So, the title is Motives from a commutative point of view. However, as a reminder, kind of take away from the last two talks, is that it's a reminder. I have introduced, so you have some fix some, some base commutative ring K. We have A, which is K algebra, associative unit will not necessarily commutative M is an A by module. And then I define the invariant, which for lack of a better word, I called with K theory of A M, which is roughly speaking, I take the tensor algebra of M generated by M over A, I take its K theory, I complete it with respect to the natural iteration by degree, and I take out the constant charge. And this is a very nice invariant. First of all, it's the structure of a trace theory, which I discussed in my last talk, which allows you to do lots of computations with it. In particular, the study of this for general A and M reduces to the situation where A is just K, just by general trace theory formula. And then if specifically K is just a perfect field of characteristic P, then the answer for this is very surprisingly easy, namely that this K theory of K and degree I actually vanishes unless I is not one, so it's only exists in one degree, which allows you to construct all sorts of explicit models so this becomes generally very accessible invariant. So today I want to discuss cyclotonic structure on this guy, but before I do that, there is something else I want to explain. So this is a very useful invariant, but this is kind of not the most fundamental. So there is another invariant, which gives rise to this guy and some other invariants, which was kind of the original idea behind whole business, rather than step. And this invariant is devoted to the notion of a cyclic node. So I had this for two categories already when I introduced this theory, but just for ordinary categories. So let me remind you of this category lambda, comma, if we consider the fiber category over the object of one, so this is objects and equipped to the map to one. And it's actually equivalent to delta, the category of finite and empty ordinals. And this is clear geometrically, so objects and lambda are both categories of quivers, right? I mean, there are different definitions, but the one I gave you works like this, but there are some quivers. And when you have a map to one, this means that one edge of the quiver becomes distinguished. There's one edge, which is now distinguished, this one. And you can just erase it. And if you erase it, you end up with basically, you know, so it's a quiver now, which is not a wheel, but just a string. And so the corresponding category, just a partial or totally other set of some elements, as this gives you the equivalence. So now if we have some small category I, and we have some functor from I opposite, that is I to sets, then we can define a simple set, which is known as the, no, I'm sorry, so definition, cyclic nerve. So I had this already last time, but without coefficients. Now I need two coefficients, so cyclic nerve of I with coefficients in F. It's a simple set, just a functor from delta opposite to sets. And this interpret as now opposite to this fiber category.  So if I have some object here, which is, yeah, I think of as my wheel quiver, then for the usual cyclic nerve, the values would be the set of configurations like this. So I have an object, each vertex, one, two and so on, I, N, N, and then there are maps, F0, F1, and so on, and then, so F, L is actually a map from L to I, L plus one, for L from zero, and then the last map. So for the usual cyclic nerve, it's again just a map of the category, but here I'm modified. Here FN actually is a map from, it's actually an element in F applied to I, N. I see, all right. So this does not have a cyclic symmetry, I broke in the cyclic symmetry by choosing one edge, but model of that, it's okay, so it gives me a superficial set. So there's a question, how does a map from two, one gives a distinguished error? Okay. Or is this a map from one? No, it's a map from one that would give me a distinguished vertex, just for this vertex. The point is that the category lambda is actually self-dual. Kind of the fanciest way to see it in terms of this functional description which I had, I mean, I described maps as functions between some categories. Well, those functions have adjoins, and you just take the adjoin. But also, if you think geometrically, you think of realization of a quiver as some kind of, you know, cellular decomposition of the circle, so there's a map from a circle to a circle, but then if you take an edge in the target and you take its pre-image, then it lies inside a single edge upstairs. So there is some kind of sort of a factor which associates to my object. The set of edges of the decomposition is actually a contravariant, and this gives me the distinguished edge. Perhaps I should draw a picture. It's not so easy to draw in this zoom thing, right? So the target is just one. Here I have some points, and then if you look at what the map actually does, you see that it has to contract all the edges, except for one to this point. So all this must be contracted. And there is exactly a single edge which is not contracted, and that's the one that I choose. I hope that's clear. Yeah. Okay, thanks. Great. So there is the cyclic nerve, and then you can consider this geometric realization with some kind of topological space, spaces. And now specifically for A, so you have P of A, which for me was the category of finally generated projective A models. And then the bi-module M defines a function of P of M from P of A opposite that's P of A to, well, to carry out the space, but at least for the sets, right? It's just a very naive one. So because P dash times P goes to home from P dash, and then M times A. And so I can consider the cyclic nerve of this category P of A of the efficiency of this function. And this can be called cyclic theory space. Perhaps I shouldn't, oh, it's a space, right? So dot would make the most of the drops. So this realization of the cyclic nerve P of A. And historically this I think is what gave rise to whole. So this was considering this guy was suggested by Goodwill, you know, somewhat famous, but unpublished letter. Waldhausen, Waldhausen, I'm about 80, 80. The point is, I mean, this is just a space, but it also has a new loop space structure if you just take direct sum of everything. So it's actually spectrum. And it's somehow better than say with K theory, which I considered because it's reasonably small. So for example, if K is a finite field, then this is just countable, right? So it should be something which is computable, but in fact it's not. So a problem, at least it's very hard to compute. There is not enough structure. For example, we don't know as far as I understand, even now we don't know what it gives for just, you know, the point, even the case of finite field. So in order to get some handle on this, we need to do something to use it to construct some other invariance, which are more amenable to computation. And in practice, what you do, you need some kind of completion. And this can be done in different ways, in fact. So for example, there is a theorem, which I believe is due to Lyndon Stowes and McCarthy, which says that if you take this, so this is a factor of two variables, you treat it as a factor of respect to variable M, this additive variable. So you can do what is known as goodwill completion, completion in the sense of goodwill calculus. Well, for people who have more algebraic like me, this just means something like proper polynomial completion with respect to M. So some factors are additive. If functions are not additive, you can write down the correction to this cross effect. This is a factor of two variables that can again be edited for not in each variable and so on. So there is a whole theory of polynomial functions developed originally by both for WDU. And you can sort of try to compute the best approximation polynomial expression to a function. And if you do it for cyclic theory, you get something which appears also in the theory of the cyclotonic phase. It's a spectrum called TR of A, efficient in M. And already this behaves nicely. So already for TR, again, if you just plug in client fields, so TR of K. Again, I can consider any M, but if I consider, if I write just TR of A, this means diagonal by model. So this again behaves as my with K theory. So this is zero unless I zero if K is finite, say perfect, positive characteristic. So this already can compute. And of course, if you look in the... There is a question, Dimitri, there is a question, the good willy completion is just the limit of a good willy tower. Yes, yes, yes. I mean, personally, I always think about the polynomial completion because it's easier to visualize algebraically. All right, and by the way, the topological Hohschild's homology appears as the first term of the towers, actually just the additive, if you want the de-tivization of this cyclic. It's basically the linear part, linear respect to M. Another thing which you can do, you can look at these guys, if you're all fine and filled, you can try to look at them as an algebraic varieties. I mean, the cyclic nerve is a simplicial set, but you can put some kind of algebraic structure on that and look at it as some kind of simplicial affine scheme and use that structure. This would also give you some completion, which is more or less the same. This has not been written down, I believe, but because, I mean, we can do it more easily by good willy completion, but it can be done, gives you the same answer. And then also there is something which should be Ethereum, but I think there is no direct reference in the literature. So I put quotes here, is that my with K theory is actually also the same. So this polynomial completion also gives me why with K theory, but up to a shift. So let me think about not using groups, but say spaces, spectrum, this will be, so I need to shift by one. So there is this loop thing, all this WK, K. This is not an literature, unfortunately, we discussed it with Thomas Nichols at some point. So I mean, you can cook up a proof if you just assemble known results, but it's probably better to do it in some kind of conceptual way, which as yet is not done. So there's no radio reference, but this is nevertheless. So kind of the take away is that the fundamental thing is the cyclic nerve, which generates all the other environments. Cyclic nerve itself is hard to compute, but then you apply also various sorts of approximation to it, and this gives you computable invariance, which