Deep Learning, whether you like it or not is here to stay, and with any tech gold-rush comes a plethora of options that can seem daunting to newcomers. If you were to start off with deep learning, one of the first questions to ask is, which framework to learn? I'd say instead of a simple trial-and-error, if you try to understand the building blocks of all these frameworks, it would help you make an informed decision. Common choices include Theano, TensorFlow, Torch, and Keras. All of these choices have their own pros and cons and have their own way of doing things.

Two self-driving cars are headed down the highway when the lead car decides to speed up to avoid being rear-ended by the second. That car, in turn, slows down to avoid hitting the first. Then a third car suddenly comes between the two, prompting the slower car to change lanes to avoid and accident. The problem: There are cars in the lanes on either side of it. What's an autonomous car to do?

Revision wars on Wikipedia amongst human editors is an all-too-common occurrence, but new research from the UK shows that similar online battles are being waged between the site's software robots. As a new study published in PLOS ONE reveals, Wikipedia's bots don't always get along, frequently undoing each other's edits. These online algorithms, each equipped with their own instructions and goals, engage in sterile "fights" over content that can persist for years. The new research shows how relatively "dumb" bots can produce complex interactions and behaviors, and how developers need to stay on top of their digital creations. This has implications not just for the quality of Wikipedia pages, but for the development of AI in general--particularly any autonomous agents set loose on the web.

A new study has found that humans aren't the only one's fighting about Wikipedia revisions. Researchers have discovered that software robots designed to improve articles on the site were'fighting' over content and undoing each other's edits for 10 years. The findings have shown that even simple autonomous algorithms can produce complex interactions that result in unintended consequences. Researchers discovered that software robots designed to improve articles on Wikipedia were'fighting' over content and undoing each other's edits for 10 years. Researchers have discovered that software robots designed to improve articles on Wikipedia have been'fighting' over content and undoing each other's edits.

Many of the top players in technology and automobiles are fervently working towards a world in which autonomous driving vehicles are commonplace. Some current vehicles, like the Tesla Model S, already offer self-driving, auto-pilot capabilities, but these are a precursor to fully autonomous vehicles that operate with virtually no human intervention. As you'd probably expect, Intel is actively working in the area as well. Massive amounts of processing power and storage are needed to churn through and store the deep learning models that will disseminate data for autonomous vehicles. Today the company posted a short video that gives a glimpse into its Autonomous Driving Lab in Chandler, Arizona.

Open forms of global constraints allow the addition of new variables to an argument during the execution of a constraint program. Such forms are needed for difficult constraint programming problems where problem construction and problem solving are interleaved, and fit naturally within constraint logic programming. However, in general, filtering that is sound for a global constraint can be unsound when the constraint is open. This paper provides a simple characterization, called contractibility, of the constraints where filtering remains sound when the constraint is open. With this characterization we can easily determine whether a constraint has this property or not. In the latter case, we can use it to derive a contractible approximation to the constraint. We demonstrate this work on both hard and soft constraints. In the process, we formulate two general classes of soft constraints.

Boolean matrix factorisation aims to decompose a binary data matrix into an approximate Boolean product of two low rank, binary matrices: one containing meaningful patterns, the other quantifying how the observations can be expressed as a combination of these patterns. We introduce the OrMachine, a probabilistic generative model for Boolean matrix factorisation and derive a Metropolised Gibbs sampler that facilitates efficient parallel posterior inference. On real world and simulated data, our method outperforms all currently existing approaches for Boolean matrix factorisation and completion. This is the first method to provide full posterior inference for Boolean Matrix factorisation which is relevant in applications, e.g. for controlling false positive rates in collaborative filtering and, crucially, improves the interpretability of the inferred patterns. The proposed algorithm scales to large datasets as we demonstrate by analysing single cell gene expression data in 1.3 million mouse brain cells across 11 thousand genes on commodity hardware.

At about lunchtime on an unseasonably warm February day, a small commercial drone hovered alongside Highlight Tower; a striking, angular glass block soaring 126m over a suburban Autobahn on the outskirts of Munich, with equally striking views. This email address is already registered. By submitting my Email address I confirm that I have read and accepted the Terms of Use and Declaration of Consent. By submitting your personal information, you agree that TechTarget and its partners may contact you regarding relevant content, products and special offers. You also agree that your personal information may be transferred and processed in the United States, and that you have read and agree to the Terms of Use and the Privacy Policy.

Editor's note: We're proud to bring NextDraft--the most righteous, most essential newsletter on the web--to WIRED.com. Every Friday you'll get a roundup of the week's most popular must-read stories from around the internet, courtesy of mastermind Dave Pell. As it becomes increasingly clear that artificial intelligence and other technologies are going to be history's most aggressive job killers, more people (in Silicon Valley and elsewhere) are re-examining the possibility that a universal basic income could provide a solution. But as NYT Mag's Annie Lowrey reports, "No experiment has been truly complete, studying what happens when you give a whole community money for an extended period of time -- when nobody has to worry where his or her next meal is coming from or fear the loss of a job or the birth of a child." But now, in a few villages in Kenya, a non-profit is looking to run the biggest such experiment yet.

As tech companies continue to dump mountains of cash into artificial intelligence (AI) development, the technology promises to greatly improve our digital lives. However, the AI ecosystem still has major problems to solve before it can advance, a new report said. The report, released Friday from Edison Investment Research, said that AI has the potential to be a major differentiator but it is still in the early stages of its development. Most of what is referred to as AI, currently, is "simply advanced statistics," the report claims. According to the Edison Investment Research report, there are three goals that must be solved for AI to move out of its infancy.