User Tools

Site Tools


public:t-720-atai:atai-22:readings

DCS-T-720-ATAI-2022 Main

T-720-ATAI-2022 Readings & Study Material

Readings README (Do not skip!)


INTELLIGENCE: THE PHENOMENON


The Human Animal [ 9,10 ]

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?

Other Kinds of Animals

Are animals other than humans also intelligent?
Note: You are encouraged to find other material on this topic (please let instructor know if you find interesting things).

Requirements for General Autonomous Intelligence [ 4,6 ]

When engineers make an artifact, like a bridge or a space rocket, they start by listing the artifact's requirements. This way, for any proposed implementation, they can check their progress by comparing the performance of a prototype to these. The below papers consider what are necessary and sufficient requirements for a machine with real intelligence. (Therefore, these speak to defining what 'intelligent systems' in fact really means.)

AI: The Research Field

Artificial intelligence (AI) started as a research field. It still is. Just like research results in physics are useful for engineering, results in AI are useful for industry. AI is still in formation, much like computer science. It is a knowledge-generating enterprise funded by the public through universities and competitive research grants. Applications of AI are funded by companies and through various other means (including competitive grants for applied research). The knowledge generated in AI research is in part determined by the nature of the enterprise - how it's organized, who are the influencers, what are low-hanging fruit, etc.

Part I: The Basics

Part II: GMI




WORLDS, TASKS & ENVIRONMENTS


Worlds [ 4,4 ]

Causation & Causal Relations

Evaluation: Approaches, Tools, Techniques [ 3,5 ]




LEARNING


Learning: General Overview [ 6,7 ]

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

Artificial Pedagogy [ 2,4 ]

METHODOLOGY & THEORY


A(G)I Theories [ 2,3 ]

Part I: GOFAI Approaches

Part II: GMI Methodology [ 5,8 ]

CONTROL & SYSTEMS


Control & Systems [ 4,6 ]

Models

Generality

Autonomy [ 3,3 ]

Resource Management: Attention, Self-Control, Integrated Cognitive Control [ 3,5 ]

UNDERSTANDING & KNOWLEDGE REPRESENTATION


Symbols & Meaning [3,5]

Semantic & Operational Closure [ 1,2 ]

About Understanding [ 3,5 ]

Reasoning [ 4,5 ]

Situatedness, Embodiment

IMPLEMENTED AGI-ASPIRING SYSTEMS


NARS [ 4,5 ]

AERA [ 3,5 ]

Drescher's Constructivist Schema System

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





FOUNDATIONAL TOPICS


Prerequisites

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 ]


Other Sources

Reinforcement Learning





Readings README

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; the readings are my top choices 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.

You are expected to read a lot of papers in this course, at least 3-4 papers per week (5 recommended). Keep at it and you'll be fine!

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.

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 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

/var/www/ailab/WWW/wiki/data/pages/public/t-720-atai/atai-22/readings.txt · Last modified: 2022/11/14 13:03 by thorisson