Understanding artificial intelligence:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Boca Raton ; London ; New York
CRC Press
2021
|
| Ausgabe: | First edition |
| Online-Zugang: | TUM01 |
| Beschreibung: | 1 Online-Ressource Illustrationen |
| ISBN: | 9781000284157 9781003080626 |
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MARC
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| 100 | 1 | |a Sabouret, Nicolas |e Verfasser |4 aut | |
| 240 | 1 | 0 | |a Comprendre l'intelligence artificielle |
| 245 | 1 | 0 | |a Understanding artificial intelligence |c Nicolas Sabouret |
| 250 | |a First edition | ||
| 264 | 1 | |a Boca Raton ; London ; New York |b CRC Press |c 2021 | |
| 264 | 4 | |c © 2021 | |
| 300 | |a 1 Online-Ressource |b Illustrationen | ||
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| 505 | 8 | |a Intro -- Half Title -- Title Page -- Copyright Page -- Dedication -- Table of Contents -- Introduction -- 1. What is Artificial Intelligence?: Understanding What a Computer, an Algorithm, a Program, and, in Particular, an Artificial Intelligence Program Are -- Computer Science and Computers -- Computers and Algorithms -- Algorithms and Computer Science -- The All-Purpose Machine -- Programs that Make Programs -- And Where Does Artificial Intelligence Fit in All This? -- A Machine That Learns? -- Obey, I Say! -- Attention: One Program Can Conceal Another! -- So, What Is AI? -- Reference -- 2. The Turing Test: Understanding That It Is Difficult to Measure the Intelligence of Computers -- What Is Intelligence? -- A Test, But Which One? -- And Then There Was Turing! -- It Chats... -- A Chatbot Named Eliza -- It's Controversial Too! -- My Computer and I Are Not Alike! -- 3. Why Is It So Difficult?: Understanding There Are Limitations to What a Machine Can Calculate -- Limitations of Computers -- A Real Headache -- Counting Operations -- This Is Getting Complex -- Squaring the Circle? -- 4. Lost in the Woods: Understanding a First Principles of AI Method -Exploration -- A Small Walk in Paris -- How a GPS Works -- Finding the Path -- It Is More Difficult Than It Looks -- The Adventurers of the Lost Graph -- So Easy! -- It's a Win-Win -- Computer Science in Three Questions -- A Clever Algorithm -- AI Is Here! -- A Head in the Stars -- The Best Is the Enemy of the Good -- Roughly Speaking, It's That Way! -- Follow the Crows -- There's a Trick! -- 5. Winning at Chess: Understanding How to Build Heuristics -- A Short History of AI -- A New Challenge -- An Old Algorithm -- From Theory to Practice -- Another Limit to Computers? -- Checkmate, von Neumann! -- The Return of Minimax -- A Heuristic to Win at Chess -- Easier Said Than Done | |
| 505 | 8 | |a A Disappointing Victory -- 6. The Journey Continues: Understanding That One Graph Can Conceal Another -- On the Intelligence of Machines -- A Very Particular Traveler -- A Healthy Walk -- A Really Difficult Problem... -- The Greedy Algorithm -- At Least, It's Fast! -- Come On, We Can Do Better! -- The Solution Space -- One Graph Can Conceal Another! -- Space Exploration -- 7. Darwin 2.0: Understanding How to Explore Space as a Group -- Natural Selection -- Computing Herds -- You've Got to Start Somewhere -- Two Beautiful Children! -- A Little More Randomness -- So, What Is This Good For? -- This Doesn't Solve Everything, However -- 8. Small but Strong: Understanding How Ants Find Their Way -- Multi-Agent Systems -- The Algorithm and the Ant -- Just Like in the Ant Colony -- Calculating a Path With Ants -- The More the Merrier! -- The Solution Emerges -- The Solution to All Our Problems? -- 9. A Bit of Tidying Up: Understanding How a Machine Learns to Classify -- Find the Odd One Out! -- From Animals to Genes -- Let's Do Some Sorting -- It's All a Matter of Distance -- Starting Over Again and Again -- There Is No Right Solution -- To Each Its Own Method -- So, Where's the Learning in All This? -- Joy and Good Humor! -- 10. Taking an AI by the Hand: Understanding That a Good Example Is Often Better Than a Long Explanation -- Ask the Program! -- From Data to Programs -- An Example Is Better Than a Long Explanation -- The Adventure Begins -- Much Ado About Nothing? -- From Image to Data -- How About We Take the Train? -- It's Logic! -- Tell Me What You Read, and I'll Tell You Who You Are -- From Symbolic to Numeric -- 11. Learning to Count: Understanding What Statistical Learning Is -- New and Old Alike? -- Riddle Me This -- Trees Again -- A Bit of Prediction -- Lots of Examples, Lots of Variables -- Too Much, It's Too Much! -- The Kitties Return | |
| 505 | 8 | |a The Mathematics of Artificial Intelligence -- Too Much Class! -- Keep Straight -- The SVM Strikes Back -- Intelligent, Did You Say Intelligent? -- Careful, Don't Swallow the Kernel! -- 12. Learning to Read: Understanding What a Neural Network Is -- Draw Me a Neuron -- More Triangles -- Just a Little Fine Tuning -- A Long Learning Process -- Where Did My Brain Go? -- One More Layer! -- Network Success -- One More Small Break -- 13. Learning as You Draw: Understanding What Deep Learning Is -- Are Video Games Bad for Your Health? -- Parallel Computing -- The Return of the Neural Networks -- Why Add More? -- The Crux of the Problem -- The Achievements of Deep Learning -- Watch Out for Tricks! -- To Each His Own Method -- 14. Winning at Go: Understanding What Reinforcement Learning Is -- The Quest for the Grail -- A Bit of Calculation -- Want to Play Again? -- Red, Odd, and Low! -- No Need To Be Stubborn -- A Bit of Reinforcement -- Stronger Than Ever! -- Step by Step -- Playing Without Risk -- Give Me the Data! -- 15. Strong AI: Understanding AI's Limitations -- Strong AI -- General Intelligence -- Artificial Consciousness -- An Uncertain Future -- How Far Can We Go? -- 16. Is There Cause for Concern?: Understanding That AI Can Be Misused -- Autonomous Systems? -- Misuse of AI -- AI Serving People -- Explaining, Always Explaining -- 17. To Infinity and Beyond!: Understanding That AI Has Good Days Ahead -- Where Are We Going? -- Doing Like Humans? -- Even More AI! -- James Allen -- John McCarthy and Patrick Hayes -- Richard Fikes, Nils Nilsson, and Drew McDermott -- Christopher Watkins, Leslie Kaelbling, and Michael Littman -- Robert Kowalski, Alain Colmerauer, and Philippe Roussel -- Edward Feigenbaum -- Lotfi Zadeh -- Allen Newell -- Judea Pearl -- Richard Richens, John Sowa, and Ronald Brachman -- Deborah McGuinness -- Acknowledgments -- They made AI. | |
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Datensatz im Suchindex
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| author | Sabouret, Nicolas |
| author_facet | Sabouret, Nicolas |
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| author_sort | Sabouret, Nicolas |
| author_variant | n s ns |
| building | Verbundindex |
| bvnumber | BV047442217 |
| classification_tum | DAT 700 |
| collection | ZDB-30-PQE |
