# A brief history of AI: how to prevent another winter (a critical review)

arXiv: https://arxiv.org/abs/2109.01517 · HTML: https://mlsanity.com/paper/2109.01517/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 109 references that are in the map.
- First posted on arXiv 3 Sep 2021.

## Builds on (most cited first, 5 of 5)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI](https://mlsanity.com/paper/1910.10045/) (2019)
- [Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans](https://mlsanity.com/paper/2008.06388/) (2020)
- [Troubling Trends in Machine Learning Scholarship](https://mlsanity.com/paper/1807.03341/) (2018)
- [The AI Index 2021 Annual Report](https://mlsanity.com/paper/2103.06312/) (2021)

## Built on by (most cited first, 8 of 8)

- [An Overview Of Temporal Commonsense Reasoning and Acquisition](https://mlsanity.com/paper/2308.00002/) (2023)
- [Large Language Models for Agent-Based Modelling: Current and possible uses across the modelling cycle](https://mlsanity.com/paper/2507.05723/) (2025)
- [PReLU: Yet Another Single-Layer Solution to the XOR Problem](https://mlsanity.com/paper/2409.10821/) (2024)
- [AI-Based Detection, Classification and Prediction/Prognosis in Medical Imaging: Towards Radiophenomics](https://mlsanity.com/paper/2110.10332/) (2021)
- [DeepDiveAI: Identifying AI Related Documents in Large Scale Literature Data](https://mlsanity.com/paper/2408.12871/) (2024)
- [What is Implementation Science; and Why It Matters for Bridging the Artificial Intelligence Innovation-to-Application Gap in Medical Imaging](https://mlsanity.com/paper/2510.13006/) (2025)
- [Semantic Web and Software Agents -- A Forgotten Wave of Artificial Intelligence?](https://mlsanity.com/paper/2503.20793/) (2025)
- [Rethinking Artificial Intelligence in Medical Imaging: Assumptions, Reality, and Reframing](https://mlsanity.com/paper/2607.27428/) (2026)

## Abstract (arXiv)

The field of artificial intelligence (AI), regarded as one of the most enigmatic areas of science, has witnessed exponential growth in the past decade including a remarkably wide array of applications, having already impacted our everyday lives. Advances in computing power and the design of sophisticated AI algorithms have enabled computers to outperform humans in a variety of tasks, especially in the areas of computer vision and speech recognition. Yet, AI's path has never been smooth, having essentially fallen apart twice in its lifetime ('winters' of AI), both after periods of popular success ('summers' of AI). We provide a brief rundown of AI's evolution over the course of decades, highlighting its crucial moments and major turning points from inception to the present. In doing so, we attempt to learn, anticipate the future, and discuss what steps may be taken to prevent another 'winter'.