Algorithms are not enough: creating general artificial intelligence
"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 thi...
Gespeichert in:
| 1. Verfasser: | |
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| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Cambridge, Massachusetts ; London, England
The MIT Press
[2020]
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| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis Inhaltsverzeichnis Inhaltsverzeichnis |
| Zusammenfassung: | "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"-- |
| Beschreibung: | Literaturangaben |
| Beschreibung: | x, 328 Seiten Illustrationen |
| ISBN: | 9780262044127 |
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Datensatz im Suchindex
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| adam_text | Contents Preface 1 ix Introduction: Intelligence, Artificial and Natural The Invention of Human Intelligence 5 Computational Intelligence 8 Natural Intelligence 9 The General in General Intelligence 11 Specialized, General, and Superintelligence Resources 18 2 Human Intelligence 1 13 21 Intelligence Testing 22 Problem Solving 25 Well-Formed Problems 25 Formal Problems 29 Insight Problems 36 Quirks of Human Intelligence 43 Conclusion 49 Resources 49 3 Physical Symbol Systems: The Symbolic Approach to Intelligence 53 Turing Machines and the Turing Test 54 The Dartmouth Summer Workshop (1956) Representation 61 Definition of General Intelligence 74 Conclusion 76 Resources 77 59
vi 4 Contents Computational Intelligence and Machine Learning 81 Limits of Expert Systems 81 Probabilistic Reasoning 84 Machine Learning 86 Varieties ofMachine Learning 88 Perceptrons and the Perceptron Learning Rule 93 Beginnings of Machine Learning 96 Reinforcement Learning 104 Summary: A Few Examples of Machine Learning Systems Conclusion 107 Resources 107 5 Neural Network Approach to Artificial Intelligence Neural Network Basics 112 Dolphin Biosonar: An Example Whole Brain Hypothesis 122 Conclusion 128 Resources 129 6 115 Recent Advances in Artificial Intelligence Watson 137 Siri and Her Relatives 140 AlphaGo 146 Self-Driving Cars 149 Poker 153 Conclusion 156 Resources 157 7 Building Blocks of Intelligence Perception and Pattern Recognition Gestalt Properties 164 Ambiguity 164 Intelligence and Language 167 Common Sense 174 Representing Common Sense 177 Resources 182 8 Expertise 185 Source of Expertise 192 IQ and Expertise 193 161 162 133 106 109
Contents vii Fluid and Crystallized Intelligence The Acquisition of Expertise 196 Resources 204 9 Intelligent Hacks and TRICS 194 207 Representations for General Intelligence Conclusion 226 Resources 227 222 10 Algorithms: From People to Computers 229 Optimal Choices: Using Algorithms to Guide Human Behavior Game Theory 251 Resources 253 11 The Coming Robopocalypse? 255 Superintelligence 257 Concerns about Superintelligence 259 Time to Interact with the World 266 Resources 275 12 General Intelligence 277 Defining Intelligence 278 Achieving General Intelligence 280 Beginning the Sketch ofArtificial General Intelligence 282 More on the Stack ofHedgehogs 288 General Intelligence Is Not Algorithmic Optimization 291 Intelligence and TRICS 291 Transfer Learning 295 Intelligence Entails Risk 299 Creativity in General Intelligence 301 Growing General Intelligence 302 Whole Brain Emulation 303 Analogy 305 Other Limitations of the Current Paradigm 306 Metaleaming 309 Insight 310 A Sketch of Artificial General Intelligence 314 Resources 317 Index 321 237
Contents Preface 1 ix Introduction: Intelligence, Artificial and Natural The Invention of Human Intelligence 5 Computational Intelligence 8 Natural Intelligence 9 The General in General Intelligence 11 Specialized, General, and Superintelligence Resources 18 2 Human Intelligence 1 13 21 Intelligence Testing 22 Problem Solving 25 Well-Formed Problems 25 Formal Problems 29 Insight Problems 36 Quirks of Human Intelligence 43 Conclusion 49 Resources 49 3 Physical Symbol Systems: The Symbolic Approach to Intelligence 53 Turing Machines and the Turing Test 54 The Dartmouth Summer Workshop (1956) Representation 61 Definition of General Intelligence 74 Conclusion 76 Resources 77 59
