In the first thirty seconds of the director and artist Paul Trillo's short film "Thank You for Not Answering," a woman gazes out the window of a subway car that appears to have sunk underwater. A man appears in the window swimming toward the car, his body materializing from the darkness and swirling water. It's a frightening, claustrophobic, violent scene--one that could have taken hundreds of thousands of dollars of props and special effects to shoot, but Trillo generated it in a matter of minutes using an experimental tool kit made by an artificial-intelligence company called Runway. The figures in the film appear real, played by humans who may actually be underwater. But another glance reveals the uncanniness in their blank eyes, distended limbs, mushy features.

People in Texas sounded off on AI job displacement, with half of people who spoke to Fox News convinced that the tech will rob them of work. With new developments in generative artificial intelligence bringing the technology to the forefront of public conversation, concerns about how it will affect jobs in the entertainment industry have risen, even contributing in a writer strike in Hollywood. But, founders of Web3 animation studio Toonstar have been using artificial intelligence in their studio for years, and told Fox News Digital it serves as an aid in the creative process. AI can "unlock creativity" and give animators a "head start" in terms of creativity, Luisa Huang, COO and co-founder of Toonstar told Fox News Digital. "But I have yet to see AI be able to put output anything … that is ready for production," she added.

TL;DR: The 2023 Complete Blender Bundle is on sale for £28.21, saving you 85% on list price. Making your own video games is more within reach than you may think. Whether you're a cozy gamer with a dream or a curious digital artist, you can design your own game with Blender. This online learning bundle can guide you through creating landscapes, characters, and animation. The 2023 Complete Blender Bundle has 50 hours of game development training by a top instructor for just £28.21.

Using characters and scenes he generated with Dall-E, writer / director Chad Nelson and creative agency Native Foreign have made the animated short Critters, which recently debuted on YouTube. The five-minute film, which was partly financed by OpenAI and is a cross between something from Pixar and a David Attenborough-style documentary, we meet a cast of cute, furry creatures who live in an imaginary jungle. While the assets were generated using AI, Chad wrote the script himself. He used actors to record the voices and the film was made together with a team of animators. His son also worked on the film, as an Unreal Engine programmer.

TL;DR: As of April 14, The 2023 Complete Blender Bundle(Opens in a new tab) is just $34.99 -- that's $620 in savings. Making your own video games is more within reach than you may think. Whether you're a cozy gamer with a dream or a curious digital artist, you can design your own game with Blender. This online learning bundle can guide you through creating landscapes, characters, and animation(Opens in a new tab). The 2023 Complete Blender bundle has 50 hours of game development training by a top instructor for just $34.99 (reg.

Meta has open-sourced an artificial intelligence project that lets anyone bring their doodles to life. The company hopes that by offering Animated Drawings as an open-source project other developers will be able to create new, richer experiences. The Fundamental AI Research (FAIR) team originally released a web-based version of the tool in 2021. It asks users to upload a drawing of a single human-like character or to select a demo figure. If you use your own doodle, you'll see a consent form that asks if Meta can use your drawing to help train its models.

In addition to identifying the content within a single image, relating images and generating related images are critical tasks for image understanding. Recently, deep convolutional networks have yielded breakthroughs in predicting image labels, annotations and captions, but have only just begun to be used for generating high-quality images. In this paper we develop a novel deep network trained end-to-end to perform visual analogy making, which is the task of transforming a query image according to an example pair of related images. Solving this problem requires both accurately recognizing a visual relationship and generating a transformed query image accordingly. Inspired by recent advances in language modeling, we propose to solve visual analogies by learning to map images to a neural embedding in which analogical reasoning is simple, such as by vector subtraction and addition. In experiments, our model effectively models visual analogies on several datasets: 2D shapes, animated video game sprites, and 3D car models.

Bayesian optimization (BayesOpt) is a powerful tool widely used for global optimization tasks, such as hyperparameter tuning, protein engineering, synthetic chemistry, robot learning, and even baking cookies. BayesOpt is a great strategy for these problems because they all involve optimizing black-box functions that are expensive to evaluate. A black-box function's underlying mapping from inputs (configurations of the thing we want to optimize) to outputs (a measure of performance) is unknown. However, we can attempt to understand its internal workings by evaluating the function for different combinations of inputs. Because each evaluation can be computationally expensive, we need to find the best inputs in as few evaluations as possible.

Image animation consists of generating a video sequence so that an object in a source image is animated according to the motion of a driving video. Our framework addresses this problem without using any annotation or prior information about the specific object to animate. Once trained on a set of videos depicting objects of the same category (e.g.

Back in July of 2020, I published a group post entitled “Philosophers on GPT-3.” At the time, most readers of Daily Nous had not heard of GPT-3 and had no idea what a large language model (LLM) is. How times have changed. Over the past few months, with the release of OpenAI’s ChatGPT and Bing’s AI Chatbot “Sydney” (which we learned a few hours after this post originally went up has “secretly” been running GPT-4) (as well as Meta’s Galactica—pulled after 3 days—and Google’s Bard—currently available only to a small number of people), talk of LLMs has exploded. It seemed like a good time for a follow-up to that original post, one in which philosophers could get together to explore the various issues and questions raised by these next-generation large language models. Here it is. As with the previous post on GPT-3, this edition of Philosophers On was put together by guest editor by Annette Zimmermann. I am very grateful to her for all of the work she put into developing and editing this post. Philosophers On is an occasional series of group posts on issues of current interest, with the aim of showing what the careful thinking characteristic of philosophers (and occasionally scholars in related fields) can bring to popular ongoing conversations. The contributions that the authors make to these posts are not fully worked out position papers, but rather brief thoughts that can serve as prompts for further reflection and discussion. The contributors to this installment of “Philosophers On” are: Abeba Birhane (Senior Fellow in Trustworthy AI at Mozilla Foundation & Adjunct Lecturer, School of Computer Science and Statistics at Trinity College Dublin, Ireland), Atoosa Kasirzadeh (Chancellor’s Fellow and tenure-track assistant professor in Philosophy & Director of Research at the Centre for Technomoral Futures, University of Edinburgh), Fintan Mallory (Postdoctoral Fellow in Philosophy, University of Oslo), Regina Rini (Associate Professor of Philosophy & Canada Research Chair in Philosophy of Moral and Social Cognition), Eric Schwitzgebel (Professor of Philosophy, University of California, Riverside), Luke Stark (Assistant Professor of Information & Media Studies, Western University), Karina Vold (Assistant Professor of Philosophy, University of Toronto & Associate Fellow, Leverhulme Centre for the Future of Intelligence, University of Cambridge), and Annette Zimmermann (Assistant..