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.

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.

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.

In cargo logistics, a key performance measure is transport risk, defined as the deviation of the actual arrival time from the planned arrival time. Neither earliness nor tardiness is desirable for customer and freight forwarders. In this paper, we investigate ways to assess and forecast transport risks using a half-year of air cargo data, provided by a leading forwarder on 1336 routes served by 20 airlines. Interestingly, our preliminary data analysis shows a strong multimodal feature in the transport risks, driven by unobserved events, such as cargo missing flights. To accommodate this feature, we introduce a Bayesian nonparametric model -- the probit stick-breaking process (PSBP) mixture model -- for flexible estimation of the conditional (i.e., state-dependent) density function of transport risk. We demonstrate that using simpler methods, such as OLS linear regression, can lead to misleading inferences. Our model provides a tool for the forwarder to offer customized price and service quotes. It can also generate baseline airline performance to enable fair supplier evaluation. Furthermore, the method allows us to separate recurrent risks from disruption risks. This is important, because hedging strategies for these two kinds of risks are often drastically different.

The blind application of machine learning runs the risk of amplifying biases present in data. Such a danger is facing us with word embedding, a popular framework to represent text data as vectors which has been used in many machine learning and natural language processing tasks. We show that even word embeddings trained on Google News articles exhibit female/male gender stereotypes to a disturbing extent. This raises concerns because their widespread use, as we describe, often tends to amplify these biases. Geometrically, gender bias is first shown to be captured by a direction in the word embedding. Second, gender neutral words are shown to be linearly separable from gender definition words in the word embedding. Using these properties, we provide a methodology for modifying an embedding to remove gender stereotypes, such as the association between between the words receptionist and female, while maintaining desired associations such as between the words queen and female. We define metrics to quantify both direct and indirect gender biases in embeddings, and develop algorithms to "debias" the embedding. Using crowd-worker evaluation as well as standard benchmarks, we empirically demonstrate that our algorithms significantly reduce gender bias in embeddings while preserving the its useful properties such as the ability to cluster related concepts and to solve analogy tasks. The resulting embeddings can be used in applications without amplifying gender bias.