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Frequently Asked Questions (FAQS);faqs.077 We generally feel that any religion that can't stand to have fun poked at it is in as sad shape as the corresponding kind of person. III. What kinds of neopagan are there, and where did they come from? Depending on who you talk to and what definitions you use, there are between 40,000 and 200,000 neopagans in the U.S.; the true figure is probably closer to the latter than the former, and the movement is still growing rapidly following a major `population explosion' in the late 1970s. The numerically largest and most influential neopagan group is the `Kingdom of Wicca' -- the modern witch covens. Modern witchcraft has nothing to do with Hollywood's images of the cackling, cauldron-stirring crone (though Wiccans sometimes joke about that one) and is actively opposed to the psychopathic Satanism that many Christians erroneously think of as `witchcraft'. Your author is an initiate Wiccan priest and coven leader of long standing. Other important subgroups include those seeking to revive Norse, Egyptian, Amerind, and various kinds of tribal pantheons other than the Greek and Celtic ones that have been incorporated into Wicca. These generally started out as Wiccan offshoots or have been so heavily influenced by Wiccan ritual technique that their people can work comfortably in a Wiccan circle and vice- versa. There are also the various orders of ceremonial magicians, most claiming to be the successors to the turn-of-the-century Golden Dawn or one of the groups founded by Alesteir Crowley during his brillant and notorious occult career. These have their own very elaborate ritual tradition, and tend to be more intellectual, more rigid, and less nature-oriented. They are sometimes reluctant to describe themselves as neopagans. The Discordians (and, more recently, the Discordian-offshoot Church of the Sub-Genius) are few in number but quite influential. They are the neopagan movement's sacred clowns, puncturing pretense and adding an essential note to the pagan festivals. Many Wiccans, especially among priests and priestesses, are also Discordians and will look you straight in the eye and tell you that the entire neopagan movement is a Discordian hoax... Neopaganism used to be largely a white, upper-middle-class phenomenon, but that has been changing during the last ten years. So called `new-collar' workers have come in in droves during the eighties. We still see fewer non-whites, proportionately, than there are in the general population, but that is also changing (though more slowly). With the exception of a few nut-fringe `Aryan' groups detested by the whole rest of the movement, neopagans are actively anti-racist; prejudice is not the problem, it's more that the ideas have tended to be accepted by the more educated segments of society first, and until recently those more educated segments were mostly white. On the East Coast, a higher-than-general-population percentage of neopagans have Roman Catholic or Jewish backgrounds, but figures suggest this is not true nationwide. There is also a very significant overlap in population with science-fiction fandom and the Society for Creative Anachronism. Politically, neopagans are distributed about the same as the general population, except that whether liberal or conservative they tend to be more individualist and less conformist and moralistic than average. It is therefore not too surprising that the one significant difference in distribution is the presence of a good many more libertarians than one would see in a same-sized chunk of the general population (I particularly register this because I'm a libertarian myself, but non-libertarians have noted the same phenomenon). These complexities are obscured by the fact that the most politically active and visible neopagans are usually ex-hippie left-liberals from the 1960s. I think the most acute generalization made about pagans as a whole is Margot Adler's observation that they are mostly self-made people, supreme individualists not necessarily in the assertive or egoist sense but because they have felt the need to construct their own culture, their own definitions, their own religious paths, out of whatever came to hand rather than accepting the ones that the mainstream offers. IV. Where do I find out more? I have deliberately not said much about mythology, or specific religious practice or aims, or the role of magic and to what extent we practice and 'believe' in it. Any one of those is a topic for another posting; but you can get a lot of information from