Algorithms Are Not Enough : Creating General Artificial Intelligence

Why a new approach is needed in the quest for general artificial intelligence. Since the inception of artificial intelligence, we have been warned about the imminent arrival of computational systems that can replicate human thought processes. Before we know it, computers will become so intelligent that humans will be lucky to kept as pets. And yet, although artificial intelligence has become increasingly sophisticatedwith such achievements as driverless cars and humanless chess-playingcomputer science has not yet created general artificial intelligence. In Algorithms Are Not Enough , Herbert Roitblat explains how artificial general intelligence may be possible and why a robopocalypse is neither imminent, nor likely. Existing artificial intelligence, Roitblat shows, has been limited to solving path problems, in which the entire problem consists of navigating a path of choicesfinding specific solutions to well-structured problems. Human problem-solving, on the other hand, includes problems that consist of ill-structured situations, including the design of problem-solving paths themselves. These are insight problems, and insight is an essential part of intelligence that has not been addressed by computer science. Roitblat draws on cognitive science, including psychology, philosophy, and history, to identify the essential features of intelligence needed to achieve general artificial intelligence. Roitblat describes current computational approaches to intelligence, including the Turing Test, machine learning, and neural networks. He identifies building blocks of natural intelligence, including perception, analogy, ambiguity, common sense, and creativity. General intelligence can create new representations to solve new problems, but current computational intelligence cannot. The human brain, like the computer, uses algorithms; but general intelligence, he argues, is more than algorithmic processes. Contents Preface 1: Introduction: Intelligence, Artificial and Natural The Invention of Human Intelligence Computational Intelligence Natural Intelligence The General in General Intelligence Specialized, General, and Superintelligence Resources 2: Human Intelligence Intelligence Testing Problem Solving Insight Problems Quirks of Human Intelligence Conclusion Resources 3: Physical Symbol Systems: The Symbolic Approach to Intelligence Turing Machines and the Turing Test The Dartmouth Summer Workshop (1956) Representation Definition of General Intelligence Conclusion Resources 4: Computational Intelligence and Machine Learning Limits of Expert Systems Probabilistic Reasoning Machine Learning Perceptrons and the Perceptron Learning Rule Beginnings of Machine Learning Reinforcement Learning Summary: A Few Examples of Machine Learning Systems Conclusion Resources 5: Neural Network Approach to Artificial Intelligence Neural Network Basics Dolphin Biosonar: An Example Whole Brain Hypothesis Conclusion Resources 6: Recent Advances in Artificial Intelligence Watson Siri and Her Relatives AlphaGo Self-Driving Cars Poker Conclusion Resources 7: Building Blocks of Intelligence Perception and Pattern Recognition Ambiguity Intelligence and Language Common Sense Representing Common Sense Resources 8: Expertise Source of Expertise IQ and Expertise Fluid and Crystallized Intelligence The Acquisition of Expertise Resources 9: Intelligent Hacks and TRICS Representations for General Intelligence Conclusion Resources 10: Algorithms: From People to Computers Optimal Choices: Using Algorithms to Guide Human Behavior Game Theory Resources 11: The Coming Robopocalypse? Superintelligence Concerns about Superintelligence Time to Interact with the World Resources 12: General Intelligence Defining Intelligence Achieving General Intelligence Creativity in General Intelligence Growing General Intelligence Whole Brain Emulation Analogy A Sketch of Artificial General Intelligence Resources Index Why a new approach is needed in the quest for general artificial intelligence.Since the inception of artificial intelligence, we have been warned about the imminent arrival of computational systems that can replicate human thought processes. Before we know it, computers will become so intelligent that humans will be lucky to be kept as pets. And yet, although artificial intelligence has become increasingly sophisticated—with such achievements as driverless cars and humanless chess-playing—computer science has not yet created general artificial intelligence. In Algorithms Are Not Enough, Herbert Roitblat explains how artificial general intelligence may be possible and why a robopocalypse is neither imminent nor likely.Existing artificial intelligence, Roitblat shows, has been limited to solving path problems, in which the entire problem consists of navigating a path of choices—finding specific solutions to well-structured problems. Human problem-solving, on the other hand, includes problems that consist of ill-structured situations, including the design of problem-solving paths themselves. These are insight problems, and insight is an essential part of intelligence that has not been addressed by computer science. Roitblat draws on cognitive science, including psychology, philosophy, and history, to identify the essential features of intelligence needed to achieve general artificial intelligence.Roitblat describes current computational approaches to intelligence, including the Turing Test, machine learning, and neural networks. He identifies building blocks of natural intelligence, including perception, analogy, ambiguity, common sense, and creativity. General intelligence can create new representations to solve new problems, but current computational intelligence cannot. The human brain, like the computer, uses algorithms; but general intelligence, he argues, is more than algorithmic processes. "The holy grail of artificial intelligence research has been the achievement of artificial general intelligence. Since the inception of artificial intelligence, machines that can perform any task that a human might have been predicted to be imminent. Some people have been enthusiastic about this prospect, but others have been terrified. Both have been disappointed. In fact, despite all of the progress in solving individual tasks, this research has not been on a road that could ever lead to general intelligence. To paraphrase the Ancient Greek poet, Archilochus, we have been building hedgehogs, when what we are after is a Fox. The fox, he said, knows many things, but the hedgehog knows one big thing. Even a stack of hedgehogs, however, cannot duplicate the intelligence of a fox. This book describes a roadmap for designing a generally intelligent fox that solves the problem of general intelligence. It brings to bear wide swaths of cognitive science, including psychology, philosophy, and history to debunk the barriers to general intelligence by identifying the essential features of intelligence that would be needed to achieve general artificial intelligence. Along the way, it makes it apparent that fears of an imminent explosion of uncontrollable computational intelligence (the so-called "singularity,") are completely unfounded"-- Provided by publisher