI because these games provide closed worlds with specific and simple rules," says Xian-Min Jin at Shanghai Jiao Tong University in China. In Go, players take turns to place a stone on a board, trying to surround and capture the opponent's stones.

In 1238, the medieval Spanish ruler Alfonso X of Castile published a tome called Libro de los Juegos, or The Book of Games. It consisted of 97 parchment pages, many with beautiful color illustrations, and contains the earliest descriptions of games such as chess, dice, and backgammon. Alfonso went on to classify games into three categories: games that are played on horseback, games played dismounted (such as fencing and wrestling), and games played seated. He divided this third category even further into games that rely on the brain, games of chance, and games that rely on both. In making these distinctions, Alfonso is the unofficial founder of a field of science known as ludology--the study of games, which has attracted much interest among mathematicians, computer scientists, sociologists, and others.

In 1238, the medieval Spanish ruler Alfonso X of Castile published a tome called Libro de los Juegos, or The Book of Games. It consisted of 97 parchment pages, many with beautiful color illustrations, and contains the earliest descriptions of games such as chess, dice, and backgammon.

This is about AlphaGo, Google DeepMind's Go playing AI that shook the technology world in 2016 by defeating one of the best players in the world, Lee Sedol. Go is an ancient board game which has so many possible moves at each step that future positions are hard to predict -- and therefore it requires strong intuition and abstract thinking to play. Because of this reason, it was believed that only humans could be good at playing Go. Most researchers thought that it would still take decades to build an AI which could think like that. In fact, I'm releasing this essay today because this week (March 8–15) marks the two-year anniversary of the AlphaGo vs Sedol match!

This prophecy came a step closer this week as legal AI proved itself more accurate than lawyers for the first time on a staple legal task –reviewing and approving contracts. The study was overseen by top US law schools and veteran corporate lawyers (including for instance Bruce Mann, a former senior partner at top US law firm, Morrison Foerster -- a Harvey Specter- like deal-maker who has handled more than 300 IPOs and over 200 mergers & acquisitions). In the controlled experiment, 20 top US-trained lawyers took on a legal AI platform, LawGeex. Both the experienced corporate lawyers and the AI pored over five unseen Non-Disclosure Agreements to find a list of common 30 issues (vetted by contract experts from Duke University and the University of Southern California). Each participant (the AI included) was given 4 hours to issues spot clauses in the contracts.

Deep Blue beat world chess champion Gary Kasparov in the final game of a tied, six-game match last May 11. Kasparov had beaten the machine in an earlier match held in February 1996. The Fredkin Prize was awarded under the auspices of AAAI; funds had been held in trust at Carnegie Mellon University. The Fredkin Prize was originally established at Carnegie Mellon University 17 years ago by Massachusetts Institute of Technology Computer Science Professor Edward Fredkin to encourage continued research progress in computer chess. The first award of $5,000 was given to two scientists from Bell Laboratories who in 1981 developed the first chess machine to achieve master status.

Je Kie, the number one Go player in the world, spent much of the game playing with the hair on his head. Time and again, he pinched the short strands between his thumb and index fingers, twisting the hair around one and then the other. His opponent, AlphaGo, the machine built by researchers at Google's DeepMind lab, merely played the game. And in the end, as seemed inevitable, it won. With the win, AlphaGo claimed victory in its three-game match with the Ke Jie, taking a 2-0 lead.

Google's computer algorithm AlphaGo narrowly beat the world's top-ranked player in the ancient Chinese board game of Go on Tuesday, earning praise for apparently surpassing human abilities in one of the last games that machines have yet to dominate and reaffirming the arrival of what its developers tout as a groundbreaking new form of artificial intelligence. AlphaGo took the first of three scheduled games against brash Chinese 19-year-old Ke Jie, the world's No. 1 player, who after the match anointed the program as the new "Go god." For the first time, AlphaGo was quite humanlike. In the past it had some weaknesses. But now I feel its understanding of Go and the judgment of the game is beyond our ability.