The latest breaking updates, delivered straight to your email inbox. If you've ever wanted to use artificial intelligence to quickly design a hybrid between a duck and a corgi, now is your time to shine. On Wednesday, OpenAI announced that anyone can now use the most recent version of its AI-powered DALL-E tool to generate a seemingly limitless range of images just by typing in a few words, months after the startup began gradually rolling it out to users. The move will likely expand the reach of a new crop of AI-powered tools that have already attracted a wide audience and challenged our fundamental ideas of art and creativity. But it could also add to concerns about how such systems could be misused when widely available.

Periodic graphs are graphs consisting of repetitive local structures, such as crystal nets and polygon mesh. Their generative modeling has great potential in real-world applications such as material design and graphics synthesis. Classical models either rely on domain-specific predefined generation principles (e.g., in crystal net design), or follow geometry-based prescribed rules. Recently, deep generative models has shown great promise in automatically generating general graphs. However, their advancement into periodic graphs have not been well explored due to several key challenges in 1) maintaining graph periodicity; 2) disentangling local and global patterns; and 3) efficiency in learning repetitive patterns. To address them, this paper proposes Periodical-Graph Disentangled Variational Auto-encoder (PGD-VAE), a new deep generative models for periodic graphs that can automatically learn, disentangle, and generate local and global graph patterns. Specifically, we develop a new periodic graph encoder consisting of global-pattern encoder and local-pattern encoder that ensures to disentangle the representation into global and local semantics. We then propose a new periodic graph decoder consisting of local structure decoder, neighborhood decoder, and global structure decoder, as well as the assembler of their outputs that guarantees periodicity. Moreover, we design a new model learning objective that helps ensure the invariance of local-semantic representations for the graphs with the same local structure. Comprehensive experimental evaluations have been conducted to demonstrate the effectiveness of the proposed method. The code of proposed PGD-VAE is availabe at https://github.com/shi-yu-wang/PGD-VAE.

You don't have to pick up a paintbrush to create a museum-worthy painting. Thanks to an AI tool called DALL-E, all you have to do is type in the picture you want to make. Now that it's finally available to the public, we'll explain how to use DALL-E to generate AI art for free. This versatile tool is excellent for novice artists and experts alike. For example, it can help you develop ideas for paintings -- and you can then tweak the images you generate, so they look perfect.

DALL-E 2 is one of the most popular AI platforms that offers users the opportunity to create amazing art using text prompts. In this article, we'll show you how to create AI art from scratch as well as edit your own images on the platform. DALL-E 2 is an AI image generation platform that allows users to create images from scratch using text prompts. It runs on an artificial intelligence program called GPT-3, which takes natural language and converts it to images. The platform also allows users to upload their own images and edit them using text prompts to create completely new works of art.

People consider creativity to be inherently human. However, artificial intelligence (AI) has reached the stage where it can be creative as well. A recent competition attracted anger from artists after it awarded a prize to an artwork created by an AI model known as Midjourney. And such software is now freely available thanks to the release of a similar model called Stable Diffusion, which is the most efficient of its kind to date. Unions of creative practitioners such as Stop AI Stealing the Show have for some time been raising concerns about the use of AI in creative fields.

Is anyone else feeling dizzy? Just when the AI community was wrapping its head around the astounding progress of text-to-image systems, we're already moving on to the next frontier: text-to-video. Late last week, Meta unveiled Make-A-Video, an AI that generates five-second videos from text prompts. Built on open-source data sets, Make-A-Video lets you type in a string of words, like "A dog wearing a superhero outfit with a red cape flying through the sky," and then generates a clip that, while pretty accurate, has the aesthetics of a trippy old home video. The development is a breakthrough in generative AI that also raises some tough ethical questions.

Were you unable to attend Transform 2022? Check out all of the summit sessions in our on-demand library now! Last week, OpenAI released Whisper, an open-source deep learning model for speech recognition. Developers and researchers who have experimented with Whisper are also impressed with what the model can do. However, what is perhaps equally important is what Whisper's release tells us about the shifting culture in artificial intelligence (AI) research and the kind of applications we can expect in the future.

Today, when DALL·E 2 has free access, it's worth using tools that allow you to save tokens What exactly is the definition of reverse engineering? To reverse-engineer is to take something apart to figure out how it's put together. Although its primary purpose is educational, reverse engineering is often used to recreate or improve upon the original product. Have you ever seen a picture on MidJourney or Stable Diffusion and wondered what inspired it? Now you can find out what the AI art generator believes is the best guess by asking it!

This paper addresses the dire need for a platform that efficiently provides a framework for running reinforcement learning (RL) experiments. We propose the CaiRL Environment Toolkit as an efficient, compatible, and more sustainable alternative for training learning agents and propose methods to develop more efficient environment simulations. There is an increasing focus on developing sustainable artificial intelligence. However, little effort has been made to improve the efficiency of running environment simulations. The most popular development toolkit for reinforcement learning, OpenAI Gym, is built using Python, a powerful but slow programming language. We propose a toolkit written in C++ with the same flexibility level but works orders of magnitude faster to make up for Python's inefficiency. This would drastically cut climate emissions. CaiRL also presents the first reinforcement learning toolkit with a built-in JVM and Flash support for running legacy flash games for reinforcement learning research. We demonstrate the effectiveness of CaiRL in the classic control benchmark, comparing the execution speed to OpenAI Gym. Furthermore, we illustrate that CaiRL can act as a drop-in replacement for OpenAI Gym to leverage significantly faster training speeds because of the reduced environment computation time.

If you've been itching to try OpenAI's image synthesis tool but have been stymied by the lack of an invitation, now's your chance. Today, OpenAI announced that it removed the waitlist for its DALL-E AI image generator service. That means anyone can sign up and use it. DALL-E is a deep learning image synthesis model that has been trained on hundreds of millions of images pulled from the Internet. It uses a technique called latent diffusion to learn associations between words and images.