Artificial Intelligence Books

Bean's book certainly presents a fresh approach to reasoning about causation. His ideas on causation under time reversal and backward causation are interesting, indeed. The reader will find this work inclusive of physics, Artificial Intelligence and Philosophy quite stimulating.

Bean is an original and careful thinker. His ideas challenge refutation. Those interested in physics, philosophy, and cybernetics will find this work to be a stimulating departure from traditional views on causality.

It's a great book and easy to read. I am learning alot from it.

Written by a student of Noam Chomsky, this book introduces the classic approach to natural language processing using Prolog. Prolog is a programming language used for writing programs that model human logic and decision making. It is particalarly well suited for natural language processing. The book assumes very limited computer programming knowledge, and should be especially useful to linguists who want to introduce themselves to natural language computing.

The book begins with an indepth discussion of the old and the new theory of the computer intelligence and artificial intelligence machines. Then it goes on to discusses topics such as, different levels of human language structure, morphological parsers, recursion (affixes on the affixes), regular and irregular morphology, rule governed creativity, top down and bottom up parsing. Practically every linguistic phenomenon discussed is accompanied with an example Prolog program.

This book is a must-have for any student of linguistics wishing to learn how to use the AI features of Prolog to do natural language computing.

A very approachable introduction to backpropagation nets, and covers Fukushima's Cognitron and Neocogniton as well.

An excellent introductory book for the first timers who wish to know about neural networks

This is an undergraduate introduction to data mining. The book doesn't go into details. It may be suitable for people who want to get a quick feel of the data mining field. People who need more details shall read more serious and comprehensive introductions. Overall I am giving 4 stars, because I liked it.

This book is an excellent introduction to data mining, concentrating primarily on decision tree induction. The material provided is presented clearly with no assumption of prior knowledge on the part of the reader. A weakness of the book is that it doesn't place the material provided within the larger context of machine learning, both in terms of breadth or depth. However, when used as a textbook the instructor could easily address this problem.

This book guides you through formal systems useful for reasoning about uncertainty. If you've ever wondered about the rationale for probability theory or for ways to overcome its limitation, this is the book for you.

The author made an effort to make the book as self-contained as possible (a remarkable achievement given what it covers), so this book is very clear. The examples are short, but illuminating and motivating, so this book is interesting. The author always tries to justify why the axioms of a theory were chosen a certain way, so this book is insightful.

Even if you have just a passing interest in probability theory, I highly recommend this book. It will not only give you reasons for the definitions in probability theory, but also powerful alternative (and often complementary) ways of reasoning about uncertainty.

If you're completely at home with first-order logic and with probability, you're may be ready to extend some of those ideas. This book examines a range of topics that push logic and probability into wider, more interesting areas.

After a brief introduction, Halpern introduces upper and lower probabilities representing partial knowledge, and other measures representing belief, plausibility, possibility, and necessity. These are built up in a rigorous way, but with plenty of physical significance at each step - these aren't just axiomatic systems put together for their inherent elegance. The next few chapters build up a logical sequence of constructs around these measures, including independence, conditioning, and expectation. I expected to see confidence intervals generalized into these terms, but Halpern may have considered those to be exercises for the reader.

From these pieces, Halpern builds frameworks for real-world decision making. This includes the ability update knowledge (and ignorance) in the presence of new facts. It also includes modal logics, based on the variability of "truth" according to the time at which an assertion is made or the person by whom it it made, and "counterfactuals" that reason about events that could have occurred but didn't. And, whenever Halpern presents a new approach, he's also careful to point out where its weaknesses are.

This isn't for beginners, by any means. The successful reader is flexible about the axioms to use in an analytic system, and is able and willing to follow along with dense logical notation. One should not expect this to cover the whole world of soft logics - traditional fuzziness gets only brief mention, for example. The best parts of this presentation extend familiar probabilistic terms (such as expectation) well beyond their original frameworks, creating a more unified view of various belief measures than I've seen elsewhere. If you have a serious interest in soft logic, formal reasoning, and mathematical tools for AI, I recommend this book very highly.

-- wiredweird

1. P2 robot was developed over 10 years at a cost of $100 million. Its successor is Asimo with the ability to walk, run, turn, greet, and deliver a coffee tray.

2. The Marsokhod rover has six wheels on movable axles allow it to climb over rocks 1.5 times the height of its deeply ridged conically shaped wheels.

