How to Perform Comprehensive Large Scale LLM ValidationTowards Data Scienceon August 22, 2025 at 2:00 am Learn how to validate large scale LLM applications
The post How to Perform Comprehensive Large Scale LLM Validation appeared first on Towards Data Science.
Learn how to validate large scale LLM applications
The post How to Perform Comprehensive Large Scale LLM Validation appeared first on Towards Data Science. Read More
Where Hurricanes Hit Hardest: A County-Level Analysis with PythonTowards Data Scienceon August 21, 2025 at 8:06 pm Use Python, GeoPandas, Tropycal, and Plotly Express to map the number of hurricane encounters per county over the past 50 years.
The post Where Hurricanes Hit Hardest: A County-Level Analysis with Python appeared first on Towards Data Science.
Use Python, GeoPandas, Tropycal, and Plotly Express to map the number of hurricane encounters per county over the past 50 years.
The post Where Hurricanes Hit Hardest: A County-Level Analysis with Python appeared first on Towards Data Science. Read More
In a first, Google has released data on how much energy an AI prompt usesMIT Technology Reviewon August 21, 2025 at 12:00 pm Google has just released a technical report detailing how much energy its Gemini apps use for each query. In total, the median prompt—one that falls in the middle of the range of energy demand—consumes 0.24 watt-hours of electricity, the equivalent of running a standard microwave for about one second. The company also provided average estimates…
Google has just released a technical report detailing how much energy its Gemini apps use for each query. In total, the median prompt—one that falls in the middle of the range of energy demand—consumes 0.24 watt-hours of electricity, the equivalent of running a standard microwave for about one second. The company also provided average estimates… Read More
MAViS: A Multi-Agent Framework for Long-Sequence Video Storytellingcs.AI updates on arXiv.orgon August 21, 2025 at 4:00 am arXiv:2508.08487v3 Announce Type: replace-cross
Abstract: Despite recent advances, long-sequence video generation frameworks still suffer from significant limitations: poor assistive capability, suboptimal visual quality, and limited expressiveness. To mitigate these limitations, we propose MAViS, an end-to-end multi-agent collaborative framework for long-sequence video storytelling. MAViS orchestrates specialized agents across multiple stages, including script writing, shot designing, character modeling, keyframe generation, video animation, and audio generation. In each stage, agents operate under the 3E Principle — Explore, Examine, and Enhance — to ensure the completeness of intermediate outputs. Considering the capability limitations of current generative models, we propose the Script Writing Guidelines to optimize compatibility between scripts and generative tools. Experimental results demonstrate that MAViS achieves state-of-the-art performance in assistive capability, visual quality, and video expressiveness. Its modular framework further enables scalability with diverse generative models and tools. With just a brief user prompt, MAViS is capable of producing high-quality, expressive long-sequence video storytelling, enriching inspirations and creativity for users. To the best of our knowledge, MAViS is the only framework that provides multimodal design output — videos with narratives and background music.
arXiv:2508.08487v3 Announce Type: replace-cross
Abstract: Despite recent advances, long-sequence video generation frameworks still suffer from significant limitations: poor assistive capability, suboptimal visual quality, and limited expressiveness. To mitigate these limitations, we propose MAViS, an end-to-end multi-agent collaborative framework for long-sequence video storytelling. MAViS orchestrates specialized agents across multiple stages, including script writing, shot designing, character modeling, keyframe generation, video animation, and audio generation. In each stage, agents operate under the 3E Principle — Explore, Examine, and Enhance — to ensure the completeness of intermediate outputs. Considering the capability limitations of current generative models, we propose the Script Writing Guidelines to optimize compatibility between scripts and generative tools. Experimental results demonstrate that MAViS achieves state-of-the-art performance in assistive capability, visual quality, and video expressiveness. Its modular framework further enables scalability with diverse generative models and tools. With just a brief user prompt, MAViS is capable of producing high-quality, expressive long-sequence video storytelling, enriching inspirations and creativity for users. To the best of our knowledge, MAViS is the only framework that provides multimodal design output — videos with narratives and background music. Read More
A Survey on Video Anomaly Detection via Deep Learning: Human, Vehicle, and Environmentcs.AI updates on arXiv.orgon August 21, 2025 at 4:00 am arXiv:2508.14203v1 Announce Type: cross
Abstract: Video Anomaly Detection (VAD) has emerged as a pivotal task in computer vision, with broad relevance across multiple fields. Recent advances in deep learning have driven significant progress in this area, yet the field remains fragmented across domains and learning paradigms. This survey offers a comprehensive perspective on VAD, systematically organizing the literature across various supervision levels, as well as adaptive learning methods such as online, active, and continual learning. We examine the state of VAD across three major application categories: human-centric, vehicle-centric, and environment-centric scenarios, each with distinct challenges and design considerations. In doing so, we identify fundamental contributions and limitations of current methodologies. By consolidating insights from subfields, we aim to provide the community with a structured foundation for advancing both theoretical understanding and real-world applicability of VAD systems. This survey aims to support researchers by providing a useful reference, while also drawing attention to the broader set of open challenges in anomaly detection, including both fundamental research questions and practical obstacles to real-world deployment.
