Master’s of Science in Artificial General Intelligence (AGI)
- Professional Courses
Description
The Master of Science in Artificial General Intelligence (AGI) is a cutting-edge, research-oriented graduate program designed to explore the theoretical, computational, and ethical foundations of human-level machine intelligence. Unlike narrow AI, AGI aims to develop systems with the ability to understand, learn, and apply knowledge across a wide range of tasks at a level comparable to—or exceeding—that of humans.
This interdisciplinary program blends machine learning, cognitive science, neuroscience, logic, linguistics, and computational theory to equip students with the skills to design next-generation intelligent systems.
Duration
2 years (4 semesters), full-time
Key Objectives
Graduates of the program will be able to:
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Understand the core theoretical frameworks of AGI and how they differ from narrow AI.
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Design and implement systems capable of general learning, reasoning, and adaptation.
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Explore the philosophical and ethical implications of creating human-like intelligence.
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Conduct independent research in AGI and contribute to its theoretical and applied development.
Target Audience
This program is designed for:
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Computer science graduates aiming to specialize in AGI
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AI researchers looking to expand into general intelligence
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Professionals in cognitive science or neuroscience interested in computational models
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Those pursuing a PhD in AGI or related fields
Admission Requirements
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Bachelor’s degree in Computer Science, Mathematics, Engineering, Cognitive Science, or related fields
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Background in programming (Python/C++), linear algebra, and probability
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GRE and/or university-specific entrance exam (if applicable)
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English proficiency (TOEFL/IELTS)
Curriculum Structure
Year 1: Foundations and Core Concepts
Semester 1
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Foundations of Artificial General Intelligence
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Advanced Machine Learning & Deep Learning
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Cognitive Architectures & Computational Models
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Philosophy of Mind and Consciousness
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Research Seminar I
Semester 2
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Probabilistic Reasoning and Decision-Making
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Reinforcement Learning & Embodied Intelligence
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Neuroscience for AGI
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Mathematical Foundations for AGI (Logic, Computability, Information Theory)
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Research Seminar II
Year 2: Advanced Topics and Research
Semester 3
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Lifelong and Continual Learning Systems
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AGI Safety, Alignment, and Ethics
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Human-AI Interaction & Cognitive Interfaces
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Elective Course I (choose from below)
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Research Proposal & Literature Review
Semester 4
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Master’s Thesis / Capstone Project
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Elective Course II
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Colloquium & Research Presentation
Elective Courses (Sample Options)
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Computational Creativity and Imagination
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Natural Language Understanding and World Models
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Robotics and Sensorimotor Integration
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Meta-Learning and Few-shot Learning
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Quantum Computation for AGI
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Artificial Consciousness and Self-awareness
Learning Methodology
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Lectures and guest talks from AGI researchers
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Lab-based experimentation with AGI prototypes
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Critical analysis of AGI architectures (e.g., AIXI, OpenCog, Soar, LIDA)
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Paper readings and collaborative projects
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Thesis work in partnership with research labs or industry
Career Opportunities
Graduates can pursue roles in:
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AGI research institutions and think tanks
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AI safety and ethics organizations
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Advanced AI labs in industry (e.g., DeepMind, OpenAI, Anthropic)
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Academic careers (PhD, teaching, research)
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Innovation roles in robotics, autonomous systems, or cognitive tech
Research and Collaborations
Students will have the opportunity to work with:
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University research labs in AI, cognitive science, and robotics
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Industry partners working on AGI-related technologies
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Open-source AGI projects (e.g., OpenCog, Hyperon, AutoGPT-based frameworks)
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Cross-disciplinary research with philosophy, psychology, and neuroscience departments
Assessment
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Coursework and lab evaluations
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Research seminar presentations
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Peer-reviewed research papers or preprints
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Final thesis or applied AGI system demonstration
Ethical Commitment
The program emphasizes the safe and beneficial development of AGI, addressing risks, value alignment, and long-term societal impact, in alignment with global initiatives such as the AI Alignment movement, Asilomar AI Principles, and UN AI Ethics Guidelines.
