8 1/2 (1963) Ai no corrida (1976) The Apartment Bob le Flambeur (1955) Bicycle Thieves (1948, 8.4) Der Untergang (2004) Double Indemnity Fallen Idol (1948, Caroll Reed) Following His Girl Friday (1940) Kiss Me Deadly (1955) La Dolce Vita (1960) La Regle du Jeu (1939)** Marnie Mouchette (1967) Night of the Hunter (1955) The Others (2001) The Seventh Seal (Det Sjunde inseglet, 1957) They Live by Night (1949) Tokyo Story (1953) Waiting for Guffman Witness for the Prosecution (1957)

<<option chkOpenInNewWindow>> Open Links in New Window <<option chkSaveEmptyTemplate>> Save Empty Template

Compared to [[Siglets]], Alternating color in list views was an easy hack. You can see the results to the left. This makes it much simpler to read the list view, especially when you have Tiddlers that might take up more than one line (as mine tend to do). The corresponding CSS required to manipulate the colors is in StyleSheet .

To Jeremy, and whoever else may look at the code, I apologize profusely for its resemblence to a cancer, or some other malignant disease. The TiddlyWiki code as of the most current release is becoming very clean and pristine, with well defined ways for adding extensions, but I completely ignored this and grafted my hacks into hallowed places, rendering them blighted and unsightly. I apologize. Yes, there are lots of bugs. * The CSS is rickety, and probably doesn't work in IE. -- [[Joe|http://www.cs.utexas.edu/~joeraii]]

//Scott Gilbert// [[Developmental Biology|http://www.amazon.com/gp/reader/0878932585/ref=sib_dp_pt/104-7094928-2123921#reader-link]]

