Artificial intelligence has made a big impact on our lives over the past few years. From online shopping bots to voice assistants like Alexa, we're now more connected than ever before thanks to AI technology. But how can we use AI in other areas? In this article, I will look at how artificial intelligence (AI) can revolutionize the world of art and design by creating new works from scratch and improving existing ones. GANs, or generative adversarial networks, are a type of deep learning model that can generate images, text and other types of data.

Synthetic data created by artificial intelligence systems, for AI systems is a growing market, as general adversarial networks (GANs) are used to train facial recognition and other biometric algorithms. The Washington Post profiles a company called Yuty, and the path it took to providing synthetic facial datasets, and reports that it is one of around 50 startups in the space. The Post notes that Gartner has forecast 60 percent of all AI training data will be synthetic by 2024. Amazon recently revealed that it relied heavily on synthetic data to train its palm biometrics. In a similar vein, OpenAI's DALL-E machine learning tool has updated a policy to allow its users to share synthetic facial images, after the tool's developers built in mechanisms to prevent its use in creating deepfakes, according to Vice.

The Allen Institute for AI (AI2), the division within the nonprofit Allen Institute focused on machine learning research, today published its work on an AI system, called Unified-IO, that it claims is among the first to perform a "large and diverse" set of AI tasks. Unified-IO can process and create images, text and other structured data, a feat that the research team behind it says is a step toward building capable, unified general-purpose AI systems. "We are interested in building task-agnostic [AI systems], which can enable practitioners to train [machine learning] models for new tasks with little to no knowledge of the underlying machinery," Jaisen Lu, a research scientist at AI2 who worked on Unified-IO, told TechCrunch via email. "Such unified architectures alleviate the need for task-specific parameters and system modifications, can be jointly trained to perform a large variety of tasks and can share knowledge across tasks to boost performance." AI2's early efforts in building unified AI systems led to GPV-1 and GPV-2, two general-purpose, "vision-language" systems that supported a handful of workloads including captioning images and answering questions.

The idea being that in an evolving artistic landscape, technology will play a huge role in the next wave of masterpieces. The hope is that AI will be able to take over for the painstaking grunt work of individual cell shading and clumsy greenscreen for movies, and much more. Currently the application has been limited to a few websites that are in beta mode (essentially a testing phase), a few invitations sent out to curious users who are sharing their findings with the rest of the internet. The results have been a mix of curious, terrifying, and hilarious. Already, a new wave of memes has surged through social media, pictures of clowns on the moon, beloved television shows overrun by dinosaurs, and celebrities eating cheese.

A photographer has created portraits of people who do not exist but were instead made with the artificial intelligence (AI) program Dall-E 2. Mathieu Stern, a French photographer, used the nascent software that is not yet easily available to the public to create photorealistic portraits of fictitious people that he documented in a YouTube video. Stern, who recently made a series of wild camera designs on the program, started by instructing Dall-E to create an image of "a young beautiful woman wearing a yellow kimono, in a tropical greenhouse." "At first the lack of information about the camera, the lens, and the general look of the image, led to rather unimpressive results," Stern explains on YouTube. "So to help Dall-E, some details must be added to the general description, like the lens, the camera, the film, and adding some words like bokeh." Stern says the best results came after adding the word "Graflex."

While the Machine Learning world was still coming to terms with the impressive results of DALL-E 2, released earlier this year, Google upped the ante by releasing its own text-to-image model Imagen, which appears to push the boundaries of caption-conditional image generation even further. Imagen, released just last month, can generate high-quality, high-resolution images given only a description of a scene, regardless of how logical or plausible such a scene may be in the real world. These impressive results no doubt have many wondering how Imagen actually works. In this article, we'll explain how Imagen works at several levels. First, we will examine Imagen from a bird's-eye view in order to understand its high-level components and how they relate to one another. We'll then go into a bit more detail regarding these components, each with its own subsection, in order to understand how they themselves work. Finally, we'll perform a Deep Dive into Imagen that is intended for Machine Learning researchers, students, and practitioners. Without further ado, let's dive in! In the past few years, there has been a significant amount of progress made in the text-to-image domain of Machine Learning. A text-to-image model takes in a short textual description of a scene and then generates an image which reflects the described scene. An example input description (or "caption") and output image can be seen below: It is important to note that high-performing text-to-image models will necessarily be able to combine unrelated concepts and objects in semantically plausible ways.

In 2020, OpenAI's machine learning algorithm GPT-3 blew people away when, after ingesting billions of words scraped from the internet, it began spitting out well-crafted sentences. This year, DALL-E 2, a cousin of GPT-3 trained on text and images, caused a similar stir online when it began whipping up surreal images of astronauts riding horses and, more recently, crafting weird, photorealistic faces of people that don't exist. Now, the company says its latest AI has learned to play Minecraft after watching some 70,000 hours of video showing people playing the game on YouTube. Compared to numerous prior Minecraft algorithms which operate in much simpler "sandbox" versions of the game, the new AI plays in the same environment as humans, using standard keyboard-and-mouse commands. In a blog post and preprint detailing the work, the OpenAI team say that, out of the box, the algorithm learned basic skills, like chopping down trees, making planks, and building crafting tables.

Experts at OpenAI have trained a neural network to play Minecraft to an equally high standard as human players. The neural network was trained on 70,000 hours of miscellaneous in-game footage, supplemented with a small database of videos in which contractors performed specific in-game tasks, with the keyboard and mouse inputs also recorded. After fine-tuning, OpenAI found the model was able to perform all manner of complex skills, from swimming to hunting for animals and consuming their meat. It also grasped the "pillar jump", a move whereby the player places a block of material below themselves mid-jump in order to gain elevation. Perhaps most impressive, the AI was able to craft diamond tools (requiring a long string of actions to be executed in sequence), which OpenAI described as an "unprecedented" achievement for a computer agent.

OpenAI has developed a neural network that can play Minecraft like humans. The Artificial Intelligence (AI) model was trained over 70,000 hours of miscellaneous in-game footage, along with a small database of videos in which specific in-game tasks were performed. Keyboard and mouse inputs are also recorded. OpenAI fine-tuned the AI, and now, it is skillful as a human-it can swim, hunt for animals, and eat. The AI can also do the pillar jump, where a player places a block of material below themselves in mid-air to gain more elevation.

Not all of us have the talent to whip up a piece of art at a moment's notice. But algorithms using machine learning are learning how to create "AI art" based on text prompts--and you can use them, too. Algorithms like DALL-E (and eventually, DALL-E 2), DALL-E mini, Craiyon, Midjourney, and more are learning how to take publicly available art and learn what makes them art. Or, at least, digest the various elements and style of a photo or artistic work and recombine them into something new. Sure, you can argue whether or not they're, in fact, "art," but the creations are unique, original, and compelling. Simply put, AI art uses a text prompt: something specific like McDonalds at the bottom of the sea, for example, or a bit more generic like the castle of time -- the prompt that generated the art at the top of this story.