Technology and data made possible advances in...technology and data. JL The Economist reports: New techniques have made training deep networks feasible. This takes a lot of number-crunching power, which became available when several AI research groups realised that graphical processing units (GPUs), the specialised chips used in PCs and video-games consoles to generate fancy graphics, were also well suited to running deep-learning algorithms. HOW HAS ARTIFICIAL intelligence, associated with hubris and disappointment since its earliest days, suddenly become the hottest field in technology? The term was coined in a research proposal written in 1956 which suggested that significant progress could be made in getting machines to "solve the kinds of problems now reserved for humans…if a carefully selected group of scientists work on it together for a summer". That proved to be wildly overoptimistic, to say the least, and despite occasional bursts of progress, AI became known for promising much more than it could deliver.

Head chef robot "Andrew" flips Japanese pancakes during the "Kingdom of Robot" press preview at the Huis Ten Bosch amusement park on July 12 in Sasebo, Nagasaki, Japan. In the last 400 years or so, since the time of the scientific revolution, we have come to find it natural to suppose that the world is comprehensible. Nature and its laws, operating in things most small as well as in the cosmos as a whole, are understandable. And, yet, the biologist J.B.S. Haldane, quoted in Samuel Arbesman's intriguing new book coming out next week, Overcomplicated: Technology at the Limits of Comprehension, has written: "Now, my own suspicion is that the universe is not only queerer than we suppose, but it is queerer than we can suppose." It isn't just the world of physics that has come to seem so exceedingly strange -- at the level of the quantum and also that of the multiverse -- but consciousness itself.

This is the second part of'A Brief History of Game AI Up to AlphaGo'. Part 1 is here and part 3 is here. In this part, we shall cover just about four decades of progress, from the first victories of computers against people at Checkers and Chess all the way up to DeepBlue's victory against humanity's then-best living Chess player. By the late 1950s, the industrious engineers at IBM were far from the only ones working on AI -- excitement for the new field filled research groups in universities from the US to the Soviet Union. One such group was made up of Allen Newell and Herbert Simon (both attendants of the Dartmouth Conference) from Carnegie Mellon University, and Cliff Shaw from RAND Corporation. They collaborated on Chess AI from 1955 to 1958, culminating in "Chess Playing Programs and the Problem of Complexity"1 which both summarized existing Chess AI research and contributed new ideas that they tested with the NSS (Newell, Shaw, and Simon) Chess program. Just as Shannon noted that master players use intuition to think selectively about moves, Newell, Shaw and Simon considered heuristics to be an important aspect of human Chess-playing.

As a High School student Carlton had been withdrawn and quiet, unsocial and uninvolved. One of his teachers had been convinced that he was using drugs because he was so pale and tired. In reality, he had been up late into the night, designing, building and refining his electrically independent computer. He drew his own blood for it, leading to symptoms of anemia. His prototype was, in retrospect, an archaic fossil as soon as it was operational, but he won a National competition with it.

An MIT-altered robot backed with AI provided scheduling recommendations to labor ward nurses at Beth Israel Deaconess Medical Center. See how it turned out. MIT researchers were recently able to demonstrate that the could train an altered Nao robot to learn the ins and outs of room scheduling in a labor ward at Beth Israel Deaconess Medical Center in Boston. Nurses accepted the robot's recommendations 90% of the time. Just to make sure the nurses weren't blindly accepting the robot's advice, the team also had the robot provide consciously bad feedback--which was also rejected at a 90% rate.

One of the surprise products of last week's Computex IT show in Taiwan was the Zenbo robot from Asus. The cute, two-wheeled, home-help robot will read stories to kids, summon help for seniors in an emergency and blast out songs while twirling around the floor to the music. But Asus is better known as a manufacturer of smartphones and laptop PCs. Home-help robots have been tried and failed before, so why did the company decide the time is right? IDG News Service spoke with Jonney Shih, chairman of Asus and the power behind much of the company's product planning and design, to find out more about Zenbo. Shih said he saw the robot as an evolution in computing -- something that followed on from the PC era, mobile computing, and the recent so-called Internet of Things.

Google acquired Kifi, an app for collecting links from across the Internet, which teams could then collaborate on. No financial terms of the deal were disclosed. The Kifi service and data will not become part of Google. The service will remain fully functional for existing users for a few more weeks, before its shut down. The company is no longer accepting new registrations. The team at Kifi will be joining the Spaces team at Google.

Editor's note: Dallas police actively guided an explosive-laden robot to kill gunman Micah Johnson when negotiations broke down after he opened fire on police and others July 7. This interview on Think -- which was recorded before the ambush took place -- focuses on the questions raised by computers and robots programmed to kill without human supervision. You may have seen it earlier this year when a company called Boston Dynamics posted a video of a humanoid robot that can walk on two legs, even over uneven terrain. Seeing a machine balance on bumpy, snowy ground in a New England forest is mesmerizing. But watching the robot get knocked to the ground by a human tester, then get up all by itself and keep going is somehow profoundly unsettling.

Chinese Premier Li Keqiang called on Saturday for greater global cooperation against terrorism, state media said, as the Asian giant seeks greater international support for its anti-terror fight. Speaking at an Asia-Europe summit, Li said various security challenges - conventional and unconventional - remain prominent even though those regions had remained generally stable and peaceful. "Acts of terrorism are common challenges faced by every nation. Countries should work more closely to fight terrorism, and build societies that are truly open and tolerant so to root out the soil where it grows," said Li. China has sought Western support for its own "war on terror" since the attacks in Paris last November.

One of the most remarkable details of a fatal collision earlier this month involving a tractor trailer and a Tesla electric car operating in self-driving mode was the fact that the car apparently mistook the side of the truck for the sky. As Tesla explained in a public statement following the accidental death, the car's autopilot was unable to see "the white side of the tractor trailer against a brightly lit sky"--which is to say, it was unable to differentiate the two. The truck was not perceived as a discrete object, in other words, but as something indistinguishable from the larger spatial environment. It was more like an elision, a continuation of the sky by deceptive means. Examples like this are tragic, to be sure, but they are also technologically interesting, in that they give momentary glimpses of where robotic perception has failed.