June 2022 Across the world, video cameras have become an accepted feature of urban life. Many cities in China now have dense networks of them, and London and New Delhi aren't far behind. Now France is playing catch-up. Concerns have been raised throughout the country. But the surveillance rollout has met special resistance in Marseille, France's second-biggest city. It's unsurprising, perhaps, that activists are fighting back against the cameras, highlighting the surveillance system's overreach and underperformance.

OpenAI isn't slowing down when it comes to building extra functions and add-ons into its ChatGPT AI bot, and one of the newest features to roll out--exclusive to paying users, for now--is ChatGPT Projects. This is a major step forward for keeping conversations and data organized in ChatGPT: It gives you the ability to put your discussions with ChatGPT in separate spaces, like folders in a filing cabinet, complete with uploaded documents, web searches, custom instructions, and whatever else you've added. You can have one project for researching birthday present ideas, for example, and one for analyzing the current state of the movie industry. It's up to you how you use them, but Projects can make a genuine difference to workflows in ChatGPT. Projects can include conversations, files, and instructions.

Has Apple, the biggest company in the world, bungled its artificial intelligence strategy? Doubts blew out into the open when one of the company's closest observers, tech analyst John Gruber, earlier this month gave a blistering critique in a blog post titled "Something Is Rotten in the State of Cupertino," referring to the home of Apple's headquarters. The respected analyst and Apple enthusiast said he was furious for not being more skeptical when the company announced last June that its Siri chatbot would be getting a major generative AI upgrade.

This study presents an empirical investigation into the energy consumption of Discriminative and Generative AI models within real-world MLOps pipelines. For Discriminative models, we examine various architectures and hyperparameters during training and inference and identify energy-efficient practices. For Generative AI, Large Language Models (LLMs) are assessed, focusing primarily on energy consumption across different model sizes and varying service requests. Our study employs software-based power measurements, ensuring ease of replication across diverse configurations, models, and datasets. We analyse multiple models and hardware setups to uncover correlations among various metrics, identifying key contributors to energy consumption. The results indicate that for Discriminative models, optimising architectures, hyperparameters, and hardware can significantly reduce energy consumption without sacrificing performance. For LLMs, energy efficiency depends on balancing model size, reasoning complexity, and request-handling capacity, as larger models do not necessarily consume more energy when utilisation remains low. This analysis provides practical guidelines for designing green and sustainable ML operations, emphasising energy consumption and carbon footprint reductions while maintaining performance. This paper can serve as a benchmark for accurately estimating total energy use across different types of AI models.

Urban systems are managed using complex textual documentation that need coding and analysis to set requirements and evaluate built environment performance. This paper contributes to the study of applying large-language models (LLM) to qualitative coding activities to reduce resource requirements while maintaining comparable reliability to humans. Qualitative coding and assessment face challenges like resource limitations and bias, accuracy, and consistency between human evaluators. Here we report the application of LLMs to deductively code 10 case documents on the presence of 17 digital twin characteristics for the management of urban systems. We utilize two prompting methods to compare the semantic processing of LLMs with human coding efforts: whole text analysis and text chunk analysis using OpenAI's GPT-4o, GPT-4o-mini, and o1-mini models. We found similar trends of internal variability between methods and results indicate that LLMs may perform on par with human coders when initialized with specific deductive coding contexts. GPT-4o, o1-mini and GPT-4o-mini showed significant agreement with human raters when employed using a chunking method. The application of both GPT-4o and GPT-4o-mini as an additional rater with three manual raters showed statistically significant agreement across all raters, indicating that the analysis of textual documents is benefited by LLMs. Our findings reveal nuanced sub-themes of LLM application suggesting LLMs follow human memory coding processes where whole-text analysis may introduce multiple meanings. The novel contributions of this paper lie in assessing the performance of OpenAI GPT models and introduces the chunk-based prompting approach, which addresses context aggregation biases by preserving localized context.

There are not one but two dimensions of bias that can be revealed through the study of large AI models: not only bias in training data or the products of an AI, but also bias in society, such as disparity in employment or health outcomes between different demographic groups. Often training data and AI output is biased for or against certain demographics (i.e. older white people are overrepresented in image datasets), but sometimes large AI models accurately illustrate biases in the real world (i.e. young black men being disproportionately viewed as threatening). These social disparities often appear in image generation AI outputs in the form of 'marked' features, where some feature of an individual or setting is a social marker of disparity, and prompts both humans and AI systems to treat subjects that are marked in this way as exceptional and requiring special treatment. Generative AI has proven to be very sensitive to such marked features, to the extent of over-emphasising them and thus often exacerbating social biases. I briefly discuss how we can use complex prompts to image generation AI to investigate either dimension of bias, emphasising how we can probe the large language models underlying image generation AI through, for example, automated sentiment analysis of the text prompts used to generate images.

