We study the residual bootstrap (RB) method in the context of high-dimensional linear regression. Specifically, we analyze the distributional approximation of linear contrasts $c^{\top} (\hat{\beta}_{\rho}-\beta)$, where $\hat{\beta}_{\rho}$ is a ridge-regression estimator. When regression coefficients are estimated via least squares, classical results show that RB consistently approximates the laws of contrasts, provided that $p\ll n$, where the design matrix is of size $n\times p$. Up to now, relatively little work has considered how additional structure in the linear model may extend the validity of RB to the setting where $p/n\asymp 1$. In this setting, we propose a version of RB that resamples residuals obtained from ridge regression. Our main structural assumption on the design matrix is that it is nearly low rank --- in the sense that its singular values decay according to a power-law profile. Under a few extra technical assumptions, we derive a simple criterion for ensuring that RB consistently approximates the law of a given contrast. We then specialize this result to study confidence intervals for mean response values $X_i^{\top} \beta$, where $X_i^{\top}$ is the $i$th row of the design. More precisely, we show that conditionally on a Gaussian design with near low-rank structure, RB simultaneously approximates all of the laws $X_i^{\top}(\hat{\beta}_{\rho}-\beta)$, $i=1,\dots,n$. This result is also notable as it imposes no sparsity assumptions on $\beta$. Furthermore, since our consistency results are formulated in terms of the Mallows (Kantorovich) metric, the existence of a limiting distribution is not required.

We propose a novel methodology, forest floor, to visualize and interpret random forest (RF) models. RF is a popular and useful tool for non-linear multi-variate classification and regression, which yields a good trade-off between robustness (low variance) and adaptiveness (low bias). Direct interpretation of a RF model is difficult, as the explicit ensemble model of hundreds of deep trees is complex. Nonetheless, it is possible to visualize a RF model fit by its mapping from feature space to prediction space. Hereby the user is first presented with the overall geometrical shape of the model structure, and when needed one can zoom in on local details. Dimensional reduction by projection is used to visualize high dimensional shapes. The traditional method to visualize RF model structure, partial dependence plots, achieve this by averaging multiple parallel projections. We suggest to first use feature contributions, a method to decompose trees by splitting features, and then subsequently perform projections. The advantages of forest floor over partial dependence plots is that interactions are not masked by averaging. As a consequence, it is possible to locate interactions, which are not visualized in a given projection. Furthermore, we introduce: a goodness-of-visualization measure, use of colour gradients to identify interactions and an out-of-bag cross validated variant of feature contributions.

A Tesla electric-powered sedan stands at a Tesla charging staiton at a highway reststop along the A7 highway on June 11, 2015 near Rieden, Germany. SAN FRANCISCO -- Some Tesla owners have used the cars' Autopilot feature to take their hands off the wheel -- and film themselves doing anything but driving. YouTube videos uploaded since Tesla introduced the self-driving feature in October show drivers playing games, pretending to sleep, and in general, not holding the steering wheel. This kind of distracted driving is exactly what Tesla Motors Inc., under federal investigation after a man using Autopilot died from injuries sustained in a May crash, says drivers should not do. The Autopilot feature is designed to allow Teslas to cruise highways without drivers steering, braking or accelerating.

Alpha: 'AI who beats Human Pilot in Tests' and in these lengthy tests; as usual the human subject is prone to getting tired. The Computer AI known as ALPHA does not get tired and is one of recent AI developments that reveal just how good the Artificial intelligence is getting. I have written on AI for some time now and it is evident the next stage of development that men will shoot for is AI use in many different areas. But areas where these AI robots, and instruments can work with humans as an aid. This is all fine and good until the Robot or AI instrument starts learning on it's own.

