Company's delayed, £319 smart speaker pitched as music-first wireless speaker that can be voice-controlled via Siri Tue 23 Jan 2018 09.44 EST Last modified on Tue 23 Jan 2018 11.13 EST Apple's delayed £319 HomePod smart speaker will finally be available to buy on Friday in the UK, US and Australia. The 17.8cm tall HomePod is pitched as a music-first wireless speaker that can be controlled by voice using Apple's Siri assistant, which can also set timers, reminders, check the weather and control smart home devices. "It brings advanced audio technologies like beam-forming tweeters, a high-excursion woofer and automatic spatial awareness, together with the entire Apple Music catalogue and the latest Siri intelligence, in a simple, beautiful design that is so much fun to use," said Philip Schiller, Apple's senior vice president of worldwide marketing. The HomePod was announced in June to ship before the end of 2017, but Apple was forced to delay shipping in November as the product needed more work. Apple could only watch as Amazon got an over three-year head start with the Echo – while Google's Home is 14 months old – and it now has some catching up to do. Ben Wood, chief of research at CCS Insight, said: "This is a critically important device for Apple given the significant head start Amazon and Google have in this area.

A Google panorama photo fail from a Reddit user has again shown how good AI can be at weirdly specific tasks and how bad it is at seeing, well, the big picture. A skier with the handle MalletsDarker snapped three photos of friends at the Lake Louise ski resort in Banff, Alberta, and as it does, Google Photos offered to stitch them together. To be sure, the algorithm did a masterful job of blending the three photos. However, it failed to grasp basics like "humans are not eighty feet tall" and turned MalletsDarker's friend into a lurking, Gulliver-sized figure. Looking at the three photos, it's easy to see why Google Photos offered to do a stitch.

Apple will release its brand new HomePod in just a couple of weeks, it has announced. The smart speaker will go on sale on 9 February. Pre-orders will open this Friday, 26 January, it said in a statement. The 7-inch speaker will cost £319 in the UK, as well as similar prices in the US and Australia when it goes on sale at the end of this week. It will go on sale in France and Germany in spring, Apple said – voice assistants often roll out slower than other products, since they rely on having local data and languages.

Artificial intelligence already is a top topic at this year's World Economic Forum in Davos, Switzerland. Monday night, amid a driving blizzard that snarled traffic around town, I hosted a small dinner featuring Carnegie Mellon University's Justine Cassell. She is associate dean of technology, strategy, and impact at the university's school of computer science, and an expert on the human role in artificial intelligence. Cassell let loose the best one-liner I've heard that combats Elon Musk's fear that the robots will kill us all. "If you're afraid of the android revolution," she said, "just stand in a puddle.

Apple's own vision for the future of home audio, the HomePod smart speaker, will begin shipping on February 9th. Pre-orders for the device open this Friday, January 26th, and are open to users in the US, UK and Australia, while those in France and Germany will be waiting until the "spring." It will, as expected, be priced at $349 / £319, and is available in either White or Space Grey. HomePod is Apple's own weapon in the fight against Amazon and Google in the war of smart speakers with voice assistants. Like its rivals, the device can listen to commands, via Siri, allowing you to control your smart home and music playback.

Netflix's long list of suggested movies and TV shows is a fantastic example of a personalized user experience. In fact, about 70 percent of everything users watch is a personalized recommendation, according to the company. Getting to that point hasn't been easy, and improving on its recommendation system is an ongoing process. Netflix has spent well over a decade developing and refining its recommendations. In 2006, it launched the Netflix Prize to search for machine learning experts who could improve its previous algorithm.

And the venerable Time magazine asked the seminal question in our minds, "Are Computers Already Smarter than Humans"? AI outperformed professional players, most notably in beating a Chinese master at "Go". Real time speech translation is becoming more powerful every day and in next five years, my Spanish would be as good as my Japanese, thanks to the AI and machine learning. These are just a few of the milestones AI reached in 2017. As artificial intelligence continues to disrupt multiple industries -- I think 2018 is going to be transformative.

CARFIT and CEA have signed an agreement to create a joint laboratory focused on Artificial Intelligence related to car vibrations and their interpretation. The lab will bring together teams from the List, a CEA Tech Institute, and from CARFIT to share knowledge and expertise. The joint lab will be dedicated to the development of artificial intelligence methods for identifying signs of mechanical failures exposed by car vibrations....

Generic generation and manipulation of text is challenging and has limited success compared to recent deep generative modeling in visual domain. This paper aims at generating plausible natural language sentences, whose attributes are dynamically controlled by learning disentangled latent representations with designated semantics. We propose a new neural generative model which combines variational auto-encoders and holistic attribute discriminators for effective imposition of semantic structures. With differentiable approximation to discrete text samples, explicit constraints on independent attribute controls, and efficient collaborative learning of generator and discriminators, our model learns highly interpretable representations from even only word annotations, and produces realistic sentences with desired attributes. Quantitative evaluation validates the accuracy of sentence and attribute generation.

Has anyone benchmarked TF and other frameworks across time? I'd be very interested in seeing how their performance compares as time goes on. Over a year ago I compared a standard LSTM in TF with the unoptimized one in my own library and did not find it any faster. Since then there has been work on JIT compilation in TF and now PyTorch, so it'd be good to know how standard RNN models written in those frameworks compared to the optimized one in the CuDNN library.