Center for Analysis and Design of Intelligent Agents

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[[/public:t-713-mers:mers-23:main|DCS-T-713-MERS-2023 Main]]


===== T-713-MERS-2023 Readings & Study Material =====



[[/public:t-713-mers:mers-23:readings?#readings_readme|Readings README]] (Do not skip!)
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===== INTELLIGENCE: THE PHENOMENON [5,6] =====

//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?//

  * [[http://en.wikipedia.org/wiki/General_intelligence_factor| The g factor]] on Wikipedia
  * [[http://en.wikipedia.org/wiki/Theory_of_multiple_intelligences| Multiple theory of intelligence]] on Wikipedia
  * [[https://arxiv.org/pdf/0706.3639.pdf|A Collection of Definitions of Intelligence]] by Legg & Hutter
  * [[https://sciendo.com/article/10.2478/jagi-2019-0002|On Defining Artificial Intelligence]] by P. Wang
  * [[https://alumni.media.mit.edu/~kris/ftp/DCAULKR-JAGI-2020.pdf|Discretionarily Constrained Adaptation Under Insuficient Knowledge & Resources]] by K. R. Thórisson
  * [[http://consc.net/papers/computation.html|A Computational Foundation for the Study of Cognition]] by D. Chalmers
  * [[https://www.csee.umbc.edu/courses/471/papers/turing.pdf|Computing Machinery and Intelligence]] by A. M. Turing
  * [[https://www.psychologytoday.com/intl/blog/consciousness-and-the-brain/201202/what-is-thought|What is Thought?]] by E. Morsella
  * [[https://en.wikipedia.org/wiki/Thought|Thought]] on Wikipedia.

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===== WORLDS & AGENTS =====
//All intelligence exists in a world. \\ What kind of world is the natural world? //

  * [[http://cadia.ru.is/wiki/_media/public:t-720-atai:draftofkrthorissonsforthcomingbookonintelligence.pdf|About Worlds - Excerpt from a draft of a
forthcoming book on Intelligence]] by K.R.Thórisson
  * [[https://bigthink.com/starts-with-a-bang/universe-made-pure-mathematics/|No, The Universe Isn't Made of Pure Mathematics]] by E. Siegel
  * [[https://bigthink.com/starts-with-a-bang/never-tell-scientist-just-a-theory/|Never Tell a Scientist It's "Just A Theory"]] by E. Siegel
  * [[https://en.wikipedia.org/wiki/Intelligent_agent|Intelligent Agents]] on Wikipedia

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===== EMPIRICAL SCIENCE [4,5]=====
// Information comes from measurements. \\ Knowledge comes from information. \\ //

  * [[https://en.wikipedia.org/wiki/Empirical_evidence|Empirical Evidence]] on Wikipedia
  * [[https://sciencing.com/what-are-comparative-experiments-12731287.html|What Are Comparative Experiments?]] by D. Verial
  * [[https://en.wikipedia.org/wiki/Causality| Causation & causality]] on Wikipedia
  * [[https://www.informationphilosopher.com/freedom/causality.html|Causality]] on The Information Philosopher
  * [[https://en.wikipedia.org/wiki/Empirical_evidence|Empirical Evidence]] on Wikipedia

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

   * [[https://ftp.cs.ucla.edu/pub/stat_ser/r284-reprint.pdf|Bayesianism and Causality, or, Why I am Only a Half-Beyesian]] by J. Pearl
   * [[https://www.informationphilosopher.com/freedom/adequate_determinism.html|Adequate Determinism]] on the Information Philosopher. 

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===== REASONING: THE PHENOMENON =====

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

  * [[https://en.wikipedia.org/wiki/Reason| Reasoning]] on Wikipedia
  * {{/public:cognitive_logic_versus_mathematical_logic_-_pei_wang.pdf|Cognitive logic versus mathematical logic}} by P. Wang
  * {{/public:towardalogicofeverydayreasoning-peiwang.pdf|Toward a logic of everyday reasoning}} by P. Wang
  * [[https://core.ac.uk/download/pdf/82164736.pdf|The limitation of Bayesiansm]] by P. Wang. 



