Anyone interested in the future of design should have a look at the new Batmobile. I was lucky enough to be photographed alongside it at the London Film and Comic Convention earlier this year, and my inner geek was impressed. Built like a tank and armour-plated, with twin machine guns mounted on a bat-black body, it's a seriously cool-looking piece of kit. But looks can be deceptive. The Batmobile certainly looked rough and tough, but it was roped off from the crowds so no one could get too close.

Emerging technologies and their possible implications for ethics, security, and even human existence have increasingly gained ground in the past two decades. Some innovations have resulted in obvious security and existential threats: a world with nuclear arms, for example. The potential of other technological shifts, however, has been more mixed. Biotechnologies, genetic engineering, and stems cells have given rise to controversial debates in which advocacy groups on both sides have convincingly put forward pros and cons. The Internet has revolutionized everything from markets to family communication in ways both beneficial and harmful.

Uplift modeling, also known as incremental modeling, true lift modeling, or net-lift modeling is a predictive modeling technique that directly models the incremental impact of a treatment (such as a direct marketing action) on an individual's behavior. Uplift modeling has applications in customer relationship management for up-sell, cross-sell and retention modeling. It has also been applied to personalized medicine. Unlike the related Differential Prediction concept in psychology, Uplift modeling assumes an active agent. All of your marketing effort are about Return on Investment (ROI), ultimately, unless you are a non-profit.

"We are absolutely certain that the use of deep-learning machines is going to allow us to have a better understanding of ISIS as a network, and a better understanding of how we can target it precisely and lead to its defeat," Work said March 30 at an event hosted by The Washington Post. Work said he recently met with a firm in Silicon Valley that can crunch vast amounts of social media data to deliver insights. The firm used its analytics capability to recount in real time how a Malaysia Airlines flight was shot down, according to Work. An official investigation concluded that a Russian Buk missile downed the airplane over Ukraine on July 17, 2014, killing 298 people. Courtney Hillson, told FCW the company he referred to is Orbital Insight, a geospatial data firm.

Change detection (CD) in time series data is a critical problem as it reveal changes in the underlying generative processes driving the time series. Despite having received significant attention, one important unexplored aspect is how to efficiently utilize additional correlated information to improve the detection and the understanding of changepoints. We propose hierarchical quickest change detection (HQCD), a framework that formalizes the process of incorporating additional correlated sources for early changepoint detection. The core ideas behind HQCD are rooted in the theory of quickest detection and HQCD can be regarded as its novel generalization to a hierarchical setting. The sources are classified into targets and surrogates, and HQCD leverages this structure to systematically assimilate observed data to update changepoint statistics across layers. The decision on actual changepoints are provided by minimizing the delay while still maintaining reliability bounds. In addition, HQCD also uncovers interesting relations between changes at targets from changes across surrogates. We validate HQCD for reliability and performance against several state-of-the-art methods for both synthetic dataset (known changepoints) and several real-life examples (unknown changepoints). Our experiments indicate that we gain significant robustness without loss of detection delay through HQCD. Our real-life experiments also showcase the usefulness of the hierarchical setting by connecting the surrogate sources (such as Twitter chatter) to target sources (such as Employment related protests that ultimately lead to major uprisings).

Lawrence Livermore's new supercomputer system uses 16 IBM TrueNorth chips developed by IBM Research (credit: IBM Research) Lawrence Livermore National Laboratory (LLNL) has purchased IBM Research's supercomputing platform for deep-learning inference, based on 16 IBM TrueNorth neurosynaptic computer chips, to explore deep learning algorithms. IBM says the scalable platform processing power is the equivalent of 16 million artificial "neurons" and 4 billion "synapses." The brain-like neural-network design of the IBM Neuromorphic System can process complex cognitive tasks such as pattern recognition and integrated sensory processing far more efficiently than conventional chips, says IBM. The technology represents a fundamental departure from computer design that has been prevalent for the past 70 years and could be incorporated in next-generation supercomputers able to perform at exascale speeds -- 50 times faster than today's most advanced petaflop (quadrillion floating point operations per second) systems. The TrueNorth processor chip was introduced in 2014 (see IBM launches functioning brain-inspired chip).

Donald Trump's speeches are nothing like that, according to Brad Hayes, a MIT scientist who programmed a Twitter bot to sound like him. Called DeepDrumpf, it uses an artificial intelligence algorithm based on Trump's language in hundreds of hours of debate transcripts. Hayes told Tech Insider he has learned a lot over the last few weeks about how Trump talks. Here is how he describes Trump's language, which differs dramatically from past presidential candidates. Trump often uses short, imperative sentences, Hayes says.

The Pentagon's No. 2 civilian official said Wednesday that the Defense Department is concerned that adversary nations could empower advanced weapons systems to act on their own, noting that while the United States will not give them the authority to kill autonomously, other countries might. Deputy Defense Secretary Robert O. Work said the Pentagon hasn't "fully figured out" the issue of autonomous machines, but continues to examine it. The U.S. military has built a force that relies heavily on the decision-making skills of its troops, but "authoritarian regimes" may find weapons that can act independently more attractive because doing so would consolidate the ability to take action among a handful of leaders, he said. "We will not delegate lethal authority to a machine to make a decision," Work said. "The only time we will… delegate a machine authority is in things that go faster than human reaction time, like cyber or electronic warfare."

The Pentagon's No. 2 civilian official said Wednesday that the Defense Department is concerned that adversary nations could empower advanced weapons systems to act on their own, noting that while the United States will not give them the authority to kill autonomously, other countries might. Deputy Defense Secretary Robert Work said the Pentagon hasn't "fully figured out" the issue of autonomous machines, but continues to examine it. The U.S. military has built a force that relies heavily on the decision-making skills of its troops, but "authoritarian regimes" may find weapons that can act independently more attractive because it consolidates the ability to take action among a handful of leaders, he said. "We will not delegate lethal authority to a machine to make a decision," Work said. "The only time we will ... delegate a machine authority is in things that go faster than human reaction time, like cyber or electronic warfare."

When TOPIO 1.0, a bipedal humanoid robot that plays table tennis against a human being, was first displayed in 2007, the news hardly received global acclaim. Artificial intelligence (AI) developments, which refer to intelligent computers and machines (equaling or surpassing human intelligence), have become increasingly widespread and commonplace. AI benefits our daily existence even in the most mundane aspects of life, and its uses are tested and applied in a variety of sectors--from medicine and healthcare to the economy, diplomacy, and war. Uses and applications of AI are extremely diverse: they range from online search engines to robotics and security. AI is the basis of unmanned vehicles (which are frequently employed by the United States abroad), space applications, and finance, where AI-based algorithms can quickly find optimal solutions or process data with speed and accuracy.