 is at telling the difference between things. So, Govind, who is the co-founder of Semantics 3, not our first speaker from Semantics 3 during this conference, has spent a lot of time working on this problem, and he's gonna tell you about some of the lessons he's learned in trying to get machines to differentiate between photos. I think that I hope I won't be mind-boggling myself. I don't know who you're going to be interested in. I, as I was introducing the work of Govind on Semantics 3, where I actually work with the ASN team, but I also work with the Wanderer team. We're going to work with, if you want to see that, somebody who we work with, who we do as a grant for cross-border management, somebody who's like a crisis presence, and over there, we're going to take the head, and over there, we're going to improve the quality of what I mean on the websites. We've got the dance, we've got what's going on, we've got the whole country. Because what there is, how we use money to get these. We do a lot of mining of data from cross-border, and we take all this data together, and run it through the network, and the data from the network side is here, they're all brought up to us, so please subscribe to the channel. These are going to be unsupervised content extractions, so we follow that data in the ASN network, we've got a lot of data, but they share a lot of structure to that benefit, very important items, which is very important, because it's not a structure, yes. We don't have a decision, so we don't require us, because we are rich, nor are we happy, that's not what we want. We don't have a decision, which is, we'll get a natural value by the structure that we use. We don't have a lot of our customers, not usually. Gallery's development, I think a lot of this is, we don't have these, the idea that they should not be left in some city, but where we are on the other hand, we also, as our side, we're making a lot of work. All the work that we're doing, it's very, it's a good concept. We require us to understand the safety, what we do in the websites, you know the movies, and you repeat it the other day, but exactly the same, that we're going to do is to protect the distribution, and just be there, and then we're going to solve the issues from all the other models. There's a natural language problem, the field is a problem, there's an on-political problem, but it's a very interesting problem. There's also a strategy problem for a few years, especially if you're from a group where we've been tripled by a scholar of the class, we're dealing with many issues, so we have to deal with that and get that, which I'm very excited to know a lot about this people, and I think that's an issue that I've always been trying to use for students as well, or it's going to be a good business like this, we're going to be able to deal with this. Again, I don't want to go, this is surprising, we're going to be able to deal with the opposite model and the class shape, the opposite model and the school shape, so again, we're going to be actually going to deal with this, I'm going to go back to first, you're setting up the model class, and then you're going to set it up, we're going to make it into the model class that you can set up, as a ASI and as a wonder why it's built in this way, and that's why we're going to be able to deal with this. But I do quite a lot of these works, but I think that's going to be at the center of my coffee. I don't think it's going to be my story, so either one way or the other half of lunch, and these five students will be all in their own mind, but I don't know how many of my colleagues are going to be able to stay here, and to solve the problem of the current question. I need to be storing the proper example structure. I'm going to be talking about a specific subprime, a specific goal that we might solve in the next program. The problem that we are in as we might solve this goal follows the problem that we needed in the middle of a few days, and I need the lesson that we're going to solve this. We need the lessons and structures available for storing. But I just want to find out why all of this coffee is not so much to deal with any sort of problem, actually. It's not a rule, you know, to go around and sit here and drink with them. I'm going to be a bit, I'm going to be a bit very interested in talking about this, what it was like as we're going to do this to you, but I also think that the challenges that we ourselves have gone through over the years, are you going to do this for the next six years or are you going to be solving this problem in the last three or four years? There's rules with which we can decide to come through. So as a company itself and as a leader of about 60,000 employees, hopefully we'll be creating some of the lessons from the five-parts C-director that we need to get forward in 40 years. So the first thing you want to do with this before, we want to be able to get a sense of what we need to manage. We need a way to measure this, we need a way to make that happen. That first thing is that we will shape the structures of the structure that we need to best possibly be able to do. We need to be able to measure the three-parts C-director and this we want to be able to get a sense of the three-parts C-director that I'll get out of this process. And it was really difficult for me to see some people who said, so we went ahead, did us a tiny sense of process for my role as part of the work we're going to do. The nine-parts C-director, which is for my example, pairs of products, go-match, we used a short-gauge industry-based approach. So if I... I think products and go-match are actually going to be able to do that. You don't know what I'm saying. But if I say that I'm going to be able to do this, which I have to be able to do, you need to have the bones in your hands and you would, like, I would say it's a little bit difficult to say that. The challenge was not to be in the go-match, the challenges of finding any single products or almost a single product that seemed to make it difficult for us. I'm going to go back, which is that it's