# A Case for Specialisation in Non-Human Entities

arXiv: https://arxiv.org/abs/2503.04742 · HTML: https://mlsanity.com/paper/2503.04742/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 23 of its 132 references that are in the map.
- First posted on arXiv 5 Feb 2025.

## Builds on (most cited first, 23 of 23)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
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- [Datasheets for Datasets](https://mlsanity.com/paper/1803.09010/) (2018)
- [Model Cards for Model Reporting](https://mlsanity.com/paper/1810.03993/) (2018)
- [The Hidden Vulnerability of Distributed Learning in Byzantium](https://mlsanity.com/paper/1802.07927/) (2018)
- [COLD-Attack: Jailbreaking LLMs with Stealthiness and Controllability](https://mlsanity.com/paper/2402.08679/) (2024)
- [Here Comes The AI Worm: Unleashing Zero-click Worms that Target GenAI-Powered Applications](https://mlsanity.com/paper/2403.02817/) (2024)
- [A Voting-Based System for Ethical Decision Making](https://mlsanity.com/paper/1709.06692/) (2017)
- [Collaborative Learning in the Jungle (Decentralized, Byzantine, Heterogeneous, Asynchronous and Nonconvex Learning)](https://mlsanity.com/paper/2008.00742/) (2020)
- [Adapting a Kidney Exchange Algorithm to Align with Human Values](https://mlsanity.com/paper/2005.09755/) (2020)
- [On the Impossible Safety of Large AI Models](https://mlsanity.com/paper/2209.15259/) (2022)
- [The Brussels Effect and Artificial Intelligence: How EU regulation will impact the global AI market](https://mlsanity.com/paper/2208.12645/) (2022)
- [Persia: An Open, Hybrid System Scaling Deep Learning-based Recommenders up to 100 Trillion Parameters](https://mlsanity.com/paper/2111.05897/) (2021)
- [An Equivalence Between Data Poisoning and Byzantine Gradient Attacks](https://mlsanity.com/paper/2202.08578/) (2022)
- [Scaling Laws Do Not Scale](https://mlsanity.com/paper/2307.03201/) (2023)
- [Tournesol: A quest for a large, secure and trustworthy database of reliable human judgments](https://mlsanity.com/paper/2107.07334/) (2021)
- [Bluesky and the AT Protocol: Usable Decentralized Social Media](https://mlsanity.com/paper/2402.03239/) (2024)
- [On Goodhart's law, with an application to value alignment](https://mlsanity.com/paper/2410.09638/) (2024)
- [Lower Bounds for Differential Privacy from Gaussian Width](https://mlsanity.com/paper/1612.02914/) (2016)
- [The poison of dimensionality](https://mlsanity.com/paper/2409.17328/) (2024)
- [Solidago: A Modular Collaborative Scoring Pipeline](https://mlsanity.com/paper/2211.01179/) (2022)
- [Google's Chrome Antitrust Paradox](https://mlsanity.com/paper/2406.11856/) (2024)

## Abstract (arXiv)

With the rise of large multi-modal AI models, fuelled by recent interest in large language models (LLMs), the notion of artificial general intelligence (AGI) went from being restricted to a fringe community, to dominate mainstream large AI development programs. In contrast, in this paper, we make a case for specialisation, by reviewing the pitfalls of generality and stressing the industrial value of specialised systems. Our contribution is threefold. First, we review the most widely accepted arguments against specialisation, and discuss how their relevance in the context of human labour is actually an argument for specialisation in the case of non human agents, be they algorithms or human organisations. Second, we propose four arguments in favor of specialisation, ranging from machine learning robustness, to computer security, social sciences and cultural evolution. Third, we finally make a case for specification, discuss how the machine learning approach to AI has so far failed to catch up with good practices from safety-engineering and formal verification of software, and discuss how some emerging good practices in machine learning help reduce this gap. In particular, we justify the need for specified governance for hard-to-specify systems.