books. Here's a basic bibliography: Adler, Margot _Drawing_Down_the_Moon_ (Random House 1979, hc) This book is a lucid and penetrating account of who the modern neo-pagans are, what they do and why they do it, from a woman who spent almost two years doing observer-participant journalism in the neo-pagan community. Especially valuable because it combines an anthropologist's objectivity with a candid personal account of her own feelings about all she saw and did and how her ideas about the neo-pagans changed under the impact of the experiences she went through. Recommended strongly as a first book on the subject, and it's relatively easy to find. There is now a revised and expanded second edition available. Starhawk _The_Spiral_Dance_ An anthology of philosophy, poetry, training exercises, ritual outlines and instructive anecdotes from a successful working coven. First-rate as an introduction to the practical aspects of magick and running a functioning circle. Often findable at feminist bookstores. Shea, Robert and Wilson, Robert Anton _Illuminatus!_ (Dell, 1975, pb) This work of alleged fiction is an incredible berserko-surrealist rollercoaster that _will_ bend your mind into a pretzel with an acid-head blitzkrieg of plausible, instructive and enlightening lies and a few preposterous and obscure truths. Amidst this eccentric tale of world-girdling conspiracies, intelligent dolphins, the fall of Atlantis, who _really_ killed JFK, sex, drugs, rock and roll and the Cosmic Giggle Factor, you will find Serious Truths about Mind, Time, Space, the Nature of God(dess) and What It All Means -- and also learn why you should on no account take them Seriously. Pay particular attention to Appendix Lamedh ("The Tactics of Magick"), but it won't make sense until you've read the rest. This was first published in 3 volumes as _The_Eye_In_The_Pyramid_, _The_ Golden_Apple_ and _Leviathan_, but there's now a one-volume trade paperback carried by most chain bookstores under SF. Campbell, Joseph W., _The_Masks_of_God_ (Viking Books, 1971, pb) One of the definitive analytical surveys of world mythography -- and readable to boot! It's in 4 volumes: I. _Primitive_Mythology_ II. _Oriental_Mythology_ III. _Occidental_Mythology_ IV. _Creative_Mythology_ The theoretical framework of these books is a form of pragmatic neo-Jungianism which has enormously influenced the neopagans (we can accurately be described as the practice for which Campbell and Jung were theorizing). Note especially his predictions in vols. I & IV of a revival of shamanic, vision-quest-based religious forms. The recent Penguin pb edition of this book should be available in the Mythology and Folklore selection of any large bookstore. Bonewits, Isaac, _Real_Magic_ (Creative Arts Books, 1979, pb) A fascinating analytical study of the psychodynamics of ritual and magick. This was Bonewits's Ph.D. thesis for the world's only known doctorate in Magic and Thaumaturgy (UC Berkeley, 1971). Hardest of the five to find but well worth the effort -- an enormously instructive, trenchant and funny book. -- Eric S. Raymond = esr@snark.thyrsus.com Xref: bloom-picayune.mit.edu comp.ai.neural-nets:8664 news.answers:4240 Path: bloom-picayune.mit.edu!enterpoop.mit.edu!ira.uka.de!uka!prechelt From: prechelt@i41s14.ira.uka.de (Lutz Prechelt) Newsgroups: comp.ai.neural-nets,news.answers Subject: FAQ in comp.ai.neural-nets -- monthly posting Supersedes: <nn.posting_720242283@i41s14.ira.uka.de> Followup-To: comp.ai.neural-nets Date: 28 Nov 1992 03:17:01 GMT Organization: University of Karlsruhe, Germany Lines: 1609 Approved: news-answers-request@MIT.Edu Expires: 2 Jan 1993 03:18:03 GMT Message-ID: <nn.posting_722920683@i41s14.ira.uka.de> Reply-To: prechelt@ira.uka.de (Lutz Prechelt) NNTP-Posting-Host: i41s18.ira.uka.de Keywords: questions, answers, terminology, bibliography Originator: prechelt@i41s18 Archive-name: neural-net-faq Last-modified: 92/11/13 (FAQ means "Frequently Asked Questions") ------------------------------------------------------------------------ Anybody who is willing to contribute any question or information, please email me; if it is relevant, I will incorporate it. But: Please format your contribution appropriately so that I can just drop it in. The monthly posting departs at the 28th of every month. ------------------------------------------------------------------------ This is a monthly posting to the Usenet newsgroup comp.ai.neural-nets (and news.answers, where it should be findable at ANY time) Its purpose is to provide basic information for individuals who are new to the field of neural