arXiv:2508.14203v1 Announce Type: cross
Abstract: Video Anomaly Detection (VAD) has emerged as a pivotal task in computer vision, with broad relevance across multiple fields. Recent advances in deep learning have driven significant progress in this area, yet the field remains fragmented across domains and learning paradigms. This survey offers a comprehensive perspective on VAD, systematically organizing the literature across various supervision levels, as well as adaptive learning methods such as online, active, and continual learning. We examine the state of VAD across three major application categories: human-centric, vehicle-centric, and environment-centric scenarios, each with distinct challenges and design considerations. In doing so, we identify fundamental contributions and limitations of current methodologies. By consolidating insights from subfields, we aim to provide the community with a structured foundation for advancing both theoretical understanding and real-world applicability of VAD systems. This survey aims to support researchers by providing a useful reference, while also drawing attention to the broader set of open challenges in anomaly detection, including both fundamental research questions and practical obstacles to real-world deployment. Read More
Harnessing Structured Knowledge: A Concept Map-Based Approach for High-Quality Multiple Choice Question Generation with Effective Distractorscs.AI updates on arXiv.orgon August 20, 2025 at 4:00 am arXiv:2505.02850v2 Announce Type: replace-cross
Abstract: Generating high-quality MCQs, especially those targeting diverse cognitive levels and incorporating common misconceptions into distractor design, is time-consuming and expertise-intensive, making manual creation impractical at scale. Current automated approaches typically generate questions at lower cognitive levels and fail to incorporate domain-specific misconceptions. This paper presents a hierarchical concept map-based framework that provides structured knowledge to guide LLMs in generating MCQs with distractors. We chose high-school physics as our test domain and began by developing a hierarchical concept map covering major Physics topics and their interconnections with an efficient database design. Next, through an automated pipeline, topic-relevant sections of these concept maps are retrieved to serve as a structured context for the LLM to generate questions and distractors that specifically target common misconceptions. Lastly, an automated validation is completed to ensure that the generated MCQs meet the requirements provided. We evaluate our framework against two baseline approaches: a base LLM and a RAG-based generation. We conducted expert evaluations and student assessments of the generated MCQs. Expert evaluation shows that our method significantly outperforms the baseline approaches, achieving a success rate of 75.20% in meeting all quality criteria compared to approximately 37% for both baseline methods. Student assessment data reveal that our concept map-driven approach achieved a significantly lower guess success rate of 28.05% compared to 37.10% for the baselines, indicating a more effective assessment of conceptual understanding. The results demonstrate that our concept map-based approach enables robust assessment across cognitive levels and instant identification of conceptual gaps, facilitating faster feedback loops and targeted interventions at scale.
arXiv:2505.02850v2 Announce Type: replace-cross
Abstract: Generating high-quality MCQs, especially those targeting diverse cognitive levels and incorporating common misconceptions into distractor design, is time-consuming and expertise-intensive, making manual creation impractical at scale. Current automated approaches typically generate questions at lower cognitive levels and fail to incorporate domain-specific misconceptions. This paper presents a hierarchical concept map-based framework that provides structured knowledge to guide LLMs in generating MCQs with distractors. We chose high-school physics as our test domain and began by developing a hierarchical concept map covering major Physics topics and their interconnections with an efficient database design. Next, through an automated pipeline, topic-relevant sections of these concept maps are retrieved to serve as a structured context for the LLM to generate questions and distractors that specifically target common misconceptions. Lastly, an automated validation is completed to ensure that the generated MCQs meet the requirements provided. We evaluate our framework against two baseline approaches: a base LLM and a RAG-based generation. We conducted expert evaluations and student assessments of the generated MCQs. Expert evaluation shows that our method significantly outperforms the baseline approaches, achieving a success rate of 75.20% in meeting all quality criteria compared to approximately 37% for both baseline methods. Student assessment data reveal that our concept map-driven approach achieved a significantly lower guess success rate of 28.05% compared to 37.10% for the baselines, indicating a more effective assessment of conceptual understanding. The results demonstrate that our concept map-based approach enables robust assessment across cognitive levels and instant identification of conceptual gaps, facilitating faster feedback loops and targeted interventions at scale. Read More
Help Your Model Learn the True SignalTowards Data Scienceon August 20, 2025 at 4:10 am An algorithm-agnostic approach inspired by Cook’s distance
The post Help Your Model Learn the True Signal appeared first on Towards Data Science.
