If you have an Apple Watch and aren't determined to buy Series 4, your upgrade is here. Apple has released watchOS 5 right on schedule, promising big updates to fitness and Siri as well as a few apps that were arguably overdue. You now have automatic workout detection, competition with friends, advanced run tracking and expanded workouts that include hiking and yoga. Siri, meanwhile, no longer needs to wait for "hey Siri" when you raise your wrist, and the Siri watch face both provides more info (including Siri Shortcuts) and third-party app support. Other prominent updates include a native podcast app, a Walkie Talkie app (above) for back-and-forth voice chats and interactive notifications.

Having a smart assistant in your home can be all sorts of useful. But when you need to set an alarm, add something to your shopping list, turn on your smart lights, and find out the weather, the process can get somewhat tedious when you have to use a wake word with every question. You can really start to feel like an evil stepsister. Thankfully, Amazon added the ability to hold a continuous conversation with your smart assistant, a feature called "Follow-Up Mode." And Google recently did the same with the Google Assistant's Continued Conversations.

This week, Getty Images announced a new partnership with Amazon that will allow the online retail giant to use images from Getty's vast image catalog on its Echo devices with a screen. While Alexa was previously able to deliver verbal answers to questions, such as "Who won the Best Actress Oscar this year," now the device will be able to show a picture to accompany the answer. The Echo Show certainly set the standard for smart assistant speakers with displays, but now Google is hot on Amazon's heels. The rumor is that Google will unveil its own Google Assistant-powered smart screen this holiday season. It's understandable, then, that Amazon is feeling the pressure to up its game when it comes to the Echo Show and smaller Echo Spot.

"I'm sorry, I didn't quite get that." Anyone who has ever tried to have a deeper conversation with a virtual assistant like Siri knows how frustrating it can be. That's despite the fact that AI systems like it are increasingly pushing into our lives, with new success stories on an almost daily basis. Not only do AIs now help radiologists detect tumours, they can act as cat repellent and even detect signals of potential alien technology from space. But when it comes to fundamental human abilities, like having a good chat, AI falls short.

That's a conclusion many shoppers will reach when spotting the JBL Link 300, the mid-range of JBL's Google Assistant-powered Link smart speaker lineup. Its $250 price tag makes for a reasonable port of entry for anyone looking to build a voice-activated, multi-room audio and home-control system on a miserly budget. And since this speaker also supports Google's Chromecast audio technology, the Link 300--and any of its bigger or smaller siblings--can be paired with any other speaker that supports Chromecast. Delivering JBL's signature "California studio monitor" sound--a warm, realistically fleshed out soundstage and presence with soul and the stamina to crank--the Link 300 outperformed the vaunted Sonos One in many of my listening tests. Side Effects), to the rich soloing sonorities of Yo-Yo Ma (Six Evolutions--Bach: Cello Suites) and the elegantly spare acoustic jazz sessions (think Sarah Vaughan, Hank Garland, Bobby Timmons, and Melody Gardot) that are stock-in-trade on TSF Jazz--Paris, my favorite compare/contrast streaming channel. The JBL Link 300 has a much larger footprint its rival smart speaker, the Sonos One, but that extra girth results in bigger bass response.

D-Link's Full HD Pan & Tilt Wi-Fi Camera (model DCS-8525LH) is the latest addition to the home networking company's growing stable of security cams. As we've come to expect from D-Link, this camera has a strong set of features, but is unfortunately hampered by an often overwhelming app experience. Design-wise, the DCS-8525LH is a clone of the DCS-5030L pan-and-tilt camera we evaluated a couple of years ago. And like that model, this one's bulky body and conspicuous antenna won't blend easily into your home unless your design scheme takes its cues from cold retro-future technology. For the most part, the specs also mirror that earlier pan-and-tilt model--a 114-degree field of view, 16-feet of night vision, 340 degrees of pan and 120 degrees of tilt, motion an audio detection--though the resolution takes a leap forward, maxing out at 1080p. It also adds integration with Google Assistant, Amazon Alexa, and IFTTT.

When Apple unveiled its new slate of iPhones on Sept. 12, executives gushed over the device's new second-generation "neural processor," a custom chip meant just for AI. But Apple didn't boast about how much the new chip was going to improve Siri--Apple's virtual assistant that got eight seconds of the 2 hour presentation--or boost battery life, as the company often claims its machine learning is used for. The real killer app for AI on the iPhone is photography. The Cupertino-based company has been slowly transitioning its cameras from devices that take a picture, into devices that capture data for AI to make into an image. It's called computational photography, and it isn't unique to Apple--researchers have been thinking about the camera as a way to capture data for AI for years.

Previous efforts in recommendation of candidates for talent search followed the general pattern of receiving an initial search criteria and generating a set of candidates utilizing a pre-trained model. Traditionally, the generated recommendations are final, that is, the list of potential candidates is not modified unless the user explicitly changes his/her search criteria. In this paper, we are proposing a candidate recommendation model which takes into account the immediate feedback of the user, and updates the candidate recommendations at each step. This setting also allows for very uninformative initial search queries, since we pinpoint the user's intent due to the feedback during the search session. To achieve our goal, we employ an intent clustering method based on topic modeling which separates the candidate space into meaningful, possibly overlapping, subsets (which we call intent clusters) for each position. On top of the candidate segments, we apply a multi-armed bandit approach to choose which intent cluster is more appropriate for the current session. We also present an online learning scheme which updates the intent clusters within the session, due to user feedback, to achieve further personalization. Our offline experiments as well as the results from the online deployment of our solution demonstrate the benefits of our proposed methodology.

LinkedIn Talent Solutions business contributes to around 65% of LinkedIn's annual revenue, and provides tools for job providers to reach out to potential candidates and for job seekers to find suitable career opportunities. LinkedIn's job ecosystem has been designed as a platform to connect job providers and job seekers, and to serve as a marketplace for efficient matching between potential candidates and job openings. A key mechanism to help achieve these goals is the LinkedIn Recruiter product, which enables recruiters to search for relevant candidates and obtain candidate recommendations for their job postings. We highlight a few unique information retrieval, system, and modeling challenges associated with talent search and recommendation systems: (1) The underlying query to the talent search system could be quite complex, combining several structured fields (such as canonical title(s), canonical skill(s), company name) and unstructured fields (such as free-text keywords). Depending on the application, the query could either consist of an explicitly entered query text and selected facets (talent search), or be implicit in the form of a job opening, or ideal candidate(s) for a job (talent recommendations).

Senior citizens often need support to live independently and actively participate in their communities. The EU-funded Alfred project came up with a Personal Interactive Assistant to help the elderly overcome the obstacles preventing them from carrying out everyday tasks. The project created a virtual'butler' to which people can talk, ask questions or give commands, and developed systems to encourage older people to socialise by suggesting and managing events, to monitor their state of health, and to help them stay physically and mentally active via personalised games. It produced 25 apps, both for immediate use and to inspire developers interested in designing new services that target the needs of senior citizens. The Alfred project brought together expertise and technology from a range of different areas: ubiquitous computing, big data, gaming, the semantic web, cyber physical systems, the internet of things, the internet of services, and human-computer interaction.