I explain Artificial Intelligence terms and news to non-experts. Last year I shared DALL·E, an amazing model by OpenAI capable of generating images from a text input with incredible results. Now is time for his big brother, DALL·E 2. And you won't believe the progress in a single year! DALL·E 2 is not only better at generating photorealistic images from text. The results are four times the resolution!

We are excited to bring Transform 2022 back in-person July 19 and virtually July 20 - 28. Join AI and data leaders for insightful talks and exciting networking opportunities. OpenAI has recently released DALL-E 2, a more advanced version of DALL-E, an ingenious multimodal AI capable of generating images purely based on text descriptions. DALL-E 2 does that by employing advanced deep learning techniques that improve the quality and resolution of the generated images and provides further capabilities such as editing an existing image, or creating new versions of it. Many AI enthusiasts and researchers tweeted about how amazing DALL-E 2 is at generating art and images out of a thin word, yet in this article I'd like to explore a different application for this powerful text-to-image model -- generating datasets to solve computer vision's biggest challenges. Caption: A DALL-E 2 generated image.

Need an illustration of a wise cat meditating in the Himalayas searching for enlightenment but don't have any oil paints? On Wednesday, OpenAI unveiled the DALL·E 2 system, which can generate artistic images from a phrase. According to the OpenAI blog, the name DALL·E is "a portmanteau of the artist Salvador Dalí and Pixar's WALL·E." The recently-released second version expands upon the original DALL·E, which debuted in January 2021. It features higher-resolution images (1024 x 1024 pixels compared to 256 x 256 pixels) and lower-latency.

We are excited to bring Transform 2022 back in-person July 19 and virtually July 20 - 28. Join AI and data leaders for insightful talks and exciting networking opportunities. Artificial intelligence research lab OpenAI made headlines again, this time with DALL-E 2, a machine learning model that can generate stunning images from text descriptions. DALL-E 2 builds on the success of its predecessor DALL-E and improves the quality and resolution of the output images thanks to advanced deep learning techniques. The announcement of DALL-E 2 was accompanied with a social media campaign by OpenAI's engineers and its CEO, Sam Altman, who shared wonderful photos created by the generative machine learning model on Twitter. DALL-E 2 shows how far the AI research community has come toward harnessing the power of deep learning and addressing some of its limits.

You may have seen some weird and whimsical pictures floating around the internet recently. And a bowl of soup that looks like a monster knitted out of wool. These pictures weren't drawn by any human illustrator. Instead, they were created by DALL-E 2, a new AI system that can turn textual descriptions into images. Just write down what you want to see, and the AI draws it for you -- with vivid detail, high resolution, and, arguably, real creativity.

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?

AI has recently crossed a utility threshold, where cutting-edge models such as GPT-3, Codex, and DALL-E 2 are actually useful and can perform tasks computers cannot do any other way. The act of producing these models is an exploration of a new frontier, with the discovery of unknown capabilities, scientific progress, and incredible product applications as the rewards. And perhaps most exciting for me personally, because the field is fundamentally about creating and studying software systems, great engineers are able to contribute at the same level as great researchers to future progress. I first got into software engineering because I wanted to build large-scale systems that could have a direct impact on people's lives. I attended a math research summer program shortly after I started programming, and my favorite result of the summer was a scheduling app I built for people to book time with the professor.

Armchairs in the shape of avocados and baby daikon radishes wearing tutus are among the quirky images created by a new piece of software from OpenAI, an Elon Musk-backed artificial intelligence lab in San Francisco. OpenAI trained the software, known as Dall-E, to generate images from short text captions. It specifically used a dataset of 12 billion images and their captions, which were found on the internet. The lab said Dall-E -- a portmanteau of Spanish surrealist artist Salvador Dali and Wall-E, a small animated robot from the Pixar movie of the same name -- had learned how to create images for a wide range of concepts. OpenAI showed off some of the results in a blog post published on Tuesday.

人工知能がプログラムを自動で作ってくれる！人間がプログラミング言語を勉強してコードを書くのは時代遅れ？【OpenAI Codex】について解説します。

"Generative A.I. Landscapes" is a collection of n 5 colorful graphic artworks created by ScriNFT ScriNFT is a experienced photographer and NFT artist who specializes in minimalist, fantasy, generative, graphic art, which is often characterized by vibrant, glowing color palettes and hard to imagine compositions. This specific 1/5 NFT collection has been created using A.I., several hours of work were required to generate of each artwork, the association of colors is incredible as well as the definition of the works themselves.