networks or are just beginning to read this group. It shall help to avoid lengthy discussion of questions that usually arise for beginners of one or the other kind. >>>>> SO, PLEASE, SEARCH THIS POSTING FIRST IF YOU HAVE A QUESTION <<<<< and >>>>> DON'T POST ANSWERS TO FAQs: POINT THE ASKER TO THIS POSTING <<<<< This posting is archived in the periodic posting archive on "pit-manager.mit.edu" [18.172.1.27] (and on some other hosts as well). Look in the anonymous ftp directory "/pub/usenet/news.answers", the filename is as given in 'Archive-name:' header above. If you do not have anonymous ftp access, you can access the archives by mail server as well. Send an E-mail message to mail-server@pit-manager.mit.edu with "help" and "index" in the body on separate lines for more information. The monthly posting is not meant to discuss any topic exhaustively. Disclaimer: This posting is provided 'as is'. No warranty whatsoever is expressed or implied, especially, no warranty that the information contained herein is correct or useful in any way, although both is intended. >> To find the answer of question number <x> (if present at all), search >> for the string "-A<x>.)" (so the answer to question 12 is at "-A12.)") And now, in the end, we begin: ============================== Questions ============================== (the short forms and non-continous numbering is intended) 1.) What is this newsgroup for ? How shall it be used ? 2.) What is a neural network (NN) ? 3.) What can you do with a Neural Network and what not ? 4.) Who is concerned with Neural Networks ? 6.) What does 'backprop' mean ? 7.) How many learning methods for NNs exist ? Which ? 8.) What about Genetic Algorithms ? 10.) Good introductory literature about Neural Networks ? 11.) Any journals and magazines about Neural Networks ? 12.) The most important conferences concerned with Neural Networks ? 13.) Neural Network Associations ? 14.) Other sources of information about NNs ? 15.) Freely available software packages for NN simulation ? 16.) Commercial software packages for NN simulation ? 17.) Neural Network hardware ? 19.) Databases for experimentation with NNs ? ============================== Answers ============================== ------------------------------------------------------------------------ -A1.) What is this newsgroup for ? The newsgroup comp.ai.neural-nets is inteded as a forum for people who want to use or explore the capabilities of Neural Networks or Neural-Network-like structures. There should be the following types of articles in this newsgroup: 1. Requests Requests are articles of the form "I am looking for X" where X is something public like a book, an article, a piece of software. If multiple different answers can be expected, the person making the request should prepare to make a summary of the answers he/she got and announce to do so with a phrase like "Please email, I'll summarize" at the end of the posting. The Subject line of the posting should then be something like "Request: X" 2. Questions As opposed to requests, questions are concerned with something so specific that general interest cannot readily be assumed. If the poster thinks that the topic is of some general interest, he/she should announce a summary (see above). The Subject line of the posting should be something like "Question: this-and-that" or have the form of a question (i.e., end with a question mark) 3. Answers These are reactions to questions or requests. As a rule of thumb articles of type "answer" should be rare. Ideally, in most cases either the answer is too specific to be of general interest (and should thus be e-mailed to the poster) or a summary was announced with the question or request (and answers should thus be e-mailed to the poster). The subject lines of answers are automatically adjusted by the news software. 4. Summaries In all cases of requests or questions the answers for which can be assumed to be of some general interest, the poster of the request or question shall summarize the ansers he/she received. Such a summary should be announced in the original posting of the question or request with a phrase like "Please answer by email, I'll summarize" In such a case answers should NOT be posted to the newsgroup but instead be mailed to the poster who collects and reviews them. After about 10 to 20 days from the original posting, its poster should make the summary of answers and post it to the net. Some care should be invested into a summary: a) simple concatenation of all the answers is not enough; instead redundancies, irrelevancies, verbosities and errors must be filtered