A tribute to the father of Artificial Intelligence (1912)

#artificialintelligence

Today I was invited to give a KeyNote Lecture about Artificial Intelligence in the beautiful city of Zaragoza, by Javier Khunel the CEO of the main business school there, media group Heraldo and CaixaBank, to a diverse audience of business owners, entrepreneurs, c level execs, intrapreneurs and many more, at a great venue The CaixaForum building. I have been in the field for the last 21 years, and the last ones as clear advocate of AI and Deep Learning, with a company in the field, and advising Emotiv Inc the Leader in Brain Computer Interfaces about Data Science and Artificial Intelligence. So it is fair to say that I play in a field I know very well. Anyway I always get my facts and figures up to date, and to my surprise I discover an amazing fact: the father of Artificial Intelligence according to the MIT Technology Review is from my own backyard so to speak, from the country I was born Spain. He develop a machine in 1912 called "El Ajedrecista" or "The Chess Player" a very limited precursor of IBM's Deep Blue, and the first true chess computer, but by all means a pioneer (electro mechanical) work in the Artificial Intelligence field, he also build an Algebraic Formula Machine and many other mostly unknown marvels.


Demis Hassabis, Google DeepMind - Artificial Intelligence and the Future

#artificialintelligence

Mar 11, 2016 AlphaGo, a computer program developed by Google DeepMind in London to play the traditional Chinese board game Go, had five matches against Se-Dol Lee, a professional Go player in Korea from March 8-15, 2016. AlphaGo won four out of the five games, a significant test result showcasing the advancement achieved in the field of general-purpose artificial intelligence (GAI), according to the company. Dr. Demis Hassabis, the Chief Executive Officer of Google DeepMind, visited KAIST on March 11, 2016 and gave an hour-long talk to students and faculty. In the lecture, which was entitled "Artificial Intelligence and the Future," he introduced an overview of GAI and some of its applications in Atari video games and Go.


Bot makes poker pros fold: What's next for AI?

#artificialintelligence

Carnegie Mellon's No-Limit Texas Hold'em software made short work of four of the world's best professional poker players in Pittsburgh at the grueling "Brains vs. Artificial Intelligence" poker tournament. Poker now joins chess, Jeopardy, go, and many other games at which programs outplay people. But poker is different from all the others in one big way: players have to guess based on partial, or "imperfect" information. "Chess and Go are games of perfect information," explains Libratus co-creator Noam Brown, a Ph.D. candidate at Carnegie Mellon. "All the information in the game is available for both sides to see.


Tech Savvy: What AlphaGo Means to the Future of Management

#artificialintelligence

AI as management assistant: The artificial intelligence program AlphaGo got a lot of attention for beating 18-time Go world champion Lee Sedol four out of five games last week. The significance of this achievement is rooted in the extraordinary number of possible moves in Go: 2.08168199382 … 10170, reportedly more than the number of atoms in the universe. That's too many possibilities for brute computing force to handle (which is how IBM's Deep Blue beat chess master Garry Kasparov 20 years ago). Yet AlphaGo, created by Google DeepMind, formerly British AI company DeepMind Technologies, mastered the 2,500-year-old board game on its own in a matter of months. "It started by studying a database of about 100,000 human matches, and then continued by playing against itself millions of times," reported science correspondent Geoff Brumfiel at NPR. Go bragging rights are nice for Google, but what does AlphaGo's victory mean for management?


AlphaGo and the Declining Advantage of Big Companies

#artificialintelligence

An ancient Chinese board game that dates back nearly 3,000 years, Go is played on a 19-by-19 square grid, with each player trying capture the opponent's territory. It was thought it would take at least another 10 years before a machine could beat a human in Go. That's like an aircraft that can fly faster and faster without the help of an engineer. How can that be possible? When machine learning first took off, it was used to predict how we click, buy, lie, or die.