T-720-ATAI-2024 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 intelligence. |
| Final Project | Gives you a bit more in-depth experience in thinking about next-stage advanced AI systems. |
| Public Presentation | You will (probably) give a public presentation of one of your programming projects in the course |
| Final Exam | To try to gauge how much of the material you actually understand – how much of it you have “ingested”. This is what the Final Exam is designed to measure. |
| How to get the most out of the course | Read the assigned reading material and watch the videos ! Do the assignments ! Think about the content ! Ask questions ! |
| 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”. 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”. What is meant is the upcoming methods, approaches, and knowledge that the field of AI may uncover. |
| Then what does “advanced” refer to here? | It refers to advancement toward a deeper understanding of the phenomenon of intelligence, and how to create a machine with 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 must make some references to the history of philosophy, AI, cybernetics and computer science along the way. |
| Methodology | Contemporary methods in AI (artificial neural networks, to be more specific) 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, © 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. |
2024©K.R.Thórisson