OK, There's clearly a lot ow work happening in linguistics these days, that I have fallen behind on reading.
The nature of the conversations here has been frustrating, because so far, it sounds like an attempt to evade the "central limit theorem" -- https://en.wikipedia.org/wiki/Central_limit_theorem There are two related ideas I'm trying to get across: one is that if you make enough observations of a phenomenon, eventually, the central-limit theorem kicks in, and smooths over random variations. Specifically, I claim that, despite MST being imperfect, a large number of observations should smooth over the imperfections. I believe this to be true, (but I could be wrong). The other idea is that the golden test corpus must avoid accidentally testing disjuncts far away from the central limit -- to avoid, as it were, making statements analogous to "Well, I flipped the coin three times, and I did not get 50-50 odds, therefore the theory doesn't work". You have to flip the coin at least N times, for some large N. Here, for MST, we don't know how big N has to be, we don't have a good plan for determining N. It's worse, cause everything is Zipfian aka 1/f noise. It is possible that BERT or other approaches allow smaller values of N to work, but this is also not clear. Its also not clear that BERT would converge to a different limit than MST - the central-limit theorem says there is only one limit -- not two. But perhaps I'm misapplying it, perhaps I'm neglecting some important effect. Without measurements, its hard to guess what that effect is (if it even exists). Anyway, I have a backlog of half-a-dozen important unread papers, so I'll try to get around to that "real soon now". --linas On Mon, Apr 1, 2019 at 12:15 AM Ben Goertzel <[email protected]> wrote: > "Replacing MST by DNN/BERT" is a strange way to put it... > > DNN/BERT builds a pretty complex and comprehensive language model, > much beyond what is done by calculation of MI values and similar > > The extraction of a parse dag satisfying syntactic constraints (no > links cross, covering all words in the sentence, connected graph) is a > conceptually simple step, and nobody is spending much time on this > step indeed... > > The question of how to assign a quantitative weight to the relation > btw two word-instances in a sentence, taking into account the specific > context in that sentence, but also the history of co-utilization of > those words (or other similar words), is less conceptually simple and > this is one place I think DNN language models can help > > Using MST or similar parsing based on numbers exported from DNN > language models is one way of extracting symbolic-ish structured > knowledge from these big messy subsymbolic probabilistic language > models... > > The DNNs in use now like BERT do not really satisfy me on a > theoretical or conceptual level, but they have been tuned to work > pretty nicely and they have been implemented pretty efficiently on > multi-GPU hardware -- so, given this and given the quality of the > recent practical results obtained with them -- I consider it well > worth exploring how to use them as tools in our pursuits for grammar > and semantics learning > > -- Ben > > On Mon, Apr 1, 2019 at 2:07 PM Linas Vepstas <[email protected]> > wrote: > > > > > > > > On Sun, Mar 31, 2019 at 10:51 PM Anton Kolonin @ Gmail < > [email protected]> wrote: > >> > >> Hi Linas, I like this thread more and more :-) > > > > I don't. I use a lot of CAPITALIZED WORDS below. There is a deep and > dark fundamental misunderstanding, and I am sometimes at wits end trying to > figure out why, and how to explain things in an understandable fashion. > >> > >> >But somehow, I suspect... Isn't this why OpenCog has "unified rule > engine" (URE) instead of link grammar at its core, > >> > >> Linas, the "extraction of phrasemes" goal approaching has been > discussed exactly in terms of MST->GL->URL on the last fall in Hong Kong > discussion: > https://docs.google.com/document/d/13YyqtGud0GAbVaFcc94kAd2LhGf7jTr5XDYgiuC294c/edit > >> > >> That is: > >> > >> 1) Do MST-parsing to get word links proto-disjuncts > >> > >> 2) Do Grammar Learning to cluster and conclude word categories and > rules with disjuncts > >> > >> 3) Do URE-kind-of-thing