Ericsson Artificial Intelligence (AI)-based network solution provides capabilities to maximize customers' network investment with a focus on …

This paper scrutinizes a database of over 4900 YouTube videos to characterize financial market coverage. Financial market coverage generates a large number of videos. Therefore, watching these videos to derive actionable insights could be challenging and complex. In this paper, we leverage Whisper, a speech-to-text model from OpenAI, to generate a text corpus of market coverage videos from Bloomberg and Yahoo Finance. We employ natural language processing to extract insights regarding language use from the market coverage. Moreover, we examine the prominent presence of trending topics and their evolution over time, and the impacts that some individuals and organizations have on the financial market. Our characterization highlights the dynamics of the financial market coverage and provides valuable insights reflecting broad discussions regarding recent financial events and the world economy.

Every Scene Text Recognition (STR) task consists of text localization \& text recognition as the prominent sub-tasks. However, in real-world applications with fixed camera positions such as equipment monitor reading, image-based data entry, and printed document data extraction, the underlying data tends to be regular scene text. Hence, in these tasks, the use of generic, bulky models comes up with significant disadvantages compared to customized, efficient models in terms of model deployability, data privacy \& model reliability. Therefore, this paper introduces the underlying concepts, theory, implementation, and experiment results to develop models, which are highly specialized for the task itself, to achieve not only the SOTA performance but also to have minimal model weights, shorter inference time, and high model reliability. We introduce a novel deep learning architecture (GeoTRNet), trained to identify digits in a regular scene image, only using the geometrical features present, mimicking human perception over text recognition. The code is publicly available at https://github.com/ACRA-FL/GeoTRNet

Fake news and misinformation spread rapidly on the Internet. How to identify it and how to interpret the identification results have become important issues. In this paper, we propose a Dual Co-Attention Network (Dual-CAN) for fake news detection, which takes news content, social media replies, and external knowledge into consideration. Our experimental results support that the proposed Dual-CAN outperforms current representative models in two benchmark datasets. We further make in-depth discussions by comparing how models work in both datasets with empirical analysis of attention weights.

Language is an important form of data in politics. Constituents express their stances and needs in text such as social media and survey responses. Politicians conduct campaigns through debates, statements of policy positions, and social media. Government staff needs to compile information from various documents to assist in decision-making. Textual data is also prevalent through the documents and debates in the legislation process, negotiations and treaties to resolve international conflicts, and media such as news reports, social media, party platforms, and manifestos. Natural language processing (NLP) is the study of computational methods to automatically analyze text and extract meaningful information for subsequent analysis. The importance of NLP for policymaking has been highlighted since the last century (Gigley, 1993).

Fox News correspondent Mark Meredith has the latest on ChatGPT on'Special Report.' "Nothing Forever," the popular AI-generated "Seinfeld" parody, was recently banned from streaming on Twitch according to the show's creators on Sunday. The show originally streamed 24/7 on Twitch since mid-December, following four characters named Larry, Fred, Yvonne and Kakler in reference to the characters Jerry, George, Elaine and Kramer from the popular 90s comedy "Seinfeld." On the show's Discord, one of the creators, Xander, reportedly explained the situation regarding its recent suspension. Here's the latest: we received a 14-day suspension due to what Larry Feinberg said tonight during a club bit," Xander reportedly said. "We've appealed the ban, and we'll let you know as we know more on what Twitch decides.

Sound clips of Donald Trump reading the'Three Little Pigs' nursery rhyme aloud and Tom Hanks reciting Pulp Fiction's'Ezekiel 25:17' may sound realistic, but they were generated by artificial intelligence. A developer created a tool, dubbed Tortoise TTS (Text-to-Speech), capable of replicating a person's voice after analyzing 20 seconds of an audio clip with them speaking. Shashank Jain, the creator of Tortoise TTS, said his main idea was to create a tool that allows us to generate podcasts based on text. 'With the arrival of ChatGPT, we can generate conversations in the format we want, provide the feed to the tool I created and outcomes a podcast between two speakers of our choice,' he told DailyMail.com. The sound clips were created with a text-to-speech AI developed by Shashank Jain, who said it was designed to generate podcasts.

Set up in 2018, Runway has been developing AI-powered video-editing software for several years. Its tools are used by TikTokers and YouTubers as well as mainstream movie and TV studios. The makers of The Late Show with Stephen Colbert used Runway software to edit the show's graphics; the visual effects team behind the hit movie Everything Everywhere All at Once used the company's tech to help create certain scenes. In 2021, Runway collaborated with researchers at the University of Munich to build the first version of Stable Diffusion. Stability AI, a UK-based startup, then stepped in to pay the computing costs required to train the model on much more data.

Craggy, grey-haired and 80 years old, Harrison Ford might seem a bit old to don his brown Fedora-style hat or crack his whip as Indiana Jones. But a trailer for his upcoming film Indiana Jones and the Dial of Destiny offers a flashback to Indy in his swashbuckling glory days. "That is my actual face at that age," the actor explained on CBS's The Late Show with Stephen Colbert. "They have this artificial intelligence (AI) programme. It can go through every foot of film that Lucasfilm owns because I did a bunch of movies for them and they have all this footage including film that wasn't printed: stock. They could mine it from where the light is coming from, the expression. Then I put little dots on my face and I say the words and they make it. Having discovered the secret of eternal youth, Ford joked: "That's what I see when I look in the mirror now." He is not the only actor to get a digital facelift with an assist from AI. Tom Hanks, Robin Wright and other cast members will play younger versions of themselves in Here, directed by Robert Zemeckis, thanks to a tool that the AI company Metaphysic says can create "high-resolution photorealistic faceswaps and de-ageing effects on top of actors' performances live and in real time without the need for further compositing or VFX work". Metaphysic's website proclaims: "We are world leaders in creating AI generated content that looks real" and suggests: "Use AI to create your own hyperreal avatar". The company has just struck a deal with the Creative Artists Agency "to develop generative AI tools and services for talent", according to the Hollywood Reporter. Just as the buzzy AI chatbot ChatGPT threatens to upend journalism, speechwriting and school essays, so AI could turn digital de-ageing from something that requires many months of highly skilled artists to something that many people can do in their bedrooms. And as the technology becomes ever more sophisticated, there are fears that deepfake technology could fall into the wrong hands and be weaponised. Olcun Tan, a German-born visual effects supervisor based in Los Angeles, reflects: "We're going through a big revolution.

While there are some who scoff at the suggestion of a "curse" on live-action video game adaptations, the creators of The Last of Us are not among them. They believed in the curse; they feared a TV show. A flop would tarnish the game's legacy. Few would have had a better claim to this skepticism than Merle Dandridge, Troy Baker, and Ashley Johnson, who played Marlene, Joel, and Ellie, respectively, in the games. Back then, decked out in black mocap suits, pandemic-ravaged America existed only in their imagination.