CommAI: Evaluating the first steps towards a useful general AI
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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.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). First posted on arXiv 31 Jan 2017.
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Citing papers per year: 2023: 1; 2024: 0; 2025: 1; 2026: 1.
3 papers.
- 2025CoLoTa: A Dataset for Entity-based Commonsense Reasoning over Long-Tail Knowledgecited by 1
- 2023Assessing Language Disorders using Artificial Intelligence: a Paradigm Shiftcited by 0
- 2026CommonWhy: A Dataset for Evaluating Entity-Based Causal Commonsense Reasoning in Large Language Modelscited by 0
- 2026CommonWhy: A Dataset for Evaluating Entity-Based Causal Commonsense Reasoning in Large Language Models13 May 2026
- 2025CoLoTa: A Dataset for Entity-based Commonsense Reasoning over Long-Tail Knowledge20 Apr 2025
- 2023Assessing Language Disorders using Artificial Intelligence: a Paradigm Shift31 May 2023