In The Future of Violence, Benjamin Wittes and Gabriella Blum discuss a disturbing hypothetical scenario. A lone actor in Nigeria, "home to a great deal of spamming and online fraud activity," tricks women and teenage girls into downloading malware that enables him to monitor and record their activity, for the purposes of blackmail. The real story involved a California man who the FBI eventually caught and sent to prison for six years, but if he had been elsewhere in the world he might have gotten away with it. Many countries, as Wittes and Blum note, "have neither the will nor the means to monitor cybercrime, prosecute offenders, or extradite suspects to the United States." Technology is, in other words, enabling criminals to target anyone anywhere and, due to democratization, increasingly at scale.

Fox News Flash top headlines for Oct. 22 are here. Check out what's clicking on Foxnews.com "----A 1986 graduate of West Point, Martin deployed to Iraq five times including stints as a company commander during Operation Desert Storm, as a battalion and brigade commander during Iraqi Freedom and he commanded the famed 1st Infantry Division at Fort Riley, Kansas. Martin also served as the commander of the Combined Joint Forces Land Component Command during the pivotal Battle of Mosul, a major multi-national offensive that helped the Iraqi government retake control of the Iraqi city from ISIS forces----" From an Army Report --- MARTIN ARMY BIO HERE --- Warrior: There is a lot of discussion about the Army's Top 6 Modernization priorities:...Long Range Precision Fires, Next Generation Combat Vehicles, Future Vertical Lift, Network, Air and Missile Defense, and Soldier Lethality…. How are they progressing and what sticks out in your mind?

It is the first graduate level, research-based AI university in the world. MBZUAI will enable graduate students, businesses, and governments to advance artificial intelligence. The university is named after His Highness Sheikh Mohamed bin Zayed Al Nahyan, Crown Prince of Abu Dhabi and Deputy Supreme Commander of the UAE Armed Forces, who has long advocated for the UAE's development of human capital through knowledge and scientific thinking to take the nation into the future. MBZUAI will introduce a new model of academia and research to the field of AI, providing students and faculty access to some of the world's most advanced AI systems to unleash its potential for economic and societal development. The announcement was made at a press conference at the University campus in Masdar City and was immediately followed by the first meeting of the MBZUAI Board of Trustees. Dr Sultan Ahmed Al Jaber, UAE Minister of State, who has been appointed Chair of the MBZUAI board of trustees and is spearheading the establishment of the University, said: "Mohamed bin Zayed University of Artificial Intelligence aligns with the vision of the UAE leadership that is based on sustainable development, progress and the overall well-being of humanity and underpinned by capacity-building and active participation in finding practical solutions based on innovation and state-of-the-art technology.

Techfest is the annual science and technology festival of Indian Institute of Technology Bombay.[1] It also refers to the independent body of students who organize this event along with many other social initiatives and outreach programs around the year. Techfest is known for hosting a variety of events that include competitions, exhibitions, lectures as well as workshops. Started in 1998 with the aim of providing a platform for the Indian student community to develop and showcase their technical prowess, it has now grown into Asia's Largest Science and Technology Festival[2] with a footfall of 1.75 lakhs in its latest edition.[3][4][5] The activities culminate in a grand three-day event in the campus of IIT Bombay which attracts people from all over the World, including students, academia, corporates and the general public.[6] The very first edition of Techfest was in 1998. The underlying spirit of Techfest was "to promote technology and scientific thinking and innovation" a motto that has been followed by every Techfest since. Techfest '98 also set the broad outlines of Techfest in the form of competitions, lectures, workshops, and exhibitions which went on to become a standard feature at every Techfest. Entrepreneurship also made an appearance in the 1999 and 2000 editions. Technoholix--Techfest in the Dark, showcasing technological entertainment at the end of each day as well as the hub of on the spot activities, made their debut during these years. Techfest 2001-2002 saw the incorporation of IIT Bombay's department oriented events like Yantriki, Chemsplash and Last Straw. Students from G H Raisoni College of Engineering got the Engineering Excellence Award for best design.