to build the rules into "phrasemes" or > "sections" or "patterns". > > > > Yes. > >> > >> However, your current discourse and our current results just show that > "no one is be able to do reasonable MST-parsing" so the above is just waste > of time, correct? > > > > No. Very much no. I'm saying the opposite of that. You can replace MST > by almost *ANYTHING* else, and the quality of your results WILL NOT CHANGE! > > > > If the quality of your results depends on the quality of MST, you are > DOING SOMETHING WRONG! > > > > I'm utterly flabbergasted. I don't know how many more times I can say > this: stop wasting time on this unimportant step! > >> > >> At the time we speak, Ben, Alexely, Sergey and Asuares are trying to > use DNN/BERT magic to do the trick 1. > > > > I want to call this "a complete waste of time". It will almost surely > not improve the quality of the results! I don't understand why four smart > people think that replacing MST by BERT will make any difference at all! > It should not matter! Nothing depends on this step! Anything at all, > anything with a probability better than random chance, is sufficient! Why > isn't this obvious? > > > > If Ben is reading this: I recall talking to Ben about this in an > ice-cream shop in Berlin, for an AGI conference, and he seemed to > understand back then. I have no idea why he changed his mind. I really do > not understand why everyone spends so much time obsessing about MST. Is > this a "color of the bike shed" problem? > https://en.wikipedia.org/wiki/Law_of_triviality > > > > MST-vs.-BERT==color-of-bike-shed > > > > Just use MST. It's simple. It works. It gives good results. Stop trying > to improve it. The interesting problems are elsewhere! Just use MST, and > move on to the good stuff! > >> > >> To my mind, that may get possible only if the DNN/BERT magic do the > trick having the steps 2 and 3 done under the hood. If this is done, in > such case, we don't need to do 2 and 3 after we have the DNN/BERT-based > model, because we can simply "milk-out" the grammar rules out of DNN/BERT > micelium for that. And we don't need the ULL as well by the way, because we > just need DNN/BERT and rows of different sorts of milk machines around it. > > > > So why are you bothering to work on ULL? > >> > >> So, instead of solving the problem of constructing the pipeline for > learning grammar from raw text we need to solve the problem of milking the > grammar out of DNN/BERT model trained on these texts, right? > > > > Because I don't think that you know how to milk lexical functions out of > DNN/BERT -- We've wasted more than a year talking about MST. Instead of > endlessly talking about MST, you could have JUST USED IT, WITHOUT ANY > MODIFICATIONS, gotten good results, and spent the year working on something > interesting! > > > > Again: replacing MST by DNN/BERT with something else will NOT IMPROVE > the accuracy! You'll have exactly the same accuracy as before, and if your > accuracy improves, it is because you are doing something wrong! > > > >> However, either way, we need to understand algorithmic machinery of how > the links assemble in disjuncts and disjuncts assemble into sections, > through the universe-scale combinatorial explosion. > > > > No. That is the OPPOSITE of what ACTUALLY HAPPENS!!!! > >> > >> And I agree that clustering and categorizing word and links (and then > disjuncts and sections, right) is part of the process - explicitly in ULL > pipeline or implicitly deep in DNN/BERT darkness. > > > > It is NOT DEEP AND DARK. I wrote not one but TWO PAPERS on this, > CASTING LIGHT ON THAT DARKNESS > > > > I'm frustrated to the 43rd degree on why I cannot seem to have a > reasonable conversation with any other human being about any of this. > > > > -- Linas > > > >> Cheers, > >> > >> -Anton > >> > >> > >> 01.04.2019 9:17, Linas Vepstas: > >> > >> > >> > >> On Thu, Mar 28, 2019 at 10:22 AM Ivan V. <[email protected]> wrote: > >>> > >>> Linas Vepstas wrote: > >>> > >>> >... knowledge extraction can be done generically, and not just on > language. > >>> > >>> If link grammar would be Turing complete, this might be possible right > away. > >> > >> > >> In my experience, thinking about Turing completeness is unproductive > and a