out (as good as possible), b) the answers shall be separated clearly c) the contributors of the individual answers shall be identifiable (unless they requested to remain anonymous [yes, that happens]) d) the summary shall start with the "quintessence" of the answers, as seen by the original poster e) A summary should, when posted, clearly be indicated to be one by giving it a Subject line starting with "Summary:" Note that a good summary is pure gold for the rest of the newsgroup community, so summary work will be most appreciated by all of us. (Good summaries are more valuable than any moderator ! :-> ) 5. Announcements Some articles never need any public reaction. These are called announcements (for instance for a workshop, conference or the availability of some technical report or software system). Announcements should be clearly indicated to be such by giving them a subject line of the form "Announcement: this-and-that" 6. Reports Sometimes people spontaneously want to report something to the newsgroup. This might be special experiences with some software, results of own experiments or conceptual work, or especially interesting information from somewhere else. Reports should be clearly indicated to be such by giving them a subject line of the form "Report: this-and-that" 7. Discussions An especially valuable possibility of Usenet is of course that of discussing a certain topic with hundreds of potential participants. All traffic in the newsgroup that can not be subsumed under one of the above categories should belong to a discussion. If somebody explicitly wants to start a discussion, he/she can do so by giving the posting a subject line of the form "Start discussion: this-and-that" (People who react on this, please remove the "Start discussion: " label from the subject line of your replies) It is quite difficult to keep a discussion from drifting into chaos, but, unfortunately, as many many other newsgroups show there seems to be no secure way to avoid this. On the other hand, comp.ai.neural-nets has not had many problems with this effect in the past, so let's just go and hope... :-> ------------------------------------------------------------------------ -A2.) What is a neural network (NN) ? [anybody there to write something better? buzzwords: artificial vs. natural/biological; units and connections; value passing; inputs and outputs; storage in structure and weights; only local information; highly parallel operation ] First of all, when we are talking about a neural network, we *should* usually better say "artificial neural network" (ANN), because that is what we mean most of the time. Biological neural networks are much more complicated in their elementary structures than the mathematical models we use for ANNs. A vague description is as follows: An ANN is a network of many very simple processors ("units"), each possibly having a (small amount of) local memory. The units are connected by unidirectional communication channels ("connections"), which carry numeric (as opposed to symbolic) data. The units operate only on their local data and on the inputs they receive via the connections. The design motivation is what distinguishes neural networks from other mathematical techniques: A neural network is a processing device, either an algorithm, or actual hardware, whose design was motivated by the design and functioning of human brains and components thereof. Most neural networks have some sort of "training" rule whereby the weights of connections are adjusted on the basis of presented patterns. In other words, neural networks "learn" from examples, just like children learn to recognize dogs from examples of dogs, and exhibit some structural capability for generalization. Neural networks normally have great potential for parallelism, since the computations of the components are independent of each other. ------------------------------------------------------------------------ -A3.) What can you do with a Neural Network and what not ? [preliminary] In principle, NNs can compute any computable function, i.e. they can do everything a normal digital computer can do. Especially can anything that can be represented as a mapping between vector spaces be approximated to arbitrary precision by feedforward NNs (which is the most often used type). In practice, NNs are especially useful for mapping problems which are tolerant of a high error rate, have lots of example data available, but to which hard and fast rules can not easily be applied. NNs are especially bad for problems that are concerned with manipulation of symbols and for problems