Slate and Future Tense are discussing Mr. Robot and the technological world it portrays throughout the show's second season. You can follow this conversation on Future Tense, and Slate Plus members can also listen to Hacking Mr. Robot, a members-only podcast series featuring Lily Newman and Fred Kaplan. In this episode of Hacking Mr. Robot, Fred and Lily discuss Episode 11. Fred Kaplan is the author of Dark Territory: The Secret History of Cyber War.

For a sequence model to be both expressive and parsimonious in its parameterization, it is crucial to equip the model with memory thus allowing its prediction at time t to depend on previously seen inputs. Recurrent neural networks form an expressive class of nonlinear sequence models. Through their many variants, such as long-short-term-memory [HS97], recurrent neural networks have seen remarkable empirical success in a broad range of domains. At the core, neural networks are typically trained using some form of (stochastic) gradient descent. Even though the training objective is non-convex, it is widely observed in practice that gradient descent quickly approaches a good set of model parameters.

Large volume of networked streaming event data are becoming increasingly available in a wide variety of applications, such as social network analysis, Internet traffic monitoring and healthcare analytics. Streaming event data are discrete observation occurred in continuous time, and the precise time interval between two events carries a great deal of information about the dynamics of the underlying systems. How to promptly detect changes in these dynamic systems using these streaming event data? In this paper, we propose a novel change-point detection framework for multi-dimensional event data over networks. We cast the problem into sequential hypothesis test, and derive the likelihood ratios for point processes, which are computed efficiently via an EM-like algorithm that is parameter-free and can be computed in a distributed fashion. We derive a highly accurate theoretical characterization of the false-alarm-rate, and show that it can achieve weak signal detection by aggregating local statistics over time and networks. Finally, we demonstrate the good performance of our algorithm on numerical examples and real-world datasets from twitter and Memetracker.

This document is an invited chapter covering the specificities of ABC model choice, intended for the incoming Handbook of ABC by Sisson, Fan, and Beaumont (2017). Beyond exposing the potential pitfalls of ABC based posterior probabilities, the review emphasizes mostly the solution proposed by Pudlo et al. (2016) on the use of random forests for aggregating summary statistics and and for estimating the posterior probability of the most likely model via a secondary random fores.

The IBM Watson AI XPRIZE is a 5 million competition, challenging teams globally, to develop and demonstrate how humans can collaborate with powerful AI technologies to tackle the world's grand challenges. The prize aims to accelerate adoption of AI technologies, and spark creative, innovative and audacious demonstrations of the technology that are truly scalable and solve societal grand challenges. To encourage innovation in any form, the competition is an open challenge in AI. Rather than set a single, universal goal for all teams, this competition will invite teams to each declare their own goal and solution to a grand challenge. The IBM Watson AI XPRIZE is a four-year competition with annual milestone competitions in 2017, 2018, and 2019.

O'Reilly has released the results of the 2016 Data Science Salary Survey. This survey is based on data from over 900 respondents to a 64-question survey about data-related tasks, tools, and the salary they receive from doing/using them. The median salary reported in the survey was US 87,000; amongst data scientists in the US, the median salary was US 106,000. Appropriately for a survey about data science, O'Reilly doesn't merely report aggregate statistics from the survey; they fit a linear regression model for a data, and extact coefficients from the model indicative of salary "bumps" (or downgrades) attributable to demographic factors. Factors that tended to increase salary included: working in cloud computing environments; working with Python; and being older.

Data scientists are in short supply. But so too are managers that understand data science and machine learning enough to spot the opportunities for using these disciplines to optimize their businesses. McKinsey Global Institute has projected that by 2018, the U.S. alone will face a shortage of 1.5 million managers and analysts with the necessary analytics and data science expertise to fill demand. To combat this problem, Data science automation specialist DataRobot announced today that it has updated its DataRobot University curriculum with Data Science for Executives, a half-day offering that teaches executives interested in the benefits of advanced data science how to identify opportunities to optimize their business using machine learning. "If you look at the success of future companies, we think it's dependent on three things," says Jeremy Achin, co-founder and CEO of DataRobot.

Two things happen like clockwork every September: Apple releases a new operating system to go on its new phones. And then everyone complaints that their old phones feel slow. The phenomenon – which can be seen in the huge surge in people searching for "iPhone slow" every year – is the result of a combination of factors. New software, the advertising of much faster new phones and heightened expectations that aren't always met combine to make the slab of glass and metal sitting on your phone feel like it's slowed down. But this year that seems to be even worse. And there seems to be something in it, after a huge number of complaints that iOS 10 slows down people's phones.

Immigration officials have deported Jose Vega-Zuniga four times, but he's always returned, and usually landed behind bars. But a recent DUI arrest culminated in a federal conviction this summer that carried up to 20 years in prison. So, prosecutors allege, he left of his own accord. Days after a federal judge issued a warrant for his arrest, Vega-Zuniga, 38, crossed the border near San Diego, sitting in the front passenger seat as his wife drove her pickup into Mexico, prosecutors said. On Wednesday, nearly a month after the brazen escape, federal authorities arrested his wife, Elba Soto, at her Moreno Valley home, according to the U.S. attorney's office in Los Angeles.

The Times Bayer bets the farm in 66 billion Monsanto deal: An American pioneer of genetically modified seeds has struck a 66 billion takeover deal with Bayer, the German chemicals giant, in the latest round of megadeal consolidations in agricultural production. Lawyers set to pass M&A paperwork to machines: Luminance, a start-up backed by Mike Lynch, the British technology investor, is aiming to reduce the time taken on due diligence that accompanies multimillion-pound M&A deals from weeks to days or even hours using a new form of artificial intelligence. Public sector job numbers plunge to record low: Employment in the public sector has fallen to a record low as private sector jobs surge ahead despite Brexit, official figures have shown. Mortgage lending falls in wake of Brexit vote: The number of mortgages advanced for house purchases fell in the first month after Britain's decision to leave the European Union. Miner in'bribery scandal' intends to quit AIM: An Africa-focused mining company that has been caught up in a bribery scandal is planning to delist its shares.