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.

There are two things companies need to be careful of, whether they are large or small. The first is to avoid any pursuit of unrealistic products that require futuristic technologies. The second is a misinterpretation of the market demand, or the quasi-demand. It means the market actually does not need the products the company thinks are necessary. So, what profound changes may we expect AI to bring to people's life in the short run?

The National Oceanic and Atmospheric Administration is predicting an above-normal 2017 Hurricane Season, with five to nine hurricanes -- two to four of them Category 3 (winds at least 111 mph) or stronger. The weakness or absence of storm-suppressing El Niño climate conditions, combined with above-normal ocean surface temperatures and average or weaker vertical wind shear across the Caribbean and Atlantic Coast are factors pointing to an active hurricane season, said Ben Friedman, acting NOAA administrator. They expect that three named storms will make landfall in the U.S. April's Tropical Storm Arlene was a rare preseason storm, but it was also an indication of an active season ahead, Friedman said Thursday during a news conference at the NOAA Center for Weather and Climate Prediction in College Park, Md. In the 25 years since Category 5 Hurricane Andrew hit South Florida, forecasting accuracy has improved 65%, said Mary Erickson, deputy director at the National Weather Service.

Goldstaub, aged 31, is the co-founder of CognitionX, which aims to bring clarity to the fast-paced world of artificial intelligence (AI). "We're like a Ghostbusters hotline," she says. "When you feel lost and wonder who you're going to call we have researchers who can answer big questions. AI will be the next industrial revolution, it's happening quicker than anything we have ever seen. This month's NHS hack is just the latest reminder of how fast technology is developing and Goldstaub says it illustrates the need for widespread education and "collaboration beyond the tech sector, with government, academia and business talking about moving forwards with new tools". Sitting in an eye-poppingly pink chair at her company's HQ at the Wayra incubator in Piccadilly, where she has a staff of 22 and is "expanding at a rate of knots", Goldstaub explains how AI has the potential to detect cancer earlier than humans can, trade stocks, increase energy efficiency, predict human rights trials and make fairer decisions in courts. Already, 76 per cent of companies use some form of AI and it could add $814 billion to the UK economy by 2035, increasing productivity by up to 40 per cent, according to a report by Accenture. So we'd better get it right. Goldstaub compares AI to a child, learning from what it hears. "If you train AI based on racist, sexist information, you can't be surprised that it becomes racist and sexist." She cites the example of the chatbot Tay, which was trained on Twitter and in less than 24 hours was making offensive remarks. "You'll have AI making decisions about whether you can get loans, about car insurance.

The sheer number of cyber-attacks and threats present in today's world is considerable. As the number of threats we face grows at an exponential rate it has become harder for cyber experts to keep up. According to the Verizon Data Report, more than seventy percent of attacks exploit known vulnerabilities with available patches. Once the vulnerabilities become known to the hackers they take advantage of them within minutes. With the number of threats growing, costing an average of $445 billion worldwide and the industry facing a shortage of 1.5 million experts, it is taking longer to detect an mitigate attacks.

Objectives: Discussions of fairness in criminal justice risk assessments typically lack conceptual precision. Rhetoric too often substitutes for careful analysis. In this paper, we seek to clarify the tradeoffs between different kinds of fairness and between fairness and accuracy. Methods: We draw on the existing literatures in criminology, computer science and statistics to provide an integrated examination of fairness and accuracy in criminal justice risk assessments. We also provide an empirical illustration using data from arraignments. Results: We show that there are at least six kinds of fairness, some of which are incompatible with one another and with accuracy. Conclusions: Except in trivial cases, it is impossible to maximize accuracy and fairness at the same time, and impossible simultaneously to satisfy all kinds of fairness. In practice, a major complication is different base rates across different legally protected groups. There is a need to consider challenging tradeoffs.