NEC Corporation and the University of Tokyo recently announced the commencement of a comprehensive industry-academia alliance for strengthening innovation in Japan. This alliance differs from others in that it has been created under an organization-to-organization agreement to promote comprehensive collaboration, including the sharing of visions and issues from the fundamental research phase to the commercialization of research results phase, the consideration of social acceptance of research results, and the development of human resources for the future. Among the first actions by this industry-academia alliance for innovation will be considering the magnitude of the impact of AI on society, to conclude the NEC/University of Tokyo Partnership Agreement for Future AI Research and Education in the field of Strategic Artificial Intelligence (AI), and to commence specific activities. Conventional industry-academia alliances have only engaged in limited, small-scale joint research related to individual technologies. However, in order to strengthen Japan's innovation and promote meaningful social changes, it will be imperative to carry out investigations on implications for ethics and legal systems with a focus on commercialization, as well as to create new collaborative alliances that are large in scale and cover broad areas, including human resource development.

PsiberLogic is a completely free, open-source fuzzy logic controller package for Python 3. Psibernetix proudly supports the amazing Python community, and is happy to contribute to Python's open-source movement. This package is for anyone seeking a high-performance, python3-callable package for creating fuzzy logic controllers. Details on ALPHA – a significant breakthrough in the application of what's called genetic-fuzzy systems are published in the most-recent issue of the Journal of Defense Management, as this application is specifically designed for use with Unmanned Combat Aerial Vehicles (UCAVs) in simulated air-combat missions for research purposes. The tools used to create ALPHA as well as the ALPHA project have been developed by Psibernetix, Inc., recently founded by UC College of Engineering and Applied Science 2015 doctoral graduate Nick Ernest, now president and CEO of the firm; as well as David Carroll, programming lead, Psibernetix, Inc.; with supporting technologies and research from Gene Lee; Kelly Cohen, UC aerospace professor; Tim Arnett, UC aerospace doctoral student; and Air Force Research Laboratory sponsors. ALPHA is currently viewed as a research tool for manned and unmanned teaming in a simulation environment.

Please introduce yourself and your Weps to our readers! We are a team of 20-somethings from Estonia who are behind Weps, the artificial intelligence chatbot website builder that works on any device. Our users can create websites by interacting with Weps in an already familiar messenger-like interface. We were just accepted into Axel Springer Plug and Play accelerator this September and are looking forward to grow together with their help! How did you get the idea to Weps?

The Best Paper award for this year's International Conference on Very Large Data Bases (VLDB) goes to "Compressed Linear Algebra for Large-Scale Machine Learning", authored by a PhD candidate at the University of Maryland and four senior researchers from IBM. Their method for compressing matrices for linear algebra operations promises to provide users significant increases in speed with less memory. In particular, the compression technology provides benefits at two different parts of the data science process. Before training a model, a data scientist typically goes through multiple iterations of feature engineering. Common feature engineering tasks include examining the data with descriptive statistics and transforming the values in columns to better suit the assumptions built into different types of machine learning models.

"The second edition was reviewed in JASA by Maiti (2004) … we now stand 10 years later with an even more impressive textbook that truly stands for what Bayesian data analysis should be. Quite a lot … this is truly the reference book for a graduate course on Bayesian statistics and not only Bayesian data analysis." Praise for the Second Edition: … it is simply the best all-around modern book focused on data analysis currently available. The second edition makes this an even more robust choice. I am thoroughly excited to have this book in hand to supplement course material and to offer research collaborators and clients at our consulting lab more sophisticated methods to solve their research problems.

Cancer is a tricky disease to treat. With more than one hundred known types, each responding differently to treatment depending on the person they're growing inside (and dozens of other factors), oncologists certainly have their work cut out for them. Machine learning could soon make their jobs a bit easier. IBM's supercomputer Watson has been applying machine learning to personalized cancer treatment for some time. After pouring over 600,000 medical reports and 1.5 million anonymized patient records and clinical trials, the data should help define the clearest path forward for doctors.

This article was originally published by Scientific American. The idea that we have brains hardwired with a mental template for learning grammar -- famously espoused by Noam Chomsky of the Massachusetts Institute of Technology -- has dominated linguistics for almost half a century. Recently, though, cognitive scientists and linguists have abandoned Chomsky's "universal grammar" theory in droves because of new research examining many different languages -- and the way young children learn to understand and speak the tongues of their communities. That work fails to support Chomsky's assertions. The research suggests a radically different view, in which learning of a child's first language does not rely on an innate grammar module. Instead the new research shows that young children use various types of thinking that may not be specific to language at all -- such as the ability to classify the world into categories (people or objects, for instance) and to understand the relations among things. These capabilities, coupled with a unique hu man ability to grasp what others intend to communicate, allow language to happen. The new findings indicate that if researchers truly want to understand how children, and others, learn languages, they need to look outside of Chomsky's theory for guidance.

We describe a probabilistic PARAFAC/CANDECOMP (CP) factorization for multiway (i.e., tensor) data that incorporates auxiliary covariates, SupCP. SupCP generalizes the supervised singular value decomposition (SupSVD) for vector-valued observations, to allow for observations that have the form of a matrix or higher-order array. Such data are increasingly encountered in biomedical research and other fields. We describe a likelihood-based latent variable representation of the CP factorization, in which the latent variables are informed by additional covariates. We give conditions for identifiability, and develop an EM algorithm for simultaneous estimation of all model parameters. SupCP can be used for dimension reduction, capturing latent structures that are more accurate and interpretable due to covariate supervision. Moreover, SupCP specifies a full probability distribution for a multiway data observation with given covariate values, which can be used for predictive modeling. We conduct comprehensive simulations to evaluate the SupCP algorithm, and we apply it to a facial image database with facial descriptors (e.g., smiling / not smiling) as covariates. Software is available at https://github.com/lockEF/SupCP .

ABSTRACT We study the relationship between online Gaussian process (GP) regression and kernel least mean squares (KLMS) algorithms. While the latter have no capacity of storing the entire posterior distribution during online learning, we discover that their operation corresponds to the assumption of a fixed posterior covariance that follows a simple parametric model. Interestingly, several well-known KLMS algorithms correspond to specific cases of this model. The probabilistic perspective allows us to understand how each of them handles uncertainty, which could explain some of their performance differences. Index Terms-- online learning, regression, Gaussian processes, kernel least-mean squares 1. INTRODUCTION Gaussian Process (GP) regression is a state-of-the-art Bayesian technique for nonlinear regression [1].

Here we study non-convex composite optimization: first, a finite-sum of smooth but non-convex functions, and second, a general function that admits a simple proximal mapping. Most research on stochastic methods for composite optimization assumes convexity or strong convexity of each function. In this paper, we extend this problem into the non-convex setting using variance reduction techniques, such as prox-SVRG and prox-SAGA. We prove that, with a constant step size, both prox-SVRG and prox-SAGA are suitable for non-convex composite optimization, and help the problem converge to a stationary point within $O(1/\epsilon)$ iterations. That is similar to the convergence rate seen with the state-of-the-art RSAG method and faster than stochastic gradient descent. Our analysis is also extended into the min-batch setting, which linearly accelerates the convergence. To the best of our knowledge, this is the first analysis of convergence rate of variance-reduced proximal stochastic gradient for non-convex composite optimization.