3. In the USA, Sandia National Laboratories has a hopper in a plastic shell the size of a grapefruit. Using a built-in compass and a gimbal mechanism with a moveable weight, it cal roll around to right itself after each jump. A small internal combustion engine with enough fuel for about 4,000 hops drives a piston into the ground, generating a leap three fee and six feet forward.

4. The Nagoya brachiator has 14 motors controlling a fully articulated body. A separate stereo-canera setup connected to a computer determines where the brachiator's arms are, updated 60 times per second. Using basic equations for swinging and knowledge of distance between handholds the Brachiators is able to swing between branches.

5. Alan DiPietro of iRobot has created robot gecko feet allowing the robot to walk up a wall. The German MAKRO Project of 1997-2000 developed a multisegment robot to inspect the interior of sewerage pipes. The snake-like robot could travel down the pipe autonomously and was seen as a cheaper and much more effective way of carrying out inspections. Shigeo Hirose built a simple snakebot with serpentine motion by placing wheels under each modular section. Snakebot II developed by Mark Yim incorporated some autonomous behavior.

6. David Barret, in 1995, built a robot tuna. Controlled by six servo motors each rated at 2 horsepower, it had force sensors at various locations along the path of its controlling tendons. Mitsubishi Heavy Industries built a robotic sea bream, in which the tail fin and two pectoral fins and controlled by desktop computer giving the robotic fish a top speed of 0.82 feet per second.

7. iRobot is interested in creating a legged robot that can scuttle along the bed of a river or lake, a robotic crab with possible uses for detecting mine detection.

8. Lucy by Steve Grand does have a lot of knowledge, but the designer of the robot claims it has the ability to mimic, "Many people still think of the brain as a passive receptor of information. I think of perception as a much more active process. As conscious beings, we don't live in a real-world-we live in a virtual world inside our heads. Most of the time this internal world is closely synchronized to the external world-our model matches reality, tracks it, and predicts it. When we dream or when we image things, we disconnect from the real world and let the model run on its own. Although the same mechanism are at work in both cases, the synchronization with reality is missing when we dream or think. The model is the crucial thing: perception is an active process, in which we use this model to predict, hypothesize about, and correct data fed in by our senses-filling in details when the data is incomplete and being surprised when reality fails to live up to the model."

9. Smelly, a University of Portsmouth robot has two tubes containing a smell sensor sensitive to alcohol. The sensor is connected to a bridge circuit and its resistance changes when an organic compound is absorbed by the sensor film, allowing the concentration to be measured. Hiroshi Kobayashi uses electric actuators beneath robot skin to create the appearance of facial expressions. The actuators are made from shape memory alloys, metals which are easily deformed when the current is flowing and returns to its original shape when the current stops.

10. Stirling Cricket uses ANN to control its movements and behave similarly to a female cricket seeking a mate. Female crickets home in on males by listening to their chirping song. Sound reaches a crickets eardrums-located on its forelegs-both directly and via internal tubes. When the robot hears a sound from its right a signal passes down to its motor via the right-hand neurons, at the same time inhibiting the passage of any signal from the left-hand neurons, and the robot cricket moves toward the source. Pine Labs have pioneered a method of sitting cultured neurons on multi-electrode substrates - 60 electrodes made of the transparent conductor indium-tin oxide on a glass substrate-allowing their electrical activity to be monitored. A gas-permeable membrane made of Teflon protects the cultured neurons and allows them to be kept alive for two years or more. Steven Potter has connected the neurons to an animat, a simulated mouse moving around a virtual maze in 3-D graphical environment. Electrical signals from the neurons are picked up by the electrodes and converted to movement commands.

11. Duke University connected electrodes to the brain of a monkey and recorded brain activity as the monkey reached for food and data from the actions were feed into a ANN. When the monkey reached for food, the ANN could predict its muscle movements and send the instructions to a robotic arm.

12. Fred and Ginger are two robots that can work together to carry out a task. Each robots that have square plates that can move forward or back, left or right.

13. Sandia National Laboratories have been developing swarm robots for find a source. Each robot continually informs others of its position and the strength of the signal it is receiving from the source. The streams of information allow each member to continually refine its search allow the robots to find the source four times faster than any published method.

14. Hiroaki Kitano established Robocup. The ultimate challenge was to develop an team of humanoid robots that could defeat the human world champions by 2050. The rules change each year as research groups get better at their tasks. The robots must recognize where they are on the field, whether they are in attack or defend mode, recognize other teammates, and execute complex trajectory projections of the ball.