distraction. > >> > >>> But somehow, I suspect... Isn't this why OpenCog has "unified rule > engine" (URE) instead of link grammar at its core, > >> > >> > >> No. It has the rule-engine because back then, I did not understand > sheaves. I'm starting to think that the rule engine is a strategic > mistake. The original idea is that rule-application is the main conceptual > abstraction of term-rewriting. One rewrites, or proves theorems by > applying sequences of rules. It turns out that discovering the right > sequence is hard. Finding correct long sequences is hard - a combinatorial > explosion. > >> > >> The openpsi system addresses some of these issues. Unfortunately, it's > current implementation is a tangle of rule-selection mechanisms, and > theories of human psychology. It's probably better than the URE, but is > currently not as powerful. > >> > >> I'm trying to place a theory of sheaves as a replacement for URE, and > as the natural generalization of openpsi, but I've successfully > self-sabotaged myself in these efforts. > >> > >>> > >>> and with URE things get much more complicated. I'm sorry, but that is > still a Gordian knot to me, considering all of my modest knowledge. > >> > >> > >> We all have modest knowledge. That is the nature of the human condition. > >> > >>> > >>> On the other hand, if someone really smart would provide automatic > grammar extraction by means of unrestricted grammar, I believe that would > be it. > >> > >> > >> Yes, that is the goal of the language-learning project. However, as > noted in my last email (on the link-grammar list) it is not enough to just > learn a semi-Thue system, declare victory, and go home. The example I gave > there: > >> > >> "I think that you should give that car a second look" > >> "you should really give that song a second listen" > >> "maybe you should give Sue a second chance". > >> > >> Learning to parse these "set phrases" or phrasemes is equivalent to > learning a semi-Thue system; however, its not enough to realize that all > three are forms of advice-giving, having "conserved" or "fixed" regions "x > YOU SHOULD y GIVE z SECOND w" where z is very highly variable having > millions of variations, and w only has a few dozen allowed variations. > Note that the words "fixed", "conserved", "variable" are words used in > genetics and proteomics and antibody structure. Its the same idea. > >> > >> The goal of learning lexical functions (LF's) is to learn that all > three are advice-giving forms, and also to learn what is, and what can be > plugged in for x,y,z,w. So, although a super-whiz-bang grammar learner > capable of learning context-sensitive languages should be able to learn "x > YOU SHOULD y GIVE z SECOND w", it still will not know the *meaning* of this > phrase. To know the *meaning*, you have to know the acceptable ranges (as > fuzzy-sets) of x,y,z,w. > >> > >> To conclude, thinking about Turing-completeness is a waste of time, > because Turing completeness only tells you that "x YOU SHOULD y GIVE z > SECOND w" is recursively enumerable; it does not tell you what it actually > means. > >> > >> Put another way: having a universal Turing machine is not the same as > knowing how some particular program works. Automagically learning a > context-sensitive grammar is not enough to know what that grammar is > "saying/doing". > >> > >> -- Linas > >> > >>> > >>> > >>> Thank you, > >>> Ivan V. > >>> > >>> > >>> čet, 28. ožu 2019. u 07:58 Anton Kolonin @ Gmail <[email protected]> > napisao je: > >>>> > >>>> Ben, Linas, > >>>> > >>>> >But we know that MST parsing is shit. Stop wasting time on MST or > trying to "improve" it. > >>>> > >>>> I think that sounds like kind of support for the concept of "dumb > explosive parsing" being advocated for 1+ year ago: > >>>> > >>>> > https://docs.google.com/document/d/14MpKLH5_5eVI39PRZuWLZHa1aUS73pJZNZzgigCWwWg/edit#heading=h.aqo9bumb3doy > >>>> > >>>> I also agree we other Linas'es reasoning in this thread. I would > consider giving it a try starting next month if we don't have a > breakthrough with DNN-MI-milking-based-MST-Parsing by that time. > >>>> > >>>> > can be done generically, and not just on language > >>>> > >>>> I think everyone in bio-informatics dreams of extracting secrets of > "dark side