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===== NON-AXIOMATIC REASONING [5,5]=====

  * [[https://sciendo.com/article/10.2478/v10229-011-0021-5|Solving a problem with or without a program]] by P. Wang
  * [[https://www.academia.edu/64812965/Abduction_in_Non_Axiomatic_Logic|Abduction in non-axiomatic logic]] by P. Wang
  * [[https://www.frontiersin.org/articles/10.3389/frai.2022.806403/full|A Model of Unified Perception and Cognition]] by P. Wang et al. 
  * [[http://matt.colorado.edu/teaching/highcog/readings/s96.pdf|Empirical case for two systems of reasoning]] by S. Sloman
  * [[https://www.youtube.com/watch?v=vOVQmUYQG1M|AGI NARS Tutorial]] by P. Wang & P. Hammer
  * [[https://www.academia.edu/64813003/A_New_Approach_for_Induction_From_a_Non_Axiomatic_Logical_Point_of_View|A New Approach for Induction: From a Non-Axiomatic Point of View]] by P. Wang. 
  * [[https://www.researchgate.net/publication/221439054_The_Logic_of_Categorization|The logic of categorization]] by P. Wang

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===== 'COMMON SENSE REASONING' [1,4]=====

  * [[http://alumni.media.mit.edu/~kris/ftp/AGI17_Understanding&CommonSense.pdf|Understanding & Common Sense]] by K.R. Thórisson
  * [[http://alumni.media.mit.edu/~kris/ftp/AGI17-UUW-DoMachinesUnderstand.pdf|Do Machines Understand?]] by K.R. Thórisson
  * [[https://paperswithcode.com/sota/question-answering-on-truthfulqa?metric=%25%20true|Question Answering on Truthful Q&A]] on Papersiwthcode.com
  * [[https://paperswithcode.com/sota/common-sense-reasoning-on-commonsenseqa|Common Sense Reasoning]] on Paperswithcode.com

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===== NON-AXIOMATIC REASONING SYSTEM (NARS) =====

  * [[https://cis.temple.edu/tagit/blogs/NARS-in-a-nutshell.html|NARS in a Nutshell]] by Tangrui (Tory) Li
  * [[https://cis.temple.edu/tagit/blogs/Non-axiomatic-truth-values.html|Non-axiomatic truth-values]] by Tangrui (Tory) Li
  * [[https://cis.temple.edu/tagit/blogs/Attention.html|Attention]] by Tangrui (Tory) Li
  * {{/public:t-713-mers:mers-23:nal_-_1_en_v.1.1.pdf|Course Material for NAL-1 (v.1.1)}} by Tangrui (Tory) Li

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===== LEARNING & KNOWLEDGE   ======

  * [[http://alumni.media.mit.edu/~kris/ftp/AGI16_growing_recursive_self-improvers.pdf|Growing Recursive Self-Improvers]] by B. Steunebrink et al.


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===== SUPPORTING TOPICS  ======

====Artificial Neural Networks====
  * {{/public:chatgptcantreason.pdf|ChatGPT Can't Reason}} by K. Arkoudas
  * [[https://arxiv.org/pdf/2305.18654.pdf|Limits of Transformers on Compositionality]] by Dziri et al.
  * [[https://arxiv.org/pdf/2307.01850.pdf|Self-Consuming Generative Models Go MAD]] by S. Alemohammad et al.
  * [[https://garymarcus.substack.com/p/what-was-60-minutes-thinking-in-that?r=8tdk6&utm_campaign=post&utm_medium=web|Gary Marcus' commentary blog on 60 Minutes interview with Geoffrey Hinton]] by G. Marcus
  * [[https://www.youtube.com/watch?v=DP454c1K_vQ|Neural and Non-Neural AI, Reasoning, Transformers, and LSTMs]] interview with Juergen Schmidhuber by Machine Learning Street Talk

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==== Probability ====
  * [[https://ftp.cs.ucla.edu/pub/stat_ser/R246.pdf|Beyesian Networks]] by J. Pearl

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=====Readings README=====
**How to Use This Page**
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//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.

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**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. 

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//EOF//