AI is asbestos in the walls of our tech society, stuffed there by monopolists run amok. What I do not do is predict the future. No one can predict the future, which is a good thing, since if the future were predictable, that would mean we couldn't change it. Now, not everyone understands the distinction. They think science-fiction writers are oracles. Even some of my colleagues labor under the delusion that we can "see the future". Then there are science-fiction fans who believe that they are the future. A depressing number of those people appear to have become AI bros. These guys can't shut up about the day that their spicy autocomplete machine will wake up and turn us all into paperclips has led many confused journalists and conference organizers to try to get me to comment on the future of AI. That's something I used to strenuously resist doing, because I wasted two years of my life explaining patiently and repeatedly why I thought crypto was stupid, and getting relentlessly bollocked by cryptocurrency cultists who at first insisted that I just didn't understand crypto.

Breakthroughs, discoveries, and DIY tips sent every weekday. A study recently published in the journal suggests something many Italians already knew--certain hand gestures really do make people seem more competent and persuasive. "One of the key takeaways for marketers is that you can use the same content, but if you pay more attention to how that content is delivered, it could have a big impact on persuasiveness," Mi Zhou, study co-author and University of British Columbia digital market research scientist, said in a statement . Zhou and her colleagues analyzed 2,184 TED Talks using AI and automated video analysis. They compared hundreds of thousands of video clips of hand features to audience engagement metrics, and asked study participants to rate the speakers and products in videos of sales pitches with different hand movements.

Scripting interfaces enable users to automate tasks and customize software workflows, but creating scripts traditionally requires programming expertise and familiarity with specific APIs, posing barriers for many users. While Large Language Models (LLMs) can generate code from natural language queries, runtime code generation is severely limited due to unverified code, security risks, longer response times, and higher computational costs. To bridge the gap, we propose an offline simulation framework to curate a software-specific skillset, a collection of verified scripts, by exploiting LLMs and publicly available scripting guides. Our framework comprises two components: (1) task creation, using top-down functionality guidance and bottom-up API synergy exploration to generate helpful tasks; and (2) skill generation with trials, refining and validating scripts based on execution feedback. To efficiently navigate the extensive API landscape, we introduce a Graph Neural Network (GNN)-based link prediction model to capture API synergy, enabling the generation of skills involving underutilized APIs and expanding the skillset's diversity. Experiments with Adobe Illustrator demonstrate that our framework significantly improves automation success rates, reduces response time, and saves runtime token costs compared to traditional runtime code generation. This is the first attempt to use software scripting interfaces as a testbed for LLM-based systems, highlighting the advantages of leveraging execution feedback in a controlled environment and offering valuable insights into aligning AI capabilities with user needs in specialized software domains.

When faced with a bit of downtime, many of my friends will turn to the same party game. It's based on the surrealist game Exquisite Corpse, and involves translating brief written descriptions into rapidly made drawings and back again. One group calls it Telephone Pictionary; another refers to it as Writey-Drawey. The internet tells me it is also called Eat Poop You Cat, a sequence of words surely inspired by one of the game's results. As recently as three years ago, it was rare to encounter text-to-image or image-to-text mistranslations in daily life, which made the outrageous outcomes of the game feel especially novel. But we have since entered a new era of image-making. With the aid of AI image generators like Dall-E 3, Stable Diffusion and Midjourney, and the generative features integrated into Adobe's Creative Cloud programs, you can now transform a sentence or phrase into a highly detailed image in mere seconds. Images, likewise, can be nearly instantly translated into descriptive text.

