On the Path to AI : (Record no. 305815)

MARC details
000 -LEADER
fixed length control field 04165nam a22004213i 4500
001 - CONTROL NUMBER
control field EBC6219747
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20240122001217.0
006 - FIXED-LENGTH DATA ELEMENTS--ADDITIONAL MATERIAL CHARACTERISTICS
fixed length control field m o d |
007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION
fixed length control field cr cnu||||||||
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 231124s2020 xx o ||||0 eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9783030435820
Qualifying information (electronic bk.)
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
Canceled/invalid ISBN 9783030435813
035 ## - SYSTEM CONTROL NUMBER
System control number (MiAaPQ)EBC6219747
035 ## - SYSTEM CONTROL NUMBER
System control number (Au-PeEL)EBL6219747
035 ## - SYSTEM CONTROL NUMBER
System control number (OCoLC)1161874734
040 ## - CATALOGING SOURCE
Original cataloging agency MiAaPQ
Language of cataloging eng
Description conventions rda
-- pn
Transcribing agency MiAaPQ
Modifying agency MiAaPQ
050 #4 - LIBRARY OF CONGRESS CALL NUMBER
Classification number Q175.4-.55
082 0# - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 303.4834
100 1# - MAIN ENTRY--PERSONAL NAME
Personal name Grant, Thomas D.
245 10 - TITLE STATEMENT
Title On the Path to AI :
Remainder of title Law's Prophecies and the Conceptual Foundations of the Machine Learning Age.
250 ## - EDITION STATEMENT
Edition statement 1st ed.
264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE
Place of production, publication, distribution, manufacture Cham :
Name of producer, publisher, distributor, manufacturer Springer International Publishing AG,
Date of production, publication, distribution, manufacture, or copyright notice 2020.
264 #4 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE
Date of production, publication, distribution, manufacture, or copyright notice �2020.
300 ## - PHYSICAL DESCRIPTION
Extent 1 online resource (163 pages)
336 ## - CONTENT TYPE
Content type term text
Content type code txt
Source rdacontent
337 ## - MEDIA TYPE
Media type term computer
Media type code c
Source rdamedia
338 ## - CARRIER TYPE
Carrier type term online resource
Carrier type code cr
Source rdacarrier
505 0# - FORMATTED CONTENTS NOTE
Formatted contents note 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.
505 8# - FORMATTED CONTENTS NOTE
Formatted contents note 9.3 G�odel, 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.
588 ## - SOURCE OF DESCRIPTION NOTE
Source of description note Description based on publisher supplied metadata and other sources.
590 ## - LOCAL NOTE (RLIN)
Local note Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2023. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.
655 #4 - INDEX TERM--GENRE/FORM
Genre/form data or focus term Electronic books.
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Wischik, Damon J.
776 08 - ADDITIONAL PHYSICAL FORM ENTRY
Relationship information Print version:
Main entry heading Grant, Thomas D.
Title On the Path to AI
Place, publisher, and date of publication Cham : Springer International Publishing AG,c2020
International Standard Book Number 9783030435813
797 2# - LOCAL ADDED ENTRY--CORPORATE NAME (RLIN)
Corporate name or jurisdiction name as entry element ProQuest (Firm)
856 40 - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier <a href="https://ebookcentral.proquest.com/lib/bacm-ebooks/detail.action?docID=6219747">https://ebookcentral.proquest.com/lib/bacm-ebooks/detail.action?docID=6219747</a>
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