Thank you very much. And so today I'll be talking about this rather boastful title of making computers actually use for two historical inquests. And so I would like to start my talk by saying that I'm very happy that I'm talking in this particular seminar because I think with Joey we kind of share a very kind of general like love for the particular. So if we talk about computers and historical linguistics, the first thing that kind of comes to your mind in general for people who have heard anything about it is a paradigm like this. So what I call tree crunching. So like you ask specialists of a language group with already mature tradition of historical linguistics, and you ask them to encode tradition to a couple of binary trees, whether this language has this on or not whether this word in this language is cognate to that word to that language or not and you just put it all into a kind of fellow genetic algorithm and you get a tree and yeah that's nice but I don't think that's the thing that's the most appealing to historical linguists, because I put my basic misgivings for to to this kind of paradigm in three and three subtitles. So first it's, it's the kind of the inverse because in general computers are supposed to help us doing things but now computer is going to run algorithm and we encode things so that computers can read them easily. That's not the happiest thing ever. The second thing is this kind of big conclusion is them, which is why I began my talk with a not to host, which is that the idea that you know historical linguistics is is not an end to itself at a means to a tree which can be compared with genetic trees or large historical movements of people, things like that. Of course those things are extremely interesting. I think everyone spend some time listening to cracks pot series about this kind of things, but that's not actually makes historical linguists fun for us. And that's not makes our fruits of labor useful for people who speak the languages or who, or who identify with those languages. And the third thing is that it relies on traditions for language groups that already had the tradition so it doesn't produce anything new for us. So how about having it the way around so let's have computers and service and let's have computers to help us create and perfect hypotheses that are classical that actually interest us and that are concrete on this specific trade of this specific language or language group and also we want to have and also we wanted to work in a sort of expanded sorry exploratory context so that we computers help us to create new traditions to bring a rigorous historical linguistics to places to language families where it didn't exist before. Okay, so the idea of capo is that of reconstruction assistant. So like what's a word processor. Well, that's what you use to write documents so that like the documents exist inside the word processor and by interacting with a word processor you write your documents. Okay, so CAD is used for technical drawings and designs and proof assistant in mathematics is to use to create computer verify formal proofs. So that's the basic idea of reconstruction assistant so the linguist or the linguists interact with this reconstruction assistant. And it a software it takes us a bunch of inputs which is basically the lexicon of daughter languages, and you gradually elaborate the assistant helps you to elaborate the linguistic reconstruction your hypothesis about the history of this language group. And at a certain point when you think that the thing is good enough it can be output into an etymological dictionary or other kinds of handbooks of the of the language group. Okay, let's say that come historical linguistics is not like the hardest problem ever for for computers. I mean, a lot of historical linguistics is just a string search and replace a lot of other things are just the statistics. So, in principle, it should, we should be using computers everywhere. But of course that's not what happened. And yeah, we basically use computers as well as what processes and with maybe some custom ruby scripts or ruby scripts or Excel magic to to to get something done but it's not a computational. And we have recently a couple of great surveys on the computational and computerized methods and like for every single sub task for every single step in the general workflow of historical linguistics. You actually get very, very good computational and computerized methods. So, what do we need? Well, let's say the technology is already here and what is needed is engineering. So, you just need to, when you have a context like when you actually want to do something and you also actually want, you actually actually want to involve something and see what we can create. So, that's the basic idea. Okay, so let's talk about some of the previous approaches and from in from an engineering point of view which is to say, how can it be made to produce concrete results of a good quality. So, let's focus on one of the most important tasks when we consider historical comparative linguistics, which are that of cognitive assignment or cognitive determination or cognizance or whatever. So, you have a bunch of words and in different languages, and you try to partition them into different cocking sets so this word is cocking it to that word to that word so you put them all together and you call it a cocking it set.  So, there are two basic computational approaches to cognitive assignment. The first one is automatic cognitive detection. So, basically it's an algorithm, which is quantitative kind of language agnostic. You just give it an input, the wordless and it tells you which word is cognitive which that's the first idea. The second idea is that of back projection etymology. So, basically you have already you know already the phonological history of the language group pretty well, and you encode them into let's call them programs that reconstruct the every daughter language form into a language form, and you kind of crisscross you triangulate the results together and see what can be fit into caucus with what others. So, automatic cognitive detection has a very long history.  So, they still had with assistance at the area. And, and you, yeah, you have them crunch words like a computer in a certain manner so you can have, you can gain rough first idea of what kind of caucus can do this. So, as, as soon as the thing gets computerized and have some actual engineering constraints, you, the basic methods rely on actual phonetic similarity which is like kind of no no to historical linguistics for historical linguistics because yeah this is kind of thing that's basically you have an impression bad means bad. So, yeah, Persian bad is cognitive English bad of course the world like this, but those algorithms will pick up those coincidental complex while fail on non obvious cognates. So, what we actually use here is this method developed by your home at least context that which uses a pair wise sequence alignment to closely mimic the behavior of comparative method basically it takes words like assume words that might be cognate are actually cognate. So, so you get a kind of score of correspondence and you use the correspondence score computed from the word this itself to to determine the search a self reference thing is very robust. And it's, most importantly, it fixes this problem over real over reliance on the actual sound value and it's has a robust independent behavior. So, yeah, the, the algorithms, especially the new ones like clicks that are great. So, if you use it on like on some kind of German English Dutch French thing it, it gives 9495% accuracy, but it cannot function as as a whole workflow by itself. So, the question is, what happens afterwards. If you get a bunch of cocksets, how can you, how are you supposed to correct them. So, this problem itself has like two dimensions. The first thing is, of course, our idea that we want to explore new language groups. So, when you develop an algorithm. You think that like there are trained specialists who can have who have this magical power to tell the true cognate from the force. But this magical power is a downstream to a very, very mature tradition of historical linguistics. So, yeah, so basically, if we don't think very deeply about this kind of problems, you're basically asking, having computers, helping people to do things that people that can already do but cannot help people doing things that they don't. And the second stuff is what I will call the Chinese typewriter problem. So, even with linguistic expertise, it's quite non trivial to think of a way to correct cognate judgments. So, this is a very logical kind of inventory of cognate judgment errors. So, over lumping, over splitting and misattribution. I think I tried to talk in like over correction or type one type two errors but it doesn't work like this. So, okay, over lumping is that if the real the true the ground truth has like those three cognates sets but in fact, I've heard them tell you that they are they all belong to one coxet and over splitting and misattribution. So, for over lumping, you need to split one cognate set into several for over splitting you need to merge several coxets together and for misattribution you need to move stuff around moves move stuff between coxets. Okay, that's the problem. So, if you have this, of course, you know that okay here are two blues, the thing you need to put here and here is one red thing you need to put here. But, well, human beings don't have like infinite memory so basically, if you put those things like physically visually simple, adjacent text opposed to each other, it's very easy to see the pattern. But the problem is that like if you actually run the next step algorithm or any kind of algorithm on the actual data you get you get a huge bunch of coxets and and it's just not feasible to to to reassign forms to do this, but across 3000 or 10,000 or more coxets. So, here's the perfect analogy so I once so I took this picture at the one of the libraries in Heidelberg. And yeah, this is this is this marvelous machine called the Chinese typewriter. So, apparently, you have a kind of mechanical cursor, and you need to locate this mechanical cursor on one of the characters and you, and you, and you, and you tie and you use the button and it moves the the this this this type to the to the paper to to to produce characters.  And you need to match this coxet here to this coxet here. And while looking at all those know it simply doesn't isn't going to work. Okay, so as a conclusion so automatic cognitive detection is a great it generates excellent first drafts of cognitive assignment, but there's no obvious way for human language to correct the result. And let's talk about the second idea that is very crucial to the development of the paper methodology.  So what's the idea. So, okay, it's the idea of Houston, who in, I think it was in the 70s who had this brilliant idea of my producing a computer generated etymological detection of conkin languages. And here's a sample entry from that dictionary. So if you can see. It's, it's, you have the product conkin word with create a fox manominee or driveway. And how do you compile the how how do you how can you make this kind of different with a computer. Well, so our conkin is one of the language groups that has an extremely developed tradition of historical linguistics so like I think back in 20s or 30s people. Yeah, understand how the sound changes very well, even for even for quite difficult language like a manominee. And so, what happens is that you can sense you know the historical phonology almost perfectly you can write programs that back projects. So what's back projects. So, yeah, you take it's a function that takes an actual proto an actual daughter language form and produces the list of possible proto language forms. Okay, you have a back projection. And so you can have computer just to go through all the daughter language dictionaries and unites them. See if this one can be reconciled with that one in terms of proto language form. So it's so from an engineering point of view, it has the unique distinction of the of actually working. So yeah, here is the dictionary. And what works is also kind of easy to explain because when you have like those three kinds of coconut judgment errors. Okay, so basically, you just need to get rid of basically homophones which is overlapping and it doesn't require this kind of matching things together in 3000 or 30,000 item set. Okay. The problem isn't actually what happens afterwards because there is, because you just need to split a coxsets that are already made so it's is afterward the problem lies before so it has the kind of let's say fragile dependency on the perfect historical phonology. So, so yeah, let's get that into a kind of, I think, exaggerated example. So if you just have a group and you write some rules, and which are quite wrong. And yeah, it compares everything and it gets you 50 etima, sorry, 15 etima. And yeah, you have your great etymological dictionary of group X that has 50 that has 15 etima and you have no absolutely no idea what went wrong because it doesn't give you when it fails it fails gracefully. You