15. The piezoelectric effect uses mechanical energy-pressure, to the polarized crystals-the bending results in an electrical current. Touch sensoring is going to be crucial allowing the robot to feel and prevent squashing items it picks up. The degree of skin material elasticity will determine the amount of electric charge.

16. Hiroshi Kobayashi work concentrates on robot facial expressions that can accurately mimic human expressions. The more real the robot looks the more human like its behavior is expected to be.

Research into artificial intelligence has been undergoing a roller coaster ride in the past four decades. Promises were made, but were never fulfilled as to the building of intelligent machines. Both the military and industry were interested in robotics, and industry got what it needed at the time, in the context of manufacturing, but these robots were by no means intelligent. Lately a new wave of optimism in artificial intelligence has appeared, and one will naturally wonder if this optimism is justified. Highly advanced intelligent machines have been predicted to arise in the next two decades, but it remains to be seen if the research in artificial intelligence will allow this to come to fruition.

This brief but insightful book is about the ongoing efforts to build intelligent robots. It gives though a healthy dose of skepticism, and that serves to remind the reader that a lot of hard work is ahead if these types of machines are to be built. The author emphasizes the viewpoint that basing intelligence on the human model as was done in the last thirty years has not resulted in advances in artificial intelligence. Therefore, the author looks to other more simple forms of life to obtain a model of intelligence. Indeed, in the book one finds robots based on snakes, monkeys, flies, cockroaches, grasshoppers, crabs, pikes, birds, orangutans, tortoises, lobsters, crickets, lampreys, dogs, and platypuses. It remains to be seen if this approach will lead to the rise of intelligent machines, but the book does give a highly interesting overview of what has been accomplished to date using this approach. The acceptance of robots and their practical use could perhaps be done best by introducing them as objects we are familiar with. Pet robots or robots that perform useful but restricted functions as already begun in the marketplace, with impressive results.

The author discusses some interesting work on just how to employ robots in the field so that they are able to function and obtain energy autonomously. Anyone who has owned a pet robot understands the aggravation of the frequent need to recharge batteries. The author gives the example of the "SlugBot", which captures real slugs, drops them into a methane-producing biomass generator, which produces electricity for the robot. The engineering difficulties of this approach are enormous of course, and the author is careful to point this out. Farmers though, would appreciate the assistance of these slug-exterminator robots. Other strategies that deal with the "recharging" problem are discussed, such as the one of building "robot ecosystems".

The author also includes a very brief discussion on "robot cars", pointing out that autonomous cars are already a reality. The legal environment though is the only real impediment to their being put into production, as the author points out. This and human factors, such as the trust that an individual must feel in permitting the car to deliver him safely to the destination, will play a major role in the acceptance of robot cars, and robots in general. Humans need to know that the robots are smart enough, and adept enough physically, to assist them in tasks that might bring them physical harm.

Robot toys in the form of "baby bots" are also discussed in the book: the "Robota doll", which was designed to react to touch and handling and to the presence of a human. The author discusses the negative reaction of child development experts to robot dolls, the claim being that children may perhaps be confused about whether the doll is really alive. She raises the question as to whether the money spent on robot doll research would best be spent on child playgroups. Her question is an interesting one, and the answer to it will determine the economic plausibility of developing robots. If a certain need can be met without robots, and at a substantially less cost, there will be no incentive to bring robots to the marketplace, in the area in question. Researchers and business people are going to have to scale down the cost for intelligent robots if they are to become normal additions to the human community.

No book about robots could be complete without a discussion of nanotechnology, and the author does this in the context of the physics. The accelerations and momenta of nanobots is not a problem that researchers need to be concerned with, contrary to the case of large robots. The author also discusses the possibility of using DNA as a "chemical glue" to assemble molecule-size nanobots. This brings in to the picture the use of genetic engineering to assist in the manufacture of these nanobots, a prospect that is utterly fascinating.

The material is presented clearly and comprehensively from the unique perspective of the SOM originator himself. The inclusion of exhaustive references is particularly useful for the prospective researcher, but, at the risk of sounding ungrateful, I'm curious as to why paper titles were not included in the citations? Overall though, a very good reference.

This is a wonderfully written, and excellent book. It assumes only minimal background knowledge but imparts a great deal of insight. I love the way that the author describes this area and the connections with deep and beautiful mathematics.