//Richard Feynman// During the Middle Ages there were all kinds of crazy ideas, such as that a piece of of rhinoceros horn would increase potency. Then a method was discovered for separating the ideas--which was to try one to see if it worked, and if it didn't work, to eliminate it. This method became organized, of course, into science. And it developed very well, so that we are now in the scientific age. It is such a scientific age, in fact, that we have difficulty in understanding how witch doctors could ever have existed, when nothing that they proposed ever really worked--or very little of it did. But even today I meet lots of people who sooner or later get me into a conversation about UFO's, or astrology, or some form of mysticism, expanded consciousness, new types of awareness, ESP, and so forth. And I've concluded that it's not a scientific world. Most people believe so many wonderful things that I decided to investigate why they did. And what has been referred to as my curiosity for investigation has landed me in a difficulty where I found so much junk that I'm overwhelmed. First I started out by investigating various ideas of mysticism and mystic experiences. I went into isolation tanks and got many hours of hallucinations, so I know something about that. Then I went to Esalen, which is a hotbed of this kind of thought (it's a wonderful place; you should go visit there). Then I became overwhelmed. I didn't realize how MUCH there was. At Esalen there are some large baths fed by hot springs situated on a ledge about thirty feet above the ocean. One of my most pleasurable experiences has been to sit in one of those baths and watch the waves crashing onto the rocky slope below, to gaze into the clear blue sky above, and to study a beautiful nude as she quietly appears and settles into the bath with me. One time I sat down in a bath where there was a beatiful girl sitting with a guy who didn't seem to know her. Right away I began thinking, "Gee! How am I gonna get started talking to this beautiful nude woman?" I'm trying to figure out what to say, when the guy says to her, "I'm, uh, studying massage. Could I practice on you?" "Sure," she says. They get out of the bath and she lies down on a massage table nearby. I think to myself, "What a nifty line! I can never think of anything like that!" He starts to rub her big toe. "I think I feel it," he says. "I feel a kind of dent--is that the pituitary?" I blurt out, "You're a helluva long way from the pituitary, man!" They looked at me, horrified--I had blown my cover--and said, "It's reflexology!" I quickly closed my eyes and appeared to be meditating. That's just an example of the kind of things that overwhelm me. I also looked into extrasensory perception, and PSI phenomena, and the latest craze there was Uri Geller, a man who is supposed to be able to bend keys by rubbing them with his finger. So I went to his hotel room, on his invitation, to see a demonstration of both mindreading and bending keys. He didn't do any mindreading that succeeded; nobody can read my mind, I guess. And my boy held a key and Geller rubbed it, and nothing happened. Then he told us it works better under water, and so you can picture all of us standing in the bathroom with the water turned on and the key under it, and him rubbing the key with his finger. Nothing happened. So I was unable to investigate that phenomenon. But then I began to think, what else is there that we believe? (And I thought then about the witch doctors, and how easy it would have been to check on them by noticing that nothing really worked.) So I found things that even more people believe, such as that we have some knowledge of how to educate. There are big schools of reading methods and mathematics methods, and so forth, but if you notice, you'll see the reading scores keep going down--or hardly going up--in spite of the fact that we continually use these same people to improve the methods. There's a witch doctor remedy that doesn't work. It ought to be looked into; how do they know that their method should work? Another example is how to treat criminals. We obviously have made no progress--lots of theory, but no progress--in decreasing the amount of crime by the method that we use to handle criminals. Yet these things are said to be scientific. We study them. And I think ordinary people with commonsense ideas are intimidated by this pseudoscience. A teacher who has some good idea of how to teach her children to read is forced by the school system to do it some other way--or is even fooled by the school system into thinking that her method is not necessarily a good one. Or a parent of bad boys, after disciplining them in one way or another, feels guilty for the rest of her life because she didn't do "the right thing," according to the experts. So we really ought to look into theories that don't work, and science that isn't science. I think the educational and psychological studies I mentioned are examples of what I would like to call cargo cult science. In the South Seas there is a cargo cult of people. During the war they saw airplanes with lots of good materials, and they want the same thing to happen now. So they've arranged to make things like runways, to put fires along the sides of the runways, to make a wooden hut for a man to sit in, with two wooden pieces on his head to headphones and bars of bamboo sticking out like antennas--he's the controller--and they wait for the airplanes to land. They're doing everything right. The form is perfect. It looks exactly the way it looked before. But it doesn't work. No airplanes land. So I call these things cargo cult science, because they follow all the apparent precepts and forms of scientific investigation, but they're missing something essential, because the planes don't land. Now it behooves me, of course, to tell you what they're missing. But it would be just about as difficult to explain to the South Sea islanders how they have to arrange things so that they get some wealth in their system. It is not something simple like telling them how to improve the shapes of the earphones. But there is one feature I notice that is generally missing in cargo cult science. That is the idea that we all hope you have learned in studying science in school--we never say explicitly what this is, but just hope that you catch on by all the examples of scientific investigation. It is interesting, therefore, to bring it out now and speak of it explicitly. It's a kind of scientific integrity, a principle of scientific thought that corresponds to a kind of utter honesty--a kind of leaning over backwards. For example, if you're doing an experiment, you should report everything that you think might make it invalid--not only what you think is right about it: other causes that could possibly explain your results; and things you thought of that you've eliminated by some other experiment, and how they worked--to make sure the other fellow can tell they have been eliminated. Details that could throw doubt on your interpretation must be given, if you know them. You must do the best you can--if you know anything at all wrong, or possibly wrong--to explain it. If you make a theory, for example, and advertise it, or put it out, then you must also put down all the facts that disagree with it, as well as those that agree with it. There is also a more subtle problem. When you have put a lot of ideas together to make an elaborate theory, you want to make sure, when explaining what it fits, that those things it fits are not just the things that gave you the idea for the theory; but that the finished theory makes something else come out right, in addition. In summary, the idea is to give all of the information to help others to judge the value of your contribution; not just the information that leads to judgement in one particular direction or another. The easiest way to explain this idea is to contrast it, for example, with advertising. Last night I heard that Wesson oil doesn't soak through food. Well, that's true. It's not dishonest; but the thing I'm talking about is not just a matter of not being dishonest; it's a matter of scientific integrity, which is another level. The fact that should be added to that advertising statement is that no oils soak through food, if operated at a certain temperature. If operated at another temperature, they all will--including Wesson oil. So it's the implication which has been conveyed, not the fact, which is true, and the difference is what we have to deal with. We've learned from experience that the truth will come out. Other experimenters will repeat your experiment and find out whether you were wrong or right. Nature's phenomena will agree or they'll disagree with your theory. And, although you may gain some temporary fame and excitement, you will not gain a good reputation as a scientist if you haven't tried to be very careful in this kind of work. And it's this type of integrity, this kind of care not to fool yourself, that is missing to a large extent in much of the research in cargo cult science. A great deal of their difficulty is, of course, the difficulty of the subject and the inapplicability of the scientific method to the subject. Nevertheless, it should be remarked that this is not the only difficulty. That's why the planes don't land--but they don't land. We have learned a lot from experience about how to handle some of the ways we fool ourselves. One example: Millikan measured the charge on an electron by an experiment with falling oil drops, and got an answer which we now know not to be quite right. It's a little bit off because he had the incorrect value for the viscosity of air. It's interesting to look at the history of measurements of the charge of an electron, after ~Millikan. If you plot them as a function of time, you find that one is a little bit bigger than Millikan's, and the next one's a little bit bigger than that, and the next one's a little bit bigger than that, until finally they settle down to a number which is higher. Why didn't they discover the new number was higher right away? It's a thing that scientists are ashamed of--this history--because it's apparent that people did things like this: When they got a number that was too high above Millikan's, they thought something must be wrong--and they would look for and find a reason why something might be wrong. When they got a number close to Millikan's value they didn't look so hard. And so they eliminated the numbers that were too far off, and did other things like that. We've learned those tricks nowadays, and now we don't have that kind of a disease. But this long history of learning how to not fool ourselves--of having utter scientific integrity--is, I'm sorry to say, something that we haven't specifically included in any particular course that I know of. We just hope you've caught on by osmosis The first principle is that you must not fool yourself--and you are the easiest person to fool. So you have to be very careful about that. After you've not fooled yourself, it's easy not to fool other scientists. You just have to be honest in a conventional way after that. I would like to add something that's not essential to the science, but something I kind of believe, which is that you should not fool the layman when you're talking as a scientist. I am not trying to tell you what to do about cheating on your wife, or fooling your girlfriend, or something like that, when you're not trying to be a scientist, but just trying to be an ordinary human being. We'll leave those problems up to you and your rabbi. I'm talking about a specific, extra type of integrity that is not lying, but bending over backwards to show how you're maybe wrong, that you ought to have when acting as a scientist. And this is our responsibility as scientists, certainly to other scientists, and I think to laymen. For example, I was a little surprised when I was talking to a friend who was going to go on the radio. He does work on cosmology and astronomy, and he wondered how he would explain what the applications of his work were. "Well," I said, "there aren't any." He said, "Yes, but then we won't get support for more research of this kind." I think that's kind of dishonest. If you're representing yourself as a scientist, then you should explain to the layman what you're doing-- and if they don't support you under those circumstances, then that's their decision. One example of the principle is this: If you've made up your mind to test a theory, or you want to explain some idea, you should always decide to publish it whichever way it comes out. If we only publish results of a certain kind, we can make the argument look good. We must publish BOTH kinds of results. I say that's also important in giving certain types of government advice. Supposing a senator asked you for advice about whether drilling a hole should be done in his state; and you decide it would be better in some other state. If you don't publish such a result, it seems to me you're not giving scientific advice. You're being used. If your answer happens to come out in the direction the government or the politicians like, they can use it as an argument in their favor; if it comes out the other way, they don't publish at all. That's not giving scientific advice. Other kinds of errors are more characteristic of poor science. When I was at Cornell, I often talked to the people in the psychology department. One of the students told me she wanted to do an experiment that went something like this--it had been found by others that under certain circumstances, X, rats did something, A. She was curious as to whether, if she changed the circumstances to Y, they would still do A. So her proposal was to do the experiment under circumstances Y and see if they still did A. I explained to her that it was necessary first to repeat in her laboratory the experiment of the other person--to do it under condition X to see if she could also get result A, and then change to Y and see if A changed. Then she would know the the real difference was the thing she thought she had under control. She was very delighted with this new idea, and went to her professor. And his reply was, no, you cannot do that, because the experiment has already been done and you would be wasting time. This was in about 1947 or so, and it seems to have been the general policy then to not try to repeat psychological experiments, but only to change the conditions and see what happened. Nowadays, there's a certain danger of the same thing happening, even in the famous field of physics. I was shocked to hear of an experiment being done at the big accelerator at the National Accelerator Laboratory, where a person used deuterium. In order to compare his heavy hydrogen results to what might happen with light hydrogen, he had to use data from someone else's experiment on light hydrogen, which was done on different apparatus. When asked why, he said it was because he couldn't get time on the program (because there's so little time and it's such expensive apparatus) to do the experiment with light hydrogen on this apparatus because there wouldn't be any new result. And so the men in charge of programs at NAL are so anxious for new results, in order to get more money to keep the thing going for public relations purposes, they are destroying--possibly--the value of the experiments themselves, which is the whole purpose of the thing. It is often hard for the experimenters there to complete their work as their scientific integrity demands. All experiments in psychology are not of this type, however. For example, there have been many experiments running rats through all kinds of mazes, and so on--with little clear result. But in 1937 a man named Young did a very interesting one. He had a long corridor with doors all along one side where the rats came in, and doors along the other side where the food was. He wanted to see if he could train the rats to go in at the third door down from wherever he started them off. No. The rats went immediately to the door where the food had been the time before. The question was, how did the rats know, because the corridor was so beautifully built and so uniform, that this was the same door as before? Obviously there was something about the door that was different from the other doors. So he painted the doors very carefully, arranging the textures on the faces of the doors exactly the same. Still the rats could tell. Then he thought maybe the rats were smelling the food, so he used chemicals to change the smell after each run. Still the rats could tell. Then he realized the rats might be able to tell by seeing the lights and the arrangement in the laboratory like any commonsense person. So he covered the corridor, and still the rats could tell. He finally found that they could tell by the way the floor sounded when they ran over it. And he could only fix that by putting his corridor in sand. So he covered one after another of all possible clues and finally was able to fool the rats so that they had to learn to go in the third door. If he relaxed any of his conditions, the rats could tell. Now, from a scientific standpoint, that is an A-number-one experiment. That is the experiment that makes rat-running experiments sensible, because it uncovers that clues that the rat is really using-- not what you think it's using. And that is the experiment that tells exactly what conditions you have to use in order to be careful and control everything in an experiment with rat-running. I looked up the subsequent history of this research. The next experiment, and the one after that, never referred to ~Mr. ~Young. They never used any of his criteria of putting the corridor on sand, or being very careful. They just went right on running the rats in the same old way, and paid no attention to the great discoveries of ~Mr. Young, and his papers are not referred to, because he didn't discover anything about the rats. In fact, he discovered all the things you have to do to discover something about rats. But not paying attention to experiments like that is a characteristic example of cargo cult science. Another example is the ESP experiments of ~Mr. Rhine, and other people. As various people have made criticisms--and they themselves have made criticisms of their own experiements--they improve the techniques so that the effects are smaller, and smaller, and smaller until they gradually disappear. All the para-psychologists are looking for some experiment that can be repeated--that you can do again and get the same effect--statistically, even. They run a million rats--no, it's people this time--they do a lot of things are get a certain statistical effect. Next time they try it they don't get it any more. And now you find a man saying that is is an irrelevant demand to expect a repeatable experiment. This is science? This man also speaks about a new institution, in a talk in which he was resigning as Director of the Institute of ~Parapsychology. And, in telling people what to do next, he says that one of things they have to do is be sure the only train students who have shown their ability to get PSI results to an acceptable extent--not to waste their time on those ambitious and interested students who get only chance results. It is very dangerous to have such a policy in teaching--to teach students only how to get certain results, rather than how to do an experiment with scientific integrity. So I have just one wish for you--the good luck to be somewhere where you are free to maintain the kind of integrity I have described, and where you do not feel forced by a need to maintain your position in the organization, or financial support, or so on, to lose your integrity. May you have that freedom.

