Learning Outcomes DCS-t-720-ATAI-24

After taking the course, diligently attending the classes and doing the assignments, thoroughly reading, and actively participating in discussions, students should be able to:

Identify key challenging research questions related to advanced machine learning and (AML) general machine intelligence (GMI)
List methodological difficulties and proposed solutions to building AML/AGI systems
Explain key components of some AML/GMI architectures, and how these relate to the creation of truly intelligent machines of the future
Students should have a good idea of:
- The limitations of current AI methodologies
- How GMI differs from “narrow AI”
- Some ongoing AML/GMI projects in industry and academia
- What the main requirements are for building complete minds
- What methodologies are currently available and applicable for building complete minds
- How software architecture plays a central role in AI, robotics, and GMI
- How to apply presently-known techniques and methodologies for building complex AI systems
- Emergence, self-organization, and synergism
Students will have had hands-on experience with:
- Selected machine learning methods, notably reinforcement learning
- One programming environment targeting GMI