Center for Analysis and Design of Intelligent Agents

Readings README (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?

• The g factor on Wikipedia
• Multiple theory of intelligence on Wikipedia
• A Collection of Definitions of Intelligence by Legg & Hutter
• On Defining Artificial Intelligence by P. Wang
• Discretionarily Constrained Adaptation Under Insuficient Knowledge & Resources by K. R. Thórisson
• A Computational Foundation for the Study of Cognition by D. Chalmers
• Computing Machinery and Intelligence by A. M. Turing
• What is Thought? by E. Morsella
• Thought on Wikipedia.

All intelligence exists in a world.
What kind of world is the natural world?

• About Worlds - Excerpt from a draft of a forthcoming book on Intelligence by K.R.Thórisson
• No, The Universe Isn't Made of Pure Mathematics by E. Siegel
• Never Tell a Scientist It's "Just A Theory" by E. Siegel
• Intelligent Agents on Wikipedia

Information comes from measurements.
Knowledge comes from information.

• Empirical Evidence on Wikipedia
• What Are Comparative Experiments? by D. Verial
• Causation & causality on Wikipedia
• Causality on The Information Philosopher
• Empirical Evidence on Wikipedia

Getting anything done efficiently requires knowledge of cause-effect relations.

• Bayesianism and Causality, or, Why I am Only a Half-Beyesian by J. Pearl
• Adequate Determinism on the Information Philosopher.

Reasoning enables systematic (logical) manipulation of information.
Reasoning is efficient large amounts of information.

• Reasoning on Wikipedia
• Cognitive logic versus mathematical logic by P. Wang
• Toward a logic of everyday reasoning by P. Wang
• The limitation of Bayesiansm by P. Wang.

• Solving a problem with or without a program by P. Wang
• Abduction in non-axiomatic logic by P. Wang
• A Model of Unified Perception and Cognition by P. Wang et al.
• Empirical case for two systems of reasoning by S. Sloman
• AGI NARS Tutorial by P. Wang & P. Hammer
• A New Approach for Induction: From a Non-Axiomatic Point of View by P. Wang.
• The logic of categorization by P. Wang

• Understanding & Common Sense by K.R. Thórisson
• Do Machines Understand? by K.R. Thórisson
• Question Answering on Truthful Q&A on Papersiwthcode.com
• Common Sense Reasoning on Paperswithcode.com

• NARS in a Nutshell by Tangrui (Tory) Li
• Non-axiomatic truth-values by Tangrui (Tory) Li
• Attention by Tangrui (Tory) Li
• Course Material for NAL-1 (v.1.1) by Tangrui (Tory) Li

• Growing Recursive Self-Improvers by B. Steunebrink et al.

• ChatGPT Can't Reason by K. Arkoudas
• Limits of Transformers on Compositionality by Dziri et al.
• Self-Consuming Generative Models Go MAD by S. Alemohammad et al.
• Gary Marcus' commentary blog on 60 Minutes interview with Geoffrey Hinton by G. Marcus
• Neural and Non-Neural AI, Reasoning, Transformers, and LSTMs interview with Juergen Schmidhuber by Machine Learning Street Talk

• Beyesian Networks by J. Pearl

How to Use This Page

Note: DO NOT SKIP READING THE BELOW TEXT

Papers under each section are ordered from most to least important, so start counting from the top.

[ x,y ]
x: necessary mandatory number of papers to be read – absolute minimum number.
y: the recommended number.
No number: Read all the papers listed.

It is your responsibility to ensure that you grasp the concepts covered in this class; the readings are my top choices (suggestions) for getting this done. However, if you are aware of alternative sources of treatment of the concepts covered in these you may prefer to read about them from your preferred source. If in doubt, ask me.

Assigned readings should be read before class.
If you do so you will already have some familiarity with the subject matter, which not only means you will remember it better but also that you can ask questions for clarification during the lecture and partially steer its direction.

Reading the papers after class is less effective.

You are expected to read all of the papers assigned in this course, at least 2-3 papers per week (4 recommended). Keep at it and you'll be fine!

Warning: Do not attempt to read papers during the group sessions as this is the absolute worst way to cover this material if you truly are interested in learning (you may of course have it open for reference).

Reading the assigned readings not at all should generally be avoided.

As you read papers from each of the following categories I want you ask yourself a few questions:

• For each paper in each category X, ask yourself:
  • What is X?
  • How does the human mind do X?
  • Do current computers do X?
  • …and …
  • Do we need (to replicate or capture) what the human mind does to achieve X to create a machine that rivals the ability of humans to do X?

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.

EOF