can then compare. Okay, so this is what I wanted to say about cyclic Ethereum. Now let me discuss cyclic Ethereum structures. So I need to discuss first equivalent aspect and so on. So let me start with the finite group. Now, first unstable. So as I mentioned last time, actually when you think about J-covariant spaces, there are two types of homotopy category you can consider. So one is the naive homotopy category, only the crude homotopy category or G-spaces. So object space is the action of G, and then we just look at maps, which are the covariant maps, which happen to be with equivalences, homotopy equivalences without regard to G, and invert them. And this gives you, so in modern language, so if I denote by point with the index G, just you know the group point, which is the quotient of the point by G. So, you know, one object with group bottom of this is G. Then this is the homotopy category of functions from this guy to spaces. That's the naive type. But then we can do more refined thing. You can again consider J-covariant spaces, and you can see the maps between them and classes of those maps up to J-covariant homotopies. And it's important to realize that this gives you different action, much larger category, which can also be defined like this. So, sort of genuine G space. This would be again a homotopy category, but now what you do, you consider the category of G orbits, or G is the category. So, a G orbit is a finite G set where G act randomly. So, it's a quotient by some subgroup, and morphisms are mapped between those, which are G-covariant. So, it's a category with, you know, some objects. I mean, since G is finite, there is a finite number of objects that correspond to subgroup. Well, isomorphism classes correspond to conjugacy class of subgroups. But still it's a category of non-verbal maps, so it's an interesting thing. There is, of course, a forgetful factor, because if you look at just the orbit where G acts on itself, sort of, so quotient by the trivial subgroup, then, automorphism, so this is just G itself. This gives you an embedding from this point model of G to orbit. So, there is a restriction of function. If you have a genuine G space, you can forget the rest of the structure, and then consider just naive, but there is something else. So, this category on the left is bigger. Alternatively, if you don't like, so orbit is something where G acts transitively. You can actually drop that condition. And we consider the category of all finite G sets. So, sets where G acts in some way, not necessarily transitively, maps are J-quarant maps. And then say that the function on gamma G to spaces is additive if it sends disjoint unions to products. So, there is always a map, let's say, contravariant. Let's hope it takes me. The fact, I'm sorry, here it's also supposed to be contravariant, yeah. Because what you associate to an orbit, if you have a space with G action, is just the space of fixed points with respect to the respect of some group. So, it's contravariant. Then for every function, there is a map like this, and I want this to be a homo-tube equivalence. So, this is my notion of additive. And then, this genuine category of G spaces, from orbit to logical spaces, can be also interpreted as, I consider factors from gamma G now, logical space and vertical equivalence, but I consider only those guys which are additive. There's a full subcategory spent by it. Okay, this was like space level unstable. Now, the slogan is that for spectra, what you do, you add, you do just one thing, you add transfer maps. And the way to do it, I mean, one way to do it, but the one which I think is the world's best is by modifying the category gamma G. So, you can see there a category, which I will denote Q gamma G, it has the same objects. So, Q gamma G, same objects, sorry, just same. Objects are again, just find it, just sets. But morphisms are now correspondences of zero as one, and morphisms are correspondences like this. Now, you could just take the isomorphism classes of those, but this would not be the good thing to do because it's too crude that destroys symmetry. So, you really should think of this Q gamma G, not as a category, but as a two category. So, here are objects, morphisms are like this, and two morphisms are isomorphism between these guys. So, for every two sets, the category of morphisms is actually the group of diagrams like this. So, this is what you do. And then, if you want now to define JQ variant spectra, then this is just a category, you notice like this, which is spectra, this is just a category, so it's a category of this Q gamma G. So, I need to consider stable things. So, this is a function from Q gamma G to spectra, not spaces, and they have to be edited. In fact, if you do it like this, you don't even need to consider, you can also put spaces here, and see the functions to spaces. And this would automatically give you a spectrum because of some version of the single machine. So, all J spectra, which are connective and appropriate sense actually correspond to spaces. So, there is a fully faithful embedding. None of this is in literature unfortunately, as far as I know, although it has been around for, I don't know, 15 years, everybody knows this, but there are no ready references, and that's part of the problem why we can't really prove some comparison theorems, which we would like to publish that. And part of the reason for that is technical because first of all, this is a two category. So, this has to be really done some kind of a categorical setup to make it work. And that's, I mean, that requires some writing, right? So, people are kind of too lazy to do it. But nevertheless, it's all true. So, this is how things are. And in particular, there is a version of the single machine for the G spaces, which was introduced by Shimaka a long time ago, which boxes Shannon Watson used. But that unfortunately is not strong enough. So, it doesn't give you enough control, it doesn't give you what I write here. So, one actually needs to strengthen that, and that's something which is still a bit of a loss. Dimitri, there was a question by Shan Tilson. So, he says he asks, so where aren't stable splitings equivalently? Stable what? Splitings? Splitings. Where? Not sure. Shan, can you be more precise in the inclusion from G spaces to G spectra? No, no, no. This is a fully faithful embedding. And, I mean, downstairs it's not G spaces anymore, because I added Q, right? It's a function from, I mean, spaces would be if I just put gamma here. Okay. And this is Q. So, if you want, so there is a function from G spaces to G spectra, which is the suspension spectrum, of course. But this corresponds to some kind of induction from gamma G to Q gamma G. But if you have a function from Q gamma G, but to spaces, then this is already fully faithful, so you don't need to. In fact, if you don't have G, you just take Q gamma, then this very close, I mean, this contains this category of pointed finite sets. And that is exactly what goes into the single machine. Okay. Yeah. All right. So, this is how it goes. Okay. So, how do you see all those fixed point functions in this language? So, for G spectra, there are two types of fixed points. There's the stocks, for example. So, there is the geometric fixed points and there is something which didn't have a name and then they called it categorical fixed points and now they probably call it genuine fixed points. So, it changes. So, one of them is very easy. One is just a valuation. So, those genuine fixed points, how does it go? So, let me denote this by psi. So, for any subgroup H, you have a fixed point function, psi H. And in terms of this function from Q gamma, this is just a valuation at the general page in fancy. And for geometric fixed points, so what you do, I first define something called inflation. The term inflation comes from the theory of makey functions. And by the way, the makey function is the same theory except target of your function are not spectra, but complexes. And then get the notion of derived makey function. So, for makey function, I actually wrote the paper about this like 10 years ago. It was not related to you. But anyway, so inflation. So, if you have some subgroup instead of G, you can consider it's normalizer, N of H. So, it's normalizer, and then you consider the quotient. It's somewhat similar to while group and group period. And then you observe that for any H, or for any H, we have a factor which does the following. So, we take, I notice by small phi H. So, we take Q gamma, and this goes to Q, U gamma G, and this goes to Q gamma W of H. So, you take a set, and you just send it to the fixed points. So, if you look at the set of fixed points, then the normalizer acts on this set. But H actually acts trivial, so the action actually factors through an action of W of H. So, as H is naturally W of H set, so that's this factor. And then you prove a lemma. So, you have the factor, and then you can consider pullback. Factor commutes with disjoint unions. So, it's an additive guys to additive guys, and then, so you get pullback from, well, W of H spectra to J spectra. This is called inflation. And then lemma is that this is actually fully faithful. This is something you have to check. I can't really comment on this. This isn't, I mean, this is not that difficult once you realize it's true, but it's kind of surprising. If you have to prove it, it's not obvious, but they won't do it. And once you do that, so once you do that, you can consider it's adjoint. So, it has an adjoint, they left adjoint, I place a hat from G W H spectra, and then the geometric fixed point function is exactly this composite, just forgetful. This evaluation at W of H, it just goes to spectra. So, geometry fixed points of some X are obtained by applying this guy, and then evaluating at the trivial orbit. I mean, not trivial, but the biggest orbit. All right, so this is how we see all those guys. And the general picture is as follows. So this category of G spectra, it's actually a triangulated category, well, stable category if you want.  