Perhaps the coolest thing about IBM's 9th "Five Innovations that will Help Change our Lives within Five Years" predictions is that none of them sound like science fiction. "With advances in artificial intelligence and nanotechnology, we aim to invent a new generation of scientific instruments that will make the complex invisible systems in our world today visible over the next five years," said Dario Gil, vice president of science & solutions at IBM Research in a statement. Among the five areas IBM sees as being key in the next five years include artificial intelligence, hyperimaging and small sensors. In five years, what we say and write will be used as indicators of our mental health and physical wellbeing. Patterns in our speech and writing analyzed by new cognitive systems will provide tell-tale signs of early-stage mental and neurological diseases that can help doctors and patients better predict, monitor and track these diseases.

The Turing award, in existence since 1966, comes with a $250,000 prize funded by Google and Intel. Last year's award went to Leslie Valiant, a Harvard University computer scientist. One past winner, Internet pioneer Vinton Cerf, says Pearl's accomplishments have "redefined the term'thinking machine'" over the past 30 years. Pearl's efforts have had "a pervasive influence not only on machine learning but on natural language processing, computer vision, robotics, computational biology, econometrics, cognitive science and statistics," Cerf said in a statement. The UCLA computer science professor is widely credited with coining the term "Bayesian Network," which refers to a statistical model ACM describes as mimicking "the neural activities of the human brain, constantly exchanging messages without benefit of a supervisor."

For example, NASA and General Motors built the 300 pound Robonaut2 - or R2 - a robot that is capable of using the same tools as humans and now works alongside them in space onboard the International Space Station. R2 can use its hands to do work beyond the scope of prior humanoid machines and can easily work safely alongside people, a necessity both on Earth and in space, NASA stated. It is also stronger: able to lift, not just hold, a 20-pound weight (about four times heavier than what other dexterous robots can handle) both near and away from its body. Granted the robot takes up valuable space station space, but it doesn't have to be fed or go to the bathroom - major advantages in space. Other robots such as the Octoroach being developed by UC Berkeley researchers can crawl into all manner of super-secret surveillance or emergency recovery applications that the human body just could not.

Perhaps the coolest thing about IBM's 9th "Five Innovations that will Help Change our Lives within Five Years" predictions is that none of them sound like science fiction. "With advances in artificial intelligence and nanotechnology, we aim to invent a new generation of scientific instruments that will make the complex invisible systems in our world today visible over the next five years," said Dario Gil, vice president of science & solutions at IBM Research in a statement. More on Network World: IBM says soon you won't need passwords; mind reading will be routine; the so-called digital divide will cease to exist and junk mail will become important Among the five areas IBM sees as being key in the next five years include artificial intelligence, hyperimaging and small sensors. In five years, what we say and write will be used as indicators of our mental health and physical wellbeing. Patterns in our speech and writing analyzed by new cognitive systems will provide tell-tale signs of early-stage mental and neurological diseases that can help doctors and patients better predict, monitor and track these diseases.

With automation and IT working together to take over routine tasks, IT workers can devote more time to innovation. Don't forget, though, that adding automation to the mix doesn't mean that IT and automation work in silos. As KPMG told Network World, the co-existence between human employees and cognitive systems is creating a new class of digital labor that can enhance human skills and expertise, allowing employees to innovate constantly. Before diving into what this means to IT, let's examine the underlying concepts. Machine learning is a branch of artificial intelligence (AI) that uses data, algorithms, and known outcomes to build systems that learn and adapt without human input.

During Black Friday and Cyber Monday 2016, consumers should watch out for scams that come through spam, insecure public networks and apps that might seem legitimate but could be taking over your phones and computers, experts say. Here are a dozen steps you can take to avoid becoming a victim. This story, "12 tips for safer Black Friday and Cyber Monday shopping" was originally published by Network World. Tim Greene covers security and keeps an eye on Microsoft for Network World.

With automation and IT working together to take over routine tasks, IT workers can devote more time to innovation. Don't forget, though, that adding automation to the mix doesn't mean that IT and automation work in silos. As KPMG told Network World, the co-existence between human employees and cognitive systems is creating a new class of digital labor that can enhance human skills and expertise, allowing employees to innovate constantly. Before diving into what this means to IT, let's examine the underlying concepts. Machine learning is a branch of artificial intelligence (AI) that uses data, algorithms, and known outcomes to build systems that learn and adapt without human input.

Artificial intelligence, machine learning and advanced algorithms are at the heart of an emerging digital world. That was one of the chiefs components of Gartner's Peter Sondergaard, senior vice president and global head of Research opening remarks at today's Gartner Symposium/ITxpo show in Orlando. More on Network World: Will future developments in the realm of Artificial Intelligence be like the wild west or a more controlled situation? "CIOs will participate in the building of a new digital platform with intelligence at the center," Sondergaard said told a crowd of more than 8,000 CIOs and IT leaders. "The new competitive differentiator is understanding the customer's intent through advanced algorithms and artificial intelligence. Creating new experiences that solve problems customers didn't realize they had."

Can the development of artificial intelligence technology be kicked up a notch? Scientists at Intelligence Advanced Research Projects Activity (IARPA) certainly hope so and recently issued a Request For Information about how AI advances could be made more quickly and consistently. "Artificial intelligence, defined here as computer simulation of cognitive processes such as perception, recognition, reasoning, and control, have captured the public's imagination for over 60 years. However, artificial intelligence research has proceeded in fits and starts over much of that time, as the field repeats a boom/bust cycle characterized by promising bursts of progress followed by inflated expectations and finally disillusionment, leading to what has become known as an "AI winter" – a long period of diminished research and funding activity," IARPA wrote. IARPA is the high-risk, high-reward research arm of the Office of the Director of National Intelligence.

What are the fundamental limitations inherent in machine learning systems? That's the central question of a potential new DARPA program known as the Fundamental Limits of Learning (Fun LoL) which according to the researchers will address how the quest for the ultimate learning machine can be measured and tracked in a systematic and principled way. "It's not easy to put the intelligence in artificial intelligence. Current machine learning techniques generally rely on huge amounts of training data, vast computational resources, and a time-consuming trial and error methodology. Even then, the process typically results in learned concepts that aren't easily generalized to solve related problems or that can't be leveraged to learn more complex concepts. The process of advancing machine learning could no doubt go more efficiently--but how much so? To date, very little is known about the limits of what could be achieved for a given learning problem or even how such limits might be determined," DARPA stated.