{"id":368,"date":"2016-11-11T14:06:49","date_gmt":"2016-11-11T14:06:49","guid":{"rendered":"http:\/\/kreps.org\/academic\/?p=368"},"modified":"2023-06-01T20:59:28","modified_gmt":"2023-06-01T20:59:28","slug":"matter-and-memory-and-deep-learning","status":"publish","type":"post","link":"http:\/\/kreps.org\/academic\/matter-and-memory-and-deep-learning\/","title":{"rendered":"Matter and Memory and Deep Learning"},"content":{"rendered":"
\"Diagnoses

Diagnoses of Matter and Memory, Project Bergson Japan<\/p><\/div>\n

David presented a paper at the \u2018Diagnoses of Matter and Memory: Bergson and the Problems of Brain, Time and Memory<\/a>\u2019 8th International Colloquium of Project Bergson in Japan, in Tokyo and Osaka, Japan, 10th-13th November 2016.<\/p>\n

David was one of six international academics invited to Japan by Project Bergson co-ordinator Yasushi Hirai, including Barry Dainton from Liverpool, Jean-Luc Petit from Strasbourg, and three others from Switzerland and the United States, to join Japanese academics working on the exploration of Bergson and the implications of his work for modern philosophy, in a colloquium of sharing, discussion, complementarity and innovative fusion from a range of disciplines.<\/p>\n

\"7th

7th Colloquium book<\/p><\/div>\n

The 7th Colloquium, in 2015, \u00a0‘The Anatomy of Matter and Memory:\u00a0Bergson and Contemporary Theories of Perception, Mind and Time<\/a>,’ was published in Japan (in Japanese) this month, with an introduction by Project Bergson curator, Professor Yasushi Hirai, in which he quotes from David’s book, ‘Bergson, Complexity, and Creative Emergence<\/a>.’ An English translation of this book is forthcoming. \u00a0The final versions of the papers in the 8th Colloquium, to be completed in the coming weeks, including David’s paper, ‘Matter and Memory and Deep Learning,’ will be published in Japan in 2017.<\/p>\n

Abstract:<\/strong> The recent phenomenon of \u2018Deep Learning,\u2019 which has given us such science-fiction-like innovations as search tools in photographic applications and the growing reality of self-driving cars, is a new form, and subset, of \u2018Machine Learning\u2019 made possible by very recent innovations in computing. Machine Learning itself has been around for some decades \u2013 essentially pattern-recognition software that requires very substantial computing resources, which were, until very recently, mostly theoretical and hard to come by. Machine Learning was one avenue of the field of Artificial Intelligence known as Narrow A.I. \u2013 the kind of \u2018artificial intelligence\u2019 that was strictly limited in scope as a first-steps starting point of what came, as a result, to be known as General A.I. General A.I., known then as simply, \u2018Artificial Intelligence\u2019, was the 1950s dream that brought us such things as Robbie the Robot, and more recently C3PO, and The Terminator: the kind of science fiction characters that remain the only manifestations of General Artificial Intelligence.<\/p>\n

\u2018Deep Learning\u2019 also continues engineering\u2019s 1940s trend of using language in a way that I will contest in this paper: a co-opting of words that have been used, in the past, to describe human activities, using them instead to describe what engineers have managed to make machines do. These co-optations reduce the richness of the word, making its referent an algorithm: a flow diagram that represents the bare essentials of what an engineer can understand and reproduce of a human activity; not the human activity itself. This diagram of the \u2018engineering possible\u2019 over-simplifies the human activity it tries to depict. With continued usage, the meaning of the word for us today has all-too-often become reduced to what the engineer has newly defined it to mean: something much less than it once was.<\/p>\n

In this paper I propose to attempt to roll back some of these co-optations, and to re-introduce some of the richness of the words that have been taken by engineering. I shall examine Turing\u2019s seminal paper on the notion of a thinking machine. I shall be using the philosophical insights of Henri Bergson, especially in his book, Matter and Memory, and the discoveries of neuroscience and complexity scientists. I will try to show that the answer to Turing\u2019s question, \u2018Can machines think?\u2019 remains a resounding, \u2018No!\u2019, and that notions such as \u2018deep learning\u2019 are in fact not only an inaccurate use of the very human experience of learning, but degrade the latter in using such a term.<\/p>\n

Reference:<\/p>\n

Kreps, D (2016) Matter and Memory<\/em> and Deep Learning, paper presented at Diagnoses of Matter and Memory: Bergson and the Problems of Brain, Time and Memory\u2019 8th International Colloquium of Project Bergson in Japan, in Tokyo and Osaka, Japan, 10th-13th November 2016.<\/p>\n