An algorithm-agnostic approach inspired by Cook’s distance
The post Help Your Model Learn the True Signal appeared first on Towards Data Science. Read More
Water Cooler Small Talk: Should ChatGPT Be Blocked at Work?Towards Data Scienceon August 19, 2025 at 7:31 pm Water cooler small talk is a special kind of small talk, typically observed in office spaces around a water cooler. There, employees frequently share all kinds of corporate gossip, myths, legends, inaccurate scientific opinions, indiscreet personal anecdotes, or outright lies. Anything goes. So, in my Water Cooler Small Talk posts, I discuss strange and usually
The post Water Cooler Small Talk: Should ChatGPT Be Blocked at Work? appeared first on Towards Data Science.
Water cooler small talk is a special kind of small talk, typically observed in office spaces around a water cooler. There, employees frequently share all kinds of corporate gossip, myths, legends, inaccurate scientific opinions, indiscreet personal anecdotes, or outright lies. Anything goes. So, in my Water Cooler Small Talk posts, I discuss strange and usually
The post Water Cooler Small Talk: Should ChatGPT Be Blocked at Work? appeared first on Towards Data Science. Read More
Hidden costs of AI implementation every CEO should knowAI Newson August 19, 2025 at 10:32 am AI has been a game-changer for many businesses, and CEOs are eager to get in on the action. Smart move! But, before you start envisioning robots handling your customer service and algorithms optimising everything from inventory to cafeteria orders, let’s talk about the elephant in the room: the costs no one mentions at those slick
The post Hidden costs of AI implementation every CEO should know appeared first on AI News.
AI has been a game-changer for many businesses, and CEOs are eager to get in on the action. Smart move! But, before you start envisioning robots handling your customer service and algorithms optimising everything from inventory to cafeteria orders, let’s talk about the elephant in the room: the costs no one mentions at those slick
The post Hidden costs of AI implementation every CEO should know appeared first on AI News. Read More
The AI Risk Spectrum: From Dangerous Capabilities to Existential Threatscs.AI updates on arXiv.orgon August 20, 2025 at 4:00 am arXiv:2508.13700v1 Announce Type: cross
Abstract: As AI systems become more capable, integrated, and widespread, understanding the associated risks becomes increasingly important. This paper maps the full spectrum of AI risks, from current harms affecting individual users to existential threats that could endanger humanity’s survival. We organize these risks into three main causal categories. Misuse risks, which occur when people deliberately use AI for harmful purposes – creating bioweapons, launching cyberattacks, adversarial AI attacks or deploying lethal autonomous weapons. Misalignment risks happen when AI systems pursue outcomes that conflict with human values, irrespective of developer intentions. This includes risks arising through specification gaming (reward hacking), scheming and power-seeking tendencies in pursuit of long-term strategic goals. Systemic risks, which arise when AI integrates into complex social systems in ways that gradually undermine human agency – concentrating power, accelerating political and economic disempowerment, creating overdependence that leads to human enfeeblement, or irreversibly locking in current values curtailing future moral progress. Beyond these core categories, we identify risk amplifiers – competitive pressures, accidents, corporate indifference, and coordination failures – that make all risks more likely and severe. Throughout, we connect today’s existing risks and empirically observable AI behaviors to plausible future outcomes, demonstrating how existing trends could escalate to catastrophic outcomes. Our goal is to help readers understand the complete landscape of AI risks. Good futures are possible, but they don’t happen by default. Navigating these challenges will require unprecedented coordination, but an extraordinary future awaits if we do.
arXiv:2508.13700v1 Announce Type: cross
Abstract: As AI systems become more capable, integrated, and widespread, understanding the associated risks becomes increasingly important. This paper maps the full spectrum of AI risks, from current harms affecting individual users to existential threats that could endanger humanity’s survival. We organize these risks into three main causal categories. Misuse risks, which occur when people deliberately use AI for harmful purposes – creating bioweapons, launching cyberattacks, adversarial AI attacks or deploying lethal autonomous weapons. Misalignment risks happen when AI systems pursue outcomes that conflict with human values, irrespective of developer intentions. This includes risks arising through specification gaming (reward hacking), scheming and power-seeking tendencies in pursuit of long-term strategic goals. Systemic risks, which arise when AI integrates into complex social systems in ways that gradually undermine human agency – concentrating power, accelerating political and economic disempowerment, creating overdependence that leads to human enfeeblement, or irreversibly locking in current values curtailing future moral progress. Beyond these core categories, we identify risk amplifiers – competitive pressures, accidents, corporate indifference, and coordination failures – that make all risks more likely and severe. Throughout, we connect today’s existing risks and empirically observable AI behaviors to plausible future outcomes, demonstrating how existing trends could escalate to catastrophic outcomes. Our goal is to help readers understand the complete landscape of AI risks. Good futures are possible, but they don’t happen by default. Navigating these challenges will require unprecedented coordination, but an extraordinary future awaits if we do. Read More