[[Cargo Cult Science]] [[Contract Thinking]] [[Rich Sutton]] [[What's Wrong with Artificial Intelligence]] [[Verification, the Key to AI]]

Momus: >"About the thing about "status thinking" versus "contract thinking". Discoveries like today's should make you veer towards a "contract" model of Japanese society. You should avoid the conclusion that a contract-based society governed my mutual interests and consensual understandings is a conspiracy." Momus: >"I think the biggest problem Marxy and I have is that Marxy is a "status thinker" and I'm a "contract thinker". Marxy, in other words, believes that everything revolves around "the truth of the situation" whereas I believe that everything revolves around a consensual contract between the actors involved. In this I am way more "Japanese" than ~Marxy." Marxy: >"A contract is not necessarily a conspiracy, but I think there's much reason to question exclusionary agreements or contracts not formed with equal access to perfect-information - both of which are dominant in Japanese society."

HelloThere NeededFunctionality [[Clippings]]

Type the text for 'Evolvability and Static vs. Dynamic Fitness' here.

Welcome to Siglet, showcasing some hacked-on features for JeremyRuston's [[TiddlyWiki|http://www.tiddlywiki.com]] (using the ServerSide backend [[pytw]]). First is [[Siglets]], little bits of CSS code based on the first tag of a Tiddly that show off its type (i.e. TODO, Research, Work, Personal, etc). Second is AlternatingLists, which are generally useful for displaying lists with Tiddlys that have long names. You can see these both in action on the left. Finally, the list of tiddlers in the upper left displays what tiddlers are currently open, and gives you a nice clean way of managing the display list without having to scroll all the way down. This implementation is just proof-of-concept, I can't really vouch for its lack of bugs (in fact, at the moment it has several). If you like the concept, I encourage you to look at the implementation and try to improve upon it, if possible (it should be easy :). It is my sincere hope that these features are "nifty" enough to warrant eventual inclusion in the mainstream TiddlyWiki. First, because the code is an ugly hack (see [[Apologia]]), and second because I don't have time to hack javascript all day (thats why god invented Python). !!!Update 9/6/05 * Added drag and drop tiddler reordering, ala [[dragn|http://www.cs.utexas.edu/~joeraii/dragn]]. !!!Update 8/10/05 * Sync with TW 1.2.31 * Merge with [[pytw|http://www.cs.utexas.edu/~joeraii/pytw]] * New StyleSheet * Added folding (from Steve Rumsby's [[YATWA|http://www.rumsby.org/yatwa/]]). Just click on the Tiddler title to fold the tiddler. !!!Update 7/27/05 and older *Sync with TW 1.2.29 *Fixed some of the annoying layout rendering bugs in firefox. *Made the Tidder List in the sidebar update in response to opening/closing tiddlers. This gives you an executive summary of what is currently open, and allows you to close whatever you want, without having to hunting in the display area. *tiddlyLink styles are now respected across all views (in tag lists as well) including border and font colors. With the current configuration, siglets are used in the sidebar, and color-coded tiddly links are used in the main view. Both styles are available in both views, however. *The toolbar button "ref" has been relocated "under" the tiddler name. This is a more natural place. However... *All of the tiddlers linking to a certain page are listed opposite the tags. *Color coded the tags themselves. This helps me immensely. Also moved the references button to the TiddlerTitle. This is more natural and saves toolbar space (considering I've already added "close all" and I intend to add "fold"). *Fixed some unicode problems. Now you can say things like: [[古今東西]] or [[σ-evolution]]！ TiddlyWiki is (c) osmosoft, and Siglet is all BSD-license code.

@@bgcolor(#ff4300):Neuroscientific basis for art. [[Ramachandran]].@@ * Aesthetic fundamentals that transcend culture. @@bgcolor(#71ff4f):Network and Complexity Theory@@ * Social Implications * Culture * Meme theory * General Computation [[http://cscs.umich.edu/~crshalizi/notebooks/complex-networks.html|http://cscs.umich.edu/~crshalizi/notebooks/complex-networks.html]] * Self organization, and emergence. @@bgcolor(#9397ff):Improving Evolutionary Search@@ * Long-term evolution problem, lock in because target is not adaptive and/or it becomes too difficult to effect positive phenotypic change. * Neutral network theory. * Evo-devo in biology, but mostly in the context of improving search efficacy (learning a problem). @@bgcolor(#ff6900):Machine Learning@@ * Or at least how to reliably build models of large data sets. * Unsupervised learning * Semi-supervised learning, including clustering and such. * [[Rich Sutton]]'s work on ~TD-learning. @@bgcolor(#9a00ff):Neural Networks@@ * Models of learning from the brain. * Unsupervised, self-organizing vs. supervised training. * How can self-organization be employed to augment Neuro-evolution? Is evolution even the correct time scale to "learn" problems? Or should there be some component of "training" afterwards? @@bgcolor(#00ffa9):Developmental Biology@@ * In general, developmental biology is really cool. At its most fundamental level development is equivalent to a computer program. The "code" is DNA, and the machinery is the cellular components. But unlike traditional computers where the results are displayed through some device, the "results" of a computation in developmental biology is the morphology, kinematics, and machinery associated with the organism itself. Thus development describes computers that can literally grow themselves from a seed. * Developmental programs are structured in a way much different from traditional programming -- the main differences include massive parallelism, shallow execution depth (shallow gene cascades, but exponential proliferation -- i.e. exploiting parallelism) tightly integrated modular reuse, etc.

version.extensions.listOpen = {major: 0, minor: 1, revision: 0}; config.macros.listOpen = { text: "Hello" }; config.macros.listOpen.handler = function(place,macroName,params) { var tiddlerDisplay = document.getElementById("tiddlerDisplay"); for(var t=0;t<tiddlerDisplay.childNodes.length;t++) { if( tiddlerDisplay.childNodes[t].id ) { var tiddlerName = tiddlerDisplay.childNodes[t].id.substr(7); /*if( tiddlerName.length > 25 ) tiddlerName = tiddlerName.substr(0,25) + "...";*/ createTiddlyLink(place,tiddlerName,true); //createTiddlyElement(place,"br",null,null,""); //refreshEditor(tiddlerName); } } }

version.extensions.listTags = {major: 0, minor: 1, revision: 0}; config.macros.listTags = { text: "Hello" }; config.macros.listTags.handler = function(place,macroName,params) { var tagged = store.getTaggedTiddlers(params[0]); for(var r=0;r<tagged.length;r++) { if( tagged[r].title.toLowerCase() != params[0].toLowerCase() ) { createTiddlyLink(place,tagged[r].title,true); createTiddlyElement(place,"br",null,null,""); } } }

Type the text for 'Measuring Evolvability as the Rate of Complexity Increase' here.

