Formulating research questions is a foundational yet challenging academic skill, one that generative AI systems often oversimplify by offering instant answers at the expense of student reflection. This protocol lays out a study grounded in constructivist learning theory to evaluate a novel AI-based Socratic Tutor, designed to foster cognitive engagement and scaffold research question development in higher education. Anchored in dialogic pedagogy, the tutor engages students through iterative, reflective questioning, aiming to promote System 2 thinking and counteract overreliance on AI-generated outputs. In a quasi-experimental design, approximately 80 German pre-service biology teacher students will be randomly assigned to one of two groups: an AI Socratic Tutor condition and an uninstructed chatbot control. Across multiple cycles, students are expected to formulate research questions based on background texts, with quality assessed through double-blind expert review. The study also examines transfer of skills to novel phenomena and captures student perceptions through mixed-methods analysis, including surveys, interviews and reflective journals. This study aims to advance the understanding of how generative AI can be pedagogically aligned to support, not replace, human cognition and offers design principles for human-AI collaboration in education.

A group of organizations, including nonprofits like LatinoProsperity and labor groups like the California Teamsters, are petitioning California Attorney General Rob Bonta to stop OpenAI from becoming a for-profit entity, The Los Angeles Times reports. OpenAI announced plans to transition to a public-benefit corporation in 2024, and reportedly has two years to pull it off or risk a large portion of the money its raised become debt. The group's primary concerns are that OpenAI "failed to protect its charitable assets" and is actively "subverting its charitable mission to advance safe artificial intelligence." OpenAI started as a nonprofit research organization studying AI, but transitioned to a for-profit company that's overseen and run by a nonprofit in 2019. That structure is legally allowed in the state of California, but the group's petition claims that OpenAI's decision to pursue a new structure is driven by a desire not to further its mission, but to provide "AI's benefits -- the potential for untold profits and control over what may become powerful world-altering technologies -- to a handful of corporate investors and high-level employees."

Anthropic is joining the ranks of OpenAI in offering a more expensive tier of its flagship chatbot. On Wednesday, the company announced Max Plan. Starting today, you can either pay 100 or 200 per month to use Claude up to 5x or 20x more than you can with Anthropic's existing Pro plan. The company told Engadget it's introducing the Max tier in response to the popularity of Claude 3.7 Sonnet. The new hybrid reasoning model, which excels at coding tasks, has been so popular with users, many are asking to use it as much as they want.

Developing new drugs to treat illnesses has typically been a slow and expensive process. However, a team of researchers at the University of Waterloo uses machine learning to speed up the development time. The Waterloo research team has created "Imagand," a generative artificial intelligence model that assesses existing information about potential drugs and then suggests their potential properties. Trained on and tested against existing drug data, Imagand successfully predicts important properties of different drugs that have already been independently verified in lab studies, demonstrating the AI's accuracy. Traditionally, bringing a successful drug candidate to market can cost between US 2 billion and US 3 billion and take over a decade to complete.

DBOT can value any public traded company on the basis of Damodaran's analysis, and generates a report to support its position in an attempt to mimic its analytic parent. Until recently, such capabilities of analytic twins for financial valuation were not feasible. However, with advances in large language models (LLMs) and generative artificial intelligence (GenAI), it has become possible to conduct valuations that marry numbers and reasoning to generate credible valuations that can be used for long-term investing. The implications for automation and support of various parts of the valuation exercise are profound. In this paper, we provide a method for creating a digital analytic twin, DBOT, which is designed to mimic the investment analysis of individual companies by Damodaran. Since DBOT can value every company in an index such as the S&P500, it also provide an analysis in a macro sense, for example, by valuing the S&P500 market index relative to the valuation of its individual components. From the perspective of generative AI, DBOT presents a multitude of challenges. First and foremost, LLMs must be able to reason over financial texts, charts, tables, and spreadsheets. Furthermore, DBOT requires the AI system to follow Damodaran's

Social media has recently been flooded with images that look like they belong in a Studio Ghibli film. Selfies, family photos and even memes have been re-imagined with the soft pastel palette characteristic of the Japanese animation company founded by Hayao Miyazaki. The update significantly improved ChatGPT's image generation capabilities, allowing users to create convincing Ghibli-style images in mere seconds. It has been enormously popular – so much so, in fact, that the system crashed due to user demand. Generative artificial intelligence (AI) systems such as ChatGPT are best understood as "style engines".

China is using generative artificial intelligence (AI) to ramp up disinformation against Taiwan to "divide" Taiwan's public, the island's National Security Bureau said. Taiwan has accused China of stepping up military drills, trade sanctions and influence campaigns against the island in recent years to force the island to accept Chinese sovereignty claims. Taiwan strongly rejects China's sovereignty claims. China staged two days of war games and live-fire drills near the democratically governed island this month, triggering concern by the United States and many of its allies.

The year that ChatGPT went viral, only two US companies--OpenAI and Google--could boast truly cutting-edge artificial intelligence. Three years on, AI is no longer a two-horse race, nor is it purely an American one. A new report published today by Stanford University's Institute for Human-Centered AI (HAI) highlights just how crowded the field has become. OpenAI and Google are still neck and neck in the race to build bleeding-edge AI, the report shows. But several other companies are closing in.

You've seen headlines about the market crash and maybe even wondered if now's your shot at finally investing. A stock-picking tool powered by OpenAI is helping regular folks identify strong opportunities with minimal risk. Meet Sterling Stock Picker, the thing that could turn your savings account into an early retirement, extra travel funds, or whatever you wish. Rather than gambling with your hard-earned dollars, this tool helps you research options that match your preferences and risk tolerance, and a lifetime subscription is just 68.99 (reg. Want to dive into the stock market but feel like you're reading a foreign language?

Metamaterials, renowned for their exceptional mechanical, electromagnetic, and thermal properties, hold transformative potential across diverse applications, yet their design remains constrained by labor - intensive trial - and - error methods and limited data interoperability. Here, we introduce CrossMatAgent -- a novel multi - agent framework that synergistically integrates large language models with state - of - the - art generative AI to revolutionize metamaterial design. By orchestrating a hierarchical team of agents -- e ach specializing in tasks such as pattern analysis, architectural synthesis, prompt engineering, and supervisory feedback -- our system leverages the multimodal reasoning of GPT - 4o alongside the generative precision of DALL - E 3 and a fine - tuned Stable Diffusion Extra Large ( XL) model. This integrated approach automates data augmentation, enhances design fidelity, and produces simulation - and 3D printing - ready metamaterial patterns. Comprehensive evaluations, including Contrastive Language - Image Pre - training ( C LIP) - based alignment, SHAP ( SHapley Additive exPlanations) interpretability analyses, and mechanical simulations under varied load conditions, demonstrate the framework's ability to generate diverse, reproducible, and application - ready designs . CrossMatAgent thus establishes a scalable, AI - driven paradigm that bridges the gap between conceptual innovation and practical realization, paving the way for accelerated metamaterial development.