It's one thing to track your heart rate, pulse or other movements with a smart watch or other consumer electronics, researchers say, but quite another to rely on the device to diagnose a disease. It's one thing to track your heart rate, pulse or other movements with a smart watch or other consumer electronics, researchers say, but quite another to rely on the device to diagnose a disease. With sensors that can collect data on body movements, heart rate, blood pressure and other metrics, the list of health trackers that go beyond activity trackers like Fitbits gets longer each year. "There's definitely an explosion of these things," says Dr. Joseph Kvedar, the vice president for connected health at Partners HealthCare in Boston, and an associate professor of dermatology at Harvard Medical School. Some of these devices will lead to a better health care system, Kvedar predicts, with cheaper, more efficient care.

Machine learning algorithms are already being used in a wide variety of applications – from smartphones to deep space. Indeed, NASA recently discovered a planet orbiting a star (much like our own sun) over 2,500 light years away by using Google's machine learning technology. The rapid rise of the Internet of Things (IoT) and big data applications are only serving to accelerate this new era of automation. While these are exciting times, it's important not to get carried away with all of the hype. AI is not going to result in mass job losses or, as some tech luminaries believe, kill us all.

Officials in Moscow, like many other city governments, are discussing how artificial intelligence technologies can help the city do its work better. But the Russian capital has perhaps the most pervasive vision for AI in government yet. "We want AI to work everywhere," Moscow's Chief Information Officer, Artem Ermolaev, tells GovInsider. His team is already trialling AI to advise doctors in treating cancer patients; help residents find new homes; and assess applications for government services and support. "The Moscow vision of the future is when artificial intelligence is equal to humans – or sometimes, artificial intelligence is higher than the human," he says.

Starting with the good news -- as you can see from the chart below, last year, at this time, 82% of VCs were either "very concerned" or "didn't feel compelled to do deals given uncertainty" and that has totally reversed with 62% being optimistic or bullish. Unsurprisingly, with VCs being more optimistic the number of VCs who planned to increased their investment activities doubled in 2017 while the number who planned to do fewer deals went down by nearly 2/3rds. And as VCs get more bullish on the investment environment and pick up the pace of their activities a natural result is the valuations tend to hold up, which bodes will for entrepreneurs raising capital in 2017. As you can see from the chart below, very few VCs are forecasting significant price drops (2%) versus a year ago when a large number were bracing for price drops (30%). We also asked VCs to weigh in on the areas of technology that most interested them in the coming five years and the overwhelming winner was in startups focused on machine learning/artificial intelligence (AI). And as a result of this interest, you can expect an increase of activity in investments in the space in the coming year, whereas many VCs thought AR / VR and Blockchain investments would be more ripe as opportunities in the 3–5 year time horizon. Since the change in government is still fresh on people's minds we asked what VCs thought about the Trump Administration. This was anonymously conducted after the election but a couple of weeks before the inauguration and the data were very clear -- VCs don't like Trump.

Netanyahu has also cautioned that Israel could act against Iran itself after an Iranian drone flew into Israel last month and an Israeli warplane was shot down while bombing air defenses in Syria. He has accused Iran of planning to build precision-guided missile factories in Lebanon, amid tensions along that border.

Of all the purported uses of Artificial Intelligence (AI), it would be hard to find one more controversial than its possible use for military purposes. In popular consciousness, the idea of military AI immediately brings to mind the notion of autonomous weapon systems or "killer robots", machines that can …

If residents of Brooklyn's Prospect Heights neighborhood have a feeling their streets have gotten safer over the past decade, while residents of the Hillcrest area of Washington, D.C., feel less secure on several of their blocks, artificial intelligence has now delivered proof that their hunches are correct. Using a new machine-learning algorithm they developed, researchers at Harvard and MIT combine photos from Google's Street View service with human perceptions of a street's safety to score whether a city block has improved or declined over time. The computer scientists and economists want their project, called Streetchange, to give urban planners artificial intelligence tools to help guide their thinking about evolving urban landscapes. Given the demographic trends researchers foresee, policymakers will need all the help they can get: By 2050, over two-thirds of the world's population is expected to live in urban areas. As that dramatic shift remakes society, it's more critical than ever for urban planners to understand the impact and implications of that transformation.

Of all the purported uses of Artificial Intelligence (AI), it would be hard to find one more controversial than its possible use for military purposes. In popular consciousness, the idea of military AI immediately brings to mind the notion of autonomous weapon systems or "killer robots", machines that can independently target and kill humans. The possible presence of such systems on battlefields has sparked a welcome international debate on the legality and morality of using these weapon systems. The controversies surrounding autonomous weapons, however, must not obscure the fact that like most technologies, AI has a number of non-lethal uses for militaries across the world, and especially for the Indian military. These are, on the whole, not as controversial as the use of AI for autonomous weapons, and, in fact, are far more practicable at the moment, with clear demonstrable benefits.

At his coming-out hearing as chairman of the Federal Reserve on Feb. 27, Jay Powell made all sorts of news in finance-land, including a suggestion that the bank saw potentially faster inflation ahead. Also notable was his assessment of the causes for the volatility that roiled Wall Street and saw trillions of dollars lost, gained, lost, and then regained in a matter of days in early February. In wonk speak, Powell remarked that he didn't think that ETFs--exchange-traded funds--were a particular culprit, though he conceded that the issue deserves further study. Powell's reassurance notwithstanding, it is at the least too soon to draw a conclusion. Too much has changed too quickly in the past few years to say with any confidence that we understand the interplay of humans and machines as applied to markets.

Originally published by Scientific American. Law enforcement's use of predictive analytics recently came under fire again. Dartmouth researchers made waves reporting that simple predictive models--as well as nonexpert humans--predict crime just as well as the leading proprietary analytics software. That the leading software achieves (only) human-level performance might not actually be a deadly blow, but a flurry of press from dozens of news outlets has quickly followed. In any case, even as this disclosure raises questions about one software tool's credibility, a more enduring, inherent quandary continues to plague predictive policing.