[[http://cadia.ru.is/wiki/public:t-720-atai:atai-22:main|DCS-T-720-ATAI-2022 Main]]


====== Learning Outcomes DCS-t-720-ATAI-22 ======



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