Zuckerberg outlines Meta’s AI vision for ‘personal superintelligence’AI Newson July 30, 2025 at 2:05 pm Meta CEO Mark Zuckerberg has laid out his blueprint for the future of AI, and it’s about giving you “personal superintelligence”. In a letter, the Meta chief painted a picture of what’s coming next, and he believes it’s closer than we think. He says his teams are already seeing early signs of progress. “Over the
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Meta CEO Mark Zuckerberg has laid out his blueprint for the future of AI, and it’s about giving you “personal superintelligence”. In a letter, the Meta chief painted a picture of what’s coming next, and he believes it’s closer than we think. He says his teams are already seeing early signs of progress. “Over the
The post Zuckerberg outlines Meta’s AI vision for ‘personal superintelligence’ appeared first on AI News. Read More
The AI Hype Index: The White House’s war on “woke AI”MIT Technology Reviewon July 30, 2025 at 3:37 pm Separating AI reality from hyped-up fiction isn’t always easy. That’s why we’ve created the AI Hype Index—a simple, at-a-glance summary of everything you need to know about the state of the industry. The Trump administration recently declared war on so-called “woke AI,” issuing an executive order aimed at preventing companies whose models exhibit a liberal…
Separating AI reality from hyped-up fiction isn’t always easy. That’s why we’ve created the AI Hype Index—a simple, at-a-glance summary of everything you need to know about the state of the industry. The Trump administration recently declared war on so-called “woke AI,” issuing an executive order aimed at preventing companies whose models exhibit a liberal… Read More
Author: Derrick D. JacksonTitle: Founder & Senior Director of Cloud Security Architecture & RiskCredentials: CISSP, CRISC, CCSPLast updated: July 28th, 2025 Safe AI Usage: The Complete Workplace Guide Scope: This Safe AI Usage guide covers AI usage for typical business applications (content creation, analysis, customer support) used by knowledge workers. For organizations developing high-risk AI systems or […]
Distilling a Small Utility-Based Passage Selector to Enhance Retrieval-Augmented Generationcs.AI updates on arXiv.orgon July 28, 2025 at 4:00 am arXiv:2507.19102v1 Announce Type: cross
Abstract: Retrieval-augmented generation (RAG) enhances large language models (LLMs) by incorporating retrieved information. Standard retrieval process prioritized relevance, focusing on topical alignment between queries and passages. In contrast, in RAG, the emphasis has shifted to utility, which considers the usefulness of passages for generating accurate answers. Despite empirical evidence showing the benefits of utility-based retrieval in RAG, the high computational cost of using LLMs for utility judgments limits the number of passages evaluated. This restriction is problematic for complex queries requiring extensive information. To address this, we propose a method to distill the utility judgment capabilities of LLMs into smaller, more efficient models. Our approach focuses on utility-based selection rather than ranking, enabling dynamic passage selection tailored to specific queries without the need for fixed thresholds. We train student models to learn pseudo-answer generation and utility judgments from teacher LLMs, using a sliding window method that dynamically selects useful passages. Our experiments demonstrate that utility-based selection provides a flexible and cost-effective solution for RAG, significantly reducing computational costs while improving answer quality. We present the distillation results using Qwen3-32B as the teacher model for both relevance ranking and utility-based selection, distilled into RankQwen1.7B and UtilityQwen1.7B. Our findings indicate that for complex questions, utility-based selection is more effective than relevance ranking in enhancing answer generation performance. We will release the relevance ranking and utility-based selection annotations for the MS MARCO dataset, supporting further research in this area.
