"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 -- You received this message because you are subscribed to the Google Groups "opencog" group. 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