a little challenge for people to do, it's a little bit of a surprise to me, right? There's almost no product yet. And in fact, that's how we are going to save money here, also. We did this simple industry-based approach, so we used two products. We basically looked at and we picked up the product that you are going to use from every skill set that you're going to have. We made sure that these two products were made by me, which means we're making products that are safe to have all of these on the go-match. And we're making products that are good for our products that are low-end sales. Why are we using this? Well, usually when products are sufficiently high-end you don't have to answer a lot of the questions and send a lot of the questions to the consumers. What we find out when we put them on the go-match is that they're low-end, high-end products that are safe to save money for the most product. So, by all means, that's what we're making products that are high-end simple products. So, by making products on the same stage, when low-end sales are high-end, you've got to work with products that are high-end products. And, of course, you can work with on-match products that are on-match products that are high-end products that are really good for your generation. I guess that's the kind of story that I'm going to tell you. In that natural product, we spent about $1,000 a month on a model product. The model would be about $5,000 a month. But the product that's in this is a little over $2,000 a month. I'll talk about that in the case because we don't want to let the children work at a high-end center. We don't want to let the children work at a high-end center. So, here's two examples of our on-match cases. We have to go with that. I had a lot of friends that enjoyed the machine. They detected the idea that all the products that are on-match came in sequence and none of them are on-match. This specification is very popular, I'd say, which we don't pay into the model. So, for example, like, you have to have a little bit of a little bit of a finite, specific detail, I'd say. But our problem, while this was in the middle, the other idea that we had in the model where it's built in had precisely the same system. Then, when they come to the same website, and then when they're not on-match. So, rather than the on-match and the actual on-match below-matches, are the image, the name, and all the other stuff, that was the biggest way to reverse the data of how we built the data. This wasn't the idea, and this wasn't just the way we were, we were not allowed to use the things. Some websites don't stand by the fact that we're not on-match characters. So, when we went to the internet, when it was needed to get simple things like, if we didn't go into the third-party and see back on-match, then, say, you know what, they probably wouldn't see back on-match and they're holding on-match. Again, I couldn't do the on-match all, but we wanted to solve the problem without actually going to the on-match. I thought we're not going to go into this. But if I don't know the difference, you just have to guess, the website can get on-match. Let's simply take away your words, you know, we've got a lot of questions from the folks that have been in the internet, smashing these possible sensitive relationships, cause the relationship between the on-match and the internet was the one that used that high-priority, the way we looked at it. The incentive relationship was the one that had a single sense of the word. And I mean, by extension, I mean, that we can get to at least 30 years from the middle of the understanding of the software, is that the models aren't quite consuming across the EU, the USA, just give you a little bit of a solving this problem. All the ones we have are in the on-match loss. They don't care about the on-match loss, they're watching their way to the on-match so they don't have to worry about it. And by extension, and we are used to this type of world, a lot of us have learned what we were doing in traditional fields, that's right, in which there is a part of that, and there is almost nothing. And by the means of rock and rocks, we do the problems now, especially the games that we work with, what they call it, they can get a view and look at it, so they learn the same as the people from who we are, we have to sort of make ourselves to be our players. So this is sort of the first necessary regard for all of us. But we decided to make a lot of the stories of all of us back then, and I think that will be the stories that we can give. And I think we should be called objective understanding, and some of these cases, I think like, I think that I say we're supposed to be able to do a lot of things again. I see that the issue is that when she goes to work, we actually start in front of that screen that says, I think it's 50% for buying a house, but there are so many things that we're going to do. And I'm sure we'll be very proud of that part. So hopefully this is some way to sort of think through the process that we're just fighting on for a long time. And you know what else? Because I get the things that are not un-eventing or checking observations, but the things that you can do with data and seeing if you can do some great stuff. And to give you an example by this slide, you'll notice this is still an example of everything. I think across this paper, there's probably a difference between other different cases where one case measures that they set up very interesting objective, which is they kind of look at the problem of the cause of people, causes of human faces, and that kind of depth. Looking at one's face, looking at your face, I know you have a lot of problems. No one is going to say, it's just how you are. And with an added scenario, like this actress, we're super surprised because she goes against that sensibility in it. If you're a teacher, you might find out they don't. Or so, because you're looking at interesting observations, and you're looking at your goals, you're not looking at 96.6%, you're likely to be a little, or you're likely to be a little bit more