This article proposes a new grand challenge for AI reasearch: to develop AI system to make major scientific discoveries in biomedical sciences that worth Nobel Prize. There are a series of human cognitive limitations that prevents us from making accerlated scientific discoveries, particularity in biomedical sciences. As a result, scientific discoveries are left behind at the level of cottage industry. AI systems can transform scientific discoveries into highly efficient practice, thereby enable us to expand our knowledge in unprecedented way. Such system may out-compute all possible hypotheses and may redefine the nature of scientific intuition, hence scientific discovery process.

Within the field, the test is widely recognized as a pioneering landmark, but also is now seen as a distraction, designed over half a century ago, and too crude to really measure intelligence. Intelligence is, after all, a multidimensional variable, and no one test could possibly ever be definitive truly to measure it. Moreover, the original test, at least in its standard implementations, has turned out to be highly gameable, arguably an exercise in deception rather than a true measure of anything especially correlated with intelligence. The much ballyhooed 2015 Turing test winner Eugene Goostman, for instance, pretends to be a thirteen-year-old foreigner and proceeds mainly by ducking questions and returning canned one-liners; it cannot see, it cannot think, and it is certainly a long way from genuine artificial general intelligence.

This week, in Geneva, Indian diplomats are closely monitoring an international expert review of the legal implications of the so-called "lethal autonomous weapons". These weapons will have the capability of selecting and engaging targets on their own. Although fully autonomous weapons are yet to register significant presence in the arsenal of any nation, many consider their development and deployment inevitable in the coming years. Rapid advances in robotics, machine-learning and big-data analytics are at once driving the so-called "fourth industrial revolution" and the transformation of modern warfare. How the leading powers mobilise and deploy these technologies will shape the balance of economic and military power among them in the coming decades.

To the computer scientist Leslie Valiant, "machine learning" is redundant. In his opinion, a toddler fumbling with a rubber ball and a deep-learning network classifying cat photos are both learning; calling the latter system a "machine" is a distinction without a difference. Valiant, a computer scientist at Harvard University, is hardly the only scientist to assume a fundamental equivalence between the capabilities of brains and computers. But he was one of the first to formalize what that relationship might look like in practice: In 1984, his "probably approximately correct" (PAC) model mathematically defined the conditions under which a mechanistic system could be said to "learn" information. Valiant won the A.M. Turing Award -- often called the Nobel Prize of computing -- for this contribution, which helped spawn the field of computational learning theory. In a 2013 book, also entitled "Probably Approximately Correct," Valiant generalized his PAC learning framework to encompass biological evolution as well.

"The robots are coming" is not something Paul Revere said during the American Revolution, but it is certainly something many people have uttered over the years. So have we finally reached the tipping point where artificial intelligence and robots will begin to take over human jobs en masse? Perhaps not, but we are closer to the time when they will be even more essential assets and presences in the workforce, explains Martin Ford, the author of the book "Rise of the Robots." I caught up with Ford at The Economist magazine's Innovation Forum event, which was held earlier this month. He pointed out that artificial intelligence is making its way into sectors that were once manned by only man, including the legal profession, where computer systems such as Watson could muscle in on human territory to provide legal counsel, and even journalism where stories are being written without direct human input about some articles.

We caught up with Matt Taylor from Numenta -- an organization whose mission is to lead a new era of machine intelligence and build computer systems around the principles of the brain. Matt shared his thoughts and insights on the open source community around their exciting projects. Find out what he says, and check out the Numenta community channel on Gitter. Tell us about a little bit about yourself and the Numenta community. How did it all begin?

