In 2015, a poll of 200 senior corporate executives conducted by the National Robotics Education Foundation identified robotics as a major source of jobs for the United States. Indeed, some 81% of respondents agreed that robotics was the top area of job growth for the nation. Not that this should come as a surprise: as the demand for smart factories and automation increases, so does the need for robots. According to Nearshore Americas, smart factories are expected to add $500 billion to the global economy in 2017. In a survey conducted by technology consulting firm Capgemini, more than half of the respondents claimed to have invested $100 million or more into smart factory initiatives over the last five years.

Among the tasks you can train a computer to perform is scanning the skies over the U.S. for the alarming number of surveillance and spy aircraft. The news web site BuzzFeed did just that, reporting this week that it employed a machine-learning algorithm to first recognize known spy planes, and then combine that model with a large set of flight-tracking data from a commercial web site. The AI project mapped thousands of surveillance flights operated by federal agencies over a four-month period, including a military contractor tracking terrorists in Africa that is also flying surveillance aircraft over U.S. cities, BuzzFeed reported. Flightradar24 gathers data from a network of ground-based receivers supplemented by Federal Aviation Administration receivers. The ground radars sweep up a flight data transmitted by aircraft transponders, including unique identifiers for each plane.

A typical cybersecurity analyst is never short of work, a lot of which can be futile. According to a 2015 Ponemon Institute study, by the end of the year the average security operations center has spent around 20,000 hours just on chasing alerts that prove to be false alarms. Traditional security systems generate a lot of noise that needs to be waded through, which creates even more work. At the same time, a vast pool of security information is published across multiple media in natural languages that can't be quickly processed and leveraged by these systems. Cognitive security, or artificial intelligence, can "understand" natural language, and is a logical and necessary next step to take advantage of this increasingly massive corpus of intelligence that exists.

Artificial intelligence means many different things to many different people. One thing that is certain is that it is coming and with it, it brings both opportunities and threats. Understanding both is essential, because already, artificial intelligence is working its way into many aspects of our lives, from search engines and personal assistants, to algorithms monitoring and controlling everything from energy consumption to traffic. Simply put, artificial intelligence is a computer capable of exhibiting intelligence. This is done through processes such as learning and problem solving.

The Forest Service has a new message for Americans: Keep your drones out of their wildfires. Even as the Forest Service uses drones both to help prevent fires, by starting prescribed burns and on occasion to help battle flames, drones are emerging as a new fire threat. People fly them into fires to get pictures that they wouldn't otherwise be able to capture. The problem is, however, that capturing those images puts firefighters at even greater risk. And, if a drone hinders the firefighting process, that can cost valuable time.

August 12, 2017 Wishful Thinking Artificial intelligence means many different things to many different people. One thing that is certain is that it is coming and with it, it brings both opportunities and threats. Understanding both is essential, because already, artificial intelligence is working its way into many aspects of our lives, from search engines and personal assistants, to algorithms monitoring and controlling everything from energy consumption to traffic. Simply put, artificial intelligence is a computer capable of exhibiting intelligence. This is done through processes such as learning and problem solving.

On December 9th, 1968, a research project funded by the US Department of Defense launched a revolution. The focus was not a Cold War adversary or even a resource rich banana republic, but rather to "augment human intellect" and the man driving it was not a general, but a mild mannered engineer named Douglas Engelbart. His presentation that day would be so consequential that it is now called The Mother of All Demos. Two of those in attendance, Bob Taylor and Alan Kay would go on to develop Engelbart's ideas into the Alto, the first truly personal computer. Later, Steve Jobs would take many elements of the Alto to create the Macintosh.

A US technology firm has developed a drone that is able to aim and fire at enemies while flying in mid-air. The Tikad drone, developed by Duke Robotics, is armed with a machine-gun and a grenade launcher. The gun can be fired only by remote control, and is designed to reduce military casualties by cutting the number of ground troops required. But campaigners warn that in the wrong hands, it will make it easier to kill innocent people. The Tikad drone, available for private sale at an undisclosed price, has won a security innovation award from the US Department of Defense, and there is interest from several military forces around the world, including Israel, reports Defense One.

These are two examples of how NASA hopes to use artificial intelligence. As far-fetched as the concept sounds, the agency is already using AI in missions on both Earth and Mars. And there are other missions in the works that could see AI exploring icy moons in search of life. This bot-friendly future stands counter to some of the fuss in the press this past week, after Facebook shut down an experiment because two artificially intelligent bots began communicating in a shorthand language instead of English. Many in the media portrayed the bots as coming up with their own language.

Among the tasks you can train a computer to perform is scanning the skies over the U.S. for the alarming number of surveillance and spy aircraft. The news web site BuzzFeed did just that, reporting this week that it employed a machine-learning algorithm to first recognize known spy planes, and then combine that model with a large set of flight-tracking data from a commercial web site. The AI project mapped thousands of surveillance flights operated by federal agencies over a four-month period, including a military contractor tracking terrorists in Africa that is also flying surveillance aircraft over U.S. cities, BuzzFeed reported. Flightradar24 gathers data from a network of ground-based receivers supplemented by Federal Aviation Administration receivers. The ground radars sweep up a flight data transmitted by aircraft transponders, including unique identifiers for each plane. The aerial gumshoes then used an algorithm called Random Forest (referred to on Github as randomForest, Random Forests, random-forest and variations of those names).