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T-720-ATAI-2019 Main

T-720-ATAI-2019 Readings & Study Material


This material is generally ordered by specificity (both within and between headings), starting with the general topic of what intelligence is, progressing towards good old-fashioned AI (aka constructionist AI or GOFAI) and then onward towards artificial general intelligence (AGI). Within each topic the papers are ordered by importance, the most important first.

The recommended minimum number of papers to be read in each category is listed in brackets [ a,b ] after the title, where a refers to the necessary mandatory number of papers to be read, and b refers to the recommended absolute minimum number (papers are ordered from most to least important, so start counting from the top). If no number is given you should read all the papers listed. The section marked “Prerequisites” are readings on the basics: Things you should already know. If you even have the slightest reason to think that some content in these is not already under your belt (e.g. you have neither recently taken an introductory course on AI nor a single psychology or philosophy course on intelligence) you really should read them (they are a quick read, for the most part).

This means you are expected to read well over 50 papers in this course, so that comes out to at least 3-4 papers per week (5 recommended). Keep at it and you'll be fine!

Note: Assigned readings should be read before class. There is a simple reason for that: You will 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. Warning: Do not attempt to read them during class - this is absolutely the worst way to cover this material (but of course you may 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 discrepacies 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.



Key Papers

Definitions of Artificial Intelligence


Key Papers [ 3,5 ]

Constructionist Systems & Methodologies


Introductory Material - Constructionist AI [ 2,3 ]

Limitations of Constructionist AI [ 2,4 ]

The Holy Grail of AI: Generality

Overview of Artificial General Intelligence [ 4,5 ]

Requirements for AGI [ 3,5 ]

Related to: Methodology, Cognitive Architectures

Thought, Cognition, Cognitive Process/es

Related to: Cognitive Architecture, Intelligence, Understanding, AI

Understanding [ 3,4 ]

Related to: Thought, Reasoning

Situatedness, Embodiment [ 1,2 ]

Related to: Symbols, Meaning, Autonomy, Bootstrapping

Autonomy [ 3,4 ]

Resource Control: Attention / Self-Control / Integrated Cognitive Control [ 4,6 ]

Self-Programming [ 4,5 ]

Related to: Reasoning, Learning, Bootstrapping

Reasoning [ 4,6 ]

Related to: Thought, Cognitive Architecture, Intelligence

(AGI) Bootstrapping / Seed A(G)I / Seed Programming [ 2,4 ]


Related to: Resource control, Attention, Reasoning

Artificial Pedagogy [ 2,4 ]

Related to: Learning, Bootstrapping

AGI Methodology [ 5,6 ]

Implemented AGI-Aspiring Systems

NARS [ 4,5 ]

AERA [ 5,5 ]

Sigma [ 0,1 ]

Open Cog [ 0,2 ]

Other Such Systems [ 0,3 ]

  • Franklin, S. (2007). (LIDA) A Foundational Architecture for Artificial General Intelligence. Advances in Artificial General Intelligence: Concepts, Architectures and Algorithms. IOS Press, Amsterdam, The Netherlands, The Netherlands, pp. 36-54. PDF
  • Anderson, J.R. & Schunn, C.D. (2000). Implications of the ACT-R learning theory: No magic bullets. Advances in instructional psychology. 5:1-34. Lawrence Erlbaum | PDF
  • Laird, J.E.; Newell, A. & Rosenbloom, P.S. (1987). SOAR: An architecture for general intelligence. Artificial Intelligence, Volume 33, Issue 1, Pages 1-64 PDF
  • Snaider, J; McCall, R. & Franklin, S. (2011). The LIDA framework as a general tool for AGI. Artificial General Intelligence, Lecture Notes in Computer Science. 2011. Volume 6830/2011. pp. 133-142 PDF

Evaluation: Worlds, Tasks, Environments [ 3,6 ]

Foundational Topics


Symbols & Meaning [3,5]

Self-Organization & Emergence [1,2]

(Phenomenal) Consciousness [0,2]

Societal Impact & Ethics [0,2]

Additional Readings & Study Material

Reinforcement Learning [ 0,2 ]

Deep Learning [ 0,1 ]

Other [ 0,3 ]

Additional Sources

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