# What the F*ck Is Artificial General Intelligence?

arXiv: https://arxiv.org/abs/2503.23923 · HTML: https://mlsanity.com/paper/2503.23923/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 25 of its 98 references that are in the map.
- First posted on arXiv 31 Mar 2025.

## Builds on (most cited first, 25 of 25)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Scaling Laws for Neural Language Models](https://mlsanity.com/paper/2001.08361/) (2020)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Energy and Policy Considerations for Deep Learning in NLP](https://mlsanity.com/paper/1906.02243/) (2019)
- [Neural-Symbolic Computing: An Effective Methodology for Principled Integration of Machine Learning and Reasoning](https://mlsanity.com/paper/1905.06088/) (2019)
- [Towards Deep Symbolic Reinforcement Learning](https://mlsanity.com/paper/1609.05518/) (2016)
- [Bad Universal Priors and Notions of Optimality](https://mlsanity.com/paper/1510.04931/) (2015)
- [There's Plenty of Room Right Here: Biological Systems as Evolved, Overloaded, Multi-scale Machines](https://mlsanity.com/paper/2212.10675/) (2022)
- [The Optimal Choice of Hypothesis Is the Weakest, Not the Shortest](https://mlsanity.com/paper/2301.12987/) (2023)
- [Generative AI vs. AGI: The Cognitive Strengths and Weaknesses of Modern LLMs](https://mlsanity.com/paper/2309.10371/) (2023)
- [Emergent Causality and the Foundation of Consciousness](https://mlsanity.com/paper/2302.03189/) (2023)
- [OpenCog Hyperon: A Framework for AGI at the Human Level and Beyond](https://mlsanity.com/paper/2310.18318/) (2023)
- [The General Theory of General Intelligence: A Pragmatic Patternist Perspective](https://mlsanity.com/paper/2103.15100/) (2021)
- [Philosophical Specification of Empathetic Ethical Artificial Intelligence](https://mlsanity.com/paper/2107.10715/) (2021)
- [Computational Dualism and Objective Superintelligence](https://mlsanity.com/paper/2302.00843/) (2023)
- [Compression, The Fermi Paradox and Artificial Super-Intelligence](https://mlsanity.com/paper/2110.01835/) (2021)
- [Computable Artificial General Intelligence](https://mlsanity.com/paper/2205.10513/) (2022)
- [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)
- [Are Biological Systems More Intelligent Than Artificial Intelligence?](https://mlsanity.com/paper/2405.02325/) (2024)
- [Position: Stop Acting Like Language Model Agents Are Normal Agents](https://mlsanity.com/paper/2502.10420/) (2025)
- [The Artificial Scientist: Logicist, Emergentist, and Universalist Approaches to Artificial General Intelligence](https://mlsanity.com/paper/2110.01831/) (2021)
- [A Circuit Complexity Formulation of Algorithmic Information Theory](https://mlsanity.com/paper/2306.14087/) (2023)
- [Parallel Verification of Natural Deduction Proof Graphs](https://mlsanity.com/paper/2311.10440/) (2023)
- [ActPC-Chem: Discrete Active Predictive Coding for Goal-Guided Algorithmic Chemistry as a Potential Cognitive Kernel for Hyperon & PRIMUS-Based AGI](https://mlsanity.com/paper/2412.16547/) (2024)

## Abstract (arXiv)

Artificial general intelligence (AGI) is an established field of research. Yet some have questioned if the term still has meaning. AGI has been subject to so much hype and speculation it has become something of a Rorschach test. Melanie Mitchell argues the debate will only be settled through long term, scientific investigation. To that end here is a short, accessible and provocative overview of AGI. I compare definitions of intelligence, settling on intelligence in terms of adaptation and AGI as an artificial scientist. Taking my cue from Sutton's Bitter Lesson I describe two foundational tools used to build adaptive systems: search and approximation. I compare pros, cons, hybrids and architectures like o3, AlphaGo, AERA, NARS and Hyperon. I then discuss overall meta-approaches to making systems behave more intelligently. I divide them into scale-maxing, simp-maxing, w-maxing based on the Bitter Lesson, Ockham's and Bennett's Razors. These maximise resources, simplicity of form, and the weakness of constraints on functionality. I discuss examples including AIXI, the free energy principle and The Embiggening of language models. I conclude that though scale-maxed approximation dominates, AGI will be a fusion of tools and meta-approaches. The Embiggening was enabled by improvements in hardware. Now the bottlenecks are sample and energy efficiency.