Philip C. Jackson - Toward Human-Level Artificial Intelligence: Representation and Computation of Meaning in Natural Language (Dover Books on Mathematics)

Toward
Human-Level
Artificial
Intelligence

Representation and Computation of Meaning in Natural Language

Philip C. Jackson, Jr.

Dover Publications, Inc.

Mineola, New York

Copyright

Copyright 2019 by Philip C. Jackson, Jr.

All rights reserved. No part of this work may be reproduced in any form without the written permission of Philip C. Jackson, Jr.

Bibliographical Note

Toward Human-Level Artificial Intelligence: Representation and Computation of Meaning in Natural Language is a new work, first published by Dover Publications, Inc., in 2019.

International Standard Book Number

ISBN-13: 978-0-486-83300-2

ISBN-10: 0-486-83300-3

Manufactured in the United States by LSC Communications

83300301

www.doverpublications.com

2 4 6 8 10 9 7 5 3 1

2019

Dedication

To the memory of my parents, Philip and Wanda Jackson.

To my wife Christine.

Table of Contents

Figures

Notation and Overview of Changes

The notation refers to chapters and sections in this book. For example, .

This book combines text from a doctoral thesis with research papers based on the thesis, and elaborates some topics with further thoughts.

Relative to the thesis (Jackson, 2014) :

The half-page Abstract has been replaced by a one-page Synopsis.

New material was added in .

2.3.3.2.2 was moved into .

.

A new .

The previous .

New epigraphs have been used for some chapters.

The infinity symbol is shown after each epigraph, to represent the potential scope of human-level artificial intelligence. Previously, each epigraph was followed by an icon for an open book.

Quotations were removed where permissions did not cover a commercial book and possible translation to foreign languages.

To improve readability, first-person pronouns are now used in several places, rather than references to the author.

Synopsis

This book advocates an approach to achieve human-level artificial intelligence, based on a doctoral thesis (Jackson, 2014).

While a Turing Test may help recognize human-level AI if it is created, the test itself does not define intelligence or indicate how to design, implement, and achieve human-level AI.

The doctoral thesis proposes a design-inspection approach: to define human-level intelligence by identifying capabilities achieved by human intelligence and not yet achieved by any AI system, and to inspect the internal design and operation of any proposed system to see if it can in principle support these capabilities.

These capabilities will be referred to as higher-level mentalities. They include human-level natural language understanding, higher-level learning, metacognition, imagination, and artificial consciousness.

To implement the higher-level mentalities, the thesis proposes a novel research approach: Develop an AI system using a language of thought based on the unconstrained syntax of a natural language; Design the system as a collection of concepts that can create and modify concepts, expressed in the language of thought, to behave intelligently in an environment; Use methods from cognitive linguistics such as mental spaces and conceptual blends for multiple levels of mental representation and computation.

The thesis endeavors to address all the major theoretical issues and objections that might be raised against this approach, or against the possibility of achieving human-level AI in principle. No insurmountable objections are identified, and arguments refuting several objections are presented.

The thesis describes the design of a prototype demonstration system, and discusses processing within the system that illustrates the potential of the research approach to achieve human-level AI.

If it is possible to achieve human-level AI, then it is important to consider whether human-level AI should be achieved. So, this book discusses economic risks and benefits of AI, considers how to ensure that human-level AI and superintelligence will be beneficial to humanity, and identifies reasons why human-level AI may be necessary for humanitys survival and prosperity.

Preface

It is important to thank everyone who helped make the thesis possible, and who contributed to my research on artificial intelligence over the years, though time and space would make any list incomplete.

I am grateful to Professor Dr. Harry Bunt of Tilburg University and Professor Dr. Walter Daelemans of the University of Antwerp, for their encouragement and insightful, objective guidance of the thesis research and exposition. It was a privilege and a pleasure to work with them. I am also grateful to the other members of the thesis review committee for their insightful questions during the thesis defense in 2014: Dr. Filip A. I. Buekens, Professor Dr. H. Jaap ven den Herik, Professor Dr. Paul Mc Kevitt, Dr. Carl Vogel, and Dr. Paul A. Vogt.

Most doctoral dissertations are written fairly early in life, when memories are fresh of all who helped along the way, and auld acquaintances are able to read words of thanks. These words are written fairly late in life, regretfully too late for some to read.

I am grateful to all who have contributed directly or indirectly to my studies and research on artificial intelligence and computer science, in particular:

John McCarthy , Michael Cunningham, Ira Pohl, Edward Feigenbaum, Bertram Raphael, William McKeeman, David Huffman, Michael Tanner, Frank DeRemer, Ned Chapin, John Grafton, James Q. Miller, Bryan Bruns, David Adam, Noah Hart, Marvin Minsky, Donald Knuth, Nils Nilsson, Faye Duchin, Douglas Lenat, Robert Tuggle, Henrietta Mangrum, Warren Conrad, Edmund Deaton, Bernard Nadel, Thomas Kaczmarek, Carolyn Talcott, Richard Weyhrauch, Stuart Russell, Igor Aleksander, Helen Morton, Richard Hudson, Vyv Frederick Evans, Michael Brunnbauer, Jerry Hobbs, Laurence Horn, Brian C. Smith, Philip N. Johnson-Laird, Charles Fernyhough, Antonio Chella, Robert Rolfe, Brian Haugh, K. Brent Venable, Jerald Kralik, Alexei Samsonovich, David J. Kelley, Peter Lindes, William G. Kennedy, Arthur Charlesworth, Joscha Bach, Patrick Langley, John Laird, Christian Lebiere, Paul Rosenbloom, John Sowa.

They contributed in different ways, such as teaching, questions, guidance, discussions, reviews of writings, permissions for quotations, collaboration, and/or correspondence. They contributed in varying degrees, from sponsorship to encouragement, lectures, comments, conversations, objective criticisms, disagreements, or warnings that I was overly ambitious. I profoundly appreciate all these contributions. To be clear, in thanking these people it is not claimed they would agree with everything Ive written or anything in particular.

It is appropriate to acknowledge the work of Noah Hart. In 1979, he asked me to review his senior thesis, on use of natural language syntax to support inference in an AI system. I advised the approach was interesting, and could be used in a system of self-extending concepts to support achieving human-level AI, which was the topic of my graduate research. Later, I forgot salient information such as his surname, the title of his paper, its specific arguments, syntax and examples, etc. It has now been over 39 years since I read his paper, which if memory serves was about 20 pages.

My research on the doctoral thesis initially investigated developing a mentalese based on conceptual graphs, to support natural language understanding and human-level AI. Eventually it was clear that was too difficult in the time available, because the semantics to be represented were at too high a level. So, I decided to explore use of natural language syntax, starting from first principles. Eventually it appeared this approach would be successful and, wishing to recognize Harts work, I used resources on the Web to identify and contact him. He provided the title in the Bibliography, but said it was unpublished and he could not retrieve a copy. He recalled about his system: