# A World-Self Model Towards Understanding Intelligence

arXiv: https://arxiv.org/abs/2203.13762 · HTML: https://mlsanity.com/paper/2203.13762/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 63 references that are in the map.
- First posted on arXiv 25 Mar 2022.

## Builds on (most cited first, 23 of 23)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [PIQA: Reasoning about Physical Commonsense in Natural Language](https://mlsanity.com/paper/1911.11641/) (2019)
- [Rich feature hierarchies for accurate object detection and semantic segmentation](https://mlsanity.com/paper/1311.2524/) (2013)
- [CommonsenseQA: A Question Answering Challenge Targeting Commonsense Knowledge](https://mlsanity.com/paper/1811.00937/) (2018)
- [Image Super-Resolution Using Deep Convolutional Networks](https://mlsanity.com/paper/1501.00092/) (2014)
- [Image Transformer](https://mlsanity.com/paper/1802.05751/) (2018)
- [Neurosymbolic AI: The 3rd Wave](https://mlsanity.com/paper/2012.05876/) (2020)
- [Towards Causal Representation Learning](https://mlsanity.com/paper/2102.11107/) (2021)
- [The Next Decade in AI: Four Steps Towards Robust Artificial Intelligence](https://mlsanity.com/paper/2002.06177/) (2020)
- [Real Image Denoising with Feature Attention](https://mlsanity.com/paper/1904.07396/) (2019)
- [FastMoE: A Fast Mixture-of-Expert Training System](https://mlsanity.com/paper/2103.13262/) (2021)
- [Rediscovering orbital mechanics with machine learning](https://mlsanity.com/paper/2202.02306/) (2022)
- [Thinking Fast and Slow: Optimization Decomposition Across Timescales](https://mlsanity.com/paper/1704.07785/) (2017)
- [Dynamic Visual Reasoning by Learning Differentiable Physics Models from Video and Language](https://mlsanity.com/paper/2110.15358/) (2021)
- [Neuro-Symbolic Artificial Intelligence: Current Trends](https://mlsanity.com/paper/2105.05330/) (2021)
- [Faster and Safer Training by Embedding High-Level Knowledge into Deep Reinforcement Learning](https://mlsanity.com/paper/1910.09986/) (2019)
- [Neurosymbolic Systems of Perception & Cognition: The Role of Attention](https://mlsanity.com/paper/2112.01603/) (2021)

## Built on by (most cited first, 1 of 1)

- [TimeToM: Temporal Space is the Key to Unlocking the Door of Large Language Models' Theory-of-Mind](https://mlsanity.com/paper/2407.01455/) (2024)

## Abstract (arXiv)

The symbolism, connectionism and behaviorism approaches of artificial intelligence have achieved a lot of successes in various tasks, while we still do not have a clear definition of "intelligence" with enough consensus in the community (although there are over 70 different "versions" of definitions). The nature of intelligence is still in darkness. In this work we do not take any of these three traditional approaches, instead we try to identify certain fundamental aspects of the nature of intelligence, and construct a mathematical model to represent and potentially reproduce these fundamental aspects. We first stress the importance of defining the scope of discussion and granularity of investigation. We carefully compare human and artificial intelligence, and qualitatively demonstrate an information abstraction process, which we propose to be the key to connect perception and cognition. We then present the broader idea of "concept", separate the idea of self model out of the world model, and construct a new model called world-self model (WSM). We show the mechanisms of creating and connecting concepts, and the flow of how the WSM receives, processes and outputs information with respect to an arbitrary type of problem to solve. We also consider and discuss the potential computer implementation issues of the proposed theoretical framework, and finally we propose a unified general framework of intelligence based on WSM.