Maybe I should write this, because this I think important to keep in mind. In fact, it's already in the original paper of May Lewis and other people where it was invented, but the longest error was different. So it's not stated cleanly in these terms. So it took me some time to extract it from there. So let me share this knowledge with you. So in general, what happens is that this guy has a semi-orthogonal decomposition. It has a filtration decomposition. It's glued out of pieces. Pieces are numbered by conjugacy classes of subgroups, or if you want isomorphism class of objects in O of G. And the pieces are just very naive things. So these are spectra with the action of W of H. It's like, if you want, let me note like this, the homotopic category of factors from some guy to spectra, as T here means stable. So this is the most naive version of the covariance table category which you can consider just spectra with action of a group. And the genuine thing is glued out of those guys. And the gluing factors, gluing data, are some sort of generalized state, homology, respect the families of some generalized state. So that's the basic picture. The category is huge, but it has those factors corresponding to subgroups which are easy. And then there's some gluing, but the gluing is not that. I mean, for example, if you restrict your attention to things which have fine homological dimension in some sense, the whole state of homology would disappear and the pieces would just be earthen. Okay, so this is the general setup for a covariance table homotopic. Now, this was finite groups. And traditionally the level of generality in topological sources, I mean, people who invented it, would be for more generally comparative groups. And one can do the version for compact league groups. Of course, now those categories have to be topologized. So the home sets would actually be some kind of homotopic types also. But the point is that we don't need it for our applications. This is maybe strange, because you would think that the circle would come into play, right? But in fact, if you look closely, nowhere in this psychotomic business, you have a genuinely a covariance spectrum with respect to the circle. Because, as I said, the G equivalent category has those sectors corresponding to subgroups. So for the circle, the subgroups are cyclic subgroups and then the circle itself. But the latter never enters the picture. So people only consider the part which corresponds to cyclic subgroups. So, effectively, it's only genuinely equivalent with respect to all those cyclic subgroups on the circle. That's all you need. You don't need the subgroups. Conversely, as I will argue in a moment, what you do need is to arrange the same cyclic subgroups in the other direction. Instead of taking kind of the embeddings from one to the other, you should take the projections. And not the kind of inverse limit, which is the group of cross-review, but conversely the projective limit, which is the profine completion of Z. So instead, let us... So instead, what we need to use is actually a version for finely generated, but profine it. Profine it. For example, Z. And then the option is that it works exactly the same as before with one modification. So where am I? So now if G is a profine, then you can... It's absolutely the same, but you slightly change the notion of finite set. So instead of finite G set, we now consider admissible G set. And this is two conditions. So first of all, for any point, the stabilizer of the point is finite index or finite. So it's a union of finite orbits, but maybe infinite union. But however, for any fixed co-finance subgroup, the fixed point set is finite. So it's a union of orbits or finite orbits, but each finite orbit can appear on a finite number of times. So they can grow, but then the size of the orbit has to move. And so this is the only modification. So this gives you some kind of category gamma and G hat. And the rest of the story is exactly the same.  I would respect them now for a profine. Okay. Now, what has it got to do with the next part? And now I can answer the question which I was asked actually at the very end of the last talk. So I said that it deserves a good answer. And this is the answer. So the question is about my definition of the category lump, which I defined categorically. Maps were some functors, but I only allowed one, which had some degree, but they only allowed functors of degree one. The question is, what if I allow all functors of degree, which is not one. And the answer is that this actually fits very nicely with this perspective point of view. So definition. So Dimitri, there are questions about the finite index. So by Remy Ponderben-De Bruin, he asks, when you say finite index, do you mean open? You are very continuity condition and objection. Yes. I mean, I'm working with profine groups with the profine topology finitely generated. And then open and profine is probably the same. Finitely generated, profine group, I mean profine completely. So it's profine topology, so open means profine. I mean, the example I actually need is on the Z-hat, but I think the theory works for all finite indexes. Okay, so definition. Cyclotomic category lump R. Objects are still the same as lambda. So these are just n. But now maps are another condition is that the degree is not zero. So there are some functions which just end everything to an object and all the maps to the identity, which are not interesting, sort of degenerate. One thing which are not degenerate in this sense. Then I allow all the degree. Now, an example of something, so if degree is one, then this is our lambda. Let's say that the map is horizontal. It is degree is one and vertical. So what's an example of a map of degree which is not one. The basic one is like one. We take some nL and this has, so this is a circle, so this has an anatomorphism of order L. And we just take the quotient. You can take the quotient and the level of the quiver. In fact, even for categories will be the quotient. So there is a quotient map. And I want to say that the function is vertical if it is isomorphic to this kind of quotient map. There is also a categorical notion of discrete cofibration. If you know what that is, then this is the quotient. And then this horizontal and vertical maps form what is known as a factorization system of lambda r in the sense of both fields, which means some things in particular. It means that every map in the cyclotomic category factors uniquely. So every map factors unique isomorphism as, you know, there is first of all some kind of horizontal map and then some kind of vertical map. Horizontal this is vertical. And conversely, if you have a diagram like this, we have a vertical map and a horizontal map. Then in this category you can form actually a Cartesian square. So there is a pullback square. This H m will be horizontal and this will be vertical. Okay. Now, if you look at this lambda r and you only consider horizontal maps and this I will denote the index of lambda r on the horizontal map and this is of course just lambda. But if you look at the vertical map and this the category of orbits for the group Z which is the same thing. I mean orbits are finite by definition. So this is the same as orbits for its profiling completion. And so lambda r combines both lambda and orbits for this group. And so in order to define the cyclotomic things and this would be kind of the clean definition of the cyclotomic spectra which is I think the most conceptually clean one. You just take this lambda r and you repeat the procedure you did for the orbits. So you first of all you need to add this joint unions. Somebody should mute it I think. Somebody mute somebody. Okay, thanks. Now add this joint unions and in fact finite but also infinite but sort of admissible in the sense which I had before. So it has formal unions you know. It has to be done. It has to Vanya. Vanya, microphone, switch it please. Sorry. Thanks. You know how to do this, right? You go to participant list and then mute somebody. You can do it. So you can see the things like that. Admissible means that for every n i number of n here. So this gives me some category which I need some notation for so let me call it what I don't know. What's in my nose lambda r gamma lambda r gamma. And then you do this Q thing. Well, for orbits you can do just correspondences and when I said correspondences I didn't tell you how the compositions are defined. Compositions are of course defined by just taking pullbacks and that's why it's not a good idea to restrict yourself to orbits because if you take two orbits and take the product it will be a g set but it can now split into several orbits. So you get the joint union sort of way in the system if you want to have compositions. And so in this case this way I had to add these joint unions here too and then what you now define now consider Q lambda r gamma, right? So objects are as in lambda r gamma and so maps are correspondences of the following types. So there's some kind of generalized L. I mean this N is actually a disjoint union of some kind, right? There is some M dot and then there is a diagram like this dot. So the only difference is that here I can allow on the right I can allow any maps whereas on the left I only allow vertical maps. And formally I need to do this because my pullbacks only exist for vertical maps and in lambda in this category lambda r gamma once I added the disjoint union vertical maps admit all pullbacks with respect to both horizontal and vertical maps. So this will define category, well two category maybe yeah. Now the definition definition cyclotomic. I put quotes here because it's not the definition of the literature and the fact that it's the same is not there is no published it's what I want to be a definition an additive this guy and why I think that this is a good definition because you get everything basically for free now once you have set this up in this way. First of all this is obviously cyclotomic spectrum this definition I mean I explained why this is a cyclic object just by considering if you have a quiver you consider a tensor algebra replace the tensor algebra with the path algebra of the quiver consider k theory for that and this is obviously functional and exactly the same thing goes for cyclotomic structure. And if you have a vertical map then there is a certain function which has an adjoint and both induce maps on k theory this is really you get for free so one, two I also had this p-typical k theory and there is also a notion of a p-typical everything which starts with actually lambda r where I only allow maps such that the degree of the map is the power of fifth prime p and then I can repeat the procedure consider adjoint unions consider they take the quotient k, there is q category and so on so there is this category of cyclotomic spectrum we can denote this place and there is a category of p-typical cyclotomic spectrum but then what's funny is that there is you can construct some