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On the path to AI = Law’s prophecies...
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SpringerLink (Online service)
On the path to AI = Law’s prophecies and the conceptual foundations of the machine learning age /
Record Type:
Language materials, printed : Monograph/item
Title/Author:
On the path to AI/ by Thomas D. Grant, Damon J. Wischik.
Reminder of title:
Law’s prophecies and the conceptual foundations of the machine learning age /
Author:
Grant, Thomas D.
other author:
Wischik, Damon J.
Description:
XXII, 147 p. 4 illus.online resource. :
Contained By:
Springer Nature eBook
Subject:
Technology—Sociological aspects. -
Online resource:
https://doi.org/10.1007/978-3-030-43582-0
ISBN:
9783030435820
On the path to AI = Law’s prophecies and the conceptual foundations of the machine learning age /
Grant, Thomas D.
On the path to AI
Law’s prophecies and the conceptual foundations of the machine learning age /[electronic resource] :by Thomas D. Grant, Damon J. Wischik. - 1st ed. 2020. - XXII, 147 p. 4 illus.online resource.
Prologue: Starting with logic -- CHAPTER 1: Two Revolutions -- CHAPTER 2: Getting past logic -- CHAPTER 3: Experience and data as input -- CHAPTER 4: Finding patterns as the path from input to output -- CHAPTER 5: Output as prophecy -- CHAPTER 6: Explanations of machine learning -- CHAPTER 7: Juries and other reliable predictors -- CHAPTER 8: Poisonous datasets, poisonous trees -- CHAPTER 9: From Holmes to AlphaGo -- CHAPTER 10:Conclusion -- EPILOGUE: Lessons in two directions.
Open Access
This open access book explores machine learning and its impact on how we make sense of the world. It does so by bringing together two ‘revolutions’ in a surprising analogy: the revolution of machine learning, which has placed computing on the path to artificial intelligence, and the revolution in thinking about the law that was spurred by Oliver Wendell Holmes Jr in the last two decades of the 19th century. Holmes reconceived law as prophecy based on experience, prefiguring the buzzwords of the machine learning age—prediction based on datasets. On the path to AI introduces readers to the key concepts of machine learning, discusses the potential applications and limitations of predictions generated by machines using data, and informs current debates amongst scholars, lawyers and policy makers on how it should be used and regulated wisely. Technologists will also find useful lessons learned from the last 120 years of legal grappling with accountability, explainability, and biased data. .
ISBN: 9783030435820
Standard No.: 10.1007/978-3-030-43582-0doiSubjects--Topical Terms:
1266803
Technology—Sociological aspects.
LC Class. No.: HM846-851
Dewey Class. No.: 303.483
On the path to AI = Law’s prophecies and the conceptual foundations of the machine learning age /
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Prologue: Starting with logic -- CHAPTER 1: Two Revolutions -- CHAPTER 2: Getting past logic -- CHAPTER 3: Experience and data as input -- CHAPTER 4: Finding patterns as the path from input to output -- CHAPTER 5: Output as prophecy -- CHAPTER 6: Explanations of machine learning -- CHAPTER 7: Juries and other reliable predictors -- CHAPTER 8: Poisonous datasets, poisonous trees -- CHAPTER 9: From Holmes to AlphaGo -- CHAPTER 10:Conclusion -- EPILOGUE: Lessons in two directions.
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This open access book explores machine learning and its impact on how we make sense of the world. It does so by bringing together two ‘revolutions’ in a surprising analogy: the revolution of machine learning, which has placed computing on the path to artificial intelligence, and the revolution in thinking about the law that was spurred by Oliver Wendell Holmes Jr in the last two decades of the 19th century. Holmes reconceived law as prophecy based on experience, prefiguring the buzzwords of the machine learning age—prediction based on datasets. On the path to AI introduces readers to the key concepts of machine learning, discusses the potential applications and limitations of predictions generated by machines using data, and informs current debates amongst scholars, lawyers and policy makers on how it should be used and regulated wisely. Technologists will also find useful lessons learned from the last 120 years of legal grappling with accountability, explainability, and biased data. .
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