People belong to multiple communities, words belong to multiple topics, and books cover multiple genres; overlapping clusters are commonplace. Many existing overlapping clustering methods model each person (or word, or book) as a non-negative weighted combination of "exemplars" who belong solely to one community, with some small noise. Geometrically, each person is a point on a cone whose corners are these exemplars. This basic form encompasses the widely used Mixed Membership Stochastic Blockmodel of networks (Airoldi et al., 2008) and its degree-corrected variants (Karrer et al. 2011; Jin et al., 2017), as well as topic models such as LDA (Blei et al., 2003). We show that a simple one-class SVM yields provably consistent parameter inference for all such models, and scales to large datasets. Experimental results on several simulated and real datasets show our algorithm (called SVM-cone) is both accurate and scalable.

Attacks against the control processor of a power-grid system, especially zero-day attacks, can be catastrophic. Earlier detection of the attacks can prevent further damage. However, detecting zero-day attacks can be challenging because they have no known code and have unknown behavior. In order to address the zero-day attack problem, we propose a data-driven defense by training a temporal deep learning model, using only normal data from legitimate processes that run daily in these power-grid systems, to model the normal behavior of the power-grid controller. Then, we can quickly find malicious codes running on the processor, by estimating deviations from the normal behavior with a statistical test. Experimental results on a real power-grid controller show that we can detect anomalous behavior with over 99.9% accuracy and nearly zero false positives.

Multiwinner voting rules are used to select a small representative subset of candidates or items from a larger set given the preferences of voters. However, if candidates have sensitive attributes such as gender or ethnicity (when selecting a committee), or specified types such as political leaning (when selecting a subset of news items), an algorithm that chooses a subset by optimizing a multiwinner voting rule may be unbalanced in its selection -- it may under or over represent a particular gender or political orientation in the examples above. We introduce an algorithmic framework for multiwinner voting problems when there is an additional requirement that the selected subset should be "fair" with respect to a given set of attributes. Our framework provides the flexibility to (1) specify fairness with respect to multiple, non-disjoint attributes (e.g., ethnicity and gender) and (2) specify a score function. We study the computational complexity of this constrained multiwinner voting problem for monotone and submodular score functions and present several approximation algorithms and matching hardness of approximation results for various attribute group structure and types of score functions. We also present simulations that suggest that adding fairness constraints may not affect the scores significantly when compared to the unconstrained case.

While robotics and artificial intelligence (AI) promise great advances in productivity, mostly they seem to worry people. Commentators talk and write endlessly about how these marvelous technologies will steal jobs from both workers and the managerial class, creating a large unemployed population. If history has anything to say, however, and it does, such fears are not only exaggerated, they are off the mark entirely. Ultimately, AI will create more new jobs than it destroys and likely in occupations heretofore nonexistent. Popular commentary on this matter maintains an almost universally downbeat tone.

Hysteria about the future of artificial intelligence (AI) is everywhere. There is no shortage of sensationalist news about how AI can cure diseases, accelerate human innovation and improve human creativity. From the headlines alone, you would think we already live in a future where AI has infiltrated every aspect of society. While AI has opened up a wealth of promising opportunities, it has also led to a mindset that can be best described as "AI solutionism". This is the attitude that, given enough data, machine learning algorithms can solve all of humanity's problems.

NEW DELHI: The government should drive cross-border collaboration on artificial intelligence research with countries like Japan, UK, Germany, Singapore, Israel and China to develop solutions that tackle social and economic challenges, a report said today. The Ministry of External Affairs and Department of Science and Technology (DST) may take the lead in developing such relationships, suggested the Assocham-PwC joint study. It observed that forming cooperative relationships with some of the front-runners such as Japan, the UK, Germany, Singapore, Israel and China to develop solutions that tackle social and economic challenges can aid and accelerate strategy formulation in artificial intelligence, machine learning and other new-age technologies in India. "Exchanging best practices and learning from prior initiatives is one way of strengthening cooperation," noted the study. The study also suggested that policy planning in artificial intelligence (AI) must be aimed at creating an ecosystem that is supportive of research, innovation and commercialisation of applications.

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Google has published a set of fuzzy but otherwise admirable "AI principles" explaining the ways it will and won't deploy its considerable clout in the domain. "These are not theoretical concepts; they are concrete standards that will actively govern our research and product development and will impact our business decisions," wrote CEO Sundar Pichai. The principles follow several months of low-level controversy surrounding Project Maven, a contract with the U.S. military that involved image analysis on drone footage. Some employees had opposed the work and even quit in protest, but really the issue was a microcosm for anxiety regarding AI at large and how it can and should be employed. Consistent with Pichai's assertion that the principles are binding, Google Cloud CEO Diane Green confirmed today in another post what was rumored last week, namely that the contract in question will not be renewed or followed with others.

ODP CORE TECHNOLOGY STACK § Best-of-breed open source technologies chosen, configured and integrated § Containerized data ingest and processing pipeline Apache Spark and Docker § Automated deployment into Amazon Web Services (AWS) and Azure § Ability to swap in-and-out technologies based on use case, as well as tailor deployments based on use case (e.g.

An Artificial Intelligence (AI) wave is sweeping the world as countries from China to the US and the UK compete aggressively and commit resources to get ahead in the race. US-based Paul Triolo, director (geotechnology) of Eurasia Group, a political risk consultancy firm, shares his views with Malini Goyal on a range of issues around AI. Edited excerpt from the interview: What makes AI of such critical significance for nations and businesses? Why are experts calling it the new arms race? I think the term arms race has unfortunately become a favored meme on artificial intelligence, in part because people have confused the progress of artificial narrow intelligence with artificial general intelligence (AGI). AGI, which most experts think we are years from developing, could provide an advantage to one country, should it choose to use the technology maliciously.