Course Outline

Introduction to AGI and Cognitive Architectures

  • What is AGI? The evolution of artificial general intelligence
  • Overview of cognitive architectures and their role in AGI
  • Key concepts and foundational theories in cognitive science

Core Cognitive Architectures

  • ACT-R: Architecture for Cognition and Learning
  • Soar: Cognitive Architecture for Problem Solving
  • CLARION: Cognitive Architecture for Action and Reflection

Integration of Cognitive Models in AGI Systems

  • How cognitive processes influence machine learning
  • Memory systems, decision-making, and attention in AGI
  • Building scalable and adaptable cognitive systems

Building and Evaluating AGI Architectures

  • Designing and simulating cognitive architectures
  • Evaluating performance and accuracy of AGI models
  • Testing AGI systems in real-world applications

Applications of AGI and Cognitive Architectures

  • Natural language processing and AGI models
  • Robotics and cognitive agents
  • Autonomous decision-making systems

Challenges and Future of AGI Development

  • Ethical considerations in AGI research
  • The future of cognitive architectures in advanced AI
  • Emerging trends and innovations in AGI systems

Summary and Next Steps

  • Key takeaways from the course
  • Resources for further learning
  • Q&A and closing remarks

Requirements

  • In-depth knowledge of artificial intelligence and machine learning
  • Experience in cognitive modeling and computational systems
  • Understanding of neural networks and deep learning

Audience

  • Cognitive scientists
  • AI researchers
  • AI system developers
 14 Hours

Delivery Options

Private Group Training

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  • Delivered online, onsite/classroom or hybrid by experts sharing real world experience

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