# Computable Artificial General Intelligence

arXiv: https://arxiv.org/abs/2205.10513 · HTML: https://mlsanity.com/paper/2205.10513/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 81 references that are in the map.
- First posted on arXiv 21 May 2022.

## Builds on (most cited first, 10 of 10)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Symbol Emergence in Cognitive Developmental Systems: a Survey](https://mlsanity.com/paper/1801.08829/) (2018)
- [Bad Universal Priors and Notions of Optimality](https://mlsanity.com/paper/1510.04931/) (2015)
- [Symbol Emergence and The Solutions to Any Task](https://mlsanity.com/paper/2109.01281/) (2021)
- [Philosophical Specification of Empathetic Ethical Artificial Intelligence](https://mlsanity.com/paper/2107.10715/) (2021)
- [Compression, The Fermi Paradox and Artificial Super-Intelligence](https://mlsanity.com/paper/2110.01835/) (2021)
- [The Archimedean trap: Why traditional reinforcement learning will probably not yield AGI](https://mlsanity.com/paper/2002.10221/) (2020)
- [The Artificial Scientist: Logicist, Emergentist, and Universalist Approaches to Artificial General Intelligence](https://mlsanity.com/paper/2110.01831/) (2021)
- [Intensional Artificial Intelligence: From Symbol Emergence to Explainable and Empathetic AI](https://mlsanity.com/paper/2104.11573/) (2021)
- [Cybernetics and the Future of Work](https://mlsanity.com/paper/2105.07870/) (2021)

## Built on by (most cited first, 6 of 6)

- [The Optimal Choice of Hypothesis Is the Weakest, Not the Shortest](https://mlsanity.com/paper/2301.12987/) (2023)
- [On the Computation of Meaning, Language Models and Incomprehensible Horrors](https://mlsanity.com/paper/2304.12686/) (2023)
- [Is Complexity an Illusion?](https://mlsanity.com/paper/2404.07227/) (2024)
- [Why Is Anything Conscious?](https://mlsanity.com/paper/2409.14545/) (2024)
- [What the F*ck Is Artificial General Intelligence?](https://mlsanity.com/paper/2503.23923/) (2025)
- [A Mind Cannot Be Smeared Across Time](https://mlsanity.com/paper/2601.11620/) (2026)

## Abstract (arXiv)

Artificial general intelligence (AGI) may herald our extinction, according to AI safety research. Yet claims regarding AGI must rely upon mathematical formalisms -- theoretical agents we may analyse or attempt to build. AIXI appears to be the only such formalism supported by proof that its behaviour is optimal, a consequence of its use of compression as a proxy for intelligence. Unfortunately, AIXI is incomputable and claims regarding its behaviour highly subjective. We argue that this is because AIXI formalises cognition as taking place in isolation from the environment in which goals are pursued (Cartesian dualism). We propose an alternative, supported by proof and experiment, which overcomes these problems. Integrating research from cognitive science with AI, we formalise an enactive model of learning and reasoning to address the problem of subjectivity. This allows us to formulate a different proxy for intelligence, called weakness, which addresses the problem of incomputability. We prove optimal behaviour is attained when weakness is maximised. This proof is supplemented by experimental results comparing weakness and description length (the closest analogue to compression possible without reintroducing subjectivity). Weakness outperforms description length, suggesting it is a better proxy. Furthermore we show that, if cognition is enactive, then minimisation of description length is neither necessary nor sufficient to attain optimal performance, undermining the notion that compression is closely related to intelligence. However, there remain open questions regarding the implementation of scale-able AGI. In the short term, these results may be best utilised to improve the performance of existing systems. For example, our results explain why Deepmind's Apperception Engine is able to generalise effectively, and how to replicate that performance by maximising weakness.