Despite significant interest in developing general purpose fact checking models, it is challenging to construct a large-scale fact verification dataset with realistic claims that would occur in the real world. Existing claims are either authored by crowdworkers, thereby introducing subtle biases that are difficult to control for, or manually verified by professional fact checkers, causing them to be expensive and limited in scale. In this paper, we construct a challenging, realistic, and large-scale fact verification dataset called FaVIQ, using information-seeking questions posed by real users who do not know how to answer. The ambiguity in information-seeking questions enables automatically constructing true and false claims that reflect confusions arisen from users (e.g., the year of the movie being filmed vs. being released). Our claims are verified to be natural, contain little lexical bias, and require a complete understanding of the evidence for verification. Our experiments show that the state-of-the-art models are far from solving our new task. Moreover, training on our data helps in professional fact-checking, outperforming models trained on the most widely used dataset FEVER or in-domain data by up to 17% absolute. Altogether, our data will serve as a challenging benchmark for natural language understanding and support future progress in professional fact checking.

Rap generation, which aims to produce lyrics and corresponding singing beats, needs to model both rhymes and rhythms. Previous works for rap generation focused on rhyming lyrics but ignored rhythmic beats, which are important for rap performance. In this paper, we develop DeepRapper, a Transformer-based rap generation system that can model both rhymes and rhythms. Since there is no available rap dataset with rhythmic beats, we develop a data mining pipeline to collect a large-scale rap dataset, which includes a large number of rap songs with aligned lyrics and rhythmic beats. Second, we design a Transformer-based autoregressive language model which carefully models rhymes and rhythms. Specifically, we generate lyrics in the reverse order with rhyme representation and constraint for rhyme enhancement and insert a beat symbol into lyrics for rhythm/beat modeling. To our knowledge, DeepRapper is the first system to generate rap with both rhymes and rhythms. Both objective and subjective evaluations demonstrate that DeepRapper generates creative and high-quality raps with rhymes and rhythms. Code will be released on GitHub.

The Foundation for the Defense of Democracies issues an alarming report about Beijing's expanding tentacles in international agencies; Eric Shawn has the Fox News exclusive. Two astronauts on Sunday made the first spacewalk outside China's new orbital station to set up cameras and other equipment using a 15-meter-long (50-foot-long) robotic arm. Liu Boming and Tang Hongbo were shown by state TV climbing out of the airlock as Earth rolled past below them. The third crew member, commander Nie Haisheng, stayed inside. Liu and Tang spent nearly seven hours outside the station, the Chinese space agency said.

Blistering progress, unparalleled success, and unmatched viability are just a few phrases associated with the infusion of AI across various industrial sectors. Artificial intelligence (AI) is at an important transition point in a world where technological advancements are at their finest. The rate of growth of AI is extremely high such that the global artificial intelligence (AI) market size, valued at $27.23 billion in 2019, is projected to reach $266.92 billion by 2027, exhibiting a CAGR of 33.2 percent during the forecast period. Content that is truly "magnificent" can be time-consuming to create and often very expensive to produce. As it becomes costlier to produce content, it also becomes even more imperative to command the interest and attention of the audiences.

THE ROBOTS ARE HERE Music industry update Illinois, USA Indie artist, Elliott Waits For No One The official music video for the critically acclaimed single "You Can't See Me" from emerging Illinois, US band Elliott Waits For No One's self-titled 2020 album was directed by English actor and television film director Tim Roth, who made his debut with the release of the popular 1982 film "Made in Britain". Following its release, the spectacular video received recognition locally, nationally and internationally not just from the music industry but from the film and television sectors as well. The video has gone on to win multiple film festival awards including the Hollywood Gold Awards, the Golden Short Film Festival, and most recently, it has been awarded a Cult Critic Movie Award. The music video is available to stream on YouTube and popular video streaming platforms.

Using machine learning, researchers calculated that in roughly 35 years its ecological range will move northward, bringing hay fever to regions it has …

Machine learning models are incredibly effective at specific sets of tasks. If applied correctly, the entire process of training one can be more than worth it.

Fake News on social media platforms has attracted a lot of attention in recent times, primarily for events related to politics (2016 US Presidential elections), healthcare (infodemic during COVID-19), to name a few. Various methods have been proposed for detecting Fake News. The approaches span from exploiting techniques related to network analysis, Natural Language Processing (NLP), and the usage of Graph Neural Networks (GNNs). In this work, we propose DEAP-FAKED, a knowleDgE grAPh FAKe nEws Detection framework for identifying Fake News. Our approach is a combination of the NLP -- where we encode the news content, and the GNN technique -- where we encode the Knowledge Graph (KG). A variety of these encodings provides a complementary advantage to our detector. We evaluate our framework using two publicly available datasets containing articles from domains such as politics, business, technology, and healthcare. As part of dataset pre-processing, we also remove the bias, such as the source of the articles, which could impact the performance of the models. DEAP-FAKED obtains an F1-score of 88% and 78% for the two datasets, which is an improvement of 21%, and 3% respectively, which shows the effectiveness of the approach.

The highly developed intelligence of humans cannot be separated from language. Mimicking that means of communication plays an important role in the development of artificial intelligence. Expert Tessa Verhoef is intrigued by language as a key to artificial intelligence. As an assistant professor at Leiden University, Netherlands (Creative Intelligence Lab, Leiden Institute of Advanced Computer Science), she is very much involved in it. That's the main goal – to use the human-human relationship as inspiration to understand how language works.

A little over a year ago, OpenAI, an artificial intelligence company based in San Francisco, stunned the world by showing a dramatic leap in what appeared to be the power of computers to form natural-language sentences, and even to solve questions, such as completing a sentence, and formulating long passages of text people found fairly human. The latest work from that team shows how OpenAI's thinking has matured in some respects. GPT-3, as the newest creation is called, emerged last week, with more bells and whistles, created by some of the same authors as the last version, including Alec Radford and Ilya Sutskever, along with several additional collaborators, including scientists from Johns Hopkins University. It is now a truly monster language model, as it's called, gobbling two orders of magnitude more text than its predecessor. But within that bigger-is-better stunt, the OpenAI team seem to be approaching some deeper truths, much the way Dr. David Bowman approached the limits of the known at the end of the movie 2001.