Art and music used to be thought of as two fields of human endeavor that could never be realized by artificial intelligence. That belief is still held by many, but in the past few decades painstaking and dedicated research in artificial intelligence has shown beyond doubt that not only can non-human machines compose music that is beautiful to listen to, but one can use these machines essentially as tutors, giving keen insight into musical composition and music theory. The musical expertise non-human machines allows a deeper and richer appreciation of music, and the music they produce will continue to stir the senses and interrupt, or perhaps dominate, the normal course of cognition.

Via a collection of research articles, this book gives a splendid representation of what was done in using the field of artificial intelligence to understand music theory and composition up until the year 1992. The last twelve years of course, thanks mostly to faster and more powerful hardware, has seen considerable advances in musical artificial intelligence. The quality of music composed by the machines is astounding, and considering that hardware is continuing to get more powerful (and cheaper), it will be interesting to see what the musical abilities of the machines will be a decade from now.

The book essentially defines itself as an overview of `cognitive musicology', which as Otto E. Laske asserts, is a field that began in the 1970s, and has as its goal the understanding of both musical thought and `musicological' thought, and their links to `musical action.' It has its origins in many different fields, such as cognitive psychology, neuroscience, artificial intelligence, and semiotics, and attempts to model musical knowledge, but does so in a way that does not separate knowledge from action. Laske wants to move away from the Cartesian paradigm, believing that it is inadequate for music research. He also believes, interestingly, that there is a `musical intelligence' that is distinct from various other types of "intelligences" that can exist in humans. Thus cognitive musicology should be viewed as a field that studies this musical cognitive system, and this study can be done independently of the research into other forms of intelligences, such as linguistic or mathematical.
Laske breaks up the field of cognitive musicology into: `local knowledge', which is knowledge about the tools and materials needed; `competence', which is knowledge about the domain; and `performance,' which is knowledge of how to perform under real-time constraints. The integration of work in cognitive musicology with computing machines is essential according to Laske, for this will allow the view of music and musicology as essentially knowledge engineering. Artificial intelligence is and essential part of cognitive musicology he argues, since it introduces a task-oriented perspective on music, which had not been done in music theory at that time.

The article by Peter Kugel follows the one by Laske, wherein Kugel argues that the strict computational framework of Laske may be inadequate for some forms of musical thought. To make his case on the limitations of computation, he introduces what he calls an `announcement condition.' This is a method by which one can tell with certainty whether a procedure has finished doing its job. This motivates the idea of a `limiting computation', which is one that allows a solution to a problem that a "regular" computation can't. Musical thinking, Kugel asserts, requires limiting computations, and he discusses various methods for finding examples of musical thinking that require more than regular computations. Interestingly, Kugel uses Cantor diagonal arguments to find, or at least indicate how to find, examples of new styles of composition. There are problems he says that can be found by "technique", but others require "insight", and the difference between these does involve the level of knowledge of the problem solver. One can turn some problems requiring insight into ones that do not, but there are some problems, such as those involving musical creation, that cannot be. No explicit examples are given however.

Many other very interesting articles follow, all discussing various aspects of how to model musical activity, connections with artificial intelligence, automated musical composition, etc. One particularly interesting article is the one by Kemal Ebcioglu on designing an expert system for harmonizing chorales in the style of Bach using first-order predicate logic. Written first in PROLOG on a VAX 11 architecture (which shows the age of the article), Ebcioglu describes how a language called BSL (for Backtracking Specification Language) was designed for the purposes at hand. The language was constructed so as to permit the coding of universal and existential quantifiers, be efficient for producing high-quality music, and yet be tractable and easy to use. An illustration of the syntax of the language is given using the 8-queens problem and an informal description is given of the semantics of the language. The search technique of backtracking plays, as the name of the language implies, a central role, but it is implemented in a manner that makes it less inefficient than the usual backtracking techniques that are implemented in LISP and PROLOG. The author then describes the CHORAL system, which allows the representation of Bach chorales and their harmonization.

Understanding Music with AI is an introduction to Cognitive Musicology, the term understood as a science of mental representations of music. The book introduces formal models for otherwise taken-for-granted musical activities such as composing, analyzing music, listening, performing music, etc. In contrast to present-day work with neural nets, theories introduced in the book test their models at the level of cognition, rather than perception or brain processes. The distinction between listening and perception, e.g., is thought to be fundamental, in that listening is based on meaning-making, and comprises perception as a mere subprocess. There is also an attempt to do justice to creativity, especially in music composition. As a consequence, criteria of theoretical adequacy are different in AI- and network-based music research. For a critique of the premises of AI work, see Marc Leman, "Adequacy criteria for models of musical cognition," in J.N. Tabor (Editor), Otto Laske: Navigating new musical horizons, The Greenwood Press, 1999, pp. 93-120. Readers not interested in these academic matters will enjoy reading the book for the intricacy of the systems displayed therein, and the pioneering spirit of the contributors. Otto Laske, Co-Editor (1992).

