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Multi-agent AI systems are taking over supply chain execution

agentic ai, ai, AI (Artificial Intelligence), AI Business Strategy, AI in Action, Artificial Intelligence, automation, explainable AI, Features, governance, Governance, Regulation & Policy, Logistics, Manufacturing & Engineering AI, multi-agent systems, physical ai, Retail & Logistics AI, supply chain, Trust, Bias & Fairness, Workforce & HR AI

Multi-agent AI systems are taking over supply chain execution as enterprise networks face diminishing returns from static dashboards, pushing logistics directors towards autonomous execution. Predictive demand models display recommendations, yet human planners still clear every action. Multi-agent systems replace that approval stage across targeted operational boundaries. Instead of waiting for weekly scheduling runs, independent software […]

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Motional and MIT AI explains self-driving car decisions

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Motional and MIT researchers have built a system that lets self-driving cars explain their decisions in real-time, tackling the black-box problem in autonomous vehicle AI. The work, published in Nature, comes from a team at Motional that includes CEO Laura Major, working alongside researchers from MIT’s Computer Science and Artificial Intelligence Laboratory. Their proposed method,

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How Agentic AI Accelerates SME Credit Decisions with SAS Viya

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This post demonstrates how Agentic AI and SAS Viya can modernize SME loan origination by combining OCR, LLMs, governed decisioning, and interactive dashboards to accelerate transparent, explainable, and scalable credit decisions. The post How Agentic AI Accelerates SME Credit Decisions with SAS Viya appeared first on SAS Blogs.

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Mend Releases AI Security Governance Framework: Covering Asset Inventory, Risk Tiering, AI Supply Chain Security, and Maturity Model

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There’s a pattern playing out inside almost every engineering organization right now. A developer installs GitHub Copilot to ship code faster. A data analyst starts querying a new LLM tool for reporting. A product team quietly embeds a third-party model into a feature branch. By the time the security team hears about any of it,

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Mend.io Releases AI Security Governance Framework Covering Asset Inventory, Risk Tiering, AI Supply Chain Security, and Maturity Model

agentic ai, ai, AI (Artificial Intelligence), AI Infrastructure, AI Shorts, Applications, Artificial Intelligence, deep learning, Editors Pick, Enterprise AI, explainable AI, Generative AI, Guardrail, Language Model, Large Language Model, Machine Learning, New Releases, Promote, Software engineering, sponsored, Staff, Tech News, Technology

There’s a pattern playing out inside almost every engineering organization right now. A developer installs GitHub Copilot to ship code faster. A data analyst starts querying a new LLM tool for reporting. A product team quietly embeds a third-party model into a feature branch. By the time the security team hears about any of it,

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Meta Superintelligence Lab Releases Muse Spark: A Multimodal Reasoning Model With Thought Compression and Parallel Agents

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Meta Superintelligence Labs recently made a significant move by unveiling ‘Muse Spark’ — the first model in the Muse family. Muse Spark is a natively multimodal reasoning model with support for tool-use, visual chain of thought, and multi-agent orchestration. https://ai.meta.com/static-resource/muse-spark-eval-methodology What ‘Natively Multimodal’ Actually Means When Meta describes Muse Spark as ‘natively multimodal,’ it means

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How to Build an Explainable AI Analysis Pipeline Using SHAP-IQ to Understand Feature Importance, Interaction Effects, and Model Decision Breakdown

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In this tutorial, we build an advanced explainable AI analysis pipeline using SHAP-IQ to understand both feature importance and interaction effects directly inside our Python environment. We load a real-world dataset, train a high-performance Random Forest model, and then apply the SHAP-IQ interaction index to compute precise, theoretically grounded explanations of model predictions. We extract

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A New Frontier for AI Agents: Transparency

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As AI agents optimize how they communicate, the shift away from human-readable language underscores why transparency and interpretability are essential for building trust in autonomous systems. The post A New Frontier for AI Agents: Transparency appeared first on SAS Blogs.

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