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| public:t-719-nxai:nxai-25:main:thecourse [2025/02/28 13:19] – [What this Course Is / Is not] thorisson | public:t-719-nxai:nxai-25:main:thecourse [2025/04/14 09:54] (current) – [What this Course Is / Is not] thorisson |
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| [[/public:T-719-NXAI:nxai-25:main|T-719-NXAI-2025 Main]] \\ | [[/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]] | [[/public:t-719-nxai:nxai-25:lecture_notes|Links to Lecture Notes]] |
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| ====Course Overview==== | ======ABOUT THIS COURSE====== |
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| | 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. | | \\ |
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| | ====General Overview==== |
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| | | Organization | The bulk of the course is built on reading 1-3 papers every day and discussing it. \\ - The first week involves 6 topics; the second week involves two of the topics and two programming assignments. The last week consists of invited talks and hands-on projects. | |
| | Implementation Assignments | Implementation Assignments are intended to give you insight into some of the core principles of intelligence. | | | 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. The content of the exam is the readings and the invited talks. | |
| | 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 thinking about the content ! \\ Ask questions in all sessions ! | |
| | 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 ==== | ====What this Course Is / Is not ==== |
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| | | Terminology | **Terms used in this course are usually taken in their most //general// sense, unless otherwise noted.** \\ Terms in AI are regularly morphed and redefined to mean something different than their typical meaning when encountered in general discourse. One example is "attention" -- a term that people use all the time but means something **very** different in the context of humans than in the context of ANN-based systems. The reasons for such term re-definitions are numerous. Here it should be noted that, since we are talking about the natural phenomenon of //intelligence//, when we use terms like "attention", "reasoning", "knowledge" and the like, we are most often talking about the intuitive term that typically would be meant if the words were used in general conversations in general discourse (e.g. between family members, friends, etc.). | |
| | \\ 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. | | | \\ 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//. | | | \\ 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, information incompleteness, and incremental information gathering and modeling//. | |
| | \\ 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. | | | \\ 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. | | | 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. | |
| | 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. | | | 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. | | | 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. | | | Meta-Cognition | The ability of a system to reason about itself, about its place in the world, and about its own reasoning. | |
| | Reasoning | The application of logical rules to knowledge. | | | Reasoning | The application of logical rules to knowledge. | |
| | | Knowledge | Structured information in a way that it can be used for goal-achievement, explanation and prediction. | |
| | | Information | A collection of data organized in a way to contain relations and some meta-data. | |
| | | Data | A measurement. | |
| | 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. | | | 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. | | | 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. | | | Transfer learning | The ability to transfer knowledge learned in one task to a different ask. Also called 'generalization'. | |
| | Autonomy | The ability to do tasks without help from a teacher (process designed to specifically help with a specific learning process). | | | 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. | | | Constructivist AI | Methodology that relies on systems acquiring their own knowledge. | |
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| 2025(c)K.R.Thórisson \\ | 2025(c)K.R.Thórisson \\ |
