Before Chris White could help disrupt Jihadi finance networks, crush weapons markets, and bust up sex-slave rings with search tools that mine the dark Web, he first had to figure out how to stop himself from plummeting through the open gun door of a banking Black Hawk helicopter. White was on his way to a forward operating base outside Kabul headquarters, as part of a secret intelligence cell to help confront the Taliban and al-Qaida, smash their encrypted online money stream, and win over the hearts and minds of the Afghanistan population. Slight and lanky and 28, White felt Dukakis-ridiculous in his unwieldy body armor and bulbous helmet with "Dr. White" scrawled in marker on duct tape across the front, and with the dust from liftoff, he was finding it hard to breathe. He was still struggling with the unfamiliar seat straps when the pilot hit the stick, sending White sliding toward the hot square of the door and the desert 200 feet below. Down there, Afghanistan was a messy, dangerous place for pretty much everybody. After nearly a decade of U.S.-led war, the American body count had hit 1,000, and civilian casualties were beyond calculation, as President Obama's 30,000-troop surge intensified the fighting that spring. Many feared the situation was only going from bad to worse. The U.S. was escalating drone strikes across the border in Pakistan. And U.S. command was under assault after Gen. Stanley McChrystal, the surge's architect, found himself without a job after he and his staff made disparaging remarks about the commander in chief in some music magazine. It is hard to imagine that only a few weeks earlier, White had been just another impossibly young-looking Harvard postdoc in flip-flops looking forward to a Cambridge summer. Helicopter gunships and war zones weren't on the radar; there were lattes in the square and rock climbing, and on the other side of campus, a prestigious fellowship in the School of Engineering and Applied Sciences, where he was working at the intersection of big data, statistics, and machine learning. He had earned academic pole position and had every expectation it would continue that way forever -- becoming a professor, building a lab, and sniping out white papers from a tenured ivory tower.

For four years, eBay has been collecting customer search data, along with search click-through rates and other customer interaction data, and feeding the information into its machine learning system. In an interview with InformationWeek, Dan Fain, the company's VP of engineering, outlined the steps being taken, and the business motivation behind them. The use-cases are worth exploring for any IT professional looking to help improve a company's bottom line by applying machine learning to customer-facing applications. Search, customer click patterns, language translations, item recommendations, and image analysis are among the key ways machine learning is being put to use at eBay, according to Fain. The first, and most important, application of machine learning is to improve search functions on eBay.com, according to Fain. "We have several machine learning models working behind the scenes to ensure we get the best search results," said Fain in our interview.

From picking our favorite restaurants to predicting weather and correcting global food shortages, artificial intelligence is already augmenting everyday life. Firmly rooted in the realm of science fiction, artificial intelligence (AI) has often felt external – something happening out there. In reality, AI is a huge part of our everyday lives. We just don't recognize it. Bank alerts of suspected fraudulent charges, smartphone notifications to exercise, Siri or Cortana's ability to recognize voices – are all examples of AI. "Artificial intelligence is basically where machines make sense, learn, interface with the external world, without human beings having to specifically program it," said Nidhi Chappell, director of machine learning at Intel. AI improves lives in many other areas too.

Movidius' low-power SoCs will integrate with Intel's RealSense technology to bring computer vision processing to IoT, VR and other devices.

For thousands of years humans believed that authority came from the gods. Then, during the modern era, humanism gradually shifted authority from deities to people. Jean-Jacques Rousseau summed up this revolution in Emile, his 1762 treatise on education. When looking for the rules of conduct in life, Rousseau found them "in the depths of my heart, traced by nature in characters which nothing can efface. I need only consult myself with regard to what I wish to do; what I feel to be good is good, what I feel to be bad is bad." Humanist thinkers such as Rousseau convinced us that our own feelings and desires were the ultimate source of meaning, and that our free will was, therefore, the highest authority of all.

Imagine the police arresting a bot and releasing it after months of custody and investigation. This is not a scenario from a futurist's blog -- it actually happened in Switzerland last year. What were the charges against the globe-trotting Swiss bot and its owners? Its name gives you an idea: Random Darknet Shopper. Created by a couple who are both artists, RDS shopped in the wrong places and bought illegal goods on the dark web, also called the "darknet", "deep web," and "darknet markets."

One Toronto firm is using cognitive artificial intelligence (AI) processes to mine big data sets in social media to help asset management firms make better decisions for investors. Based in Toronto, Buzz Indexes claims that machine learning has evolved to the point where developing models to monitor and understand the context within the millions of posts and comments around stocks and investments made on online social media platform such as Twitter is a reality. The company initially launched its Buzz Social Media Insights Index this past spring and offers regular methodology and stock rebalancing updates, most recently this past month. According to Buzz Indexes founder Jamie Wise, the offering takes a big data approach to social media such as Twitter, aggregating chatter on investment opportunities to track potential actionable insights: "Each month we look at an index of the 100 most talked about stocks in the media landscape and the tone and depth of the conversation," he said. Specifically, the firm reviews social media platforms, online news sites and web forums to identify "influencers" whose tweets, comments and posts are most likely to impact collective opinion.

On October 14-15, 2016, the NYU Center for Mind, Brain and Consciousness in conjunction with the NYU Center for Bioethics will host a conference on "The Ethics of Artificial Intelligence". Recent progress in artificial intelligence (AI) makes questions about the ethics of AI more pressing than ever. Existing AI systems already raise numerous ethical issues: for example, machine classification systems raise questions about privacy and bias. AI systems in the near-term future raise many more issues: for example, autonomous vehicles and autonomous weapons raise questions about safety and moral responsibility. AI systems in the long-term future raise more issues in turn: for example, human-level artificial general intelligence systems raise questions about the moral status of the systems themselves.

This interview is part of a Machine Learning series. We invited Adam Geitgey, Director of Software Engineering at Groupon, to talk about the difference between machine learning and the older artificial intelligence effort and the progress we've made so far. JAXenter: How are you involved in machine learning? Adam Geitgey: My professional background is primarily in traditional software development, not machine learning. I've worked on scaling large-scale websites, building backend systems, building mobile apps and other things like that.

As machine learning increasingly affects domains protected by anti-discrimination law, there is much interest in the problem of algorithmically measuring and ensuring fairness in machine learning. Across academia and industry, experts are finally embracing this important research direction that has long been marred by sensationalist clickbait overshadowing scientific efforts. This sequence of posts is a sober take on the subtleties and difficulties in engaging productively with the issue of fairness in machine learning. Prudence is necessary, since a poor regulatory proposal could easily do more harm than doing nothing at all. In this first post, I will focus on a sticky idea I call demographic parity that through its many variants has been proposed as a fairness criterion in dozens of papers.