On the Path to AI : : Law's Prophecies and the Conceptual Foundations of the Machine Learning Age.
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Place / Publishing House: | Cham : : Springer International Publishing AG,, 2020. ©2020. |
Year of Publication: | 2020 |
Edition: | 1st ed. |
Language: | English |
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Physical Description: | 1 online resource (163 pages) |
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Grant, Thomas D. On the Path to AI : Law's Prophecies and the Conceptual Foundations of the Machine Learning Age. 1st ed. Cham : Springer International Publishing AG, 2020. ©2020. 1 online resource (163 pages) text txt rdacontent computer c rdamedia online resource cr rdacarrier Intro -- Prologue-Starting with Logic -- Holmes and His Legacy -- A Note on Terminology: Machine Learning, Artificial Intelligence, and Neural Networks -- Notes -- Contents -- About the Authors -- Abbreviations -- 1 Two Revolutions -- 1.1 An Analogy and Why We're Making It -- 1.2 What the Analogy Between a Nineteenth Century Jurist and Machine Learning Can Tell Us -- 1.3 Applications of Machine Learning in Law-And Everywhere Else -- 1.4 Two Revolutions with a Common Ancestor -- 2 Getting Past Logic -- 2.1 Formalism in Law and Algorithms in Computing -- 2.2 Getting Past Algorithms -- 2.3 The Persistence of Algorithmic Logic -- 3 Experience and Data as Input -- 3.1 Experience Is Input for Law -- 3.2 Data Is Input for Machine Learning -- 3.3 The Breadth of Experience and the Limits of Data -- 4 Finding Patterns as the Path from Input to Output -- 4.1 Pattern Finding in Law -- 4.2 So Many Problems Can Be Solved by Pure Curve Fitting -- 4.3 Noisy Data, Contested Patterns -- 5 Output as Prophecy -- 5.1 Prophecies Are What Law Is -- 5.2 Prediction Is What Machine Learning Output Is -- 5.3 Limits of the Analogy -- 5.4 Probabilistic Reasoning and Prediction -- 6 Explanations of Machine Learning -- 6.1 Holmes's "Inarticulate Major Premise" -- 6.2 Machine Learning's Inarticulate Major Premise -- 6.3 The Two Cultures: Scientific Explanation Versus Machine Learning Prediction -- 6.4 Why We Still Want Explanations -- 7 Juries and Other Reliable Predictors -- 7.1 Problems with Juries, Problems with Machines -- 7.2 What to Do About the Predictors? -- 8 Poisonous Datasets, Poisonous Trees -- 8.1 The Problem of Bad Evidence -- 8.2 Data Pruning -- 8.3 Inferential Restraint -- 8.4 Executional Restraint -- 8.5 Poisonous Pasts and Future Growth -- 9 From Holmes to AlphaGo -- 9.1 Accumulating Experience -- 9.2 Legal Explanations, Decisions, and Predictions. 9.3 Gödel, Turing, and Holmes -- 9.4 What Machine Learning Can Learn from Holmes and Turing -- 10 Conclusion -- 10.1 Holmes as Futurist -- 10.2 Where Did Holmes Think Law Was Going, and Might Computer Science Follow? -- 10.3 Lessons for Lawyers and Other Laypeople -- Epilogue: Lessons in Two Directions -- A Data Scientist's View -- A Lawyer's View -- Selected Bibliography -- Index. Description based on publisher supplied metadata and other sources. Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2024. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries. Electronic books. Wischik, Damon J. Print version: Grant, Thomas D. On the Path to AI Cham : Springer International Publishing AG,c2020 9783030435813 ProQuest (Firm) https://ebookcentral.proquest.com/lib/oeawat/detail.action?docID=6219747 Click to View |
language |
English |
format |
eBook |
author |
Grant, Thomas D. |
spellingShingle |
Grant, Thomas D. On the Path to AI : Law's Prophecies and the Conceptual Foundations of the Machine Learning Age. Intro -- Prologue-Starting with Logic -- Holmes and His Legacy -- A Note on Terminology: Machine Learning, Artificial Intelligence, and Neural Networks -- Notes -- Contents -- About the Authors -- Abbreviations -- 1 Two Revolutions -- 1.1 An Analogy and Why We're Making It -- 1.2 What the Analogy Between a Nineteenth Century Jurist and Machine Learning Can Tell Us -- 1.3 Applications of Machine Learning in Law-And Everywhere Else -- 1.4 Two Revolutions with a Common Ancestor -- 2 Getting Past Logic -- 2.1 Formalism in Law and Algorithms in Computing -- 2.2 Getting Past Algorithms -- 2.3 The Persistence of Algorithmic Logic -- 3 Experience and Data as Input -- 3.1 Experience Is Input for Law -- 3.2 Data Is Input for Machine Learning -- 3.3 The Breadth of Experience and the Limits of Data -- 4 Finding Patterns as the Path from Input to Output -- 4.1 Pattern Finding in Law -- 4.2 So Many Problems Can Be Solved by Pure Curve Fitting -- 4.3 Noisy Data, Contested Patterns -- 5 Output as Prophecy -- 5.1 Prophecies Are What Law Is -- 5.2 Prediction Is What Machine Learning Output Is -- 5.3 Limits of the Analogy -- 5.4 Probabilistic Reasoning and Prediction -- 6 Explanations of Machine Learning -- 6.1 Holmes's "Inarticulate Major Premise" -- 6.2 Machine Learning's Inarticulate Major Premise -- 6.3 The Two Cultures: Scientific Explanation Versus Machine Learning Prediction -- 6.4 Why We Still Want Explanations -- 7 Juries and Other Reliable Predictors -- 7.1 Problems with Juries, Problems with Machines -- 7.2 What to Do About the Predictors? -- 8 Poisonous Datasets, Poisonous Trees -- 8.1 The Problem of Bad Evidence -- 8.2 Data Pruning -- 8.3 Inferential Restraint -- 8.4 Executional Restraint -- 8.5 Poisonous Pasts and Future Growth -- 9 From Holmes to AlphaGo -- 9.1 Accumulating Experience -- 9.2 Legal Explanations, Decisions, and Predictions. 