Tesla CEO Elon Musk speaks at the Model X launch event in Fremont, Calif. SAN FRANCISCO – Tesla is known for making an elite 100,000 electric sedan. Well watch out automakers, Elon Musk just announced his company will be pursuing small SUVs, a pickup truck and even larger scale commercial trucks in a bid to revolutionize transportation as we know it. That bombshell was just part of Musk's long-touted Tesla "Master Plan: Part Deux," which outlined a strategy that pivots the company from a builder of niche automobiles to a producer of a broad range of passenger and commercial vehicles that eschew fossil fuel. Also noted in the plan, which Musk published on the company's website late Wednesday, is the company's more well-known pursuit of the consumer solar business through its planned acquisition of SolarCity.

On a previous post we learned how to train a machine learning classifier that is able to detect the different aspects mentioned on hotel reviews. With this aspect classifier, we were able to automatically know if a particular review was talking about cleanliness, comfort & facilities, food, Internet, location, staff and/or value for money. We also learned how to combine this classifier with the sentiment analysis classifier to get interesting insights and answer questions like are guests loving the location of a particular hotel but complaining about its cleanliness? These are the kind of questions we aim to answer with this tutorial and that will lead us to some interesting insights. The source code used for this process is available in this repository.

Over the course of the past year or so, there have been a number of incredible tech projects aimed at stopping the spread of Ebola. One approach that we've never come across before, however, involves plotting the possible spread of Ebola and other "filoviruses" of the same family by predicting which bat species they're most likely to be carried by. That's exactly the goal of a team of scientists, who recently used machine learning techniques to build just such a model. Their work may help prevent future spillover events in which it is important to predict which species of wildlife help spread contagion. "This work entailed collecting intrinsic features describing the world's bat species -- 1,116 species altogether -- and training a machine learning algorithm on these data to learn which features best predict the bat species that carry filoviruses," lead author of the study Barbara Han, a disease ecologist at the Cary Institute of Ecosystem Studies, tells Digital Trends.

We first ran into the predictive marketing startup Datatrics from the Netherlands at the PAPI's Connect event in Valencia earlier this year, where they competed in the first ever AI Startup Battle. The Dutch startup offers marketing teams an easy and actionable way to leverage Machine Learning with its innovative data management platform, which we believe sets a great example for other startups in showing how BigML can add to their competitive edge and supercharge their growth. So we interviewed Bas Nieland, CEO and co-founder of Datatrics to find out more. Can you tell us what was the motivation behind starting Datatrics? Bas Nieland: Nowadays digital marketers are awashed with data due to the fragmentation of consumer attention on many more channels.

The traditional definition of artificial intelligence is the ability of machines to execute tasks and solve problems in ways normally attributed to humans. Some tasks that we consider simple--recognizing an object in a photo, driving a car--are incredibly complex for AI. Machines can surpass us when it comes to things like playing chess, but those machines are limited by the manual nature of their programming; a 30 gadget can beat us at a board game, but it can't do--or learn to do--anything else. This is where machine learning comes in. Show millions of cat photos to a machine, and it will hone its algorithms to improve at recognizing pictures of cats.

Automation is the past, current and next big thing. For a long time, getting robots and software to work for us has been the Holy Grail of business. In theory it makes everything cheaper, more reliable, more powerful and it frees humans up to work on creative projects. Ever since the first industrial revolution, capitalists have looked for ways to extract human labour from the means of production and replace it with smart systems. This, of course, was initially driven by greed.

For more than a decade, technology investors and entrepreneurs obsessed over social media and mobile apps that helped people do things like find new friends, fetch a ride home or crowdsource a review of a product or a movie. Now Silicon Valley has found its next shiny new thing. And it does not have a "Like" button. The new era centers on artificial intelligence and robots, a transformation that many believe will have a payoff on the scale of the personal computing industry or the commercial Internet, two previous generations that spread computing globally. Computers have begun to speak, listen and see, as well as sprout legs, wings and wheels to move unfettered in the world.

The government will introduce new rules on running Japanese language schools to eliminate poorly managed ones and keep the educational quality at an adequate level, sources said Wednesday. The Justice Ministry will revise the relevant ordinance soon, more clearly stating disqualifying conditions and making its screening more stringent, the sources said. There were 549 approved Japanese language schools in fiscal 2015, which ended in March. Due to Japan's declining population, the government aims to promote the establishment of Japanese language schools to attract more highly skilled foreign workers, but inappropriate operations at some schools have surfaced recently. A man running a Japanese language school in Fukuoka Prefecture was convicted in May of finding part-time jobs for students who worked more hours than allowed by law so they could earn money for school fees.

This paper is concerned with the numerical solution of model-based, Bayesian inverse problems. We are particularly interested in cases where the cost of each likelihood evaluation (forward-model call) is expensive and the number of un- known (latent) variables is high. This is the setting in many problems in com- putational physics where forward models with nonlinear PDEs are used and the parameters to be calibrated involve spatio-temporarily varying coefficients, which upon discretization give rise to a high-dimensional vector of unknowns. One of the consequences of the well-documented ill-posedness of inverse prob- lems is the possibility of multiple solutions. While such information is contained in the posterior density in Bayesian formulations, the discovery of a single mode, let alone multiple, is a formidable task. The goal of the present paper is two- fold. On one hand, we propose approximate, adaptive inference strategies using mixture densities to capture multi-modal posteriors, and on the other, to ex- tend our work in [1] with regards to effective dimensionality reduction techniques that reveal low-dimensional subspaces where the posterior variance is mostly concentrated. We validate the model proposed by employing Importance Sam- pling which confirms that the bias introduced is small and can be efficiently corrected if the analyst wishes to do so. We demonstrate the performance of the proposed strategy in nonlinear elastography where the identification of the mechanical properties of biological materials can inform non-invasive, medical di- agnosis. The discovery of multiple modes (solutions) in such problems is critical in achieving the diagnostic objectives.

This paper characterizes hierarchical clustering methods that abide by two previously introduced axioms -- thus, denominated admissible methods -- and proposes tractable algorithms for their implementation. We leverage the fact that, for asymmetric networks, every admissible method must be contained between reciprocal and nonreciprocal clustering, and describe three families of intermediate methods. Grafting methods exchange branches between dendrograms generated by different admissible methods. The convex combination family combines admissible methods through a convex operation in the space of dendrograms, and thirdly, the semi-reciprocal family clusters nodes that are related by strong cyclic influences in the network. Algorithms for the computation of hierarchical clusters generated by reciprocal and nonreciprocal clustering as well as the grafting, convex combination, and semi-reciprocal families are derived using matrix operations in a dioid algebra. Finally, the introduced clustering methods and algorithms are exemplified through their application to a network describing the interrelation between sectors of the United States (U.S.) economy.