Since the release of ChatGPT in late 2022, generative artificial intelligence has trickled down from adults in their offices to university students in campus libraries to teenagers in high school hallways. Now it's reaching the youngest among us, and parents and teachers are grappling with the most responsible way to introduce their under-13s to a new technology that may fundamentally reshape the future. Though the terms of service for ChatGPT, Google's Gemini and other AI models specify that the tools are only meant for those over 13, parents and teachers are taking the matter of AI education into their own hands. Inspired by a story we published on parents who are teaching their children to use AI to set them up for success in school and at work, we asked Guardian readers how and why – or why not – others are doing the same. Though our original story only concerned parents, we have also included teachers in the responses published below, as preparing children for future studies and jobs is one of educators' responsibilities as well.

ARTICLE TEMPLATE Beyond Detection: Designing AI-Resilient Assessments with Automated Feedback Tool to Foster Critical Thinking and Originality Muhammad Sajjad Akbar a a University of Sydney, Australia; ARTICLE HISTORY Compiled April 1, 2025 ABSTRACT The growing prevalence of generative AI tools such as ChatGPT has raised urgent concerns about their impact on student learning, particularly their potential to erode critical thinking and creativity in academic contexts. As students increasingly use these tools to complete assessments, foundational cognitive skills are at risk of being bypassed, challenging the integrity of higher education and the authenticity of student work. Current AI-generated text detection tools are fundamentally inadequate in addressing this challenge. They produce unreliable, unverifiable outputs and are highly susceptible to false positives and false negatives, especially when students apply obfuscation techniques such as paraphrasing, translation, or structural rewording. These tools rely on shallow statistical features rather than contextual or semantic understanding, making them unsuitable as definitive indicators of AI misuse. In response, this research proposes an AI-resilient, assessment-based solution that shifts focus from reactive detection to proactive assessment design. The solution is delivered through a web-based Python tool that integrates Bloom's Taxonomy with advanced natural language processing techniques including GPT-3.5 Turbo, BERT-based semantic similarity, and TF-IDF metrics to evaluate the AI-solvability of assignment tasks. By analyzing both surface-level and semantic features, the tool helps educators assess whether a task targets lower-order thinking (e.g., recall, summarization), which is more easily completed by AI, or higher-order skills (e.g., analysis, evaluation, creation), which are more resistant to AI automation. This framework empowers educators to intentionally design cognitively demanding AI-resistant assessments that promote originality, critical thinking, and fairness. By addressing the design of root issue assessment rather than relying on flawed detection tools, this research contributes a sustainable and pedagogically sound strategy to uphold academic standards and foster authentic learning in the era of AI. KEYWORDS Generative AI; ChatGPT; AI-resilient; Bloom's Taxonomy; Automated Assessments; AI-solvability;Automated Feedback; appendices 1. Introduction Integrating AI-technology with innovative thinking skills in higher education (HE) environment has grown more challenging due to rapid digital innovation and ubiquitous data availability. In applied education, innovative thinking is essential. It is charac-CONTACT Muhammad Sajjad Akbar. It entails thinking creatively to come up with original solutions to issues, enhance workflows, or open up new possibilities.

We introduce the concept of "Design Agents" for engineering applications, particularly focusing on the automotive design process, while emphasizing that our approach can be readily extended to other engineering and design domains. Our framework integrates AI-driven design agents into the traditional engineering workflow, demonstrating how these specialized computational agents interact seamlessly with engineers and designers to augment creativity, enhance efficiency, and significantly accelerate the overall design cycle. By automating and streamlining tasks traditionally performed manually, such as conceptual sketching, styling enhancements, 3D shape retrieval and generative modeling, computational fluid dynamics (CFD) meshing, and aerodynamic simulations, our approach reduces certain aspects of the conventional workflow from weeks and days down to minutes. These agents leverage state-of-the-art vision-language models (VLMs), large language models (LLMs), and geometric deep learning techniques, providing rapid iteration and comprehensive design exploration capabilities. We ground our methodology in industry-standard benchmarks, encompassing a wide variety of conventional automotive designs, and utilize high-fidelity aerodynamic simulations to ensure practical and applicable outcomes. Furthermore, we present design agents that can swiftly and accurately predict simulation outcomes, empowering engineers and designers to engage in more informed design optimization and exploration. This research underscores the transformative potential of integrating advanced generative AI techniques into complex engineering tasks, paving the way for broader adoption and innovation across multiple engineering disciplines.

Following the rapid increase in Artificial Intelligence (AI) capabilities in recent years, the AI community has voiced concerns regarding possible safety risks. To support decision-making on the safe use and development of AI systems, there is a growing need for high-quality evaluations of dangerous model capabilities. While several attempts to provide such evaluations have been made, a clear definition of what constitutes a "good evaluation" has yet to be agreed upon. In this practitioners' perspective paper, we present a set of best practices for safety evaluations, drawing on prior work in model evaluation and illustrated through cybersecurity examples. We first discuss the steps of the initial thought process, which connects threat modeling to evaluation design. Then, we provide the characteristics and parameters that make an evaluation useful. Finally, we address additional considerations as we move from building specific evaluations to building a full and comprehensive evaluation suite.