Deepmind's new video AI, Transframer, can handle a whole range of image and video tasks – and dream up 30-second videos from a single frame. Generative AI systems have moved from research labs to industrial and consumer applications in recent years, kicked off by OpenAI's large-scale language model GPT-3. Then last April, the company introduced the DALL-E 2 imaging system, which indirectly spawned alternatives such as Midjourney and Stable Diffusion. Google sister Deepmind is now showing Transframer, an AI model that could offer a glimpse of the next generation of generative AI models. Deepmind's Transframer is a visual prediction framework that can solve eight image modeling and processing tasks at once, such as depth estimation, instance segmentation, object recognition or video prediction.

This work was created with DALL-E 2. The prompt used for the character creation is thoughtfully constructed. This gives uniqueness and unrepeatability to the work. Once came out from the algorithm, the image resolution is pretty small so is enhanced, then is painted in Adobe Photoshop using an oil brush. The resulted image has a very good print quality.

Artificial intelligence-powered (AI) image generators have exploded in popularity and apps like DALL-E, Midjourney, and more recently Stable Diffusion are exciting and tantalizing technology enthusiasts. To train these systems, each AI tool is fed millions of images. DALL-E 2, for example, was trained on approximately 650 million image-text pairs that its creator, OpenAI, scraped from the internet. PetaPixel reached out to OpenAI and asked if it only used public domain and creative commons images, but the company did not respond to our requests as of publication. However, the company has previously declined to publicly disclose the details of the images used to train DALL E-2.

Swedish artist Simon Stålenhag is known for haunting paintings that blend natural landscapes with the eerie futurism of giant robots, mysterious industrial machines, and alien creatures. Earlier this week, Stålenhag appeared to experience some dystopian dread of his own when he found that artificial intelligence had been used to mimic his style. The act of AI imitation was performed by Andres Guadamuz, a reader in intellectual property law at the University of Sussex in the UK who has been studying legal issues around AI-generated art. He used a service called Midjourney to create images resembling Stålenhag's spooky style, and posted them to Twitter. Guadamuz says he created the images to highlight the legal and ethical questions that algorithms that generate art may raise.

Is it going to take over the world? All questions a novice like myself is thinking whenever someone far more clued-up on the ever changing advancement of technology turns the conversation onto the dreaded topic of Artificial Intelligence (AI). Usually I let them dribble on and myself stay silent in the hope that our chat comes to an end, however, you illustrators and writers out there may want to be paying close attention to the recent craze sweeping through twitter boards and reddit threads. Open AI (based in San Francisco) has been growing in popularity recently on account of its new Playground and Dall-E 2 systems. The Playground system is a new predictive language tool in which you input a question or a command and in a matter of seconds an AI responds with cohesive and calculated language.

Modern deep generative models can assign high likelihood to inputs drawn from outside the training distribution, posing threats to models in open-world deployments. While much research attention has been placed on defining new test-time measures of OOD uncertainty, these methods do not fundamentally change how deep generative models are regularized and optimized in training. In particular, generative models are shown to overly rely on the background information to estimate the likelihood. To address the issue, we propose a novel frequency-regularized learning FRL framework for OOD detection, which incorporates high-frequency information into training and guides the model to focus on semantically relevant features. FRL effectively improves performance on a wide range of generative architectures, including variational auto-encoder, GLOW, and PixelCNN++. On a new large-scale evaluation task, FRL achieves the state-of-the-art performance, outperforming a strong baseline Likelihood Regret by 10.7% (AUROC) while achieving 147$\times$ faster inference speed. Extensive ablations show that FRL improves the OOD detection performance while preserving the image generation quality. Code is available at https://github.com/mu-cai/FRL.

I recently got access to OpenAI's DALL-E 2 instance. It's a lot of fun, but beyond its obvious application as a cornucopia of funny cat avatars, I think it's now fit to use in certain kinds of creative work. There are already plenty of good articles out there on the model's strengths and weaknesses, so I won't go over that here other than to note that it's not a threat to high art. It's got an idea of what things look like and how they can visually fit together, but it's very vague on how they work (e.g. However, with human guidance and a carefully chosen domain, it can still do some very impressive things.

It's clear that AI art is growing fast and will continue to capture the imaginations of people around the world – especially now it's in the hands of TikTokers. It's a clear indication of how fast the disruptive technology is growing in popularity and hitting the mainstream. Verdict has contacted TikTok and Open AI for comment. GlobalData is the parent company of Verdict and its sister publications.

AI art has been bursting into the mainstream thanks to the likes of DALL-E 2 and MidJourney. The tools allow anyone to create almost any image they can dream of from just a short text prompt. The results can be very, very strange, but artists, designers and brands are learning how to make the technology work for them, sometimes very successfully. But if you've been impressed so far, it seems the next advance is already on the way: AI video generators. AI art generators work based on text prompts.

Language models work by taking huge amounts of text uploaded to the internet and using it to train artificial intelligence software to guess what kinds of responses might come after certain questions or comments. The models have become so good at predicting the right response that engaging with one often feels like having a conversation with a knowledgeable human. Google and other companies, including OpenAI and Microsoft, have poured resources into building better models and training them on ever-bigger sets of text, in multiple languages.