The United Arab Emirates (UAE) will host graduate-level courses in artificial intelligence (AI) at a new university in its capital, Abu Dhabi. The Mohamed bin Zayed University of Artificial Intelligence (MBZUAI) aims to enable students, businesses and governments to increase the use of AI technology. Sultan Ahmed Al Jaber, UAE minister of state, said: "Mohamed bin Zayed University of Artificial Intelligence is an open invitation from Abu Dhabi to the world to unleash AI's full potential. "AI is already changing the world, but we can achieve so much more if we allow the limitless imagination of the human mind to fully explore it." The university, which will be located in Masdar City, Abu Dhabi, is tapping experience from across the world with a board of trustees from respected universities in the UK, the US and China. MBZUAI will offer postgraduate (MSc and PhD) courses in computer vision, machine learning and natural language processing. It is now open for admission to the academic year 2021-2022. Registration will begin in August 2020, with the first students starting the following month. All graduate students admitted will be offered a full scholarship and other benefits, including a monthly allowance, health insurance and accommodation. It will also help graduates find work and internships with local and global companies. MBZUAI has partnered with the Abu Dhabi-based Inception Institute of Artificial Intelligence for the supervision of PhD students and curriculum development, as well as collaborative research. Michael Brady, interim president at MBZUAI, said: "Following decades of research into machine learning and artificial intelligence, we are now at a turning point in the widespread application of advanced intelligence.

Humans can't keep pace with the rapid growth of new cyberthreats.

"The unpredictability of the outcomes of today's decision models often stems from an inability to properly capture and account for the uncertainty factors linked to these models' "behavior" in a business context. Decision intelligence provides a framework that brings together traditional and advanced techniques to design, model, align, execute, monitor and tune decision models." A growing number of companies are using DI to guide their decision-making including Google, IBM, Microsoft and many others across sectors. This intensive, one-day course will provide a hands-on, interactive, deep dive into DI for tehcnical and non-technical professionals. This is the only Decision Intelligence course currently available in New York City, being taught by one of the leading pioneers in the field, Dr. Lorien Pratt! "Curious to know what the psychology of avoiding lions on the savannah has in common with responsible AI leadership and the challenges of designing data warehouses?

You know for sure AI has formally gone mainstream when it is a hot topic among government circles. To give a few instances -- the Central Board of Secondary Education (CBSE) has partnered with Microsoft to enable tech skills development, while the Indian army is seeking intuitive AI assistance in its weapon systems and surveillance programmes. And more recently, the Housing Ministry has ordered AI algorithms to cut down bookkeeping corruption incidences. But the most prominent development has been the proposal of Rs.7,500-crore plan by Niti Aayog to create an institutional framework for AI in the country. And while the public sector is poised to play a crucial role in the growth of India's future, the increasing adoption of AI in the corporate world is calling for experts to create responsible AI technologies.

Researchers at the San Diego Supercomputer Center at UC San Diego and UC San Diego School of Medicine have received two National Science Foundation (NSF) planning grants worth a combined $2 million under a new NSF initiative to invest in research collaborations between academia, industry, government and communities that enable capabilities beyond what is currently possible in either the private or public sectors. Called Convergence Accelerator awards, the first set of grants has been awarded to research teams, according to a recent NSF release. These projects will evaluate how employers can use sophisticated artificial intelligence tools to connect with the workers they need, while seeking ways to develop the future U.S. workforce with the universities that will educate people and the companies that will employ them. A total of 43 new awards totaling $39 million will support projects across the country. Both grants, which support one of NSF's'Big Ideas' called Harnessing the Data Revolution, are focused on the area of Open Knowledge Networks, which pool many types of information and ideas so they can be accessed and leveraged to create new understanding.

Chris Wiggins, Chief Data Scientist at The New York Times, presented "Data Science at the New York Times" at Rev. Wiggins advocated that data scientists find problems that impact the business; re-frame the problem as a machine learning (ML) task; execute on the ML task; and communicate the results back to the business in an impactful way. He covered examples of how his team addressed business problems with descriptive, predictive, and prescriptive ML solutions. This post provides distilled highlights, a transcript, and a video of the session. In the Rev session, "Data Science at The New York Times", Chris Wiggins provided insights into how the Data Science group at The New York Times helped the newsroom and business be economically strong by developing and deploying ML solutions. Wiggins advised that data scientists ingest business problems, re-frame them as ML tasks, execute on the ML tasks, and then clearly and concisely communicate the results back to the organization. He advocated that an impactful ML solution does not end with Google Slides but becomes "a working API that is hosted or a GUI or some piece of working code that people can put to work". Wiggins also dove into examples of applying unsupervised, supervised, and reinforcement learning to address business problems. Wiggins also indicated that data science, data engineering, and data analysis are different groups at The New York Times. The data science group, in particular, includes people from a "wide variety of intellectual trainings" including cognitive science, physics, finance, applied math, and more. Wiggins closed the session with indicating how he looks forward to hiring from even more diverse job applications. For more insights from this session, watch the video or read through the transcript. I have about 30 minutes with you. I'm going to try to tell you all about data science at the New York Times, and in case I run out of time my email address and my Twitter are here. If you don't remember anything else, just remember we're hiring.