kind of inflation function and it becomes actually an equivalence once you invert p no, not p conversely, once you localize it p like this actually let me write this in words equivalence p locally so I had to split this with k theory into pieces to obtain the p-typical one but once you do the cyclotomic story then this becomes kind of automatic just the same k-type and then you can also describe this can be described in terms of as I said this filtration also can be described in terms of sectors in terms of semi orthogonal decomposition and gluing day in fact in this case there will be only one sector this will be just the circle so this would be spectral in action of a circle and as I said this generalized state which enters the picture often vanishes in this case in fact if you are p-local the relevant vanishes in all cases except for the psychic group of order p so p-square, p-cube and so on all these guys don't contribute so the only thing you get is actually order p and this gives you this Nikolaos short description this works like magic just because you know we are lucky and this semi orthogonal decomposition is almost orthogonal there is only one possible gluing and there are no conditions on the gluing either so this is some kind of free free algebra situation so this suggests that this is a very easy and direct description of psychotomic spectrum and this is exactly what Nikolaos and Schultz did for computations this is of course what you want to use and this is what people use nowadays with which success but for conceptual reasons I think the correct definition is the one that I gave and so these are some motivations and the kind of the main motivation for looking at this picture for me is that it also works with coefficients so kind of the whole gist of my lectures was probably that you should if you can you should generalize your things to non-commutative setting and allow coefficients because then at some point you can reduce the linear output so it's crucial to work with coefficients so with coefficients coefficients so WK of KM is not a psychotomic spectrum anymore we lose the cyclic symmetry but it's still a genuine spectrum aspect to particularly have those fifth points and then there's this KHH and this actually appears just as a geometric fixed point with respect to the whole group and as a reminder the guy itself should be a model of the written proof yet but it should be the same as TR how am I doing this time okay my time is up but let me maybe yeah, yeah practically done so to recapitulate so this W K as I said should be the same as TR historically TR was defined in terms of THH but actually the relationship is symmetric in fact so TR THH in fact TR is maybe more fundamental so this can be recovered as fixed points TR and then historically it's the other way around TR was obtained as inverse limit of categorical fixed points from THH but the difference is that what I have here you can do with coefficients and then it's much easier to prove because you reduce to A equal to K so without coefficients of course TR of A as we saw is just this inverse limit so without coefficients THH has this some kind of residual it has some kind of non-complete S1 equivalent spectrum structure meaning that it's a spectrum of S1 action equivalent to respect to all the circuit subgroups and then have this so this stands for maybe it's better to do it in my notation my notation was like this so in this sense the two things define each other but I think the picture with THH is kind of more relevant for I mean it's closer to both things on nature I think this is it, I think this is all I wanted to tell you and thank you for your attention in these three lectures and it was a pleasure talking to you so thanks a lot for the talk so are there questions about okay so maybe I have a first question so you said on WK AM with coefficient there's no cyclotomy structures so can you explain why? because it's a cyclic symmetry when you choose M okay but if is there a choice of M where you can see if you take the diagonal by and this gives you the the point is that even if you break the cyclic symmetry so you break the S1 action you still get the Z-hat structure which I think is important to use okay thanks so any other questions okay so I think there's no more questions so thanks again for your nice and rich series of talk Calais Dimitri and okay we meet in half an hour so thanks


Hey everyone, welcome back to the Python image library module tutorial series, and I think we are finally ready to move on You know, I mean we've been stuck in the land of simple image for a while now But let's check out what more the Python image library has to offer for us. Let's move on to image ops Now image ops stands for image operations and they're kind of like ready-made Processing operations for messing with your image and doing lots of really cool stuff So the module is a little experimental not all of it works And the things that do work typically only work on either gray scale images that are just black and white or RGB images things that don't have an alpha channel or a transparency like Band to them. So that's why in the last tutorial. I wanted to show you Image mode and strip and merge and everything like that So I'm going to build up some functionality here if the mode is going to equal RGB a if we have the alpha channel Which we know that the file that we're working with right now does what we'll do is we'll change it to you know the regular RGB Band simple RGB without the alpha channel so we can use some of the functions in our image ops module up here So you guys know we need to run image dot load RGB and a are going to equal image dot split and Okay, did I say image dot strip earlier? I meant to say image dot split. I keep getting these things Messed up. I keep saying strip when I meant to say split So I think actually yeah, I got to go back and change the video name even alright. Here we go We're gonna split the image and I didn't think that ticks any arguments in it No, it doesn't take any arguments So what we can do from there is we can make this new image equal to image dot merge the RGB Mode and the tuple for RGB So now I've got our image now what we can do is we can actually go through with the image ops Argument what I want to show off here is first the invert function What this one does is it will return an inverted image So we'll set image equal to what's being returned to us and we'll do image ops because the function lives inside the module Invert and we'll pass in the image which of course we have to be image here now if I run this code Check it out. I'll go to my desktop here and Here is our modified pie image with all the colors inverted super cool, right? Let's check out some of the other functions that the the module can offer us. There is posterize posterize and The values that I've been playing with the ones that work for me are integers One this works. Okay This probably is not the best image to show it off with there are plenty of others that I could be using here and That will actually have a little bit more display and better look to it But okay back to what I was talking about the images the integers that work for me go up until about eight Eight doesn't seem to have much of a change in the image But if I go any higher Like if I go to nine back to it if I try and run this it gives me an error If I do like one back to what we had before but if I go like 1.2 It doesn't like that. It always wants an integer floats don't work for me So I can't get any lower than one and zero is just not gonna do anything zero is gonna get us a black image so One through eight seem to work okay for me and you should play around with these on other images other than the Python logo because There's not a whole lot of change in this simple like only three colors in the image, you know All right. There's also solar eyes. This is what I was checking out image dot solar rise Actually image equals image ops. Sorry image ops on solar eyes Passing the image and what this takes is a threshold Typically the threshold is 128 if you call the function without the arguments It'll run to 128 and Everything all the colors that are greater than this 128 value will be inverted So if we set this to like zero All the colors will be inverted and we'll have the same Outputs or at least changed modification as the invert function that we had up top 255 is going to get us to like all the colors That are being solarized Or inverted and now if I go 256 though It gets us back to our original image so that's kind of the limit there So that's around to play with it if you want to but those are three functions that I've been using and they're in the Image ops module so we can move on from the simple image In the PIL the Python image library and now we're checking out image operations. So inverts Posterize and solarize I'm gonna keep this functionality that converts it to an RGB image if there is an alpha channel And you might want to do the same thing because this is gonna be pretty handy when we're looking through more of the image ops Functions because typically those only work with RGB Try it. Let's see if I try and run this Without them it tells me nope This is not supported for this image type if I just try and simply invert it I think posterize won't work either. I Doubt solarize would to begin with either No, so yeah Definitely keep this functionality for more of the stuff that we look through with the image ops module. So okay Thanks for checking out this video guys I hope this is a building up a little bit of a toolkit and a repertoire for what you can use with images and Python and We'll get to learn more stuff in the coming tutorials. Thanks guys


Hi everyone, in this short tutorial, I'm gonna show you how to create a repository in Jupyter. In order to do that, I'm gonna click on Windows start menu and I'm going to open Anaconda. Then I'm going to choose Jupyter notebook and click on it. Now after the Jupyter server is started and localhost is opened, I'm gonna click on this new button and I'm gonna click here to create new folder. Now my new folder has name untitled folder, you can see that it's created a few seconds ago. I'm gonna select it and I'm going to scroll up and click this rename button and now I'm gonna rename my directory. I'm gonna choose name my repo for example and here it is and that's my new repository for projects in Jupyter. That's all in this tutorial. Thanks for watching, see you in the next tutorial. Don't forget to subscribe to our channel. Bye!