| contents | Intro -- Half Title -- Title Page -- Copyright Page -- Dedication -- Table of Contents -- Introduction -- 1. What is Artificial Intelligence?: Understanding What a Computer, an Algorithm, a Program, and, in Particular, an Artificial Intelligence Program Are -- Computer Science and Computers -- Computers and Algorithms -- Algorithms and Computer Science -- The All-Purpose Machine -- Programs that Make Programs -- And Where Does Artificial Intelligence Fit in All This? -- A Machine That Learns? -- Obey, I Say! -- Attention: One Program Can Conceal Another! -- So, What Is AI? -- Reference -- 2. The Turing Test: Understanding That It Is Difficult to Measure the Intelligence of Computers -- What Is Intelligence? -- A Test, But Which One? -- And Then There Was Turing! -- It Chats... -- A Chatbot Named Eliza -- It's Controversial Too! -- My Computer and I Are Not Alike! -- 3. Why Is It So Difficult?: Understanding There Are Limitations to What a Machine Can Calculate -- Limitations of Computers -- A Real Headache -- Counting Operations -- This Is Getting Complex -- Squaring the Circle? -- 4. Lost in the Woods: Understanding a First Principles of AI Method -Exploration -- A Small Walk in Paris -- How a GPS Works -- Finding the Path -- It Is More Difficult Than It Looks -- The Adventurers of the Lost Graph -- So Easy! -- It's a Win-Win -- Computer Science in Three Questions -- A Clever Algorithm -- AI Is Here! -- A Head in the Stars -- The Best Is the Enemy of the Good -- Roughly Speaking, It's That Way! -- Follow the Crows -- There's a Trick! -- 5. Winning at Chess: Understanding How to Build Heuristics -- A Short History of AI -- A New Challenge -- An Old Algorithm -- From Theory to Practice -- Another Limit to Computers? -- Checkmate, von Neumann! -- The Return of Minimax -- A Heuristic to Win at Chess -- Easier Said Than Done A Disappointing Victory -- 6. The Journey Continues: Understanding That One Graph Can Conceal Another -- On the Intelligence of Machines -- A Very Particular Traveler -- A Healthy Walk -- A Really Difficult Problem... -- The Greedy Algorithm -- At Least, It's Fast! -- Come On, We Can Do Better! -- The Solution Space -- One Graph Can Conceal Another! -- Space Exploration -- 7. Darwin 2.0: Understanding How to Explore Space as a Group -- Natural Selection -- Computing Herds -- You've Got to Start Somewhere -- Two Beautiful Children! -- A Little More Randomness -- So, What Is This Good For? -- This Doesn't Solve Everything, However -- 8. Small but Strong: Understanding How Ants Find Their Way -- Multi-Agent Systems -- The Algorithm and the Ant -- Just Like in the Ant Colony -- Calculating a Path With Ants -- The More the Merrier! -- The Solution Emerges -- The Solution to All Our Problems? -- 9. A Bit of Tidying Up: Understanding How a Machine Learns to Classify -- Find the Odd One Out! -- From Animals to Genes -- Let's Do Some Sorting -- It's All a Matter of Distance -- Starting Over Again and Again -- There Is No Right Solution -- To Each Its Own Method -- So, Where's the Learning in All This? -- Joy and Good Humor! -- 10. Taking an AI by the Hand: Understanding That a Good Example Is Often Better Than a Long Explanation -- Ask the Program! -- From Data to Programs -- An Example Is Better Than a Long Explanation -- The Adventure Begins -- Much Ado About Nothing? -- From Image to Data -- How About We Take the Train? -- It's Logic! -- Tell Me What You Read, and I'll Tell You Who You Are -- From Symbolic to Numeric -- 11. Learning to Count: Understanding What Statistical Learning Is -- New and Old Alike? -- Riddle Me This -- Trees Again -- A Bit of Prediction -- Lots of Examples, Lots of Variables -- Too Much, It's Too Much! -- The Kitties Return The Mathematics of Artificial Intelligence -- Too Much Class! -- Keep Straight -- The SVM Strikes Back -- Intelligent, Did You Say Intelligent? -- Careful, Don't Swallow the Kernel! -- 12. Learning to Read: Understanding What a Neural Network Is -- Draw Me a Neuron -- More Triangles -- Just a Little Fine Tuning -- A Long Learning Process -- Where Did My Brain Go? -- One More Layer! -- Network Success -- One More Small Break -- 13. Learning as You Draw: Understanding What Deep Learning Is -- Are Video Games Bad for Your Health? -- Parallel Computing -- The Return of the Neural Networks -- Why Add More? -- The Crux of the Problem -- The Achievements of Deep Learning -- Watch Out for Tricks! -- To Each His Own Method -- 14. Winning at Go: Understanding What Reinforcement Learning Is -- The Quest for the Grail -- A Bit of Calculation -- Want to Play Again? -- Red, Odd, and Low! -- No Need To Be Stubborn -- A Bit of Reinforcement -- Stronger Than Ever! -- Step by Step -- Playing Without Risk -- Give Me the Data! -- 15. Strong AI: Understanding AI's Limitations -- Strong AI -- General Intelligence -- Artificial Consciousness -- An Uncertain Future -- How Far Can We Go? -- 16. Is There Cause for Concern?: Understanding That AI Can Be Misused -- Autonomous Systems? -- Misuse of AI -- AI Serving People -- Explaining, Always Explaining -- 17. To Infinity and Beyond!: Understanding That AI Has Good Days Ahead -- Where Are We Going? -- Doing Like Humans? -- Even More AI! -- James Allen -- John McCarthy and Patrick Hayes -- Richard Fikes, Nils Nilsson, and Drew McDermott -- Christopher Watkins, Leslie Kaelbling, and Michael Littman -- Robert Kowalski, Alain Colmerauer, and Philippe Roussel -- Edward Feigenbaum -- Lotfi Zadeh -- Allen Newell -- Judea Pearl -- Richard Richens, John Sowa, and Ronald Brachman -- Deborah McGuinness -- Acknowledgments -- They made AI. |
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| dewey-full | 006.3 |
| dewey-hundreds | 000 - Computer science, information, general works |
| dewey-ones | 006 - Special computer methods |
| dewey-raw | 006.3 |
| dewey-search | 006.3 |
| dewey-sort | 16.3 |
| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik |
| discipline_str_mv | Informatik |
| edition | First edition |
| format | Electronic eBook |
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| id | DE-604.BV047442217 |
| illustrated | Not Illustrated |
| index_date | 2024-07-03T18:01:24Z |
| indexdate | 2024-07-10T09:12:16Z |
| institution | BVB |
| isbn | 9781000284157 9781003080626 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-032844369 |
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| owner_facet | DE-91 DE-BY-TUM |
| physical | 1 Online-Ressource Illustrationen |
| psigel | ZDB-30-PQE ZDB-30-PQE TUM_PDA_PQE_Kauf |
| publishDate | 2021 |
| publishDateSearch | 2021 |
| publishDateSort | 2021 |
| publisher | CRC Press |
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| spelling | Sabouret, Nicolas Verfasser aut Comprendre l'intelligence artificielle Understanding artificial intelligence Nicolas Sabouret First edition Boca Raton ; London ; New York CRC Press 2021 © 2021 1 Online-Ressource Illustrationen txt rdacontent c rdamedia cr rdacarrier Intro -- Half Title -- Title Page -- Copyright Page -- Dedication -- Table of Contents -- Introduction -- 1. What is Artificial Intelligence?: Understanding What a Computer, an Algorithm, a Program, and, in Particular, an Artificial Intelligence Program Are -- Computer Science and Computers -- Computers and Algorithms -- Algorithms and Computer Science -- The All-Purpose Machine -- Programs that Make Programs -- And Where Does Artificial Intelligence Fit in All This? -- A Machine That Learns? -- Obey, I Say! -- Attention: One Program Can Conceal Another! -- So, What Is AI? -- Reference -- 2. The Turing Test: Understanding That It Is Difficult to Measure the Intelligence of Computers -- What Is Intelligence? -- A Test, But Which One? -- And Then There Was Turing! -- It Chats... -- A Chatbot Named Eliza -- It's Controversial Too! -- My Computer and I Are Not Alike! -- 3. Why Is It So Difficult?: Understanding There Are Limitations to What a Machine Can Calculate -- Limitations of Computers -- A Real Headache -- Counting Operations -- This