Before I go any further it's probably worth establishing what a Deepfake is and isn't. A technique by which a digital image or video can be superimposed onto another, which maintains the appearance of an unedited image or video. The term is often misinterpreted, and that's potentially as a result of definitions like this. The concept of manipulating images and video in this way is certainly not a new concept. Visual effects artists working on Hollywood films back in the '90s would probably describe parts of their job as something very similar to this.

A machine learning engineer is the jack of all trades.

Mobile Augmented Reality (MAR) integrates computer-generated virtual objects with physical environments for mobile devices. MAR systems enable users to interact with MAR devices, such as smartphones and head-worn wearables, and performs seamless transitions from the physical world to a mixed world with digital entities. These MAR systems support user experiences by using MAR devices to provide universal accessibility to digital contents. Over the past 20 years, a number of MAR systems have been developed, however, the studies and design of MAR frameworks have not yet been systematically reviewed from the perspective of user-centric design. This article presents the first effort of surveying existing MAR frameworks (count: 37) and further discusses the latest studies on MAR through a top-down approach: 1) MAR applications; 2) MAR visualisation techniques adaptive to user mobility and contexts; 3) systematic evaluation of MAR frameworks including supported platforms and corresponding features such as tracking, feature extraction plus sensing capabilities; and 4) underlying machine learning approaches supporting intelligent operations within MAR systems. Finally, we summarise the development of emerging research fields, current state-of-the-art, and discuss the important open challenges and possible theoretical and technical directions. This survey aims to benefit both researchers and MAR system developers alike.

Short textual descriptions of entities provide summaries of their key attributes and have been shown to be useful sources of background knowledge for tasks such as entity linking and question answering. However, generating entity descriptions, especially for new and long-tail entities, can be challenging since relevant information is often scattered across multiple sources with varied content and style. We introduce DESCGEN: given mentions spread over multiple documents, the goal is to generate an entity summary description. DESCGEN consists of 37K entity descriptions from Wikipedia and Fandom, each paired with nine evidence documents on average. The documents were collected using a combination of entity linking and hyperlinks to the Wikipedia and Fandom entity pages, which together provide high-quality distant supervision. The resulting summaries are more abstractive than those found in existing datasets and provide a better proxy for the challenge of describing new and emerging entities. We also propose a two-stage extract-then-generate baseline and show that there exists a large gap (19.9% in ROUGE-L) between state-of-the-art models and human performance, suggesting that the data will support significant future work.

Lucy, the virtual being AI from Fable Studio's Wolves in the Walls virtual reality experience, is getting around. Now she has busted the fourth wall and moved into the real, or virtual world, of the virtual Sundance Film Festival. This week, Lucy appeared as a guest at Sundance. She appeared in Zoom sessions with other attendees, and they were able to quiz her. As an artificial intelligence character, she responded with her own comments and views on watching expressionist movies at Sundance.

A new study led by the University of Exeter and the Center for Whale Research suggests killer whales may socialise with each other based on age and gender, with younger whales and females more sociable than other groups. The research used drone cameras to study one pod of southern resident killer whales off the US coast of Washington State, in the Pacific Ocean. Around 10 hours of footage was captured over 10 days.

Bethesda is known for its deliberate way of hiding clues in plain sight. Because of this, "The Elder Scrolls 6" fans now speculate that the latest trailer of "Starfield" contains a hint about the map of the next "The Elder Scrolls" installment. Xbox and Bethesda launched a new "Starfield" trailer during the team's showcase at this year's E3. The two-minute trailer was packed with details, which was something the team intentionally did, Bethesda Executive Producer Todd Howard said. Interestingly, fans did not let the trailer go unexplored.

The e-bot7 Conversational AI platform will be available as an intelligent chatbot and will make the company's customer service even more efficient.

UserWay, the leading web accessibility company, announced today the release of its AI-Powered Accessibility Widget in the Shopify app store. UserWay's widget automatically solves accessibility issues on websites to create a more inclusive and accessible user experience. By embedding the UserWay widget in their sites, Shopify store owners ensure that their shops are inclusive of the more than one billion people living with disabilities. Creating websites that are fully accessible is challenging, requiring a high level of effort and expertise to cover hundreds of accessibility guidelines. A recent analysis of the top one million websites detected failures to meet the Web Content Accessibility Guidelines (WCAG) in more than 97% of home pages.

Model deployment is one of the most important skills you should have if you're going to work with NLP models. Model deployment is the process of integrating your model into an existing production environment. The model will receive input and predict an output for decision-making for a specific use case. There are different ways you can deploy your NLP model into production, you can use Flask, Django, Bottle e.t.c .But in today's article, you will learn how to build and deploy your NLP model with FastAPI. In part 1, we will focus on building an NLP model that can classify movie reviews into different sentiments.