Social media popularity and importance is on the increase, due to people using it for various types of social interaction across multiple channels. This social interaction by online users includes submission of feedback, opinions and recommendations about various individuals, entities, topics, and events. This systematic review focuses on the evolving research area of Social Opinion Mining, tasked with the identification of multiple opinion dimensions, such as subjectivity, sentiment polarity, emotion, affect, sarcasm and irony, from user-generated content represented across multiple social media platforms and in various media formats, like text, image, video and audio. Therefore, through Social Opinion Mining, natural language can be understood in terms of the different opinion dimensions, as expressed by humans. This contributes towards the evolution of Artificial Intelligence, which in turn helps the advancement of several real-world use cases, such as customer service and decision making. A thorough systematic review was carried out on Social Opinion Mining research which totals 485 studies and spans a period of twelve years between 2007 and 2018. The in-depth analysis focuses on the social media platforms, techniques, social datasets, language, modality, tools and technologies, natural language processing tasks and other aspects derived from the published studies. Such multi-source information fusion plays a fundamental role in mining of people's social opinions from social media platforms. These can be utilised in many application areas, ranging from marketing, advertising and sales for product/service management, and in multiple domains and industries, such as politics, technology, finance, healthcare, sports and government. Future research directions are presented, whereas further research and development has the potential of leaving a wider academic and societal impact.

In recent years, data-intensive AI, particularly the domain of natural language processing and understanding, has seen significant progress driven by the advent of large datasets and deep neural networks that have sidelined more classic AI approaches to the field. These systems can apparently demonstrate sophisticated linguistic understanding or generation capabilities, but often fail to transfer their skills to situations they have not encountered before. We argue that computational situated grounding provides a solution to some of these learning challenges by creating situational representations that both serve as a formal model of the salient phenomena, and contain rich amounts of exploitable, task-appropriate data for training new, flexible computational models. Our model reincorporates some ideas of classic AI into a framework of neurosymbolic intelligence, using multimodal contextual modeling of interactive situations, events, and object properties. We discuss how situated grounding provides diverse data and multiple levels of modeling for a variety of AI learning challenges, including learning how to interact with object affordances, learning semantics for novel structures and configurations, and transferring such learned knowledge to new objects and situations.

Artificial intelligence promises to drive the growth of the United States economy and improve the quality of life of all Americans.

The Texas Artificial Intelligence Center of Excellence will "evangelize" AI efforts to deliver government services, the state's Department of Information …

America is locked in a quantum computer race with China. The latest developments from Chinese scientists show a "significant computing breakthrough, achieving quantum computational advantage," according to state media Xinhua News Agency. Thursday, China's top quantum research group published a new research paper in the journal Science, titled "Quantum computational advantage using photons," outlines how a quantum computer prototype detected up to 76 photons through Gaussian boson sampling (GBS), a standard simulation algorithm, Xinhua said, adding that its ability to process complex problems is exponentially faster than most supercomputers. Called "Jiuzhang," the supercomputer prototype can conduct large-scale GBS 100 trillion times faster than the world's fastest supercomputer. Researchers said their prototype processes 10 billion times faster than the 53-qubit quantum computer developed by Google.

WASHINGTON (Reuters) - U.S. President Donald Trump signed an executive order on Thursday setting guidance for federal agency use of artificial intelligence in government decision-making. The White House said Trump was setting nine principles for the design, development, acquisition and use of AI in government in an effort "to foster public trust and confidence in the use of AI, and ensure that the use of AI protects privacy, civil rights, and civil liberties." The order directs agencies to prepare inventories of AI-use cases throughout their departments and directs the White House to develop a road map for policy guidance for administrative use. Michael Kratsios, U.S. chief technology officer, said the order "will foster public trust in the technology, drive government modernization and further demonstrate America's leadership in artificial intelligence." The Trump administration has made artificial intelligence a priority, earlier issuing guidance to federal agencies aimed at limiting "overreach" in regulating the use of AI by private companies, while urging agencies to use AI to eliminate outdated regulations.

Artificial intelligence (AI) for translation is something Google and other companies have provided for individuals. It can be accessed on your phone. However, translation is still a much larger and complex issue than many people realize. The business community has many complex and unique needs that add to the challenge of accurate and reliable translation, and AI is showing increasing capability. One of the keys to business translation is the simple reality that each business sector has its own terms, phrases, and even idioms.

When soldiers are teamed with robots, the human need to interfere may negate the benefits of robotic assistance, a new US military project has discovered. But letting military artificial intelligence proceed without human supervision raises troubling ethical questions. The System-of-Systems Enhanced Small Unit (SESU) project foresees a team of around 200 to 300 soldiers augmented with swarms of small drones and robotic ground vehicles.

The world's first satellite launching drone, developed by a US-based space startup, will will be able to carry a new payload into orbit every 180 minutes, the firm claims. Aevum says the massive 80ft long drone, named the Ravn X, is fully autonomous, 70 per cent reusable, and can take off and land on runways as short as a mile long. Working in partnership with the US Space Force, the firm says it is'completely reimagining access to space' by focusing on autonomy and better logistics. The drone can take off from any runway to reach high altitude where it deploys a second stage that takes a small payload the rest of the way to space. After it has launched the second stage rocket into low Earth orbit, the drone flies itself back to its home runway, lands and then parks up in its hanger.

After the buyer used the weapon to kill his estranged wife and two others, the site successfully invoked Section 230 to avoid liability. More recently, Grindr, a dating app, took cover behind Section 230 when Matthew Herrick, an actor in New York, sued the site as a result of false profiles that were created by an ex-boyfriend. The profiles, which included Herrick's home and work addresses, suggested that Herrick had rape fantasies, and that any resistance he put up was part of the fantasy. As a consequence, hundreds of men showed up at his apartment door or at his workplace, at all hours, month after month, forcibly demanding sex. "You look at that law, and it seems very narrow," Herrick's lawyer, Carrie Goldberg, told me.