vi 4 Contents Computational Intelligence and Machine Learning 81 Limits of Expert Systems 81 Probabilistic Reasoning 84 Machine Learning 86 Varieties ofMachine Learning 88 Perceptrons and the Perceptron Learning Rule 93 Beginnings of Machine Learning 96 Reinforcement Learning 104 Summary: A Few Examples of Machine Learning Systems Conclusion 107 Resources 107 5 Neural Network Approach to Artificial Intelligence Neural Network Basics 112 Dolphin Biosonar: An Example Whole Brain Hypothesis 122 Conclusion 128 Resources 129 6 115 Recent Advances in Artificial Intelligence Watson 137 Siri and Her Relatives 140 AlphaGo 146 Self-Driving Cars 149 Poker 153 Conclusion 156 Resources 157 7 Building Blocks of Intelligence Perception and Pattern Recognition Gestalt Properties 164 Ambiguity 164 Intelligence and Language 167 Common Sense 174 Representing Common Sense 177 Resources 182 8 Expertise 185 Source of Expertise 192 IQ and Expertise 193 161 162 133 106 109
Contents vii Fluid and Crystallized Intelligence The Acquisition of Expertise 196 Resources 204 9 Intelligent Hacks and TRICS 194 207 Representations for General Intelligence Conclusion 226 Resources 227 222 10 Algorithms: From People to Computers 229 Optimal Choices: Using Algorithms to Guide Human Behavior Game Theory 251 Resources 253 11 The Coming Robopocalypse? 255 Superintelligence 257 Concerns about Superintelligence 259 Time to Interact with the World 266 Resources 275 12 General Intelligence 277 Defining Intelligence 278 Achieving General Intelligence 280 Beginning the Sketch ofArtificial General Intelligence 282 More on the Stack ofHedgehogs 288 General Intelligence Is Not Algorithmic Optimization 291 Intelligence and TRICS 291 Transfer Learning 295 Intelligence Entails Risk 299 Creativity in General Intelligence 301 Growing General Intelligence 302 Whole Brain Emulation 303 Analogy 305 Other Limitations of the Current Paradigm 306 Metaleaming 309 Insight 310 A Sketch of Artificial General Intelligence 314 Resources 317 Index 321 237
Contents Preface 1 ix Introduction: Intelligence, Artificial and Natural The Invention of Human Intelligence 5 Computational Intelligence 8 Natural Intelligence 9 The General in General Intelligence 11 Specialized, General, and Superintelligence Resources 18 2 Human Intelligence 1 13 21 Intelligence Testing 22 Problem Solving 25 Well-Formed Problems 25 Formal Problems 29 Insight Problems 36 Quirks of Human Intelligence 43 Conclusion 49 Resources 49 3 Physical Symbol Systems: The Symbolic Approach to Intelligence 53 Turing Machines and the Turing Test 54 The Dartmouth Summer Workshop (1956) Representation 61 Definition of General Intelligence 74 Conclusion 76 Resources 77 59
vi 4 Contents Computational Intelligence and Machine Learning 81 Limits of Expert Systems 81 Probabilistic Reasoning 84 Machine Learning 86 Varieties ofMachine Learning 88 Perceptrons and the Perceptron Learning Rule 93 Beginnings of Machine Learning 96 Reinforcement Learning 104 Summary: A Few Examples of Machine Learning Systems Conclusion 107 Resources 107 5 Neural Network Approach to Artificial Intelligence Neural Network Basics 112 Dolphin Biosonar: An Example Whole Brain Hypothesis 122 Conclusion 128 Resources 129 6 115 Recent Advances in Artificial Intelligence Watson 137 Siri and Her Relatives 140 AlphaGo 146 Self-Driving Cars 149 Poker 153 Conclusion 156 Resources 157 7 Building Blocks of Intelligence Perception and Pattern Recognition Gestalt Properties 164 Ambiguity 164 Intelligence and Language 167 Common Sense 174 Representing Common Sense 177 Resources 182 8 Expertise 185 Source of Expertise 192 IQ and Expertise 193 161 162 133 106 109