On March 27, a large group of artists and creators from across the web noticed the frightening extent to which a once-beloved, highly influential community platform of theirs had, like so many others, fallen prey to the artificial intelligence juggernauts plundering the internet. As VFX animator Romain Revert (Minions, The Lorax) pointed out on X, the bots had come for his old home base of DeviantArt. Its social accounts were promoting "top sellers" on the platform, with usernames like "Isaris-AI" and "Mikonotai," who reportedly made tens of thousands of dollars through bulk sales of autogenerated, dead-eyed 3D avatars. The sales weren't exactly legit--an online artist known as WyerframeZ looked at those users' followers and found pages of profiles with repeated names, overlapping biographies and account-creation dates, and zero creations of their own, making it apparent that various bots were involved in these "purchases." It's not unlikely, as WyerframeZ surmised, that someone constructed a low-effort bot network that could hold up a self-perpetuating money-embezzlement scheme: Generate a bunch of free images and accounts, have them buy and boost one another in perpetuity, inflate metrics so that the "art" gets boosted by DeviantArt and reaches real humans, then watch the money pile up from DeviantArt revenue-sharing programs. After Revert declared this bot-on-bot fest to be "the downfall of DeviantArt," myriad other artists and longtime users of the platform chimed in to share in the outrage that these artificial accounts were monopolizing DeviantArt's promotional and revenue apparatuses.

When artificial intelligence image generators first rolled out, they seemed like magic. Churning out detailed imagery in minutes was, from one angle, a technical marvel. From another angle, though, it looked like mere mimicry. The models were trained on billions of images without anyone asking the humans behind them for permission. "They have sucked the creative juices of millions of artists," says Eva Toorenent, an illustrator who serves as the Netherlands adviser for the European Guild for Artificial Intelligence Regulation.

This research delves into the intersection of illustration art and artificial intelligence (AI), focusing on how illustrators engage with AI agents that embody their original characters (OCs). We introduce 'ORIBA', a customizable AI chatbot that enables illustrators to converse with their OCs. This approach allows artists to not only receive responses from their OCs but also to observe their inner monologues and behavior. Despite the existing tension between artists and AI, our study explores innovative collaboration methods that are inspiring to illustrators. By examining the impact of AI on the creative process and the boundaries of authorship, we aim to enhance human-AI interactions in creative fields, with potential applications extending beyond illustration to interactive storytelling and more.

Natural language explanations have the potential to provide rich information that in principle guides model reasoning. Yet, recent work by Lampinen et al. (2022) has shown limited utility of natural language explanations in improving classification. To effectively learn from explanations, we present FLamE, a two-stage few-shot learning framework that first generates explanations using GPT-3, and then finetunes a smaller model (e.g., RoBERTa) with generated explanations. Our experiments on natural language inference demonstrate effectiveness over strong baselines, increasing accuracy by 17.6% over GPT-3 Babbage and 5.7% over GPT-3 Davinci in e-SNLI. Despite improving classification performance, human evaluation surprisingly reveals that the majority of generated explanations does not adequately justify classification decisions. Additional analyses point to the important role of label-specific cues (e.g., "not know" for the neutral label) in generated explanations.

OneDoor Studios CMO Dan Cobb discusses the adaption of the YA series'Calculated.' A Hollywood film studio is leveraging a new real-time design and artist development process to adapt a popular young adult (YA) series, including an industry-first application of artificial intelligence (AI) that gives fans and artists active input in creating character design, sets and special effects. Dan Cobb, the Chief Marketing Officer (CMO) of OneDoor Studios, said development is underway on "Calculated," an adaption of the YA sci-fi series by Nova McBee. On a mission to become the "World's First Fan-Funded and Fan-Created Film Studio," Cobb and his team have developed a relationship with AI artists on the WeGo.One's Discord channel. The artists, who are required to have deep knowledge of the source material, liaise with investors and the author to spawn images using MidJourney V5 Pro and a combination of other similar generative image technologies to build the film's storyboard, enhance concept art and develop shot lists.

Join top executives in San Francisco on July 11-12, to hear how leaders are integrating and optimizing AI investments for success. Adobe announced a series of AI initiatives at its annual conference, Adobe Summit, on Tuesday, including a new set of tools that can generate images on demand using only text prompts. The initiatives are part of a broad effort by Adobe, the software giant behind popular creative apps like Photoshop and Illustrator, to inject more artificial intelligence into its creative products. They include a new tool called Firefly that will allow users to create images just by entering text descriptions into the software. Firefly is powered by a type of AI known as generative adversarial network (GAN), which has also been used by companies like OpenAI to build systems that can generate images, videos and speech.