# CommAI: Evaluating the first steps towards a useful general AI

arXiv: https://arxiv.org/abs/1701.08954 · HTML: https://mlsanity.com/paper/1701.08954/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Jan 2017.

## Built on by (most cited first, 3 of 3)

- [CoLoTa: A Dataset for Entity-based Commonsense Reasoning over Long-Tail Knowledge](https://mlsanity.com/paper/2504.14462/) (2025)
- [Assessing Language Disorders using Artificial Intelligence: a Paradigm Shift](https://mlsanity.com/paper/2305.20046/) (2023)
- [CommonWhy: A Dataset for Evaluating Entity-Based Causal Commonsense Reasoning in Large Language Models](https://mlsanity.com/paper/2605.12918/) (2026)

## Abstract (arXiv)

With machine learning successfully applied to new daunting problems almost every day, general AI starts looking like an attainable goal. However, most current research focuses instead on important but narrow applications, such as image classification or machine translation. We believe this to be largely due to the lack of objective ways to measure progress towards broad machine intelligence. In order to fill this gap, we propose here a set of concrete desiderata for general AI, together with a platform to test machines on how well they satisfy such desiderata, while keeping all further complexities to a minimum.