HTC has just launched one of the most unusual smartphones we've ever seen. The HTC U11 is the Taiwanese company's answer to the Samsung Galaxy S8, and it's very different to anything that's come before it. It's designed to be squeezed by users, a new type of interaction that HTC rather ambitiously hopes will find a place in history somewhere alongside the likes of the computer mouse and the touchscreen. The I.F.O. is fuelled by eight electric engines, which is able to push the flying object to an estimated top speed of about 120mph. The giant human-like robot bears a striking resemblance to the military robots starring in the movie'Avatar' and is claimed as a world first by its creators from a South Korean robotic company Waseda University's saxophonist robot WAS-5, developed by professor Atsuo Takanishi and Kaptain Rock playing one string light saber guitar perform jam session A man looks at an exhibit entitled'Mimus' a giant industrial robot which has been reprogrammed to interact with humans during a photocall at the new Design Museum in South Kensington, London Electrification Guru Dr. Wolfgang Ziebart talks about the electric Jaguar I-PACE concept SUV before it was unveiled before the Los Angeles Auto Show in Los Angeles, California, U.S The Jaguar I-PACE Concept car is the start of a new era for Jaguar.

Gut reaction: You won't want to hide the HTC U11 in a protective case. HTC's new flagship phone is just too stunning to conceal behind a chintzy polycarb shell. Imbued with HTC's new "liquid" design aesthetic, the U11 has an impossibly glossy finish, evoking the T-1000 Terminator for those old enough to remember that robot assassin. From a basic specs perspective, the U11 is a solid but not remarkable Android 7.1 phone. It includes a state-of-the-silicon-art Snapdragon 835 processor running up to 2.45 GHz, a 3,000 mAh battery, and a 5.5-inch quad HD (2560x1440) display.

Trekkies used to (and might still) believe that only every other Star Trek movie was actually good, and I'd argue that applies to HTC's flagship releases, too. The M9 was an improvement over its predecessor, but the A9 didn't thrill. HTC hit its stride with 2016's 10 while the subsequent U Ultra... well, it was kind of a mess. Thankfully, we didn't have to wait too long before the Taiwanese phone maker took another stab at a flagship. Say hello to the $649/£649 HTC U11, a smartphone that blends high-end power with a striking aesthetic, a slew of pressure sensors and a truly awful name.

HTC has revealed its newest smartphone, which can be squeezed in order to take selfies and launch other apps. The tech giant's new U11 device houses technology called Edge Sense, based around sensors built into the side of the device that respond to being squeezed. The Taiwanese manufacturer has also become the first to include support for both the Google Assistant and Amazon Alexa smart virtual assistants. The tech giant's new U11 device houses technology called Edge Sense, based around sensors built into the side of the device that respond to being squeezed The HTC U11, which will cost £649 / $649, was unveiled during a live event in Taiwan and is the tech giant's challenger to the new Samsung Galaxy S8 and the iPhone 7. The Edge Sense technology can be used to open the camera and take selfies, as well as launch certain apps and trigger voice control features. It also works when the phone is wet and if the user if wearing gloves, HTC said, and it will also support voice dictation for messages.

Apple (AAPL) is looking to stay ahead of the artificial intelligence development curve with the purchase of Lattice Data, a company that uses AI technology to turn so-called "dark" data into more usable data sets The deal closed a couple of weeks ago at $200 million, according to a report from TechCrunch. On Sunday, Apple issued a generic statement on the deal that said it "buys smaller technology companies from time to time, and we generally do not discuss our purpose or plans." Founded in 2015, Lattice Data claims that its work is "human-caliber quality at machine-caliber scale." As part of the deal, about 20 engineers from Lattice Data are now working at Apple. This is a common practice in the tech sphere.

work is creative, problem-solving work which requires high levels of education and is usually well-rewarded. Manual work is skilled dexterous physical work. It usually does not require high levels of education and is typically poorly-paid and highly-precarious (i.e.

While presenting own advanced Robo-Advisory Platform, Micha? Ró?a?ski, Managing Partner at EARP and CEO at Empirica, will illustrate the most important issues of building tailored FinTech software in his session at 20th Cloud Expo. He will share experiences we have gained for over 6 years of developing solutions for financial institutions and FinTech companies, including robo-advisors. We welcome all FinTech innovators interested in how properly implemented technology can move their businesses forward.

Personalized recommendations have become a common feature of many modern online services. In particular on e-commerce sites, one value of such recommendations is that they help consumers find items of interest in large product assortments more quickly. Many of today's sites take advantage of modern recommendation technologies to create personalized item suggestions for consumers navigating the site. However, limited research exists on the use of personalization and recommendation technology when consumers rely on the site's catalog search functionality to discover relevant items. In this work we explore the value of personalizing search results on e-commerce sites using recommendation technology. We design and evaluate different personalization strategies using log data of an online retail site. Our results show that considering several item relevance signals within the recommendation process in parallel leads to the best ranking of the search results. Specifically, the factors taken into account include the users' general interests, their most recent browsing behavior, as well as the consideration of current sales trends.

Humans do not interact with digital systems for the pleasure of clicking buttons. Interactions, in particular those that are quite complex, time-consuming, and sometimes tedious aim at problem solving. Wicked problems are particularly tough. They are characterized by the phenomenon that they tend to change when being tackled. One may see the interaction’s semantics as the outcome which–in the case of those wicked problems, at least–is initially unforseeable. The treatment of wicked problems motivates a certain paradigmatic shift from conventional tools to intelligent assistant systems. However intelligent the digital assistant system, the syntax of human system interaction may be formally represented and is comparably simple in structure. How does such a simple interaction syntax lead to the emergence of intelligent solutions? To what extent do very high expectations of functionality and service impose requirements on the syntax behind? Original concepts of meme media lead both to theoretical insights and to some practical solutions to wicked problems. Data analysis, visualization, and exploration is an attractive application domain. Concepts of interactions scenarios, a meme media implementation, and an application case study are presented.

Building on a survey of previous theories of serendipity and creativity, we advance a sequential model of serendipitous occurrences. We distinguish between serendipity as a service and serendipity in the system itself, clarify the role of invention and discovery, and provide a measure for the serendipity potential of a system. While a system can arguably not be guaranteed to be serendipitous, it can have a high potential for serendipity. Practitioners can use these theoretical tools to evaluate a computational system's potential for unexpected behaviour that may have a beneficial outcome. In addition to a qualitative features of serendipity potential, the model also includes quantitative ratings that can guide development work. We show how the model is used in three case studies of existing and hypothetical systems, in the context of evolutionary computing, automated programming, and (next-generation) recommender systems. From this analysis, we extract recommendations for practitioners working with computational serendipity, and outline future directions for research.