That's what we'll say to that, and we'll know something happened. In the meantime, should I come to class? There's the list of explanations. Missing all the good jokes. Okay, so let's... Before we start, I'm trying... So, A, have you heard of Lambda Calculus before? Yes. Nose and yeses. Okay. Yeah, I'm recording. No, is this the PowerPoints online? No, not yet. I didn't know if I'd get to it today or not. I'm making good progress. Okay. So, let's try to... We're going to come at this with a completely fresh, new, different mindset. Right?  It just describes a way of performing computation. So, then the Lambda is the character that we're going to use. So, actually, this is great. You only need to ever use with this one Greek symbol. And that's Lambda. And it's actually really easy to draw to, right? Yeah. Which is a super awesome benefit. Okay. Also, before we start, I should be very clear. We're actually going to go into some pretty cool, like, formal stuff. So, we're going to... So, you've studied Turing machines. Right? So, why do we study Turing machines? Do you program on a Turing machine? Yeah. Yeah? Do you program on a Turing machine? Yes. I was laughing. Oh. Anybody program on a Turing machine? No. No. Why do we study Turing machines? Because we tell you to have professors. That's what you're supposed to say. But why do we do that? Because they're just as powerful and expressive as the algorithms that we study. Yeah. Right? So, a Turing machine is great because it allows us to have representation without worrying about a machine. Right?  Fundamentally, can't do anything more than a Turing machine. It just doesn't quicker. It does it quicker, and it actually exists. Those are two big differences, right? Turing machine has infinite tape and infinite memory, which doesn't exist in any system ever yet. Right? So, it helps us think about what types of things can we compute. Right? We know if we can do it for a Turing machine, then we can write a program to do it. Right? And so that helps us think about what is possible. Right? This is actually getting more into the computer science of computer science. Right? That's like, there's the famous quote that computer science is about computers like astronomy is about telescopes. Right? We're actually talking about computation more abstractly than the computer or programming. Right? We programmed to instantiate that and to get the computers and the machines to actually do something. Right? But that's not necessary. So, why I want to talk about that is Lambda Calculus is a different way of thinking about computations. So, it's actually... People have shown that you can express a Turing machine with Lambda Calculus and you can express Lambda Calculus with a Turing machine. So, it's... In that sense, it is just as expressive, right? So, anything you can do with Lambda Calculus, you can do with a Turing machine, you can do with your computer. And I'll actually make an argument as we go into this that actually Lambda Calculus is a lot closer to some family of programming languages. Right? So, if you can understand kind of how Lambda Calculus works, this actually can help you. It's actually much more applicable. So, you can write programs where you talk about read-in one and move the tape right or left. But here, we talk about function applications and defining functions and invoking functions, calling functions, and we have functions, returning functions, which is kind of what you're doing in homework four. But also, even the syntax is actually very similar to Lisp and the Lisp and Scheme family of languages. That's actually because they're derived from here, but it's actually a real programming language that you can do a lot of really cool stuff in. Okay, question before we get started. So, the other really cool thing that I like about it is that, like I said, Turing machine, right, is like read something and move left or right and read or write something else, right? Those are the fundamental operations. But that really, what applicability does that have to you in your life as a programmer? Almost nothing, right? I mean, you just don't write programs and write zero or one and move in a direction, right? That doesn't really make sense. What do you use most often? Well, what do you hopefully use as a good software engineering technique most often? What was that, a debugger? Okay. But like, how do you construct your programs with your fingers typing, yes? Also that, huh? Incremental, yeah, you incrementally develop it. Do you just have it in like one thing that was like, do all this stuff? Probably. Yes? You're gonna have trouble. Functionally? Yeah, or functionally or object, right? You separate some of that functionality and you encapsulate it so that it can be reused, right? And so, let me calculate this, when you boil it down, the fundamental structure here is functions and function applications. So in this aspect, it actually does mimic programming very well, right? Okay, you can define, can you define functions in a Turing machine? I think kind of you can, but not in the language itself, right? The language doesn't really support that, right?  Yeah, yeah, you can, but it's not, it doesn't feel clean and it doesn't feel like what you do in your daily programming life, right? You define functions, those functions accept parameters and they do some computation and that's all, right? So this is, and this is the crazy thing, is lambda calculus, the only things you can do are define functions and apply functions. That's it. Those are the, there's no addition operator, nothing. But it is still as expressive as a Turing machine. You can still do every, it's actually kind of crazy, right? Everything you want to do on a computer, can be done with using functions and applying functions, which is exactly how you think about programming normally. So the key thing is that you have the ability to apply anonymous functions, right? So what does anonymous function mean? A function without a name, yes. It's kind of like the start of a Western, right? A name with no name, walked into a solar place, right? So these are the two key things. We can define anonymous functions. We actually can't even give functions names, but we can define anonymous functions and we can apply those functions. So what do we see that applying functions mean? Project four, a homework four. There's like a node that says apply, what does that mean? To use or invoke, right? We can define anonymous functions and we can invoke those functions. Those are the only things we can do, but we can still do everything possible that you can do in any programming language. And so ML, Haskell, F-sharp closure, there's a lot of kind of functional programming and also the Lisp family of languages derive and get a lot of inspiration from Lambda calculus. Is Lisp another one of them? Yes, Lisp actually follows almost directly as we'll see. So if you've ever done any Lisp or scheme programming, the syntax is actually exactly the same here with Lambda calculus. We have parentheses that we use to delineate our structures, but we're not there yet, we're still a little bit. Okay, so some history. I should also, did I already say this? I don't think I said it yet. So I don't have a crazy strong theoretical background. I'm a hacker really fundamentally. This is why some of your cries for me to teach 355 or something go unanswered because that is not, I don't have that formal mathematical background. But I still really like Lambda calculus even though we're going to talk about it really formally because it is very intuitive and it has very clear relations to programming languages and I think it's going to help you think about languages and how you program. I think it's going to help you think about it differently. But with all that said, so if you see something that's wrong or you think isn't correct, like ask me. And I'll try to, we'll try to answer it. Okay. So actually the study, this is actually kind of crazy. So back in 19, sorry, 1893 I'm going to butcher all these names. You said you did German, can you Frege? Frege? I think it's a German name. Frege? That sounds French. But anyways he first studied the use of functions in logic. So they were trying to understand logic, right? So you've, I believe you've taken classes on logic, right? First-order logic, Boolean logic, right? And so he was interested in trying to study well how do we, what do functions mean in logic? How can we use that there? Then in the 20s, Schunthengel in the 1920s he studied how combinators which are kind of what we're going to get to are a very specific type of function and how those can be applied to formal logic. And they started to see that like, wow, actually this logic like ands, ors, can actually be applied and used using functions which seems kind of crazy, right? Why would there be a relation between? So what's a mathematical function? Okay, that is an example of a mathematical function. Yes. But what does a function like mean? A set of ordered pairs. A set of ordered pairs. Yeah, so I like to think of it as a mapping, right? So you have you can think about it as ordered pairs, right? Match one thing to another thing. If you have more tuples or whatever, then you're mapping some of the input to some of the output, right? So all a function does is map things, right? And they started to see that like actually with this mapping, you can actually do logic and you can do first-order logic and all kinds of logic. Church in 1930, so Alfonzo Church introduced Lambda Calculus and unfortunately this is what happens sometimes when you do things with research Clean, who remembers Clean, or is it Cleaning? I actually can't remember. Was it Cleaning? Yeah. He did the Cleaning Star, right? From regular expressions.  