Is Getting Complex -- Squaring the Circle? -- 4. Lost in the Woods: Understanding a First Principles of AI Method -Exploration -- A Small Walk in Paris -- How a GPS Works -- Finding the Path -- It Is More Difficult Than It Looks -- The Adventurers of the Lost Graph -- So Easy! -- It's a Win-Win -- Computer Science in Three Questions -- A Clever Algorithm -- AI Is Here! -- A Head in the Stars -- The Best Is the Enemy of the Good -- Roughly Speaking, It's That Way! -- Follow the Crows -- There's a Trick! -- 5. Winning at Chess: Understanding How to Build Heuristics -- A Short History of AI -- A New Challenge -- An Old Algorithm -- From Theory to Practice -- Another Limit to Computers? -- Checkmate, von Neumann! -- The Return of Minimax -- A Heuristic to Win at Chess -- Easier Said Than Done A Disappointing Victory -- 6. The Journey Continues: Understanding That One Graph Can Conceal Another -- On the Intelligence of Machines -- A Very Particular Traveler -- A Healthy Walk -- A Really Difficult Problem... -- The Greedy Algorithm -- At Least, It's Fast! -- Come On, We Can Do Better! -- The Solution Space -- One Graph Can Conceal Another! -- Space Exploration -- 7. Darwin 2.0: Understanding How to Explore Space as a Group -- Natural Selection -- Computing Herds -- You've Got to Start Somewhere -- Two Beautiful Children! -- A Little More Randomness -- So, What Is This Good For? -- This Doesn't Solve Everything, However -- 8. Small but Strong: Understanding How Ants Find Their Way -- Multi-Agent Systems -- The Algorithm and the Ant -- Just Like in the Ant Colony -- Calculating a Path With Ants -- The More the Merrier! -- The Solution Emerges -- The Solution to All Our Problems? -- 9. A Bit of Tidying Up: Understanding How a Machine Learns to Classify -- Find the Odd One Out! -- From Animals to Genes -- Let's Do Some Sorting -- It's All a Matter of Distance -- Starting Over Again and Again -- There Is No Right Solution -- To Each Its Own Method -- So, Where's the Learning in All This? -- Joy and Good Humor! -- 10. Taking an AI by the Hand: Understanding That a Good Example Is Often Better Than a Long Explanation -- Ask the Program! -- From Data to Programs -- An Example Is Better Than a Long Explanation -- The Adventure Begins -- Much Ado About Nothing? -- From Image to Data -- How About We Take the Train? -- It's Logic! -- Tell Me What You Read, and I'll Tell You Who You Are -- From Symbolic to Numeric -- 11. Learning to Count: Understanding What Statistical Learning Is -- New and Old Alike? -- Riddle Me This -- Trees Again -- A Bit of Prediction -- Lots of Examples, Lots of Variables -- Too Much, It's Too Much! -- The Kitties Return The Mathematics of Artificial Intelligence -- Too Much Class! -- Keep Straight -- The SVM Strikes Back -- Intelligent, Did You Say Intelligent? -- Careful, Don't Swallow the Kernel! -- 12. Learning to Read: Understanding What a Neural Network Is -- Draw Me a Neuron -- More Triangles -- Just a Little Fine Tuning -- A Long Learning Process -- Where Did My Brain Go? -- One More Layer! -- Network Success -- One More Small Break -- 13. Learning as You Draw: Understanding What Deep Learning Is -- Are Video Games Bad for Your Health? -- Parallel Computing -- The Return of the Neural Networks -- Why Add More? -- The Crux of the Problem -- The Achievements of Deep Learning -- Watch Out for Tricks! -- To Each His Own Method -- 14. Winning at Go: Understanding What Reinforcement Learning Is -- The Quest for the Grail -- A Bit of Calculation -- Want to Play Again? -- Red, Odd, and Low! -- No Need To Be Stubborn -- A Bit of Reinforcement -- Stronger Than Ever! -- Step by Step -- Playing Without Risk -- Give Me the Data! -- 15. Strong AI: Understanding AI's