AGI: A Deep Dive into Cognitive Architectures

• 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

AGI (Artificial General Intelligence) explores the creation of machines that can perform tasks requiring human-like cognitive functions. This course delves into cognitive architectures, examining theories, systems, and frameworks that aim to replicate human-like thought processes in machines.

This instructor-led, live training (online or onsite) is aimed at advanced-level cognitive scientists, AI researchers, and developers who are interested in deepening their knowledge of AGI and cognitive architectures.

By the end of this training, participants will be able to:

• Understand the fundamental principles of cognitive architectures.
• Explore various AGI models and their applications.
• Analyze cognitive processes and their integration in AI systems.
• Evaluate different cognitive architecture frameworks for AGI.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

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