Contents vii Fluid and Crystallized Intelligence The Acquisition of Expertise 196 Resources 204 9 Intelligent Hacks and TRICS 194 207 Representations for General Intelligence Conclusion 226 Resources 227 222 10 Algorithms: From People to Computers 229 Optimal Choices: Using Algorithms to Guide Human Behavior Game Theory 251 Resources 253 11 The Coming Robopocalypse? 255 Superintelligence 257 Concerns about Superintelligence 259 Time to Interact with the World 266 Resources 275 12 General Intelligence 277 Defining Intelligence 278 Achieving General Intelligence 280 Beginning the Sketch ofArtificial General Intelligence 282 More on the Stack ofHedgehogs 288 General Intelligence Is Not Algorithmic Optimization 291 Intelligence and TRICS 291 Transfer Learning 295 Intelligence Entails Risk 299 Creativity in General Intelligence 301 Growing General Intelligence 302 Whole Brain Emulation 303 Analogy 305 Other Limitations of the Current Paradigm 306 Metaleaming 309 Insight 310 A Sketch of Artificial General Intelligence 314 Resources 317 Index 321 237
|
| adam_txt |
Contents Preface 1 ix Introduction: Intelligence, Artificial and Natural The Invention of Human Intelligence 5 Computational Intelligence 8 Natural Intelligence 9 The General in General Intelligence 11 Specialized, General, and Superintelligence Resources 18 2 Human Intelligence 1 13 21 Intelligence Testing 22 Problem Solving 25 Well-Formed Problems 25 Formal Problems 29 Insight Problems 36 Quirks of Human Intelligence 43 Conclusion 49 Resources 49 3 Physical Symbol Systems: The Symbolic Approach to Intelligence 53 Turing Machines and the Turing Test 54 The Dartmouth Summer Workshop (1956) Representation 61 Definition of General Intelligence 74 Conclusion 76 Resources 77 59
vi 4 Contents Computational Intelligence and Machine Learning 81 Limits of Expert Systems 81 Probabilistic Reasoning 84 Machine Learning 86 Varieties ofMachine Learning 88 Perceptrons and the Perceptron Learning Rule 93 Beginnings of Machine Learning 96 Reinforcement Learning 104 Summary: A Few Examples of Machine Learning Systems Conclusion 107 Resources 107 5 Neural Network Approach to Artificial Intelligence Neural Network Basics 112 Dolphin Biosonar: An Example Whole Brain Hypothesis 122 Conclusion 128 Resources 129 6 115 Recent Advances in Artificial Intelligence Watson 137 Siri and Her Relatives 140 AlphaGo 146 Self-Driving Cars 149 Poker 153 Conclusion 156 Resources 157 7 Building Blocks of Intelligence Perception and Pattern Recognition Gestalt Properties 164 Ambiguity 164 Intelligence and Language 167 Common Sense 174 Representing Common Sense 177 Resources 182 8 Expertise 185 Source of Expertise 192 IQ and Expertise 193 161 162 133 106 109
Contents vii Fluid and Crystallized Intelligence The Acquisition of Expertise 196 Resources 204 9 Intelligent Hacks and TRICS 194 207 Representations for General Intelligence Conclusion 226 Resources 227 222 10 Algorithms: From People to Computers 229 Optimal Choices: Using Algorithms to Guide Human Behavior Game Theory 251 Resources 253 11 The Coming Robopocalypse? 255 Superintelligence 257 Concerns about Superintelligence 259 Time to Interact with the World 266 Resources 275 12 General Intelligence 277 Defining Intelligence 278 Achieving General Intelligence 280 Beginning the Sketch ofArtificial General Intelligence 282 More on the Stack ofHedgehogs 288 General Intelligence Is Not Algorithmic Optimization 291 Intelligence and TRICS 291 Transfer Learning 295 Intelligence Entails Risk 299 Creativity in General Intelligence 301 Growing General Intelligence 302 Whole Brain Emulation 303 Analogy 305 Other Limitations of the Current Paradigm 306 Metaleaming 309 Insight 310 A Sketch of Artificial General Intelligence 314 Resources 317 Index 321 237
