Artificial intelligence is already in use across surveillance networks around the world. At high security sites like prisons, nuclear facilities or government agencies, it's commonplace for security systems to set up a number of rules-based alerts for their video analytics. So if an object on the screen (a person, or a car, for instance) crosses a designated part of the scene, an alert is passed on to the human operator. The operator surveys the footage, and works out if further action needs to be taken... BRS Labs' AISight is different because it doesn't rely on a human programmer to tell it what behaviour is suspicious. It learns that all by itself.

In about A.D. 1300 the Anasazi people abandoned Long House Valley. To this day the valley, though beautiful in its way, seems touched by desolation. It runs eight miles more or less north to south, on the Navajo reservation in northern Arizona, just west of the broad Black Mesa and half an hour's drive south of Monument Valley. To the west Long House Valley is bounded by gently sloping domes of pink sandstone; to the east are low cliffs of yellow-white sedimentary rock crowned with a mist of windblown juniper. The valley floor is riverless and almost perfectly flat, a sea of blue-gray sagebrush and greasewood in sandy reddish soil carried in by wind and water. Today the valley is home to a modest Navajo farm, a few head of cattle, several electrical transmission towers, and not much else. Yet it is not hard to imagine the vibrant farming district that this once was. The Anasazi used to cultivate the valley floor and build their settlements on low hills around the valley's perimeter. Remains of their settlements are easy to see, even today. Because the soil is sandy and the wind blows hard, not much stays buried, so if you leave the highway and walk along the edge of the valley (which, by the way, you can't do without a Navajo permit), you frequently happen upon shards of Anasazi pottery, which was eggshell-perfect and luminously painted. On the site of the valley's eponymous Long House--the largest of the ancient settlements--several ancient stone walls remain standing. Last year I visited the valley with two University of Arizona archaeologists, George Gumerman and Jeffrey Dean, who between them have studied the area for fifty or more years. Every time I picked up a pottery shard, they dated it at a glance. By now they and other archaeologists know a great deal about the Anasazi of Long House Valley: approximately how many lived here, where their dwellings were, how much water was available to them for farming, and even (though here more guesswork is involved) approximately how much corn each acre of farmland produced. They have built up a whole prehistoric account of the people and their land. But they still do not know what everyone would most like to know, which is what happened to the Anasazi around A.D. 1300. "Really, we've been sort of spinning our wheels in the last eight to ten years," Gumerman told me during the drive up to the valley. "Even though we were getting more data, we haven't been able to answer that question."

Google, Microsoft, and Yahoo have teamed up to encourage Web page operators to make the meaning of their pages understandable to search engines. The move may finally encourage widespread use of technology that makes online information as comprehensible to computers as it is to humans. If the effort works, the result will be not only better search results, but also a wave of other intelligent apps and services able to understand online information almost as well as we do. The three big Web companies launched the initiative, known as Schema.org, It defines an interconnected vocabulary of terms that can be added to the HTML markup of a Web page to communicate the meaning of concepts on the page.

When some of the world's most advanced rescue robots are foiled by nothing more complex than a doorknob, you get a good sense of the challenge of making our homes and workplaces more automated. At the DARPA Robotics Challenge, a contest held over the weekend in California, two dozen extremely sophisticated robots did their best to perform a series of tasks on an outdoor course, including turning a valve, climbing some steps, and opening a door (see "A Transformer Wins DARPA's $2 Million Robotics Challenge"). Although a couple of robots managed to complete the course, others grasped thin air, walked into walls, or simply toppled over as if overcome with the sheer impossibility of it all. At the same time, efforts by human controllers to help the robots through their tasks may offer clues as to how human-machine collaboration could be deployed in various other settings. "I think this is an opportunity for everybody to see how hard robotics really is," says Mark Raibert, founder of Boston Dynamics, now owned by Google, which produced an extremely sophisticated humanoid robot called Atlas (see "10 Breakthrough Technologies 2014: Agile Robots").

