Explainability in the MLOps Cycle - MLOps Podcast Episode 138
Explore explainability in MLOps with Dattaraj Rao. Gain insights on rule-based systems, ML model deployment, and emerging trends in AI. Learn about responsible AI and practical applications in various industries.
Performance Co-Pilot TreeTop - Predictive Analytics for System Performance Analysis
Explore automated system performance analysis using dynamic ensemble, statistical methods, causal inference, anomaly detection, and explainable AI for rapid data distillation and real-time performance insights.
Hybrid AI for Knowledge Representation and Model-based Image Understanding
Explore hybrid AI for spatial reasoning and image understanding, combining knowledge representation with data-driven approaches in medical imaging applications.
Beyond Truth and Falsehood - Logic as a Calculus of Events in AI
Explore logic's evolving role in AI, from truth calculus to event calculus, enabling advancements in probabilistic reasoning, machine learning, and explainable AI systems.
Explainable and Robust AI for VILMA Virtual Laboratory
Explore explainable AI and uncertainty quantification in atmospheric research. Learn about SLISEMAP, a supervised manifold visualization method for local explanations and dimensionality reduction.
Deep Distillation and Explainable Neural Networks (ENNs) - Lecture
Explore neurosymbolic architecture ENN, its bottom-up training, and deep distillation with Milo Lin. Gain insights into explainability, biological inspiration, and practical applications.
Human-Centered Explainable AI
Explore human-centered explainable AI with Professor Mark Reidl, focusing on key concepts, latest research, and practical applications in this emerging field of artificial intelligence.
Machine Learning Models to Interpretable Rules
Explore interpretable machine learning with rulelists and rulesets. Learn hands-on use cases using 'imodels' and 'tidyrules' packages, and discover how to convert rulesets to SQL for production.
Exploring AI's Role in Fine Art: An Introduction to Explainable Fine Art
Explore AI's impact on fine art through an introduction to explainable fine art, delving into innovative technologies and interdisciplinary perspectives.
Towards Reverse Algorithmic Engineering of Neural Networks
Explore the algorithmic decision-making process of neural networks through NeuroSAT case study, examining how ML models learn and apply combinatorial features and confidence-based variable selection in SAT solving.
A Journey Towards Explainable AI and Its Societal Implications - From Trust Issues to Complex Models
Explore the principles of explainable AI, from feature-based to data-driven models, examining key algorithms like LIME and Shapley while understanding their broader societal impact and applications in search systems.
ML Observability for Mission-Critical Use Cases
Explore key components and challenges of ML Observability for critical AI deployments, including explainability, monitoring, and risk mitigation. Learn from real-world case studies.
Seeing the Unseen - AI, Ethics and the Future of Transparency
Explore AI ethics, transparency, and societal impact with expert panelists. Discuss emerging dilemmas, legislation, and user experiences in AI development and implementation.
Explainable Neuroimaging AI/ML for FDA and Prospective QML
Explore explainable AI in neuroimaging, focusing on FDA requirements and future quantum machine learning applications. Gain insights into transparent algorithms for medical decision-making.
How to Re-Code LLMs Layer by Layer with Tensor Network Substitutions
Explore strategies for recoding Large Language Models using tensor network substitutions to enhance explainability, compression, and performance on smaller devices.
Logic for Explainable AI - Tutorial
Explore a comprehensive theory of explainable AI using symbolic logic, covering necessary and sufficient conditions for decisions, minimal perturbations, and applications to various classifier types.
Black-Box and Glass-Box Explanation in Machine Learning
Explore intelligibility and explanation in machine learning, comparing glass-box and black-box methods with new techniques for providing explanations.
Explainability for Diarization - JSALT 2023 Team Presentation
Explore explainability techniques for speaker diarization in this JSALT 2023 presentation, focusing on innovative approaches and their practical applications.
Towards Compositional Interpretability for XAI
Explore a mathematical framework using category theory and string diagrams to understand AI model interpretability, focusing on compositional structures and their explainability benefits in neural networks and transformers.
Introduction to Artificial Intelligence and Machine Learning
Discover the fundamentals of AI and ML, from core algorithms to real-world applications across industries, while exploring future trends and key challenges in modern technology.
Artificial Intelligence and Machine Learning - An Introduction to Core Concepts and Applications
Discover the fundamentals of AI and ML, exploring core components, real-world applications, future trends, and key challenges in technology transformation across multiple industries.