just have no idea. So, it works only in this very limited context of like scaling up reliable theory of historical phonology to to an etymological dictionary. And but but we are trying to do something else which is this exploratory stuff when you have a group that you don't really know about then what you can do about it. Okay, so while for the algorithms, the challenge lies in how can you find a way to correct the results. And here the challenge lies in the in the need to have a way to for linguists to discover and gradually refine the historical phonology. Okay, so that's the basic idea. So yeah, for both. But if we look at them like from this kind of engineering workflow point of view, they have, they both have this the fatal fatal disadvantage of being a non explain non exploratory non iterative so yeah, you need to already have a perfect group you're working on before you can enlist the help of computer into it. And of course, if you know the group perfectly why do you need a computer. Okay, so let's distill this discussion into the design goals. So, in order to have something that we actually would like would want to use the first goal is what I call optimal human computer cooperation. So yeah, so the ideal is to have the computer working like some kind of very intelligent, unless the assistant, or more like the student and you're the director. And yeah, the student does everything to the ability but then ask you. Okay, so how can I get this so you get to generate the so so you do the interesting this generating hypothesis, giving intuitive judgments and computers do the work like enforcing consistency keeping track of details and that's the ideal. And the second design goal is incremental improvement so yeah so as we have seen for the for the kind of back projecting technology thing so it really needs a good hypothesis of historical phonology. And so, which in most cases we don't have so yeah, the good system should encourage hypothesis to be gradually built and refined a crude set of hypotheses should still reward the language with more and better information so that the scientists can later use those new information gained from the bad hypothesis to make a good hypothesis. And here is a third thing which is a non destructiveness so yeah basically you need to think really hard so that if the human language is something then. Yeah, it shouldn't be thrown away, except when the human linguists themselves think the thing is wrong. Let's talk about our workflow, which is this thing called CAPA computer sets approach to language reconstruction. So the context is that we in this in this framework of the ERC project beyond boundaries and we want to do an etymology reduction of the language languages. Okay, so we have a lot of digitalized data. And, but now we're working on this, working on just finding a way to gracefully let us make this make a first version of the dictionary, I think, from the six languages in the certain parallel lexicon kind of source. Okay, the context is that of monosyllabic languages. So, okay. Okay, so some people think that monosyllabic languages means that most of the words in those languages like our monosyllabic which doesn't exist with which doesn't really exist, I think. But it's still a very important kind of typological type. So, yeah, the morphology is mostly compositional, and the productive morphine boundary coincides with the syllable boundary. And also, so there is little that happens between syllables, either dichronically or saying synchronically. So with monosyllabic languages, basically, it kind of allows us to like forget about words and take syllables at the basic unit as a phonological evolution and etymologicalization. So, yeah, here's an example of like the cognizance for monosyllabic languages.  So all those and, and this word hair is like a flesh hair. So, this head thing goes here, this brain goes here and the head hair thing also goes here while the second syllable is going to be etymology, etymologyized on its own in this, in another, a coxet. That's the basic idea of monosyllabic languages. It saves us a lot of work. And yeah, so CAPER is developed for monosyllabic languages with its limitations. So, later, if we're going to make it more useful for other kinds of languages, we need to think more about it. So, the actual document the CAPER workflow allows us to work on is what I call the bipartite hypothesis, which is made of two parts. So you have the coxets and you have the phonological history. So, okay, the coxets are the coxets. So, a word in the general case or a syllable in this monosyllabic case belongs to coxets. Nothing fancy here. And the other thing is what we call the phonological history. So, basically, yeah, you write them in as finite state transducers in the formal language. So, I don't have time to talk about trans, how great transducers are today, but basically it's the idea is that it gets you to write the historical evolution and SPE style, which everybody kind of knows, and also permit and also like automatically permits you to project forwards and project backwards. So, yeah, that's what we need. So, first you. So, the phonological history is actually composed of like two parts. So, we're looking at this example where Proto permission bar gives more time for. So, first you need to define what the what a legal syllable and the proto language is. So, for example, here, while you, yeah, the legal syllable is composed of the initial and the rhyme and the initials are those and the rhyme are R and E. You can see that bar. Well, it's B fits in the initial here and R fits in the rhyme here. Nothing fancy. Then we need to define the individual sound changes that lead from the proto language to more time. So, for example, here's a rule of devising which turns B into P. And here is a rule that turns R into all so that we get actually we get ball from bar. And finally, we have this transducer called more time, which defines not any legal more chancellable but like an perfectly inherited more chancellable, which is a lot is it's leash derived from proto family, excluding loans and energy and other shenanigans. So, yeah, so how do you define it?  And so here you get essentially an ordered list of sound changes apply to the proto language syllable. So, an interesting point of the caper view of hypothesis of the data will on which the theory on which you actually welcome is that you don't actually do any proto forms. I mean, if you take a look here you might think that, well, the linguists need to write got to stop all and age for the head and it shouldn't work like this because basically the proto forms should not be provided by the human linguist but and third from the phonological history and the daughter language form so we get for example here it means tears. And yeah, so this word reconstructs in HRLC reconstructs to this would be reconstructed to all those different proto family syllables and this would reconstruct to all those and this would So you do some kind of set. The theory operation. In fact, it doesn't always work so yeah, some kind of complex complex heuristics to tell that the the actual reconstruction of the of the whole caucus it is likely to be be. That's how it's supposed to work. So why do we do this, because it permits a kind of rapid iteration of phonological history. If you do any kind of reconstruction like on hand, you, one of, one of the, one of the, and not very Sony but very annoying problem is this kind of thing so like if you, if you change your hypothesis and so if you change your proto language for the And every time you do such a change a lot of the proto language forms need to be replaced. And so yeah basically we just get lazy and like never update your reconstruction except when you have like a complete new vision of how things are supposed to work and then this kind of things which are still quite counterproductive because I think and we will see later that actually the best way to to to make good to make good reconstruction to make good hypothesis is to iterate very quickly on the on the whole theory And so we got transducers we got to the this phonological history and what are they used for well The first insight of the Cape methodology is that you just blindly do a backward projection whenever possible and very often forward prediction and that provides a lot of context to the linguist. So, for example, we get this word here cloudy. So, yeah, the in the inferred reconstruction is zoo X. So, in this, and for the assay with Mao, so for example, the syllable, so is so yeah the computer runs the transducer backwards to do a projection from Zhao and they see that it is it is reconstructed as zoo X so it a chord it is harmonious it conforms with the with the with the projected with the projected reconstruction of the whole coxet so you get to take care and you get different kinds of process. So, here's the kind of process so for example, Mao, and at least according to our current hypothesis, it can only come from do not zoo. So, here it reconstruct this word back reproducing do and tells you that, well, it does have a reconstruction but it's not a reconstruction that conforms to the projected reconstruction of the coxet. And here's another example, which is, which, so if you get a memory, you can get a dope or deep.  And here, of course, what's wrong is not the, it's not the phonological theory but you should move it out to create another coxet. Okay, so that's the basic idea of back projecting everywhere which gives you a lot of context and we'll take a look at that later also. And so the basic organization of the Cape her workflow is basically what you would expect so you process the source word list to a kind of computer readable form. And you get the bootstrapping stage where the next that algorithm generates the first draft of the cognitive assignment. And then you get iterative improvement before deciding that you are going to publish that as an iterative dictionary. So the interesting thing here is the actual iterative improvement. So the idea is that, so basically, since we have this bipartite hypothesis, so you're supposed to, like when you make a better transducer, then it could somehow contributes into the into the coxet editing process to create better coxets and vice versa. So you can have this is exactly you, you work on transducers and then you work on coxets with new stuff you feel very happy and you get your new stuff and you use that back to create better transducers and you feel very happy and yet better yet better until you get the final result. So let's talk about this iterative improvement and both and both parts. So the more interesting part is this that a cognitive assignment. So how are you going to solve those seemingly intractable problems of of creating coxets. So here is this misattribution coxet error. And so, well, I will put it like this, since I put it like this, then, of course, how are you going to correct them dragging them around. Okay, so with back projection, you get a very nice stuff. Let's just take a look. So we just looked at this cloudy word, and maybe something doesn't really belong here so we're going to working on it. So we create a new coxet. So it's just as easy as dragging the word from the old coxet to the new coxet. So let's take a look at how the how the how the reconstruction actually change. Okay, now we put up here since stuff doesn't have reconstruction according to our current theory so there, there's no reconstruction for this coxet, then we drag the other Okay, this one does have a reconstruction which is deep or do so it is inferred that basically this this new coxet likely has reconstruction of deep or do. Okay, so that's the basic idea you allow language to drag words around, and you use transducers to to to provide this most useful kind of context that can be provided to the linguist. Okay. Of course, we just saw that on our board we just have three coxets and have that possible, we have a lot of them. Well, so that's a problem that needs to be looked into very seriously. So, here's what we want. So, first, partial visibility, the idea is that you, you shouldn't let yourself like be overwhelmed with this with the ocean of coxets know it wouldn't work. So, either like you wish to prioritize them. So maybe some are more important more in more reliable or like you'd end up in the smaller etymological lexicon or whatever. But if you don't have that then just give me some some random coxets so that I don't have to work on 330,000 of them at once. The second idea is that of grouping so since we have this over splitting or misattribution relation where we really do need the coxets to be visible to the human eye on the same screen in order for human beings to realize that okay maybe we should track it from here or maybe we should merge two coxets together. So we need to find a way to group together the coxets that are likely to have an over splitting or misattribution relation. Okay, here's what we do. So, first, we have a criterion called a reliable coxets. So the idea is that, well, since in the etymological dictionary, usually word netimon is justified by more than one example so yeah, at least it wouldn't do as any harm if we just declare that we just we only want to see the coxets which already has a reconstruction which is justified by words from more than one language. And the second idea is that of course so basically you take a reliable coxets and you also you try to see if there are other coxets reliable or unreliable that has the same that can have the same. That can have the same reconstruction. So you make an equivalence class you just partition the coxets into different boards where the boards are those where the coxets in the boards just share one or more reconstructions. So, what does that do well in doing this with transducers you make only one small part of coxets visible, and also since they're visible in board, you every board contains coxets that are very likely to have an over splitting or misattribution relations and since they since some of their words already share reconstruction so yeah they may be very very very related. Okay, boards also offers us this task management benefit of chunking it up to manage what it's so that if we take a look at some lovely boards. Okay, here's a board. Here's a board with a lot of coxets, but not that lot. Here's another board. Notice that for example, here is a coxet that is reconstructed to band here's another and some of the, and some of the glosses are all we all or whole so yeah they should be much together.  