Center for Analysis and Design of Intelligent Agents

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[[/public:T-719-NXAI:nxai-25:main|T-719-NXAI-2025 Main]] \\
[[/public:T-719-NXAI:nxai-25:main:lecture_notes|Links to Lecture Notes]]


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====Course Overview====

|  Organization  | The bulk of the course is built up of 3 "sprints" on 3 key topics, over a period of 3 weeks. The general organization of these is such: \\ - The first week involves all three topics and preparing for the practical projects; the second two weeks involve two of the topics and two programming assignments. In the last 2 days students do presentations of their projects to the group.  |
|  Implementation Assignments  | Implementation Assignments are intended to give you insight into some of the core principles of intelligence.   |
|  Presentation  | You will give a presentation of one of your implementation projects in the course.   |
|  Exam  | The Exam is designed to measure how much of the material you //actually have understood// -- how much of it you have //"ingested"// -- through a written exam of 2 hours.   |
|  How to get the most out of the course  | Pay attention and spend a lot of time reading in the first week. Spend time on the assignments ! \\ Think about the content ! \\ Ask questions in the lecture sessions !  |

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====What this Course Is / Is not ====

|  \\ Intelligence  | **This course is about a phenomenon we often refer to as "intelligence".** \\ A number of features of natural intelligence remain unexplained. Like the focus of any good scientist should reflect, it is the //unexplained// and //ill-understood// aspects of this phenomenon that is our key focus here.  |
|  \\ GMI / AGI  | A number of terms have been used to refer to the various aspects that people study WRT intelligence. We use the terms "general machine intelligence" (GMI) and "artificial general intelligence" (AGI), in their most general sense (no pun intended), to refer to the various aspects of intelligence that allow an agent to deal with //variety, incompleteness, and incremental information gathering//.  | 
|  \\ Advanced topics  | The main focus of course is //not// the latest and greatest methods to come out of the field called "AI". Instead, it is about unanswered //scientific questions//. \\ Nevertheless, we will make some references to contemporary AI methods along the way (much of which is ANN-based), and you may even learn something about them. What is meant by "next-generation" are the upcoming-yet-unadapted ideas, methods, and approaches that the field of AI is developing now and which will soon make contemporary AI methods mostly irrelevant.    |
|  Then what does "next-generation" refer to here?  | \\ It refers to advancement toward a deeper understanding of the phenomenon of intelligence, and how to create machines based on these.    |
|  \\ History  | The phenomenon of intelligence has been studied for ages. Some of the early notable contributions were the Greek philosophers' musings on reasoning and logic. This is not a history course, but we may make some references to the history of philosophy, AI, cybernetics, and computer science along the way.  |

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====Important Terms in This Course====
|  Methodology  | Contemporary methods in AI (specifically, artificial neural networks, reinforcement learning, and related methods) **will not suffice** for addressing the full scope of the phenomenon of intelligence, as observed in nature.    |
|  Attention  | The ability to manage resources, including computational (thought), information from the external environment, energy, and time.   |
|  Meta-Cognition  | The ability of a system to reason about itself.  |
|  Reasoning  | The application of logical rules to knowledge.  |
|  Learning  | Acquisition of knowledge - models of experience - that enables more successful (a) completion of tasks, (b) creation of goals, subgoals and plans, (c) prediction and explanation of the world.   |
|  Cumulative Learning   | Incremental, continual acquisition of knowledge in such a way as generally making it more useful over time.  |
|  Transfer learning   | The ability to transfer knowledge learned in one task to a different ask.  |
|  Autonomy   | The ability to do tasks without help from a teacher (process designed to specifically help with a specific learning process).  |
|  Constructionist AI   | Methodology that relies heavily on human coding for building intelligent systems.  |
|  Constructivist AI   | Methodology that relies on systems acquiring their own knowledge.  |

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2025(c)K.R.Thórisson  \\