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.

As artificial intelligence becomes increasingly popular for generating images, a question has roiled the art world: Can AI create art? At bitforms gallery in San Francisco, the answer is yes. An exhibit called "Artificial Imagination" is on display through late December and features works that were created with or inspired by the generative AI system DALL-E as well as other types of AI. With DALL-E, and other similar systems such as Stable Diffusion or Midjourney, a user can type in words and get back an image. August Kamp's 2022 digital image "new experimental version, state of the art" is part of the exhibit "Artificial Imagination" at bitforms gallery in San Francisco. The exhibit features art made with and inspired by OpenAI's AI image generation system DALL-E.

With the spread of the use of Text2Img diffusion models such as DALL-E 2, Imagen, Mid Journey and Stable Diffusion, one challenge that artists face is selecting the right prompts to achieve the desired artistic output. We present techniques for measuring the effect that specific words and phrases in prompts have, and (in the Appendix) present guidance on the selection of prompts to produce desired effects.

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People get up in arms whenever the hand of the artist is detached from the final artwork. "Are photographs real art?" they muttered in the 19th Century. "God I hate this Pollock guy," cried haters witnessing a splattered canvas that the artist seemingly never touched. So it's no wonder that AI image-generators have got art historians in a twist, as more artists make use of these tools to inform their practice. I love diving into what gets people's blood boiling in the art world, and this summer AI crept its way onto the leaderboard of irritants.

Check out the on-demand sessions from the Low-Code/No-Code Summit to learn how to successfully innovate and achieve efficiency by upskilling and scaling citizen developers. In the world of artificial intelligence (AI) and machine learning (ML), 2022 has arguably been the year of foundation models, or AI models trained on a massive scale. From GPT-3 to DALL-E, from BLOOM to Imagen -- another day, it seems, another large language model (LLM) or text-to-image model. But until now, there have been no AI benchmarks to provide a standardized way to evaluate these models, which have developed at a rapidly-accelerated pace over the past couple of years. Don't miss our new special issue: Zero trust: The new security paradigm.

Now picture Lee 43 years later, stumbling onto DALL-E, an artificial intelligence that generates original works of art based on human-supplied prompts that can literally be as simple as "a picture of a train." As he types in words to create image after image, the wow is back. "It feels like a miracle," he says. "When the results appeared, my breath was taken away and tears welled in my eyes. Our machines have crossed a threshold. All our lives, we have been reassured that computers were incapable of being truly creative.

PROteolysis TArgeting Chimeras (PROTACs) are an emerging therapeutic modality for degrading a protein of interest (POI) by marking it for degradation by the proteasome. Recent developments in artificial intelligence (AI) suggest that deep generative models can assist with the de novo design of molecules with desired properties, and their application to PROTAC design remains largely unexplored. We show that a graph-based generative model can be used to propose novel PROTAC-like structures from empty graphs. Our model can be guided towards the generation of large molecules (30–140 heavy atoms) predicted to degrade a POI through policy-gradient reinforcement learning (RL). Rewards during RL are applied using a boosted tree surrogate model that predicts a molecule's degradation potential for each POI.