Artificial Intelligence is stupid and causal reasoning won't fix it
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.
[2008.07371] Artificial Intelligence is stupid and causal reasoning won't fix it --> Computer Science > Computers and Society arXiv:2008.07371 (cs) [Submitted on 20 Jul 2020] Title: Artificial Intelligence is stupid and causal reasoning won't fix it Authors: John Mark Bishop View a PDF of the paper titled Artificial Intelligence is stupid and causal reasoning won't fix it, by John Mark Bishop View PDF Abstract: 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
saved by
related reading
- I Will Fucking Piledrive You If You Mention AI Again — Ludicityludic.mataroa.blog
- Why Tool AIs Want to Be Agent AIs · Gwern.netgwern.net
- [1911.01547] On the Measure of Intelligencearxiv.org
- [2104.12871] Why AI is Harder Than We Thinkarxiv.org
- The hot mess theory of AI misalignment: More intelligent agents behave less coherently | Jascha’s blogsohl-dickstein.github.io
- Is AI Reasoning Right for the Wrong Reasons? | Quanta Magazinequantamagazine.org
- Is causality the missing piece of the AI puzzle?qualcomm.com
- Faithful, Interpretable Model Explanations via Causal Abstraction | SAIL Blogai.stanford.edu
- From Philosophy to Math to Engineering - Machine Intelligence Research Instituteintelligence.org
- To Build Truly Intelligent Machines, Teach Them Cause and Effect | Quanta Magazinequantamagazine.org
- How Causal Abstraction Underpins Computational Explanationarxiv.org
- [2602.06337] Can Post-Training Transform LLMs into Causal Reasoners?arxiv.org