Goto

Collaborating Authors

Rise of the Thinking Machines: AI Beats Humans At Their Own Game

#artificialintelligence

If you happen to have a free 30 hours or so, I would highly recommend watching Google's AlphaGo program take on one of the best players in the world at the ancient Chinese board game Go. If you don't have that much time, you could instead just watch the 6-hour third match, where the program wrapped up the best of five series. It's literally history being made. Some news outlets have covered this feat, but I don't think many people understand how monumental this actually is. Back in 1997, when Garry Kasparov was beaten by IBM's Deep Blue in chess, people were more excited about the future of computing.


Computers and Thought

Classics

E.A. Feigenbaum and J. Feldman (Eds.). Computers and Thought. McGraw-Hill, 1963. This collection includes twenty classic papers by such pioneers as A. M. Turing and Marvin Minsky who were behind the pivotal advances in artificially simulating human thought processes with computers. All Parts are available as downloadable pdf files; most individual chapters are also available separately. COMPUTING MACHINERY AND INTELLIGENCE. A. M. Turing. CHESS-PLAYING PROGRAMS AND THE PROBLEM OF COMPLEXITY. Allen Newell, J.C. Shaw and H.A. Simon. SOME STUDIES IN MACHINE LEARNING USING THE GAME OF CHECKERS. A. L. Samuel. EMPIRICAL EXPLORATIONS WITH THE LOGIC THEORY MACHINE: A CASE STUDY IN HEURISTICS. Allen Newell J.C. Shaw and H.A. Simon. REALIZATION OF A GEOMETRY-THEOREM PROVING MACHINE. H. Gelernter. EMPIRICAL EXPLORATIONS OF THE GEOMETRY-THEOREM PROVING MACHINE. H. Gelernter, J.R. Hansen, and D. W. Loveland. SUMMARY OF A HEURISTIC LINE BALANCING PROCEDURE. Fred M. Tonge. A HEURISTIC PROGRAM THAT SOLVES SYMBOLIC INTEGRATION PROBLEMS IN FRESHMAN CALCULUS. James R. Slagle. BASEBALL: AN AUTOMATIC QUESTION ANSWERER. Green, Bert F. Jr., Alice K. Wolf, Carol Chomsky, and Kenneth Laughery. INFERENTIAL MEMORY AS THE BASIS OF MACHINES WHICH UNDERSTAND NATURAL LANGUAGE. Robert K. Lindsay. PATTERN RECOGNITION BY MACHINE. Oliver G. Selfridge and Ulric Neisser. A PATTERN-RECOGNITION PROGRAM THAT GENERATES, EVALUATES, AND ADJUSTS ITS OWN OPERATORS. Leonard Uhr and Charles Vossler. GPS, A PROGRAM THAT SIMULATES HUMAN THOUGHT. Allen Newell and H.A. Simon. THE SIMULATION OF VERBAL LEARNING BEHAVIOR. Edward A. Feigenbaum. PROGRAMMING A MODEL OF HUMAN CONCEPT FORMULATION. Earl B. Hunt and Carl I. Hovland. SIMULATION OF BEHAVIOR IN THE BINARY CHOICE EXPERIMENT Julian Feldman. A MODEL OF THE TRUST INVESTMENT PROCESS. Geoffrey P. E. Clarkson. A COMPUTER MODEL OF ELEMENTARY SOCIAL BEHAVIOR. John T. Gullahorn and Jeanne E. Gullahorn. TOWARD INTELLIGENT MACHINES. Paul Armer. STEPS TOWARD ARTIFICIAL INTELLIGENCE. Marvin Minsky. A SELECTED DESCRIPTOR-INDEXED BIBLIOGRAPHY TO THE LITERATURE ON ARTIFICIAL INTELLIGENCE. Marvin Minsky.


