How Brexit may affect artists and the art market. George Lucas abandons plans for a museum in Chicago. Miami models allege that an artist asked them to do intimate things with a rope. The Chicago Tribune says good riddance. Plus, how the referendum may affect artists.

Google has made no secret of its A.I. ambitions, and on Thursday it announced the next step in its bold plans to realize them: a brand-new research group in Europe focused squarely on machine learning. Based in Google Research offices in Zurich, Switzerland, the new group will focus on three key areas of artificial intelligence: machine intelligence, machine perception, and natural language processing and understanding, according to a blog post by Emmanuel Mogenet, head of Google Research for Europe. It will research ways to improve machine-learning infrastructure and enable the technology for practical use, for instance. Researchers will also work closely with linguists to advance natural language understanding, Mogenet said. Zurich, meanwhile, is home to Google's largest engineering office outside the U.S. Researchers there developed the engine that powers Knowledge Graph as well as the conversation engine that powers the Google Assistant in its Allo messaging app. Google's presence in Europe hasn't been entirely smooth, however: It's facing ongoing scrutiny over antitrust concerns and tax issues.

Genpact (NYSE: G), a global leader in digitally-powered business process management and services, announces that it has entered into a definitive agreement to acquire PNMsoft, a Gartner Magic Quadrant-rated dynamic workflow, case management and work optimization solutions provider based around Tel Aviv, Israel. PNMsoft complements and easily integrates pre-existing systems of records that typically host manual process work, and will act as a core component in Genpact's digital portfolio whose roadmap comprises close to 100 digital solution components ("digital assets"). Terms of the transaction were not disclosed. Closing is subject to satisfaction of certain customary conditions and expected in the third quarter. The transaction is not expected to be material to current year financial performance.

In the daily edtech trenches, the forest is easily lost for the trees. Technological minutiae in the classroom carry such immense consequences that it can be hard to think beyond tomorrow's software update, nevermind next year's LMS rollout. In his opening keynote at the ISTE 2016 conference, noted physicist Dr. Michio Kaku showed educators the forest that he and others believe will encircle the classroom of the future. And oh, what a forest it might be. According to Dr. Kaku, talking wallpaper, data-reading toilets and other technologies that seem like miracles today are a mere fifty years away.

Sidewalk Labs, a top-secret urban innovation division run under Google's parent company Alphabet, wants to improve city life for city-dwellers by reinventing public parking and transportation. Its first testing grounds - Columbus, Ohio - may host subsidized ride-sharing, a service that finds free parking spots, and an artificial intelligence platform that will help meter maids fine more people, all in the Buckeye State capital. Earlier in 2016, Sidewalk Labs announced plans to buy up land in major US cities and transform the parcels into ultra-high tech municipalities. In conjunction with the US Department of Transportation, it launched the Smart City Challenge to identify a launchpad for its innovations. The Guardian revealed on Monday never-before-seen documents and proposals from the Smart City Challenge.

Sidewalk Labs, a top-secret urban innovation division run under Google's parent company Alphabet, wants to improve city life for city-dwellers by reinventing public parking and transportation. Its first testing grounds - Columbus, Ohio - may host subsidized ride-sharing, a service that finds free parking spots, and an artificial intelligence platform that will help meter maids fine more people, all in the Buckeye State capital. Earlier in 2016, Sidewalk Labs announced plans to buy up land in major US cities and transform the parcels into ultra-high tech municipalities. In conjunction with the US Department of Transportation, it launched the Smart City Challenge to identify a launchpad for its innovations. The Guardian revealed on Monday never-before-seen documents and proposals from the Smart City Challenge.

Contagions such as the spread of popular news stories, or infectious diseases, propagate in cascades over dynamic networks with unobservable topologies. However, "social signals" such as product purchase time, or blog entry timestamps are measurable, and implicitly depend on the underlying topology, making it possible to track it over time. Interestingly, network topologies often "jump" between discrete states that may account for sudden changes in the observed signals. The present paper advocates a switched dynamic structural equation model to capture the topology-dependent cascade evolution, as well as the discrete states driving the underlying topologies. Conditions under which the proposed switched model is identifiable are established. Leveraging the edge sparsity inherent to social networks, a recursive $\ell_1$-norm regularized least-squares estimator is put forth to jointly track the states and network topologies. An efficient first-order proximal-gradient algorithm is developed to solve the resulting optimization problem. Numerical experiments on both synthetic data and real cascades measured over the span of one year are conducted, and test results corroborate the efficacy of the advocated approach.

In a conventional signal detection problem, the goal is to design a system for detecting a specific signal of interest [1]. The performance of such systems degrades if the signal evolves over time or for other known signals. Due to the advent of Big Data applications, modern detection systems are expected to perform signal detection tasks for different signal models. Hence, it is desirable to build a universal system which is flexible enough to generalize to several signal models. This paper considers a Wireless Sensor Network (WSN) consisting of a number of sensors and a FC. WSNs often operate with severe resource limitations. Consequently, minimizing the system complexity in terms of communication is critical [2]. For example, resources can be conserved if the nodes do not transmit irrelevant or redundant data. Such transmissions can be avoided through dimensionality reduction [3].

The input data features set for many data driven tasks is high-dimensional while the intrinsic dimension of the data is low. Data analysis methods aim to uncover the underlying low dimensional structure imposed by the low dimensional hidden parameters by utilizing distance metrics that consider the set of attributes as a single monolithic set. However, the transformation of the low dimensional phenomena into the measured high dimensional observations might distort the distance metric, This distortion can effect the desired estimated low dimensional geometric structure. In this paper, we suggest to utilize the redundancy in the attribute domain by partitioning the attributes into multiple subsets we call views. The proposed methods utilize the agreement also called consensus between different views to extract valuable geometric information that unifies multiple views about the intrinsic relationships among several different observations. This unification enhances the information that a single view or a simple concatenations of views provides.

Approximate Bayesian Computation (ABC) is a framework for performing likelihood-free posterior inference for simulation models. Stochastic Variational inference (SVI) is an appealing alternative to the inefficient sampling approaches commonly used in ABC. However, SVI is highly sensitive to the variance of the gradient estimators, and this problem is exacerbated by approximating the likelihood. We draw upon recent advances in variance reduction for SVI [6][13] and likelihood-free inference using deterministic simulations [12] to produce low variance gradient estimators of the variational lower-bound. By then exploiting automatic differentiation libraries [8] we can avoid nearly all model-specific derivations. We demonstrate performance on three problems and compare to existing SVI algorithms. Our results demonstrate the correctness and efficiency of our algorithm.