of the genome" with something like that ;-) > >>>> > >>>> Cheers, > >>>> > >>>> -Anton > >>>> > >>>> > >>>> 28.03.2019 1:24, Linas Vepstas пишет: > >>>> > >>>> Hi Anton, > >>>> > >>>> I've cc'ed the link-grammar mailing list, because I describe below > some concepts for word-sense disambiguation. I'm also cc'ing the opencog > mailing list and ivan vodisek, because after studying hilbert systems, I > think he's ready to think about how knowledge extraction can be done > generically, and not just on language. > >>>> > >>>> -- Linas > >>>> > >>>> On Mon, Mar 25, 2019 at 1:39 AM Anton Kolonin @ Gmail < > [email protected]> wrote: > >>>>> > >>>>> Hi Linas, > >>>>> > >>>>> >I'd call it "interesting", but maybe not "golden" > >>>>> > >>>>> These are randomly selected sentences from "Gutenberg Children" > corpus: > >>>>> > >>>>> > http://langlearn.singularitynet.io/data/cleaned/English/Gutenberg-Children-Books/lower_LGEng_token/ > >>>>> > >>>>> "Gutenberg Children silver standard" is LG-English parses: > >>>>> > >>>>> > http://langlearn.singularitynet.io/data/parses/English/Gutenberg-Children-Books/test/GCB-LG-English-clean.ull > >>>>> > >>>>> "Gutenberg Children gold standard" is subset of "silver standard" > with semi-random selection of sentences skipping direct speech and doing > manual verification of the links. > >>>>> > >>>>> So as long as we are training on "Gutenberg Children" corpus, having > the test on the same "Gutenberg Children" seems reasonable, right? > >>>> > >>>> > >>>> Yes. You still need to verify that each word in the "golden" corpus > occurs at least N=10 or 20 times in the training corpus. The dependency of > accuracy on N is not generally known, but it is very clear that if a word > occurs only N=3 times in the training corpus, then whatever is learned > about it will be very low quality. > >>>> > >>>>> > >>>>> But thanks, we may have put mire effort in removal of ancient > constructions and words even if these are present in the corpus. > >>>> > >>>> If you consistently train on 19th century literature, and then > evaluate 19th-century literature comprehension, that's fine. Just don't > expect it to work for 21st century blog posts. > >>>> > >>>> The strongest effect will be the N=number of observations effect. > >>>> > >>>>> > >>>>> >Anyway -- you only indicate pair-wise word-links. Is the omission > of disjuncts intentional? > >>>>> > >>>>> If you have all links in the sentence, you can construct all of the > disjuncts with o ambiguity, correct? > >>>> > >>>> No, but only because you did not indicate the link-type. The whole > point of a clustering step is to obtain a link-type; if you discard it, you > will never get better-than-MST results. The link-type is critical for > obtaining the word-classes. The whole point of learning is to learn the > word-classes; you've learned very little, if you know only word-pairs. > >>>> > >>>> Consider this example: > >>>> > >>>> I saw wood > >>>> I saw some wood > >>>> > >>>> A solution that would be "almost perfect" (or "golden") would be this: > >>>> > >>>> saw: {performer-of-actions}- & {sculptable-mass}+; > >>>> saw: {observer}- & {viewable-thing}+; > >>>> > >>>> These disambiguate the two different senses of the word "saw". It's > impossible to have word-sense disambiguation without actually having these > disjuncts. The word-pairs alone are not sufficient to report the link-type > connecting the words. Clustering gives the other dictionary entries: > >>>> > >>>> I: {performer-of-actions}+ or {observer}+; > >>>> wood: {sculptable-mass}- or ({quantity-determiner}- & > {viewable-thing}-); > >>>> some: {quantity-determiner}+; > >>>> > >>>> Thus, the pronoun "I" also belong to two different word-sense > categories: performers and observers. Compare to: > >>>> > >>>> "The chainsaw saws wood" -- a "chainsaw" can be a "performer of > actions" but cannot be an "observer". > >>>> "The dog saw some wood" -- dogs can be observers. They can perform > some actions; like run, jump, but they cannot saw, hammer, cut, stab. > >>>> > >>>> The link-type is absolutely crucial to understanding a word. The > language-learning project is all about learning the link-types. Without > correct link-type assignments, you cannot have correct