Artificial Intelligence
Artificial Intelligence Books
Product Description
Artificial Intelligence is the study of how to build or program computers to enable them to do what minds can do. This volume discusses the ways in which computational thoughts and computer modeling can aid our understanding of human and animal minds. Major theoretical approaches are outlined, as well as some promising recent developments. Fundamental philosophical questions are discussed by the side of with topics such as: the differences between symbolic and connectionist AI, plotting and problem solving, knowledge representation, learning, expert systems, thought, natural language, creativity, and human-computer interaction. This volume is apposite for any psychologist, philosopher, or computer scientist wanting to know the current state of the art in this area of cognitive science.
Key Features
* Up-to-date account of how computational thoughts and techniques are relevant to psychology
* Includes discussions of “classical” (symbolic) AI, of connectionism (neural nets), of evolutionary programming, and of A-Life
* Discusses a wide range of psychology from low-level thought to creativity
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This book is a collection of articles that give a honest representation of the status of artificial intelligence in the mid 1990’s. In only a decade since those times, the field has expanded considerably, frequently due to applications and the rise of the Internet. Controversy as to the nature and characterization of machine intelligence continues of course, and one finds both intense criticism and uncritical optimism of the nature and future of artificial intelligence. Most of this takes place in the philosophical literature, but there are also places in mainstream AI circles where predictions of future developments are overly optimistic. This optimism is refreshing but it can be a distraction for those that are sincerely working to develop helpful applications of artificial intelligence. But also, cynicism and negativism have also thwarted research in AI, there having been a few cases that showed much promise, but were abandoned because the researchers were convinced by others that their thoughts were unsound. The AM and EURISKO efforts in automated mathematics, which are discussed briefly in this book, are excellent examples of this.
All of the articles in this book are fascinating, but there are a few that stand out due to their penetrating insight on matters that are still of fantastic interest in artificial intelligence. One of these is the article entitled “Creativity” by Margaret A. Boden, who is the editor of the book, and who has done some outstanding work in the elucidation of what it means for a machine (human or otherwise) to be creative. Her insights on this theme are many, and in the attitude of this reviewer her works should be required reading for all those interested in the origins of creativity and attempts to implement it in non-human machines.
Boden asserts that every case of creativity cannot be clarified by a single scientific theory, one reason for this being that an AI develop must be evaluated, and such an evaluation is outside the realm of science. In addition, there is a high variability in creative psychological processes, which disqualify a general understanding of them. Lastly, creativity is very idiosyncratic but Boden is careful to top out that it is not random, but instead theme to constraints in `conceptual space’. Boden does not define conceptual chairs from a mathematical standpoint in her article, but she does discuss their utility in modeling creativity in AI, and the role of AI models in making more rigorous the conceptual chairs employed by musicologists, literary critics, etc. Boden’s thoughts in this article on the mapping, exploring, and transforming conceptual chairs can be viewed in the context of dynamical systems, and such a view allows them to be more easily coded into a machine language. Boden discusses several examples of AI models of the arts, such as connectionist models of music, the AARON program for generating line drawings, and the Letter Spirit project, which tries to develop the perception of alphabetic style. She points out the pluses and minuses in each of these examples, such as the limited compositional ability of connectionist models, the limited evaluative and self-correcting powers of AARON, and the lack of Letter Spirit in being able to justify its own decisions. AI models of science are also discussed, and Boden concludes that most of these are `data driven’ and cannot identify relevance for themselves. She believes that they can learn, but their discoveries are `exploratory’, and do not succeed in changing their own conceptual chairs.
The status of machine intelligence for scientific discovery has changed quite a bit since this article was written. Techniques from inductive judgment programming coupled with quicker hardware are making the reality of automated scientific discovery closer with every passing year in the twenty-first century. With more powerful hardware on the horizon, these developments give evidence of a time when drawing on the efforts of their human tutors, machines will be able to reckon across scientific domains and formulate hypotheses and creative thoughts that may far surpass anything that has been done by human scientists. The time scales needed for this scientific discovery to take place may be so small that it might be trying for human observers to assimilate these new consequences in order to evaluate their efficacy and applicability. The machines though may have their own opinions on the utility of the thoughts and theories they derive.
Rating: 4 / 5