Reasoning about Object Recognition
Dive into object recognition reasoning, exploring embodied AI and metacognition concepts with insights from an expert in the field of neuro-symbolic artificial intelligence.
Ideas on Conducting Experiments in AI and ML
Explore various experimental approaches in AI and ML, focusing on methodologies used in cutting-edge research and practical applications.
Markov Random Fields, Markov Chains, and Markov Logic Networks - Probabilistic Graphical Models Tutorial
Explore Markov Random Fields, Logic Networks, and Chains. Dive into probabilistic graphical models with Professor Simari, covering key concepts and applications in AI and machine learning.
Prompt Engineering for Neuro-Symbolic AI - Part 2
Dive into advanced prompt engineering techniques, exploring the intersection of symbolic methods and deep learning for more effective AI interactions and development.
Neuro-Symbolic Graph Reasoning and Knowledge Graph Analysis - From Theory to Implementation
Explore neuro-symbolic AI frameworks and graph-based reasoning through practical examples, from biomedical research applications to Python implementations and logical rule learning.
Building Trustworthy AI with Common Sense - Lecture
Explore building trustworthy AI with common sense for robust decision-making, discovery, and misinformation tackling. Learn about CARE principles and neuro-symbolic architectures for explainable, adaptive AI systems.
From NLP to NLU: Why We Need Varied, Comprehensive, and Stratified Knowledge - Neuro-Symbolic AI
Explore the importance of knowledge in AI systems for improved language understanding, addressing challenges in generative AI, and enhancing neuro-symbolic approaches for safer, more explainable AI.
PyReason and Abduction for Trajectory Generation
Explore combining PyReason's annotated logic with abductive inference for robust human movement trajectory generation, countering anomaly detection.
Constraint Satisfaction Problems: Consistency Algorithms - Part 6
Explore consistency algorithms for constraint satisfaction problems in AI, covering formulation, solving methods, and domain splitting.
Causal Analysis and Decision Intelligence for Manufacturing at Bosch
Explore neuro-symbolic AI applications in manufacturing, focusing on causal analysis and decision intelligence for improved processes at Bosch. Learn about root-cause analysis and cognitive architectures for decision-making.
Neuro-Symbolic AI for Mental Healthcare
Explore neuro-symbolic AI methods for enhancing mental healthcare interventions, focusing on social media data analysis, clinical knowledge integration, and improved conversational systems.
Knowledge-infused Learning for Autonomous Driving (KL4AD)
Explore knowledge-infused learning techniques for autonomous driving, focusing on neuro-symbolic AI and its potential to enhance interpretability, explainability, and robustness in real-world applications.
Neuro Symbolic AI
Explore Neuro Symbolic AI with Dr. Amit Sheth, delving into cutting-edge concepts and applications in this emerging field of artificial intelligence.
Language Understanding Using Neuro-symbolic AI: The Need for Varied, Comprehensive, and Stratified Knowledge
Explore neuro-symbolic AI for language understanding, focusing on diverse knowledge types and their stratification to enhance comprehension and reasoning capabilities.
Multi-Modal, Pre-Trained Models in Verifiable Sequential Decision-Making - Metacognitive AI
Explore multi-modal, pre-trained models in verifiable sequential decision-making for metacognitive AI, focusing on latest advancements and applications in artificial intelligence.
Neuro-Symbolic AI with AllegroGraph
Explore Neuro-Symbolic AI's fusion of neural networks and symbolic AI. Learn how AllegroGraph's Knowledge Graph Platform enables complex reasoning and data integration for advanced AI applications.
Uncertainty Quantification in Metacognition
Explore uncertainty quantification in metacognition and its applications in AI research, focusing on DARPA-funded efforts and advancements in neuro-symbolic approaches.
Towards Neuro-Symbolic AI with Knowledge Graphs and Large Language Models
Explore the fusion of neuro-symbolic AI, knowledge graphs, and large language models. Learn how combining these approaches can enhance AI systems' factual understanding and decision-making capabilities.
NeurASP: Model Training - Part 2
Explore NeurASP's end-to-end training process, connecting ASP programs to neural architectures for gradient flow. Learn model training techniques in this neuro-symbolic framework.
Technical Shortcomings of Deep Learning - Part 1
Explore technical challenges in deep learning, including data hunger, limited transfer capacity, open-ended inference, and explainability. Gain insights from Gary Marcus's critical appraisal.
Technical Shortcomings of Deep Learning - Part 2
Explore technical challenges in deep learning, including integration with prior knowledge, correlation vs. causation, and data distribution issues. Gain insights into limitations and potential improvements.