that need short-term memory. ------------------------------------------------------------------------ -A4.) Who is concerned with Neural Networks ? Neural Networks are interesting for quite a lot of very dissimilar people: - Computer scientists want to find out about the properties of non-symbolic information processing with neural nets and about learning systems in general. - Engineers of many kinds want to exploit the capabilities of neural networks on many areas (e.g. signal processing) to solve their application problems. - Cognitive scientists view neural networks as a possible apparatus to describe models of thinking and conscience (High-level brain function). - Neuro-physiologists use neural networks to describe and explore medium-level brain function (e.g. memory, sensory system, motorics). - Physicists use neural networks to model phenomena in statistical mechanics and for a lot of other tasks. - Biologists use Neural Networks to interpret nucleotide sequences. - Philosophers and some other people may also be interested in Neural Networks for various reasons. ------------------------------------------------------------------------ -A6.) What does 'backprop' mean ? [anybody to write something similarly short, but easier to understand for a beginner ? ] It is an abbreviation for 'backpropagation of error' which is the most widely used learning method for neural networks today. Although it has many disadvantages, which could be summarized in the sentence "You are almost not knowing what you are actually doing when using backpropagation" :-) it has pretty much success on practical applications and is relatively easy to apply. It is for the training of layered (i.e., nodes are grouped in layers) feedforward (i.e., the arcs joining nodes are unidirectional, and there are no cycles) nets. Back-propagation needs a teacher that knows the correct output for any input ("supervised learning") and uses gradient descent on the error (as provided by the teacher) to train the weights. The activation function is (usually) a sigmoidal (i.e., bounded above and below, but differentiable) function of a weighted sum of the nodes inputs. The use of a gradient descent algorithm to train its weights makes it slow to train; but being a feedforward algorithm, it is quite rapid during the recall phase. Literature: Rumelhart, D. E. and McClelland, J. L. (1986): Parallel Distributed Processing: Explorations in the Microstructure of Cognition (volume 1, pp 318-362). The MIT Press. (this is the classic one) or one of the dozens of other books or articles on backpropagation :-> ------------------------------------------------------------------------ -A7.) How many learning methods for NNs exist ? Which ? There are many many learning methods for NNs by now. Nobody can know exactly how many. New ones (at least variations of existing ones) are invented every week. Below is a collection of some of the most well known methods; not claiming to be complete. The main categorization of these methods is the distiction of supervised from unsupervised learning: - In supervised learning, there is a "teacher" who in the learning phase "tells" the net how well it performs ("reinforcement learning") or what the correct behavior would have been ("fully supervised learning"). - In unsupervised learning the net is autonomous: it just looks at the data it is presented with, finds out about some of the properties of the data set and learns to reflect these properties in its output. What exactly these properties are, that the network can learn to recognise, depends on the particular network model and learning method. Many of these learning methods are closely connected with a certain (class of) network topology. Now here is the list, just giving some names: 1. UNSUPERVISED LEARNING (i.e. without a "teacher"): 1). Feedback Nets: a). Additive Grossberg (AG) b). Shunting Grossberg (SG) c). Binary Adaptive Resonance Theory (ART1) d). Analog Adaptive Resonance Theory (ART2, ART2a) e). Discrete Hopfield (DH) f). Continuous Hopfield (CH) g). Discrete Bidirectional Associative Memory (BAM) h). Temporal Associative Memory (TAM) i). Adaptive Bidirectional Associative Memory (ABAM) j). Kohonen Self-organizing Map (SOM) k). Kohonen Topology-preserving Map (TPM) 2). Feedforward-only Nets: a). Learning Matrix (LM) b). Driver-Reinforcement Learning (DR) c). Linear Associative Memory (LAM) d). Optimal Linear Associative Memory (OLAM) e). Sparse Distributed Associative Memory (SDM) f). Fuzzy Associative Memory (FAM) g). Counterprogation (CPN) 2. SUPERVISED LEARNING (i.e. with a "teacher"): 