# A Berkeley View of Systems Challenges for AI

arXiv: https://arxiv.org/abs/1712.05855 · HTML: https://mlsanity.com/paper/1712.05855/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Dec 2017.

## Built on by (most cited first, 15 of 15)

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- [Shifting Capsule Networks from the Cloud to the Deep Edge](https://mlsanity.com/paper/2110.02911/) (2021)
- [Hiding Behind Backdoors: Self-Obfuscation Against Generative Models](https://mlsanity.com/paper/2201.09774/) (2022)
- [Fairness and Explainability in Automatic Decision-Making Systems. A challenge for computer science and law](https://mlsanity.com/paper/2206.03226/) (2022)
- [The Contribution of XAI for the Safe Development and Certification of AI: An Expert-Based Analysis](https://mlsanity.com/paper/2408.02379/) (2024)
- [Learning in Deep Factor Graphs with Gaussian Belief Propagation](https://mlsanity.com/paper/2311.14649/) (2023)
- [Robust Distributed Bayesian Learning with Stragglers via Consensus Monte Carlo](https://mlsanity.com/paper/2112.09794/) (2021)
- [David and Goliath: An Empirical Evaluation of Attacks and Defenses for QNNs at the Deep Edge](https://mlsanity.com/paper/2404.05688/) (2024)
- [A Scalable Approach for Safe and Robust Learning via Lipschitz-Constrained Networks](https://mlsanity.com/paper/2506.23977/) (2025)

## Abstract (arXiv)

With the increasing commoditization of computer vision, speech recognition and machine translation systems and the widespread deployment of learning-based back-end technologies such as digital advertising and intelligent infrastructures, AI (Artificial Intelligence) has moved from research labs to production. These changes have been made possible by unprecedented levels of data and computation, by methodological advances in machine learning, by innovations in systems software and architectures, and by the broad accessibility of these technologies. The next generation of AI systems promises to accelerate these developments and increasingly impact our lives via frequent interactions and making (often mission-critical) decisions on our behalf, often in highly personalized contexts. Realizing this promise, however, raises daunting challenges. In particular, we need AI systems that make timely and safe decisions in unpredictable environments, that are robust against sophisticated adversaries, and that can process ever increasing amounts of data across organizations and individuals without compromising confidentiality. These challenges will be exacerbated by the end of the Moore's Law, which will constrain the amount of data these technologies can store and process. In this paper, we propose several open research directions in systems, architectures, and security that can address these challenges and help unlock AI's potential to improve lives and society.