For years, artist Steve Coulson wanted to make his own comic. "The problem has always been – I can't draw," he says. But in 2022, Coulson published a beautiful comic called Summer Island. The 40-page folk-horror story about a sea god festival features detailed illustrations with a coherent visual style-- all created with the help of artificial intelligence. As AI image generators, such as OpenAI's popular DALL-E and DALL-E2, become more widespread, some forecast the death of human artforms.

For months, the "first" AI art program, DALL-E, has been hidden behind a beta wall that has limited access. Now it's open to everyone to try out, with a generous amount of credits, to boot. Each signup adds 50 credits to your account, with each credit generating four 1024 1024 images from a single prompt from the OpenAI server. You'll get 15 new credits per month, though the credits do not roll over. OpenAI also has placed content limits on the type of images you can generate, forbidding violence, sexual acts (including nudity), politicians, and public figures.

Although the effect is rather crude, the system offers an early glimpse of what's coming next for generative artificial intelligence, and it is the next obvious step from the text-to-image AI systems that have caused huge excitement this year. Meta's announcement of Make-A-Video, which is not yet being made available to the public, will likely prompt other AI labs to release their own versions. It also raises some big ethical questions. In the last month alone, AI lab OpenAI has made its latest text-to-image AI system DALL-E available to everyone, and AI startup Stability.AI launched Stable Diffusion, an open-source text-to-image system. But text-to-video AI comes with some even greater challenges.

Meta unveiled its Make-a-Scene text-to-image generation AI in July, which like Dall-E and Midjourney, utilizes machine learning algorithms (and massive databases of scraped online artwork) to create fantastical depictions of written prompts. As its name implies, Make-a-Video is, "a new AI system that lets people turn text prompts into brief, high-quality video clips," Zuckerberg wrote in a Meta blog Thursday. Functionally, Video works the same way that Scene does -- relying on a mix of natural language processing and generative neural networks to convert non-visual prompts into images -- it's just pulling content in a different format. "Our intuition is simple: learn what the world looks like and how it is described from paired text-image data, and learn how the world moves from unsupervised video footage," a team of Meta researchers wrote in a research paper published Thursday morning. Doing so enabled the team to reduce the amount of time needed to train the Video model and eliminate the need for paired text-video data, while preserving "the vastness (diversity in aesthetic, fantastical depictions, etc.) of today's image generation models."

Several months after launching DALL-E 2 as a part of a limited beta, OpenAI today removed the waitlist for the AI-powered image-generating system, which will remain in beta but let anyone sign up and begin using it. Pricing will carry over from the waitlist period, with first-time users getting a finite amount of credits that can be put toward generating or editing an image or creating a variation of existing images. "More than 1.5 million users are now actively creating over 2 million images a day with DALL-E -- from artists and creative directors to authors and architects -- with about 100,000 users sharing their creations and feedback in our Discord community," OpenAI wrote in a blog post. "Learning from real-world use has allowed us to improve our safety systems, making wider availability possible today." OpenAI has yet to make DALL-E 2 available through an API, though the company notes in the blog post that one is in testing.

After nine generations of Pokémon, it can sometimes feel like the game developers are just pulling random words from a hat and seeing what they come up with. I still can't decide if Klefki, the sentient keyring Pokémon, is a stroke of genius or madness. Justin Pinkney, a machine learning researcher at Lambda Labs, created a "text to Pokémon" generator by fine-tuning Stable Diffusion, an AI image generator that functions in the same vein as DALL-E 2. Fine tuning #stablediffusion to make Pokemon! I wrote a quick guide on fine tuning your own Stable Diffusion: https://t.co/hLWrOjEPTm I also released my Pokemon model, you can try it out on Replicate: https://t.co/3sVQrk54wZ

Internet art and image archives are already flooded with images developed with the use of artificial intelligence. Expect even more images of high imagination or photos of dubious origin now that the AI image generator that arguably started the current artificial image craze, DALL-E, is open and available to all. In a Wednesday blog post, DALL-E developer OpenAI said already have 1.5 million users creating more than 2 million AI-generated images a day. Using data and feedback, the company said they have made their filters stronger at rejecting any images made to emulate sexual, violent, or poltiical content. There is no current API available for DALL-E, but apparently one's in development.

We live in exciting times where every week, we have announcements on cutting-edge technology. A few months ago, OpenAI dropped state of the art text-to-image model DALL·E 2. Only a few people got early access to experience a new AI system that can create realistic images from a description using natural language. It is still closed to the public. A few weeks later, Stability AI launched the open-source version of DALLE2 called the Stable Diffusion model. This launch has changed everything.

Transfer learning is crucial in training deep neural networks on new target tasks. Current transfer learning methods always assume at least one of (i) source and target task label spaces overlap, (ii) source datasets are available, and (iii) target network architectures are consistent with source ones. However, holding these assumptions is difficult in practical settings because the target task rarely has the same labels as the source task, the source dataset access is restricted due to storage costs and privacy, and the target architecture is often specialized to each task. To transfer source knowledge without these assumptions, we propose a transfer learning method that uses deep generative models and is composed of the following two stages: pseudo pre-training (PP) and pseudo semi-supervised learning (P-SSL). PP trains a target architecture with an artificial dataset synthesized by using conditional source generative models. P-SSL applies SSL algorithms to labeled target data and unlabeled pseudo samples, which are generated by cascading the source classifier and generative models to condition them with target samples. Our experimental results indicate that our method can outperform the baselines of scratch training and knowledge distillation. For training deep neural networks on new tasks, transfer learning is essential, which leverages the knowledge of related (source) tasks to the new (target) tasks via the joint-or pre-training of source models. There are many transfer learning methods for deep models under various conditions (Pan & Yang, 2010; Wang & Deng, 2018). For instance, domain adaptation leverages source knowledge to the target task by minimizing the domain gaps (Ganin et al., 2016), and fine-tuning uses the pre-trained weights on source tasks as the initial weights of the target models (Yosinski et al., 2014).

Regie.ai, a startup using OpenAI's GPT-3 text-generating system to create sales and marketing content for brands, today announced that it raised $10 million in Series A funding led by Scale Venture Partners with participation from Foundation Capital, South Park Commons, Day One Ventures and prominent angel investors. The fresh investment comes as VCs see a growing opportunity in AI-powered, copy-generating adtech companies, whose tech promise to save time while potentially increasing personalization. Previously a software engineer at Google and Meta, Sridhar is a data scientist by trade, having developed enterprise-scale AI systems that detect duplicate images and rank search results. Millen formerly was a VP at T-Mobile, leading the national sales teams (e.g., strategic accounts and public sector). With Regie, Sridhar says he and Millen aimed to create a way for companies to communicate with their customers via channels like email, social media, text, podcasts, online advertising and more.