mlsanityData to 7 Oct 2026

Model of models -- Part 1

arXiv 2308.04600 ↗First posted 8 Aug 2023cs.AI cs.LO cs.SCcited by 0

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Abstract (arXiv)

This paper proposes a new cognitive model, acting as the main component of an AGI agent. The model is introduced in its mature intelligence state, and as an extension of previous models, DENN, and especially AKREM, by including operational models (frames/classes) and will. This model's core assumption is that cognition is about operating on accumulated knowledge, with the guidance of an appropriate will. Also, we assume that the actions, part of knowledge, are learning to be aligned with will, during the evolution phase that precedes the mature intelligence state. In addition, this model is mainly based on the duality principle in every known intelligent aspect, such as exhibiting both top-down and bottom-up model learning, generalization verse specialization, and more. Furthermore, a holistic approach is advocated for AGI designing, and cognition under constraints or efficiency is proposed, in the form of reusability and simplicity. Finally, reaching this mature state is described via a cognitive evolution from infancy to adulthood, utilizing a consolidation principle. The final product of this cognitive model is a dynamic operational memory of models and instances. Lastly, some examples and preliminary ideas for the evolution phase to reach the mature state are presented.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 19 of its 138 references that are in the map. First posted on arXiv 8 Aug 2023.

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19 of 138 references.

References · 19

  1. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,302
  2. 2022PaLM: Scaling Language Modeling with Pathwayscited by 5,304
  3. 2020CodeBERT: A Pre-Trained Model for Programming and Natural Languagescited by 1,286
  4. 2019Attention is not Explanationcited by 916
  5. 2019The Neuro-Symbolic Concept Learner: Interpreting Scenes, Words, and Sentences From Natural Supervisioncited by 369
  6. 2019Deep Learning for Anomaly Detection: A Surveycited by 357
  7. 2018code2seq: Generating Sequences from Structured Representations of Codecited by 177
  8. 2022Evolution through Large Modelscited by 131
  9. 2023Deductive Verification of Chain-of-Thought Reasoningcited by 122
  10. 2022Scene Graph Generation: A Comprehensive Surveycited by 73
  11. 2022Parsel: Algorithmic Reasoning with Language Models by Composing Decompositionscited by 62
  12. 2018Structured Neural Summarizationcited by 53
7 more
  1. 2023Introduction to Latent Variable Energy-Based Models: A Path Towards Autonomous Machine Intelligencecited by 52
  2. 2023Memory Augmented Large Language Models are Computationally Universalcited by 45
  3. 2022Neural Networks are Decision Treescited by 16
  4. 2020Visual Relationship Detection using Scene Graphs: A Surveycited by 8
  5. 2020Autoencoderscited by 2
  6. 2022A General Search-based Framework for Generating Textual Counterfactual Explanationscited by 2
  7. 2017Using NLU in Context for Question Answering: Improving on Facebook's bAbI Taskscited by 1

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