We can merge things together. Here's a very good board with which contains basically only one etymon. However, this, there is one word that doesn't look the same which is this yeah this yeah it's just a word for water but the, but the algorithm failed to understand that the year is not the same as the other keywords so let's move it out of it. And here we get a word that is reconstructed as big and that means to go to battle. So, and here you get the same meaning but the the actual the actual reconstruction, even the superficial form kind of allows you to make this decision. And of course the fire short is the related idea to go to battle so you can merge all those coxets together and let's take a look at so this is the, I'm not really working here. This is just the results of the first fishing is you don't have a lot of things here. And yeah. Okay, that's the basic idea of it. So, this process of using transducers to put board to put coxets into visible boards we call fishing and why do you call it fishing because basically with every improvement on the phonological history on the transducers. It doesn't happen, which makes more reliable coxets visible to us. So, if you do a new bad transducer for a new language or you improve and transducer for language already has one, then either more coxets are just as reliable, or some little small coxets actually have a reconstruction and can be attached to another reliable coxet. That's the basic idea. So we have this little nice fish icon to to indicate the new cost of that's just when that just got fished up. Here's how the fishing works. So we color code everything in this way so this gray box represents the results of next that algorithm, which is a huge bunch of possible coxets waiting to be corrected. So somehow I'm being very hand wavy here. You book yourself up and put together a rough first draft of historical phonology. And with fishing, you actually fish out a lot of coxets into this red portion, which I call visible or boarded. Then with the cognate assignment interface, the one that we just seen a lot of videos of human linguists can edit the first bunch of body coxets into human curated high quality coxets. Then you get better hypotheses of historical phonology. And it fish out coxets with this these better historical phonology. And you all edit them again using the interface. So that's the idea. So with this fishing thing you basically finally have an incremental kind of way out from the from the conundrum of too many coxets. Okay, here's the interesting thing that I wish to talk about a bit with the wrecks or basically because your first transducers are not perfect. They are going to pick up some things that with later transducers, the fishing algorithm thinks that they shouldn't belong on the board. So they get assigned this acute dead fish icon. And if we take a life, if we take a natural look at those dead fish things, it's very interesting. So for example, this means tile like the tile you put on the rooftop of houses. And the first thing you notice that is that they look a little bit too similar for for actual cognates. Maybe this should be all or maybe that should be. But no, they are why va va. And the second thing is that they they receive quite disparate. So this one is why with the zero tone. And this one is why with each tone and this one doesn't have reconstruction it isn't inherited looking syllable. So what happens of course if you speak Chinese you know that it's, it's basically the the Chinese word for tile so with. So you see basically that by using algorithms and transducers together it also gives it also weeds out the lens if you're interested in the inherited part of the next. Okay, so here's the idea so we're trying to build an iterative workflow which allow you to work on one of the two parts of the bipartite hypothesis. And when you get better transducers, it can help you to fish coxies and also transducers kind of contribute directly to the UI so you are helped and spoiled by the transducers to create better coxies, which maybe can be used to create better transducers. So this, this interface and this efficient workflow basically combines the two approaches previously discussed that automatic cognitive detection that have back projection etymology. So, so for the math type, you know, if you do math or if you do math or physics, you know the idea of like one object devolved into degrades into another in an extreme context. So if you imagine that the cognitive detection algorithm says that every word is not cognitive with every word it will with any word and it's basically degrades into back projection etymology and it's basically both but of course it's the actual interesting thing. So it kind of combines the advantages of both methods, especially with regards to the scope of data. So, automatic cognitive detection algorithms are not really fancy about a small imperfections, but on the other hand it misses non obvious cogniz the kind that gives you aha. I remember like learning I think last year it's really ridiculous that I hadn't done last year that enough is genug it's, it's, it's trivial when you think of it but if you know the historical phonology but it wouldn't click and then wouldn't click to the algorithmizer and back projection etymology gives you those aha's but if there is a little imperfection either in the actual forms or in your version of the historical phonology, then it's just, it doesn't doesn't appear. So, on the one hand, we don't want this over fascinates making data invisible. But on the other hand, we do want to know if modern reflexes are a lot because it's not. So, the Cape awake a by putting the human linguist in the center of this approach provides both kind of information so yeah it doesn't give the kind of information that's the algorithms give and also the kind of information, the, the back really strict rigorous etymological method and then it gives you all this information and it lets you to decide what to do with it. Okay, now let's talk about transducers. So the thing with transducers is that, well, while you can have those like basic stuff. You have the algorithm creating the caucus and you can correct the caucus for transducers you need to create things X and the hello and which makes the situation quite frustrating. So, here's what we want to first you to create your, your historical knowledge that you need to something to work on, you need your words that you think maybe are cognitive with each other, and you need a visualization mechanism to to give your patterns to make you think about things. And also, since transducers, or any kinds of hypothesis of historical knowledge are unmanageable, fragile jagger notes you do need some way to decompose it into discrete tasks of manageable size within some feedback so you know you did something good and not bad. Okay. So, we work from correspondence patterns. So, the old masters of historical knowledge, of course, thinks that the correspondence are the real things and the reconstruction so may call them here restitution. They're not real. They're just some kind of algebraic notation of the real correspondence patterns. Okay, it doesn't really work like this, but it gives us a quite good idea of like, it's important in the whole strategy. So, in the context that we kind of where we are from so for the linguistics of China or Southeast Asia or other sign of Tibetan languages. There is this kind of thing of putting actual attested forms into correspondence pattern tables. So, here's an example so here is the reconstruction of one for a proto meow and you get this proto, proto initial.  And so you can see that they correspond to each other. And here's an, here's an less exhaustive example is a diabolical reconstructing prototype so you get four legs and five words but the basic idea the same. Also, the idea that of marking of explicitly marking non regular correspondence. So, for example, here it says that it says that the tone isn't regular. It says that the vowel isn't regular. Okay, in the most general case, generating correspondence patterns from caucus is generally hard problem because basically to do this kind of pattern to need alignment, which means that you need to take literally and that they and that the and layer and kind of fit them together into into this kind of table where you can say that you have Spanish L which corresponds to Italian L which corresponds to a Romanian L which corresponds to French L and etc etc. However, first we know that we don't really need the actual, let's say the actual algebraic expression of the correspondence patterns so we need them mostly as a visualization tool so I think we can argue that nothing which corresponds with T which corresponds with P which corresponds to nothing completely misses the point. And so, but if you, if you don't look at this, but if you use this thing as, as a kind of index to generate this kind of China or Southeast Asia kind of complete tables with examples, it, it makes the pattern extremely clear so yeah, even if it's nothing to T to P to nothing which is completely as a line, but we look at this and we, and we get a lot of ideas on where this might come from. Okay, so the actual alignment is not important. And of course, at least for the current incarnation of the paper methodology we're working with monosyllabic languages so we do a very trivial thing alignment by phonotactics. So yeah, this is like, this is like a phonology, this is like a linguistic theory of 1000 AD and you just divide your syllable into initial medial nucleus, coder and tone. And so yeah, you, for every language, the, the language right to kind of pass, which passes this thing into, into the schema. And also the schema is combined this way so initial means initial plus media and then you get media and you get rhyme which is media plus nucleus plus coder and you get tone. So yeah, the important thing is that you see this is, you don't take, you don't need to take alignment as some kind of very, very fine thing but as a tool to help you to see the actual patterns. Okay, so here's how it works. So, for example, we have those languages. So we're going to study at sea. The interface is very primitive, but it's me doing the programming so how can I be otherwise. Okay, so you get the correspondence you waited a bit for to get the report and so it gives you a title chapter titles initials rhymes and you give those you get those rhymes with everywhere, you get the actual forms and also the back projections. Let's turn two languages into three languages. So we'll still need to wait a little bit to get the actual results. Here they are. Okay, let's look at rhymes again. There is absolutely no interface. So yeah, you just search around in this document. Okay, so yeah. Here is a turn correspondence. Okay, that's the basic idea. And with back projection is a really great example of how back projection really helps you in reconstructing stuff. So for example, we see this, we see this correspondence pattern. So, I try and send out correspond to the in a T and the in my room. So for example, for chicken, cock, hen or the same thing, but yeah, they should be much together, but yeah, there are basically two examples. And so for bark, it's good job, and what kind of rain, and this one is good job for a little while. So yeah, when you just take a look at this, then good job and of course, it's really difficult to think of any hypothesis that can like explain them. But of course, you don't need to do that. So, the idea is to enable you to do this, a gradual refining when you create hypothesis on the largest, the most regular stuff. And what did that give you. Well, after you created them and you take a look here. Okay, so basically it's really easy to see that you have, you have a grouping, which makes our time and send out to have a good while have a proto language, well, I see and module have proto language. So yeah, basically for those two words and probably others, you get a good in some languages and in some other languages and you can and you can like make more sophisticated hypothesis from here.  