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public:t-720-atai:atai-24:learning_outcomes

DCS-T-720-ATAI-2024 Main

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



2024©K.R.Thórisson

/var/www/cadia.ru.is/wiki/data/pages/public/t-720-atai/atai-24/learning_outcomes.txt · Last modified: 2024/04/29 13:33 by 127.0.0.1

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