likely, maybe my heart will be broken if it goes wrong. And also, it's like a conceivably different than conceivably different because it's the speciality of the problem. Never mind that the body affects actuals, we're not consuming it. I'm having a little bit of an air out, and we spoke with the face of the six, what we did, and the feeling of the lesson about the accomplished people, so some folks will be able to say, don't, I'm a little bit of a negative case, they put out a negative example of people who are under pressure, doctors, lawyers, and I will show that, that if you do, people will wear more blood shirts for their program, because that's what's going on here. In America, for example, people who wear cross-leather, not all people wear cross-leather, because they're 19 young, and the days are very January. So we're going to pick up on that, guys. So part of the thing is, these things go just up for when they're really high, that's what's going on here, that's how you can stand it. And the day's right, this is a few more questions about this. I hope you're not going to get into this context, but it's going to be just through not making the world a better place. So that makes it best that we're not going to have something that's going to be all over the place. Okay, that was great. Like I said, we have four versions of signals from text, I'll just try again. I want to finish it off, four versions of signals that we've got. So first of all, we decided to do a text problem. With the text problem, the problem was this way. Again, the problem in all of it, we managed to get it to be about 85%. But when we actually looked at the data, we found that there was a huge queue. The problem has been really well done sometimes, comes well done sometimes. And actually, the problem seems to be a huge thing, observation, what to do with it all. And then the problem here was this way. So the last one we do, we'll be focusing on this one. Great, really shout out to all of you. Very important to hear all this, and I'm proud of this, because me, I know I'm from the U.S., so me, I was on a lot of trouble, and me, as I used to be, no sort of underline rule here. And as soon as one person would make a whole business, I used to back. And a lot of people, I used to be used to be used to be. And I didn't mind all of that, but I wanted to do a specific back. And then I was going to say, I know the better handle. Now, I didn't know how to handle logic. The other side was the problem. And that was not the second takeaway of this. People used to sit down with knowledge based on this. Knowledge based on this. And in the Russian part, where you're talking about, even though it's music, if you know it's music, you sometimes forget that you're almost a part of the citizens of the learning part. You buy knowledge and that knowledge, and you want the Bible, especially when you're really your case, you want the Bible, you need your knowledge and your knowledge. You think about this, and it's not about why you want this, you're not learning very well. And the way in which I think about this, is I ask myself this question. You'll find, you'll remember, that human with no prior knowledge is being a part of the learning, but a prior living being is a part of the problem. And so you walk up the hallway, start following everything you want, and you'll find that you're a citizen of the smart person. And you will just learn from the main piece, that for you, you want this question, for you, you really want this question, and you want to let it go, you really need to know that, and so you're way forward, and you're pushing your position on the next question. Other than the new video, this is against the way that I've been in other days, like how you've worked out my income. The other thing that I learned from this is, in trying to figure out the problem, and I think it's important to give you all these problems and I just want to let you know that, in the idea of being very well-earned, and being strong and proud and proud, you really need to know that, and I think it's important to really accept that you sort of learn from the ideas. So as a scientist, what do you really want to do? You want to work with a high level, what do you really want to do? You can work with a high level of education, there's your new education, and your new education, and your new education, you do that with any of the lines, you go higher, and you do the same, and you work with a smaller level. And how do you do that? You push up the handle, you push up the movie, it's certainly not as easy as it was, but there's sort of, you know, I think it's on the road, I think that there's a big value behind it, or it doesn't have to be spent a lot of hours for it to be released. Because we like sometimes, even when we're done with it, you know, the time you spend your time, is it necessary to be in that action, so to say, look, that is just looking at your ideas, but something that I don't have in my head, that I don't think that's, that's just, I don't need to pay power to work, I'm just like, I don't need to do this. Ah, that's a lot. Hopefully, let's get it, because I know a lot of you need a chance, you know, to be able to do it. So next up, when we build our next model, we then have to build a business model, all of the same projects that we're going to take a step, but the challenge now was, I mean, my policy, how we're going to make this next, the business is down the road, we have to look at it right in the sales, but we've had a next model, the image model, all of which are people who are panicking, nice to be able to do it. But I thought, how we're going to do it, we're going to have a nice deal of power, we're going to actually be seeing it, and a lot of people are going to know, that we're going to have more power, more sales power. What we're going to do in this case, is build a model, build a model, right? And I mean, it's easy, and what we're going to do is, you move a bunch of people back in the space, as you build a share of the space, you have another, but the other thing, even, I can decisionally, back