Download PDF Thinking about Android Epistemology (American Association for Artificial Intelligence)
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Thinking about Android Epistemology (American Association for Artificial Intelligence)
Download PDF Thinking about Android Epistemology (American Association for Artificial Intelligence)
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About the Author
Kenneth M. Ford is Founder and Director of the Florida Institute for Human and Machine Cognition (IHMC) in Pensacola.
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Product details
Series: American Association for Artificial Intelligence
Paperback: 384 pages
Publisher: AAAI Press; Updated Version edition (March 24, 2006)
Language: English
ISBN-10: 0262562170
ISBN-13: 978-0262562171
Product Dimensions:
6 x 0.5 x 9 inches
Shipping Weight: 14.4 ounces
Average Customer Review:
4.6 out of 5 stars
4 customer reviews
Amazon Best Sellers Rank:
#1,164,852 in Books (See Top 100 in Books)
First of all I must admit that I bought this book because I liked the title of it: "Thinking about Android Epistemology" sounds quite exciting and when I looked at the content I was sure that this was the book I had been waiting for. To tell you the truth so far it has met if not exceeded my expectations.Maybe therefore the rating is just four stars, still, it has lived up to my hopes for some interesting and instructive hours in my favorite easy-chair.One thing that I especially appreciate in this book is that it consists of short articles, scientific in their orientation, and therefore make it easy to get the author's point after a few pages. Especially I liked Marvin Minksy's report on "Alienable Rights", where he lets an "Apprentice" and a "Surveyor" discuss this item in a funny and instructive way and at the same time put human cognition on the test bench.However, I should mention that I am not a computer scientist and that I am more interested in the philosophical and psychological point of view since and what will be the consequences and the fall out of these coming changes in human self-awareness. I am convinced that we live in an age of fundamental change - I am thinking of consciousness, singularity, machines - where our human centered beliefs and convictions are proved and will be changed in a way that we not yet can imagine.Therefore I like to take part of the opinions of philosphers like Daniel Dennet, Marvin Minsky, the grand old man in computer science, or Herbert Simon who bring this subject down to earth and let even the interested layman take part and let him understand which changes are to be awaited. I would like to thank Kenneth M. Ford Clark Glymour and Patrick J. Hayes for putting together this book and publish it and put the "science of intelligence" on "computational wings".
Good product at a good price.
Android Epistemology, as defined in this book, seeks to answer fundamental questions about the nature of Artificially Intelligent machines. The theme of the book, "thinking about" machine intelligence, unfolds in a set of often-entertaining essays by philosophers, cognitive scientists and computer scientists. The topics explored here are quite diverse, and are typically presented in an informal, discursive style. Curious computer scientists specializing in AI will find the book useful for the purpose of surveying part of the philosophical underpinnings of AI. Especially insightful was the historical introduction by the book's editors, which included a trace of the intellectual heritage of AI pioneers to philosopher mentors. Conversely, non-computer scientists should have their minds expanded by essays such as those by Simon and Churchland, which clearly and concisely spell out and rigorously defend designs of architectures of machine intelligence.The book is divided into four parts, each containing essays that roughly seek to answer the following questions:1. Can machines be intelligent? (Part I)2. Is humans and machine intelligence based on the same underlying design principles? (Part II)3. What limitations, if any, to designing intelligent systems are provided by the frame problem? (Part III)4. What are the range of human traits that machines can exhibit? (part IV)Among the essays in part one, Clark Glymour's entertaining "Silicon Reflections" shows by way of a clever fable that the claim that networks comprised of "artificial sensory and motor nerves" cannot have mental states, i.e., can't think, feel, or understand, is hard to defend. The key is imagining an advance in medical technology whereby hybrid brains, part electro-mechanical, part brain matter, are possible. The underlying argument is a sort of Sorites paradox: if the result of replacing one brain cell in a brain with mental states with a mechanical equivalent is also a brain with mental states, then repeating this "operation" one more time should have the same effect; hence repeating it until the brain has completely been mechanized will produce something that has mental states. To avoid this conclusion, the "Dretskeans" (read: Searleans, deniers of the mechanical mind) are forced to either extreme or ad-hoc positions. This essay also incorporates the theme, repeated in the closing article of this book and in other publications by the editors, that machine intelligence as a technology offers humans a sort of cognitive prosthesis, a way of augmenting the native capabilities of the human mind.An essential reading for anyone interested in the foundations of machine intelligence is Herb Simon's contribution. The language here is remarkably clear, lucid and bold. It cuts through the rhetoric and nonsense that accompanied much of the debate around the Chinese room argument, giving each premise in the argument against AI its proper amount of space (which is often less than a sentence). Among the themes Simon discusses here that still make up the fundamental challenges