You don't have to be a Go champion to have artificial intelligence change your game. You get in your car, and your Apple iPhone tells you what traffic looks like where you're going--before you ask. We're all on the road with Tesla's self-driving cars, which are redefining what driving means. The artificial intelligence calendar assistant Amy emails three of your friends to figure out a meeting time that works for everyone--and nails it. Thankfully, chatting with Amazon's Alexa is a lot more entertaining than, say, would be Hal, the fictional artificial intelligence from the film 2001: A Space Odyssey.

A key function of data science at Twitch is using behavioral data to build data products that improve our products and services. Some examples of products that data science has helped to launch include the AutoMod chat moderation system, the similar channel recommendations used for Auto Hosting, and the recommendation system for VODs. This post discusses some of the tradeoffs involved when building data products and presents our approach for scaling predictive models to millions of users. The decision to build a data product at Twitch is often the result of exploratory analysis performed by a data scientist. For example, an investigation of our user communities may result in findings about which types of channels different groups of users are likely to follow. We can use these insights to build predictive models, such as a recommendation system that identifies similar channels on our platform.

Most smart home devices, like smart light bulbs or connected door locks, are most useful when they're capable of communicating with one another. That's why companies like Apple and Google have developed their own respective "languages" for enabling these devices to stay in sync. But some argue that this interconnectivity hasn't completely solved the smart home's biggest issues. In order to get those benefits, consumers must choose one specific platform, like Apple's HomeKit or Google's Works With Nest, which could limit the choices they have when shopping for smart home products. Andy Rubin, the creator of Android and CEO of newly launched tech company Essential, is trying to change that.

We tackle the problem of collaborative filtering (CF) with side information, through the lens of Gaussian Process (GP) regression. Driven by the idea of using the kernel to explicitly model user-item similarities, we formulate the GP in a way that allows the incorporation of low-rank matrix factorisation, arriving at our model, the Tucker Gaussian Process (TGP). Consequently, TGP generalises classical Bayesian matrix factorisation models, and goes beyond them to give a natural and elegant method for incorporating side information, giving enhanced predictive performance for CF problems. Moreover we show that it is a novel model for regression, especially well-suited to grid-structured data and problems where the dependence on covariates is close to being separable.

Despite incredible recent advances in machine learning, building machine learning applications remains prohibitively time-consuming and expensive for all but the best-trained, best-funded engineering organizations. This expense comes not from a need for new and improved statistical models but instead from a lack of systems and tools for supporting end-to-end machine learning application development, from data preparation and labeling to productionization and monitoring. In this document, we outline opportunities for infrastructure supporting usable, end-to-end machine learning applications in the context of the nascent DAWN (Data Analytics for What's Next) project at Stanford.

Google's upcoming Android O software could release in early August, according to David Ruddock of Android Police. The blogger shared on Twitter Wednesday that the new operating system could launch during the first or second week of August, detailing that roll out could take place earlier than the release of Android 7.0 Nougat. Among highlighted features includes faster roll outs, battery improvements, faster boot times, updated emoji, smart text selection, picture-in-pictures, updates to the home screen and the night light feature, and a new initiative called Android Go, which brings features of the new system to low-end Android devices. Additionally, Google announced that Kotlin will now be an official Android programming language. Other Google announcements included Google Lens, Google Assistant for iPhone, Daydream 2.0, a standalone VR headset, updates to Google Photos, Google Home, Daydream and Tango, among others.

By nature, tech companies tend to be possessive about their features: Amazon wants to keep its Alexa virtual assistant on its devices, Apple devices will feature Siri exclusively and so on. But if Amazon has its way, the virtual assistants could work alongside each other. At the Wired Business Conference Wednesday, David Limp, senior vice president of devices at Amazon, said the company believes there should be more openness among competing virtual assistant platforms, USA Today reported. Read: Apple's Siri Is More Popular Than Amazon Alexa For Virtual Assistant Users, Report Says Limp said Amazon would be willing to work alongside partners like Apple or Google to ensure their competing virtual assistants would be compatible. "You should be able to tell'Alexa, ask Siri X,' " Limp said.

When Apple unveiled its Siri-powered HomePod speaker on Monday, it was widely seen as a response to similar devices like the Amazon Echo and Google Home. After all, all three gadgets have voice-activated assistants, all play music, and all can deliver news headlines, weather reports and control smart home devices. But Amazon Senior Vice President of Devices David Limp says there are big differences between Apple's HomePod and Amazon's Echo devices. "It's a little different philosophically than how we're looking at Echo," said Limp, who spoke with reporters at the Wired Business Conference on Wednesday. "We see [Echo devices] as endpoints for assistants; you'll want them in every room . . . If you think about putting a device in every room, times $350, it becomes an expensive purchase."

When David Limp thinks about the future of Alexa, the AI assistant he oversees at Amazon, he imagines a world not unlike Star Trek--a future in which you could be anywhere, asking anything, and an ambient computer would be there to fulfill your every need. "Imagine a world in the not-so-distant future where you could have infinite computing power and infinite storage," Limp said today at WIRED's 2017 Business Conference in New York. "If you take off the constrains of servers and building up infrastructure, what could you do?" Limp, who has worked at Amazon since 2010 as the senior vice president for devices, sees Alexa as a critical part of this future. Already, you can shout "Hey, Alexa," and get the assistant to tell you the weather forecast, turn off the lights, hail an Uber, or thousands of other things that Amazon and developers have trained it to do. But Limp says there's still plenty more work to be done before we live in the AI-assisted future he thinks about every day, and much of that effort has to do with training machines to better understand humans.

NEW YORK-- Alexa and Siri, rival voice assistants on Amazon's Echo and Apple's iPhone, don't directly communicate with one another. But the Amazon executive in charge of Alexa and the Amazon Echo said he'd welcome the idea. "You should be able to tell Alexa, ask Siri X,'" said David Limp, senior vice president of devices at Amazon. "If Apple or Google want to come calling, my phone number is out there, they can call...I don't know if I can envision it but I hope that will happen on behalf of customers," Limp told a group of journalists at the Wired Business Conference Wednesday in New York. Limp has a strong interest in Apple's entry into the voice-activated, artificially-intelligent infused speaker space, which also includes Google Home and Microsoft's Cortana on the Harmon Kardon Invoke speaker.