*''"Link-neighborhood"'' representation. Simple representation in the header that shows what tiddlers ref this, but without the explicit "ref" button. Simple graphical would be good. * ''delicious tag integration'' (show delicious entries with same tags) ** e.g. [[http://del.icio.us/rss/joeraii/mathematics,article|http://del.icio.us/rss/joeraii/mathematics,article]] can be scraped * Export to plain text (latex?) * Datagarden integration (upload a file to datagarden, have it visible here somewhere. pref: special files tag) This may be possible with the newest base version. * Probably should make the toolbar follow the screen. This way it will never be somewhere out of access range. !!!!TODO ''May be not our fault:'' * Having no tags is a state to which tags cannot be added. !!!!GOAL * Maximizing serendipity through preemptive search. !!!!Envisioning the perfect Text Editor * ''Organize papers efficiently''. Attach notes to papers. Extract citation contexts (i.e. the last time I cited this paper, it was with this kind of reference). List these. Have searches show context, ala google search. In general scholar.google.com already does this. Maybe keep a wiki of read paper summaries? * ''Delicious/search integration'' -- may be better realized through spotlight integration. Attach delicious links, or other websites, or documents to a certain word, ~EASILY. i.e. right click and say get spotlight results. * ''Vim editing''. The vim gui editor sucks. Is there some way to extract just the text input widget and embed it in some kind of real GUI? Preferably with integrated TODO (iCal integrated) and autosave/session maintenance. Something like kVim for osx would be sweet. * ''~Metadata...'' creation date, tag documents into certain categories. Allow many categories: i.e. URGENT, but also research, gecco, etc. * Really what I want is a way to attach notes and tags to a folder and its contents. This should be fairly realizable through the automator. * Find related information about a document.

''Population vs. representational evolvability, or better: when is a dynamic fitness function necessary (when the representation is unable to perform estimation-of-distribution)?'' When a single organism has the local phenotypic distribution averaged into its fitness, then it can exhibit selection for evolvability. An alternate way of achieving this is through an adaptive fitness function, which ensures that "evolvable" phenotypes, i.e. those that learn the structure of the fitness function are rewarded. [[Designing Evolvable Representations]] / [[An Empirical Measure of Evolvability]] / (The rise of) [[Evolvability in Artificial Evolutionary Systems]] !!!Short Term: * Theorify the measurement process. Currently there are some issues: if an algorithm only lowers its baseline performance, does it have more evolvability than before? (can we take the log of the lowest point?) Adaptive fitness function necessary? * Evolvability is the structure of the local space, more or less, what then, is the ability to choose the local space? Trick question: they're both the same thing. * Work towards a journal publication. Risto will be able to help with this. * Expand to more domains. More difficult kinds of symmetry? What can such a test elucidate? * More theoretical approach to quantifying "information provided about underlying problem structure." Can this be described in an information-theoretic manner? Also can we pin down what training-response shapes are good? !!!Long Term: * Link "underlying problem structure" to the "human knowledge" from NFL work. * What do we want ultimately? Good long-term behavior in evolutionary algorithms. Or really any machine learning algorithm. The problem is convergence and the decline of an easy increasing gradient once search reaches a certain place. The ratio of deleterious mutation to profitable ones becomes so low as to render improvement statistically impossible. Another explanation: it requires too many deleterious mutations to make the final improvement, i.e. we have to undo too much prior evolution in order to catch the remaining 5% or 1% fitness needed. So what we want instead is an algorithm that makes sure to structure early evolution such that these fitness targets are not lost later. What methods can we use to extract underlying structure? Self-organization is a method already optimized for doing this.

''No Free Lunch Refutation'' * Basically when we talk about things like "averaging over all possible problems" we get into issues discussed by Gregory Chaitin, e.g. most of the elements of the set of all possible problems are uncomputable numbers, e.g. purely random. This can be easily shown to render the search spaces purely random as well: the "number" that represents the search space contains all of the (finite) fitness values stored in the following way: enumerate each fitness value as a coordinate on an n-dimensional space (the number n can "vary" but must be bounded, this is easily done except in the real world), then run over the ordered list, concatenating the binary representation of the fitness of the coordinate to yield the real number representing the search space. Random = random. * Now, what do the NFL results really mean, if basically every problem we're averaging over is completely random? Of course all algorithms will perform the same. We're basically just averaging over a terrible problem that we cannot learn from at all. * The solution? Only look at performance on subsets of problems. Proposed by Droste98perhaps Also look at Igel03classes ''Introduction'' What is NFL? Introduce the core problem and explain how it has been applied to cover all search and optimization.

These interface options are saved in your browser Your username for signing your edits. <<option txtUserName>> <<option chkSaveBackups>> Save Backups <<option chkAutoSave>> Auto Save <<option chkGenerateAnRssFeed>> Generate an RSS Feed <<option chkRegExpSearch>> Reg Exp Search <<option chkCaseSensitiveSearch>> Case-sensitive Search <<option chkAnimate>> Enable Animations See AdvancedOptions

* ''Marc Toussaint'' * ''"[[Perspective: Evolvability]]" Kirschner and Gerhart'' Evolvability is an organism's capacity to generate heritable phenotypic variation. * ''"[[Evolvability and Static vs. Dynamic Fitness]]" Glickman and Sycara'' Static fitness functions cause loss of variability/variation and thus degradation of evolvability. * ''"[[Some Representation and Ecological Aspects of Evolvability]]" Tim Taylor'' The aspects of the system providing the "drive" for evolvability has been neglected. Even if a system has a capacity for high evovlability, it will not realize this capacity if the appropriate selection pressures are absent. * ''"[[Measuring Evolvability as the Rate of Complexity Increase]]" Nehaniv'' Theoretical measure of complexity. * ''"[[String Rewriting Grammar Optimized Using an Evolvability Measure]]" Hideaki Suzuki'' ** What is an objective measure for evolvability? ** What are the necessary conditions for an ~ALife system to be highly evolvable? ** How can we optimize an ~ALife system design in terms of evolvability? * ''"[[Evolvability of an RNA Virus is Determined by its Mutational Neighborhood]]" Christina Burch and Lin Chao (Nature)'' * ''"[[Evolvability is a Selectable Trait]]" David Earl and Michael Deem (PNAS)''

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* [[What's Wrong with Artificial Intelligence]] * [[Verification, the Key to AI]]

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[[Siglets]] are bits of CSS code that correspond to a specific tag. In the sidebar at left, the small colored squares are siglets generated from the first tag of each tiddler, note how the color is preserved in the Tiddler title, and the body as well. The shape is preserved across the sidebars and the popups, and the color is preserved everywhere. Using many colors and shapes helps your brain to develop associations faster, as well as makes it easier to scan the page for important information (e.g. TODO entries). Siglets help you keep track of some kind type or class information associated with a certain Tiddly entry, for example a TODO item tiddly, or a tiddly about Work, etc. They allow you to quickly scan a list of tiddlys and pick out what is necessary for a certain task. For example you can tag TODO tiddlies with an obnoxious color so that you make them want to go away. @@color(orange):For something really cool look at the popup menus.@@ @@color(yellow):Currently siglets don't render correctly in the tiddler body or title.@@ This is due to my lack of understanding of div elements, and how to make them not line-break. Ultimately this might require completely re-engineering siglets, but for now, changing the text background color will suffice (although it is ugly, somewhat distracting, and makes things difficult to read in some cases). The CSS code for doing the color, shape, etc., can be found in the top of StyleSheet .

