Alexa's smart home skills aren't just for turning on the lights or locking your door these days -- now, they can help fulfill your culinary ambitions. Amazon has added cooking abilities to its Smart Home Skill framework, letting you control microwaves (and eventually conventional ovens) with your voice. Instead of pressing umpteen buttons, you can simply ask Alexa to "defrost 3lbs of chicken." Suffice it to say this could be helpful if you don't want to start cooking right away, or if you just hate your microwave's interface. Expect to see the cooking features in use very soon.

Microsoft unveiled its Cortana-powered thermostat, called GLAS, back in July, and now we have more details on it. The software giant partnered with Johnson Controls, the maker of the first in-room thermostat, to create the device, and it's a beauty. It also comes with a hefty price tag: $319, and is available for pre-order now for delivery in March 2018. GLAD is powered by a Windows 10 IoT Core and features a gorgeous translucent touch display. You can control the temperature, check the weather, monitor both indoor and outdoor air quality and more.

A.O. Scott is a film critic at the New York Times. He sits down with Isaac Chotiner to discuss the year in movies, being a film critic in the age of Rotten Tomatoes, and wrestling with Hollywood in a post–Harvey Weinstein world.

Microsoft didn't have much luck in the digital music realm, from the Zune to its Groove Music service. At the end of 2017, the company switched all of its existing Groove users over to Spotify and shut Groove down. But now it turns out the move came with an unforeseen complication, as reported by Neowin. Cortana, Microsoft's smart assistant, can no longer recognize individual songs. Previously, users could use the song identification feature by pressing Cortana's music icon while a song was playing.

Robotics is already changing how we live, shop, invest, travel, and soon, robo-caregivers will transform how we provide care. Advances in AI will deliver extraordinarily innovative services in support of our loved ones. However, the use of robots to care for our children, elderly and disabled will also give rise to some very human questions. Caregiving is social science jargon for providing unpaid support to a family member or friend who has physical, psychological or development needs. Most caregivers do not know what a caregiver is or even know that they are caregivers.

Angela has been a PCMag reporter since January 2012. Prior to joining the team, she worked as a reporter for SC Magazine, covering everything related to hackers and computer security. Angela has also written for The Northern Valley Suburbanite in New Jersey, The Dominion Post in West Virginia, and the Uniontown-Herald Standard in Pennsylvania. She is a graduate of West Virginia University's Perely Isaac Reed School of Journalism.

This straightforward order to display pictures of delicious fried confections, spoken into a Google Pixel 2 smartphone with the Google Assistant, is the type of command that users have been executing in Alphabet Inc.'s GOOGL, 1.13% GOOG, 1.00% search engine for years. Behind the scenes, however, the response to this type of query now leverages an enormous amount of machine-learning technology that Google has spent years and billions of dollars developing, in hopes of being a leader in artificial intelligence. For that command to function, software produced by Alphabet-owned Google needed to deploy image content analysis systems, voice recognition and a host of other technologies that revolve around machine learning and AI, mostly pumped through high-tech data centers the company has built. It also decided to make the hardware that runs it, with an eye on pushing the abilities of its services to new places in 2018 and beyond. Since 2013, Alphabet has ramped up its infrastructure spending, pouring $57.36 billion into capital expenditures--roughly $10 billion a year.

If Alexa has replaced other popular web-based search engines in your household, you are not alone. In fact, I'm guessing many of today's children will grow up researching school papers via robots, rather than traditional computers. What is the business case for robot-based assistance? Turns out many companies have already found ways to use artificially intelligence (AI) to increase efficiency and accuracy in their daily workplaces in the form of robotic "personal assistants." I know what you're thinking: I want in on that action!

The chapters in this book examine the state of today's agent technology and point the way toward the exciting developments of the next millennium. Contributors include Donald A. Norman, Nicholas Negroponte, Brenda Laurel, Thomas Erickson, Ben Shneiderman, Thomas W. Malone, Pattie Maes, David C. Smith, Gene Ball, Guy A. Boy, Doug Riecken, Yoav Shoham, Tim Finin, Michael R. Genesereth, Craig A. Knoblock, Philip R. Cohen, Hector J. Levesque, and James E. White, among others. Held at San Francisco's W Hotel, the conference included work from researchers and practitioners who are developing novel user interface and interaction paradigms that incorporate advanced reasoning and modeling techniques. In the past few years, user interfaces have faced increasingly challenging tasks, larger numbers of users with a wide range of computer skills, and the widespread use of new platforms such as mobile devices. These trends have led to a need for advanced techniques for communication and collaboration, personalization and adaptation of behavior, agent-based assistance, integrated multimodal interfaces, and a variety of intelligent front ends for complex environments and tasks.

As such systems must crucially rely on an accurate and complete model of user preferences, the acquisition of this model becomes the central subject of this article. Many tools used today do not satisfactorily assist users to establish this model because they do not adequately focus on fundamental decision objectives, help them reveal hidden preferences, revise conflicting preferences, or explicitly reason about tradeoffs. As a result, users fail to find the outcomes that best satisfy their needs and preferences. In this article, we provide some analyses of common areas of design pitfalls and derive a set of design guidelines that assist the user in avoiding these problems in three important areas: user preference elicitation, preference revision, and explanation interfaces. For each area, we describe the state of the art of the developed techniques and discuss concrete scenarios where they have been applied and tested. However, automated decision systems cannot effectively search the space of possible solutions without an accurate model of a user's preferences. Preference acquisition is therefore a fundamental problem of growing importance. Without an adequate interaction model and system guidance, it is difficult for users to establish a complete and accurate model of their preferences. More specifically, we face the following difficulties: First, inadequate elicitation tools can easily mislead users to focus on means objectives rather than fundamental decision objectives and force them to state preferences in the wrong order. For example, a user who commits to the choice of minivans (means objective) for spacious baggage space (fundamental) is not focusing on the values and could risk missing alternatives offered by station wagons. In value-focus thinking, Keeney (1992) suggests that the specification and clarification of values should not be overtaken by the set of alternatives too rapidly. This theory has a direct implication on the order in which the system initially elicits user preferences. Second, users are not aware of all preferences until they see them violated. For example, a user does not think of stating a preference for the intermediate airport until a solution proposes an airplane change in a place the user dislikes. This observation sheds light on the interaction design guideline on how to help users discover their hidden preferences. Finally, preferences can be inconsistent.