"The proliferation of modeling and predictive approaches based on data-driven and machine learning techniques has helped to expose various social …

Why Ongo is using artificial intelligence.

The ad features a humanoid robot that plays on the idea that things like AI and algorithms will never be as good as the human touch you get from …

In a world of deep fakes, facial recognition and machine learning, an ethical framework to guide the development and rollout of AI is becoming …

The second commitment, dubbed "AI use Ontarians can trust," says that risk-based rules will be implemented to ensure safe, equitable and secure use of …

Understanding the 3D world from 2D projected natural images is a fundamental challenge in computer vision and graphics. Recently, an unsupervised learning approach has garnered considerable attention owing to its advantages in data collection. However, to mitigate training limitations, typical methods need to impose assumptions for viewpoint distribution (e.g., a dataset containing various viewpoint images) or object shape (e.g., symmetric objects). These assumptions often restrict applications; for instance, the application to non-rigid objects or images captured from similar viewpoints (e.g., flower or bird images) remains a challenge. To complement these approaches, we propose aperture rendering generative adversarial networks (AR-GANs), which equip aperture rendering on top of GANs, and adopt focus cues to learn the depth and depth-of-field (DoF) effect of unlabeled natural images. To address the ambiguities triggered by unsupervised setting (i.e., ambiguities between smooth texture and out-of-focus blurs, and between foreground and background blurs), we develop DoF mixture learning, which enables the generator to learn real image distribution while generating diverse DoF images. In addition, we devise a center focus prior to guiding the learning direction. In the experiments, we demonstrate the effectiveness of AR-GANs in various datasets, such as flower, bird, and face images, demonstrate their portability by incorporating them into other 3D representation learning GANs, and validate their applicability in shallow DoF rendering.

Fox News Flash top entertainment and celebrity headlines are here. Check out what's clicking today in entertainment. Jennifer Aniston is still looking for love -- but she refuses to find it online. The "Friends" alum, 52, said in an interview published on Wednesday that she "absolutely" will not try dating apps to find a new partner. "Absolutely no," she told People.

SAN ANTONIO, June 22, 2021 (GLOBE NEWSWIRE) -- CloudCommerce, Inc. (CLWD), a technology driven provider of digital advertising solutions, today announced that SWARM, the Company's AI-driven advertising solution, reduced media costs by more than 60% for Energy in Focus, a web based platform that showcases diverse information on energy in California. Based on the first-round results, the client has committed to a second round. Energy in Focus turned to CloudCommerce to better understand which creative initiatives would be best for their different audiences, such as b2b partners and its public advocacy audience. SWARM analyzed the top 5 previous posts from Facebook and used artificial intelligence to develop creative variations which ran on other media platforms. The result: the cost was reduced by more than 60%.

The intricate detail is extremely complex and a patentable secret, but it's all about the use of algorithms that combine computational linguistics, contextual memory, deep learning. The AI parses words as input from voice or text from resources, so that it'understands' the relationship between words (as concepts or things) not just by simple keyword association (e.g. car transport) but by well-defined meta-labels, which refer to relationships between language, concepts, objects and questions from an infinite number of possible (and impossible) relationships. A limited set of relation types are used, using global identifiers with unambiguous denotations. This is combined with semantic'Extraction Transformation and Load' (ETL) processes from structured databases, forming strong associations and disassociations during the AI's training. An example of a Knowledge Graph (KG) to draw upon vast amounts of varied information might be a recommendation system for TV shows, movies, songs and albums from an online entertainment provider, to help find relationships between actors, artistes, titles and series.

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.