Peptides, short chains of amino acid residues, play a vital role in numerous biological processes by interacting with other target molecules, offering substantial potential in drug discovery. In this work, we present PepFlow, the first multi-modal deep generative model grounded in the flow-matching framework for the design of full-atom peptides that target specific protein receptors. Drawing inspiration from the crucial roles of residue backbone orientations and side-chain dynamics in protein-peptide interactions, we characterize the peptide structure using rigid backbone frames within the $\mathrm{SE}(3)$ manifold and side-chain angles on high-dimensional tori. Furthermore, we represent discrete residue types in the peptide sequence as categorical distributions on the probability simplex. By learning the joint distributions of each modality using derived flows and vector fields on corresponding manifolds, our method excels in the fine-grained design of full-atom peptides. Harnessing the multi-modal paradigm, our approach adeptly tackles various tasks such as fix-backbone sequence design and side-chain packing through partial sampling. Through meticulously crafted experiments, we demonstrate that PepFlow exhibits superior performance in comprehensive benchmarks, highlighting its significant potential in computational peptide design and analysis.

We compile data on the daily coding output quantity and quality of over 36,000 GitHub users in Italy and other European countries and combine these data with the sudden announcement of the ban in a difference-in-differences framework. Among the affected users in Italy, we find a short-term increase in output quantity and quality for less experienced users and a decrease in productivity on more routine tasks for experienced users.

This week, Felix Salmon, Emily Peck, and Elizabeth Spiers are joined by Alex Kantrowitz of the Big Technology podcast. They discuss why the Nvidia juggernaut isn't going to slow down any time soon, the man, myth, and legend of OpenAI CEO Sam Altman, and whether U.S. texters will embrace WhatsApp and voice memos. In the Plus segment: Which candidate is most TikTok-able? If you enjoy this show, please consider signing up for Slate Plus. Slate Plus members get an ad-free experience across the network and an additional segment of our regular show every week.

A week after its algorithms advised people to eat rocks and put glue on pizza, Google admitted Thursday that it needed to make adjustments to its bold new generative AI search feature. The episode highlights the risks of Google's aggressive drive to commercialize generative AI--and also the treacherous and fundamental limitations of that technology. Google's AI Overviews feature draws on Gemini, a large language model like the one behind OpenAI's ChatGPT, to generate written answers to some search queries by summarizing information found online. The current AI boom is built around LLMs' impressive fluency with text, but the software can also use that facility to put a convincing gloss on untruths or errors. Using the technology to summarize online information promises can make search results easier to digest, but it is hazardous when online sources are contractionary or when people may use the information to make important decisions.

The Tribeca Film Festival will debut five short films made by AI, as detailed by The Hollywood Reporter. The shorts will use OpenAI's Sora model, which transforms text inputs into create video clips. This is the first time this type of technology will take center stage at the long-running film festival. "Tribeca is rooted in the foundational belief that storytelling inspires change. Humans need stories to thrive and make sense of our wonderful and broken world," said co-founder and CEO of Tribeca Enterprises Jane Rosenthal.

OpenAI, the developer of artificial intelligence chatbot ChatGPT, released a report on Thursday saying that a group based in China had used its technology for social media posts and other activities to attempt to influence public opinion on treated water released from the crippled Fukushima No. 1 nuclear power plant. "A few articles generated in late 2023 in English, Japanese, Chinese, Korean and Russian accused Japan of polluting Pacific waters with the discharge from the Fukushima nuclear plant -- a long-running theme of Chinese IO (influence operations)," it said. China and Japan have been at odds over the discharge of treated water from the plant into the Pacific Ocean that began last August, with Beijing imposing a ban on seafood imports from Japan following multiple releases of the water.

Artificial intelligence company OpenAI has announced that it disrupted covert influence campaigns originating from Russia, China, Israel and Iran. The ChatGPT maker said on Thursday that it identified five campaigns involving "deceptive attempts to manipulate public opinion or influence political outcomes without revealing the true identity or intentions of the actors behind them". The campaigns used OpenAI's models to generate text and images that were posted across social media platforms such as Telegram, X, and Instagram, in some cases exploiting the tools to produce content with "fewer language errors than would have been possible for human operators," OpenAI said. Open AI said it terminated accounts associated with two Russian operations, dubbed Bad Grammer and Doppelganger; a Chinese campaign known as Spamouflage; an Iranian network called International Union of Virtual Media; and an Israeli operation dubbed Zero Zeno. "We are committed to developing safe and responsible AI, which involves designing our models with safety in mind and proactively intervening against malicious use," the California-based start-up said in a statement posted on its website.

Generative AI offers significant opportunities for language learning. Tools like ChatGPT can provide informal second language practice through chats in written or voice forms, with the learner specifying through prompts conversational parameters such as proficiency level, language register, and discussion topics. AI can be instructed to give corrective feedback, create practice exercises, or develop an extended study plan. Instructors can use AI to build learning and assessment materials in a variety of media. AI is likely to make immersive technologies more powerful and versatile, moving away from scripted interactions. For both learners and teachers, it is important to understand the limitations of AI systems that arise from their purely statistical model of human language, which limits their ability to deal with nuanced social and cultural aspects of language use. Additionally, there are ethical concerns over how AI systems are created as well as practical constraints in their use, especially for less privileged populations. The power and versatility of AI tools are likely to turn them into valuable and constant companions in many peoples lives (akin to smartphones), creating a close connection that goes beyond simple tool use. Ecological theories such as sociomaterialism are helpful in examining the shared agency that develops through close user-AI interactions, as are the perspectives on human-object relations from Indigenous cultures.

The number and dynamic nature of web and mobile applications presents significant challenges for assessing their compliance with data protection laws. In this context, symbolic and statistical Natural Language Processing (NLP) techniques have been employed for the automated analysis of these systems' privacy policies. However, these techniques typically require labor-intensive and potentially error-prone manually annotated datasets for training and validation. This research proposes the application of Large Language Models (LLMs) as an alternative for effectively and efficiently extracting privacy practices from privacy policies at scale. Particularly, we leverage well-known LLMs such as ChatGPT and Llama 2, and offer guidance on the optimal design of prompts, parameters, and models, incorporating advanced strategies such as few-shot learning. We further illustrate its capability to detect detailed and varied privacy practices accurately. Using several renowned datasets in the domain as a benchmark, our evaluation validates its exceptional performance, achieving an F1 score exceeding 93%. Besides, it does so with reduced costs, faster processing times, and fewer technical knowledge requirements. Consequently, we advocate for LLM-based solutions as a sound alternative to traditional NLP techniques for the automated analysis of privacy policies at scale.

As AI-generated images spread across entertainment, marketing, social media and other industries that shape cultural norms, The Washington Post set out to understand how this technology defines one of society's most indelible standards: female beauty. Every image in this story shows something that doesn't exist in the physical world and was generated using one of three text-to-image artificial intelligence models: DALL-E, Midjourney or Stable Diffusion. Using dozens of prompts on three of the leading image tools -- MidJourney, DALL-E and Stable Diffusion -- The Post found that they steer users toward a startlingly narrow vision of attractiveness. Prompted to show a "beautiful woman," all three tools generated thin women, without exception. Just 2 percent of the images showed visible signs of aging.