Artificial Intelligence: Why the Thoughts and Ideas framed in the early stages are often the most…



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Artificial Intelligence: Why the Thoughts and Ideas framed in the early stages are often the most Inspiring

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Many people think that if an article or paper in AI or machine learning is older than three years, it is not worth reading. But the assumption is wrong, and you run the risk of increasing your knowledge with essential gaps. Of course, some papers refer to the first early works, but often this is done superficially to justify the author’s point of view.

It is not uncommon for papers written in the early stages of technology to be the most valuable. The authors do more or less basic research and are mostly free from superfluous external influences. They have a clearer view of the things they are looking at. Therefore, my tip: If you want to dive into AI, read first essential articles or works written in the early stages of technology or science.

Below I have listed some resources (a mixture of historical, philosophical, and technical) to consider as you begin your ‘career’ for a basic understanding of AI:

Charles Babbage/Ada Lovelace:

source:Sketch of The Analytical Engine (fourmilab.ch)

Sketch of The Analytical Engine (fourmilab.ch)

In 1842, the Italian mathematician Luigi Federico Menabrea published a description of the engine based on a lecture by Babbage in French. In 1843, the description was translated into English and extensively annotated by Ada Lovelace, who had become interested in the engine eight years earlier. In recognition of her additions to Menabrea’s paper, which included a way to calculate Bernoulli numbers using the machine (widely considered to be the first complete computer program), she has been described as the first computer programmer. (Wikipedia)

Alan M. Turing:

Alan M. Turing (source: Wikipedia)

COMPUTING MACHINERY AND INTELLIGENCE

1. The Imitation Game I propose to consider the question, “Can machines think?” This should begin with definitions of the meaning of the terms “machine” and “think.” The definitions might be framed so as to reflect so far as possible the normal use of the words, but this attitude is dangerous, If the meaning of the words “machine” and “think” are to be found by examining how they are commonly used it is difficult to escape the conclusion that the meaning and the answer to the question, “Can machines think?” is to be sought in a statistical survey such as a Gallup poll. But this is absurd. Instead of attempting such a definition I shall replace the question by another, which is closely related to it and is expressed in relatively unambiguous words. (Alan M. Turing)

John McCarthy:

The Philosophy of AI and the AI of Philosophy

Artificial intelligence (AI) has closer scientific connections with philosophy than do other sciences, because AI shares many concepts with philosophy, e.g. action, consciousness, epistemology (what it is sensible to say about the world), and even free will. This article treats the philosophy of AI but also analyzes some concepts common to philosophy and AI from the standpoint of AI.

More articles of John McCarthy

Hubert L. Dreyfus

Source MIT

What Computers Still Can’t Do — A Critique of Artificial Reason

When it was first published in 1972, Hubert Dreyfus’s manifesto on the inherent inability of disembodied machines to mimic higher mental functions caused an uproar in the artificial intelligence community. The world has changed since then. Today it is clear that “good old-fashioned AI,” based on the idea of using symbolic representations to produce general intelligence, is in decline (although several believers still pursue its pot of gold), and the focus of the Al community has shifted to more complex models of the mind. It has also become more common for AI researchers to seek out and study philosophy. For this edition of his now classic book, Dreyfus has added a lengthy new introduction outlining these changes and assessing the paradigms of connectionism and neural networks that have transformed the field.

Marvin Minsky

Source: Amazon

The Society of Mind (Video Lectures)

Marvin Minsky — one of the fathers of computer science and cofounder of the Artificial Intelligence Laboratory at MIT — gives a revolutionary answer to the age-old question: “How does the mind work?”
Minsky brilliantly portrays the mind as a “society” of tiny components that are themselves mindless. Mirroring his theory, Minsky boldly casts The Society of Mind as an intellectual puzzle whose pieces are assembled along the way. Each chapter — on a self-contained page — corresponds to a piece in the puzzle. As the pages turn, a unified theory of the mind emerges, like a mosaic. Ingenious, amusing, and easy to read, The Society of Mind is an adventure in imagination.

Douglas R Hofstadter

source: Amazon

Godel, Escher, Bach: An Eternal Golden Braid

Douglas Hofstadter’s book is concerned directly with the nature of “maps” or links between formal systems. However, according to Hofstadter, the formal system that underlies all mental activity transcends the system that supports it. If life can grow out of the formal chemical substrate of the cell, if consciousness can emerge out of a formal system of firing neurons, then so too will computers attain human intelligence. Gödel, Escher, Bach is a wonderful exploration of fascinating ideas at the heart of cognitive science: meaning, reduction, recursion, and much more.

Stuart J. Russell, Peter Norvig

source: Amazon

Artificial Intelligence: A Modern Approach

The long-anticipated revision of Artificial Intelligence: A Modern Approach explores the full breadth and depth of the field of artificial intelligence (AI). The 4th Edition brings readers up to date on the latest technologies, presents concepts in a more unified manner, and offers new or expanded coverage of machine learning, deep learning, transfer learning, multiagent systems, robotics, natural language processing, causality, probabilistic programming, privacy, fairness, and safe AI.

Ian Goodfellow, Yoshua Bengio, Aaron Courville

Source: Amazon

Deep Learning

An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives.

“Written by three experts in the field, Deep Learning is the only comprehensive book on the subject.”
~ Elon Musk,

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