Probabilistic Foundations of Metacognition via Hybrid AI

Overview

Discover a new probabilistic theory of metacognition through hybrid AI, explaining experimental results at the intersection of symbolic methods and deep learning.

Syllabus

• Introduction to Metacognition
Definition and importance of metacognition in cognitive science
Overview of traditional and contemporary theories
• Fundamentals of Probabilistic Reasoning
Probability theory basics
Bayesian inference and its applications
Probabilistic graphical models
• Overview of Hybrid AI
Defining hybrid AI: Combining symbolic methods and deep learning
Advantages and challenges of hybrid approaches
• Symbolic Methods in AI
Logic-based systems and rule-based reasoning
Applications of symbolic AI in cognitive modeling
• Deep Learning Foundations
Neural network architectures
Techniques for training deep learning models
Interpretability in deep learning
• Probabilistic Models in Deep Learning
Introduction to probabilistic neural networks
Variational inference and probabilistic programming
• Hybrid AI for Metacognition
Integrating symbolic reasoning with deep learning
Case studies of hybrid AI systems in cognitive tasks
• Probabilistic Approach to Metacognition
Developing a probabilistic framework for metacognition
Modeling human-like reflective thinking in AI
• Experimental Results and Analysis
Overview of experimental studies in metacognition
Analyzing hybrid AI models against empirical data
• Applications and Future Directions
Implementing metacognitive features in AI systems
Ethical and practical implications of metacognitive AI
• Course Conclusion and Project
Developing a hybrid AI model with metacognitive capabilities
Summary of key concepts and future research directions

Subjects

Computer Science