parses. > >>>> > >>>> ... which is 100% of the problem with MST. The problem with MST is > not so much that "its not accurate" -sure, it is not terribly accurate. But > even if MST or some MST-replacement was 100% accurate, it would still be > "wrong" because it fails to indicate the link-type. If you want to > understand a sentence, you MUST know the link-types! > >>>> > >>>> Otherwise, you just have "green ideas sleep furiously", which parses, > but only because the link types have been erased, or made stupid. Here's a > stupid grammar: > >>>> > >>>> ideas: {adjective}- & {verb}+; > >>>> green: {adjective}+; > >>>> > >>>> which allows "green ideas" to parse. But of course, this is wrong; > it should have been: > >>>> > >>>> ideas: {noospheric-modifier}- & {concept-manipulating-verb}+; > >>>> green: {physical-object-modifier}+; > >>>> > >>>> and now it is clear that "green ideas" cannot parse, because the > link-types clash. > >>>> > >>>> * If you cluster down to 5 or 6 clusters (adjective, verb, noun ...) > you will get very low quality grammars. > >>>> > >>>> * If you cluster to 200 or 300 clusters, you get sort-of-OK grammars. > This is what deep-learning/neural-nets do: this is why the deep-learning > systems seem to give nice results: 200 or 300 features is enough to start > having adequate functional distinctions (e.g. the famous "king - > male+female=queen" example, or "paris-france+germany=berlin" example) > >>>> > >>>> * If you cluster to 3K to 8K clusters, you start having a quite > decent model of language > >>>> > >>>> * Note that wordnet has 117K "synsets". > >>>> > >>>> Note that in the above example: > >>>> wood: {sculptable-mass}- or ({quantity-determiner}- & > {viewable-thing}-); > >>>> > >>>> the things in the curly-braces are effectively "synsets". > >>>> > >>>> The next set of goal-posts is to have disjuncts, of maybe low-medium > quality, and use these to extract ontologies. e.g. > >>>> {sculptable-mass} is-a {mass} is-a {physical-thing} is-a {thing} > >>>> > >>>> You can try to do this by clustering but there are probably better > ways of discovering ontology. > >>>> > >>>> > >>>>> > >>>>> >Also -- no hint of any word-classes or part-of-speech tagging? This > is surely important to evaluate as well, or is this to be done in some > other way? i.e. to evaluate if "Pivi" was correctly clustered with other > given names? Or that lama/llama was clustered with other four-legged > animals? > >>>>> > >>>>> We don't have that in MST-Parsing, right? We need this corpus to > assess the quality of the MST-Parsing so we don't need part-of-speech > information for that. > >>>> > >>>> But we know that MST parsing is shit. Stop wasting time on MST or > trying to "improve" it. We already know that it is close to a high-entropy > path to structure; trying to squeeze a few more percent of entropy is not > worth the effort, not at this time. Focus on finding a high-entropy > structure extraction algorithm, don't waste time on MST. > >>>> > >>>> You should be focusing on extracting disjuncts, word-classes, > word-senses, and trying to improve the quality of those. If you obtain a > high-entropy path to these structures, the quality of your parses will > automatically improve. Focus on the entropy numbers. Try to maximize that. > >>>> > >>>>> The clustering is able to do that anyway - see the graphs in the end > of the last year report: > >>>>> > >>>>> > https://docs.google.com/document/d/1gxl-hIqPQCYPb9NNkyA3sBYUyfwvJFvT1hZ5ZpXsaPc/edit#heading=h.twoiv52o0tou > >>>>> > >>>>> >Also -- I can't tell -- is it free of loops, or are loops allowed? > Allowing loops tends to provide stronger, more accurate parses. Loops act > as constraints. > >>>>> > >>>>> The loops and crossing links are not allowed in the MST-Parser now. > If we allow them in the test corpus, how could it make assessment of > MST-Parses better? > >>>>> > >>>>> Note, that we ARE working we MST-Parses now - accordingly to Ben's > directions. > >>>> > >>>> > >>>> Not to say bad things about Ben, but I'm certain he has not actually > thought about this problem very much. He is very very busy doing other > things; he is not thinking about this stuff. I have repeatedly tried to > explain the issues to him, and its quite clear that he is far away from > understanding them, from working at the level that I would like to have you > and your team work at. > >>>> > >>>> I'm trying to have you make small, quantified baby-steps, to verify > the accuracy of your methods and data. What I'm seeing is that you are > attempting to make giant-steps, without verification, and then getting > low-quality results, without understanding the root causes for them. You > can't dig yourself out of a ditch, and digging harder and more furiously > won't raise the accuracy of the parse results. > >>>> > >>>> --linas > >>>> > >>>>> We have your MST-Parser-less idea on the map but we are NOT trying > it now: > >>>>> > >>>>> https://github.com/singnet/language-learning/issues/170 > >>>>> > >>>>> We may try it after we explore the account for costs > >>>>> > >>>>> https://github.com/singnet/language-learning/issues/183 > >>>>> > >>>>> Thanks, > >>>>> > >>>>> -Anton > >>>>> > >>>>> 24.03.2019 9:24, Linas Vepstas пишет: > >>>>> > >>>>> Also, BTW, link-grammar cannot parse "I just stood there, my hand on > the knob, trembling like a leaf." correctly. It is one of a class of > sentences it does not know about. Which is maybe OK, because ideally, the > learned grammar will be able to do this. But today, LG cannot. > >>>>> > >>>>> --linas > >>>>> > >>>>> On Sat, Mar 23, 2019 at 9:12 PM Linas Vepstas < > [email protected]> wrote: > >>>>>> > >>>>>> Anton, > >>>>>> > >>>>>> It's certainly an unusual corpus, and it might give you rather low > scores. I'd call it "interesting", but maybe not "golden". Although I > suppose it depends on your training corpus. Here are some problems that > pop out: > >>>>>> > >>>>>> First sentence -- > >>>>>> "the old beast was whinnying on his shoulder" -- the word > "whinnying" is a fairly rare English verb -- you could read half-a-million > wikipedia articles, and not see it once. You could read lots of > 19th-century or early-20th century cowboy/adventure novels, (like what > you'd find on Project Gutenberg) and maybe see it some fair amount. Even > then -- to "whinny on a shoulder" seems bizarre.. I guess he's hugging the > horse? How often does that happen, in any cowboy novel? "to whinny on > something" is an extremely rare construction. It will work only if you've > correctly categorized "whinny" as a verb that can take a preposition. Are > your clustering algos that good, yet, to correctly cluster rare words into > appropriate verb categories? > >>>>>> > >>>>>> Second sentence .. "Jims" is a very uncommon name. Frankly, I've > never heard of it as a name before. Your training data is going to be > extremely slim on this. And lack of training data means poor statistics, > which means low scores. Unless -- again, your clustering code is good > enough to place "Jims" in a "proper name" cluster... > >>>>>> > >>>>>> "the lama snuffed blandly" -- "snuffed" is a very uncommon, almost > archaic verb. These days, everyone spells llama with two ll's not one. > Unless your talking about Buddhist monks, its a typo. > >>>>>> > >>>>>> "you understand?" is .. awkward. Common in speech, uncommon in > writing. Unlikely that you'll have enough training data for this. > >>>>>> > >>>>>> "Willard" is an uncommon name. Does your training corp[us have a > sufficient number of mentions of Willard? Do you have clustering working > well enough to stick "Willard" into a cluster with other names? > >>>>>> > >>>>>> "it is so with Sammy Jay" is clearly archaic English. > >>>>>> > >>>>>> "he hasn't any relations here" is clearly archaic, an > olde-fashioned construction. > >>>>>> > >>>>>> "Pivi said not one word" - again, a clearly old-fashioned > construction. Does the training set contain enough examples of "Pivi" to > recognize it as a name? Are names clustering correctly? > >>>>>> > >>>>>> Any sentence with an inversion is going to sound old-fashioned. All > of the sentences in that corpus sound old-fashioned. Which maybe is OK if > you are training on 19th century Gutenberg texts .. but its certainly not > modern English. Even when I was a child, and I read those old > crumbly-yellow paper adventure books, part of the fun was that no one > actually talked that way -- not at school, not at home, not on TV. It was > clearly from a different time and place -- an