Delighted to hear the Bank of England announce that mathematician, war hero and computer science pioneer Alan Turing will be pictured on a new £50 banknote. Decades after his untimely death, Alan Turing continues to be an inspiring visionary. He was an outstanding mathematician and a pioneer of computer science and artificial intelligence. In 1936, at age 24, he published "On Computable Numbers, with an Application to the Entscheidungsproblem," a scholarly paper that is widely recognized as foundational to the field of computer science. But he is best known as a World War II hero who devised code-breaking machines that ultimately helped the Allies defeat the Axis Powers.

Artificial Intelligence ("AI") is no longer the stuff of sci-fi movies or alien invasions. The technology has permeated everyday life from Siri and Alexa to Facebook and Google. While marketing teams have been relying on AI for years to help streamline business efforts and target consumers, employers have finally joined in on the hype. While the use of AI can be an efficient and cost effective means for employers to handle tasks such as talent acquisition, compensation analysis, and administrative functions, it is not without its challenges. As lawmakers on the federal and state level struggle to catch up with the rapidly changing technology, it is imperative for employers to stay ahead of the curve and ensure that their use of AI is not exposing them to costly litigation. AI is often used in the workplace to assist employers with recruitment through the use of algorithms to make hiring decisions.

Fake news and social media posts are such a threat to U.S. security that the Defense Department is launching a project to repel "large-scale, automated disinformation attacks," as the top Republican in Congress blocks efforts to protect the integrity of elections. The Defense Advanced Research Projects Agency wants custom software that can unearth fakes hidden among more than 500,000 stories, photos, videos and audio clips. If successful, the system after four years of trials may expand to detect malicious intent and prevent viral fake news from polarizing society. "A decade ago, today's state of the art would have registered as sci-fi -- that's how fast the improvements have come," said Andrew Grotto at the Center for International Security at Stanford University. "There is no reason to think the pace of innovation will slow any time soon."

We provide the first polynomial-time algorithm for recognizing if a profile of (possibly weak) preference orders is top-monotonic. Top-monotonicity is a generalization of the notions of single-peakedness and single-crossingness, defined by Barbera and Moreno. Top-monotonic profiles always have weak Condorcet winners and satisfy a variant of the median voter theorem. Our algorithm proceeds by reducing the recognition problem to the SAT-2CNF problem.

Prior work poses a diversity paradox for science. Diversity breeds scientific innovation, and yet, diverse individuals have less successful scientific careers. But if diversity is good for innovation, why is science not rewarding diversity? We answer this question by utilizing a near-population of ~1.03 million US doctoral recipients from 1980-2015 and their careers into publishing and faculty roles. The article uses text analysis and machine learning techniques to answer a series of questions: How can we detect scientific innovation? Does diversity breed innovation? And are the innovations of diverse individuals adopted and rewarded? Our analyses show that underrepresented groups produce higher rates of scientific novelty. However, their novel contributions are discounted: e.g., innovations by gender minorities are taken up by other scholars at lower rates than innovations by gender majorities, and innovations by gender and racial minorities result in fewer academic positions. This suggests an unfair system in which diverse individuals innovate, but their innovations are disproportionately ignored and fail to convert into career success at the same rate as majority groups. In sum, there may be an unwarranted reproduction of stratification in academic careers that discounts diversity's role in innovation and partly explains the underrepresentation of some groups in academia.

Recent developments within deep learning are relevant for nonlinear system identification problems. In this paper, we establish connections between the deep learning and the system identification communities. It has recently been shown that convolutional architectures are at least as capable as recurrent architectures when it comes to sequence modeling tasks. Inspired by these results we explore the explicit relationships between the recently proposed temporal convolutional network (TCN) and two classic system identification model structures; Volterra series and block-oriented models. We end the paper with an experimental study where we provide results on two real-world problems, the well-known Silverbox dataset and a newer dataset originating from ground vibration experiments on an F-16 fighter aircraft.

Abstractive summarization systems aim to produce more coherent and concise summaries than their extractive counterparts. Popular neural models have achieved impressive results for single-document summarization, yet their outputs are often incoherent and unfaithful to the input. In this paper, we introduce SENECA, a novel System for ENtity-drivEn Coherent Abstractive summarization framework that leverages entity information to generate informative and coherent abstracts. Our framework takes a two-step approach: (1) an entity-aware content selection module first identifies salient sentences from the input, then (2) an abstract generation module conducts cross-sentence information compression and abstraction to generate the final summary, which is trained with rewards to promote coherence, conciseness, and clarity. The two components are further connected using reinforcement learning. Automatic evaluation shows that our model significantly outperforms previous state-of-the-art on ROUGE and our proposed coherence measures on New York Times and CNN/Daily Mail datasets. Human judges further rate our system summaries as more informative and coherent than those by popular summarization models.

Hypersonic missiles, stealthy cruise missiles, and weaponized artificial intelligence have so reduced the amount of time that decision makers in the United States would theoretically have to respond to a nuclear attack that, two military experts say, it's time for a new US nuclear command, control, and communications system. Give artificial intelligence control over the launch button. In an article in War on the Rocks titled, ominously, "America Needs a'Dead Hand,'" US deterrence experts Adam Lowther and Curtis McGiffin propose a nuclear command, control, and communications setup with some eerie similarities to the Soviet system referenced in the title to their piece. The Dead Hand was a semiautomated system developed to launch the Soviet Union's nuclear arsenal under certain conditions, including, particularly, the loss of national leaders who could do so on their own. Given the increasing time pressure Lowther and McGiffin say US nuclear decision makers are under, "[I]t may be necessary to develop a system based on artificial intelligence, with predetermined response decisions, that detects, decides, and directs strategic forces with such speed that the attack-time compression challenge does not place the United States in an impossible position."

The Joint Artificial Intelligence Center, stood up just last year, has plans to hire an ethicist to help guide the Defense Department's development and application of artificial intelligence technologies. "One of the positions we are going to fill will be somebody who is not just looking at technical standards, but who is an ethicist," said Air Force Lt. Gen. Jack Shanahan, the JAIC's director. "We are going to bring in someone who will have a deep background in ethics, and then the lawyers within the department will be looking at how we actually bake this into the Department of Defense." Speaking Aug. 30 at the Pentagon, Shanahan provided an update on the JAIC, where he's been since January. He'd previously led Project Maven, an artificial intelligence machine-learning pathfinder project under the undersecretary of defense for intelligence.

Before you start reading, think of 3 possible scenarios for the future of Artificial Intelligence (AI). If I asked you to think of 3 possible scenarios for the future of AI, I am guessing you'd think of the bad first: Takeover scenario -- Terminator-style. Computers and robots dominate human species, take over our planet, and eventually wipe us off the face of Earth. Or, that the power of AI will be held, and used by a handful of tyrants whose sole purpose is to enslave the rest of us. You might've also thought of a hybrid scenario, where we lose some of our humanity to gain far superior computational and physical power. And finally, you might've even thought of brighter days where robots work for human species who now enjoy their Universal Basic Income (UBI), follow their "passions" or their "useless" creative endeavors, and live without a single worry in the world. Even though these are the most commonly talked about scenarios, I think we are missing the most probable scenarios somewhere in the "boring AI outcomes" section. First of all, AI, being as hyped of a topic as it is, attracts attention, and attention is usually not maintained by analyzing history and political philosophy and coming up with a possible outcome based on that. Attention is maintained by either fear or hope for a better tomorrow (i.e. That's why these'common scenarios' are not only the most written but also the most read about scenarios. If you haven't picked it up already, you'll be reading about one of the "boring AI outcomes".