So then in 1936, he Church revised it and published Lambda Calculus and that's kind of one of his first relevant to computation. And it's been even refined further. So people have added type systems and done all kinds of additions to Lambda Calculus. And even as we'll see this kind of turned into some modernish programming languages like with Lisp. So the idea is going to stem from here. Okay. So we're going to treat this like we're learning a new programming language. So it's essentially what we're doing. So what do we need to know to learn a new programming language? What does that mean? We need syntax which defines what a valid program looks like and what else? What was that? Semantics. Yeah, we need this semantics, right? Which goes over all those things that everyone was saying, right? We need to understand how to write a valid program in this language which is the syntax, right? And which now you all know how to write a parser to validate the syntax against a grammar. And we also need to know the semantics. What does it actually mean? What does, when I say it defines an anonymous function, what does that actually mean? Oh, I thought that was you had a question. So we're going to start off, our syntax is actually going to be incredibly simple which is actually one of the benefits of, and I'm going to keep talking about Lisp because I actually really like it a lot, but it's actually one of the benefits of a Lisp-like language is that the syntax is incredibly simple, right? Yes, it's incredible, and that actually has really good metaprogramming capabilities in using Lisp with macros, but that's the good thing about that. So an expression can either be an ID, so it's our base case, an expression can be lambda-id dot-expression, or an expression can be expression, space expression, and then we want some disambiguation rules, so we want to be able to use parentheses to group expressions. So an expression can be any one of these four things. These are the only four things that an expression can be. Does anybody recall this syntax for C along that is? Or the syntax for your project 5 language or project 4 language? It's a lot more complex involved, but that's all you need. That's the whole language? This is the entire language, the syntax of the language. And it's... Really, it's crazy. This can do any type of computation you want, just with this. What do these things mean? It doesn't matter yet. We haven't talked about what they mean. We need to make sure that we understand the syntax just like when we learn regular expressions. We need to go over the syntax of regular expressions, and then we get into what do regular expressions mean. So here's our language. So is this an expression in lambda calculus? Yes, by rule 1. Is this a valid expression in lambda calculus? Look how easy the lambda is to do. It's kind of like a crooked H, almost. What was that? Or an upside down Y, that's the other way. But it's a little too... Oh, yeah. And then reversed. It's not quite an upside down Y. Maybe in a mirror. Okay. Is this a lambda expression? An expression in lambda calculus? Yes. Is this... What's missing? What's wrong? What? Right, so we have a first lambda. We need an ID and then a dot. Right? So here, this is not a valid lambda expression. Here we have lambda x dot yz. Yeah, so what's the question? Let's go back to your original question before you answer it. So I was wondering how you could have two ID since you did it. Right. The second rule gives us lambda id dot expression. And we have expression is an expression and an expression. Then we have each of those as an ID. But is that... well, let's kind of peek forward a little bit. Is that the only way to parse that string? What's the other way? I'm not a computer. Right? So it's going to be that we have lambda id dot... What if this is the expression? Right? And so the outer one is expression, expression. So basically the question is, is this y associated with this rule or is that yz associated with this expression, right? So we'll get into actually how to disambiguate this. But I want you to realize that yes, this grammar is incredibly simple, right? Can you tell me? Yes. Yeah, so at the top, rule 3 says this is expression and this whole thing is an expression, right? And then rule 2 says that this is lambda id dot expression. And we know that this is an expression because of rule 4. And then we see we have expression, space expression, because of the parentheses. And then here we have expression and expression. Right? But we have clearly ambiguous syntax, right? So how to parse this, x, y, z? What are the two different ways? Yeah, so we have expression goes to expression z and then expression goes to x, y. Or the other way around. We have expression where x is on the left and then we have expression where y and z. So those that have looked at Project 5 bonus project. Why does the Project 5 bonus project handle this? Versus expression is just like this, right? It never has recursion, right? So like in Project 5, normally you can't have an arbitrary long expression, right? You just have something plus something. Right? You can't do something plus something plus something plus something plus something. Something plus something times something divided by something else. What's the first rule? What first rule? And like what by parsing through each line you take the first rule that applies to that and then you continue on like in the line of Project 5. Yes, but you need to know how I need to know which parser to create from this string, right? So like from 5 plus 2 times 3 I should by my knowledge of math, right? It should be first 5 times 3 because that's higher precedence than the plus. So you either need to specify that specifically, like by having disambiguation rules outside the grammar or you can change the grammar. So good practice. Look at Project 5 grammar, the bonus of Project 5 grammar. See how that supports it. How that grammar supports it. Okay, more examples, right? We saw how to parse this lambda x dot x y. We know that this is a valid is valid by the syntax but we don't know exactly what parser to create, right? One of the examples is we have expression lambda x expression and then we have expression x expression y or expression expression as the first one and then on the right we have y and on the other side we have lambda x x. Does that make sense? This is the example we saw so these are the two different ways to parse that string. The first one is we are going to add some disambiguation rules outside the grammar. So these are important, these are going to be something that we all are going to agree on by fiat because I said so. Right? So the first one, right, if we have e goes to ee it's going to be left associated. So using parentheses to disambiguate x, y, z we know that it's going to first do x, y and then z. This is how it's going to parse. Now remember we don't know exactly what it's going to do, right? But we know this is how we're going to parse it. We're first well, this thing is lower down on the tree, right? x, z and then y. So we can extend this with w, x, y, z this will be what? w, x, y, z so it will be w, x, like this and then y, z. Is that left, left associated? Yeah, so it's going to build it out like this. Right? Left associated just means we always go left most, right? So we do the left most here and we say oh it's still ambiguous, right? So we do the left most of this one. The same rules applies over and over. Okay. So we're going to write lambda id dot expression. So the idea is we're going to extend as far to the right as possible starting with lambda id dot. So for instance here x and y are both going to be part of the expression. Right? When we write it like this lambda dot x, y this means the same thing as lambda x dot parentheses x, y. Just like when we see this lambda x dot, lambda x dot x. Right? This inner one is going to be just x. And the outer one is going to include this expression in here. Let's stop here for now and on Monday we're going to get into more examples of these. Okay, let's preview something really quickly because I want to get why this is really important to understand. We need to be able to understand things like this just by looking at it. What's with the we'll get there when we get there but this is just something like this too. We need to understand exactly what this does syntax. It's like a programming language. You have to understand and know how to parse it. Yes. So I'll do either Monday or Tuesday I will do Monday.  So if there's anything on the practice midterm for practice this, then you would assume that there will be something like that on the midterm. It just takes a little time. It actually would be really good. This is really good timing because we can get familiar with regular expressions and disambiguation rules first before we get into all this. You don't sound convinced. Let's see, where's another really good one? I'll have the white comment here. Yeah, that's good one. I'll just go on to this.


