Autonomous agents that address day-to-day digital tasks (e.g., ordering groceries for a household), must not only operate multiple apps (e.g., notes, messaging, shopping app) via APIs, but also generate rich code with complex control flow in an iterative manner based on their interaction with the environment. However, existing benchmarks for tool use are inadequate, as they only cover tasks that require a simple sequence of API calls. To remedy this gap, we built $\textbf{AppWorld Engine}$, a high-quality execution environment (60K lines of code) of 9 day-to-day apps operable via 457 APIs and populated with realistic digital activities simulating the lives of ~100 fictitious users. We then created $\textbf{AppWorld Benchmark}$ (40K lines of code), a suite of 750 natural, diverse, and challenging autonomous agent tasks requiring rich and interactive code generation. It supports robust programmatic evaluation with state-based unit tests, allowing for different ways of completing a task while also checking for unexpected changes, i.e., collateral damage. The state-of-the-art LLM, GPT-4o, solves only ~49% of our 'normal' tasks and ~30% of 'challenge' tasks, while other models solve at least 16% fewer. This highlights the benchmark's difficulty and AppWorld's potential to push the frontiers of interactive coding agents. The project website is available at https://appworld.dev/.

Whenever AI companies present a vision for the role of artificial intelligence in the future of searching the internet, they tend to underscore the same points: instantaneous summaries of relevant information; ready-made lists tailored to a searcher's needs. They tend not to point out that generative-AI models are prone to providing incorrect, and at times fully made-up, information--and yet it keeps happening. Early this afternoon, OpenAI, the maker of ChatGPT, announced a prototype AI tool that can search the web and answer questions, fittingly called SearchGPT. The launch is designed to hint at how AI will transform the ways in which people navigate the internet--except that, before users have had a chance to test the new program, it already appears error prone. The tool then pulls up a list of festivals that it states are taking place in Boone this August, the first being An Appalachian Summer Festival, which according to the tool is hosting a series of arts events from July 29 to August 16 of this year. Someone in Boone hoping to buy tickets to one of those concerts, however, would run into trouble.

Hollywood's video game performers voted to go on strike Thursday, throwing part of the entertainment industry into another work stoppage after talks for a new contract with major game studios broke down over artificial intelligence protections. The strike – the second for video game voice actors and motion capture performers under the Screen Actors Guild-American Federation of Television and Radio Artists (Sag-Aftra) – will begin at 12.01am Friday. The move comes after nearly two years of negotiations with gaming giants, including divisions of Activision, Warner Bros and Walt Disney Co, over a new interactive media agreement. Sag-Aftra negotiators say gains have been made over wages and job safety in the video game contract, but that the studios will not make a deal over the regulation of generative AI. Without guardrails, game companies could train AI to replicate an actor's voice, or create a digital replica of their likeness without consent or fair compensation, the union said.

OpenAI on Thursday announced a new AI-powered search engine prototype called SearchGPT. The move marks the company's entry into a competitive search engine market dominated by Google for decades. On its website, OpenAI described SearchGPT as "a temporary prototype of new AI search features that give you fast and timely answers with clear and relevant sources." The company plans to test out the product with 10,000 initial users and then roll it into ChatGPT after gathering feedback. The launch of SearchGPT comes amid growing competition in AI-powered search.

Technologies that use artificial intelligence (AI) have become ubiquitous. AI technologies have produced numerous economic and social benefits, such as rapidly and reliably assisting radiologists with accurate diagnostic interpretations of medical images. Many harms of AI have also been documented, such as racial biases in predictive models used in the criminal justice system, and gender discrimination in automated screening of job applications. Some AI technologies have exacerbated biases that disproportionately affect historically marginalized communities, such as LGBTQ populations and members of racial, ethnic, and religious minorities.4 Generative AI technologies are now widely available, and the potential harms are substantial: although anyone can use ChatGPT to draft messages and DALL-E to create artwork, others can use these tools to quickly produce deceptive news stories with specious images--misinformation that can spread quickly through social media.

Fox News correspondent CB Cotton reports that different agencies disagree on the events at the Trump rally shooting. Fox News drones provided a new perspective on the sight lines between Thomas Matthew Crooks, former President Trump and the Secret Service counter sniper teams at the fateful rally in Butler, Pennsylvania, on Thursday. Fox News correspondent CB Cotton also detailed the sight lines that Crooks had when firing on Trump. A drone recreation shows that Crooks was largely concealed from Secret Service counter snipers by a large tree, though he still had an angle on the former president. The Secret Service agent who neutralized Crooks was stationed on a building behind Trump.

When Reddit said last month it would block unauthorized data scraping from its site, most of us assumed it was to tackle chatbot training. It turns out the site/service/fandom battleground also appears to be blocking search engines other than Brave and Google, the latter of which reportedly inked a deal earlier this year with Reddit worth 60 million annually. A Reddit spokesperson told Engadget the empty search results are because these engines won't agree to the company's requirements for AI training. The company says it's in discussions with several of them. Bing and DuckDuckGo both appear to be affected.

Point-based image editing enables accurate and flexible control through content dragging. However, the role of text embedding in the editing process has not been thoroughly investigated. A significant aspect that remains unexplored is the interaction between text and image embeddings. In this study, we show that during the progressive editing of an input image in a diffusion model, the text embedding remains constant. As the image embedding increasingly diverges from its initial state, the discrepancy between the image and text embeddings presents a significant challenge. Moreover, we found that the text prompt significantly influences the dragging process, particularly in maintaining content integrity and achieving the desired manipulation. To utilize these insights, we propose DragText, which optimizes text embedding in conjunction with the dragging process to pair with the modified image embedding. Simultaneously, we regularize the text optimization process to preserve the integrity of the original text prompt. Our approach can be seamlessly integrated with existing diffusion-based drag methods with only a few lines of code.

Recommender systems have played a critical role in diverse digital services such as e-commerce, streaming media, social networks, etc. If we know what a user's intent is in a given session (e.g. do they want to watch short videos or a movie or play games; are they shopping for a camping trip), it becomes easier to provide high-quality recommendations. In this paper, we introduce IntentRec, a novel recommendation framework based on hierarchical multi-task neural network architecture that tries to estimate a user's latent intent using their short- and long-term implicit signals as proxies and uses the intent prediction to predict the next item user is likely to engage with. By directly leveraging the intent prediction, we can offer accurate and personalized recommendations to users. Our comprehensive experiments on Netflix user engagement data show that IntentRec outperforms the state-of-the-art next-item and next-intent predictors. We also share several findings and downstream applications of IntentRec.

In this work, we introduce several schemes to leverage description-augmented embedding similarity for dataless intent classification using current state-of-the-art (SOTA) text embedding models. We report results of our methods on four commonly used intent classification datasets and compare against previous works of a similar nature. Our work shows promising results for dataless classification scaling to a large number of unseen intents. We show competitive results and significant improvements (+6.12\% Avg.) over strong zero-shot baselines, all without training on labelled or task-specific data. Furthermore, we provide qualitative error analysis of the shortfalls of this methodology to help guide future research in this area.