Learning Outcomes DCS-t-720-ATAI-21

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) artificial general intelligence (AGI)
  • List methodological difficulties and proposed solutions to building AML/AGI systems
  • Explain key components of some AML/AGI 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 AGI differs from “narrow AI”
  • Some AML/AGI projects in progress
  • 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 AGI
  • 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 AGI