On a screen a pair of big, dark-lashed eyes grow out from amorphous smudges of orange and blue. A nose follows, and then a heavily lipsticked mouth, with the recognisably exaggerated aesthetic of a drag queen. But before the face takes a complete form it has already begun to dissolve into something – or someone – else. But these faces take drag's contortions one step further. The faces in the video, called Queering the Dataset, are all deepfakes generated by a hacked artificial intelligence (AI) system.

The power of deepfake tech to hone digital effects into incredibly realistic video can't be underestimated. We've seen a top-level Tom Cruise impersonator transformed with a high-level deepfake artist, and now companies -- and film studios -- are taking notice. Luke Skywalker's CGI face in The Mandalorian was met with a lot of criticism, and one fan's efforts to improve it resulted in a new job. Lucasfilm has hired YouTuber Shamook to ensure future projects won't have wobbly representations of actors that are either much older or perhaps even deceased now. The latter, however, remains an ethical conundrum in itself, as demonstrated by the recent Anthony Bourdain documentary.

Researchers found that 80% of marketers surveyed believe more than 25% of all marketing tasks will be automated in some way during the next five years. Meanwhile, 41% of marketers reported they enjoyed a spike in revenue after adopting AI-powered selling tools. Plus, 56% of marketers surveyed believe that AI will create more marketing jobs than it eliminates during the next decade. The study, dubbed "2021 State of Marketing AI Report Released," is available for free download on the Web. Entrepreneur Dave Kerpen is all-in on auto-text writer Copy.ai.

Luke Skywalker's CGI face in the character's The Mandalorian cameo was met with a lot of criticism, and fans even tried to fix the scene with various tools and programs. One of those fans did so well, Lucasfilm has hired him to help it ensure its upcoming projects won't feature underwhelming de-aging and facial visual effects. That fan is a YouTuber known as Shamook, who uses deepfake technology to improve upon bad CG effects and to put actors in shows and movies they never starred in. In the comments section of a video that replaces Christian Bale with Robert Pattinson as the Batman in Christopher Nolan's film, Shamook wrote that he joined Lucasfilm/Industrial Light & Magic (ILM) a few months ago. When asked what his role within the company is, he said his official title is "Senior Facial Capture Artist."

We can see Pr value here, and there are three stars associated with this Pr value. This basically means that we can reject the null hypothesis which states that there is no relationship between the age and the target columns. But since we have three stars over here, this null hypothesis can be rejected. There is a strong relationship between the age column and the target column. Now, we have other parameters like null deviance and residual deviance.

Simulation has emerged as a popular method to study the long-term societal consequences of recommender systems. This approach allows researchers to specify their theoretical model explicitly and observe the evolution of system-level outcomes over time. However, performing simulation-based studies often requires researchers to build their own simulation environments from the ground up, which creates a high barrier to entry, introduces room for implementation error, and makes it difficult to disentangle whether observed outcomes are due to the model or the implementation. We introduce T-RECS, an open-sourced Python package designed for researchers to simulate recommendation systems and other types of sociotechnical systems in which an algorithm mediates the interactions between multiple stakeholders, such as users and content creators. To demonstrate the flexibility of T-RECS, we perform a replication of two prior simulation-based research on sociotechnical systems. We additionally show how T-RECS can be used to generate novel insights with minimal overhead. Our tool promotes reproducibility in this area of research, provides a unified language for simulating sociotechnical systems, and removes the friction of implementing simulations from scratch.

Twenty-five years ago, my colleagues Miyuki Kamachi and Jiro Gyoba and I designed and photographed JAFFE, a set of facial expression images intended for use in a study of face perception. In 2019, without seeking permission or informing us, Kate Crawford and Trevor Paglen exhibited JAFFE in two widely publicized art shows. In addition, they published a nonfactual account of the images in the essay "Excavating AI: The Politics of Images in Machine Learning Training Sets." The present article recounts the creation of the JAFFE dataset and unravels each of Crawford and Paglen's fallacious statements. I also discuss JAFFE more broadly in connection with research on facial expression, affective computing, and human-computer interaction.

Mr Green's fellow co-founder and chief technology officer Andrew Rabinovich acknowledges that building emotion-detecting AI is difficult but he argues that using multiple data sources - what people are typing as well as their facial expressions, for instance - can improve the overall accuracy of the system. This, the pair suggest, can give a broad sense of how engaged the audience is.

New feature comes on the heels of VERB's Attribution feature announced in May 2021 for the verbLIVE livestream ecommerce platform NEWPORT BEACH, Calif. and SALT LAKE CITY, July 26, 2021 (GLOBE NEWSWIRE) -- Verb Technology Company, Inc. (Nasdaq: VERB) ("VERB" or the "Company"), a leader in interactive video-based sales enablement applications, including interactive livestream ecommerce, webinar, CRM and marketing applications for entrepreneurs and enterprises, today announced that it is introducing A I capabilities to its sales enablement platform. The new feature set called "Pulse" is the first iteration of VERB's artificial intelligence initiatives designed to make it easy for anyone to sell, giving pros and newbies alike a real competitive advantage. The new feature will be available in August 2021. Designed by sales people for sales people, Pulse helps automate management of their customer relationships and interactions. Based on prior activities and behavior, Pulse guides users through behavior-driven prompts, reminders, and suggested actions for specific customers.

Narrative generation and analysis are still on the fringe of modern natural language processing yet are crucial in a variety of applications. This paper proposes a feature extraction method for plot dynamics. We present a dataset that consists of the plot descriptions for thirteen thousand TV shows alongside meta-information on their genres and dynamic plots extracted from them. We validate the proposed tool for plot dynamics extraction and discuss possible applications of this method to the tasks of narrative analysis and generation.