I see a few wary faces, a few hungry faces. I'm feeling very proud to announce that we moved to our final panel discussion for the day, ladies and gentlemen. We'll be discussing the motion of status of media education in India. The panel chair for this session, please welcome Mr. Suman Kejha, Executive Editor BW Business World. Joining him on stage to debate the motion, please welcome Professor Stephen Quinn, Principal Mojo Media Insights Professor Devesh Kishore, Professor Emeritus Makanlal Chaturvedi National University of Journalism and Mass Communication Noida Campus Professor Biswajith Das, Director Center for Culture, Media and Governance Jamia Milya Islamya New Delhi Professor Sike Sardana, Member Academic Council Kushabhau Thakre University of Journalism and Mass Communication Raipur Professor Ujwal Chaudhary, Dean School of Media Pearl Academy, Delhi and Mumbai Mr. Sajal Mukherjee, Director APJ Institute of Mass Communication Dwarga New Delhi Professor Hemant Joshi, Course Director H.J. IIMC, now will be Hindi Journalism Professor Sunil Khan Bahra, Head Tejpur University, I request all my panelists to please join the chair on stage. Good evening ladies and gentlemen, so in a way we have been discussing this since this morning, the status of media education in India we have a very distinguished panel here, now you know in a different context we say that we get the democracy we deserve similarly we get the media we deserve, but then do we have the necessary apparatus of the media world to create a world of media persons, to create the infrastructure to support democracy, to support the fourth state we have a very distinguished panel here and maybe start with Professor Stephen Quinn, Principal Mojo Media Insights what do you think, what do you think about the discussion sir? I feel like for 32 years and I don't understand India, so how can I talk about education in this country? I'll talk about the result of the cricket last night. The result, second point that what is your combination between the different areas and imparting it through labs in the morning session it was said that the example was given of that film where it was said entertainment, entertainment, entertainment I would say no, not that you can have give the emphasis on reading, writing and practice these three elements have to be kept in mind, while we are imparting education in India we do not have any organization which can train the faculty members in imparting the education I am surprised in this country when a teacher is appointed in intermediate class in high school he should have along with the master degree a beard degree also but when he or she comes to the university he can have the same master degree without beard well what does it mean? it means that our teacher who comes as a faculty member he has no training whatsoever how to impart teaching program to the students this is my second point there should be a national council of education, media education, research and training which is very important if this council is given this responsibility maybe it can be located at Indian Institute of Mass Communication or it can be located in any university but the point is that there should be a certified national council of media education, research and training where we can organize time to time the courses related to media training for our faculty members third point I wanted to mention it we should have media labs we find in many institutions media labs are missing print media lab should be there where you should have all the softwares to bring out a daily newspaper or weekly newspaper you should have a broadcasting lab at least you should have equipments in case of television you should have all those rating equipments along with camera and all that what I am trying to emphasize is if you do not have the labs then how are you going to train him so therefore this council will also be given the responsibility to find out whether these organizations have all these facilities are not I think if these three small points which I have raised are taken care of there will be lot of improvement in the present status of media education in this country thank you now it said that like actors, actors cannot be created they are born actors similarly journalists cannot be created in classrooms you know they are born journalists so professor Biswajid Das you come from distinguished Jamiya it is not here so can we go on to professor Sardana please professor Sardana what are your thoughts can we create journalists in the classroom good evening everybody even though I did my PG diploma in journalism from Punjabi University in 1955 but the basics of public lessons I have learned the hard way I spent 31 years in BHEL starting from PRO and ending up as general manager and then took to teaching I spent another 18 years in teaching my impression is that the teachers in media schools they need to be given orientation in the exact working of that particular media what kind of situation is this that a person who is a gold medalist in MA, APR or journalism or something he does MP, he does PhD and he becomes assistant professor and becomes professor and he has not entered into a newspaper office even one day I also know that he has not entered a corporate public lesson department even in one day so what is important is that these teachers they should be seconded to the media houses they should go and see there what kind of work is there number one and second is that the senior persons who have spent lifetime in the practice of media they should be actively associated with the media schools so that they can bring all the experiences that they have learned I have seen right from the state level, local level, state level, national level, international level how public lessons is done and there is no shortcut there is no injection or capsule that you just take and there is no question of whining and dining it is a very serious matter and it has to be dealt with very carefully and intelligently then only PR can be taught and third point which I would like to make is that the students somebody asked a question how can we make the students fearless we cannot make the students fearless it is just it is inborn it is inherent in the person what kind of and then but and last point which I would like to make that the history of media teachers or media persons they should be made compulsory to be read by the students those so that they know how a person what kind of challenges he faced and how did he rise from A to B and C and ultimately on the top thank you very much thank you sir one important point he raised was that the classroom does not serve much purpose in fact we learn the hard way when we are on the job you know so it is on the job training when we learn on the job in the newsroom that the real learning starts so next question that I want to pose to Prof. Ujwal Chaudhary is has the classroom become redundant the entire process of communication that we imbibe in the newsroom in the journalism lessons that we imbibe in newsrooms or say in the PR consultancy so what is the role that the classroom fulfills here I would not say that the classroom is redundant I would rather say that the classroom can be made much more impactful if certain changes are done and I would not restrict to journalism I would rather look at the entire domain of media and communication so from platforms like print and TV the focus now has to also include mobile journalism and convergent journalism where the same story is actually expressed in various manner in different volumes and sizes across various media so 256 characters for Twitter 2 to 3 paragraphs for online 7 to 8 paragraphs or maybe around 500 words for newspaper a more detailed story for magazine a podcast for radio or online so therefore there has to be the same story ability given to our youngsters to transform the same story into various formats and sizes cinema earlier it was cinema focus in the classrooms as well we moved on to television focus what we now need to make the classrooms also aware and alert to web entertainment because it's the web entertainment that's calling the shots in the times to come earlier in Brandcom the focus was only on advertising we moved on to PR and events as has been already mentioned but what's now needed is an integrated brand communication or a digital brand communication now this part of the awareness is still very low in the classroom we had a lot of discussion on academic research we moved on to communication research what we perhaps now need to bring research centricity in the practice of communication we need to bring in contextual or applied research due to lack of time I'm not giving examples and detailing verbose expression our expressions were very verbose lot of words were used what we need to move is visual expression and data based expression so infographics use of infographics use of visuals audio and video both to tell the same story which we used so many words to tell earlier we need to also move we have traditionally thought that journalism as a reporting is about 5Ws and 1H and views are about opinions opinionated articles we need to move to a new formula of storytelling storytelling through multiple means again I'm not getting into details of it we had our education which is very degree centric in fact the only purpose that many of the universities serve is only dishing out degrees we need to move to job and then we brought in job centricity with the talk of placements in the private sector what we perhaps now need is to move to career centricity skill centricity beyond jobs and degrees there has also been the functions of communication which were information, entertainment, education perhaps we need to now move to new areas of engagement and interaction so it's not how many people you reach the power of the media is understood by that rather how many people you engage even if you reach a smaller number so engagement is the key word earlier our focus was to learn more of media vehicles pre-in TV, radio now the focus is shifting to communication and conversation rather so it's not it doesn't matter if a particular brand in the communication in the ecosystem is criticized in a social