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--- public:t-720-atai:atai-25:learning_outcomes [2025/01/06 16:52] – thorisson
+++ public:t-720-atai:atai-25:learning_outcomes [2025/01/06 17:03] (current) – thorisson
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 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)
+  * 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
@@ Line 23: / Line 23: @@
     * Selected machine learning methods, notably reinforcement learning
     * One programming environment targeting GMI
+*<sub>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).</sub>
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 (c)K.R.Thórisson