But of course, when when your whole mind is taken by by by those kind of very basic laborious work of collecting correspondence pattern, you can't really see that and here you can really evolve the hypothesis in a very, in a very effortless manner. Okay, so the next problem is debugging. So yeah, you have ordered sound changes and you have feeding them bleeding and whatever.  So yeah, this in itself would be very difficult. So the idea is to is to factor the whole change into tiny tiny atomic changes and in a comparative manner. Okay, so I think it's clear with an example. So let's continue our video. We're still studying at Ebola and Mario and now I'm looking for. Now I'm looking for those cross marks those cross marks mean say that for example, the Norwood here doesn't even cannot come from a proto Burmese etymon according to the present theory of the of the historical knowledge of Mario. So yeah, I'm I'll be looking for those kind of things. I look for crosses crosses crosses crosses crosses crosses crosses crosses crosses crosses. And here's a good one. Okay, so if you look at it, then there are two crosses here. So you have a whole correspondence pattern for which the con theory doesn't work for any of the forms. So for far and for rely the the Mario form does not correspond the actual assess it Mario from doesn't correspond to anything. And for to measure it corresponds to something but you see, in every other languages care wise we have some possible so here is a problem. So what's the problem here. You have this, you have this air vowel and proto Burmese, which gives a in at sea and air in Bola is the same vowel is the same about everywhere in at sea and Bola a and air. However, in Mario, sometimes it's air and sometimes it's our so earlier we have like forgotten and we didn't see that some vowel some of the air vowels could become our and what's the condition. Okay, so the busher, no, and the gun. Okay, here is it. So basically, and you have the last and the, well, is the, I think the the the sound change from proto Burmese word to Mario, it could be quite, could be quite recent. So the idea is that maybe the proto Burmese vowel air changes into our after dealers, including the labial villa approximate. Okay, now we found our problem. So, for demonstration purposes, purposes, I start with, I start with inventing a very bad solution. So here's a bad solution. So first, here's a sound law that changes air into our mechanically, mechanically without taking into account the phonetic conditioning. Also, the placement of the, also the placement of the, the sound law is wrong. The sound changes wrong. So you get air to our here. But of, but here is is an already existing example and Mario, an existing sound training Mario where our changes into all. So, of course, we can, we can, we can predict that our bad version changes into our which then get changes in all which is what we want. Okay. So what we get. Well, for the words that we're actually interested about, we get nothing. So, what did not have a reconstruction. So for example, far doesn't have English now neither. So, yeah, the only, the only difference is that it was the wrong is the theoretical reconstruction was there but in current theoretical reconstruction the wall. However, and here we see the beauty of the Capeham interface. So, yeah, basically, you have two rows. The first row corresponds to the old transducer and the second row correspond to the new transducer. And it checks if your, if your new transducer actually explains the current forms better or worse than the old transducer. In this case, it's worse, and you get, and of course, since it's a systematic degradation of the, of the quality of the hypothesis you get a huge swath of red with frowning faces across a whole correspondence pattern which is capable of telling you that something went terribly wrong. So now we are reminded of our historical linguistic 101 and the idea that, well, some laws have consequences. And so we have, so we put it into a better place.  Okay. Now we are going to do things seriously. So we remember that it's okay. It goes after Vilas. So what are the Vilas, KG, and we remember there is this W and maybe aspiration. So yeah, it happens after Vilas. Wonderful. We have, okay, what happened, gosh, okay. So yeah, it did it right for far and measure, but not for rely. Why? Because of course, yeah, rely has this nir. And yeah, you see, yeah, I meant to do this thing wrong in the beginning for demonstration purposes, but now it caught me actually in an actual error. Okay, here's the right version of the sound law. And okay. Okay, so yeah. And yeah, I should feel very happy about this, this kind of thing, because when you get like a whole correspondence pattern with smiley faces and green, then yeah, then it's a new situation, a new sound change that is now properly accounted where it wasn't before. However, we still need to change, we still need to check if it doesn't make anything worse. So you do. Okay, because it's very bad. I type the frowning face into search. And search tells me that there is no frowning face in the thing. Okay, so I'm very happy about my new sound law in Bola where air changes into R after Vilas. Okay, so the discussion so yeah for the for the transducer debugging part. Well, so first you get this visualization of the China SCST style forward correspondence charts, which provides the necessary and minimal information for generating the correct hypothesis. And the next thing is incrementality so basically yeah it's very dangerous to change transducers so we have this kind of atomic change.  And if you do an improvement is an improvement and if it's not it's not. The third thing is iterative iterativeness iterativity. So yeah, the, what do coxies do in this path well when you have better coxies you get more examples. For example, I think the reason that we didn't catch this art is that there were two few coxies in the first version of the fishing so so when you have one example you're not really sure. one example you are not really sure if something needs to be done about it and also when they're checked when they're properly um when when you when you when you get the rubbish out of any out of a computer algorithm generated uh coxet you also get less red herrings and you get just a much uh much clearer picture of the of the historical phonology yeah so that's basically it so yeah we just talked about the uh transducer debugging as a part of an iterative workflow so when you get better coxets they can they can generate a good correspondence patterns for you to um debug your transducers to make improvements in your hypotheses in in the iterative and instant feedback way that's basically it so yeah uh yeah so sorry for this thing being a bit a bit technical but I think if we're really going to talk about how to make computers actually useful for historical linguistics we'll need to see from the beginning to the end how you can uh how you can make this uh this workflow uh consistent with itself and iterative and stuff okay that's it and so uh future directions okay so yeah we're still working on the current incarnation of the uh if the k-perme methodology so um with a view of producing a really good um entomological dictionary with the language languages and uh frankly I don't program that well so yeah but yeah just just an idea so um putting it more seriously so we see that a lot of things are greatly simplified by the fact that we are working on monosyllabic languages so the um basically the thing is that uh the first the alignment problem which is really hard is simplified so in order to address the alignment problem I think we'll at some point need to um make much better transducers than than the kind of you know it's like a peak it's like a parallel parallel regular expressions versus real regular expressions as long as you have this bi-directionality I think we should actually explore ways to to put more stuff into it and also uh since we since with monosyllabic languages we don't really care about like uh paradigmatic morphology or analogy which um which which destroys the perfect love to get that uh relationship between the inflected form in the daughter language and the inflected form in the source in the proto language and yeah uh first we need to think about um like even encoding them in the in the rigorous and machine readable and machine verifiable and way is an open problem so let's end let me end this talk um by saying some good words about transducers so I think at some point with this and other technologies so it can it can bring us to uh to something that is uh in French we say democratiser which in English is like de gatecaped so um for Indo-European linguistics you you you get an actual discipline and people work on it so yeah so people talk in the um laziest uh most symbolic way possible but for them it's it's doable but like for for minority languages we need to be very frank and I don't think we're we're getting we're getting we're getting we're getting 300 professors beckering with each other on on one tiny language family um and and the in the jungles of north Laos it doesn't work like this so so what we can do is to is to make is to make our hypothesis transparent and um so easy to change and easy to understand and so uh kind of uh kind of less institutionalized less uh intense cooperation could still uh move us towards a uh more recordable future for the historical linguistics of world languages okay that's it uh thank you very much


I'm Jared Hartke, rice extension agronomist for the University of Arkansas System Division of Agriculture. I've just given a quick update on the 2022 season as we see it so far. It's been yet another in a series of struggles getting planted with another very wet spring that we've fought our way through. Really we were tracking well behind in terms of planting progress all the way through the end of April. And then finally we started to get some strings of dry weather, not anything big at once, but getting some actual field days and suddenly found ourselves from being very far behind and potentially record behind at one point really to what turned out to look more like a middle of the road planting progress once it was all said and done, but still the vast majority of our rice crop planted into May. In an even numbered year like this one, we've talked about this a lot through the spring and the winter, we would normally expect to see 1.4, 1.5 million acres of rice. We do an annual kind of up-down rotation, 1.1 or so million acres in an odd numbered year, 1.4 plus in an even numbered year. So we're expected to be around 1.1 million acres this year. That doesn't sound like much of a decline from last year's 1.2 million until you look at it through the lens of where we normally rotate to that much higher number. So as far as the state is concerned and the state's rice industry is concerned, it's a much bigger reduction than it looks like on paper, one year to the next. So that part's going to be concerning for the industry this year and certainly what happens going into next year. But really even beyond that, certainly a huge focus through this year has been input costs and where we're on fertilizer, specifically urea, phosphate, potash, those and really what that means for profitability. Our rice prices have increased this year like those of other commodities, however they haven't increased at the same rate for rice that they have for the other. So we still have very much an uphill battle this year in terms of reaching a profit. So even very good yields this year could still be a little bit of a struggle on the rice side if something doesn't continue to further improve. The bigger issue now is going to turn toward water and availability with the amount of heat and lack of rainfall we've now had for weeks at this point. Now that we can stop griping about getting too much rain, now we haven't had it in forever and there's still not really any in the extended forecast for quite some time. So obviously trying to irrigate rice but also even starting very early on irrigating other crops. Putting quite a strain on our overall management at this point and that's kind of a down-the-road concern of how we'll be able to keep up as this season progresses. Normally by around this time we would say that we largely have the crop laid by as in we're flooded, we're sprayed up, we're pretty clean and we're more focused on you know keeping this crop irrigated and you know trying to make our way to harvest primarily but we are still working on a lot of those cleanup applications and trying to get a fair amount of rice to flood and again laid by a lot later. Looks like it's going to run all the way up into July for that and technically there are still a few rice fields being planted and even been planted in the last week or so. Guys still playing catch up from the early season delays doing all they can to get in some of their last acres now.


