T-713-MERS-2023 Main
Links to Lecture Notes
| Organization | The bulk of the course is built up of 5 “sprints” on 5 key topics, each covering 2 weeks. The general organization of these is such: - The first week involves presentation of the sprint's topic(s), assignments, and other related things. - The second week involves online discussion and Q&A related to assignments. |
| Software Assignments | To enable you to get insight into some of the core principles of the next phase in artificial intelligence. |
| Short Analytical Essay | To allow you to delve into a topic of particular interest to you. |
| Final Exam | To try to gauge how much of the material you actually understand – how much of it you have “ingested”. |
| How to get the most out of the course | Read the assigned reading material, watch the videos and do the projects ! Do the assignments ! Think about the content ! Ask questions ! |
| Intelligence | This course is about a phenomenon we often refer to as “intelligence” with a special focus on its central topic, intelligence. A number of features of natural intelligence remain unexplained, for instance, how reasoning enters into learning and thought. It is the unexplained and ill-understood aspects that are the key focus of any scientific education. |
| 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” in (AGI) the most general sense (no pun intended) of these terms, 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”. However, we will make some references to such methods along the way, and you may even learn something about them. But that is not what is meant by “advanced”. |
| “Advanced”? | The term as used here refers to advancement toward a deeper understanding of the phenomenon of reasoning and its role in intelligence. |
| History | The phenomenon of reasoning 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 must make some references to the history of philosophy, AI, cybernetics and computer science along the way. |
| Reasoning | The application of logical rules to knowledge. |
| 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. |
| Learning | Acquisition of knowledge that enables more successful completion of tasks. |
| Life-long learning | Incremental acquisition of knowledge throughout a (non-trivially long) lifetime. |
| Autonomy | The ability to do tasks without interference / help from others. |
| Methodology | Present methods in AI will not suffice for addressing the full scope of the phenomenon of intelligence, as see in nature. |
| Constructionist AI | Methodology that relies heavily on human coding for building intelligent systems. Not to be confused with Constructivist AI. |
| Constructivist AI | Methodology that relies on systems acquiring their own knowledge. |
2023©K.R.Thórisson