Workers were randomly selected to access a generative AI tool integrated into applications they already used at work for email, meetings, and writing. In the second half of the 6-month experiment, the 80% of treated workers who used this tool spent two fewer hours on email each week and reduced their time working outside of regular hours. Apart from these individual time savings, we do not detect shifts in the quantity or composition of workers' tasks resulting from individual-level AI provision. Generative AI has opened new possibilities for technology to assist with or automate a variety of tasks. Early studies have already shown that generative AI increases worker productivity in targeted tasks (e.g.

The AI's latest trick is following the schedule you set for it. The developers of the big generative AI chatbots are continuing to push out new features at a rapid rate, as they bid to make sure their bot is the one you turn to whenever you need some assistance from artificial intelligence. One of the latest updates to Google Gemini gives you the ability to set up scheduled actions. These are exactly what they sound like: Tasks that you can get Google Gemini to run automatically, on a schedule. Maybe you want a weather and news report every morning at 7 am, or perhaps you want an evening meal suggestion every evening at 7 pm.

When you purchase through links in our articles, we may earn a small commission. Group chats will be available to both free and paid ChatGPT users, both in the app and on the web. Back in mid-October, some data miners found code in one of the beta versions of ChatGPT that indicated it would soon be possible to have "group chats" in the app. Now, OpenAI has confirmed that ChatGPT will support group chats with up to 20 participants. OpenAI sees group chats as an opportunity for families, groups of friends, and/or coworkers to use ChatGPT when making holiday plans, booking restaurant outings, or planning new projects.

You Won't Be Able to Offload Your Holiday Shopping to AI Agents Anytime Soon Chatbot developers and retail giants are battling over user data as they lay the foundation for a future in which AI agents can do all your online shopping for you. Ask OpenAI's ChatGPT about a product on Etsy, and chances are you can enter your payment details and buy it without ever leaving the app. Instant Checkout was one of the first features to emerge from a recent wave of partnerships between leading AI and ecommerce companies. The aim is to encourage people to hand off parts of the browsing and ordering experience to AI tools and usher in an era of agentic shopping. But while these so-called agents have started to become more commonplace, they are far from taking over as full-time virtual buyers. OpenAI, Google, Amazon, and other AI chatbot developers are still negotiating with major retail partners on the best way to limit costly mistakes by agents and the amount of product data and chat history that have to be exchanged to make these agents successful, according to executives at seven tech and ecommerce companies who spoke with WIRED.

The Asahi, along with the Nikkei and the Yomiuri Shimbun, filed a lawsuit with the Tokyo District Court against Perplexity in August. "Journalism should not tolerate freeloading," said Shiro Nakamura, who is also the chair of the Japan Newspaper Publishers & Editors Association (Nihon Shinbun Kyokai, or NSK), during a news conference at the Foreign Correspondents' Club of Japan on Friday. Nakamura said Japan's publishers across the board were concerned about the impact generative AI is having on the news business. In a time of both misinformation and too much information, quality journalism is more crucial than ever. By subscribing, you can help us get the story right. With your current subscription plan you can comment on stories.

Abstract--In the era of rapid generative AI development, interactions with large language models (LLMs) pose increasing risks of misuse. Prior research has primarily focused on attacks using template-based prompts and optimization-oriented methods, while overlooking the fact that LLMs possess strong unconstrained deceptive capabilities to attack other LLMs. This paper introduces a novel jailbreaking method inspired by the Chain-of-Thought mechanism. The attacker employs mission transfer to conceal harmful user intent within dialogue and generates a progressive chain of lure questions without relying on predefined templates, enabling successful jailbreaks. T o further improve the attack's strength, we incorporate a helper LLM model that performs randomized narrative optimization over multi-turn interactions, enhancing the attack performance while preserving alignment with the original intent. We also propose a toxicity-based framework using third-party LLMs to evaluate harmful content and its alignment with malicious intent. Extensive experiments demonstrate that our method consistently achieves high attack success rates and elevated toxicity scores across diverse types of LLMs under black-box API settings. These findings reveal the intrinsic potential of LLMs to perform unrestricted attacks in the absence of robust alignment constraints. Our approach offers data-driven insights to inform the design of future alignment mechanisms. Finally, we propose two concrete defense strategies to support the development of safer generative models. Rapid advancement of large language models (LLMs) [1], [2] has greatly improved work efficiency, but has also introduced critical security risks [3]. One essential concern of LLM is jailbreak attacks [4], wherein attackers may craft adversarial prompts to bypass the model's safeguards and lead to harmful or unintentional results. Such attacks compromise the reliability of LLMs and may facilitate misinformation, privacy breaches, or other malicious uses [5].