History of artificial intelligence - Wikipedia, the free encyclopedia

#artificialintelligence

The history of artificial intelligence (AI) began in antiquity, with myths, stories and rumors of artificial beings endowed with intelligence or consciousness by master craftsmen; as Pamela McCorduck writes, AI began with "an ancient wish to forge the gods."[1] The seeds of modern AI were planted by classical philosophers who attempted to describe the process of human thinking as the mechanical manipulation of symbols. This work culminated in the invention of the programmable digital computer in the 1940s, a machine based on the abstract essence of mathematical reasoning. This device and the ideas behind it inspired a handful of scientists to begin seriously discussing the possibility of building an electronic brain. The Turing test was proposed by British mathematician Alan Turing in his 1950 paper Computing Machinery and Intelligence, which opens with the words: "I propose to consider the question, 'Can machines think?'" The term'Artificial Intelligence' was created at a conference held at Dartmouth College in 1956.[2] Allen Newell, J. C. Shaw, and Herbert A. Simon pioneered the newly created artificial intelligence field with the Logic Theory Machine (1956), and the General Problem Solver in 1957.[3] In 1958, John McCarthy and Marvin Minsky started the MIT Artificial Intelligence lab with 50,000.[4] John McCarthy also created LISP in the summer of 1958, a programming language still important in artificial intelligence research.[5] In 1973, in response to the criticism of James Lighthill and ongoing pressure from congress, the U.S. and British Governments stopped funding undirected research into artificial intelligence. Seven years later, a visionary initiative by the Japanese Government inspired governments and industry to provide AI with billions of dollars, but by the late 80s the investors became disillusioned and withdrew funding again. McCorduck (2004) writes "artificial intelligence in one form or another is an idea that has pervaded Western intellectual history, a dream in urgent need of being realized," expressed in humanity's myths, legends, stories, speculation and clockwork automatons.[6] Mechanical men and artificial beings appear in Greek myths, such as the golden robots of Hephaestus and Pygmalion's Galatea.[7] In the Middle Ages, there were rumors of secret mystical or alchemical means of placing mind into matter, such as J?bir ibn Hayy?n's Takwin, Paracelsus' homunculus and Rabbi Judah Loew's Golem.[8] By the 19th century, ideas about artificial men and thinking machines were developed in fiction, as in Mary Shelley's Frankenstein or Karel?apek's


The Real Risks of Artificial Intelligence

Communications of the ACM

The vast increase in speed, memory capacity, and communications ability allows today's computers to do things that were unthinkable when I started programming six decades ago. Then, computers were primarily used for numerical calculations; today, they process text, images, and sound recordings. Then, it was an accomplishment to write a program that played chess badly but correctly. Today's computers have the power to compete with the best human players. The incredible capacity of today's computing systems allows some purveyors to describe them as having "artificial intelligence" (AI). They claim that AI is used in washing machines, the "personal assistants" in our mobile devices, self-driving cars, and the giant computers that beat human champions at complex games. Remarkably, those who use the term "artificial intelligence" have not defined that term. I first heard the term more than 50 years ago and have yet to hear a scientific definition. Even now, some AI experts say that defining AI is a difficult (and important) question--one that they are working on. "Artificial intelligence" remains a buzzword, a word that many think they understand but nobody can define. Application of AI methods can lead to devices and systems that are untrustworthy and sometimes dangerous.


Bitly

#artificialintelligence

Defining artificial intelligence isn't just difficult; it's impossible, not the least because we don't really understand human intelligence. Paradoxically, advances in AI will help more to define what human intelligence isn't than what artificial intelligence is. But whatever AI is, we've clearly made a lot of progress in the past few years, in areas ranging from computer vision to game playing. AI is making the transition from a research topic to the early stages of enterprise adoption. Companies such as Google and Facebook have placed huge bets on AI and are already using it in their products. But Google and Facebook are only the beginning: over the next decade, we'll see AI steadily creep into one product after another. We'll be communicating with bots, rather than scripted robo-dialers, and not realizing that they aren't human. We'll be relying on cars to plan routes and respond to road hazards. It's a good bet that in the next decades, some features of AI will be incorporated into every application that we touch and that we won't be able to do anything without touching an application. Given that our future will inevitably be tied up with AI, it's imperative that we ask: Where are we now? What is the state of AI? And where are we heading? Descriptions of AI span several axes: strength (how intelligent is it?), Each of these axes is a spectrum, and each point in this many-dimensional space represents a different way of understanding the goals and capabilities of an AI system.