adventure. > >>>>>> > >>>>>> Anyway -- you only indicate pair-wise word-links. Is the omission > of disjuncts intentional? Also -- no hint of any word-classes or > part-of-speech tagging? This is surely important to evaluate as well, or is > this to be done in some other way? i.e. to evaluate if "Pivi" was > correctly clustered with other given names? Or that lama/llama was > clustered with other four-legged animals? > >>>>>> > >>>>>> Also -- I can't tell -- is it free of loops, or are loops allowed? > Allowing loops tends to provide stronger, more accurate parses. Loops act > as constraints. > >>>>>> > >>>>>> -- Linas > >>>>>> > >>>>>> On Thu, Mar 21, 2019 at 11:09 PM Anton Kolonin @ Gmail < > [email protected]> wrote: > >>>>>>> > >>>>>>> Hi Linas, Andes and whoever understands LG and English well enough > both. > >>>>>>> > >>>>>>> Attached are first 100 sentences for GC "gold standard" - manually > checked based on LG parses. > >>>>>>> > >>>>>>> We are expecting more to come in the next two weeks. > >>>>>>> > >>>>>>> To enable that, please have cursory review of the corpus and let > us know if there are corrections still needed so your corrections will be > used as a reference to fix the rest and keep going further. > >>>>>>> > >>>>>>> Thank you, > >>>>>>> > >>>>>>> -Anton > >>>>>>> > >>>>>>> > >>>>>>> -- > >>>>>>> You received this message because you are subscribed to the Google > Groups "lang-learn" group. > >>>>>>> To unsubscribe from this group and stop receiving emails from it, > send an email to [email protected]. > >>>>>>> To post to this group, send email to [email protected]. > >>>>>>> To view this discussion on the web visit > https://groups.google.com/d/msgid/lang-learn/bde76364-a578-4ab8-8ac5-2f49f794072b%40gmail.com > . > >>>>>>> For more options, visit https://groups.google.com/d/optout. > >>>>>> > >>>>>> > >>>>>> > >>>>>> -- > >>>>>> cassette tapes - analog TV - film cameras - you > >>>>> > >>>>> > >>>>> > >>>>> -- > >>>>> cassette tapes - analog TV - film cameras - you > >>>>> > >>>>> -- > >>>>> -Anton Kolonin > >>>>> skype: akolonin > >>>>> cell: +79139250058 > >>>>> [email protected] > >>>>> https://aigents.com > >>>>> https://www.youtube.com/aigents > >>>>> https://www.facebook.com/aigents > >>>>> https://medium.com/@aigents > >>>>> https://steemit.com/@aigents > >>>>> https://golos.blog/@aigents > >>>>> https://vk.com/aigents > >>>> > >>>> > >>>> > >>>> -- > >>>> cassette tapes - analog TV - film cameras - you > >>>> -- > >>>> You received this message because you are subscribed to the Google > Groups "lang-learn" group. > >>>> To unsubscribe from this group and stop receiving emails from it, > send an email to [email protected]. > >>>> To post to this group, send email to [email protected]. > >>>> To view this discussion on the web visit > https://groups.google.com/d/msgid/lang-learn/CAHrUA36dE5ihtcCaqPv_q4qgmbEy-yX6kTkUHyLZmjk6d4VfOg%40mail.gmail.com > . > >>>> For more options, visit https://groups.google.com/d/optout. > >>>> > >>>> -- > >>>> -Anton Kolonin > >>>> skype: akolonin > >>>> cell: +79139250058 > >>>> [email protected] > >>>> https://aigents.com > >>>> https://www.youtube.com/aigents > >>>> https://www.facebook.com/aigents > >>>> https://medium.com/@aigents > >>>> https://steemit.com/@aigents > >>>> https://golos.blog/@aigents > >>>> https://vk.com/aigents > >> > >> > >> > >> -- > >> cassette tapes - analog TV - film cameras - you > >> > >> -- > >> -Anton Kolonin > >> skype: akolonin > >> cell: +79139250058 > >> [email protected] > >> https://aigents.com > >> https://www.youtube.com/aigents > >> https://www.facebook.com/aigents > >> https://medium.com/@aigents > >> https://steemit.com/@aigents > >> https://golos.blog/@aigents > >> https://vk.com/aigents > > > > > > > > -- > > cassette tapes - analog TV - film cameras - you > > > > -- > Ben Goertzel, PhD > http://goertzel.org > > "Listen: This world is the lunatic's sphere, / Don't always agree > it's real. / Even with my feet upon it / And the postman knowing my > door / My address is somewhere else." -- Hafiz > -- cassette tapes - analog TV - film cameras - you -- You received this message because you are subscribed to the Google Groups "opencog" group. 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