In 1905, an Ohio farmer survived a railroad accident that cost him both of his legs. Two years later, he founded the Ohio Willow Wood company, using the namesake timber to hand-carve prosthetic limbs. The company grew, surviving the Great Depression and a fire that destroyed the plant, and still thrives today in rural Ohio. Few who work there now might remember the curious footnote in the company's history that occurred during World War II, when the rebuilt factory was diversified to build parts for PT boats and B-17 bombers. Today, it is ironic to consider a company that specializes in prosthetic limbs building parts for the war machine that unfortunately increases demand on companies making prosthetic limbs.

Inequality goes back to the Stone Age. Thirty thousand years ago, bands of hunter-gatherers in Russia buried some members in sumptuous graves replete with thousands of ivory beads, bracelets, jewels and art objects, while other members had to settle for a bare hole in the ground. Nevertheless, ancient hunter-gatherer groups were still more egalitarian than any subsequent human society, because they had very little property. Property is a pre-requisite for long-term inequality. Following the agricultural revolution, property multiplied and with it inequality.

Mutants like the X-Men might be much more likely to emerge in a human society living on another planet than here are on Earth, because radiation can really mess with evolution. Space agencies and private companies like Elon Musk's SpaceX are trying to make space travel more sustainable, learn about how the human body responds to long-term microgravity conditions and isolation and figure out how to grow crops in outer space, among other exercises -- all as people set their sights on places like Mars to explore and colonize. But what will happen to us once we get there? It's possible the first settlers will evolve into a new human species entirely, given enough time. On Earth, that amount of time is somewhere between thousands and millions of years, according to anthropologist John Hawks, a professor at the University of Wisconsin-Madison.

President Donald Trump's recent executive order calling for a sweeping review of the H-1B visa program has raised alarm in STEM-related industries that rely heavily on an international supply of high-skilled labor. Harrison Brody is a PhD student in neuroscience at Yale University and a member of Yale Science Diplomats. Current policy for H-1B visas, which permit highly skilled foreigners to work in the US temporarily, prohibits employers from undercutting wages or favoring foreign workers over Americans. But the president, along with a sizable bipartisan contingent, claims that the program has enabled private employers, especially those in the tech industry, to flood the labor market and provide temporary training for workers who eventually set up shop abroad. These grievances may be legitimate, but the conversation has largely ignored another industry that depends on the H-1B visa program: academic scientific research.

Humanoid robots are more than just sci-fi novelties in South Korea, which holds the top spot for the world's highest robot density – beating out China, Japan, Germany and the United States. Robot density refers to the ratio of industrial robots to employees. To maintain its lead, the South Korean government has committed $450 million to robotics over the next five years. 'The Highs' is a series which looks at countries that rank the highest in certain categories – for better or worse.

US research firm International Data Corporation predicts that by 2020, businesses will spend over $100 billion to protect themselves from hacking, up from the estimated $74 billion budget last year. However, new technologies such as artificial intelligence and quantum computing can reportedly help prevent cyberattacks. Artificial intelligence, for instance, could enhance threat detection, shorten defense response time, and improve ways of distinguishing real efforts from those that can be ignored, the Financial Times noted. "Before artificial intelligence, we'd have to assume that a lot of the data - say 90 percent - is fine. We only would have bandwidth to analyze this 10 percent," Daniel Driver from UK defense group's Chemring Technology Solution said. IBM is also developing its own AI security platform called Watson.

We consider the problem of classifying data manifolds where each manifold represents invariances that are parameterized by continuous degrees of freedom. Conventional data augmentation methods rely upon sampling large numbers of training examples from these manifolds; instead, we propose an iterative algorithm called M_{CP} based upon a cutting-plane approach that efficiently solves a quadratic semi-infinite programming problem to find the maximum margin solution. We provide a proof of convergence as well as a polynomial bound on the number of iterations required for a desired tolerance in the objective function. The efficiency and performance of M_{CP} are demonstrated in high-dimensional simulations and on image manifolds generated from the ImageNet dataset. Our results indicate that M_{CP} is able to rapidly learn good classifiers and shows superior generalization performance compared with conventional maximum margin methods using data augmentation methods.

Security strategies need to undergo a radical evolution. Tomorrow's security devices will need to see and interoperate with each other to recognize changes in the networked environment, anticipate new risks and automatically update and enforce policies. The devices must be able to monitor and share critical information and synchronize responses to detected threats. One nascent technology that has been getting a lot of attention recently, lays the foundation for such an automated approach. It's called Intent-Based Network Security (IBNS).

A future with highways full of self-driving cars or robot friends that can actually hold a decent conversation may not be far away. That's because we're living in the middle of an "artificial intelligence boom" -- a time when machines are becoming more and more like the human brain. That's partly because of an emerging subcategory of AI called "deep learning." It's a process that's often trying to mimic the human brain's neocortex, which helps humans with language processing, sensory perception and other functions. Essentially, deep learning is when machines figure out how to recognize objects.

A disembodied voice sounded over a loudspeaker. Take cover," it warned to anyone within earshot. Then, the sirens began to wail. Erin Delaney assumed it was a drill. She peeked down the hallway to see how other people were responding. Then she hit the deck. The NATO base in Kabul where Delaney had been working for weeks was being attacked. Delaney, 24, had never had any military training. She grew up in San Diego, traveled up the coast for college at UC Berkeley, and spent the next two years nestled in the safe, Tesla-filled San Francisco bubble, working in the compliance department at Dropbox. Now, with her nose to the ground, she was getting a taste--however brief--of life in a war zone. She flipped over the visitor's badge she'd received when she first arrived at the base. In case of attack, it said, she should stay on the ground for two minutes. Assuming nothing dire happened, she was to shelter in place until the shelling stopped. So, for about an hour, that's what she did.