Contents Preface 1 ix Introduction: Intelligence, Artificial and Natural The Invention of Human Intelligence 5 Computational Intelligence 8 Natural Intelligence 9 The General in General Intelligence 11 Specialized, General, and Superintelligence Resources 18 2 Human Intelligence 1 13 21 Intelligence Testing 22 Problem Solving 25 Well-Formed Problems 25 Formal Problems 29 Insight Problems 36 Quirks of Human Intelligence 43 Conclusion 49 Resources 49 3 Physical Symbol Systems: The Symbolic Approach to Intelligence 53 Turing Machines and the Turing Test 54 The Dartmouth Summer Workshop (1956) Representation 61 Definition of General Intelligence 74 Conclusion 76 Resources 77 59
vi 4 Contents Computational Intelligence and Machine Learning 81 Limits of Expert Systems 81 Probabilistic Reasoning 84 Machine Learning 86 Varieties ofMachine Learning 88 Perceptrons and the Perceptron Learning Rule 93 Beginnings of Machine Learning 96 Reinforcement Learning 104 Summary: A Few Examples of Machine Learning Systems Conclusion 107 Resources 107 5 Neural Network Approach to Artificial Intelligence Neural Network Basics 112 Dolphin Biosonar: An Example Whole Brain Hypothesis 122 Conclusion 128 Resources 129 6 115 Recent Advances in Artificial Intelligence Watson 137 Siri and Her Relatives 140 AlphaGo 146 Self-Driving Cars 149 Poker 153 Conclusion 156 Resources 157 7 Building Blocks of Intelligence Perception and Pattern Recognition Gestalt Properties 164 Ambiguity 164 Intelligence and Language 167 Common Sense 174 Representing Common Sense 177 Resources 182 8 Expertise 185 Source of Expertise 192 IQ and Expertise 193 161 162 133 106 109
Contents vii Fluid and Crystallized Intelligence The Acquisition of Expertise 196 Resources 204 9 Intelligent Hacks and TRICS 194 207 Representations for General Intelligence Conclusion 226 Resources 227 222 10 Algorithms: From People to Computers 229 Optimal Choices: Using Algorithms to Guide Human Behavior Game Theory 251 Resources 253 11 The Coming Robopocalypse? 255 Superintelligence 257 Concerns about Superintelligence 259 Time to Interact with the World 266 Resources 275 12 General Intelligence 277 Defining Intelligence 278 Achieving General Intelligence 280 Beginning the Sketch ofArtificial General Intelligence 282 More on the Stack ofHedgehogs 288 General Intelligence Is Not Algorithmic Optimization 291 Intelligence and TRICS 291 Transfer Learning 295 Intelligence Entails Risk 299 Creativity in General Intelligence 301 Growing General Intelligence 302 Whole Brain Emulation 303 Analogy 305 Other Limitations of the Current Paradigm 306 Metaleaming 309 Insight 310 A Sketch of Artificial General Intelligence 314 Resources 317 Index 321 237
Contents Preface 1 ix Introduction: Intelligence, Artificial and Natural The Invention of Human Intelligence 5 Computational Intelligence 8 Natural Intelligence 9 The General in General Intelligence 11 Specialized, General, and Superintelligence Resources 18 2 Human Intelligence 1 13 21 Intelligence Testing 22 Problem Solving 25 Well-Formed Problems 25 Formal Problems 29 Insight Problems 36 Quirks of Human Intelligence 43 Conclusion 49 Resources 49 3 Physical Symbol Systems: The Symbolic Approach to Intelligence 53 Turing Machines and the Turing Test 54 The Dartmouth Summer Workshop (1956) Representation 61 Definition of General Intelligence 74 Conclusion 76 Resources 77 59
vi 4 Contents Computational Intelligence and Machine Learning 81 Limits of Expert Systems 81 Probabilistic Reasoning 84 Machine Learning 86 Varieties ofMachine Learning 88 Perceptrons and the Perceptron Learning Rule 93 Beginnings of Machine Learning 96 Reinforcement Learning 104 Summary: A Few Examples of Machine Learning Systems Conclusion 107 Resources 107 5 Neural Network Approach to Artificial Intelligence Neural Network Basics 112 Dolphin Biosonar: An Example Whole Brain Hypothesis 122 Conclusion 128 Resources 129 6 115 Recent Advances in Artificial Intelligence Watson 137 Siri and Her Relatives 140 AlphaGo 146 Self-Driving Cars 149 Poker 153 Conclusion 156 Resources 157 7 Building Blocks of Intelligence Perception and Pattern Recognition Gestalt Properties 164 Ambiguity 164 Intelligence and Language 167 Common Sense 174 Representing Common Sense 177 Resources 182 8 Expertise 185 Source of Expertise 192 IQ and Expertise 193 161 162 133 106 109
