# Artificial Intelligence is stupid and causal reasoning won't fix it

arXiv: https://arxiv.org/abs/2008.07371 · HTML: https://mlsanity.com/paper/2008.07371/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jul 2020.

## Built on by (most cited first, 20 of 20)

- [Causal Parrots: Large Language Models May Talk Causality But Are Not Causal](https://mlsanity.com/paper/2308.13067/) (2023)
- [Can Foundation Models Talk Causality?](https://mlsanity.com/paper/2206.10591/) (2022)
- [Large Language Models: The Need for Nuance in Current Debates and a Pragmatic Perspective on Understanding](https://mlsanity.com/paper/2310.19671/) (2023)
- [Can Large Language Models emulate an inductive Thematic Analysis of semi-structured interviews? An exploration and provocation on the limits of the approach and the model](https://mlsanity.com/paper/2305.13014/) (2023)
- [The Role of AI in Peer Support for Young People: A Study of Preferences for Human- and AI-Generated Responses](https://mlsanity.com/paper/2405.02711/) (2024)
- [Learning to Prompt in the Classroom to Understand AI Limits: A pilot study](https://mlsanity.com/paper/2307.01540/) (2023)
- [Causality from Bottom to Top: A Survey](https://mlsanity.com/paper/2403.11219/) (2024)
- [Narrating Causal Graphs with Large Language Models](https://mlsanity.com/paper/2403.07118/) (2024)
- [Pearl Causal Hierarchy on Image Data: Intricacies & Challenges](https://mlsanity.com/paper/2212.12570/) (2022)
- [Artificial Intelligence and Aesthetic Judgment](https://mlsanity.com/paper/2309.12338/) (2023)
- [The Full Rights Dilemma for A.I. Systems of Debatable Personhood](https://mlsanity.com/paper/2303.17509/) (2023)
- [What Does 'Human-Centred AI' Mean?](https://mlsanity.com/paper/2507.19960/) (2025)
- [BAMBI: Developing Baby Language Models for Italian](https://mlsanity.com/paper/2503.09481/) (2025)
- ["Weak AI" is Likely to Never Become "Strong AI", So What is its Greatest Value for us?](https://mlsanity.com/paper/2103.15294/) (2021)
- [ChatGPT (Feb 13 Version) is a Chinese Room](https://mlsanity.com/paper/2304.12411/) (2023)
- [Can humans help BERT gain "confidence"?](https://mlsanity.com/paper/2309.06580/) (2023)
- [A Moonshot for AI Oracles in the Sciences](https://mlsanity.com/paper/2406.17836/) (2024)
- [Human-AI Co-Embodied Intelligence for Scientific Experimentation and Manufacturing](https://mlsanity.com/paper/2511.02071/) (2025)
- [No More Maybe-Arrows: Resolving Causal Uncertainty by Breaking Symmetries](https://mlsanity.com/paper/2603.01052/) (2026)
- [A Polyphonic Conception of AI Understanding](https://mlsanity.com/paper/2609.36079/) (2026)

## Abstract (arXiv)

Artificial Neural Networks have reached Grandmaster and even super-human performance across a variety of games: from those involving perfect-information (such as Go) to those involving imperfect-information (such as Starcraft). Such technological developments from AI-labs have ushered concomitant applications across the world of business - where an AI brand tag is fast becoming ubiquitous. A corollary of such widespread commercial deployment is that when AI gets things wrong - an autonomous vehicle crashes; a chatbot exhibits racist behaviour; automated credit scoring processes discriminate on gender etc. - there are often significant financial, legal and brand consequences and the incident becomes major news. As Judea Pearl sees it, the underlying reason for such mistakes is that, 'all the impressive achievements of deep learning amount to just curve fitting'. The key, Judea Pearl suggests, is to replace reasoning by association with causal-reasoning - the ability to infer causes from observed phenomena. It is a point that was echoed by Gary Marcus and Ernest Davis in a recent piece for the New York Times: 'we need to stop building computer systems that merely get better and better at detecting statistical patterns in data sets - often using an approach known as Deep Learning - and start building computer systems that from the moment of their assembly innately grasp three basic concepts: time, space and causality'. In this paper, foregrounding what in 1949 Gilbert Ryle termed a category mistake, I will offer an alternative explanation for AI errors: it is not so much that AI machinery cannot grasp causality, but that AI machinery - qua computation - cannot understand anything at all.