I think I am a pretty intelligent and well read guy. I am a fan of a variety of different types of SF, but this book simply didn't work for me. I've seen people who are obvious fans of this book lambaste the one-star reviewer in the comments section saying they must be semi-literate, inbred NASCAR fans if they didn't like this book. To you people I say, "Grow up." I'll stack my IQ up against yours any day.

Like many other one star reviews, I point to the heavy usage of unexplained jargon. More importantly, however, is that the book is so disjointed that it is difficult to determine precisely what is taking place at any given time. Is there a plot? I couldn't figure this out and after forcing myself to read half the book I decided that life was too short and set it aside.

Peter Hamilton is an SF writer who does a good job of creating futuristic technologies and presenting them in a way that the reader gets it and becomes immersed in the world he creates. I simply could not get into Gibson's world. I'm not sure I would want to.

I understand this book launched the Cyberpunk movement. Excuse me my ignorance, but I guess I don't really understand what Cyberpunk is. If this is it, then I'll happily steer clear. Give me a good John Varley book any day.

If you want to read an excellent SF story that shows a fantastical future with bizarre implications of powerful AI, then check out Varley's Steel Beach. I cannot recommend it enough.

I found this book to be horrendous, if not outright painful. Perhaps the cyberpunk genre isn't my bag, but considering that my trade currently is (and has been for almost a decade now) computer programming, it should warrant a greater appreciation for the technical aspects of the novel. Unfortunately, the ideas within Neuromancer were so far fetched that it just came off as cartoonish.

In my opinion, Gibson awkwardly complicates ideas/vocabulary, in an attempt to show off erudition in technology and history, but comes off as pseudointellectual and immature. The style offers little payoff (if any) when the definition of terms manifest in later chapters and distracts from an already weak premise. The detective elements offered a hint of something to come, but the incongruous jargon and unlikeable characters left much to be desired.

I have to admit it that Neuromancer is the first fictional book (out of hundreds) I wasn't able to see to the end. I read 174 pages out of 270, and threw in the towel. Granted, Gibson occasional offered descriptive imagery which many tout poetic. Despite this, it took everything I had just to finish chapter after trite chapter, finding that with each completed page I was farther and farther away from an enjoyable plot.

While I did enjoy the book, it wasn't anywhere the world's greatest novel that many seem to say it is. The plot was shallow, the characters were decent but also a little shallow. The world was an ok futuristic setting, defiantly fits as a cyberpunk genre.

The book is a little confusing, many of the aspects are never really explained. And the ending was a build to something great and then just fizzled out. But even with that being said I defiantly would recommend reading it because it's a ok novel.

gibson does an excellent job at creating and transporting us to a world and culture which is very much unlike and like the world we currently reside. as always there is a strangeness to the tone and i find myself very much stuck in whatever mood gibson desires the reader to feel. and then he makes you laugh with delight at the sheer imagination he exhibits. in other words, i really wish he was one of those really prolific authors so instead of an occasional treat we would receive a glutton's feast.

Neuromancer bends your thoughts and concepts with its theme of man integrating self with computerized Artificial Intelligence - what we commonly dub as AI.

Written in a style reminiscent to James Cain, Micky Spillane, Dashiell Hammett and other authors of that 19th century mystery genre, the book keeps you on your toes about what will happen when the mainframe's "matrix" ghosts collide - will their be a pulse eliminating computer use for a period of time, or will things improve?

The writing revolves around an antihero - not a guy who does this for the "good." He is a washed up hacker who abuses his system with drugs. He became washed up when "He'd made the classic mistake, the one he'd sworn he'd never make. He stole from his employers. . . They damaged his nervous system with a wartime Russian mycotoxin."And, so the protagonist Case is offered a second chance in this book, by a man named Armitage and a woman named Molly.

By now, you may have guessed that some thing of this book are familiar - a rebellious young man melding with a computer: sounds like Neal in the blockbuster trilogy of "The Matrix." Wikipedia hints of this being the story which influenced the same. There definitely is a similarity. In the end, when Case is as confused as the "Matrix" audiences, he asks the computer generated human form, "So what are you." The computer responds, "I'm the matrix, Case."

