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An AI Security Agent for University ACMIS: Multi-Vector Threat Detection and Automated Response6h◆Learning Transferable Latent User Preferences for Human-Aligned Decision Making6h◆Beyond a Single Direction: Chain-of-Thought Disrupts Simple Steering of Refusal6h◆Teaching the Way, Not the Answer: Privileged Tutoring Distillation for Multimodal Policy Optimization6h◆Repair the Core, Not the Symptom: Planning-Graph Repair for Long-Horizon Agents6h◆AIMO Interpretability Challenge6h◆IndustryForge-27B: A Domain-Enhanced Multimodal Foundation Model for Industrial CAD6h◆ECHO: An Auditable Dual-Path Memory System for Long-Horizon Agents6h◆Ethical LLM-Assisted Research: A Framework for Responsible Delegation, Verification, and Epistemic Value6h◆Differential Functionalism: Jacobians Shape the Structure of Phenomenal Experience6h◆Unified Agentic Video Editing Across Levels of Complexity and Creativity6h◆ER-EDF: A Psychology-Grounded Emotion Regulation Framework for Speech Empathetic Dialogue Generation in Large Audio-Language Models6h◆Diffusion Subgoal Planning for Long-Horizon Offline Goal-Conditioned Reinforcement Learning6h◆Penumbra: Sample-Efficient Adversarial Search for Regulatory Obligations6h◆Not Self-Decidable: LLMs Cannot Draw the Boundary of What an Agent Verifier Can Check6h◆TorchTPU - A Hybrid Eager/Compiled Architecture for Native PyTorch Execution on TPU6h◆AM-Bench: A Unified Taxonomy and Evaluation Suite for Agentic Misalignment6h◆HSPR: Recovering Software-Agent Benchmark Scores via Hierarchical Posterior Inference6h◆Typhoon: Gradient-Based Task-Specific Masking for Masked Language Models6h◆Entropic perspective on AI resilience, data-minimal learning and optimal control6h◆An AI Security Agent for University ACMIS: Multi-Vector Threat Detection and Automated Response6h◆Learning Transferable Latent User Preferences for Human-Aligned Decision Making6h◆Beyond a Single Direction: Chain-of-Thought Disrupts Simple Steering of Refusal6h◆Teaching the Way, Not the Answer: Privileged Tutoring Distillation for Multimodal Policy Optimization6h◆Repair the Core, Not the Symptom: Planning-Graph Repair for Long-Horizon Agents6h◆AIMO Interpretability Challenge6h◆IndustryForge-27B: A Domain-Enhanced Multimodal Foundation Model for Industrial CAD6h◆ECHO: An Auditable Dual-Path Memory System for Long-Horizon Agents6h◆Ethical LLM-Assisted Research: A Framework for Responsible Delegation, Verification, and Epistemic Value6h◆Differential Functionalism: Jacobians Shape the Structure of Phenomenal Experience6h◆Unified Agentic Video Editing Across Levels of Complexity and Creativity6h◆ER-EDF: A Psychology-Grounded Emotion Regulation Framework for Speech Empathetic Dialogue Generation in Large Audio-Language Models6h◆Diffusion Subgoal Planning for Long-Horizon Offline Goal-Conditioned Reinforcement Learning6h◆Penumbra: Sample-Efficient Adversarial Search for Regulatory Obligations6h◆Not Self-Decidable: LLMs Cannot Draw the Boundary of What an Agent Verifier Can Check6h◆TorchTPU - A Hybrid Eager/Compiled Architecture for Native PyTorch Execution on TPU6h◆AM-Bench: A Unified Taxonomy and Evaluation Suite for Agentic Misalignment6h◆HSPR: Recovering Software-Agent Benchmark Scores via Hierarchical Posterior Inference6h◆Typhoon: Gradient-Based Task-Specific Masking for Masked Language Models6h◆Entropic perspective on AI resilience, data-minimal learning and optimal control6h◆
News/No one is surprised that Nvidia’s Jensen Huang thinks AI fears are overblown
theverge
PublishedSeptember 20, 2026 at 6:50 PM
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No one is surprised that Nvidia’s Jensen Huang thinks AI fears are overblown

No one is surprised that Nvidia’s Jensen Huang thinks AI fears are overblown
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The man who may stand to make the most money from the AI boom seems to think he knows better than anyone else, including researchers who have studied and worked on AI for decades. In an interview with CBS Sunday Morning, he claimed there was a "0% chance" of AI being the end of the […]

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