Social media has become prevalent and important for social networking and opinion sharing in recent years [1]. By changing the way we perceive and interact with the world, social media has changed our lives profoundly [2], [3]. With millions of posts and replies uploaded every day on social media such as Facebook, Twitters and YouTube, it is an abundant and informative data source of public opinions; thus, it has attracted lots of attention from both academia and industry to understand people and society [4]-[6]. Most previous text mining-based social media analysis focused on Twitter and Facebook [7]. YouTube, generally considered as a video platform, the values of its text comments below videos have long been underestimated. Being the second most popular website in the world [8] and having 1.9 billion active users [9], YouTube is an attractive source of research in social media analysis with immense potentials. Recent developments in autonomous vehicle technology have helped bring self-driving vehicles to the forefront of public interest [10].

Answer Set Programming (ASP) is a well-known declarative problem solving approach based on nonmonotonic logic programs, which has been successfully applied to a wide range of applications in artificial intelligence and beyond. To address the needs of modern applications, HEX-programs were introduced as an extension of ASP with external atoms for accessing information outside programs via an API style bi-directional interface mechanism. To evaluate such programs, conflict-driving learning algorithms for SAT and ASP solving have been extended in order to capture the semantics of external atoms. However, a drawback of the state-of-the-art approach is that external atoms are only evaluated under complete assignments (i.e., input to the external source) while in practice, their values often can be determined already based on partial assignments alone (i.e., from incomplete input to the external source). This prevents early backtracking in case of conflicts, and hinders more efficient evaluation of HEX-programs. We thus extend the notion of external atoms to allow for three-valued evaluation under partial assignments, while the two-valued semantics of the overall HEX-formalism remains unchanged. This paves the way for three enhancements: first, to evaluate external sources at any point during model search, which can trigger learning knowledge about the source behavior and/or early backtracking in the spirit of theory propagation in SAT modulo theories (SMT). Second, to optimize the knowledge learned in terms of so-called nogoods, which roughly speaking are impossible input-output configurations. Shrinking nogoods to their relevant input part leads to more effective search space pruning. And third, to make a necessary minimality check of candidate answer sets more efficient by exploiting early external evaluation calls. As this check usually accounts for a large share of the total runtime, optimization is here particularly important. We further present an experimental evaluation of an implementation of a novel HEX-algorithm that incorporates these enhancements using a benchmark suite. Our results demonstrate a clear efficiency gain over the state-of-the-art HEX-solver for the benchmarks, and provide insights regarding the most effective combinations of solver configurations.

Amazon in late April announced that it would expand its tech hub in Vancouver, planning to hire 3,000 engineers with experience in cloud computing and machine learning. The company also announced plans to add 2,000 tech jobs in the Boston area, focused on improving the company's Alexa and Audible technologies. Amazon Web Services in June announced it would be adding 1,000 engineers to its Dublin, Ireland, offices over the next two years, including software development engineers, network development engineers, security and big-data specialists, and development operations engineers. Walmart in June told VentureBeat that it would be adding 2,000 tech workers to its Walmart Labs operations in Silicon Valley; Bentonville, Arkansas; Reston, Virginia; and Bangalore, India, with many focused on using machine learning to improve its online shopping tools. PayPal India announced in July that it would be hiring 600 additional engineers this year, including application developers, back-end developers, data engineers, and product managers.

The inventor of the first neurocomputer, Dr. Robert Hecht-Nielsen, defines a neural network as – "...a computing system made up of a number of simple, highly interconnected processing elements, which process information by their dynamic state response to external inputs." Artificial neural networks are one of the most important tools used in machine learning. They are brain-inspired systems which are designed for reproducibility and the repetition of a test or complete experiment that we learn. Neural networks help us cluster and classify. Typically, a neural network is initially trained, or fed large amounts of data.

They eat in Google's cafeterias, ride its commuter shuttles and work alongside its celebrated geeks. They aren't entitled to stock and can't enter certain offices. Many don't have health insurance. Before each weekly Google all-hands meeting, trays of hors d'oeuvres and, sometimes, kegs of beer are carted into an auditorium and satellite offices around the globe for employees, who wear white badges. Those without white badges are asked to return to their desks. Google's Alphabet Inc. employs hordes of these red-badged contract workers in addition to its full-fledged staff. They serve meals and clean offices.

IBM (NYSE: IBM) has announced that several new European clients have selected IBM Watson Internet of Things (IoT) technologies, the company said. New contracts signed with Spanish electricity grid operator Red Elà ctrica de Espaà a, Italian elderly care provider Cooperativa Sole, Dutch telecommunications operator Tele2 and Israeli manufacturer of smart air conditioning Electra Group are examples of IBM s commitment to transforming business and improving operations with the power of Artificial Intelligence (AI)-enabled, IBM Cloud-based Internet of Things (IoT) technologies. Red Elà ctrica de Espaà a (http://www.ree.es/en), the sole transmission agent and operator of the national electricity system in Spain has selected IBM Watson IoT technologies as part of its Intelligent Asset Management initiative project. Israel s manufacturer and distributor of consumer goods, is collaborating with IBM to create smart air conditioning solutions, which incorporate Watson IoT technology.

"How on earth did I end up running an AI company?" Robert Bassett Cross asks himself as he sits down over coffee. For the former army officer and special operations expert, it's been an unlikely journey. He spent years fighting in Iraq and Afghanistan before launching what could be the UK's next bet in big data and artificial intelligence. Unsurprisingly, Bassett Cross does not come across as your typical tech chief executive.

A new generation of celebrities is selling out concerts, starring in commercials, and amassing huge Instagram followings. But none of them exist--corporeally, anyway. In recent years, and starting in Japan, technology and social media have spawned a digital demimonde of computer-generated stars, ranging from fake musicians and models to company mascots who appear as holograms (like Betty Crocker, with AI). When they're not entertaining you, they're trying to convince you of their humanity, and even the more cartoonish among them have fleshed-out personalities. In a way, it's the purest expression of celebrity, which has always been an elaborate illusion.

Even the wonderful stuff has its unforeseen consequences, its dark consequences. Today's workout leads to tomorrow's soreness. Someone's wedded bliss is someone else's broken heart. Even a delectable In-n-Out double-double generates gastrointestinal discomfort. The same is true, of course, for transportation systems.

China is planning to upgrade its naval power with unmanned AI submarines that aim to provide an edge over the fleets of their global counterparts. A report by the South China Post on Sunday revealed Beijing's plans to build the automated subs by the early 2020s in response to unmanned weapons being developed in the US. The subs will be able to patrol areas in the South China Sea and Pacific Ocean that are home to disputed military bases. While the expected cost of the submarines has not been disclosed, they're likely to be cheaper than conventional submarines as they do not require life-supporting apparatus for humans. However, without a human crew, they'll also need to be resilient enough to be at sea without onboard repairs possible.