Going on everyone Sam here with late in sports cards. I'm in 22 penini flawless football hobby Two box full case break number 12 random team style So you look again at your random results as always can find these on the website under the results tab as well as the link In chat John D up top with Jags got a scroll down one to see mark R and the bangles It's good. Look everybody. Let's do it Do some more flawless? More flawless never bad thing here, Sean. Oh There's bucks and I thought Milwaukee get them out of here. That's got needs to make some water in it's Wednesday night, Steve Right here we go that'll work over there Good night that happens empty lid Starting off with a Drake London the guy who's on the track six of 24 the Falcons Six of 15 is Bailey zappy. You can recappy this Five of ten going to the Bears that is gonna be a Dan Hampton Nice one there congrats on that Chicago Dan Hampton for that 85 defense 14 to go on the next one everybody true How about a Ronnie lot that is gonna be seven of eight to go into the Niners. That's awesome Uh, I don't think yet tie until this next box. I swung their Niners got sand fran This is sick 13 to 15 for the Patriots is Drew Bledsoe Drew Bledsoe player worn on that as well logo that lot was sick. I love Ronnie lot dude Such a dog Yeah Three of five Jameson Williams on the emerald signature emerald diamond signature congrats on that one their lions We've got mark D picking up the commanders in 14 and 15. Thank you mark And then Jason L took a spot. I didn't see where it was though, but thank you Jason I saw you. I saw your spot coming. I don't know what it was though and Nice one here for the Broncos Seven up ten jersey number John L. Way Wallace great. I'd say you can make this because you're numbered that is nasty Congrats on that congrats Denver Going to Aaron M. All right, we've got a Going to the Niners debo Samuel 15 of 15 for the Niners Going to the Eagles four of ten Devonte Smith mark, what up buddy mark? I swung there for the Eagles And then nice one here going to the commanders 11 of 20 Sam Howell congrats Washington going to mark. There you go, buddy Frank L with a spot in 13. Thank you Frank right again full sellout promo tonight if all the brakes sell out $1,000 of break credit to three winners ten goldpacks to seven the other seven winners unreal man You like it mark. Thank you buddy through Super Bowl one Yeah, that was a good box right there man. I was a very good first box. Come on. We'll see something huge in this one Yeah, there's gonna be something huge in this the DeCordians they're playing it That's nasty that's nasty that is nasty. Oh, all right. Oh Oh We got a really potential Really cool card coming up everybody really cool card seven of ten is gonna be a cave-on tibido going to the Giants Seven of ten cave-on at tibido. Congrats Giants. Nice one here. It's gonna be Micah Parsons DeMarcus where that is Player worn DeMarcus where very nice Three colors Parsons two colored where should be Swiss stop peeking.  You get to see everything first and flawless Seven of ten is gonna be Harold Landry. Nice one there for the Titans Two-colored patch auto we've got nice four or five Damian Pierce rookie auto going to the Texans congrats on that Houston you mag this Damian Pierce In my hand. Yeah You guys are bots We've got four or five Donald driver go into the packers flawless flyers That's sick. Love me. Love Donald drivers auto too. He was a beast He was a beast 13 13 to go on 13 13 on 13. Oh, that's so sick Five of 20 James Cook on the RPA. God. That's insane, dude Tago, baby. Oh This next one's so so sick, dude Going to Chae B Congrats on the bills. That's go to Paul. Congrats Paul on the RPA. Oh, dude. This one's awesome That one's so awesome Platinum one of one auto the flying Hawaiian Troy Palamalu Go into the Steelers Congrats mark 2021 flawless. Oh That's so cool, dude, even though he was a stealer as a huge fan of defense that was one of my guys one of my guys nothing but respect for Troy Palamalu one of one that's so nasty Like that. You like that. I do like that. I do like that as my boxes are fused together It's crazy Crazy God that's a beauty, dude All right, we've got Roger Staubach 1 of 15 on the blue emerald blue diamond, I should say Another nice one here is going to the Jets 25 of 25 Joe Namath player worn at two colored patch Congrats Jets and Really nice one here go to the Eagles 16 of 25 star swatch signatures Jalen Hertz Three colored patch auto Congrats to the Philadelphia Eagles mark R. There you go, buddy Hell yeah, man. Let's do a quick recap. We've got Micah Parsons the Marcus where Kavon Tibidow Drake London Bailey Zappie Donald Driver Harold Landry Jameson Williams Drew Bledsoe Dan Hampton Ronnie lot Jalen Hertz to 25 Roger Staubach Joe Namath one of one Troy Palamalu So nasty James Cook to 20 Damian Pierce to five Sam Howell to 20 Devontae Smith to 10 Debo Samuel to 15 and John Elway Jersey numbered 7 to 10 That'll do it for the break all thank you again for the fill up next number 13. Let's sell it out


I've been watching you for so long man. Having you on my channel, it's a big thing man. Thank you, thank you. You know short people do it, it's hard. It's hard man.  I used to be a subscriber when I was around 30K. Good to see you today. In my country. Thank you. And I love what you're doing now, as far as promoting Africa and showing people the continent. Because a lot of people have one view of Africa. And I feel the way that people change their perception is when they see. And so you're helping contribute to that narrative. Exactly. That is why Africa to the world is very important. Because we need to change the narratives. Every one thing that Africa is this way, and I'm trying to show the other way. And I've been doing this for like a year, not even a year, like five months. And you're already 250K. No, no, no, that was when I was in China. When I was coming back to Africa, I had like 100K. And five months in Africa, 450K. You know why? Because not many people are telling this story, supplying them that. And they're so interested in what is going on in there. They're like, wow, look. We have watched so far, we've never in Africa. Wow. This is crazy. Like, y'all balking? Show them, show them, show them. So look, y'all just balking. I'll take it up on my channel today. Look, you have a balcony. And look, they use nice materials. Yeah. There's a pool out here. Look, it's building. And like on the content. Let's do an MTV criticism. I don't have the biggest place, just normal place. Like, you come do your work here. Boom. Nice little sofa. Everything. Don't put the drink. We don't put more alcohol. No. Right. You got one bedroom here. And this is glow. This is one of the biggest people in social media. When you want to have the blue check. You know, fancy blue check light. This is the glow. One of the best storytellers.  Oh my God, I'm so special. But she's an actual storyteller that gives substance and content to what she does.  Yeah. And then, but like, yeah, it's just so fresh. You can be comfortable when you come to Ghana. Now, most people would say, boom. You've got the oven, right? You've got the oven. Microwave. Now, for your heads. What is this here? It's a fridge. A fridge? What do you say behind the camera? What's this? It's like a microwave. A microwave. This is an oven and microwave. See in Africa. Oh, that's a fridge. I don't want to shake the head too much. That's a TV. Bedroom, we're literally getting on a flight in an hour, but I said I have to come shake with my bro. Exactly. Really making the narrative better and showing people the reality of what's going on now. But we're about to go downstairs to the pool. No, they know you. My audience already know you. They know me all. Exactly. Because I know most people recommended your channel to me before, so I think most of them know you. It's just that they haven't subscribed.  I don't know. We have elevated. Look, we have elevated. And if you see the brand, this is Otis. And if you are not intelligent to know what Otis is, just do your research. Otis is the number one brand when it comes to elevators. And so I joke a lot. I don't want to make content boring when you like it. Yeah, exactly. Because me, I like that energy in my videos. And I'm glad that you're having the same energy. And so one of the things that happens with Otis with social media not always has to be famous to make an impact, right? You can do things in life where you have a huge impact, if you have a vision, that other people can be, you know, in front of the camera making it happen. And so Otis is a multi-billion-dollar company a year annually, right? And most people, you'd walk by that, you wouldn't even notice. Their website is old. You don't even know anything. So I want people not to always think, how many Instagram followers, how many of them. But you can make real impact without actually being famous. Even when you look at the owner of this building, he's also a young African. Nada. Yes. And so he's a founder of this. He has multiple buildings going up. We need to let them know. Sometimes when I vlog, I try to show the beauty of Africa. People keep on telling me that these things are boring. But this whole luxury, you can tell or whatever, it's all about Africa. Anything Ghanaian. That's what people don't get. When you were walking out, he was walking in. You just missed him coming out. And so it's just such a special thing when I think people can see. They're like, oh, this is a Ghanaian that has created this, the vision for this. Every little detail is from him. And he has like a new vision of how, you know, not only Ghana, but how Africa can be. Because I think it's important that, you know, especially the youth, they can see that they can become something special and you can create something amazing. Before I let it go, there's something that you need to educate us. Just a social media guy. You've been a lot. How are you able to do all these things? Because like, people don't understand.  But I can employ you. You know, like that kind of things. I wanted to say something like to the young Africans, like being on social media, not just being on Instagram and posting pictures or something, but being on influencer. What are you going to tell anybody out there who is looking for to be someone like you? Because they're just on social media. You know what I mean? So like, what is the message for the young people? I would say one of the biggest things to realize, especially with technology, is especially before, we're in an old generation, right? There's, and I respect the old, I have to learn from the old that, you know, they're very wise. But when you're young, you have so much youth. You have so much energy. And with technology, you're able to create so much abundance very quickly because this is one of the greatest generations in history that you can make an impact worldwide and make income really quickly. When you look at like a platform like Instagram, right? Facebook acquired them in 15 months. They created a billion-dollar company with 11 employees in 15 months, right? Now, when you look back in the day, so take a brand like Coca-Cola and what it takes to become a billion-dollar company. First, you have to do the branding. You have to get the packaging, right? You have to create the product. And then, not only that, you have to have distribution, right? You have to do shipping.  