9.3 Gödel, Turing, and Holmes -- 9.4 What Machine Learning Can Learn from Holmes and Turing -- 10 Conclusion -- 10.1 Holmes as Futurist -- 10.2 Where Did Holmes Think Law Was Going, and Might Computer Science Follow? -- 10.3 Lessons for Lawyers and Other Laypeople -- Epilogue: Lessons in Two Directions -- A Data Scientist's View -- A Lawyer's View -- Selected Bibliography -- Index. |
author_facet |
Grant, Thomas D. Wischik, Damon J. |
author_variant |
t d g td tdg |
author2 |
Wischik, Damon J. |
author2_variant |
d j w dj djw |
author2_role |
TeilnehmendeR |
author_sort |
Grant, Thomas D. |
title |
On the Path to AI : Law's Prophecies and the Conceptual Foundations of the Machine Learning Age. |
title_sub |
Law's Prophecies and the Conceptual Foundations of the Machine Learning Age. |
title_full |
On the Path to AI : Law's Prophecies and the Conceptual Foundations of the Machine Learning Age. |
title_fullStr |
On the Path to AI : Law's Prophecies and the Conceptual Foundations of the Machine Learning Age. |
title_full_unstemmed |
On the Path to AI : Law's Prophecies and the Conceptual Foundations of the Machine Learning Age. |
title_auth |
On the Path to AI : Law's Prophecies and the Conceptual Foundations of the Machine Learning Age. |
title_new |
On the Path to AI : |
title_sort |
on the path to ai : law's prophecies and the conceptual foundations of the machine learning age. |
publisher |
Springer International Publishing AG, |
publishDate |
2020 |
physical |
1 online resource (163 pages) |
edition |
1st ed. |
contents |
Intro -- Prologue-Starting with Logic -- Holmes and His Legacy -- A Note on Terminology: Machine Learning, Artificial Intelligence, and Neural Networks -- Notes -- Contents -- About the Authors -- Abbreviations -- 1 Two Revolutions -- 1.1 An Analogy and Why We're Making It -- 1.2 What the Analogy Between a Nineteenth Century Jurist and Machine Learning Can Tell Us -- 1.3 Applications of Machine Learning in Law-And Everywhere Else -- 1.4 Two Revolutions with a Common Ancestor -- 2 Getting Past Logic -- 2.1 Formalism in Law and Algorithms in Computing -- 2.2 Getting Past Algorithms -- 2.3 The Persistence of Algorithmic Logic -- 3 Experience and Data as Input -- 3.1 Experience Is Input for Law -- 3.2 Data Is Input for Machine Learning -- 3.3 The Breadth of Experience and the Limits of Data -- 4 Finding Patterns as the Path from Input to Output -- 4.1 Pattern Finding in Law -- 4.2 So Many Problems Can Be Solved by Pure Curve Fitting -- 4.3 Noisy Data, Contested Patterns -- 5 Output as Prophecy -- 5.1 Prophecies Are What Law Is -- 5.2 Prediction Is What Machine Learning Output Is -- 5.3 Limits of the Analogy -- 5.4 Probabilistic Reasoning and Prediction -- 6 Explanations of Machine Learning -- 6.1 Holmes's "Inarticulate Major Premise" -- 6.2 Machine Learning's Inarticulate Major Premise -- 6.3 The Two Cultures: Scientific Explanation Versus Machine Learning Prediction -- 6.4 Why We Still Want Explanations -- 7 Juries and Other Reliable Predictors -- 7.1 Problems with Juries, Problems with Machines -- 7.2 What to Do About the Predictors? -- 8 Poisonous Datasets, Poisonous Trees -- 8.1 The Problem of Bad Evidence -- 8.2 Data Pruning -- 8.3 Inferential Restraint -- 8.4 Executional Restraint -- 8.5 Poisonous Pasts and Future Growth -- 9 From Holmes to AlphaGo -- 9.1 Accumulating Experience -- 9.2 Legal Explanations, Decisions, and Predictions. 9.3 Gödel, Turing, and Holmes -- 9.4 What Machine Learning Can Learn from Holmes and Turing -- 10 Conclusion -- 10.1 Holmes as Futurist -- 10.2 Where Did Holmes Think Law Was Going, and Might Computer Science Follow? -- 10.3 Lessons for Lawyers and Other Laypeople -- Epilogue: Lessons in Two Directions -- A Data Scientist's View -- A Lawyer's View -- Selected Bibliography -- Index. |
isbn |
9783030435820 9783030435813 |
callnumber-first |
Q - Science |
callnumber-subject |
Q - General Science |
callnumber-label |
Q175 |
callnumber-sort |
Q 3175.4 255 |
genre |
Electronic books. |
genre_facet |
Electronic books. |
url |
https://ebookcentral.proquest.com/lib/oeawat/detail.action?docID=6219747 |
illustrated |
Not Illustrated |
dewey-hundreds |
300 - Social sciences |
dewey-tens |
300 - Social sciences, sociology & anthropology |
dewey-ones |
303 - Social processes |
dewey-full |
303.4834 |
dewey-sort |
3303.4834 |
dewey-raw |
303.4834 |
dewey-search |
303.4834 |
oclc_num |
1161874734 |
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On the Path to AI : Law's Prophecies and the Conceptual Foundations of the Machine Learning Age. |
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