One of the best things about Chromebooks is that they're built to last. Thanks to automatic security and feature updates from Google, along with a lightweight browser-based operating system, longtime users may find that their laptops run as well, if not better, than they did on day one. But despite Chromebooks' theoretical longevity, it's possible for Google to cut their lives short. Per the company's End of Life policy, Chromebooks and other Chrome OS devices are only entitled to five years of feature and security updates. After that, Google doesn't guarantee that these systems will run safely or properly.

When we reviewed the Amazon Echo last year, we hailed it as the best home-based voice-controlled product at the consumer level. It's become even more versatile and powerful since then. It can control your home's smart lighting, lock your smart lock, play music, provide weather forecasts, order a pizza, and more. This affordable, always-on personal assistant can manage not only your home, but much of your digital life. It's so useful, we predicted many folks would want more than one Echo in their home.

This tutorial aims to provide an introduction to machine learning and scikit-learn "from the ground up". We will start with core concepts of machine learning, some example uses of machine learning, and how to implement them using scikit-learn. Going in detail through the characteristics of several methods, we will discuss how to pick an algorithm for your application, how to set its parameters, and how to evaluate performance.

This tutorial aims to provide an introduction to machine learning and scikit-learn "from the ground up". We will start with core concepts of machine learning, some example uses of machine learning, and how to implement them using scikit-learn. Going in detail through the characteristics of several methods, we will discuss how to pick an algorithm for your application, how to set its parameters, and how to evaluate performance.

How Artificial Intelligence Could Help Transform The Oil Industry (oilprice.com) There's another way to transform the oil industry. Give up, you old white bald guys. Electric is here, get used to it.

Last month, some people tweeting about Pokémon Go became unwitting subjects in an experiment that could presage a worrying new kind of online attack. Industry researchers trained machine-learning software to write tweets like a human to reply to some people using the hashtag #Pokemon, in a demonstration of how advances in software that understands language could be used to trick people online. Roughly a third of people targeted by the software clicked on a benign link sent along by the software to test how convincing it was. That's much higher than the 5 to 10 percent success rate typical for automated "phishing" messages aimed at tricking people into clicking links to deliver malware or steal passwords, says John Seymour, a senior data scientist at security company ZeroFOX. The machine-learning system comes close to the roughly 40 percent success rate of "spearphishing" messages handcrafted to trick a specific person, he says.

Uber launches self-driving cars in a trial in Pittsburgh this week. I was in an Uber returning from the airport when this was reported on the BBC by EV subscriber, Rory Cellan Jones. My Uber driver heard the headline and leant forward to increase the volume, ears pricking up. This is the sharp end of automation. Even if you hold the reasonable belief that work isn't going to go anywhere soon, and that we'll continuously reinvent things for humans to do, the question is not about the statistics in aggregate.

SINCE nobody really knows how brains work, those researching them must often resort to analogies. A common one is that a brain is a sort of squishy, imprecise, biological version of a digital computer. But analogies work both ways, and computer scientists have a long history of trying to improve their creations by taking ideas from biology. The trendy and rapidly developing branch of artificial intelligence known as "deep learning", for instance, takes much of its inspiration from the way biological brains are put together. The general idea of building computers to resemble brains is called neuromorphic computing, a term coined by Carver Mead, a pioneering computer scientist, in the late 1980s.

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