Google has launched a project to use artificial intelligence to create compelling art and music, offering a reminder of how technology is rapidly changing what it means to be a musician, and what makes us distinctly human. Google's Project Magenta, announced Wednesday, aims to push the state of the art in machine intelligence that's used to generate music and art. "We don't know what artists and musicians will do with these new tools, but we're excited to find out," said Douglas Eck, the project's leader in a blog post. "Daguerre and later Eastman didn't imagine what Annie Liebovitz or Richard Avedon would accomplish in photography. Surely Rickenbacker and Gibson didn't have Jimi Hendrix or St. Vincent in mind." Google has already released a song demonstrating the technology.

This tutorial will describe how a surprisingly small amount of code can be used to build a recommendation engine using the MapR Sandbox for Hadoop with Apache Mahout and Elasticsearch. This tutorial will run on the MapR Sandbox. The tutorial also requires Elasticsearch and Mahout to be installed on the sandbox. Step 1: Indexing the movie meta data in Elasticsearch In Elasticsearch, documents contain fields which are, by default, all indexed. Typically documents are written as a single-level JSON structure.

Artificial intelligence has long fascinated the technology industry and it's easy to see why. Creating a piece of software that can think, and act, like a human mind would be one of the biggest advancements in computer science ever, and that might even be an understatement. The interest in A.I. has meant that big companies, like Google or Facebook, are hiring teams of engineers and researchers to build the technology into their products. While humans have not created anything close to the human mind, machines and software are becoming much better at predicting what we want based on a much smaller set of information. Google, for example, spent much of its annual I/O conference talking about advances in A.I. and how the company plans to implement the technology.

Siri is expected to expand beyond the iPhone and iPad to third-party apps and Macs, a move designed to hike the usefulness and IQ of Apple's personal digital assistant. Analysts who cover Apple predict an expanded role for Siri will be one highlight of Apple's Apple Worldwide Developers Conference, which begins Monday in San Francisco.

In U.S. Congress Discusses AI, Automation, Robotics and Basic Income we see senior U.S. Government people start to try and understand the changing world… Nikolas Badminton is a world-respected futurist speaker that researches, speaks, and writes about the future of work, how technology is affecting the workplace, how workers are adapting, the sharing economy, and how the world is evolving. He appears at conferences in Canada, USA, UK, and Europe. Email him to book him for your radio, TV show, or conference.

Further, recent psychoacoustical evidence suggest the approach bears some resemblance with human auditory processing for sound textures (McDermott et al., 2013; Nelken and de Cheveigné, 2013). In an influential 2007 article, Aucouturier, Defreville & Pachet (Aucouturier et al., 2007) applied a BOF model to categorize both polyphonic music and soundscapes. Their results showed that, while BOF was a meriting model for their polyphonic music dataset, it was spectacularly effective for soundscapes, reaching accuracies of 96%. The contrast, they interpreted, lied in differences in the temporal structure of both types of stimuli, with music being more formally organized and soundscapes more easily summarized by statistics. In a later companion study (Aucouturier and Defreville, 2009), they showed that soundscapes could be time-shuffled without altering listeners' perception of their acoustic similarity, while music could not. While more work was needed for music, the authors therefore concluded that BOF was a sufficient model to approximate human perception for soundscapes, practically ruling out the need to recognize the local acoustic events in a texture in order to identify it.

Heterogeneity is often natural in many contemporary applications involving massive data. While posing new challenges to effective learning, it can play a crucial role in powering meaningful scientific discoveries through the understanding of important differences among subpopulations of interest. In this paper, we exploit multiple networks with Gaussian graphs to encode the connectivity patterns of a large number of features on the subpopulations. To uncover the heterogeneity of these structures across subpopulations, we suggest a new framework of tuning-free heterogeneity pursuit (THP) via large-scale inference, where the number of networks is allowed to diverge. In particular, two new tests, the chi-based test and the linear functional-based test, are introduced and their asymptotic null distributions are established. Under mild regularity conditions, we establish that both tests are optimal in achieving the testable region boundary and the sample size requirement for the latter test is minimal. Both theoretical guarantees and the tuning-free feature stem from efficient multiple-network estimation by our newly suggested approach of heterogeneous group square-root Lasso (HGSL) for high-dimensional multi-response regression with heterogeneous noises. To solve this convex program, we further introduce a tuning-free algorithm that is scalable and enjoys provable convergence to the global optimum. Both computational and theoretical advantages of our procedure are elucidated through simulation and real data examples.

We present a new framework for online Least Squares algorithms for nonlinear modeling in RKH spaces (RKHS). Instead of implicitly mapping the data to a RKHS (e.g., kernel trick), we map the data to a finite dimensional Euclidean space, using random features of the kernel's Fourier transform. The advantage is that, the inner product of the mapped data approximates the kernel function. The resulting "linear" algorithm does not require any form of sparsification, since, in contrast to all existing algorithms, the solution's size remains fixed and does not increase with the iteration steps. As a result, the obtained algorithms are computationally significantly more efficient compared to previously derived variants, while, at the same time, they converge at similar speeds and to similar error floors.

In this paper, we will investigate the efficacy of IMAT (Iterative Method of Adaptive Thresholding) in recovering the sparse signal (parameters) for linear models with missing data. Sparse recovery rises in compressed sensing and machine learning problems and has various applications necessitating viable reconstruction methods specifically when we work with big data. This paper will focus on comparing the power of IMAT in reconstruction of the desired sparse signal with LASSO. Additionally, we will assume the model has random missing information. Missing data has been recently of interest in big data and machine learning problems since they appear in many cases including but not limited to medical imaging datasets, hospital datasets, and massive MIMO. The dominance of IMAT over the well-known LASSO will be taken into account in different scenarios. Simulations and numerical results are also provided to verify the arguments.

Many years ago (don't ask me how I know this!) the hamburger chain Burger King began branding themselves with this slogan: "Have it your way!" It was pure marketing genius! The idea that you could order something, in this case a hamburger, at a fast food dispensary that would be tailor-made to your specific personal tastes was revolutionary – it set them apart from their competitors. Something similar happened when Amazon.com, one of the first major online stores in the Internet era, began suggesting books (and other products) to their customers that were an amazingly good match to each individual's personal tastes. Of course, Amazon accommodated its customers with this value-added service by invoking a scientific procedure, data science applied to customer data, not by asking customers directly (as did Burger King).