In the past few months, there has been a suite of new artificial intelligence products that go far beyond what has been made available to the public before. Last week, the high-profile suspension of a Google employee after he went public about an AI chat bot that he thought was (almost certainly incorrectly) sentient put a spotlight on just how far AI has come. One major advancement has been the new AI model Generative Pre-trained Transformer-3 (GPT-3) by research firm OpenAI, released in 2020. Since its initial release, OpenAI has slowly rolled out access to the model for various uses -- carefully allowing access to it due to fear of the powerful technology being misused. Just how powerful is this technology? Rather than telling you, why don't we get the AI to tell you?

I hope everyone is enjoying the latest breakthrough in artificial intelligence (AI) as much as I am. In one of the latest AI developments, a new computer programme - DALL-E 2 - generates images from a text prompt. Give it the phrase "Club Penguin Bin Laden", and it will go off and draw Osama as a cartoon penguin. For some, this was more than a bit of fun: it was further evidence that we shall soon be ruled by machines. Sam Altman, chief executive of the now for-profit Open AI company which provides the model that underpins DALL-E, suggested that a generalised intelligence (AGI) was close at hand.

In the past few months, there has been a suite of new artificial intelligence products that go far beyond what has been made available to the public before. Last week, the high-profile suspension of a Google employee after he went public about an AI chat bot that he thought was (almost certainly incorrectly) sentient put a spotlight on just how far AI has come. One major advancement has been the new AI model Generative Pre-trained Transformer-3 (GPT-3) by research firm OpenAI, released in 2020. Since its initial release, OpenAI has slowly rolled out access to the model for various uses -- carefully allowing access to it due to fear of the powerful technology being misused. Just how powerful is this technology?

On the internet, nightmare fuel is common place. The latest source: Dall-E Mini, an AI tool capturing attention on social media thanks to the weird, funny and occasionally disturbing images it creates out of text prompts. Dall-E Mini lets you type a short phrase describing an image, one that theoretically exists only in the deep recesses of your soul, and within a few seconds, the algorithm will manifest that image onto your screen. Odds are you've seen some Dall-E Mini images popping up in your social media feeds as people think of the wildest prompts they can -- perhaps it's Jon Hamm eating ham, or Yoda robbing a convenience store. This isn't the first time art and artificial intelligence have captured the internet's attention. There's a certain appeal to seeing how an algorithm tackles something as subjective as art.

In scrolling through your social media feeds of late, there's a good chance you've noticed illustrations accompanied by captions. The pictures you're seeing are likely made possible by a text-to-image program called DALL-E. Before posting the illustrations, people are inserting words, which are then being converted into images through artificial intelligence models. For example, a Twitter user posted a tweet with the text, "To be or not to be, rabbi holding avocado, marble sculpture." The attached picture, which is quite elegant, shows a marble statue of a bearded man in a robe and a bowler hat, grasping an avocado.

DALLE 2 is a groundbreaking tool for creating gorgeous, AI art. New DALLE 2 alternatives is already here. Machine Learning changed the landscape of art.

Diffusion Models are generative models which have been gaining significant popularity in the past several years, and for good reason. A handful of seminal papers released in the 2020s alone have shown the world what Diffusion models are capable of, such as beating GANs[6] on image synthesis. Most recently, practitioners will have seen Diffusion Models used in DALL-E 2, OpenAI's image generation model released last month. Given the recent wave of success by Diffusion Models, many Machine Learning practitioners are surely interested in their inner workings. In this article, we will examine the theoretical foundations for Diffusion Models, and then demonstrate how to generate images with a Diffusion Model in PyTorch.

Want to see a picture of Jesus Christ laughing at a meme on his phone, Donald Trump as the Nevermind baby, or Karl Marx being slimed at the Nikelodeon Kid's Choice awards? If you've been on Twitter or Instagram in the past couple of weeks, it's been hard to miss odd-looking formulations of these kinds of scenarios in the form of AI art. DALL-E (and DALL-E mini), the creator of these artworks, is a neural network that can take a text phrase and transform it an image. It was trained by looking at millions of images on the internet along with accompanying text and it learned to create pictures of things you'd never expect to be combined, such as an avocado armchair. Text to image technology is proceeding at a rapid pace, and the full DALL-E model is able to produce scarily clear images based on the input you provide, while the mini version is still clunky enough to capture the weird internet style that makes them instantly meme-able.

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. Many artificial intelligence (AI) algorithms are used to classify, organize or reason about data. Generative algorithms create data using models of the world to synthesize images, sounds and videos that often look increasingly realistic. The algorithms begin with models of what a world must be like and then they create a simulated world that fits the model. Generative AIs are frequently found in various content creation roles.

I have some exciting news (for me, anyway). Starting next week, I'll be going on leave from UT Austin for one year,to work at OpenAI. They're the creators of the astonishing GPT-3 and DALL-E2, which have not only endlessly entertained me and my kids, but recalibrated my understanding of what, for better and worse, the world is going to look like for the rest of our lives. Working with an amazing team at OpenAI, including Jan Leike, John Schulman, and Ilya Sutskever, my job will be think about the theoretical foundations of AI safety and alignment. What, if anything, can computational complexity contribute to a principled understanding of how to get an AI to do what we want and not do what we don't want?