So that was an old school. That's like a 10-15-year process to create a billion-dollar company. Now, you have one idea and then you have access to the internet so it collapses time frame. Everything of you're an influencer doing something.   Oh, I have to be shaking. But you really don't. You can really make... So I make 98% of my money behind the camera, right? So I have a financial education company with a business partner. I have a marketing company. I do a lot of things behind. And storytelling, creating videos is just like my art and my passion. And so I tell people, if you have an idea, learn marketing, right? Learn how to collect emails. Learn how to monetize your skill set. Not just, hey, I'll be an influencer. Think about how you can own what you have. And that's what I love about this new narrative. Before, you had to take 5 million from Maybelline. But now, you can create your own products and then distribute them directly to the consumer. But that's what I say with the price, is know your value. And especially here in Africa, right? Know your value of what you can create. Because they're behind. It's going to be like 10 years before they get... If you're like, oh, these brands are not paying, create your own. Figure out how to monetize, you know, and obviously the banking system here is different. They use, like, MTM, send money, all these things in the shoes. And you can come shake your places and then even take over the nation. Take over the nation? I guess I'm the one who's going to take over the nation. It was really nice meeting you. But tell them, describe Ghana in 3 words before I let you go. Ghana in 3 words. A few words. A place you have to visit. A place you have to visit. Thank you so much.


How's it going everyone Caleb here with Leighton sports cards next tonight box number 75 of the Leighton sports cards almanac For Ryan T. Thank you so much man and good luck here Let's do it Ryan T number 75 Here we go There Ryan Trey young red Don Russ optic rated rookie mint nine number two ninety nine Nice card there Trey optic nine Very cool. Congrats Ryan T 1819 Don Russ optic True color match 1099 mint nine Congrats there Ryan. Thanks again, man. We'll get it right out to you


We're moving on to our disruptors category and to do the honors may I invite on stage a very special couple who's flown in tonight especially for the evening Mr. K. R. P. Reddy director advertising in marketing Saqshi Group and Mrs. Rani Reddy director corporate communications of the Saqshi Group request you both to please come up on stage to do the honors. Let's find out all the shortlist What we were very clear about is is there is is is one ecosystem in an industry getting supplanted by another ecosystem so it's not just about one brand winning against the other it's about a set of fundamental shift in the ecosystem both from a demand as well as a supply site or if there's a fundamental shift in consumer preferences that are coming through I think those were the kind of criteria that we used to judge who were the winners and who were the guys who were nominated for sure he was in regularity with me my colleague Benikar instructor let's find out who the trophy goes to


As-salamu alaykum. Tonight, Muslims in Canada and around the world will mark the end of Ramadan and celebrate Eid al-Fitr. After a month of prayer and fasting, Eid is a time for faith and spirituality, a time for traditions and community, and a time for joy and happiness. From Vancouver and Calgary to Toronto and Halifax, Eid celebrations will be taking place right across the country tonight. But as you come together this year, Canadians will be recognising the many contributions you make. From my family to yours, Sophie and I wish you all a joyful Eid. Eid Mubarak.


Welcome, my friends, to the Depression 2 Expression podcast. Man, just changed the strings on my guitar. Very bright, very crisp. Just listen. Very crisp, very crisp. Oh, yeah. Oh, man, that sounds good. So, um, if you're, let's say, a serious person, maybe a serious advocate for mental health and wellness in a traditional sense, maybe don't continue listening. This is a weird one, but I did a video years ago about this idea of insanity, and the word's fun to me. I love the word insane, but I think we have the definition really, really mixed up, because if you see someone in a psych ward mental institution where like that person's probably insane, get them in a straight jacket, right? I'd like to present to you a new definition of insane, and when it would be appropriate to actually call someone literally insane, because we think that, oh, someone's really depressed.  We think that's insane. In fact, with these existential crises and saying, what is the meaning of life? Where do I fit in?  You actually can't be more grounded in reality than that. You're really, really existing in that moment, right? But when we talk about insanity, that's, you're floating a little too high. You're not really in reality. You're so far removed from reality that you're removed from other people around you. Ah, you see where I'm going with this? So I'd like to say a few, I guess I'm going to do this. I love this though. You're really getting to know me a little more through these episodes. I'm trying to make them more fun, and I'm having fun.  So a lot of you will drop off, but some new peeps will come in. So how do you know if you're insane or not? So we have to know, okay, just because I feel depressed, and just because I feel anxious, and just because I had to deal with bipolar disorder and schizophrenia. No, that's not insane. That's not insane at all. I would argue that insanity is far more removed from reality. So I made a list. See, I actually have paper. This is the very first time I've brought notes into the podcast booth. So, number one.  That is an insane thing to do. How can you do that in the right mind of a human being? Someone conscious, someone grounded in reality, who is surrounded by other people.  People hack in their darts, and they'll flick it in the sidewalk. And my mind, I can't comprehend. This is what's so interesting to me about human beings. Because I preach to kids and to adults about how we're all the same in our psychological needs. And this need for security, control, they need to be loved, understood. They need to belong, this sense of community and understanding. So I get that we're all the same in that way. But, oh my God, are we different on a psychological level just in a different way?  You can do that to your home. Can anyone feel me on that? That to me just blows my mind. Just blows my mind. Now I know what you're thinking. Scott, I've said this before. Scott, what's with the self-righteousness, man? I thought you kind of preached against that. Why are you so judgmental? No. No, that's not how it works. I've said this before, you can judge a decision and not judge the person. That person who smokes cigarettes every day and flicks their butts in the street and doesn't give a damn. To me, the person deserves respect, maybe. The person you got to get to know them, they're probably a good human being.  I don't want to get into politics, but you know, the storming of the Capitol building. We have the right to protest in America. I'm not an American, so, you know, I don't hope I'm not overstepping. Of course I'm not overstepping. I have the microphone. But, you know, they want to send a message and they want to storm the Capitol and, you know, send a message and ground themselves in reality that way. Not the smartest decision by the outcome. Probably not. I don't know what the argument is that that turned out to be what people expected it to be. That obviously wasn't the way it intended to unfold. So you can judge a decision and not a person. You can judge decisions and not the whole group of people. So insanity to me is people who are not necessarily rooted in reality as people we call, quote unquote, mentally ill. And that's what really frustrates me is the whole mental health system and how we view people with certain diagnoses and we call them insane and not part of society because they talk to themselves on the street. I've seen someone talk to themselves on the street thousands of times, hundreds of times, living in Toronto. I don't call that person insane. I see the person on Bay Street smoking a little dart and flicking it in the sewer. That person's insane to me. Lock them up. Lock them up, dude. I'm starting a new society, all right, where those people are in the hospitals and we get all the people dealing with schizophrenia, psychosis, depression, anxiety. We're on Bay Street, baby, and we're loving life, all right? So that's number one. Number two, you're insane if in high school you actually follow the trend of popped callers. Oh my god, I might call a friend right now and see if he remembers that. Let me take out my phone. I thought I'd just have a blast with this episode. Oh my gosh, remember pop callers were nuts. I'm going to call my buddy right now. Let's put him on speaker. And if he popped his caller in high school, I don't think I could be his friend anymore. Hey, buddy. Hey, buddy, you're on a podcast. Do you mind? Yeah. Whoa, nice. Okay, so I'm a better friend. Yo, I just have a quick question. Do you ever follow the trend in high school where you popped your caller? And that's when you made all your money and you're rich now. Yes, thank god. Okay, we can stay friends. Okay, I'll call you later. All right. See you, bro. Bye. And we have, oh my god, thank god I can still be his friend. Okay, so popping your caller. It's amazing these trends that we follow as it's really interesting in high school. I have so many things to talk about and I love how many of you listen to this stuff and enjoy it. It warms my heart so much. It really does. I love doing this. More than I liked video for a time but podcasting, this is so much fun for me. It's so fun. I should be paying you. You should be paying me. Someone should be paying someone. This is just a fun exchange. So in high school, you're brought into a building at an age where you are so socially susceptible to trends. Your growing pubes, your hormones are just in insane flux and you're put into a building where you have to learn new stuff. You have to find your social group. You have to deal with hormonal changes. And on top of that, you will literally do anything, anything to avoid loneliness and to avoid rejection and isolation. That's it. That is the tribal effect. So high school is literally like our primal instincts kick in. So when someone popped that caller and thought it was a cool thing, the cool guy did it, you literally had no choice but to pop that caller with him. So you saw all these people where I'm from with their Hollister golf shirts with their callers popped. And I never popped my caller. Just like my buddy I called right there. I never did it once.  