Limitations -- Strong AI -- General Intelligence -- Artificial Consciousness -- An Uncertain Future -- How Far Can We Go? -- 16. Is There Cause for Concern?: Understanding That AI Can Be Misused -- Autonomous Systems? -- Misuse of AI -- AI Serving People -- Explaining, Always Explaining -- 17. To Infinity and Beyond!: Understanding That AI Has Good Days Ahead -- Where Are We Going? -- Doing Like Humans? -- Even More AI! -- James Allen -- John McCarthy and Patrick Hayes -- Richard Fikes, Nils Nilsson, and Drew McDermott -- Christopher Watkins, Leslie Kaelbling, and Michael Littman -- Robert Kowalski, Alain Colmerauer, and Philippe Roussel -- Edward Feigenbaum -- Lotfi Zadeh -- Allen Newell -- Judea Pearl -- Richard Richens, John Sowa, and Ronald Brachman -- Deborah McGuinness -- Acknowledgments -- They made AI. Erscheint auch als Sabouret, Nicolas Understanding Artificial Intelligence Milton : CRC Press LLC,c2020 Druck-Ausgabe, Hardcover 978-0-367-53136-2 Erscheint auch als Druck-Ausgabe, Paperback 978-0-367-52435-7 |
| spellingShingle | Sabouret, Nicolas Understanding artificial intelligence Intro -- Half Title -- Title Page -- Copyright Page -- Dedication -- Table of Contents -- Introduction -- 1. What is Artificial Intelligence?: Understanding What a Computer, an Algorithm, a Program, and, in Particular, an Artificial Intelligence Program Are -- Computer Science and Computers -- Computers and Algorithms -- Algorithms and Computer Science -- The All-Purpose Machine -- Programs that Make Programs -- And Where Does Artificial Intelligence Fit in All This? -- A Machine That Learns? -- Obey, I Say! -- Attention: One Program Can Conceal Another! -- So, What Is AI? -- Reference -- 2. The Turing Test: Understanding That It Is Difficult to Measure the Intelligence of Computers -- What Is Intelligence? -- A Test, But Which One? -- And Then There Was Turing! -- It Chats... -- A Chatbot Named Eliza -- It's Controversial Too! -- My Computer and I Are Not Alike! -- 3. Why Is It So Difficult?: Understanding There Are Limitations to What a Machine Can Calculate -- Limitations of Computers -- A Real Headache -- Counting Operations -- This Is Getting Complex -- Squaring the Circle? -- 4. Lost in the Woods: Understanding a First Principles of AI Method -Exploration -- A Small Walk in Paris -- How a GPS Works -- Finding the Path -- It Is More Difficult Than It Looks -- The Adventurers of the Lost Graph -- So Easy! -- It's a Win-Win -- Computer Science in Three Questions -- A Clever Algorithm -- AI Is Here! -- A Head in the Stars -- The Best Is the Enemy of the Good -- Roughly Speaking, It's That Way! -- Follow the Crows -- There's a Trick! -- 5. Winning at Chess: Understanding How to Build Heuristics -- A Short History of AI -- A New Challenge -- An Old Algorithm -- From Theory to Practice -- Another Limit to Computers? -- Checkmate, von Neumann! -- The Return of Minimax -- A Heuristic to Win at Chess -- Easier Said Than Done A Disappointing Victory -- 6. The Journey Continues: Understanding That One Graph Can Conceal Another -- On the Intelligence of Machines -- A Very Particular Traveler -- A Healthy Walk -- A Really Difficult Problem... -- The Greedy Algorithm -- At Least, It's Fast! -- Come On, We Can Do Better! -- The Solution Space -- One Graph Can Conceal Another! -- Space Exploration -- 7. Darwin 2.0: Understanding How to Explore Space as a Group -- Natural Selection -- Computing Herds -- You've Got to Start Somewhere -- Two Beautiful Children! -- A Little More Randomness -- So, What Is This Good For? -- This Doesn't Solve Everything, However -- 8. Small but Strong: Understanding How Ants Find Their Way -- Multi-Agent Systems -- The Algorithm and the Ant -- Just Like in the Ant Colony -- Calculating a Path With Ants -- The More the Merrier! -- The Solution Emerges -- The Solution to All Our Problems? -- 9. A Bit of Tidying Up: Understanding How a Machine Learns to Classify -- Find the Odd One Out! -- From Animals to Genes -- Let's Do Some Sorting -- It's All a Matter of Distance -- Starting Over Again and Again -- There Is No Right Solution -- To Each Its Own Method -- So, Where's the Learning in All This? -- Joy and Good Humor! -- 10. Taking an AI by the Hand: Understanding That a Good Example Is Often Better Than a Long Explanation -- Ask the Program! -- From Data to Programs -- An Example Is Better Than a Long Explanation -- The Adventure Begins -- Much Ado About Nothing? -- From Image to Data -- How About We Take the Train? -- It's Logic! -- Tell Me What You Read, and I'll Tell You Who You Are -- From Symbolic to Numeric -- 11. Learning to Count: Understanding What Statistical Learning Is -- New and Old Alike? -- Riddle Me This -- Trees Again -- A Bit of Prediction -- Lots of Examples, Lots of Variables -- Too Much, It's Too Much! -- The Kitties Return The Mathematics of Artificial Intelligence -- Too Much Class! -- Keep Straight -- The SVM Strikes Back -- Intelligent, Did You Say Intelligent? -- Careful, Don't Swallow the Kernel! -- 12. Learning to Read: Understanding What a Neural Network Is -- Draw Me a Neuron -- More Triangles -- Just a Little Fine Tuning -- A Long Learning Process -- Where Did My Brain Go? -- One More Layer! -- Network Success -- One More Small Break -- 13. Learning as You Draw: Understanding What Deep Learning Is -- Are Video Games Bad for Your Health? -- Parallel Computing -- The Return of the Neural Networks -- Why Add More? -- The Crux of the Problem -- The Achievements of Deep Learning -- Watch Out for Tricks! -- To Each His Own Method -- 14. Winning at Go: Understanding What Reinforcement Learning Is -- The Quest for the Grail -- A Bit of Calculation -- Want to Play Again? -- Red, Odd, and Low! -- No Need To Be Stubborn -- A Bit of Reinforcement -- Stronger Than Ever! -- Step by Step -- Playing Without Risk -- Give Me the Data! -- 15. Strong AI: Understanding AI's Limitations -- Strong AI -- General Intelligence -- Artificial Consciousness -- An Uncertain Future -- How Far Can We Go? -- 16. Is There Cause for Concern?: Understanding That AI Can Be Misused -- Autonomous Systems? -- Misuse of AI -- AI Serving People -- Explaining, Always Explaining -- 17. To Infinity and Beyond!: Understanding That AI Has Good Days Ahead -- Where Are We Going? -- Doing Like Humans? -- Even More AI! -- James Allen -- John McCarthy and Patrick Hayes -- Richard Fikes, Nils Nilsson, and Drew McDermott -- Christopher Watkins, Leslie Kaelbling, and Michael Littman -- Robert Kowalski, Alain Colmerauer, and Philippe Roussel -- Edward Feigenbaum -- Lotfi Zadeh -- Allen Newell -- Judea Pearl -- Richard Richens, John Sowa, and Ronald Brachman -- Deborah McGuinness -- Acknowledgments -- They made AI. |
| title | Understanding artificial intelligence |
| title_alt | Comprendre l'intelligence artificielle |
| title_auth | Understanding artificial intelligence |
| title_exact_search | Understanding artificial intelligence |
| title_exact_search_txtP | Understanding artificial intelligence |
| title_full | Understanding artificial intelligence Nicolas Sabouret |
| title_fullStr | Understanding artificial intelligence Nicolas Sabouret |
| title_full_unstemmed | Understanding artificial intelligence Nicolas Sabouret |
| title_short | Understanding artificial intelligence |
| title_sort | understanding artificial intelligence |
| work_keys_str_mv | AT sabouretnicolas comprendrelintelligenceartificielle AT sabouretnicolas understandingartificialintelligence |