A 29-year-old Nepalese student in Tokyo has found herself stuck in limbo with her dreams derailed, and the state of Japan's language schools is to blame. A survivor of human trafficking in the past, the woman, who wished to be identified only by her last name, Puri, came to Japan in 2014 as an exchange student. Brimming with high expectations at the time, she said she was determined to acquire a master's degree in sociology, with an emphasis on a subject dear to her, women's rights. Imagine her disappointment, then, when her dream was cut short by the Japanese-language school in Tokyo where she was studying. The school taught her only the very basics of the language, lumped her in with unmotivated students who frequently fell asleep in class and -- to her shock -- informed her that a vocational school was the only educational path it could prepare her for.

SINGAPORE – A boom in consumer drone sales has spawned a counter-industry of startups aiming to stop drones flying where they shouldn't, by disabling them or knocking them out of the sky. Dozens of startup firms are developing techniques -- from deploying birds of prey to firing gas through a bazooka -- to take on unmanned aerial vehicles (UAVs) that are being used to smuggle drugs, drop bombs, spy on enemy lines or buzz public spaces. The arms race is fed in part by the slow pace of government regulation for drones. In Australia, for example, different agencies regulate drones and counter-drone technologies. "There are potential privacy issues in operating remotely piloted aircraft, but the Civil Aviation Safety Authority's role is restricted to safety. Privacy is not in our remit," a CASA official said.

I don't want to tell data scientists and AI developers what to do in any given situation. I want to give scientists and engineers tools for thinking about problems. We surely can't predict all the problems and ethical issues in advance; we need to be the kind of people who can have effective discussions about these issues as we anticipate and discover them. What are some of the ethical questions that AI developers and researchers should be thinking about? Even though we're still in the earliest days of AI, we're already seeing important issues rise to the surface: issues about the kinds of people we want to be, and the kind of future we want to build.

"Nanodegree" is a registered trademark of Udacity. Udacity is not an accredited university and we don't confer traditional degrees. Udacity Nanodegree programs represent collaborations with our industry partners who help us develop our content and who hire many of our program graduates.

The UK's Investigatory Powers Act is now in effect, placing Britain under some of the widest-ranging spying powers ever seen. The law – passed last month but going into effect on 30 December – is intended as an update to Britain's often unwieldy surveillance legislation. But it also includes a large set of new powers – including the ability to collect the browsing records of everyone in the country and have them read by authorities as diverse as the Food Standards Agency and the Department for Work and Pensions. Most of the central parts of the act are now in force. That includes new powers to gather and retain data on citizens, and new ways to force technology companies and others to hand over the data that they have about people to intelligence agencies.

Smart devices like the Amazon Echo record snippets of conversation when they're being used. NPR's Scott Simon asks Scott Stein of CNET to explain what happens to the data that is collected.

Domain Mondo's weekly review of technology news: Feature • Tech Story of the Year: Tay, Microsoft's Artificial Intelligence (AI) Chatterbot: "As many of you know by now, on Wednesday [March 23, 2016] we launched a chatbot called Tay. We are deeply sorry for the unintended offensive and hurtful tweets from Tay, which do not represent who we are or what we stand for, nor how we designed Tay. Tay is now offline and we'll look to bring Tay back only when we are confident we can better anticipate malicious intent that conflicts with our principles and values ... The logical place for us to engage with a massive group of users was Twitter. Unfortunately, in the first 24 hours of coming online, a coordinated attack by a subset of people exploited a vulnerability in Tay. Although we had prepared for many types of abuses of the system, we had made a critical oversight for this specific attack. We take full responsibility for not seeing this possibility ahead of time. We will take this lesson forward as well as those from our experiences in China, Japan and the U.S. Right now, we are hard at work addressing the specific vulnerability that was exposed by the attack on Tay."--Learning from Tay's introduction blogs.microsoft.com

We consider the \emph{Threshold Bandit} setting, a variant of the classical multi-armed bandit problem in which the reward on each round depends on a piece of side information known as a \emph{threshold value}. The learner selects one of $K$ actions (arms), this action generates a random sample from a fixed distribution, and the action then receives a unit payoff in the event that this sample exceeds the threshold value. We consider two versions of this problem, the \emph{uncensored} and \emph{censored} case, that determine whether the sample is always observed or only when the threshold is not met. Using new tools to understand the popular UCB algorithm, we show that the uncensored case is essentially no more difficult than the classical multi-armed bandit setting. Finally we show that the censored case exhibits more challenges, but we give guarantees in the event that the sequence of threshold values is generated optimistically.

We propose a criterion for discrimination against a specified sensitive attribute in supervised learning, where the goal is to predict some target based on available features. Assumingdata about the predictor, target, and membership in the protected group are available, we show how to optimally adjust any learned predictor so as to remove discrimination according to our definition. Our framework also improves incentives by shifting the cost of poor classification from disadvantaged groups to the decision maker, who can respond by improving the classification accuracy. We enourage readers to consult the more complete manuscript on the arXiv.

Many real phenomena, including behaviors, involve strategic interactions that can be learned from data. We focus on learning tree structured potential games where equilibria are represented by local maxima of an underlying potential function. We cast the learning problem within a max margin setting and show that the problem is NP-hard even when the strategic interactions form a tree. We develop a variant of dual decomposition to estimate the underlying game and demonstrate with synthetic and real decision/voting data that the game theoretic perspective (carving out local maxima) enables meaningful recovery.