Stop us if you've heard this before: Microsoft encourages you not to visit its competition. You may have noticed that if you visit Bing.com and then search for Google, Microsoft might remind you that it too has a search engine. More recently, Microsoft is now encouraging you to remain within its ecosystem and use Copilot instead of venturing elsewhere to Google, OpenAI, or Meta. When searching for "Claude" within Microsoft Edge -- I use Edge with Bing set at its search engine -- I tried looking up "Claude," the AI tool developed by Anthropic. While Bing dutifully returned the link as well as related information, it also reminded me that "Your Copilot is here," accompanied with a Copilot-specific search box.

Generative artificial intelligence assistants like ChatGPT are cutting into traditional online search traffic, depriving news sites of visitors and impacting the advertising revenue they desperately need, in a crushing blow to an industry already fighting for survival. "The next three or four years will be incredibly challenging for publishers everywhere. No one is immune from the AI summaries storm gathering on the horizon," warned Matt Karolian, vice president of research and development at Boston Globe Media. "Publishers need to build their own shelters or risk being swept away."

Artificial intelligence - driven adaptive learning systems are reshaping education through data - driven adaptation of learning experiences. Yet many of these systems lack transparency, offering limited insight into how decisions are made. Most explainable AI (XAI) techniques focus on technical outputs but neglect user roles and comprehension. This paper proposes a hybrid framework that integrates traditional XAI techniques with generative AI models and u ser personalisation to generate multimodal, personalised explanations tailored to user needs. We redefine explainability as a dynamic communication process tailored to user roles and learning goals. We outline the framework ' s design, key XAI limitations in education, and research directions on accuracy, fairness, and personalisation. Our aim is to move towards explainable AI that enhances transparency while supporting user - centred experiences.

With the development of generative artificial intelligence (GenAI) tools to create art, stakeholders cannot come to an agreement on the value of these works. In this study we uncovered the mixed opinions surrounding art made by AI. We developed two versions of a dance performance augmented by technology either with or without GenAI. For each version we informed audiences of the performance's development either before or after a survey on their perceptions of the performance. There were thirty-nine participants (13 males, 26 female) divided between the four performances. Results demonstrated that individuals were more inclined to attribute artistic merit to works made by GenAI when they were unaware of its use. We present this case study as a call to address the importance of utilizing the social context and the users' interpretations of GenAI in shaping a technical explanation, leading to a greater discussion that can bridge gaps in understanding.

Today's algorithm-driven interfaces, from recommendation feeds to GenAI tools, often prioritize engagement and efficiency at the expense of user agency. As systems take on more decision-making, users have less control over what they see and how meaning or relationships between content are constructed. This paper introduces "Hypertextual Friction," a conceptual design stance that repositions classical hypertext principles--friction, traceability, and structure--as actionable values for reclaiming agency in algorithmically mediated environments. Through a comparative analysis of real-world interfaces--Wikipedia vs. Instagram Explore, and Are.na vs. GenAI image tools--we examine how different systems structure user experience, navigation, and authorship. We show that hypertext systems emphasize provenance, associative thinking, and user-driven meaning-making, while algorithmic systems tend to obscure process and flatten participation. We contribute: (1) a comparative analysis of how interface structures shape agency in user-driven versus agent-driven systems, and (2) a conceptual stance that offers hypertextual values as design commitments for reclaiming agency in an increasingly algorithmic web.

How can AI move beyond imitation toward genuine creativity? This paper proposes the E.A.R.T.H. framework, a five-stage generative pipeline that transforms model-generated errors into creative assets through Error generation, Amplification, Refine selection, Transform, and Harness feedback. Drawing on cognitive science and generative modeling, we posit that "creative potential hides in failure" and operationalize this via structured prompts, semantic scoring, and human-in-the-loop evaluation. Implemented using LLaMA-2-7B-Chat, SBERT, BERTScore, CLIP, BLIP-2, and Stable Diffusion, the pipeline employs a composite reward function based on novelty, surprise, and relevance. At the Refine stage, creativity scores increase by 52.5% (1.179 to 1.898, t = -5.56, p < 0.001), with final outputs reaching 2.010 - a 70.4% improvement. Refined slogans are 48.4% shorter, 40.7% more novel, with only a 4.0% drop in relevance. Cross-modal tests show strong slogan-to-image alignment (CLIPScore: 0.249; BERTScore F1: 0.816). In human evaluations, the generated outputs were consistently rated highly, demonstrating strong creative quality and expressive clarity. Feedback highlights stylistic precision and emotional resonance. These results demonstrate that error-centered, feedback-driven generation enhances creativity, offering a scalable path toward self-evolving, human-aligned creative AI.

In collaborative systems with complex tasks relying on distributed resources, trust evaluation of potential collaborators has emerged as an effective mechanism for task completion. However, due to the network dynamics and varying information gathering latencies, it is extremely challenging to observe and collect all trust attributes of a collaborating device concurrently for a comprehensive trust assessment. In this paper, a novel progressive trust evaluation framework, namely chain-of-trust, is proposed to make better use of misaligned device attribute data. This framework, designed for effective task completion, divides the trust evaluation process into multiple chained stages based on task decomposition. At each stage, based on the task completion process, the framework only gathers the latest device attribute data relevant to that stage, leading to reduced trust evaluation complexity and overhead. By leveraging advanced in-context learning, few-shot learning, and reasoning capabilities, generative AI is then employed to analyze and interpret the collected data to produce correct evaluation results quickly. Only devices deemed trustworthy at this stage proceed to the next round of trust evaluation. The framework ultimately determines devices that remain trustworthy across all stages. Experimental results demonstrate that the proposed framework achieves high accuracy in trust evaluation.

Tran* sat across from me, phone in hand, scrolling. "I just wanted to make sure I didn't say the wrong thing," he explained, referring to a disagreement with his partner. "So I asked ChatGPT what I should say." He read the chatbot-generated message aloud. It was articulate, logical and composed – too composed.

OpenAI's Sam Altman, flanked by President Trump and Softbank's Masayoshi Son, announced a hugely ambitious investment in data centers across America to support all the artificial intelligence we're going to be using. Months in, the project has been scaled back to a single, power-hungry data center in Ohio. Subscribe to Slate Plus to access ad-free listening to the whole What Next family and all your favorite Slate podcasts. Subscribe today on Apple Podcasts by clicking "Try Free" at the top of our show page. Sign up now at slate.com/whatnextplus to get access wherever you listen.

Fox News anchor and executive editor Bret Baier has the latest on fears over the'darker side' of artificial intelligence on'Special Report.' Generative Artificial Intelligence can create lifelike imaging and audio, which is likely why an increasing number of film studios are incorporating A.I. into special effects. It comes just two years after Hollywood's largest union went on strike, in part over the impact A.I. would bring. "Popular culture movies like The Terminator have created a very dark dystopian version of what this could look like," White House A.I. and Crypto Czar David Sacks said. "The version of the future of A.I. that I think is probably most accurate if you want to pop cultural references is Star Trek Enterprise. Think about the ship computer in that. It can perform tasks for you. But it doesn't have a will of its own, it doesn't' have a mind of its' own. It's there to help the crew, and it needs to be supervised by humans."