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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; https://doi.org/10.3390/philosophies4020027
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)
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Abstract

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
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Dessalles, J.-L. From Reflex to Reflection: Two Tricks AI Could Learn from Us. Philosophies 2019, 4, 27.

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