That was probably it. I avoided insanity in that moment. I really did. So are you insane? Have you passed the test yet? So a few of you might have failed already. If you flicked cigarette butts on the street, smoke for all I care.  And two, did you pop your caller in high school? All right, so how are you doing on the test? Okay. Oh, this is a good one. Number three, this might be a long episode, but there's a few good ones you're going to love. Do you wave after somebody lets you in to their lane when you're driving? You have to give at least the nod a wave. I'll even give a double wave once I'm in. You signal they let you in. You give the left hand up. You turn into the lane. And once you keep going, you give the second hand up in your rear view mirror to acknowledge once again. That is part of being human in North America at least. You need to give the single or at least the double. And I know Seinfeld definitely touched on this one, but you need to do that. If you are part of society, if you are grounded in reality, that is what you need to do. I don't care, man. Oh my God, this was a while ago. I'm driving home and I'm just, I'm not going to go into detail, but I'm balling my eyes out. I am in just despair. I am just dying of depression. I could barely drive the car and I got into the left lane. Someone let me in and I still managed. I still managed to raise the hand. I don't want to hear excuses. I was dying, man, and I'm still like, oh, it's the right thing to do. And then I lifted the hand. It was amazing. You have to actually follow the rules of reality. All right? Remember, we're not talking about insanity in the traditional sense. We're talking about, are you part of society?  Can we share the world together? The person who is insane does not fit in the bubble of society that actually constitutes many other bubbles in realities. We got to work together. We got to work together. Now, a lot of you won't relate to this, but I just, you know, we are having a conversation at dinner about stray dogs and these shelters for dogs and how a lot of people during COVID have rescued these dogs. And there's so many organizations that go to the Middle East and there's some in places in Europe and I think South America where there are dogs that are just stray dogs. And I forget where it was. Maybe it was somewhere in India. And I may be wrong about this, but I'll just say India for now, that it was years ago and it was a sign of status if you had a German shepherd. And if India is the wrong place, forgive me. It's status like, and you have raised your status if you have a German shepherd. This is a very luxurious dog. So everyone started getting German shepherds. And this organization actually deals with this specific thing. People used to buy German shepherds and then you have this status and then everyone figured out, oh shit, that's a lot of work to take care of a dog. I got to clean up after it and I got to buy the cage and we got to train it and take it for walks and take it to the vet and get its shots and take it for haircuts and nail clipping. And so what people did was they just took the dog and let it out on the street and went home. Like that's what people do. That's where these dogs come from, bro. Isn't that insane?  And drive, let the dog out of a car van and then go home for dinner. That is boom. That is unbelievably insane. Yet I'm going to say it again. Yet we call the people insane who are dressed raggedy, have very little money and talk to themselves on the street. But how do we regard these people who can let go a dog in the middle of the night and just go back home for dinner? That is unbelievable. Holy jeez. Okay, we got a lot more to go through. This one is going to be, well, okay, let me go through this. The whole psychiatric and mental health system is basically insane. Think about how far removed the treatment for people who are dealing with mental trauma. Think of how that's dealt with. We're not actually giving people what they need. And I've worked at Psych Ward before. It's what you expect. It's what you literally see in the movies of the nurse's station and it's all glass in the middle. They have that window that opens for people's pills and they're wearing slippers and it's all white with no green plants, nothing. And nothing's in the room except a bed because if there's anything that they can hurt themselves with, well, you can't have that, especially a plant who knows what they're going to do with the leaves. So the way we treat people who are dealing with a mental illness is insane. First of all, just to get treatment is hard enough. If you're dealing with extreme symptoms and you need to see someone right away, we're always like, oh man, don't bring them to emergency. Don't bring them to ER. Well, people have no choice. They don't know where to turn. Family doctors closed.  They're great at prescribing antidepressants, but that's about it. Okay, but then psychiatrist, I have to wait three months. Oh shoot, I can't do that. Psychologist is 200 bucks an hour. Shoot, I don't have the money right now. Okay, well, the next option is the hospital. We got nowhere to turn. That whole system is insane. And once they're in the hospital, revolving around medication and having people, trying to heal people's minds while they're in a white room, not going outside, not exercising, not having healthy food. You see where I'm going with this? Like that is absolute torture. And if you hear people who have actually been admitted to these hospitals, I was talking to a friend of mine who was admitted to a hospital. It's like, yeah, you can get better at the hospital, and then you're released, and then you have PTSD. And then you have to go to more therapy because you're trying to actually deal with what happened in there. Man, oh man. Like that, I think we're going to look back. You know, we're looking back at the 40s and 50s of how we were treating men and women with depression and how it wasn't talked about and how it was a lot of medication. Same as now, but we're going to look back in 50 years and just really just kick ourselves in the face for how we dealt with people with mental illness and how we were really the pill-pushing generation as well. It's so sad. So the other one on the list, and I think we can all agree on that one, is that the whole psychiatric system, the whole system, the whole mental health system is absolutely insane. All right, cool. I'm going to go through a few more. If you think 5G towers cause COVID, that's an insane thing. Okay, show me, like, come on now. Oh my, people are pushing down towers. Remember at the beginning? They thought 5G was creating COVID. Oh my God. I'm freaking out. And that's the other thing about social media, right? You can create these groups and find a few people that think the same thing and then there's this social proof and it becomes an actual reality and truth because truth is really based on how many people believe it, right?   If a million people believe it, well, it's got to be something there. There's got to be something. So with this 5G thing, people are pushing down towers, lighting towers on fire. How removed from reality are you? When was the last science class you went to? There's educated and then there's just being ill-informed. You don't need to go to university or college to be a smart person. In fact, my gosh, man, I wouldn't even recommend a lot of people go to college or university if they don't know exactly what they want to do. But to think that 5G was causing COVID, people are like, yeah, the towers all here, see the higher rates of COVID were where all the towers are. Oh, interesting. Well, that's because that's where the freakin' highest population is, dude. That's why they have more towers to serve more people. Oh my goodness. And TikTok is just spreading so much misinformation about, well, everything. TikTok is another terrible invention. All right, so that's another test of insanity. If you're on that train of believing, whoops, that was my guitar. If you're on that train of believing 5G cause and is causing COVID, I don't know. I don't know what to tell you. Just insane. Okay, but did you pass? Let's keep going. If you think Kanye West is an actual intellectual, that's an insane thing to think.  Maybe in reference to other celebrities. Possibly. But the dude just rambles and here to there and here to there. He went on the Joe Rogan podcast. People are like, yo, Kanye is sick. This guy is just high level. I'm just like, the stuff he says is so basic and just out of order. I think we have some false idols in our world. Maybe that's why it's weird. Like, why do we idolize the people that we do? Do we not have better examples of people to look up to in our own personal lives? And that's going to be another episode too. Yeah, let's make that another episode. Because I think that's a lot of reasons why people voted for Donald Trump. I think that's why people identify with their political organization. And I think we honor and idolize some false people because we don't have people we respect in our own lives in an immediate environment. That's why having a father in the household is so, so important. Anyways. If you drink coffee, you're insane. No, just kidding. No, no, no. Enjoy the coffee. This one is, if you drink a double double, then that's insane. Come on now. You can't say you like coffee if you're having two creamies and two shuggies. That's not coffee. That's cream and sugar. Come on, man. So an easy way to do this, so you're drinking coffee flavored coffee, as my dad says, would be to start with the double double. Then go to one and a half, one and a half. Then go to one and one. Then go to 0.75 and 0.75. Slowly restrict yourself. You, man, it's amazing what you get used to. It's amazing. Another great example is people salting their food. It's amazing that you salt it while it's cooking and everything. Salt there, but once it gets to the table, salt isn't really necessary. If you're having good food, we think that we need it because we've been doing it for so long. Give your body and mind some credit that you can actually change and just forget what something was like. Like for me, bread, sugar. I haven't had bread in so long and guess what? I don't miss it anymore. It's not a thing. Give your body and mind some credit to actually say, man, I've been having double doubles for so long. I've been popping my collar for so long. Maybe, just maybe, I can change. I think I can. Give yourself some credit. Have some confidence in yourself. Now the hardest thing for human beings is behavior modification. That's why people sign up to do coaching with me. It's not because they need some incredible advice and some newfound perspective. Sometimes, yeah. But it's like, Scott, I've tried before and I just quit after a week of meditation. I quit after a week of this diet and I quit after a week of exercising and like this. I just need to stick to the plan. And it's like, boom, that's it. I had to pay a lot of money for coaching just so I could stick to the plan when I was getting off medication. That's it, man. People have a lot of trouble sticking to things. You try hard. Yes, but you don't try long. Play the long game, baby. Play the long game. Okay, let's go through a few more. This is going to be a series. I'd love to make this a series because there's so many things of judging decisions, not judging people. So get rid of your double-double. Have your coffee black or maybe just one creamy cream. All right, cool, cool, cool. If you gave your kids a cell phone, by grade four, even grade five, even in elementary school, ooh, you're playing Russian roulette, man. Oh my goodness, giving kids phones at such a young age. The dad is there. It's so bad for your kids. No! The new generation. I'm working for the new generation. I'm living for the new generation. I'm living for the new generation. I want the world to succeed for another 500 years at least, right? Oh, but no, then the parents are giving their kids Facebook accounts and phones and letting them bring their phones into their bedroom. No! And then in the US and Canada, that's exactly when children are sending nude photos on Snapchat. They get shared to thousands of people on child pornography sites. And then parents can actually get arrested. Why? Because the phone's under their name because they signed the contract because the child is not of legal age. No!  