arXiv:2507.19102v1 Announce Type: cross
Abstract: Retrieval-augmented generation (RAG) enhances large language models (LLMs) by incorporating retrieved information. Standard retrieval process prioritized relevance, focusing on topical alignment between queries and passages. In contrast, in RAG, the emphasis has shifted to utility, which considers the usefulness of passages for generating accurate answers. Despite empirical evidence showing the benefits of utility-based retrieval in RAG, the high computational cost of using LLMs for utility judgments limits the number of passages evaluated. This restriction is problematic for complex queries requiring extensive information. To address this, we propose a method to distill the utility judgment capabilities of LLMs into smaller, more efficient models. Our approach focuses on utility-based selection rather than ranking, enabling dynamic passage selection tailored to specific queries without the need for fixed thresholds. We train student models to learn pseudo-answer generation and utility judgments from teacher LLMs, using a sliding window method that dynamically selects useful passages. Our experiments demonstrate that utility-based selection provides a flexible and cost-effective solution for RAG, significantly reducing computational costs while improving answer quality. We present the distillation results using Qwen3-32B as the teacher model for both relevance ranking and utility-based selection, distilled into RankQwen1.7B and UtilityQwen1.7B. Our findings indicate that for complex questions, utility-based selection is more effective than relevance ranking in enhancing answer generation performance. We will release the relevance ranking and utility-based selection annotations for the MS MARCO dataset, supporting further research in this area. Read More
A Reproducibility Study of Product-side Fairness in Bundle Recommendationcs.AI updates on arXiv.orgon July 22, 2025 at 4:00 am arXiv:2507.14352v1 Announce Type: cross
Abstract: Recommender systems are known to exhibit fairness issues, particularly on the product side, where products and their associated suppliers receive unequal exposure in recommended results. While this problem has been widely studied in traditional recommendation settings, its implications for bundle recommendation (BR) remain largely unexplored. This emerging task introduces additional complexity: recommendations are generated at the bundle level, yet user satisfaction and product (or supplier) exposure depend on both the bundle and the individual items it contains. Existing fairness frameworks and metrics designed for traditional recommender systems may not directly translate to this multi-layered setting. In this paper, we conduct a comprehensive reproducibility study of product-side fairness in BR across three real-world datasets using four state-of-the-art BR methods. We analyze exposure disparities at both the bundle and item levels using multiple fairness metrics, uncovering important patterns. Our results show that exposure patterns differ notably between bundles and items, revealing the need for fairness interventions that go beyond bundle-level assumptions. We also find that fairness assessments vary considerably depending on the metric used, reinforcing the need for multi-faceted evaluation. Furthermore, user behavior plays a critical role: when users interact more frequently with bundles than with individual items, BR systems tend to yield fairer exposure distributions across both levels. Overall, our findings offer actionable insights for building fairer bundle recommender systems and establish a vital foundation for future research in this emerging domain.
arXiv:2507.14352v1 Announce Type: cross
Abstract: Recommender systems are known to exhibit fairness issues, particularly on the product side, where products and their associated suppliers receive unequal exposure in recommended results. While this problem has been widely studied in traditional recommendation settings, its implications for bundle recommendation (BR) remain largely unexplored. This emerging task introduces additional complexity: recommendations are generated at the bundle level, yet user satisfaction and product (or supplier) exposure depend on both the bundle and the individual items it contains. Existing fairness frameworks and metrics designed for traditional recommender systems may not directly translate to this multi-layered setting. In this paper, we conduct a comprehensive reproducibility study of product-side fairness in BR across three real-world datasets using four state-of-the-art BR methods. We analyze exposure disparities at both the bundle and item levels using multiple fairness metrics, uncovering important patterns. Our results show that exposure patterns differ notably between bundles and items, revealing the need for fairness interventions that go beyond bundle-level assumptions. We also find that fairness assessments vary considerably depending on the metric used, reinforcing the need for multi-faceted evaluation. Furthermore, user behavior plays a critical role: when users interact more frequently with bundles than with individual items, BR systems tend to yield fairer exposure distributions across both levels. Overall, our findings offer actionable insights for building fairer bundle recommender systems and establish a vital foundation for future research in this emerging domain. Read More
Author: Derrick D. JacksonTitle: Founder & Senior Director of Cloud Security Architecture & RiskCredentials: CISSP, CRISC, CSSPLast updated June 2nd, 2025 Article 2 in the Executive AI Leadership Series Building Your AI Governance Framework: From Strategy to Implementation The Reality Check You’ve made the case. The board understands AI governance isn’t optional. But what matters is […]
A comparative analysis showing how different approaches to AI frameworks serve distinct organizational needs while maintaining industry alignment. In this article you will learn: The Reality Check As organizations race to implement AI systems, they’re juggling security threats, regulatory demands, and business pressures all at once. It’s messy, and frankly, it’s not surprising. Around 70% […]
AI Governance Hub / AI Use Case Tracker Why Your Organization Needs a Comprehensive AI Use Case Tracker And What to Track: The 40-Field Guide for Complete AI Visibility Derrick D. Jackson | CISSP, CRISC, CCSP | Updated May 2025 ∼12 min read 40Fields 5Categories 8Frameworks On This Page Introduction 40 Fields Explorer Risk Tier […]
The Culprit Ransomware is predicted to hit someone every 2 seconds by 2031. When it happens, you’re looking at 3+ weeks of downtime and millions in recovery costs. Here’s what actually works to prevent it, based on environments where people have successfully fought these attacks. Windows Environment Get endpoint protection that isn’t garbage Most antivirus is […]
The Need for Explainable AI Between 2013 and 2019, the Dutch tax authority’s algorithm flagged 26,000 families as potential fraudsters. The system worked exactly as programmed, spotting patterns in childcare benefit claims. But when investigators finally understood what the algorithm was doing, they discovered it was using nationality as a hidden factor. The result: thousands […]