Case's entry into the computer - jacking up - brings on communications with the dead - Linda and Flatline. Reminiscent to Phil Dick's "Ubik." And like the Phil Dick novel, "Neuromancer" entails a David versus Goliath International Corporation - against the conglomerate which created and sponsored the hardware which intentionally or unintentionally creates the AI which confronts mankind.

This book also reminds me of Dan Brown's "Digital Fortress" - a geek's equivalent to "The Da Vinci Code" as the chase is not about church relics, but about computer software. The complexities and intricacies of the computer are more described in Brown's book, but conceptually there are many parallels.

Gibson won the science-fiction "triple crown" for this novel --the Nebula Award, the Philip K. Dick Award, and the Hugo Award in 1984 (could there be a better year to win?). Interestingly, having read it today, I could grasp some concepts - LED, pixels, RAM, ROM, firewalls - which I probably would not have understood in 1984. In many ways, it still is too descriptive of the computer concepts for this reader. But, the accuracy of the same astounds me and proves that he was a knowledgeable "computer person" who also is a gifted fiction writer.

Androids is my favorite sci-fi book of all time & is the inspiration for one of the best movies of all time, Blade Runner. The movie & book are very different. Androids deals with future competing religions, the extinction of all animal life & humanities' use of android pets, mood enhancing technologies & other aspects that Blade Runner does not even touch. However the "bad guy" Roy's character (played by Rutger Hower) is much more nuanced in the film; we genuinely empathize with this complex character, even while he is committing unspeakable acts. Finally, the cinematography & music of Bladerunner are unmatched.

Having watched Blade Runner before reading this, I feel that my perspective throughout the book was a bit tainted, but I enjoyed it. It's a solid read if you enjoy science fiction, if I do say so myself.

Philip K Dick did very well on this book. I was a little dissapointed though in the fact that the retirement of the Nexus 6 was straight to the point and quick unlike the movie Blade Runner where there was more suspense. But with the exception of that it is a quick read and brilliant.

Do Androids Dream of Electric Sheep? follows a bounty hunter working for the San Francisco police in the year 2021. This particular bounty hunter doesn't track down humans, but rather androids. If this sounds familiar at all, it might be because the book was adapted into the movie Blade Runner (Four-Disc Collector's Edition) starring Harrison Ford. For those who have seen the film version, do not expect much consistency between the book and movie. It's probably more accurate to say that Bladerunner was inspired by this book, rather than adapted from it.

Our bounty hunter, Rick Deckard, is assigned to track down a half dozen androids of a new and more intelligent type than any previously created. Along the way, his experiences cause him to question a good deal about himself including his profession.

After reading this book, I can see why Dick is sometimes compared to Kafka as a writer. There is an odd, surreal quality to the world he creates and a recitation of the entire plot would sound fairly absurd in parts. Yet, I found it a compelling read even if I didn't have a perfect literal understanding of every scene. I would compare this to an abstract painting that evokes an emotion from you even if you're not quite certain why. I found the book to be thought provoking and enjoyable but if you're looking for a straightforward action tale in a sci fi setting you are unlikely to happy with this tale.

If you think you know what this book is about because you've seen the movie Blade Runner, you are mistaken. Only the character names and some of the settings / situations were lifted from this book for the movie. As in most books, there is a lot more going on here. Because the movie is so highly engrained in our (real?) memories, it is difficult to talk about one without contrasting it to the other, sadly. That said, this is a classic that any SF fan (philosophy major, medical student, or engineer) should read.

Blade Runner completely missed the invented religion / technology of Mercerism and the mood organ device. Later authors like William Gibson have PKD to thank for pioneering concepts such as these. How can a religion and technology be one?

In the book, Mercerism combined with nuclear fallout explain why animals are so expensive (and coveted) in the future. Why does an electric sheep exist (pride, vanity, religious devotion)? The mood organ usage contains references to the cold war (and presumed imminent nuclear war) - husband and wife "dialing up" the desire to win an argument at all costs.

The double yellow center line between human and androids is blurred often- taking the reader across into oncoming traffic. Did Deckard pass the VK test? Rachel and Pris are the same model android? What does it mean to have feelings? Why would an android seek revenge?

This was my first Kindle novel purchase. I no longer have a desire to dial 888 on my mood organ (desire to watch TV regardless of what is on). I'm going to dial up more PKD, Gibson, and others instead!

BTW, to get the "Hit me with a rock" reference, you have to read the book...