media what's important is that people are engaging with the brand good or bad for both sorts of conversation earlier almost no management media management content was there limited media management was brought in still now in most places what's now needed and I would like to humbly put almost entire media education of India does not have media sales as a paper does not have OTT as a content of media education there is not sensitive to the new requirements of audience measurements people do talk about TRPs and all but there is bark now hardly it's a part of any education there so audience measurements, media sales media management, understanding of OTT and web entertainment these are the new areas we believed in recorded shooting we did armchair or within studio recorded shooting we moved on to practicing live shooting but what's now is required to go beyond and bring about augmented and virtual reality shooting AR, VR shooting again not getting into details we always had a concept centricity we were focusing on ideas and concepts we moved to context perhaps now what's required as well is a design thinking in the context of media design thinking which brings concept context and packaging of the content together and the last point we always believed that media is about agenda setting and the classrooms taught that I would rather would suggest and then we said mirroring the society but today what's required is to understand media to open the gates not close it as agenda setters and bring about a lot of user generated content into the media the whole concept of new media and mainstream media this division has to actually be broken and there has to be a seamless continuous media between so called mainstream and so called new media without detailing I just wanted to put these points in front thank you professor thank you very much now a journalist without credibility is no journalist at all so what is the role that the classroom can play in instilling values like credibility in a journalist so I would request professor Sajal Mukherjee director APJ to hold forth on this piece since morning we were attending all the panels we heard all the panels and most of the points were covered there but to Chris and give you the answer the credibility of an institute college or centers is dependent on the kind of curriculum that they are conducting how balanced they are industry interaction industry interaction that how they are inviting or encouraging the industry professional to come and take classes and interact with the students the kind of infrastructure and facilities that they are providing and the kind of placement they are giving they are legacy now these are the basic points where credibility of an institute is being judged and there are also institutes which are creating their credibility or through their marketing or PR effect so basically you have two streams one is the credible institution and there are a lot of visible institutions now in this the most preferred institute where people like to go and take the industry people take their students for their jobs and all they offer is the institute which are more credible and visible they are an institute which are low on credibility but high on visibility but they are just attracting the students and there are institute which are low on visibility and high on credibility their focus is basically to create and turn out students which are industry ready they are students which can go into the academics and the research and pursue their knowledge pursuit so this is how broadly you can look at the institute and judge the institute in terms of the credibility parameter thank you Professor Sajal Mukherjee now may request may request Professor Sunil Kanth Behera head Tehpuri University I will go there I am basically a teacher you know so I request you to wind up the discussion and also pass the continuity I will, I will I might be a little caustic I was not here the whole day very good evening to all of you I know we are in a very informal setting that makes me feel that I am in Delhi and we are talking about a very serious subject the panel was for status of media education I have been teaching communication for the last 38 years I do not call myself a media educator I am teaching communication of which media is a part a component may be a very important component but I teach communication we are here in Delhi we talk in unison about the requirements of education today we do not talk about the commitment we do not talk about the difficulties we do not talk about the challenges we simply talk about a sugar coated view that it should be like this I will not agree to it I will stay for 5-7 minutes I am sure you will not listen but that is my job as a teacher education communication education there is growth in India when we started in 60s maybe there were 20 institutions educating on communication but today we have about 1000 institutions of varied nature varied nomenclature offering these courses but there is lot of diversities and widespread disparities in education today if we think about the geographic disparity a university in Delhi cannot be equal with an institute in Odisha we have metro centers of education capital cities have universities and colleges and institutes and in rural areas also we have communication departments and there is a lot of disparity what we teach here in Delhi will not be acceptable to the students of northeast or to the students of Odisha they come from different backgrounds talking about the institutions again we have government universities central universities, state universities private universities media and journalism schools they have different hizndas they have different problems we cannot standardize in one session I was listening to standardization of communication education that itself is not possible and also not feasible maybe we can have some commonalities in the curriculum but those need to be contextualized in different institutions and in different settings talking about the nomenclature again we are giving them diplomas we are giving them short term skill based courses we are teaching them at graduate level we are teaching them at post graduate MPL and PSA level but unfortunately in the UDC communication is still today a vocational subject we do not have that kind of recognition as other disciplines have still it is being offered as a vocational subject and of course we have grown quantitatively we have grown but qualitatively it is much less than desired there is a change of if we look at the journey of we are talking much about media growth media has changed education has also changed earlier we were teaching journalism today we are teaching communication as a discipline we are teaching not only the skills but we are teaching the sociology of communication the ecology of communication the economics of communication we are teaching them all because we are not simply training them to be employed in the media organizations we also have a duty to train them to become teachers train them to become policy makers that is our purpose our purpose is not simply to be job driven and again there is a difference earlier we were teaching journalism we are now teaching communication we were teaching earlier writing skills today Prof. Chaudhary talked about we have to teach them not only writing skills but we have to teach them visual skills we have to teach them audio visual skills we have to teach them the digital skills and moreover we have to teach them the technological skills because media has become technology driven today and honestly speaking if this teaching them the technological skills have somehow is over set out the creative skills of the discipline students are more into I will take two more minutes earlier education media education was content driven now it is technology driven earlier we were teaching what is news today we are teaching them what is fake news earlier we were teaching them information scarcity today we are teaching them information overload earlier we were teaching them only functions of communication and today we are teaching them dysfunctions of communication we were teaching them the information order today we are teaching them what is information disorder what is misinformation What is misinformation? What is disinformation? What is mal-information? So we have moved lot of if we are teaching them print and analog we are now teaching them digital and mojo so Growth is there qualitatively and quantitatively, but certainly there is a lack of commitment I will say from ourselves Over these 45 or 50 years we have failed We have been debating on how to make media education more relevant, but we have been debating even today much depend on the younger faculties, I'm very Proud to say that today we have a Band of younger faculty members who are keen to take this education forward But again the UGC has made it they Running after this API is forced. I don't blame them for that, but that is the reality So think about these things and I have tried to show you the mirror Not with sugar coated words, but that is the reality go to a Department in a rural area and you will find the low-intake quality Not all the students are like the deli student. So when you think about education think about the family perspective and Think about the solution. Thank you very much very relevant points. Thank you so much professor Please have you see So that brings to an end this enlightening session of discussions on the state of media education in India Over to you Thank you so much gentlemen. I'd like to call upon Mr. Sagar Kaushik Go founder please and strides foundation to kindly come on stage and felicitate all of our speakers Mr. Kaushik Thank you gentlemen, thank you so much I'd request professor Stephen Quinn to stay on and deliver the guest of honor speech to round off the day Sorry friends This is my duty. I believe to thank extent for media and business world and particularly Prapsasurvi Dahiya for taking of this and also Publishing the directory Which perhaps? No other teacher in the country has succeeded in doing this a big round of a plot for Surabhi Dahiya thing for her wonderful work of publishing the directory. Thank you very much