Does the breakthrough to general AI need more data and computing power above all else? Yann LeCun, Chief AI Scientist at Meta, comments on the recent debate about scaling sparked by Deepmind's Gato. The recent successes of large AI models such as OpenAI's DALL-E 2, Google's PaLM and Deepmind's Flamingo have sparked a debate about their significance for progress towards general AI. Deepmind's Gato has recently given a particular boost to the debate, which has been conducted publicly, especially on Twitter. Gato is a Transformer model trained with numerous data modalities, including images, text, proprioception or joint moments.

DALL·E 2 is a new AI system that can create realistic images and art from a description in natural language. DALL·E 2 can create original, realistic images and art from a text description. It can combine concepts, attributes, and styles. DALL·E 2 can make realistic edits to existing images from a natural language caption. It can add and remove elements while taking shadows, reflections, and textures into account.

OpenAI, a San Francisco Artificial Intelligence company closely affiliated with Microsoft, launched an A.I. system and neural network in January 2021 known as DALL-E. Named using a pun of the surrealist artist Salvador Dalí and Pixar's famous movie WALL-E, DALL-E creates images from text.In this blog, we'll let you in on everything you should know about DALL-E, its variation DALL-E 2, and share ten of the most creative AI-generated images of Dall-E 2. Picture of a dog wearing a beret and a turtleneck generated by the DALL-E 2 image generation software. Now, you may be wondering what DALL-E is all about. It's an AI tool that takes a description of an object or a scene and automatically produces an image depicting the scene/object. DALL-E also allows you to edit all the wonderful AI-generated images you've created with simple tools and text modifications.

Can there be a movie or a comic book with AI-generated characters, sets & plots? It is getting closer to possibility and let's get a preview. Automatically generating stuff with these artificially intelligent systems is the trend. One subset of this is image creation from text input. Can we use this to create picture stories?

As foundation models (e.g., GPT-3, PaLM, DALL-E 2) become more powerful and ubiquitous, the issue of responsible release becomes critically important. In this blog post, we use the term release to mean research access: foundation model developers making assets such as data, code, and models accessible to external researchers. Deploying to users for testing and collecting feedback (Ouyang et al. 2022; Scheurer et al. 2022; AI Test Kitchen) and deploying to end users in products (Schwartz et al. 2022) are other forms of release that are out of scope for this blog post. Foundation model developers presently take divergent positions on the topic of release and research access. For example, EleutherAI, Meta, and the BigScience project led by Hugging Face embrace broadly open release (see EleutherAI's statement and Meta's recent release). In contrast, OpenAI advocates for a staged release and currently provides the general public with only API access; Microsoft also provides API access, but to a restricted set of academic researchers.

New research from UC Berkeley and Adobe offers a way to directly edit the hyperreal content that can be created by a Generative Adversarial Network (GAN), but which can't usually be controlled, animated, or freely manipulated in a manner long familiar to Photoshop users and CGI practitioners. Titled BlobGAN, the method involves creating a grid of'blobs' – mathematical constructs that map directly to content within the latent space of the GAN. By moving the blobs, you can move the'objects' in a scene representation, in an intuitive manner that's nearer to CGI and CAD methods than many of the current attempts to map and control the GAN's latent space: Scene manipulation with BlobGAN: as the'blobs' are moved by the user, the disposition of latent objects and styles in the GAN are correspondingly altered. For more examples, see the paper's accompanying video, embedded at the end of this article, or at https://www.youtube.com/watch?v KpUv82VsU5k Since blobs correspond to'objects' in the scene mapped out in the GAN's latent space, all the objects are disentangled a priori, making it possible to alter them individually: Objects can be resized, shrunk, cloned, and removed, among other operations. Blobs can be duplicated in the interface, and their corresponding latent representations will also be'copied and pasted'.

If you've seen photos of a teapot shaped like an avocado or read a well-written article that veers off on slightly weird tangents, you may have been exposed to a new trend in artificial intelligence (AI). Machine learning systems called DALL-E, GPT and PaLM are making a splash with their incredible ability to generate creative work. These systems are known as "foundation models" and are not all hype and party tricks. So how does this new approach to AI work? And will it be the end of human creativity and the start of a deep-fake nightmare?

Rather, it's because people value their scarcity, prospect their future price, appreciate their proof-of-concept of a new artform, or love the artist who created them. However, a new trend in the art world, AI-generated art, may soon disrupt the multibillion-dollar NFT space. First, let's look at how collections of NFTs are created today. In most high-profile NFT projects, artists design multiple classes of varying attributes such as hair color, background color, or skin tone. Then, artists will mix and match these attributes to create a collection of "unique" NFTs, where no two NFTs look the same.

How to design a house without an architect? This is Machine Learning's idea: A new type of architectural service #architects #architecture #construction...

Marcelo Rinesi remembers what it was like to watch Jurassic Park for the first time in a theater. The dinosaurs looked so convincing that they felt like the real thing, a special effects breakthrough that permanently shifted people's perception of what's possible. After two weeks of testing DALL-E 2, the CTO of the Institute for Ethics and Emerging Technologies thinks AI might be on the verge of its own Jurassic Park moment. Last month, OpenAI introduced the second generation version of DALL-E, an AI model trained on 650 million images and text captions. It can take in text and spit out images, whether that's a "Dystopian Great Wave off Kanagawa as Godzilla eating Tokyo" or "Teddy bears working on new AI research on the moon in the 1980s."