No, man, please. And if you do, don't let them bring it into their bedroom. They have the internet. Oh my gosh, 70% of 12-year-olds have already watched porn on their phones. You know that mom and dad listening? Come on now. I know you give them a phone because parenting and life is hard beyond measure. It is the hardest thing ever. Yep. But you know what's harder? It's getting a knock on the door from the police at 4 a.m. saying we just saw Timmy's dick online. That is... That's reality, man. I'm not just making jokes here. This is serious business. You ground yourself in the reality of how dangerous phones can be for children. How much trouble they can get into. And they do. And it just like, ah, that wouldn't be me, that wouldn't be my son or daughter. Nope. It could be. Alright, and last one. You're insane if you don't exercise. Not that you can't exercise. Come on, if you're listening to this in a wheelchair, don't get mad. You're the strongest and coolest person ever. But if you're not exercising, like if you're not walking, running, lifting weights, doing bands, doing yoga, sports, gin-ups, I don't understand. I don't get it. I didn't come from that privileged of a background that I was just like nobody knows the secrets of exercise except me. Like it's not an upbringing thing. I don't know what it is. But I meet people and they're just like, Scott, I'm so anxious, man. I'm feeling so low lately. I just don't know why. What's your exercise regime like? They're like, I don't exercise. I'm like, oh. Okay, cool. Like what? So you feel bad and you don't exercise and you wonder why? What? This person judging the decisions like we have to know by now. We don't need any more studies on exercise, scientists. We know. We know that exercise is just as if not more effective than most antidepressants. They've done double-blind studies and control groups. They've done this with thousands and thousands of people over so many years, even 6 to 60 since Prozac. They've done this. They've done testing. Or was it 80s Prozac? They've done the tests. Exercise helps with depression. Exercise helps with anxiety. And I know at the beginning exercise doesn't feel good but I guarantee if you get into a rhythm you're going to crave it because your body wants to move. It was designed to move. Your body wasn't designed to sit still. You know humans are the fastest runners out of any animal in the world. That's right. We evolved to hunt. We evolved to run. We evolved to move. So you have to give your body what it's demanding. You have to actually pay attention to your body in reality. And if we float too much and don't listen to our body and are just consumed by thought all the time and lost in that that's insane. You know we got to get back to basics people. That's what the world is demanding of us. That's what our bodies are demanding of us. We have new technologies that come out all the time. We have new ways to think of things and this new app comes out and this new thing comes out and we get so consumed and the new and flashy and shiny things but people get bored of it so quickly. And what do we do? Eventually everyone comes back to this basic acknowledgement that humans just need a few things. We need water. We need shelter. We need security. We need people around us. That's it. We need a sense of community. We need security. We need a bit of control in our lives. That's it. That's it. Why are we always looking for something new? Why are we always looking for the shiny object? Getting back basics is what we need to do. Ground ourselves in reality. Don't flick the cigarettes. Don't pop the collar. Wave at people when they let you in their lane. Don't leave your new dog to fend for itself on the street. Exercise. Don't give your kids a phone in elementary school. Treat people with respect if they need psychiatric care. If you're going to drink coffee or don't have a double-double just go with one cream. No sugar, baby. Kanye West is not an intellectual. He's kind of dumb. And 5G did not cause COVID. All right. Now if you're on this side, you did it. You're not insane. Congratulations.


So, this is the spread line from the coalition conference here in Toronto, the technology conference with 25,000 participants, and I'm very happy to have Tim Draper here, a 33-year venture investor, and he's also a very good specialist on blockchain and Bitcoin, as you can see from me. Yeah, I have a Bitcoin tie, in any case, I'm promoting it. So, Tim, just as a start-up, what are you expecting from this conference and what did you find in the highlights? This has been a great conference, thanks for having me on your show. I have met a wide variety of people, I think that I've learned a lot about Toronto, and I was here 20 years ago, and now I'm back, and I don't think it'll be another 20 years before I come back. I really enjoyed the energy, the excitement behind all these entrepreneurs. I think this is a great conference. Yeah, it's also a conference about technology, and blockchain is one of the important technologies which is there to establish trust between the peer network we are in the world. And you talked about, you know, we are becoming a more global hive of people, and it's not so much about governments and nations. So what is the importance of blockchain in your environment? So here in the venture capital business, we're always looking for new technologies that can potentially transform industries. And so when the internet came around, we were very excited, and we funded a lot of companies, and the internet transformed communications and information and gaming and entertainment and media. But now we have new technologies, and they start with Bitcoin, this magic technology that has somehow allowed us to have a trusted global currency that isn't tied to any political force, and it's open, transparent, global, frictionless. The technology that built Bitcoin also has other applications. So for instance, the blockchain allows for a perfect ledger. It keeps perfect track of money, or it could also keep perfect track of data. And a smart contract is a contract that's built into software, so you don't have to define whether different words mean different things. We all know where the money goes. And so smart contracts could end up being really a very important part of commerce and business going forward. And then on the other side, we have all this data, and data is getting more and more important as we can do more and more things with it. And as data evolves, and people do this deep learning on the data, they can create artificial intelligence. They can create an intelligence by making judgment calls based on the data and the smart learning or the deep learning they've done. Well, if you start combining those technologies, they're bigger than the internet ever was. You have the ability to transform insurance and, well, not just banking and commerce and finance, it's things like insurance and real estate, health care, government. All of these things have the potential to be completely transformed. And this is the time. And I feel like those industries are 10 to 100 times bigger than the industries that were affected by the internet. So we're in a big opportunity. It was a big disruption of how we work together and it's not only economic but it's also how societies work together. Because we are from the Project Management Institute, and right now we are leading this project. And Project Management in the modern fashion is a very young profession, you know, we're only 50 years old and it's about 50 years since the Department of Defense started with that. So did you run in your business in some project management corners? Yeah, it turns out that every entrepreneur is a project manager. They have a vision, they have a direction they want to head. Sometimes it evolves. But generally, they want to go somewhere and they want to take their team with them and they have to organize the project. And it's one project after another to finally get to the vision. And Project Management is like the key to success in any business. You need to, I mean, whether you're producing a movie or you're creating a new internet company or you're transforming the world through decentralization, in every case you're going to need to organize people, get them going in some direction and move forward. And we mentioned Bitcoin and Blockchain. It's interesting, you can actually have automatic payments that are tied to various tasks in your project management. And I'll give you a movie example. You know, when Lucasfilm has all those credits that come after a Star Wars film, like 15,000 people have to get paid about 25 cents every quarter. And it costs Lucasfilm about $7 to send those checks. Well, now all of that can be built into a Bitcoin wallet and so the waterfall of money will come in and it can be even more efficient than that. It can be like, you go to the movie theater, here's your $20, it then spreads all the way through the waterfall and that guy who was the assistant to the assistant to the lighting guy gets his 25 cents worth, his .0001 Bitcoin into his wallet immediately. And so the flow is immediate and projects will be much more efficient. So I believe that Bitcoin and all of these other technologies are going to have a major effect on project management. Thank you. And you know, this is as good as it gets at Tim Graeber, who really acknowledged the importance of project management. Thank you very much. Terrific. Thank you. Thank you very much. Terrific. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you.