LOS ANGELES--Will.i.am sounds congested and subdued, his batteries running low after a high-octane appearance on The Ellen Show earlier that day. He's longing for 2025, when an artificial intelligence will be able to tell him to take it easy. "Will, you sound like you are stuffy," he says in the voice of this fictional AI. "What did you eat recently? Stay away from sweets because they are causing you more mucus. You should get some rest tonight. I'm going to cancel your appointments from 7 pm. I've already bought you some epsom salts from CVS. Go and pick them up."

A California court has dismissed part of a lawsuit brought by Twitter that challenges U.S. government restrictions on what it can say about surveillance requests on its users. Twitter sued the government in 2014, alleging that the restrictions, which are common to all Internet service providers, infringe its First Amendment right to free speech. Earlier this year, the Department of Justice asked the federal district court in Oakland, California, to toss out the lawsuit. It argued that the Foreign Intelligence Surveillance Court (FISC) is a more suitable venue to hear the dispute, and that part of Twitter's argument didn't stand because the company isn't disputing document classification decisions made by the government. On Monday, a judge agreed with the government's latter argument but denied its request to shift the case to FISC.

Google's TensorFlow has been publicly available since November, 2015, and there is no disputing that, in a few short months, it has made an impact on machine learning in general, and on deep learning specifically. There is evidence of widespread acceptance via blog posts, academic papers, and tutorials all over the web. It is, of course, difficult to estimate true adoption rates, but TensorFlow's Github repository has nearly twice the number of stars of both the next most-starred machine learning project, Scikit-learn, and closest deep learning project, Berkeley Vision and Learning Center's Caffe. While not concretely indicative of TensorFlow having become the leader in the space, it is fairly easy to surmise that, given its fairly recent release, there has been considerable interest in, and use of, Google's deep learning library. For the most part, TensorFlow is relatively straightforward to use, and neural network afficianados without experience using the library could look at a given network's code and get an intuititive sense of what is going on.

It must be tough being a politician. Even as far back as 380 BCE, the Greek philosopher Plato remarked "those who are too smart to engage in politics are punished by being governed by those who are dumber." The jokes -- at each successive political candidate's expense -- haven't stopped flowing since. Like any good race to the White House, the current campaign has seen a slew of witty, sarcastic, and yes, sometimes hurtful social media comments, memes, and jokes, leveled squarely at the 21 main runners. But which of these social media posts show a real hate for the politician in the virtual cross-hairs, and which suggest the author actually likes -- even loves -- the potential future POTUS?

Details of Google's DeepMind data-sharing agreement with the UK NHS revealed that the AI system would have access to millions of patient records. The agreement, according to the NHS, is for direct clinical use only. Google's deal with the National Health Service (NHS) will allow artificial intelligence units to access as many as 1.6 million private patient records, a new report has revealed. The data-sharing agreement, according to New Scientist, which first unmasked its true nature, would give Google's DeepMind unrestricted access to sensitive data that the Royal Free NHS Trust has. As early as 2014, Google has partnered with several scientists to understand human health.

Warren Buffett isn't known for blown calls. But the billionaire investment guru and head of the holding company Berkshire Hathaway has become increasingly touchy around one of his top positions: International Business Machines. "We feel fine or we won't own it. We've never sold a share of IBM," Buffett told CNBC in an interview Monday. The fact that shares in the computing giant have fallen some 15 percent since he first started building his stake in the company in early 2011 hasn't phased him.

Deep neural networks (DNNs) are capable of learning to identify shapes, so "we're on the right track in developing machines with a visual system and vocabulary as flexible and versatile as ours," say KU Leuven researchers. "For the first time, a dramatic increase in performance has been observed on object and scene categorization tasks, quickly reaching performance levels rivaling humans," they note in an open-access paper in PLOS Computational Biology. Categorization accuracy for models created by three DNNs (CaffeNet, VGG-19, and GoggLeNet) for three types of images (color, grayscaled, silhouette). For each type, mean human performance is indicated by a gray horizontal line, with the gray surrounding band depicting 95% confidence intervals. Error bars (vertical black lines) depict 95% confidence intervals.

Over the last two decades, California and the federal government have faced harsh criticism for failing to take stronger actions to stop a highly contagious disease that has killed millions of trees along coastal regions from Big Sur to portions of Oregon. Now, a new computer modeling study suggests that the "sudden oak death" epidemic, which emerged in 1995, has grown too big and is spreading too fast to eradicate statewide. The analysis is the first to integrate knowledge of the pathogen with topography, weather and resources like government budgets to predict the likely effects of various management strategies over such a large area -- in this case, California's 163,707 square miles of land. The results are somewhat hopeful: Because the epidemic's growth rate increases with its size, focusing on restoring and treating small, local forests is now the most practical and cost-effective option for managing the destructive fungus, Phtophthora ramorum. The findings were published Monday in the Proceedings of the National Academy of Sciences.

There are a lot of clustering algorithms to choose from. The standard sklearn clustering suite has thirteen different clustering classes alone. So what clustering algorithms should you be using? As with every question in data science and machine learning it depends on your data. A number of those thirteen classes in sklearn are specialised for certain tasks (such as co-clustering and bi-clustering, or clustering features instead data points).

April on /r/MachineLearning brings top posts in deep learning video tutorials and books, the TensorFlow Playground, deep conversation centered on an xkcd comic from 2014, Microsoft cognitive APIs, and a meta-conversation on the subreddit's direction. The Google Developer YouTube channel has launched a new video series, titled Machine Learning Recipes. There are 3 videos in the playlist, as of this writing. The series, hosted by Josh Gordon, consists of video topics such as "What Makes a Good Feature?" and "Visualizing a Decision Tree." This link is directly to the first of the videos.