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http://www.cs.utexas.edu/~joeraii/datagarden

Type the text for 'Some Representation and Ecological Aspects of Evolvability' here.

* Measure evolvability as average change in fitness.

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[[Here|http://www.phiffer.org/tiddly]]

//Joseph Reisinger, Kenneth O. Stanley and Risto Miikkulainen (2005). Towards an Empirical Measure of Evolvability, Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2005) Workshop ~Program.// Genetic representations that do not employ a one-to-one mapping of genotype to phenotype are known as indirect encodings, and can be much more efficient than direct encodings for complex problems. Increasing a representation's capacity to facilitate effective search, i.e. its evolvability, has long been a goal of Evolutionary ~Computation. However, currently no benchmarks exist to measure evolvability. One reason is that it is difficult to decouple a representation's capacity to evolve under any fitness function, i.e. the latent evolvability, and its performance on a specific benchmark. Towards this goal, a method is proposed in this paper that measures the representation's ability to extract invariant properties from a changing fitness function. The test is applied to three distinct representations and it is able to distinguish all three. Ultimately, this test can serve as the foundation for performing controlled experiments determining what factors contribute to evolvability.

[[Rich Sutton]] 11/15/2001 It is a bit unseemly for an AI researcher to claim to have a special insight or plan for how his field should proceed. If he has such, why doesn't he just pursue it and, if he is right, exhibit its special fruits? Without denying that, there is still a role for assessing and analyzing the field as a whole, for diagnosing the ills that repeatedly plague it, and to suggest general solutions. The insight that I would claim to have is that the key to a successful AI is that it can tell for itself whether or not it is working correctly. At one level this is a pragmatic issue. If the AI can't tell for itself whether it is working properly, then some person has to make that assessment and make any necessary modifications. An AI that can assess itself may be able to make the modifications itself. The Verification Principle: An AI system can create and maintain knowledge only to the extent that it can verify that knowledge itself. Successful verification occurs in all search-based AI systems, such as planners, game-players, even genetic algorithms. Deep Blue, for example, produces a score for each of its possible moves through an extensive search. Its belief that a particular move is a good one is verified by the search tree that shows its inevitable production of a good position. These systems don't have to be told what choices to make; they can tell for themselves. Image trying to program a chess machine by telling it what kinds of moves to make in each kind of position. Many early chess programs were constructed in this way. The problem, of course, was that there were many different kinds of chess positions. And the more advice and rules for move selection given by programmers, the more complex the system became and the more unexpected interactions there were between rules. The programs became brittle and unreliable, requiring constant maintainence, and before long this whole approach lost out to the "brute force" searchers. Although search-based planners verify at the move selection level, they typically cannot verify at other levels. For example, they often take their state-evaluation scoring function as given. Even Deep Blue cannot search to the end of the game and relies on a human-tuned position-scoring function that it does not assess on its own. A major strength of the champion backgammon program, ~TD-Gammon, is that it does assess and improve its own scoring function. Another important level at which search-based planners are almost never subject to verification is that which specifies the outcomes of the moves, actions, or operators. In games such as chess with a limited number of legal moves we can easily imagine programming in the consequences of all of them accurately. But if we imagine planning in a broader AI context, then many of the allowed actions will not have their outcomes completely known. If I take the bagel to Leslie's office, will she be there? How long will it take to drive to work? Will I finish this report today? So many of the decisions we take every day have uncertain and changing effects. Nevertheless, modern AI systems almost never take this into account. They assume that all the action models will be entered accurately by hand, even though these may be most of the knowledge in or ever produced by the system. Finally, let us make the same point about knowledge in general. Consider any AI system and the knowledge that it has. It may be an expert system or a large database like ~CYC. Or it may be a robot with knowledge of a building's layout, or knowledge about how to react in various situations. In all these cases we can ask if the AI system can verify its own knowledge, or whether it requires people to intervene to detect errors and unforeseen interactions, and make corrections. As long as the latter is the case we will never be able to build really large knowledge systems. They will always be brittle and unreliable, and limited in size to what people can monitor and understand themselves. "Never program anything bigger than your head" And yet it is overwhelmingly the case that today's AI systems are not able to verify their own knowledge. Large ontologies and knowledge bases are built that are totally reliant on human construction and maintainence. "Birds have wings" they say, but of course they have no way of verifying this.

[[Rich Sutton]] 11/12/2001 I hold that AI has gone astray by neglecting its essential objective --- the turning over of responsibility for the decision-making and organization of the AI system to the AI system itself. It has become an accepted, indeed lauded, form of success in the field to exhibit a complex system that works well primarily because of some insight the designers have had into solving a particular problem. This is part of an anti-theoretic, or "engineering stance", that considers itself open to any way of solving a problem. But whatever the merits of this approach as engineering, it is not really addressing the objective of AI. For AI it is not enough merely to achieve a better system; it matters how the system was made. The reason it matters can ultimately be considered a practical one, one of scaling. An AI system too reliant on manual tuning, for example, will not be able to scale past what can be held in the heads of a few programmers. This, it seems to me, is essentially the situation we are in today in AI. Our AI systems are limited because we have failed to turn over responsibility for them to them. Please forgive me for this which must seem a rather broad and vague criticism of AI. One way to proceed would be to detail the criticism with regard to more specific subfields or subparts of AI. But rather than narrowing the scope, let us first try to go the other way. Let us try to talk in general about the longer-term goals of AI which we can share and agree on. In broadest outlines, I think we all envision systems which can ultimately incorporate large amounts of world knowledge. This means knowing things like how to move around, what a bagel looks like, that people have feet, etc. And knowing these things just means that they can be combined flexibly, in a variety of combinations, to achieve whatever are the goals of the AI. If hungry, for example, perhaps the AI can combine its bagel recognizer with its movement knowledge, in some sense, so as to approach and consume the bagel. This is a cartoon view of AI -- as knowledge plus its flexible combination -- but it suffices as a good place to start. Note that it already places us beyond the goals of a pure performance system. We seek knowledge that can be used flexibly, i.e., in several different ways, and at least somewhat independently of its expected initial use. With respect to this cartoon view of AI, my concern is simply with ensuring the correctness of the AI's knowledge. There is a lot of knowledge, and inevitably some of it will be incorrrect. Who is responsible for maintaining correctness, people or the machine? I think we would all agree that, as much as possible, we would like the AI system to somehow maintain its own knowledge, thus relieving us of a major burden. But it is hard to see how this might be done; easier to simply fix the knowledge ourselves. This is where we are today.

Each genotype g can be written as a tuple (x, σ), where x is the phenotype and σ is the neutral traits. The main point is that σ evolves just like x. "σ is basically how good the mutational phenotypic variability matches the distribution of good organisms." σ is a parameter describing the space around this x. Is this a useful separation? Aren't there degrees of neutrality? Can two genotypes have the same neutral traits and phenotypes, but different evolvability structures? -- __Of course__ This occurs when it is the non-neutral traits that create x, then looking at only σ for the evolvability structure is misleading. From notes pg. 50 7/2/05.