Contents vii Fluid and Crystallized Intelligence The Acquisition of Expertise 196 Resources 204 9 Intelligent Hacks and TRICS 194 207 Representations for General Intelligence Conclusion 226 Resources 227 222 10 Algorithms: From People to Computers 229 Optimal Choices: Using Algorithms to Guide Human Behavior Game Theory 251 Resources 253 11 The Coming Robopocalypse? 255 Superintelligence 257 Concerns about Superintelligence 259 Time to Interact with the World 266 Resources 275 12 General Intelligence 277 Defining Intelligence 278 Achieving General Intelligence 280 Beginning the Sketch ofArtificial General Intelligence 282 More on the Stack ofHedgehogs 288 General Intelligence Is Not Algorithmic Optimization 291 Intelligence and TRICS 291 Transfer Learning 295 Intelligence Entails Risk 299 Creativity in General Intelligence 301 Growing General Intelligence 302 Whole Brain Emulation 303 Analogy 305 Other Limitations of the Current Paradigm 306 Metaleaming 309 Insight 310 A Sketch of Artificial General Intelligence 314 Resources 317 Index 321 237 |
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| spelling | Roitblat, Herbert L. Verfasser (DE-588)1158199384 aut Algorithms are not enough creating general artificial intelligence Herbert L. Roitblat Cambridge, Massachusetts ; London, England The MIT Press [2020] © 2020 x, 328 Seiten Illustrationen txt rdacontent n rdamedia nc rdacarrier Literaturangaben "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"-- Künstliche Intelligenz (DE-588)4033447-8 gnd rswk-swf Artificial intelligence Künstliche Intelligenz (DE-588)4033447-8 s DE-604 Digitalisierung BSB München - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=032405781&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis Digitalisierung BSB München - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=032405781&sequence=000003&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis Digitalisierung BSB München - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=032405781&sequence=000005&line_number=0003&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Roitblat, Herbert L. Algorithms are not enough creating general artificial intelligence Künstliche Intelligenz (DE-588)4033447-8 gnd |
| subject_GND | (DE-588)4033447-8 |
| title | Algorithms are not enough creating general artificial intelligence |
| title_auth | Algorithms are not enough creating general artificial intelligence |
| title_exact_search | Algorithms are not enough creating general artificial intelligence |
| title_exact_search_txtP | Algorithms are not enough creating general artificial intelligence |
| title_full | Algorithms are not enough creating general artificial intelligence Herbert L. Roitblat |
| title_fullStr | Algorithms are not enough creating general artificial intelligence Herbert L. Roitblat |
| title_full_unstemmed | Algorithms are not enough creating general artificial intelligence Herbert L. Roitblat |
| title_short | Algorithms are not enough |
| title_sort | algorithms are not enough creating general artificial intelligence |
| title_sub | creating general artificial intelligence |
| topic | Künstliche Intelligenz (DE-588)4033447-8 gnd |
| topic_facet | Künstliche Intelligenz |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=032405781&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=032405781&sequence=000003&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=032405781&sequence=000005&line_number=0003&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT roitblatherbertl algorithmsarenotenoughcreatinggeneralartificialintelligence |
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