CAMBRIDGE, United States, March 19 (Xinhua) -- Google's computer program AlphaGo stunned the world this week by defeating a top professional player at Go, a board game that has trillions of possible moves and often requires players' intuition. "The AlphaGo success shows again that this is a golden era for the development of intelligent software that can be as intelligent or more intelligent than humans in narrow domains of intelligence," Tomaso Poggio, professor of Department of Brain and Cognitive Sciences at Massachusetts Institute of Technology (MIT), told Xinhua. While many people around the world witnessed the achievement accomplished by artificial intelligence (AI) with excitement, some also expressed a renewed wariness over the technology and wonder: It is a boon or a bane for mankind? AI, a field that has existed for more than 50 years, grows along with the development of cognitive science and neuroscience. Poggio regards the subject of intelligence as the greatest one in science, because it has to do with the question of how human brain works and what is the mind.

Science fiction authors have long contemplated the development of artificial intelligence. Typically, it is imagined as an application for military technology or some high-minded academic pursuit. However, in our real world, artificial intelligence may emerge for a much more pedestrian reason. Libreri argued that it's essential for VR to make human characters more believable. Sony Computer Entertainment announced a price and release date for its Playstation VR at the conference.

Google built its empire by establishing itself as a monolith of search. Now, it intends to build another by doing the opposite: fragmenting its efforts to secure a multitude of "smaller" victories, each of them worth billions. Leveraging its colossal reserves and analytic prowess, Google is becoming, in effect, the largest healthcare startup incubator in history -- so who are these leading analytic minds at the helm of this effort? They're actually not minds at all, but advanced computers that can make decisions based on technology called "machine learning." While that particular development may sound a bit uncanny, the truth is far more heartening than scary.

By now, the fact that transatlantic democratic capitalism, once the engine of postwar prosperity, has run into trouble can hardly be denied by anyone with the courage to browse a daily newspaper. Hunger, homelessness, toxic chemicals in the water supply, the lack of affordable housing: all these issues are back on the agenda, even in the most prosperous of countries. This appalling decline in living standards was some time in the making – 40 years of neoliberal policies are finally taking their toll – so it shouldn't come as a shock. However, coupled with the spillover effects of wars in the Middle East – first the refugees, now the increasingly regular terrorist attacks in the heart of Europe – our economic and political malaise looks much more ominous. It's hardly surprising that the insurgent populist forces, on both left and right, have such an easy time bashing the elites.

We may have reached an inflection point. The problem space of chess is so astonishingly large that incremental increases in hardware speed and algorithms are unlikely to result in meaningful gains from here on out. Turns out I was kinda … totally completely wrong. The number of possible moves, or "problem space", of Chess is indeed astonishingly large, estimated to be 1050: Deep Blue was interesting because it forecast a particular kind of future, a future where specialized hardware enabled brute force attack of the enormous chess problem space, as its purpose built chess hardware outperformed general purpose CPUs of the day by many orders of magnitude. In the heady days of 1997, Deep Blue could evaluate 200 million chess positions per second.

A novel written by artificial intelligence was a finalist in Japan's Hoshi Shinichi Literary Award. The award is named after Hoshi Shinichi, a Japanese science fiction author whose books include The Whimsical Robot and Greetings from Outer Space. The unique contest accepts submissions from humans and machines, and judges for the prize, now in its third year, weren't told which novels were written by humans and which were penned by human-AI teams. This year was the first time the committee received submissions written by AI programs. The AI's novel is called The Day A Computer Writes A Novel, or Konpyuta ga shosetsu wo kaku hi in Japanese.

Google's artificial intelligence (AI) ethics board, established when Google acquired London AI startup DeepMind in 2014, remains one of the biggest mysteries in tech, with both Google and DeepMind refusing to reveal who sits on it. Google set up the board at DeepMind's request after the cofounders of the 400 million research-intensive AI lab said they would only agree to the acquisition if Google promised to look into the ethics of the technology it was buying into. Business Insider asked Google once again who is on its AI ethics board and what they do but it declined to comment. A number of AI experts told Business Insider that it's important to have an open debate about the ethics of AI given the potential impact it's going to have on all of our lives. Artificial intelligence is the field of building computer systems that understand and learn from observations without the need to be explicitly programmed, as defined by Nathan Benaich, an AI investor at venture capital firm Playfair Capital.

First went checkers, then fell chess. Now, a computer program has defeated the world's top player in the ancient east Asian board game of Go -- a major milestone for artificial intelligence that brings to a close the era of board games as benchmarks in computing. At the Four Seasons Hotel in Seoul, Google DeepMind's AlphaGo capped a 3-0 week on Saturday against Lee Sedol, a giant of the game. Lee and AlphaGo were to play again Sunday and Tuesday, but with AlphaGo having already clinched victory in the five-game match, the results are in and history has been made. It was a feat that experts had thought was still years away.

As I walked around my office on a recent morning, a female voice on my iPhone narrated the objects I passed. "Brick," "wall," "telephone," she said matter-of-factly. The voice paused when I came upon a bike hung on a wall-mounted rack, then intoned, "bicycle." The voice is part of a free image-recognition app called Aipoly that's trying to make it easier for those with vision impairments to recognize their surroundings. To use it, you point the phone's rear camera at whatever you want it to identify, and Aipoly will speak what it sees (or, at least, what it thinks it sees) and show the object's name on the phone's display.

Scheduled Sampling was at NIPS last year; the reviews are also online. This is actually the third time I've tried to make my way through this paper, and to force myself to not give up again, I'm writing my impressions here. Given that this paper is about two things I know a fair amount about (imitation learning and neural networks), I kept getting frustrated at how hard it was for me to actually understand what was going on and how it related to things we've known for a long time. So this post is trying to ease entry for anyone else in that position. What is the problem this paper is trying to solve?