In August, Sonos announced it was working on Alexa integration. It would let millions of Sonos owners control their music with their voice and do all of the pizza-ordering, game-playing, life-improving things Amazon's voice assistant can do. The move provided a clear indication of where, after 15 years, Sonos sees its future. And yet seven months later, Alexa has 10,000 skills, and Sonos ain't one of them. Gadgets like Amazon's Echo are starting to take over people's living rooms.

Artificial intelligence is shaping up as the next industrial revolution, poised to rapidly reinvent business, the global economy and how people work and interact with each other. Andrew Ng, chief scientist at Chinese internet giant Baidu Inc. and co-founder of education startup Coursera, and Neil Jacobstein, chair of the artificial intelligence and robotics department at Silicon Valley think tank Singularity University, sat down with The Wall Street Journal's Scott Austin to discuss AI's opportunities and challenges. What is Baidu focused on? NG: For large enterprises like Baidu, AI creates two big pockets of opportunities. One is our core business.

I have always wanted to hear the sound of a music composition by a computer system, so a New York based startup has created an AI that has the ability to make original music demanded by listeners. Popular music composers named Drew Silverstein, Sam Estes and Michael Hobe who have previously composed for video games, movies and sitcoms are the idea generators of Amper, the AI music composer. According to Drew Silverstein who described his latest invention as "an emotive AI composer, performer, and producer" primarily designed for collaborating ideas with humans and come up with more creative compositions. Besides, users can interact with Amper through its simple, yet interactive UI, which can be handled by anyone. Even people with zero knowledge about music composition can also use it.

The facility location problem is widely used for summarizing large datasets and has additional applications in sensor placement, image retrieval, and clustering. One difficulty of this problem is that submodular optimization algorithms require the calculation of pairwise benefits for all items in the dataset. This is infeasible for large problems, so recent work proposed to only calculate nearest neighbor benefits. One limitation is that several strong assumptions were invoked to obtain provable approximation guarantees. In this paper we establish that these extra assumptions are not necessary---solving the sparsified problem will be almost optimal under the standard assumptions of the problem. We then analyze a different method of sparsification that is a better model for methods such as Locality Sensitive Hashing to accelerate the nearest neighbor computations and extend the use of the problem to a broader family of similarities. We validate our approach by demonstrating that it rapidly generates interpretable summaries.

After being trained, classifiers must often operate on data that has been corrupted by noise. In this paper, we consider the impact of such noise on the features of binary classifiers. Inspired by tools for classifier robustness, we introduce the same classification probability (SCP) to measure the resulting distortion on the classifier outputs. We introduce a low-complexity estimate of the SCP based on quantization and polynomial multiplication. We also study channel coding techniques based on replication error-correcting codes. In contrast to the traditional channel coding approach, where error-correction is meant to preserve the data and is agnostic to the application, our schemes specifically aim to maximize the SCP (equivalently minimizing the distortion of the classifier output) for the same redundancy overhead.

You can't have failed to notice the rise of the AI-powered assistants on phones, from Siri to Google Assistant to Cortana... and there are apparently more on the way in the near future. These apps offer a lot of the same features, though there are some differences too. Below we've taken four of the most well-known digital assistants of the moment and put them through their paces to see where their strengths and weaknesses lie, and to see whether some offer a better level of assistance than others. Apple was the first to put a voice-controlled assistant on a phone and Siri has grown from being a basic interface to your iPhone's functions to something much smarter and astute - and of course it's found its way to macOS now as well. Siri's still happier answering general questions it can look up on the internet rather than anything personal or specific, due in part to Apple's determination to put user privacy first (which means not collecting a load of information from your emails or your location like Google does) - but it can still bring up messages, calendar entries and so on.

The South by Southwest Conference promises to have a very different tone than last year, when then-President Obama was warmly welcomed for a keynote presentation on civic engagement in the 21st century. For the creators, marketers and entrepreneurs descending this weekend on Austin, Texas, politics in the wake of President Trump will surely be top of mind, perhaps even overshadowing some of the innovation in virtual reality and artificial intelligence. Instead of undermining the value for marketers eager to enlist technology in their work, however, the dynamic might highlight connections that are increasingly important to recognize. "Rather than a piece of technology or launch of a new app, this year's conference will really be about the way all the things happening in politics are being threaded through what everyone does," said David Grant, president of PopSugar Studios, the video unit at publisher PopSugar. "While in the past typically the focus is on a few new toys to play with, this year it is about how do these new toys affect journalism access and the ability to distinguish between real and fake news?"

What does the concept of artificial intelligence (AI) conjure up in the mind of the consumer? Will our children be replaced by fun-loving androids as the eponymous 2001 Steven Spielberg film A.I. suggests? Or perhaps grown adults will suddenly be replaced by robots in the workplace? While all of that may seem a little far-fetched, in reality, AI is already integral to many consumers' daily lives – in the form of image and voice recognition on mobile devices; personalised viewing suggestions on streaming platforms such as Amazon Video or Netflix; or voice interaction/recognition analysis incorporated into search engines such as Google. AI has also gained recognition in the healthcare sector – machine learning applications that have the potential to assist hospital staff in routine tasks such as keeping a patient's treatment records up to date are being tested and the voice-controlled Amazon Echo device, Alexa, can assist patients at home with tasks such as reminding them to take medication or arranging a GP appointment.

Ian Bogost (previously) describes the "deflationary" use of "artificial intelligence" to describe the most trivial computer science innovations and software-enabled products, from Facebook's suicide detection "AI" (a trivial word-search program that alerts humans) to the chatbots that are billed as steps away from passing a Turing test, but which are little more than glorified phone trees, and on whom 40% of humans give up after a single conversational volley. Georgia Tech artificial intelligence researcher Charles Isbell says it's "Making computers act like they do in the movies." Isbell suggests two features necessary before a system deserves the name AI. First, it must learn over time in response to changes in its environment. Fictional robots and cyborgs do this invisibly, by the magic of narrative abstraction. But even a simple machine-learning system like Netflix's dynamic optimizer, which attempts to improve the quality of compressed video, takes data gathered initially from human viewers and uses it to train an algorithm to make future choices about video transmission.

So why is Google giving you this right-wing conspiracy acid-trip? Basically, it's quoting Google's own "featured snippets," results that show up at the top of the page for certain search terms. Unlike the "Knowledge Graph," which shows reliable, well-sourced replies, featured snippets "provide an automatic and algorithmic match to a given search query," Google told Recode. In other words, the information is first surfaced by a bot from not-necessarily-reliable sources, then read back to you by Google Home (powered by Google Assistant AI). The device focuses on featured snippets because it would be impractical to read a list of search results.