昔と今、東洋と西洋のこと

pytw is a simple serverside backend written in pure python. Its available separate from siglet, [[here|http://www.cs.utexas.edu/~joeraii/pytw]]

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Tag Cloud by Clint Checketts: [[http://15black.bluedepot.com/twtests/tagcloud.htm|http://15black.bluedepot.com/twtests/tagcloud.htm]] ... done siglet style. <<tagCloud>> ==:( unfortunately the popup context menus don't appear== ==Still a few display glitches.== Seems to render OK in firefox, weird little bounding-box bug with Safari. No clue about IE (I care though... sort of... email me).

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customer.vpls.net 11 fj5010.inktomisearch.com 11 llf320072.crawl.yahoo.net 11 vs841.rosehosting.com 11 204-85-65-68.wc.rl-nc.ntc-com.net 11 crawler5202.ask.com 11 adsl-70-237-163-80.dsl.sfldmi.sbcglobal.net 10 a32248.upc-a.chello.nl 10 crawler4094.ask.com 10 llf520075.crawl.yahoo.net 10 lj611238.inktomisearch.com 10 llf320058.crawl.yahoo.net 10 crawl-66-249-70-186.googlebot.com 10 pool-71-162-238-179.phlapa.fios.verizon.net 10 llf320028.crawl.yahoo.net 9 lrouen-151-71-3-10.w80-11.abo.wanadoo.fr 9 c-68-53-107-41.hsd1.tn.comcast.net 9 gate-de.freescale.com 9 llf320052.crawl.yahoo.net 9 livebot-65-55-212-188.search.live.com 9 s0106001346ffd205.cg.shawcable.net 9 host-81-191-156-59.bluecom.no 9 ha-fw2.ko.com 9 pool-72-92-80-16.phlapa.fios.verizon.net 8 llf320057.crawl.yahoo.net 8 64.124.85.72.become.com 8 rz311349.crawl.yahoo.net 8 a37223.upc-a.chello.nl 8 livebot-207-46-98-49.search.live.com 8 76-191-220-246.dsl.dynamic.sonic.net 8 ip67-94-101-190.z101-94-67.customer.algx.net 8 c-68-54-174-199.hsd1.md.comcast.net 8 80.209.50.59.broad.hk.hi.dynamic.163data.com.cn 8 ce01pc08.netcabo.net 8 u.azdigw02.index.or.jp 8 c-24-218-203-192.hsd1.ma.comcast.net 8 93-97-212-249.zone5.bethere.co.uk 8 ct501304.inktomisearch.com 8 h13092.upc-h.chello.nl 8 free.mekensleep.com 8 64-198-88-100.xwires.net 8 78-86-158-143.zone2.bethere.co.uk 8 opncrd1.search.mud.yahoo.com 8 12-223-248-214.client.insightbb.com 8 lj512211.crawl.yahoo.net 8 1-user1.scnet.cz 7 79-116-33-200.rdsnet.ro 7 wbs-196-2-124-250.wbs.co.za 7 76-10-151-76.dsl.teksavvy.com 7 lm501022.inktomisearch.com 7 d199-126-170-17.abhsia.telus.net 7 crawler5107.ask.com 7 c-24-62-122-110.hsd1.ma.comcast.net 7 121.97.105.207.bti.net.ph 7 g2spf.jeeves.ask.info 7 egspd42059.teoma.com 7 crawler5103.ask.com 7 ca527-ch01-bl05.tx-dallas0.sa.earthlink.net 7 c952c226.virtua.com.br 7 gate-zil.freescale.com 7 rz311186.crawl.yahoo.net 7 crawl1.nat.svl.searchme.com 7 119-144.127-70.tampabay.res.rr.com 7 egspd42468.ask.com 7 dsl081-073-002.sfo1.dsl.speakeasy.net 7 lj602245.inktomisearch.com 7 81-5-142-218.dsl.eclipse.net.uk 7 troglodyte.passereaux.jmsp.net 7 shepherd.logos.k.u-tokyo.ac.jp 6 ac5-webproxy74.direcpc.com 6 bi01p1.nc.us.ibm.com 6 llf531291.crawl.yahoo.net 6 ec2-67-202-41-3.compute-1.amazonaws.com 6 host-85-27-88-50.brutele.be 6 c906b577.virtua.com.br 6 c-24-6-248-82.hsd1.ca.comcast.net 6 millionsquarefeet.com 6 82-170-36-132.dsl.ip.tiscali.nl 6 ool-4578dc40.dyn.optonline.net 6 213.213.196.173.brutele.be 6 pcd640034.netvigator.com 6 lj611546.crawl.yahoo.net 6 hs-out-f136.google.com 6 ip4da43c88.direct-adsl.nl 6 cdev6017.yst.corp.yahoo.com 6 egspd42122.ask.com 6 p57a5c439.dip.t-dialin.net 6 linac.txcorp.com 6 ip-216-36-99-90.dsl.chi.megapath.net 6 llf320010.crawl.yahoo.net 6 c-67-160-173-220.hsd1.or.comcast.net 6 lap81-1-82-245-69-185.fbx.proxad.net 6 cpe-24-93-107-171.columbus.res.rr.com 6 xd-22-78-a8.bta.net.cn 6 rchnccpxysg1.fnc.fujitsu.com 6 cdt4000.inktomisearch.com 6 213-162-100-101.matthe005.adsl.metronet.co.uk 6 cpe-24-193-236-148.ucwphilly.res.rr.com 6 83.72.231.124.ip.tele2adsl.dk 6 dsl254-071-197.nyc1.dsl.speakeasy.net 6 msnbot-65-55-211-84.search.msn.com 6 bnxoft.dravanet.hu 6 aclermont-ferrand-157-1-38-190.w86-194.abo.wanadoo.fr 6 lj9076.inktomisearch.com 6 host-12-22-56-33.certive.com 6 70-57-149-211.mpls.qwest.net 6 dj332033.crawl.yahoo.net 6 lj611828.inktomisearch.com 6 212-127-133-126.cable.quicknet.nl 6 p54ae7163.dip.t-dialin.net 6 b3091140.crawl.yahoo.net 6 lm502000.inktomisearch.com 5 ordi61-235.icmcb-bordeaux.cnrs.fr 5 c-67-190-50-11.hsd1.co.comcast.net 5 60-240-249-194.tpgi.com.au 5 pat.ccf.org 5 livebot-207-46-98-52.search.live.com 5 92-236-87-153.cable.ubr19.aztw.blueyonder.co.uk 5 lj601931.inktomisearch.com 5 64-142-43-45.dsl.static.sonic.net 5 crawl-66-249-71-84.googlebot.com 5 a79204.upc-a.chello.nl 5 midd-cache-2.server.ntli.net 5 crawl-66-249-65-146.googlebot.com 5 ryan.pediatrics.wisc.edu 5 c-69-140-159-221.hsd1.md.comcast.net 5 pc243.ulstein.no 5 c-76-97-232-234.hsd1.ga.comcast.net 5 adsl-070-145-012-229.sip.sdf.bellsouth.net 5 crawler2010.ask.com 5 122x215x159x81.ap122.ftth.ucom.ne.jp 5 lj910005.inktomisearch.com 5 crawl-66-249-65-43.googlebot.com 