1). Feedback Nets: a). Brain-State-in-a-Box (BSB) b). Fuzzy Congitive Map (FCM) c). Boltzmann Machine (BM) d). Mean Field Annealing (MFT) e). Recurrent Cascade Correlation (RCC) f). Learning Vector Quantization (LVQ) 2). Feedforward-only Nets: a). Perceptron b). Adaline, Madaline c). Backpropagation (BP) d). Cauchy Machine (CM) e). Adaptive Heuristic Critic (AHC) f). Time Delay Neural Network (TDNN) g). Associative Reward Penalty (ARP) h). Avalanche Matched Filter (AMF) i). Backpercolation (Perc) j). Artmap k). Adaptive Logic Network (ALN) l). Cascade Correlation (CasCor) ------------------------------------------------------------------------ -A8.) What about Genetic Algorithms ? [preliminary] [Who will write a better introduction?] There are a number of definitions of GA (Genetic Algorithm). A possible one is A GA is an optimization program that starts with some encoded procedure, (Creation of Life :-> ) mutates it stochastically, (Get cancer or so :-> ) and uses a selection process (Darwinism) to prefer the mutants with high fitness and perhaps a recombination process (Make babies :-> ) to combine properties of (preferably) the succesful mutants. Some GA discussion tends to happen in comp.ai.neural-nets. Another loosely relevant group is comp.theory.self-org-sys. Perhaps it is time for a comp.ai.ga, comp.theory.ga or maybe comp.ga There is a GA mailing list which you can subscribe to by sending a request to GA-List-Request@AIC.NRL.NAVY.MIL You can also try anonymous ftp to ftp.aic.nrl.navy.mil in the /pub/galist directory. There are papers and some software. For more details see (for example): "Genetic Algorithms in Search Optimisation and Machine Learning" by David Goldberg (Addison-Wesley 1989, 0-201-15767-5) or "Handbook of Genetic Algorithms" edited by Lawrence Davis (Van Nostrand Reinhold 1991 0-442-00173-8) or "Classifier Systems and Genetic Algorithms" L.B. Booker, D.E. Goldberg and J.H. Holland, Techreport No. 8 (April 87), Cognitive Science and Machine Intelligence Laboratory, University of Michigan also reprinted in : Artificial Intelligence, Volume 40 (1989), pages 185-234 ------------------------------------------------------------------------ -A10.) Good introductory literature about Neural Networks ? 0.) The best (subjectively, of course -- please don't flame me): Hecht-Nielsen, R. (1990). Neurocomputing. Addison Wesley. Comments: "A good book", "comprises a nice historical overview and a chapter about NN hardware. Well structured prose. Makes important concepts clear." Hertz, J., Krogh, A., and Palmer, R. (1991). Introduction to the Theory of Neural Computation. Addison-Wesley: Redwood City, California. Comments: "My first impression is that this one is by far the best book on the topic. And it's below $30 for the paperback."; "Well written, theoretical (but not overwhelming)"; It provides a good balance of model development, computational algorithms, and applications. The mathematical derivations are especially well done"; "Nice mathematical analysis on the mechanism of different learning algorithms"; "It is NOT for mathematical beginner. If you don't have a good grasp of higher level math, this book can be really tough to get through." 1.) Books for the beginner: Aleksander, I. and Morton, H. (1990). An Introduction to Neural Computing. Chapman and Hall. (ISBN 0-412-37780-2). Comments: "This book seems to be intended for the first year of university education." Beale, R. and Jackson, T. (1990). Neural Computing, an Introduction. Adam Hilger, IOP Publishing Ltd : Bristol. (ISBN 0-85274-262-2). Comments: "It's clearly written. Lots of hints as to how to get the adaptive models covered to work (not always well explained in the original sources). Consistent mathematical terminology. Covers perceptrons, error-backpropagation, Kohonen self-org model, Hopfield type models, ART, and associative memories." Dayhoff, J. E. (1990). Neural Network Architectures: An Introduction. Van Nostrand Reinhold: New York. Comments: "Like Wasserman's book, Dayhoff's book is also very easy to understand". McClelland, J. L. and Rumelhart, D. E. (1988). Explorations in Parallel Distributed Processing: Computational Models of Cognition and Perception (software manual). The MIT Press. Comments: "Written in a tutorial style, and includes 2 diskettes of NN simulation programs that can be compiled on MS-DOS or Unix (and they do too !)"; "The programs are pretty reasonable as an introduction to some of the things that NNs can do."; "There are *two* editions of this book. One comes with disks for the IBM PC, the other comes with disks for the Macintosh".