The significance of social media has increased manifold in the past few decades as it helps people from even the most remote corners of the world to stay connected. With the advent of technology, digital media has become more relevant and widely used than ever before and along with this, there has been a resurgence in the circulation of fake news and tweets that demand immediate attention. In this paper, we describe a novel Fake News Detection system that automatically identifies whether a news item is "real" or "fake", as an extension of our work in the CONSTRAINT COVID-19 Fake News Detection in English challenge. We have used an ensemble model consisting of pre-trained models followed by a statistical feature fusion network , along with a novel heuristic algorithm by incorporating various attributes present in news items or tweets like source, username handles, URL domains and authors as statistical feature. Our proposed framework have also quantified reliable predictive uncertainty along with proper class output confidence level for the classification task. We have evaluated our results on the COVID-19 Fake News dataset and FakeNewsNet dataset to show the effectiveness of the proposed algorithm on detecting fake news in short news content as well as in news articles. We obtained a best F1-score of 0.9892 on the COVID-19 dataset, and an F1-score of 0.9073 on the FakeNewsNet dataset.

Commonsense knowledge acquisition and reasoning have long been a core artificial intelligence problem. However, in the past, there has been a lack of scalable methods to collect commonsense knowledge. In this paper, we propose to develop principles for collecting commonsense knowledge based on selectional preference. We generalize the definition of selectional preference from one-hop linguistic syntactic relations to higher-order relations over linguistic graphs. Unlike previous commonsense knowledge definition (e.g., ConceptNet), the selectional preference (SP) knowledge only relies on statistical distribution over linguistic graphs, which can be efficiently and accurately acquired from the unlabeled corpus with modern tools. Following this principle, we develop a large-scale eventuality (a linguistic term covering activity, state, and event)-based knowledge graph ASER, where each eventuality is represented as a dependency graph, and the relation between them is a discourse relation defined in shallow discourse parsing. The higher-order selectional preference over collected linguistic graphs reflects various kinds of commonsense knowledge. Moreover, motivated by the observation that humans understand events by abstracting the observed events to a higher level and can thus transferring their knowledge to new events, we propose a conceptualization module to significantly boost the coverage of ASER. In total, ASER contains 438 million eventualities and 648 million edges between eventualities. After conceptualization with Probase, a selectional preference based concept-instance relational knowledge base, our concept graph contains 15 million conceptualized eventualities and 224 million edges between them. Detailed analysis is provided to demonstrate its quality. All the collected data, APIs, and tools are available at https://github.com/HKUST-KnowComp/ASER.

… discovered through Artificial Intelligence (AI) technology and police field follow-up, which majorly contributed in the success of the arrest operation.

Songen is an iOS app that lets you generate new royalty-free music with just a few taps of the screen. It does this with the help of an AI-assisted engine that essentially sketches out a song idea for you. The app generates music based on the genre you select. Songen then generates 10 song ideas and you'll have the option to save the ones you like. In the next step, you can refine the song by adjusting the tempo, changing the key and swapping instrumentations.

A Canadian organisation has used artificial intelligence to create a new Nirvana song – and it’s unnervingly realistic.  Over the Bridge, based in Toronto, is a nonprofit dedicated to ‘change the conversation about mental health and recovery in the music community.’ Its latest project, titled Lost Tapes of the 27 Club, is focused on honouring …

Artificial intelligence company Torch. … AI alone plans to add 70 jobs in Leawood this year, and CEO Brian Weaver said it could add 400 local jobs in …

Advance endoscopy and similar technologies that use Artificial Intelligence (AI) and robotics will dramatically reduce the need for the open brain and …

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SAN FRANCISCO, April 02, 2021 (GLOBE NEWSWIRE) -- Holberton, making software engineering education affordable and accessible globally, today announced the appointment of a new Machine Learning and Mathematics Team to build out a comprehensive program to accelerate training students in the key tenets of Artificial Intelligence (AI), the engine of the New Economy. On LinkedIn, there are currently 60,000 machine learning jobs open in the U.S. alone. Many are technology giants such as Twitter and TikTok. But increasingly traditional tech companies are investing in machine learning and recruiting machine learning engineers: even companies like McDonald's. According to LinkedIn, machine learning has created one of the biggest employment opportunities of 2021.

This is a very fast gameplay rate where the reaction of PyGAD is some microseconds that responds to every falling behavior. This is why you would feel the bird is stable and not jumping.