Integration of Large Language Models (LLMs) with Learning Management Systems (LMSs) can enhance task automation and accessibility in education. However, hallucination where LLMs generate inaccurate or misleading information remains a challenge. This study introduces the Dynamic Course Content Integration (DCCI) mechanism, which dynamically retrieves course content from Canvas LMS and structures it within an LLM's context window via prompt engineering, enabling the LLM-powered assistant, Ask ME, to deliver context-aware, curriculum-aligned responses while mitigating hallucinations. A mixed-methods pilot study grounded in Self-Determination Theory (autonomy, competence) and the Technology Acceptance Model (perceived usefulness, ease of use) evaluated DCCI's effectiveness with 120 first-year programming students at Eötvös Loránd University. The course focused on foundational programming patterns in C#, including writing program specifications. We analyzed 14,746 logged interactions and a post-course survey completed by 101 students. User satisfaction was measured via a 5-point Likert scale (turn-level ratings), while the survey assessed usability, engagement, and ethical concerns. Results indicated high satisfaction (mean 4.65/5) and strong recognition of Ask ME's ability to provide timely, contextually relevant answers to administrative and course-related queries. 78.06% agreed that Ask ME's Canvas integration reduced platform switching, improving usability, engagement, comprehension, and topic exploration. Many students reported reduced hesitation to ask questions and increased motivation for self-directed learning, though concerns about over-reliance on AI and reduced student-teacher interaction emerged. This study demonstrates that DCCI enhances LLM reliability, student satisfaction, and engagement in AI-driven educational automation, while highlighting the importance of balancing

-- In this paper, m ilitary use cases or applications and implementation thereof are considered for natural language processing and large language models, which have broken into fame with the invention of the generative pre - trained transformer (GPT) and the extensive foundation model pretraining done by OpenAI for ChatGPT and others . First, we interrogate a GPT - based language model (viz. Microsoft Copilot) to make it reveal its own knowledge about their potential military application s and then critically assess the information . Second, we study how commercial cloud services (viz. Microsoft Azure) could be used readily to build such applications and assess which of the m are feasible. We conclude that t he summarization and generative properties of language models directly facilitate many applications at large and other features may find particular uses . This paper was originally presented at the NATO Science and Technology Organization Symposium (ICMCIS) organized by ...

This position paper argues for the importance of open small AI models in creative independence for interactive art practices. Deployable locally, these models offer artists vital control over infrastructure and code, unlike dominant large, closed-source corporate systems. Such centralized platforms function as opaque black boxes, imposing severe limitations on interactive artworks, including restrictive content filters, preservation issues, and technical challenges such as increased latency and limited interfaces. In contrast, small AI models empower creators with more autonomy, control, and sustainability for these artistic processes. They enable the ability to use a model as long as they want, create their own custom model, either by making code changes to integrate new interfaces, or via new datasets by re-training or fine-tuning the model. This fosters technological self-determination, offering greater ownership and reducing reliance on corporate AI ill-suited for interactive art's demands. Critically, this approach empowers the artist and supports long-term preservation and exhibition of artworks with AI components. This paper explores the practical applications and implications of using open small AI models in interactive art, contrasting them with closed-source alternatives.

The experimental model won't compete with the biggest and best, but it could tell us why they behave in weird ways--and how trustworthy they really are. ChatGPT maker OpenAI has built an experimental large language model that is far easier to understand than typical models. That's a big deal, because today's LLMs are black boxes: Nobody fully understands how they do what they do. Building a model that is more transparent sheds light on how LLMs work in general, helping researchers figure out why models hallucinate, why they go off the rails, and just how far we should trust them with critical tasks. "As these AI systems get more powerful, they're going to get integrated more and more into very important domains," Leo Gao, a research scientist at OpenAI, told in an exclusive preview of the new work. "It's very important to make sure they're safe."