Next Article in Journal
Sports Tournaments and Social Choice Theory
Next Article in Special Issue
On Theoretical Incomprehensibility
Previous Article in Journal
Interaction Histories and Short-Term Memory: Enactive Development of Turn-Taking Behaviours in a Childlike Humanoid Robot
Previous Article in Special Issue
AlphaGo, Locked Strategies, and Eco-Cognitive Openness
Open AccessArticle

From Reflex to Reflection: Two Tricks AI Could Learn from Us

LTCI, Telecom Paris, Institut Polytechnique de Paris, 91120 Paris, France
Philosophies 2019, 4(2), 27;
Received: 15 March 2019 / Revised: 29 April 2019 / Accepted: 20 May 2019 / Published: 24 May 2019
(This article belongs to the Special Issue Philosophy and Epistemology of Deep Learning)
Deep learning and other similar machine learning techniques have a huge advantage over other AI methods: they do function when applied to real-world data, ideally from scratch, without human intervention. However, they have several shortcomings that mere quantitative progress is unlikely to overcome. The paper analyses these shortcomings as resulting from the type of compression achieved by these techniques, which is limited to statistical compression. Two directions for qualitative improvement, inspired by comparison with cognitive processes, are proposed here, in the form of two mechanisms: complexity drop and contrast. These mechanisms are supposed to operate dynamically and not through pre-processing as in neural networks. Their introduction may bring the functioning of AI away from mere reflex and closer to reflection. View Full-Text
Keywords: machine learning; complexity; simplicity; cognition; contrast machine learning; complexity; simplicity; cognition; contrast
MDPI and ACS Style

Dessalles, J.-L. From Reflex to Reflection: Two Tricks AI Could Learn from Us. Philosophies 2019, 4, 27.

Show more citation formats Show less citations formats

Article Access Map by Country/Region

Back to TopTop