Course Description:Large Language Models (LLMs) have revolutionized AI through remarkable pattern matching capabilities. However, the path to Artificial General Intelligence (AGI) requires advancing beyond unconscious (System 1) to conscious (System 2) processing. This research-oriented course explores fundamental approaches to elevate LLMs toward AGI capabilities through conscious reasoning, planning, and decision-making. Core Research Questions: 1. How can we enable LLMs to transition from pattern matching to conscious deliberation? 2. What frameworks support robust reasoning and verifiable decisions? 3. How do we implement planning and temporal awareness in LLM systems? 4. What role does multi-LLM agent collaboration play in advancing toward AGI capabilities? The course examines: 1. Theoretical foundations of consciousness in AI 2. Multi-LLM Agent Collaborative Intelligence (MACI) frameworks 3. Entropy-guided information exchange 4. Constitutional AI principles 5. Temporal reasoning and planning architectures. Through lectures, discussions, and hands-on projects, students will explore practical implementations across various domains. While healthcare provides immediate applications (diagnosis, treatment planning), the principles apply broadly to any field requiring AGI-level reasoning capabilities. Prerequisites: Machine Learning, Deep Learning
3 units · Letter or Credit/No Credit
Course Description:Large Language Models (LLMs) have revolutionized AI through remarkable pattern matching capabilities. However, the path to Artificial General Intelligence (AGI) requires advancing beyond unconscious (System 1) to conscious (System 2) processing. This research-oriented course explores fundamental approaches to elevate LLMs toward AGI capabilities through conscious reasoning, planning, and decision-making. Core Research Questions: 1. How can we enable LLMs to transition from pattern matching to conscious deliberation? 2. What frameworks support robust reasoning and verifiable decisions? 3. How do we implement planning and temporal awareness in LLM systems? 4. What role does multi-LLM agent collaboration play in advancing toward AGI capabilities? The course examines: 1. Theoretical foundations of consciousness in AI 2. Multi-LLM Agent Collaborative Intelligence (MACI) frameworks 3. Entropy-guided information exchange 4. Constitutional AI principles 5. Temporal reasoning and planning architectures. Through lectures, discussions, and hands-on projects, students will explore practical implementations across various domains. While healthcare provides immediate applications (diagnosis, treatment planning), the principles apply broadly to any field requiring AGI-level reasoning capabilities. Prerequisites: Machine Learning, Deep Learning
Offered in Winter 2026 at Stanford University.