5 eve-micro183.ucdavis.edu 5 msnbot-65-55-210-90.search.msn.com 5 cpe-68-175-79-43.nyc.res.rr.com 5 inetgate.nl.sykes.com 5 60-240-249-195.tpgi.com.au 5 dsl092-052-049.phl1.dsl.speakeasy.net 5 c-76-126-34-72.hsd1.ca.comcast.net 5 wuw-td41difgig.dybb.com 5 modemcable106.129-131-66.mc.videotron.ca 5 llf320059.crawl.yahoo.net 5 ppp154-114.lns1.mel3.internode.on.net 5 24-136-10-126.hnc-bsr1.chi-hnc.il.cable.rcn.com 5 irl-crawler3.cs.tamu.edu 5 gw9.vslesy.cz 5 c-67-185-136-97.hsd1.wa.comcast.net 5 c-174-51-186-218.hsd1.co.comcast.net 5 adsl-75-0-237-48.dsl.crchtx.sbcglobal.net 5 cpe0014380e1523-cm00159a041c3c.cpe.net.cable.rogers.com 5 cpe-70-92-240-161.wi.res.rr.com 5 c-e543e353.04-2-6b736412.cust.bredbandsbolaget.se 5 lj612345.inktomisearch.com 5 195-234-101-098.static.anw.at 5 adsl23-146.ath.forthnet.gr 5 192.90.231.222.megaegg.ne.jp 5 dhcp106.fis.unical.it 5 proxy-5v.club-internet.fr 5 agp.stanford.edu 5 c-69-181-82-141.hsd1.ca.comcast.net 5 liberty-proxy.lmig.com 5 crawl-0.cuill.com 5 crawl-66-249-71-249.googlebot.com 5 p5113-ipad504marunouchi.tokyo.ocn.ne.jp 5 smtp.stumbleupon.com 5 71.62-245-81.adsl-dyn.isp.belgacom.be 5 crawl-66-249-72-193.googlebot.com 5 0030bd02f6ca.click-network.com 5 msnbot-65-55-210-97.search.msn.com 5 173-117-99-124.pools.spcsdns.net 5 quest.securenet-server.net 5 74.72.344a.static.theplanet.com 5 cpc3-duns1-0-0-cust203.lutn.cable.ntl.com 5 c-24-11-95-175.hsd1.mi.comcast.net 5 llf520011.crawl.yahoo.net 5 70-90-41-249-michigan.hfc.comcastbusiness.net 5 sgl69-1-82-231-119-148.fbx.proxad.net 5 cpe-74-65-252-243.nyc.res.rr.com 5 c-67-161-78-120.hsd1.ca.comcast.net 5 d0.ew.sanomabp.hu 5 proxy3.tufts-health.com 5 cmbg-cache-1.server.ntli.net 4 59-126-95-40.hinet-ip.hinet.net 4 lana.stanford.edu 4 ip-213-190-58-131.static.b4net.lt 4 125-224-67-234.dynamic.hinet.net 4 crs057.goo.ne.jp 4 host-24-149-166-106.patmedia.net 4 244.203.118.70.cfl.res.rr.com 4 c-24-118-138-74.hsd1.mn.comcast.net 4 livebot-65-55-212-189.search.live.com 4 bibibobka.wiccidu.net 4 lns-bzn-47f-62-147-133-44.adsl.proxad.net 4 mered7-finjangw.ser.netvision.net.il 4 viola.sys.wakayama-u.ac.jp 4 dsl-202-52-60-180.nsw.veridas.net 4 77-22-254-220-dynip.superkabel.de 4 73-96-177-194.serverdedicati.seflow.net 4 ip-122-152-140-205.asianetcom.net 4 218-214-113-66.people.net.au 4 12-219-144-158.client.mchsi.com 4 lj602189.inktomisearch.com 4 w3gw.bss.boeing.com 4 msnbot-65-55-104-163.search.msn.com 4 llf520133.crawl.yahoo.net 4 www-gw3.credit-suisse.com 4 nat2-174.ghnet.pl 4 crawl-66-249-66-145.googlebot.com 4 cdm-75-109-248-160.asbnva.dh.suddenlink.net 4 class23.smith.udel.edu 4 dyn-83-157-222-13.ppp.tiscali.fr 4 cpe-72-130-116-29.socal.res.rr.com 4 akilebo.monbo.net 4 adsl-065-005-168-023.sip.dab.bellsouth.net 4 lj611480.crawl.yahoo.net 4 181.0.111.218.kmr02-home.tm.net.my 4 crawl26-public.alexa.com 4 njproxy1.avaya.com 4 pool-71-249-56-113.nycmny.east.verizon.net 4 adsl-99-185-108-226.dsl.crchtx.sbcglobal.net 4 llf320019.crawl.yahoo.net 4 cpe-74-69-35-235.rochester.res.rr.com 4 lj2137.inktomisearch.com 4 g195076.upc-g.chello.nl 4 lputeaux-151-41-9-87.w217-128.abo.wanadoo.fr 4 43-177.126-70.tampabay.res.rr.com 4 adsl-19-19-53.asm.bellsouth.net 4 mgt32.sid.cam.ac.uk 4 62-101-126-237.ip.fastwebnet.it 4 llf320039.crawl.yahoo.net 4 pcp01494921pcs.rte20201.de.comcast.net 4 crawler5213.ask.com 4 livebot-65-55-209-53.search.live.com 4 techcity5.rain.fr 4 h-74-1-169-252.phlapafg.covad.net 4 p18.eregie.pub.ro 4 proxy03phl.sap-ag.de 4 mail.bfkh.hu 4 llf531148.crawl.yahoo.net 4 82-34-145-205.cable.ubr04.gray.blueyonder.co.uk 4 3209ds1-rdo.0.fullrate.dk 4 12-205-235-186.client.mchsi.com 4 dsl-180-165.clm.centurytel.net 4 blueice2n1.de.ibm.com 4 p54b31383.dip0.t-ipconnect.de 4 84-203-5-240.mysmart.ie 4 cpe-60-228-46-128.nsw.bigpond.net.au 4 bad-bart.lcp.nrl.navy.mil 4 crawl-66-249-66-141.googlebot.com 4 ct501007.inktomisearch.com 4 pool-96-243-13-155.bflony.east.verizon.net 4 proxy.dexia.be 4 p54bd5206.dip.t-dialin.net 4 ven06-1-82-234-157-28.fbx.proxad.net 4 host-213-213-196-215.brutele.be 4 lj511275.crawl.yahoo.net 4 dslb-084-056-145-051.pools.arcor-ip.net 4 cpe-65-28-165-35.neb.res.rr.com 4 foobar.sita.aero 4 customer1.aao.org 4 esprx02x.nokia.com 4 86-104-26-115.dcn.ro 4 webcache.cs.bham.ac.uk 4 cblmdm72-240-157-1.buckeyecom.net 4 neppsu01.northropgrumman.com 4 c-71-56-7-87.hsd1.ga.comcast.net 4 yzlab12.chem.nyu.edu 4 laubervilliers-151-11-31-39.w193-251.abo.wanadoo.fr 4 mv521031.inktomisearch.com 4 79-113-41-14.rdsnet.ro 4 llf520039.crawl.yahoo.net 4 70-35-68-92.pbtcomm.net 4 dj101001.inktomisearch.com 4 tp-s2-c91-3.router.hinet.net 4 82-95.8-67.tampabay.res.rr.com 4 174.34.157.98.rdns.ubiquityservers.com 4 c-24-4-34-191.hsd1.ca.comcast.net 4 216.215.139.10.nw.nuvox.net 4 smtp.cbsh.com 4 host244-114.pool83221.interbusiness.it 4 host81-156-155-32.range81-156.btcentralplus.com 