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The Reality Based Guide to Machine Learning & Artificial Intelligence
Chapter 1: Machines can’t learn
A brief primer on the universal rules of logic with a specific focus on violations of logical laws (e.g. logical contradictions). Readers will come to understand how when the words ‘machine’ and ‘learning” are combined in that order, because of the definitions of each word in the term, the result is a logical contradiction. They will also be introduced to the concept of logical impossibility and how what is logically impossible is nonsense and thus absurd. Once again the term machine learning will be used as the example case.
All* Of Your Machine Learning Questions Answered
*(Six) Part II In The Series — FAQs About The Future
Chapter 2: Artificial Intelligence does not currently exist and may never
We begin with a short (instantaneous) review of all the currently existing artificial intelligence systems and artificial intelligences (zero) and move quickly into why it may never actually be possible to create an artificial intelligence. Readers will be introduced to the neuroscientific literature and be exposed to the wide variety of theories of learning each of which has some claim to correctness. They will then be shown various artificial neural networks and asked to choose the correct theory of learning to apply to any given network. It should be clear by the end of the chapter that since we do not even agree about how the human brain functions or how to define a neural network it is impossible to make an artificial one and then propose it as the basis of some artificial “intelligence.”, a term for which there is even less agreement on meaning then for learning. The problems of embodiment and the senses will also be described in detail.
All* Of Your Artificial Intelligence Questions Answered
*(Eight) Part I In The Series — FAQs About The Future