When The Wall Street Journal first had the audacity to create a magazine and event series called The Future of Everything, I had to stop myself from rolling my eyes. But then my curiosity got the best of me and I started reading about everything from the problems with fully autonomous vehicles to the new jobs being created as the era of artificial intelligence shows up in the next generation of software. All of this got me thinking about the future of marketing. Having been in the digital marketing industry for the past 24 years, I've witnessed the utter chaos that has plagued the industry (and some would argue it still does). When I first started my career in 1994, all the industry pundits were advocating that television was dead and that broadcast media would soon be replaced with on-demand programming.

Two Canadian startups have been named grantees of Microsoft's AI for Accessibility initiative, a $25 million, five year grant program launched last year to help NGOs, academics, researchers, and inventors accelerate their work for people with disabilities. "We have a huge opportunity and a responsibility to be making technology smarter and more useful for people with disabilities." AI for Accessibility wants to amplify human capability for people around the world with disabilities, by funding relevant projects that leverage and apply AI technology. The program is part of Microsoft's broader AI for Good initiative. This new round of 11 grantees brings the total projects included in the program to 32, which are spread across 13 countries.

Deep learning (DL) neural networks have only recently been employed to interpret chest radiography (CXR) to screen and triage people for pulmonary tuberculosis (TB). No published studies have compared multiple DL systems and populations. We conducted a retrospective evaluation of three DL systems (CAD4TB, Lunit INSIGHT, and qXR) for detecting TB-associated abnormalities in chest radiographs from outpatients in Nepal and Cameroon. All 1196 individuals received a Xpert MTB/RIF assay and a CXR read by two groups of radiologists and the DL systems. Xpert was used as the reference standard.

Classical results on the statistical complexity of linear models have commonly identified the norm of the weights $\|w\|$ as a fundamental capacity measure. Generalizations of this measure to the setting of deep networks have been varied, though a frequently identified quantity is the product of weight norms of each layer. In this work, we show that for a large class of networks possessing a positive homogeneity property, similar bounds may be obtained instead in terms of the norm of the product of weights. Our proof technique generalizes a recently proposed sampling argument, which allows us to demonstrate the existence of sparse approximants of positive homogeneous networks. This yields covering number bounds, which can be converted to generalization bounds for multi-class classification that are comparable to, and in certain cases improve upon, existing results in the literature. Finally, we investigate our sampling procedure empirically, which yields results consistent with our theory.

Matching two sets of items, called set-to-set matching problem, is being recently raised. The difficulties of set-to-set matching over ordinary data matching lie in the exchangeability in 1) set-feature extraction and 2) set-matching score; the pair of sets and the items in each set should be exchangeable. In this paper, we propose a deep learning architecture for the set-to-set matching that overcomes the above difficulties, including two novel modules: 1) a cross-set transformation and 2) cross-similarity function. The former provides the exchangeable set-feature through interactions between two sets in intermediate layers, and the latter provides the exchangeable set matching through calculating the cross-feature similarity of items between two sets. We evaluate the methods through experiments with two industrial applications, fashion set recommendation, and group re-identification. Through these experiments, we show that the proposed methods perform better than a baseline given by an extension of the Set Transformer, the state-of-the-art set-input function.

Graphs and networks are a standard model for describing data or systems based on pairwise interactions. Oftentimes, the underlying relationships involve more than two entities at a time, and hypergraphs are a more faithful model. However, we have fewer rigorous methods that can provide insight from such representations. Here, we develop a computational framework for the problem of clustering hypergraphs with categorical edge labels --- or different interaction types --- where clusters corresponds to groups of nodes that frequently participate in the same type of interaction. Our methodology is based on a combinatorial objective function that is related to correlation clustering but enables the design of much more efficient algorithms. When there are only two label types, our objective can be optimized in polynomial time, using an algorithm based on minimum cuts. Minimizing our objective becomes NP-hard with more than two label types, but we develop fast approximation algorithms based on linear programming relaxations that have theoretical cluster quality guarantees. We demonstrate the efficacy of our algorithms and the scope of the model through problems in edge-label community detection, clustering with temporal data, and exploratory data analysis.