A shortage of humans to fight cybersecurity battles is causing companies to turn to machines. As we reported last year, a large skills gap is causing hiring difficulties in the cybersecurity industry. According to the Information Systems Audit and Control Association, less than one in four candidates who apply for such positions is qualified. The organization predicts that the lack of qualified applicants will lead to a global shortage of two million cybersecurity professionals by 2019. On the other hand, criminals can commandeer thousands of computers to form a botnet that can then be used to launch attacks.

Ryan Hemphill is an attorney and private equity and venture capital executive based in New York City. Ryan also is the founder anc CEO of The Open Road Foundation, a nonprofit corporation serving wounded U.S. veterans and their families with employment and education services through partnerships in the automotive sector. As part of their latest endeavor to improve care for our country's combat vets, the Department of Veterans Affairs has invested in a rapidly advancing form of intelligence: the artificial kind. AI has been hailed by most forecasters as a revolutionary force in all manner of fields, from transportation to predicting the weather, and this exciting wave of possibility promises to transform the healthcare sphere, as well. The opportunity to use this growing tech to improve veteran healthcare has rightfully attracted positive attention to some intriguing new initiatives.

The deputy director of a large county human services agency, she's been wrestling all week with staff turnover and media coverage about long wait times for services. Heading home on Friday evening, she worries that she might spend the rest of her career playing defense at work. After a Saturday morning of chauffeuring her kids to soccer games and music lessons, Natalie collapses on the couch. She relaxes to music from one of her favorite radio stations, wondering how Pandora always manages to serve up exactly the songs that fit her mood. After she's had a chance to unwind, Siri gives her the week's top headlines, reminds her that her niece's graduation is coming up, recommends a gift for the niece, and, when Natalie confirms the choice, places an order. Later, Natalie's fitness band reminds her that it's time to head to the gym for a session with her trainer. On the way to the gym, Waze alerts her to an accident ahead and automatically routes her around it.

Roman Trusov is a researcher at XIX.ai. Recent studies by Google Brain have shown that any machine learning classifier can be tricked to give incorrect predictions, and with a little bit of skill, you can get them to give pretty much any result you want. This fact steadily becomes worrisome as more and more systems are powered by artificial intelligence -- and many of them are crucial for our safe and comfortable life. Lately, safety concerns about AI were revolving around ethics -- today we are going to talk about more pressuring and real issues. Machine learning algorithms accept inputs as numeric vectors.

The Whitney embedding theorem gives an upper bound on the smallest embedding dimension of a manifold. If a data set lies on a manifold, a random projection into this reduced dimension will retain the manifold structure. Here we present an algorithm to find a projection that distorts the data as little as possible.

Modern neural networks are often augmented with an attention mechanism, which tells the network where to focus within the input. We propose in this paper a new framework for sparse and structured attention, building upon a smoothed max operator. We show that the gradient of this operator defines a mapping from real values to probabilities, suitable as an attention mechanism. Our framework includes softmax and a slight generalization of the recently-proposed sparsemax as special cases. However, we also show how our framework can incorporate modern structured penalties, resulting in more interpretable attention mechanisms, that focus on entire segments or groups of an input. We derive efficient algorithms to compute the forward and backward passes of our attention mechanisms, enabling their use in a neural network trained with backpropagation. To showcase their potential as a drop-in replacement for existing ones, we evaluate our attention mechanisms on three large-scale tasks: textual entailment, machine translation, and sentence summarization. Our attention mechanisms improve interpretability without sacrificing performance; notably, on textual entailment and summarization, we outperform the standard attention mechanisms based on softmax and sparsemax.

Policy analysts wish to visualize a range of policies for large simulator-defined Markov Decision Processes (MDPs). One visualization approach is to invoke the simulator to generate on-policy trajectories and then visualize those trajectories. When the simulator is expensive, this is not practical, and some method is required for generating trajectories for new policies without invoking the simulator. The method of Model-Free Monte Carlo (MFMC) can do this by stitching together state transitions for a new policy based on previously-sampled trajectories from other policies. This "off-policy Monte Carlo simulation" method works well when the state space has low dimension but fails as the dimension grows. This paper describes a method for factoring out some of the state and action variables so that MFMC can work in high-dimensional MDPs. The new method, MFMCi, is evaluated on a very challenging wildfire management MDP.

This week on "Off the Shelf", Brian Abbe, senior vice president, Troy Abbott and Eric Billies, principals from Booz Allen Hamilton discuss the government's growing use of unmanned systems. Beyond drones, the Booz Allen Hamilton team discuss emerging trends in robotics, marine systems, autonomous munitions and Artificial Intelligence (AI). The Booz Allen Hamilton team also identify and discuss key challenges in the market including cyber, and the technological and operation hurdles moving from "unmanned systems" to autonomous systems. Finally, the team provides their insights on current, rapid pace of technological innovation and what it means for the federal government. Sponsored Survey: Agency managers view cost, security and analytics as criteria for adopting new data storage technologies.

Eric Schmidt, the executive chairman of Google parent company Alphabet, has warned that China is poised to overtake the US in the field of artificial intelligence (AI) if the US government doesn't act soon. Speaking at the Artificial Intelligence and Global Security Summit on Wednesday, the former Google CEO said: "Trust me, these Chinese people are good." He added: "They are going to use this technology for both commercial as well as military objectives with all sorts of implications." China published its AI strategy in July and said that it wanted to be the world leader in AI by 2030. "It's pretty simple," said Schmidt, who claims to have read the report.

An estimated 1 million words (War & Peace twice over!) have been written speculating the much-anticipated outcome. Won't these new AI headquarters spawn the next 10 Amazons? To find these cities, don't just follow the city infrastructure, tax breaks, big companies, or nice weather. Go where the 50,000 AI engineers/scientists are today and where they are going tomorrow -- especially the 10,000 specialized researchers. Recruiting a top AI researcher is like recruiting an NFL quarterback: 8-figure, multi-year contracts, the whole nine yards.