for engineers of machine intelligence are:1. The focus on the response-time requirements of models that we build for decision-makers, devising concise, tractable representations of a complex search state for problem solving. He notes that the set of representations forms an ordered class on which notions of equivalence can be defined. He stresses the need for scalability and the importance of laboratory prototypes. It is clear that Simon always envisioned sophisticated agents observing and changing the world.2. The importance of "nearly decomposable" systems. This implies a layered architecture for control and deliberation with different levels of abstraction. Simon is clearly aware of the challenges of complexity in intelligent systems, and his comments about decomposable systems are also relevant to issues related to verification.3. The fact that processing in intelligent systems, whether human or machine, is distributed and parallel. Consequently, architectural issues of structure and "style" (how components interact) are important.4. Reasoning with "Ill-structured phenomena", part of what today is called reasoning under uncertainty. Simon recognizes that imposing structure on ill-structured phenomena often forces a non-propositional representational framework. This insight is clearly reflected in the field of AI today.Simon also boldly asserts that some arguments against machine intelligence are based on a failure to draw the proper distinctions between what is essential for mind vs. what is not. In the latter category he discusses things like intention, consciousness, motivation, and awareness. Simon's article offers a complete and general set of principles that form the underpinnings for an architecture of machine intelligence. Another nice essay in part two is Paul Churchland's technically detailed and crisp response to the charge that the content of consciousness cannot be mapped to an activation pattern in the brain, because the latter differ between individuals, whereas the former do not.The best essays in part three are contributions by Daniel Dennett and Henry Kyburg. The frame problem, as Dennett notes, is an "installation problem", a problem of creating a concise, finite model of action that can be used by an android to autonomously plan actions. The connection to autonomy is required; teleoperated systems, or systems like the MER rovers who are commanded remotely on the ground, do not suffer from the frame problem. Dennett speculates that the solution may reside in a shift in representational paradigm to something that would be referred to today as state-based planning. On this paradigm, an agent can be viewed as continuously observing the state of the world (a vector of values) and executing a policy on that state, construed as a function from states to actions. A policy can be viewed as a very large look-up table, and no enumeration of consequences of actions is ever required. Of course, devising a policy incurs its own technical challenges; the primary problem is the exponential size of the state space (in the number of variables). Indeed, the main challenge to such state-based approaches is in managing this complexity, but at the same time the frame problem dissolves. Dennett combines a serious discussion with playful stabs at academic philosophers, who emerge as simultaneously intellectually lazy (coming up with meaningful explanations are "not their problem") and expert at pointing out the obvious.Kyburg picks up on many of Dennett's themes in his contribution. His notion of practical certainty anticipates recent developments in probabilistic robotics [thrun]. Specifically, his description of how beliefs are updated from new observations seems to map directly into what filtering algorithms do. Again, shifting the representational paradigm from propositions to one based on utilities dissolves the frame problem into a belief distribution.Part four contains in general the weakest entries in the collection. The essay by Sterrett proposes a variation of the Turing Test for intelligence in terms of the ability of intelligent agents to "override instincts or habits". The problem with this "test" is that it is clearly not empirically verifiable. A native Martian watching a MER rover traverse around a large rock might conclude it is overriding its habit of traversing in a straight line. The designers of the AutoNav system on MER would no doubt respond that its actions are completely habitual; faced with similar obstacles, the AutoNav system would always respond in the way observed. Sterrett's essay in general suffers from an obviously superficial understanding of AI architectures. The reader gets the sense of being invited to be impressed by the fact that an academic philosopher with very little technical knowledge of AI is able to come to grips successfully and accept the idea of machine intelligence.However, this book is in general a stimulating, fun read.
Thinking About Android Epistemology, by Kenneth M. Ford, Clark Glymour and Patrick J. Hayes, includes papers by the editors and other major researchers in Artificial Intelligence including Marvin Minsky, Herb Simon, Anatol Rapoport, Douglas Lenat, and Daniel Dennett. From technical discussions about the Frame Problem to engaging commentary on philosophical topics such as the role of "human essence" and intelligence, many of the major issues of Artificial Intelligence are presented in a clear and easy to comprehend manner by many of the field's pioneers. To varying degrees each paper in the collection stands as a coherent and self-contained contribution. However, much is to be gained by reading the collection from start to finish. The book has few low points and serves as an excellent overview of AI. After reading the book I came away with the view that AI may be the ultimate conjunction of science and philosophy.
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