Learning Outcomes DCS-t-720-ATAI-25

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

*_{Unless otherwise noted, we use psychological terms such as 'learning,' 'intelligence,' 'thinking,' 'perceiving' and 'reasoning' in their most general sense (as opposed to the narrow sense that companies, researchers outside the field of AI, and many journalists tend to use them, that is, in their most narrow, technical sense).}