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#4931 Explainable Generative-AI

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#4931 Explainable Generative-AI #4931  Describe the current state of explainable generative-AI Explainable AI (XAI) for generative architectures has shifted from traditional post-hoc surface attributions (like LIME, SHAP, and raw token attention heatmaps) toward mechanistic interpretability , internal representation probing , and structural verification . Because generative models construct high-dimensional, multi-step sequential outputs rather than scalar classifications, explaining why an output was generated requires inspecting the model’s internal computation graphs, factual recall mechanisms, and execution traces. Core Methodological Pillars Mechanistic Interpretability & Circuit Analysis: Instead of viewing transformer weights as an inscrutable black box, mechanistic interpretability treats networks as computational circuits. Techniques focus on identifying induction heads, indirect object identification circuits, and tracing how attention heads write to an...
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#4930 Top Problems

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#4930 Top Problems #4930  For AI, Quantum Computing, Block Chain, Nuclear Fusion, World Energy needs, World Water needs, World Food needs, list the top 5 problems that need to be solved, in order of importance, to enable progress Artificial Intelligence (AI) Safety, Alignment, and Controllability: Guaranteeing complex multi-agent and frontier models adhere strictly to intended behavioral bounds, resist prompt injection/jailbreaking, and eliminate catastrophic failure modes in mission-critical environments. Deterministic Verification and Explainability: Overcoming the "black box" probabilistic nature of deep neural networks to produce formally verifiable, auditable, and interpretable logic for high-stakes aerospace, medical, and legal deployments. Data Quality, Curvature, and Synthetic Saturation: Addressing model collapse caused by recursive training on synthetic web data while acquiring high-fidelity, multimodal domain datasets with reliable ground truth. Energy, C...
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