Will this year mark the first drone delivery, or the first time you encounter a robot at work or in your home? We saw significant advancements in robotics and artificial intelligence in 2015 (see "What Robots and AI Learned in 2015"). The world's largest economy has embarked on an audacious effort to fill its factories with advanced manufacturing robots. The government of China hopes this will help the country retain its vast manufacturing industry as workers' wages rise, and manufacturing becomes more efficient and technologically advanced around the world (see "China Wants to Replace Millions of Workers with Robots"). The project will require robots that are significantly more advanced and cost-efficient, and the economic and technological ripples could be felt around the world.

An open letter calling for a ban on lethal weapons controlled by artificially intelligent machines was signed last week by thousands of scientists and technologists, reflecting growing concern that swift progress in artificial intelligence could be harnessed to make killing machines more efficient, and less accountable, both on the battlefield and off. But experts are more divided on the issue of robot killing machines than you might expect. The letter, presented at the International Joint Conference on Artificial Intelligence in Buenos Aires, Argentina, was signed by many leading AI researchers as well as prominent scientists and entrepreneurs including Elon Musk, Stephen Hawking, and Steve Wozniak. "Artificial Intelligence (AI) technology has reached a point where the deployment of such systems is--practically if not legally--feasible within years not decades, and the stakes are high: autonomous weapons have been described as the third revolution in warfare, after gunpowder and nuclear arms." Rapid advances have indeed been made in artificial intelligence in recent years, especially within the field of machine learning, which involves teaching computers to recognize often complex or subtle patterns in large quantities of data.

Few people ever need to deal with a stricken nuclear reactor, but that skill could turn out to be important for the evolution of smarter robots. In Pomona, California, this week, 25 of the world's most advanced humanoid robots will take part in a contest inspired by the challenge of stabilizing a nuclear reactor that's leaking dangerous radioactive material. Teams from universities across the U.S., as well as Japan, China, and Europe, are bringing robots that will try to walk across piles of rubble, climb ladders, operate power tools, and drive buggies, among other chores. Each challenge is inspired by something that might have helped stabilize the Fukushima Daiichi nuclear plant in Japan after it was damaged by an earthquake in 2011. Considerable academic kudos will go to whichever team completes the most tasks within the allotted time by the end of the contest.

Former Enron executive Vincent Kaminski is a modest, semi-retired business school professor from Houston who recently wrote a 960-page book explaining the fundamentals of energy markets. His most lasting legacy, however, may involve thousands of e-mails he wrote more than a decade ago at the energy-services company. Kaminski, a former managing director for research who warned repeatedly about concerning practices he saw at Enron, is among more than 150 senior executives whose e-mail boxes were dumped onto the Internet by the Federal Energy Regulatory Commission (FERC) on March 26, 2003. In the name of serving the public's interest during its investigation of Enron, the federal agency made the controversial decision to post online more than 1.6 million e-mails that Enron executives sent and received from 2000 through 2002. FERC eventually culled the trove to remove the most sensitive and personal data, after receiving complaints (see PDF).

In a small, dark, room off a long hallway within a sprawling complex of buildings in Silicon Valley, an array of massive flat-panel displays and video cameras track Grit Denker's every move. Denker, a senior computer scientist at the nonprofit R&D institute SRI, is showing off Bright, an intelligent assistant that could someday know what information you need before you even ask. Initially, Bright is meant to cut down on the cognitive overload faced by workers in high-stress, data-intensive jobs like emergency response and network security. Bright may, for instance, aid network administrators in trying to stop the spread of a fast-moving virus by quickly providing crucial infection information, or help 911 operators send the right kind of assistance to the scene of an accident. But like many other technologies developed at SRI, such as the digital personal assistant Siri (now owned by Apple), Bright could eventually trickle down to laptops and smartphones.

Sentry duty is a tough assignment. Most of the time there's nothing to see, and when a threat does pop up, it can be hard to spot. In some military studies, humans are shown to detect only 47 percent of visible dangers. A project run by the Defense Advanced Research Projects Agency (DARPA) suggests that combining the abilities of human sentries with those of machine-vision systems could be a better way to identify danger. It also uses electroencephalography to identify spikes in brain activity that can correspond to subconscious recognition of an object.