If the world's most famous film directors designed a car, what would it look like? Thanks to artificial intelligence, we now have the answer. A car scrap collection firm based in the UK used DALL-E-2 - an AI platform that creates images based on text inputs - to imagine what cars designed by Wes Anderson, Christopher Nolan, Alfred Hitchcock and James Cameron, among others, would look like. The results show how powerful this particular artificial intelligence system, which was built by OpenAI, can be when given this type of assignment. British-born director Christopher Nolan is recognized for his work on the Batman trilogy as well as films like Interstellar, which explored many quantum physics themes, and the World War II film Dunkirk.

Recently, a man used the AI image generator Midjourney to enter a fine arts contest for the Colorado State Fair under the "Digital Arts/Digitally-Manipulated Photography" category. His piece won the top prize, sparking conversation from artists about the validity of AI in art. Many have flocked to AIs like Midjourney and DALL-E 2, which are designed to create illustrations from simple, one-sentence prompts, for their low-effort input and high-quality output. DALL-E 2 can "create realistic images and art from a description in natural language." The program, along with Midjourney and other image generation AI like Stable Diffusion, dominated internet searches for months.

In today's world of marketing, it is necessary to have visually appealing content. Visual material has become an essential area of focus for every company as a result of the widespread availability of gadgets for mass communication and extended visual advancements. Similarly, artificial intelligence is also gaining ground and it is proving to be the most revolutionary technological advancement thus far. The integration of visual content with artificial intelligence is the key to acquiring and retaining loyal customers; its absence from the overarching marketing strategy of any production raises a red flag that could ultimately result in a smaller market share for that company.

We provide a new multi-task benchmark for evaluating text-to-image models. We perform a human evaluation comparing the most common open-source (Stable Diffusion) and commercial (DALL-E 2) models. Twenty computer science AI graduate students evaluated the two models, on three tasks, at three difficulty levels, across ten prompts each, providing 3,600 ratings. Text-to-image generation has seen rapid progress to the point that many recent models have demonstrated their ability to create realistic high-resolution images for various prompts. However, current text-to-image methods and the broader body of research in vision-language understanding still struggle with intricate text prompts that contain many objects with multiple attributes and relationships. We introduce a new text-to-image benchmark that contains a suite of thirty-two tasks over multiple applications that capture a model's ability to handle different features of a text prompt. For example, asking a model to generate a varying number of the same object to measure its ability to count or providing a text prompt with several objects that each have a different attribute to identify its ability to match objects and attributes correctly. Rather than subjectively evaluating text-to-image results on a set of prompts, our new multi-task benchmark consists of challenge tasks at three difficulty levels (easy, medium, and hard) and human ratings for each generated image.

GitHub Copilot dubs itself as an "AI pair programmer" for software developers, automatically suggesting code in real time. According to GitHub, Copilot is "powered by Codex, a generative pretrained AI model created by OpenAI" and has been trained on "natural language text and source code from publicly available sources, including code in public repositories on GitHub." However, a class-action lawsuit filed against GitHub Copilot, its parent company Microsoft, and OpenAI claims open-source software piracy and violations of open-source licenses. "The spirit of open source is not just a space where people want to keep it open," says Sal Kimmich, an open-source developer advocate at Sonatype, machine learning engineer, and open source contributor and maintainer. "We have developed processes in order to keep open source secure, and that requires traceability, observability, and verification. Copilot is obscuring the original provenance of those [code] snippets."

The popularity of text-to-image artificial intelligences could be their own downfall – if the pictures they produce proliferate too much, they could contaminate the data sets that new models are trained on, harming performance. AI tools like DALL-E 2, Midjourney and Stable Diffusion can create pictures of whatever people request, and their images are increasingly being shared online.

AI Week at Betaworks begins Monday, 11/28/22 Betaworks is hosting a week of events & discussions to explore the new creative frontiers opened up by generative AI. We've witnessed unprecedented developments in how artists & engineers are using generative AI to add creative agency to how we work & play. Over the course of 4 days we will be showcasing insights & projects from an exciting roster of builders & researchers. About this Event: Over the last two thousand years, text has inexplicably become the most ubiquitous & the least ergonomic user interface element in the world. We've moved from hand-scribed writing to software word processing, but the underlying material of knowledge is the same - a trail of cryptic characters, lined up next to each other.

The robustness of the methodology of this particular study is of less interest than the phenomenon in general, only because of the recent ubiquity of the debate. That is, if people see AI-generated images as art, and are moved by them, then what? What do we need human artists for? For Swedish artist Jonas Lund, DALL·E, Midjourney and other existing forms of AI "cannot be art without an artist … Without an artist, it's not art, it's something else." In other words, just because the outputs of these sophisticated AI systems are images, doesn't mean they're art.

The tech industry might be reeling from a wave of layoffs, a dramatic crypto-crash, and ongoing turmoil at Twitter, but despite those clouds some investors and entrepreneurs are already eyeing a new boom--built on artificial intelligence that can generate coherent text, captivating images, and functional computer code. But that new frontier has a looming cloud of its own. A class-action lawsuit filed in a federal court in California this month takes aim at GitHub Copilot, a powerful tool that automatically writes working code when a programmer starts typing. The lawsuit is at an early stage, and its prospects are unclear because the underlying technology is novel and has not faced much legal scrutiny. But legal experts say it may have a bearing on the broader trend of generative AI tools. AI programs that generate paintings, photographs, and illustrations from a prompt, as well as text for marketing copy, are all built with algorithms trained on previous work produced by humans.

Lawyer and developer Matthew Butterick announced last month that he'd teamed up with the Joseph Saveri Law Firm to investigate Copilot. They wanted to know if and how the software infringed upon the legal rights of coders by scraping and emitting their work without proper attribution under current open-source licenses. Now, the firm has filed a class-action lawsuit in the District Court of Northern California in San Francisco. "We are challenging the legality of GitHub Copilot," Butterick said. "This is the first step in what will be a long journey. As far as we know, this is the first class-action case in the US challenging the training and output of AI systems. It will not be the last. AI systems are not exempt from the law. Those who create and operate these systems must remain accountable," he continued in a statement.