The global spending on the artificial intelligence (AI) market is also estimated to reach $118.6 billion by 2025. A Business Wire research unveiled that the amount spent on cloud AI in the media and entertainment (M & E) industry is anticipated to reach $1,860.9 million by 2025 from $329 million in 2019. The worldwide AI market adoption rate is estimated to reach $118.6 billion by 2025 [source: www.statista.com] Here are some of the examples of how AI is changing the media landscape. The AI market for social media is estimated to reach 3,714.89 million at 28.77% CAGR by 2025.

This is a place to share machine learning research papers, journals, and articles that you're reading this week. If it relates to what you're researching, by all means elaborate and give us your insight, otherwise it could just be an interesting paper you've read. Please try to provide some insight from your understanding and please don't post things which are present in wiki. Preferably you should link the arxiv page (not the PDF, you can easily access the PDF from the summary page but not the other way around) or any other pertinent links. Besides that, there are no rules, have fun.

The Linear Baseline model solves a convex problem and thus will converge to roughly the same optimum. This gives you basically an Idea for how informative naive correlation between features are. Logistic Regressions and Support Vector Machines are a common choice here in my experience. Regarding nonlinear models, the random forrest classifier is neat. XGBoost (Gradient Boosted Decision forrest) from the package of the same name is also realy good.

There have been a number of techniques that have demonstrated the generation of multimedia data for one modality at a time using GANs, such as the ability to generate images, videos, and audio. However, so far, the task of multi-modal generation of data, specifically for audio and videos both, has not been sufficiently well-explored. Towards this, we propose a method that demonstrates that we are able to generate naturalistic samples of video and audio data by the joint correlated generation of audio and video modalities. The proposed method uses multiple discriminators to ensure that the audio, video, and the joint output are also indistinguishable from real-world samples. We present a dataset for this task and show that we are able to generate realistic samples. This method is validated using various standard metrics such as Inception Score, Frechet Inception Distance (FID) and through human evaluation.

The platform, TAIAO (Time-Evolving Data Science / Artificial Intelligence for Advanced Open Environmental Science), brings together huge data sets, …

Lilt, the modern language service and technology provider, today announced it was named a winner in the Business Intelligence Group's Artificial Intelligence Excellence Awards program. Lilt's localization solution combines a community of the world's best professional translators with its AI-powered translation platform, bringing human-powered, technology-assisted translations to global enterprises like Intel, ASICS, Canva, DigitalOcean, WalkMe, and others. "We're thrilled to be recognized as a winner of the Artificial Intelligence Excellence Awards," said Spence Green, CEO of Lilt. "As a language service and technology provider, our AI and machine learning platform enables our customers to provide their customers with a consistent global experience, regardless of what language they speak." Lilt provides businesses with the ability to offer the same global experience to all customers, partners, and employees irrespective of language.

While Google's optional Sleep Sensing feature is the headline here, there's a lot to love about this display from a performance standpoint. Google Assistant is quick to respond and execute commands--this being a Google product, there's no support for Amazon Alexa or Apple's Siri--bolstered by accurate response to gesture commands. The Hub's machine learning chip (the same chip you'll find in the Nest Mini and Nest Audio), offers 1.2 TeraOPS of processing power to help Google learn your most common commands and respond to them faster than ever before. The Nest Hub (second-gen) can help track your sleep, manage your Google-enabled smart home, and more. The 7-inch touchscreen display houses three far-field microphones to process voice commands.

If it ain't broke, don't fix it. That seems to be the idea behind the second-gen Google Nest Hub, which--at first glance, anyway--could easily be mistaken for the original. This new Nest Hub keeps the first's slim, fabric-covered base and "floating" seven-inch display, along with onboard Google Assistant, an attractive, easy-to-use interface, plenty of video, music, and other entertainment options, and some of the best home automation functionality you'll find in a smart display. Sounds like a yawner, right? Well, that's literally true when it comes to the Nest Hub deux's major new feature: Sleep Sensing, an opt-in feature that allows the display to monitor your sleep without the need for a wristband. Powered by a tiny, built-in radar, the Nest Hub can actually sense your breathing as you slumber, and it'll give you detailed reports about your sleep history and quality.

Ambi Robotics, formerly Ambidextrous Laboratories Inc., emerges from stealth today to introduce AmbiOS, an advanced robot operating system based on simulation-to-reality artificial intelligence (AI). AmbiOS rapidly trains robots before deployment to pick and pack items for fast and affordable integration. The $6.1 million seed funding, led by Bow Capital, Vertex Ventures and The House Fund, supports efforts to deploy more AI-powered robotic systems across Ambi Robotics' U.S. customer base as e-commerce and shipping demand continues to surge. This press release features multimedia. "Using our unique approach to deep learning AI based on simulation-to-reality transfer, AmbiOS quickly configures our systems for a variety of sensors, robots and package categories," says Ken Goldberg, co-founder of Ambi Robotics.

How did Michael Crichton, Sean Connery, and Wesley Snipes factor into the creation of a preeminent robotics firm? The story begins on the movie set of the 1993 action thriller "Rising Sun," starring Connery and Snipes and based off the Crichton novel of the same name. It was during a week of filming under the hot California sun that Raibert, then a professor at MIT, realized there was more work to do. "We were providing robots for the background of a scene in the movie," said Raibert. "And we were there for a week. And it was a week of hell."