[[http://cadia.ru.is/wiki/public:t-720-atai:atai-21:main|DCS-T-720-ATAI-2021 Main]]


====== Learning Outcomes DCS-t-720-ATAI-21 ======


===== English =====

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