Deep generative models have demonstrated effectiveness in learning compact and expressive design representations that significantly improve geometric design optimization. However, these models do not consider the uncertainty introduced by manufacturing or fabrication. Past work that quantifies such uncertainty often makes simplifying assumptions on geometric variations, while the "real-world", "free-form" uncertainty and its impact on design performance are difficult to quantify due to the high dimensionality. To address this issue, we propose a Generative Adversarial Network-based Design under Uncertainty Framework (GAN-DUF), which contains a deep generative model that simultaneously learns a compact representation of nominal (ideal) designs and the conditional distribution of fabricated designs given any nominal design. This opens up new possibilities of 1)~building a universal uncertainty quantification model compatible with both shape and topological designs, 2)~modeling free-form geometric uncertainties without the need to make any assumptions on the distribution of geometric variability, and 3)~allowing fast prediction of uncertainties for new nominal designs. We can combine the proposed deep generative model with robust design optimization or reliability-based design optimization for design under uncertainty. We demonstrated the framework on two real-world engineering design examples and showed its capability of finding the solution that possesses better performances after fabrication.

Not to be outdone by Meta's Make-A-Video, Google today detailed its work on Imagen Video, an AI system that can generate video clips given a text prompt (e.g., "a teddy bear washing dishes"). While the results aren't perfect -- the looping clips the system generates tend to have artifacts and noise -- Google claims that Imagen Video is a step toward a system with a "high degree of controllability" and world knowledge, including the ability to generate footage in a range of artistic styles. As my colleague Devin Coldewey noted in his piece about Make-A-Video, text-to-video systems aren't new. Earlier this year, a group of researchers from Tsinghua University and the Beijing Academy of Artificial Intelligence released CogVideo, which can translate text into reasonably-high-fidelity short clips. But Imagen Video appears to be a significant leap over the previous state-of-the-art, showing an aptitude for animating captions that existing systems would have trouble understanding.

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!