Text-to-image diffusion models that can generate and edit photorealistic images have become a hot AI research area, with their incredible synthetic images garnering widespread mainstream media coverage. An advanced image generation approach, diffusion models have surpassed previous high-performance methods such as GANs (generative adversarial networks) in both image fidelity and diversity and are now demonstrating their potential in text-to-image generation. In the new paper Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding, a Google Brain research team advances this research field with Imagen, a text-to-image diffusion model that combines the deep language understanding of transformer-based large language models and the photorealistic image generation capabilities of diffusion models to achieve a new state-of-the-art FID score of 7.27 on the COCO dataset. Imagen's training data was drawn from massive datasets of image and English alt-text pairs. Like previous text-to-image models, Imagen's "wow" factor lies in its ability to generate photorealistic and high-resolution images from fanciful prompts such as "A cute corgi lives in a house made out of sushi" or "A dragon fruit wearing a karate belt in the snow."

Who will be liable for harmful speech generated by large language models? As advanced AIs such as OpenAI's GPT-3 are being cheered for impressive breakthroughs in natural language processing and generation -- and all sorts of (productive) applications for the tech are envisaged from slicker copywriting to more capable customer service chatbots -- the risks of such powerful text-generating tools inadvertently automating abuse and spreading smears can't be ignored. Nor can the risk of bad actors intentionally weaponizing the tech to spread chaos, scale harm and watch the world burn. Indeed, OpenAI is concerned enough about the risks of its models going "totally off the rails", as its documentation puts it at one point (in reference to a response example in which an abusive customer input is met with a very troll-esque AI reply), to offer a free content filter that "aims to detect generated text that could be sensitive or unsafe coming from the API" -- and to recommend that users don't return any generated text that the filter deems "unsafe". But, given the novel nature of the technology, there are no clear legal requirements that content filters must be applied.

Originally published on Towards AI the World's Leading AI and Technology News and Media Company. If you are building an AI-related product or service, we invite you to consider becoming an AI sponsor. At Towards AI, we help scale AI and technology startups. Let us help you unleash your technology to the masses. A paper that tries to lay down a pedagogical foundation for the understanding of how neural networks make decisions.

Originally published on Towards AI the World's Leading AI and Technology News and Media Company. If you are building an AI-related product or service, we invite you to consider becoming an AI sponsor. At Towards AI, we help scale AI and technology startups. Let us help you unleash your technology to the masses. If you thought Dall-E 2 delivered impressive results, wait till you see what this latest Google Brain model can accomplish.

It is NOT a great time for OpenAI right now. It's been just over a month since DALL·E 2 was released and just a few days ago, Google decides to enter the ring with Imagen. In comparison, Imagen is a slap in the face for DALLE·2 mainly because it outperforms DALLE·2 in terms of AI Image generation precision and quality. If by now you are wondering WTF is ImageGen and DALLE·2, how does this technology work in simple lingo, as well as what makes Google's Imagen so superior then take a walk with me through this article as we shall discover together my amigo. So both technologies in simple terms, allow you to generate images from text.

Art is a form of expression. Its message may be symbolic or religious, historical or political. Art is there to elicit an emotional response, and to'move' us. Artificial Intelligence, or AI for short, is the simulation of human intelligence processed by machines. Its original purpose is there to replace routine jobs and repetitive tasks like separation and segregation of materials.

The first version of DALL·E was a GPT-3 style transformer decoder that autoregressively generated a 256 256 image based on textual input and an optional beginning of the image. If you want to understand how a GPT-like transformer works, here is a great visual explanation by Jay Alammar. A text is encoded by BPE-tokens (max. Because of the dVAE, some details and high-frequency features are lost in generated images, so some blurriness and smoothness are the features of the DALL·E-generated images. The transformer is a large model with 12B parameters. It consisted of 64 sparse transformer blocks with a complicated set of attention mechanisms inside, consisting of 1) classical text-to-text masked attention, 2) image-to-text attention, and 3) image-to-image sparse attention.

"In the past few years, our tolerance of sloppy thinking has led us to repeat many mistakes over and over. If we are to retain any credibility, this should stop. It is hard to say where [we] have gone wronger, in underestimating language or overestimating computer programs." In April, Open AI released a neural network model called DALL-E 2 that blew people's minds; last week a new model came out from Google Brain called Imagen, and it was even better. Both turn sentences into art, and even a hardened skeptic like myself can't help but be amazed.

Artificial intelligence research group OpenAI (co-founded by Elon Musk, among others) has created DALL-E 2, a text-to-image generation program. The system takes a description written by a user and produces an image. The demo video is worth a watch (2 min). Here's a DALL-E 2 generated image from the description "Shiba Inu dog wearing a beret and black turtleneck." Bonus: DALL-E comes from "Salvador Dalí" combined with "WALL-E".

The architecture list is down below.