Artificial intelligence has gone through some dismal periods, which those in the field gloomily refer to as "AI winters." This is not one of those times; in fact, AI is so hot right now that tech giants like Google, Facebook, Apple, Baidu, and Microsoft are battling for the leading minds in the field. The current excitement about AI stems, in great part, from groundbreaking advances involving what are known as "convolutional neural networks." This machine learning technique promises dramatic improvements in things like computer vision, speech recognition, and natural language processing. You probably have heard of it by its more layperson-friendly name: "Deep Learning." Few people have been more closely associated with Deep Learning than Yann LeCun, 54. Working as a Bell Labs researcher during the late 1980s, LeCun developed the convolutional network technique and showed how it could be used to significantly improve handwriting recognition; many of the checks written in the United States are now processed with his approach. Between the mid-1990s and the late 2000s, when neural networks had fallen out of favor, LeCun was one of a handful of scientists who persevered with them. He became a professor at New York University in 2003, and has since spearheaded many other Deep Learning advances. More recently, Deep Learning and its related fields grew to become one of the most active areas in computer research. Which is one reason that at the end of 2013, LeCun was appointed head of the newly-created Artificial Intelligence Research Lab at Facebook, though he continues with his NYU duties. LeCun was born in France, and retains from his native country a sense of the importance of the role of the "public intellectual." He writes and speaks frequently in his technical areas, of course, but is also not afraid to opine outside his field, including about current events. IEEE Spectrum contributor Lee Gomes spoke with LeCun at his Facebook office in New York City. The following has been edited and condensed for clarity. IEEE Spectrum: We read about Deep Learning in the news a lot these days.

William Hertling is the author of Avogadro Corp, A.I. Apocalypse, The Last Firewall, The Turing Exception, and the upcoming Kill Process. These near-term science-fiction novels explore the emergence of artificial intelligence (AI), the coexistence of humans and smart machines, and the impact of social reputation, technological unemployment, and other near-future issues. His novels have been called "frighteningly plausible," "tremendous," and "must-read." Hertling's Singularity Series novels have been endorsed by and received wide attention from tech luminaries including Harper Reed (CTO for the Obama Campaign), Ben Huh (CEO Cheezburger), and Chris Anderson (CEO 3DRobotics, former Editor-in-Chief Wired). His first novel for children, The Case of the Wilted Broccoli, was published in 2014. Hertling grew up a digital native in the early days of bulletin board systems. His first experiences with net culture occurred when he wired seven phone lines into the back of his Apple IIe and hosted an online chat system. A frequent speaker on the future of technology, science fiction, and indie publishing, Hertling has spoken at SXSW Interactive, Defrag, OryCon, University of Colorado, Willamette Writers Conference, and many other conferences. Did you start off wanting to become a writer, or did you stumble into it? WH: I very much stumbled into it, although, in retrospect, there were a few hints ahead of time.

Jeffrey Hawkins is the American founder of Palm Computing (where he invented the Palm Pilot) and Handspring (where he invented the Treo). He has since turned to work on neuroscience full-time, founded the Redwood Center for Theoretical Neuroscience (formerly the Redwood Neuroscience Institute) in 2002, founded Numenta in 2005 and published On Intelligence describing his memory-prediction framework theory of the brain. In 2003 he was elected as a member of the National Academy of Engineering "for the creation of the hand-held computing paradigm and the creation of the first commercially successful example of a hand-held computing device." Hawkins also serves on the Advisory Board of the Secular Coalition for America and offers advice to the coalition on the acceptance and inclusion of nontheism in American life. Andrew Yan-Tak Ng is Chief Scientist at Baidu Research in Silicon Valley.

AI was very popular 30 years ago, then disappeared, and is now making a big come back because of new robotic technologies: driver-less cars, automated diagnostic, IoT (including vacuum cleaning and other household robots), automated companies with zero employee, soldier robots, and much more. Will AI replace data scientists? I think so, though data scientists will be initially replaced by "low intelligence" yet extremely stable and robust systems. There has been a lot of discussions about the automated statistician. I am myself developing data science techniques such as Jackknife regression that are simple, robust, suitable for black-box, machine-to-machine communications or other automated use, and easy to understand and pilot by the layman, just like a Google driver-less car can be "driven" by an 8 years old kid.