4 proxy1.credit-agricole.fr 4 c-24-17-56-254.hsd1.wa.comcast.net 4 inetgw-63-sec.nhs.uk 4 avelizy-155-1-81-120.w90-2.abo.wanadoo.fr 4 c-24-34-119-59.hsd1.ma.comcast.net 4 pool-71-241-188-56.buff.east.verizon.net 4 sfilter124-2.itc.swri.edu 4 pool-68-161-5-122.ny325.east.verizon.net 4 glenlivet.hud.ac.uk 4 60-240-249-200.tpgi.com.au 4 p9nb97hizd.adsl.datanet.hu 4 a81-84-64-57.cpe.netcabo.pt 4 68-76-132-236.ded.ameritech.net 4 211.253.244.43.ap.zero-isp.net 4 206-169-247-2.gen.twtelecom.net 4 proxy1.search.scd.yahoo.net 4 ool-435131a2.dyn.optonline.net 4 cpe-60-228-6-5.nsw.bigpond.net.au 4 pool-162-84-154-227.ny5030.east.verizon.net 4 i155219.upc-i.chello.nl 4 c-75-68-209-133.hsd1.vt.comcast.net 4 200-112-147-146.bbt.net.ar 4 194-218-21-14.customer.telia.com 4 84-105-239-66.cable.quicknet.nl 4 wc24.setooz.com 4 adsl-customer-233.77-pool83.137.orobiacom.it 4 53.red-83-44-93.dynamicip.rima-tde.net 4 dneg-fw.dneg.com 4 cpe-71-74-161-107.indy.res.rr.com 4 c-67-168-202-157.hsd1.or.comcast.net 4 cpe-70-93-240-248.san.res.rr.com 4 wc21.setooz.com 4 i220-108-208-159.s02.a013.ap.plala.or.jp 4 d122.nexlink.net 4 213-0-232-107.dialup.nuria.telefonica-data.net 4 bi01p1.co.us.ibm.com 4 ool-44c31b18.dyn.optonline.net 4 74-138-213-43.dhcp.insightbb.com 4 pslux.cec.eu.int 4 lj611664.inktomisearch.com 4 llf320046.crawl.yahoo.net 4 ip-253-genesys.miratech.ua 4 ppp19.pm3-2.isb-ch.in.localnet.com 4 llf520027.crawl.yahoo.net 4 gate.tms-itdienst.de 4 wc20.setooz.com 3 trotsky.ucdavis.edu 3 adsl151-7.ath.forthnet.gr 3 hmbg-5f777f96.pool.einsundeins.de 3 cache.promax-ck.pl 3 ntoska151230.oska.nt.adsl.ppp.infoweb.ne.jp 3 crawl-66-249-71-72.googlebot.com 3 host-92-4-178-14.as43234.net 3 nott-cache-3.server.ntli.net 3 c59e000c3.dhcp.bluecom.no 3 75-161-114-139.albq.qwest.net 3 llf320062.crawl.yahoo.net 3 ceg-netc-noauth.alcatel.fr 3 p54a1528c.dip.t-dialin.net 3 llf520049.crawl.yahoo.net 3 p21195-adsao01motoma-acca.hiroshima.ocn.ne.jp 3 82-41-112-252.cable.ubr05.dund.blueyonder.co.uk 3 ip70-187-59-144.pn.at.cox.net 3 f053036129.adsl.alicedsl.de 3 d47-69-73-183.nap.wideopenwest.com 3 69-161-44-202.sbtnvt.adelphia.net 3 adsl-75-34-100-187.dsl.austtx.sbcglobal.net 3 inetgw.ummhc.org 3 adsl-69-232-229-77.dsl.pltn13.pacbell.net 3 proxy.corp.comindico.com.au 3 ws202.deh.ehnr.state.nc.us 3 90-156-82-143.magma-net.pl 3 82-39-113-2.cable.ubr03.newy.blueyonder.co.uk 3 72-161-54-57.dyn.centurytel.net 3 98.47.150.119.ap.yournet.ne.jp 3 bl1sch2041312.phx.gbl 3 adsl-69-232-214-144.dsl.pltn13.pacbell.net 3 lns-bzn-49f-81-56-213-41.adsl.proxad.net 3 dmz1.bsiag.com 3 mai-gla-cpk.coxinc.com 3 p54a155f0.dip.t-dialin.net 3 81.202.215.165.dyn.user.ono.com 3 adijon-106-1-4-177.w80-11.abo.wanadoo.fr 3 crawl-66-249-65-210.googlebot.com 3 client-190.40.101.6.speedy.net.pe 3 82-69-6-69.dsl.in-addr.zen.co.uk 3 210.211.130.219.bb.vsnl.net.in 3 ppp-69-238-230-58.dsl.pltn13.pacbell.net 3 dsl28-137-100.fastxdsl.nl 3 c-68-45-34-32.hsd1.nj.comcast.net 3 info.mckesson.com 3 webmail.revolutionprep.com 3 www.websnapr.com 3 82-169-31-184-mx.xdsl.tiscali.nl 3 host162-227-static.105-80-b.business.telecomitalia.it 3 p54a030bf.dip0.t-ipconnect.de 3 bl6-25-152.dsl.telepac.pt 3 by1sch4030213.phx.gbl 3 beluga.di.transpac.fr 3 d141-60-151.home.cgocable.net 3 m125.net81-65-54.noos.fr 3 0x50a69a56.arcnxx20.adsl-dhcp.tele.dk 3 user-0c8hs8c.cable.mindspring.com 3 adsl-70-230-184-51.dsl.stlsmo.sbcglobal.net 3 c-24-126-2-69.hsd1.wv.comcast.net 3 crawl-66-249-66-194.googlebot.com 3 bl5-159-15.dsl.telepac.pt 3 vit94-1-81-57-211-133.fbx.proxad.net 3 llf310004.crawl.yahoo.net 3 i125-203-123-126.s02.a013.ap.plala.or.jp 3 p5082b96b.dip0.t-ipconnect.de 3 bl1sch2033911.phx.gbl 3 updraft.nsstc.nasa.gov 3 livebot-65-55-210-90.search.live.com 3 bl6-10-52.dsl.telepac.pt 3 dig.poyry.com 3 konji.ifs.hr 3 modemcable070.26-56-74.mc.videotron.ca 3 home-213-240-225-101.megalan.bg 3 h83-174-251-182.dyn.ufamts.ru 3 livebot-65-55-209-74.search.live.com 3 msnbot-65-55-211-87.search.msn.com 3 crawl-66-249-66-130.googlebot.com 3 adsl-67-114-152-206.dsl.sntc01.pacbell.net 3 79-113-46-166.rdsnet.ro 3 nor75-10-82-226-75-140.fbx.proxad.net 3 user-0cdfb1i.cable.mindspring.com 3 c-71-228-217-171.hsd1.tn.comcast.net 3 crawl-66-249-71-56.googlebot.com 3 crawl-66-249-72-148.googlebot.com 3 h3211.upc-h.chello.nl 3 anancy-157-1-96-101.w86-218.abo.wanadoo.fr 3 proxy-4m.club-internet.fr 3 col31-1-87-89-48-186.dsl.club-internet.fr 3 mail.euroclear.com 3 webcache-kotnet-2.kuleuven.ac.be 3 host52-148.pool877.interbusiness.it 3 69-173-78-103.albyny.adelphia.net 3 crawler5105.ask.com 3 18981098186.user.veloxzone.com.br 3 inac.erlm.siemens.de 3 c-69-180-16-96.hsd1.ga.comcast.net 3 cpe-024-211-144-200.nc.res.rr.com 3 extproxy1.siemens.at 3 blueice1n1.de.ibm.com 3 llf520054.crawl.yahoo.net 3 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