Special Issue "Philosophy and Epistemology of Deep Learning"

A special issue of Philosophies (ISSN 2409-9287).

Deadline for manuscript submissions: 15 May 2019

Special Issue Editors

Guest Editor
Dr. Hector Zenil

1. Algorithmic Dynamics Lab, Unit of Computational Medicine, Science for Life Laboratory, Center of Molecular Medicine, Karolinska Institute, Stockholm, Sweden. 2. Algorithmic Nature Group, Laboratory of Scientific Research for the Natural and Digital Sciences, Paris, France. 3. Oxford Immune Algorithmics, Oxford University Innovation, Oxford, U.K.
Interests: algorithmic learning; information theory; complex systems; philosophy of information; randomness; systems biology; digital philosophy; reprogrammability; cellular automata; measures of sophistication
Guest Editor
Prof. Dr. Selmer Bringsjord

Rensselaer AI & Reasoning Laboratory Cognitive Science Department—School of HASS, Rensselaer Polytechnic Institute, New York, USA
Website | E-Mail
Interests: cognitive science; robotics; computer science; logic & philosophy; technology; artificial intelligence

Special Issue Information

Dear Colleagues,

Current popular approaches to Machine Learning (ML), Deep Learning (DL) and Artificial Intelligence (AI) are mostly statistical in nature, and are not well equipped to deal with abstraction and explanation. In particular, they cannot generate candidate models or make generalizations directly from data to discover possible causal mechanisms. One method that researchers are resorting to in order to discover how deep learning algorithms work involves using what are called ‘generative models’ (a possible misnomer). They train a learning algorithm and handicap it systematically whilst asking it to generate examples. By observing the resulting examples they are able to make inferences about what may be happening in the algorithm at some level. 
However, current trends and methods are widely considered black-box approaches that have worked amazingly well in classification tasks, but provide little to no understanding of causation and are unable to deal with forms of symbolic computation such as logical inference and explanation. As a consequence, they also fail to be scalable in domains they have not been trained for, and require tons of data to be trained on, before they can do anything interesting—-and they require training every time they are presented with (even slightly) different data. 
Furthermore, how other cognitive features, such as human consciousness, may be related to current and future directions in deep learning, and whether such features may prove advantageous or disadvantageous remains an open question.
The aim of this special issue is thus to attempt to ask the right questions and shed some light on the achievements, limitations and future directions in reinforcement/deep learning approaches and differentiable programming. Its particular focus will be on the interplay of data and model-driven approaches that go beyond current ones, which for the most part are  based on traditional statistics. It will attempt to ascertain whether a fundamental theory is needed or whether one already exists, and to explore the implications of current and future technologies based on deep learning and differentiable programming for science, technology and society.

Dr. Hector Zenil
Prof. Dr. Selmer Bringsjord
Guest Editors

Manuscript Submission Information

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Published Papers (1 paper)

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Open AccessArticle AlphaGo, Locked Strategies, and Eco-Cognitive Openness
Received: 11 January 2019 / Revised: 3 February 2019 / Accepted: 11 February 2019 / Published: 16 February 2019
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Locked and unlocked strategies are at the center of this article, as ways of shedding new light on the cognitive aspects of deep learning machines. The character and the role of these cognitive strategies, which are occurring both in humans and in computational [...] Read more.
Locked and unlocked strategies are at the center of this article, as ways of shedding new light on the cognitive aspects of deep learning machines. The character and the role of these cognitive strategies, which are occurring both in humans and in computational machines, is indeed strictly related to the generation of cognitive outputs, which range from weak to strong level of knowledge creativity. I maintain that these differences lead to important consequences when we analyze computational AI programs, such as AlphaGo, which aim at performing various kinds of abductive hypothetical reasoning. In these cases, the programs are characterized by locked abductive strategies: they deal with weak (even if sometimes amazing) kinds of hypothetical creative reasoning, because they are limited in what I call eco-cognitive openness, which instead qualifies human cognizers who are performing higher kinds of abductive creative reasoning, where cognitive strategies are instead unlocked. Full article
(This article belongs to the Special Issue Philosophy and Epistemology of Deep Learning)
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