public:t-720-atai:atai-22:thecourse

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.
Short Essay	To allow you to delve into a topic of particular interest to you.
Final Project	Gives you a bit more in-depth experience in thinking about next-stage advanced AI systems.
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 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 is our key focus.
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) 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”.
Then what does “advanced” refer to here?	It refers to advancement toward a deeper understanding of the phenomenon of intelligence.
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	Present methods in AI will not suffice for addressing the full scope of the phenomenon of intelligence, as see 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 that enables more successful completion of tasks.
Life-long learning	Incremental acquisition of knowledge throughout a (non-trivially long) lifetime.
Transfer learning	The ability to transfer what has been learned in one task to another.
Autonomy	The ability to do tasks without interference / help from others.
Constructionist AI	Methodology that relies heavily on human coding for building intelligent systems.
Constructivist AI	Methodology that relies on systems acquiring their own knowledge.