Current clinical practice guidelines for managing Coronary Artery Disease (CAD) account for general cardiovascular risk factors. However, they do not present a framework that considers personalized patient-specific characteristics. Using the electronic health records of 21,460 patients, we created data-driven models for personalized CAD management that significantly improve health outcomes relative to the standard of care. We develop binary classifiers to detect whether a patient will experience an adverse event due to CAD within a 10-year time frame. Combining the patients' medical history and clinical examination results, we achieve 81.5% AUC. For each treatment, we also create a series of regression models that are based on different supervised machine learning algorithms. We are able to estimate with average R squared = 0.801 the time from diagnosis to a potential adverse event (TAE) and gain accurate approximations of the counterfactual treatment effects. Leveraging combinations of these models, we present ML4CAD, a novel personalized prescriptive algorithm. Considering the recommendations of multiple predictive models at once, ML4CAD identifies for every patient the therapy with the best expected outcome using a voting mechanism. We evaluate its performance by measuring the prescription effectiveness and robustness under alternative ground truths. We show that our methodology improves the expected TAE upon the current baseline by 24.11%, increasing it from 4.56 to 5.66 years. The algorithm performs particularly well for the male (24.3% improvement) and Hispanic (58.41% improvement) subpopulations. Finally, we create an interactive interface, providing physicians with an intuitive, accurate, readily implementable, and effective tool.

Newspapers are a popular form of written discourse, read by many people, thanks to the novelty of the information provided by the news content in it. A headline is the most widely read part of any newspaper due to its ap - pearance in a bigger font and sometimes in colour print. In this paper, we sug - gest and implement a method for computing inferences from English news headlines, excluding the information from the context in which the headlines appear. This method attempts to generate the possible assumptions a reader formulates in mind upon reading a fresh headline. The generated inferences could be useful for assessing the impact of the news headline on readers includ - ing children. The understandability of the current state of social affairs depends greatly on the assimilation of the headlines. As the inferences that are indepen - dent of the context depend mainly on the syntax of the headline, dependency trees of headlines are used in this approach, to find the syntactical structure of the headlines and to compute inferences out of them.

Critical infrastructure systems such as electric power networks, water networks, and transportation systems play a major role in the welfare of any community. In the aftermath of disasters, their recovery is of paramount importance; orderly and efficient recovery involves the assignment of limited resources (a combination of human repair workers and machines) to repair damaged infrastructure components. The decision maker must also deal with uncertainty in the outcome of the resource-allocation actions during recovery. The manual assignment of resources seldom is optimal despite the expertise of the decision maker because of the large number of choices and uncertainties in consequences of sequential decisions. This combinatorial assignment problem under uncertainty is known to be \mbox{NP-hard}. We propose a novel decision technique that addresses the massive number of decision choices for large-scale real-world problems; in addition, our method also features an experiential learning component that adaptively determines the utilization of the computational resources based on the performance of a small number of choices. Our framework is closed-loop, and naturally incorporates all the attractive features of such a decision-making system. In contrast to myopic approaches, which do not account for the future effects of the current choices, our methodology has an anticipatory learning component that effectively incorporates \emph{lookahead} into the solutions. To this end, we leverage the theory of regression analysis, Markov decision processes (MDPs), multi-armed bandits, and stochastic models of community damage from natural disasters to develop a method for near-optimal recovery of communities. Our method contributes to the general problem of MDPs with massive action spaces with application to recovery of communities affected by hazards.

A new bill will soon make it illegal to snoop on your neighbor with a drone. Called the Drone Integration and Zoning Act, the proposal deems airspace up to 200 feet over someone's home as their private property meaning punishments for trespassing could be enforced. The motion aims to distribute some of the Federal Aviation Administration's (FAA) authority over the nation's airspace to localities and private citizens by redefining'navigable airspace'. Sen. Mike Lee said from Utah, proposed the bill to congress on Wednesday stating, 'The FAA cannot feasibly or efficiently oversee millions of drones in every locality throughout the country.' 'The reason that the states have sovereign police powers to protect the property of their citizens is because issues of land use, privacy, trespass, and law enforcement make sense at the state and local level.' 'The best way to ensure public safety and allow this innovative industry to thrive is to empower the people closest to the ground to make local decisions in real time and that is exactly what the Drone Integration and Zoning Act does.'