The parent company of hacked extramarital dating site Ashley Madison has agreed to pay an $11.2m (£8.57m) settlement to US-based users of the site, ending a two-year court battle. Ruby Life Inc agreed to pay the settlement following a number of class-action lawsuits "alleging inadequate data security practices and misrepresentations regarding Ashley Madison". It will pay for, among other things, "payments to settlement class members who submit valid claims for alleged losses resulting from the data breach and alleged misrepresentations as described further in the proposed settlement agreement". The plaintiffs, a collection of three separate class-action lawsuits consolidated together, alleged that the company "misrepresented that they had taken reasonable steps to ensure AshleyMadison.com was secure and that the data breach resulted in the public release of certain personal information contained in AshleyMadison.com accounts and included account information of some users who had paid a fee to delete their information from the AshleyMadison.com Ruby Life inc, formerly known as Avid Life Media, has new leadership following the departure of the executive team in April 2016.

Appearing before a meeting of the National Governor's Association on Saturday, Tesla CEO Elon Musk described artificial intelligence as "the greatest risk we face as a civilization" and called for swift and decisive government intervention to oversee the technology's development. "On the artificial intelligence front, I have access to the very most cutting edge AI, and I think people should be really concerned about it," an unusually subdued Musk said in a question and answer session with Nevada governor Brian Sandoval. Musk has long been vocal about the risks of AI. But his statements before the nation's governors were notable both for their dire severity, and his forceful call for government intervention. "AI's a rare case where we need to be proactive in regulation, instead of reactive. Because by the time we are reactive with AI regulation, it's too late," he remarked.

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Tesla and Space X chief executive Elon Musk has pushed again for the proactive regulation of artificial intelligence because "by the time we are reactive in AI regulation, it's too late". Because I think by the time we are reactive in AI regulation, it'll be too late," Musk told the meeting. "AI is a fundamental risk to the existence of human civilisation." While Musk has repeatedly shared his worries over AI and its development that is seen as inevitable in some regard, words appeared to hit home with multiple governors of the 32 taking part in the meeting, with follow-up questions looking for suggestions for how to go about regulating AI's development.

Among the myriad of desirable properties discussed in the context of forgetting in Answer Set Programming (ASP), strong persistence naturally captures its essence. Recently, it has been shown that it is not always possible to forget a set of atoms from a program while obeying this property, and a precise criterion regarding what can be forgotten has been presented, accompanied by a class of forgetting operators that return the correct result when forgetting is possible. However, it is an open question what to do when we have to forget a set of atoms, but cannot without violating this property. In this paper, we address this issue and investigate three natural alternatives to forget when forgetting without violating strong persistence is not possible, which turn out to correspond to the different possible relaxations of the characterization of strong persistence. Additionally, we discuss their preferable usage, shed light on the relation between forgetting and notions of relativized equivalence established earlier in the context of ASP, and present a detailed study on their computational complexity.

The doctrine of double effect ($\mathcal{DDE}$) is a long-studied ethical principle that governs when actions that have both positive and negative effects are to be allowed. The goal in this paper is to automate $\mathcal{DDE}$. We briefly present $\mathcal{DDE}$, and use a first-order modal logic, the deontic cognitive event calculus, as our framework to formalize the doctrine. We present formalizations of increasingly stronger versions of the principle, including what is known as the doctrine of triple effect. We then use our framework to simulate successfully scenarios that have been used to test for the presence of the principle in human subjects. Our framework can be used in two different modes: One can use it to build $\mathcal{DDE}$-compliant autonomous systems from scratch, or one can use it to verify that a given AI system is $\mathcal{DDE}$-compliant, by applying a $\mathcal{DDE}$ layer on an existing system or model. For the latter mode, the underlying AI system can be built using any architecture (planners, deep neural networks, bayesian networks, knowledge-representation systems, or a hybrid); as long as the system exposes a few parameters in its model, such verification is possible. The role of the $\mathcal{DDE}$ layer here is akin to a (dynamic or static) software verifier that examines existing software modules. Finally, we end by presenting initial work on how one can apply our $\mathcal{DDE}$ layer to the STRIPS-style planning model, and to a modified POMDP model.This is preliminary work to illustrate the feasibility of the second mode, and we hope that our initial sketches can be useful for other researchers in incorporating DDE in their own frameworks.

Artificial intelligence is a growing field for tech companies, but among futurists and other tech industry figures, the potential unintended consequences of AI are an equally growing concern. Count Elon Musk among those worried about AI's possible effects. At the National Governors Association's 2017 meeting, Musk warned about the disruptive effects in robotics and other fields, cautioning that people "should be really concerned about AI." "Until people see robots going down the street killing people, they don't know how to react because it seems so ethereal," Musk said. "AI is a rare case where I think we need to be proactive in regulation instead of reactive. Because I think, by the time we are reactive in AI regulation, it's too late."

Appearing before a meeting of the National Governor's Association on Saturday, Tesla CEO Elon Musk described artificial intelligence as "the greatest risk we face as a civilization" and called for swift and decisive government intervention to oversee the technology's development. "On the artificial intelligence front, I have access to the very most cutting edge AI, and I think people should be really concerned about it," an unusually subdued Musk said in a question and answer session with Nevada governor Brian Sandoval. Musk has long been vocal about the risks of AI. But his statements before the nation's governors were notable both for their dire severity, and his forceful call for government intervention. "AI's a rare case where we need to be proactive in regulation, instead of reactive. Because by the time we are reactive with AI regulation, it's too late," he remarked.

Lots of algorithms go bad unintentionally. Some of them, however, are made to be criminal. Algorithms are formal rules, usually written in computer code, that make predictions on future events based on historical patterns. To train an algorithm you need to provide historical data as well as a definition of success. We've seen finance get taken over by algorithms in the past few decades. Trading algorithms use historical data to predict movements in the market. Success for that algorithm is a predictable market move, and the algorithm is vigilant for patterns that have historically happened just before that move.

We are currently going through one of those periodic phases of "automation anxiety" when we become convinced that the robots are coming for our jobs. These fears are routinely pooh-poohed by historians and economists. The historians point out that machines have been taking away jobs since the days of Elizabeth I – who refused to grant William Lee a patent on his stocking frame on the grounds that it would take work away from those who knitted by hand. And while the economists concede that machines do indeed destroy some jobs, they point out that the increased productivity that they enable has generally created more new jobs (and industries) than they displaced. Faced with this professional scepticism, tech evangelists and doom-mongers fall back on the same generic responses: that historical scepticism is based on the complacent assumption that the past is a reliable guide to the future; and that "this time is different".