Zoning codes are a century old, and the lifeblood of all major U.S. cities (except arguably Houston), determining what can be built where and what activities can take place in a neighborhood. Yet as their complexity has risen, academics are increasingly exploring whether their rule-based systems for rationalizing urban space could be replaced with dynamic systems based on blockchains, machine learning algorithms, and spatial data, potentially revolutionizing urban planning and development for the next one hundred years. These visions of the future were inspired by my recent chats with Kent Larson and John Clippinger, a dynamic urban thinking duo who have made improving cities and urban governance their current career focus. Larson is a principal research scientist at the MIT Media Lab, where he directs the City Science Group, and Clippinger is a visiting researcher at the Human Dynamics Lab (also part of the Media Lab), as well as the founder of non-profit ID3. One of the toughest challenges facing major U.S. cities is the price of housing, which has skyrocketed over the past few decades, placing incredible strain on the budget of young and old, singles and families alike.

The Food and Drug Administration has approved for marketing new decision support software that could alert doctors of a potential stroke in one of their patients. The software, which uses artificial intelligence, analyzes computed tomography results and notifies providers if findings of a stroke or potential stroke are detected. "The software device could benefit patients by notifying a specialist earlier, thereby decreasing the time to treatment," says Robert Ochs, acting deputy director for radiological health in FDA's Center for Devices and Radiological Health. The vendor is Viz.AI, born at Stanford University. Viz.AI is computer-aided triage software using an artificial intelligence algorithm to analyze images for indications associated with stroke.

Analyzing large X-ray diffraction (XRD) datasets is a key step in high-throughput mapping of the compositional phase diagrams of combinatorial materials libraries. Optimizing and automating this task can help accelerate the process of discovery of materials with novel and desirable properties. Here, we report a new method for pattern analysis and phase extraction of XRD datasets. The method expands the Nonnegative Matrix Factorization method, which has been used previously to analyze such datasets, by combining it with custom clustering and cross-correlation algorithms. This new method is capable of robust determination of the number of basis patterns present in the data which, in turn, enables straightforward identification of any possible peak-shifted patterns. Peak-shifting arises due to continuous change in the lattice constants as a function of composition, and is ubiquitous in XRD datasets from composition spread libraries. Successful identification of the peak-shifted patterns allows proper quantification and classification of the basis XRD patterns, which is necessary in order to decipher the contribution of each unique single-phase structure to the multi-phase regions. The process can be utilized to determine accurately the compositional phase diagram of a system under study. The presented method is applied to one synthetic and one experimental dataset, and demonstrates robust accuracy and identification abilities.

As the prevalence and everyday use of machine learning algorithms, along with our reliance on these algorithms grow dramatically, so do the efforts to attack and undermine these algorithms with malicious intent, resulting in a growing interest in adversarial machine learning. A number of approaches have been developed that can render a machine learning algorithm ineffective through poisoning or other types of attacks. Most attack algorithms typically use sophisticated optimization approaches, whose objective function is designed to cause maximum damage with respect to accuracy and performance of the algorithm with respect to some task. In this effort, we show that while such an objective function is indeed brutally effective in causing maximum damage on an embedded feature selection task, it often results in an attack mechanism that can be easily detected with an embarrassingly simple novelty or outlier detection algorithm. We then propose an equally simple yet elegant solution by adding a regularization term to the attacker's objective function that penalizes outlying attack points.

Dynamic programming (DP) solves a variety of structured combinatorial problems by iteratively breaking them down into smaller subproblems. In spite of their versatility, DP algorithms are usually non-differentiable, which hampers their use as a layer in neural networks trained by backpropagation. To address this issue, we propose to smooth the max operator in the dynamic programming recursion, using a strongly convex regularizer. This allows to relax both the optimal value and solution of the original combinatorial problem, and turns a broad class of DP algorithms into differentiable operators. Theoretically, we provide a new probabilistic perspective on backpropagating through these DP operators, and relate them to inference in graphical models. We derive two particular instantiations of our framework, a smoothed Viterbi algorithm for sequence prediction and a smoothed DTW algorithm for time-series alignment. We showcase these instantiations on two structured prediction tasks and on structured and sparse attention for neural machine translation.

