*Apr. 13, 2025: This page is work in progress – will be ready one week before course starts.*
Make sure to read the required number of papers (first number inside the brackets - the second one is the “minimum recommended”) and make sure to not fall behind on readings (I assign you only a few papers per day for a good reason - so you can get through them in time for the discussion session on that day). Note: We will interweave content from prior sessions in the following ones, so if you fall behind two or more days in a row, you will be significantly challenged to keep up (there are subtleties in the content that is really key to understanding the content and passing the course - you may feel like you're following along the discussion, but there will likely be important things you're missing).
Guidelines for how to read in this course (seriously! - do not skip).
What is intelligence?
How do experts talk about it?
What has been uncovered?
What uniquely separates the phenomenon of intelligence from other similar phenomena in the world?
What is reasoning for?
What kinds of processes does reasoning consist of?
How can the sub-processes of reasoning be coordinated at runtime?
What is learning?
Do different kinds of learning exist?
What are the component processes of learning?
How can these processes be unified in a single coherent system?
Is “machine learning” comparable to human (kinds of) learning?
How is autonomy defined?
What are the levels of autonomy?
What are the minimum requirements for different autonomy levels?
How can autonomy be achieved in an artificial system?
Is learning necessary for autonomy?
Are symbols and words the same thing?
Is the relation between words and symbols bijective?
Can anything be a model of anything?
How are symbols related to models?
How are models of causal relations made?
Where does meaning come from?
Who and what is meaning for?
Are there different kinds of meaning?
How does reasoning fit into the concept of meaning?
Is meaning necessary for understanding?
What role does a cognitive architecture play in intelligence?
How is cognitive architecture different from software architecture?
How does reasoning, goals, understanding and meaning come into a cognitive architecture?
How does reasoning fit into the concept of meaning?
Is meaning necessary for understanding?
The Subsumption Architecture is definitely GOFAI-style architecture: With baked-in hand-coded goals and control structures, these systems are notoriously difficult to build for autonomous adaptation of any kind. But they are fun to build, robust and easy to debug.
This is the definitive reasoning architecture, under development since the 1990s.
The Reykjavik University architecture that has been shown to learn very complex tasks by observation.
One of the earliest examples of implemented self-guided learning systems.
Sigma doesn't learn, but it's a great tool for learning about generality and autonomy.
Originally based on NARS (see above), the latest incarnation of the OpenCOG is called .
Note: DO NOT SKIP READING THE BELOW TEXT
As you read papers from each of the following categories I want you ask yourself a few questions:
If you can answer them satisfactorily when you're done reading you're good! Even if you can't you'll be fine if you: Write down the discrepancies and bring them to class in the form of questions. There is no such thing as a 'stupid question' when you're learning something new!
2025©K.R.Thórisson