We are excited to announce Google Cloud Platform Education Grants for computer science faculty and students. Starting today, faculty in the United States who teach courses in computer science or related subjects can apply for free credits for students to use across the full complement of Google Cloud Platform tools, without having to submit a credit card. These credits can be used anytime during the 2016-17 academic year. Consider the work of Duke University undergrad Brittany Wenger. After watching several women in her family suffer from breast cancer, Brittany used her knowledge of artificial intelligence to create Cloud4Cancer, an artificial neural network built on top of Google App Engine.

During his 15 years as president of the Service Employees International Union, Andy Stern was a controversial figure. He suffered his share of criticism from inside and outside the union. There was, however, no disputing his success in making SEIU the largest and fastest growing union in the country and a powerful political machine that was instrumental in electing President Obama and getting the Affordable Care Act passed. During Stern's tenure as national organizing director and president, he introduced and implemented strategies of industry-wide organizing and bargaining to counter the changing reality of employers who were becoming large and international. He took SEIU out of the AFL-CIO and formed a new labor federation called Change to Win, because he felt the mainstream labor movement was too conservative about organizing and limited its power by refusing to consolidate smaller unions into bigger and more powerful ones.

Graphics-chip company NVIDIA (NASDAQ:NVDA) has dominated the market for accelerators in recent years, with its Tesla GPUs being used for both high-performanceIn this point alphabet can not compete with Apple. Tesla has become a big business for NVIDIA -- during the past 12 months, the company's data-center segment generated nearly 400 million of revenue. Intel (NASDAQ:INTC) has been eyeing this market for some time, but its Xeon Phi line of accelerator cards has so far failed to make much of an impact. Knights Landing, the latest Xeon Phi product from Intel, could change that. I first talked about Knights Landing two years ago, and following a major delay, the product is finally shipping in volume to customers.

A Baxter robot of Rethink Robotics picks up a business card as it performs during a display at the World Economic Forum, in China's port city Dalian Thomson Reuters "There's just doesn't seem to be many blacksmith jobs these days." At first glance, this would be a ridiculous thing to say. We live in a modern society and machines do a way better job of making things from metal anyways. What if machines are better at driving long-haul trucks? What if machines are better servers at McDonald's?

The result is that the massive computer power so harnessed helps us to analyse what has happened in the past and, with the use of predictive analytics techniques, opens a window leading to accurate predictions. Undoubtedly, artificial intelligence is fast becoming a major technology for prescriptive analytics, the step beyond predictive analytics that helps us determine how to implement and/or optimise optimal decisions. In business applications, it can assess future risks and quantify probabilities, giving us insights into how to improve market penetration, customer satisfaction, security analysis, trade execution and fraud detection and prevention, while proving indispensable in land and air-traffic control, national security and defence, not to mention a host of healthcare applications such as patientspecific treatments for diseases and illnesses. Typically, the giant search engine firm Google is a pioneer in the field of artificial intelligence, developing self-driving automobiles, smartphone assistants and other examples of machine learning, while it is no secret that Facebook founder Mark Zuckerberg and actor Ashton Kutcher recently invested 40 million in a project focusing on developing artificial brains. In science fiction films such as Matrix, we have seen how futuristic devices will facilitate facial recognition, interpret human comments and perform complex language translations.

According to some prominent voices in the tech world, artificial intelligence presents a looming existential threat to humanity: Warnings by luminaries like Mr Elon Musk and Professor Nick Bostrom about "the singularity" - when machines become smarter than humans - have attracted millions of dollars and spawned a multitude of conferences. But this hand-wringing is a distraction from the very real problems with artificial intelligence today, which may already be exacerbating inequality in the workplace, at home and in our legal and judicial systems. Sexism, racism and other forms of discrimination are being built into the machine-learning algorithms that underlie the technology behind many "intelligent" systems that shape how we are categorised and advertised to. Take a small example from last year: Users discovered that Google's photo app, which applies automatic labels to pictures in digital photo albums, was classifying images of black people as gorillas. Google apologised; it was unintentional. But similar errors have emerged in Nikon's camera software, which misread images of Asian people as blinking, and in Hewlett-Packard's Web camera software, which had difficulty recognising people with dark skin tones.