This article presents an overview of applications of logic programming, classifying them based on the abstractions and implementations of logic languages that support the applications. The three key abstractions are join, recursion, and constraint. Their essential implementations are for-loops, fixed points, and backtracking, respectively. The corresponding kinds of applications are database queries, inductive analysis, and combinatorial search, respectively. We also discuss language extensions and programming paradigms, summarize example application problems by application areas, and touch on example systems that support variants of the abstractions with different implementations.

When an Iranian drone flew into Israeli airspace this month, it set off a rapid series of strikes and counterstrikes that deepened fears over whether a new, catastrophic war was brewing in the Middle East. That flare-up ended quickly, if violently, with the drone destroyed and an Israeli jet downed after bombing sites in Syria. But the day of fighting drew new attention to how deeply Iran has embedded itself in Syria, redrawing the strategic map of the region. Tactical advisers from Iran's Islamic Revolutionary Guards Corps are deployed at military bases across Syria. Its commanders regularly show up at the front lines to lead battles.

Late last year, a St. Louis tech executive named Emre Şarbak noticed something strange about Google Translate. He was translating phrases from Turkish -- a language that uses a single gender-neutral pronoun "o" instead of "he" or "she." But when he asked Google's tool to turn the sentences into English, they seemed to read like a children's book out of the 1950's. The ungendered Turkish sentence "o is a nurse" would become "she is a nurse," while "o is a doctor" would become "he is a doctor." The website Quartz went on to compose a sort-of poem highlighting some of these phrases; Google's translation program decided that soldiers, doctors and entrepreneurs were men, while teachers and nurses were women.

New workplace trends such as automation, AI, and the gig economy are generating a need for policies that create jobs and work that is more fulfilling. This summer, the UK government published a long-awaited independent review of employment practices in the modern economy, led by Matthew Taylor, chief executive of the London-based Royal Society for the encouragement of Arts, Manufactures and Commerce (RSA). His review and policy recommendations addressed questions about automation in the workplace, the influence of the so-called gig economy, and the need to create better work. Taylor sat down recently with James Manyika, chairman of the McKinsey Global Institute, whose recent research agenda has tackled many of the same and similar topics (notably the future of work, independent work, automation, and declining productivity). What follows are edited highlights of their conversation. It begins with Taylor outlining the scope of the UK report. Matthew Taylor: We've been exploring three questions. One is who's being exploited, how they're being exploited, and what might we do about it. Second, an understanding of the incentives that are driving changes in the labor market.

Paladion, a global cyber defence company that provides AI-Driven Managed Detection and Response Services, will participate in Artelligence Forum as a Silver Sponsor. The event, which is scheduled to take place on April 25 and 26, 2018 in Dubai, is presented by Khaleej Times and MIT Sloan Management Review GCC to bring government leaders, business leaders and technology innovators together to explore the applications of Artificial Intelligence across different industries. Paladion is consistently rated and recognised by independent advisory firms for its capabilities in Artifical Intelligence in Cyber, Security Automation, and next generation cyber security. Speaking about AI in cybersecurity and this initiative by Khaleej Times and MIT Sloan Management Review GCC, Amit Roy, EVP and Regional Head EMEA, Paladion, said, "Cyberattackers are leveraging AI to increase the speed, volume, and sophistication of attacks. Enterprises must evolve to combat such threats. Enterprises today must recognize the ineffectiveness of a rules and signature-based defence on modern attacks, and adopt AI driven cyber security to defend against AI based cyber threats. Paladion's AI-driven MDR Service offers the only way to collect, analyze, and process the massive volume of data produced by AI-driven cyberattacks. We are excited to showcase our AI-driven cyber security service at the Artelligence Forum."