# Towards A Measure Of General Machine Intelligence

arXiv: https://arxiv.org/abs/2109.12075 · HTML: https://mlsanity.com/paper/2109.12075/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 66 references that are in the map.
- First posted on arXiv 24 Sep 2021.

## Builds on (most cited first, 18 of 18)

- [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)
- [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021)
- [GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding](https://mlsanity.com/paper/1804.07461/) (2018)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [Program Synthesis with Large Language Models](https://mlsanity.com/paper/2108.07732/) (2021)
- [The Pile: An 800GB Dataset of Diverse Text for Language Modeling](https://mlsanity.com/paper/2101.00027/) (2020)
- [CodeBLEU: a Method for Automatic Evaluation of Code Synthesis](https://mlsanity.com/paper/2009.10297/) (2020)
- [Are we done with ImageNet?](https://mlsanity.com/paper/2006.07159/) (2020)
- [Unsupervised Translation of Programming Languages](https://mlsanity.com/paper/2006.03511/) (2020)
- [SPoC: Search-based Pseudocode to Code](https://mlsanity.com/paper/1906.04908/) (2019)
- [DreamCoder: Growing generalizable, interpretable knowledge with wake-sleep Bayesian program learning](https://mlsanity.com/paper/2006.08381/) (2020)
- [Neuro-Symbolic Program Synthesis](https://mlsanity.com/paper/1611.01855/) (2016)
- [Leveraging Grammar and Reinforcement Learning for Neural Program Synthesis](https://mlsanity.com/paper/1805.04276/) (2018)
- [Learning to Infer Graphics Programs from Hand-Drawn Images](https://mlsanity.com/paper/1707.09627/) (2017)
- [AGENT: A Benchmark for Core Psychological Reasoning](https://mlsanity.com/paper/2102.12321/) (2021)
- [TF-Coder: Program Synthesis for Tensor Manipulations](https://mlsanity.com/paper/2003.09040/) (2020)
- [The Lovelace 2.0 Test of Artificial Creativity and Intelligence](https://mlsanity.com/paper/1410.6142/) (2014)

## Built on by (most cited first, 2 of 2)

- [Rethinking the Evaluating Framework for Natural Language Understanding in AI Systems: Language Acquisition as a Core for Future Metrics](https://mlsanity.com/paper/2309.11981/) (2023)
- [A Vision Toward Energy-Efficient Domain-Specific Artificial Intelligence Models and Agents](https://mlsanity.com/paper/2510.22052/) (2025)

## Abstract (arXiv)

To build general-purpose artificial intelligence systems that can deal with unknown variables across unknown domains, we need benchmarks that measure how well these systems perform on tasks they have never seen before. A prerequisite for this is a measure of a task's generalization difficulty, or how dissimilar it is from the system's prior knowledge and experience. If the skill of an intelligence system in a particular domain is defined as it's ability to consistently generate a set of instructions (or programs) to solve tasks in that domain, current benchmarks do not quantitatively measure the efficiency of acquiring new skills, making it possible to brute-force skill acquisition by training with unlimited amounts of data and compute power. With this in mind, we first propose a common language of instruction, a programming language that allows the expression of programs in the form of directed acyclic graphs across a wide variety of real-world domains and computing platforms. Using programs generated in this language, we demonstrate a match-based method to both score performance and calculate the generalization difficulty of any given set of tasks. We use these to define a numeric benchmark called the generalization index, or the g-index, to measure and compare the skill-acquisition efficiency of any intelligence system on a set of real-world tasks. Finally, we evaluate the suitability of some well-known models as general intelligence systems by calculating their g-index scores.