up with the whole thing. That's the second thing that we did, and that's the thing, we were in new environments, and what we're going to do is also, yep, okay, that's my video, that's where it is, right? The new model didn't go better, and it gets a little bit better. That's the next one I was talking about. My model didn't go better, so I'm going to end up with it. That's the new model, which again, is a problem, because the one that's not very good, as a human being might know that, was when I was 12 o'clock, I simply didn't know what was going on. So it was beginning with, at least, my human being probably didn't understand how to draw the model. And then the question was, in any case, why are you all going to keep it from seeing, we're going to get this game by just one question. So again, another, so on the part where you can't get a question, there's no way you're going to keep it from seeing, because these are, we're just guessing that, the more well-versed, the more steep the demo, which is like, you're not this other, we're not going to buy it from someone else. So we mounted the model, and we learned to consider, that it's a decision-layer, to remind you of the older model, which is the part of the player that didn't want to use the text. Had we been looking for, not the new model, but a stronger one, we'd say. Now we're going to be drawing a correlation between the user text, and we're going to look at the cards, Mij, XA, Mij, XB, who in these layers had to map to one vector space, who all the textures had to map to another vector space, those correlations, those go, which you don't understand, it really do map to space, which also has the proper focus, you're going to learn to look at the best models, the best of the new symbols. And this is because they've been working as you need to understand to each other, and then we're going to go on in the base, you're going to learn to look at each other, and there's a good relation of barrier. So the challenge of working as, how do you make that, work with people that are already in their processes? And then we're going to come to another interesting demo, which is by, I know it's that way, if I can do this with video audio, they can't exactly say how to make it. They want to do it with video, they can't do it with video audio, and you need to listen to that, by activation map of the shape here, and they found that in some cases, some people are happy just to video, some people are happy just to hear our inputs, this might have to be overvolting, because we're together, but the movement of the process was for correlation, there was no understanding the way it appears to the piece, we were doing the effects, because we were doing the whole thing, so it was the same for everything that was going on, which, it's a good thing for me, it's the second one that started it, and this was the reason for my problem, it was probably an interesting solution, which is the way it's easier to solve the problem, but it didn't find a way to do it, where they didn't care about the audio video, which is why they didn't pay for it, working it out, it was easier than text, but it really was the worst time I'd come because I don't think it was just a text, it was more than a text, in other words, most of the speech seemed like it was, but most of the speech seemed like it was, it was shared, shared as it is, where most of the results are, you need to be able to talk about it, and then you go ahead and do all the things, which you can argue with each other, and simply on the recording, really, each other. This model, I think, would be, it's not the case by what I just said, but it's very, it's focused on this example, which I thought was very cool. I managed, I really thought it would be important, like 100% of the speech was shared as a speech, where what do you want me to do, why not use it as a focus in language? Well, yes, this has been something for years, they did the audio model, so they always, like the audio, they had to try to switch, they would be audio model, important to the representation space, we saw the audio model, and they built it on supply for about half a string, so they put the model in the ideal audio, and I guess that they dropped the audio in the video, in the audio, in the audio model, and it's amazing, the model learned, and here I am using video, and by the way, they changed that video over and over, in the audio, in the model, to see what do you see, which is kind of crazy, where it sets, because if you can infer, the model is in the same language, the same language, then you understand how the speech is actually useful, which I was afraid of, and it would be hard to understand why, I wouldn't follow this very well, and I just have to tell us more about the direction, so let's enter into this, I mean, even if you want to be perfect, you can follow the audio model, in order to be enough, you can handle all these things with a smile, the problem is kind of like sometimes, and you can now, when you see, I don't know the signals, okay, sometimes you realize, that you are going to be an additional mandatory provision to be like, you could just, I mean, in order to be able to do so, you still have to be able to realize all that, what it is that they're going with, that there is a potential, there are ways, but you can follow the one, you can also try to think that, the question is kind of like correlations, that's interesting, but I don't know, the other thing I don't know is, coming back to the iPhone's problem, well, at first, we were thinking about how much we could be able to use, and can each be, do a different fit, so we need to get all that feature functions, buy the model and all the things that we want to learn, and then we realized, there are ways that there are both the order system, the order system, and the parameters of the model, and the moves that you can do is, change the objective of the model, which in this case, was involving a supervised model, a supervised model, Apple supervised model, something like that, to change the objective of the model, as opposed to the things that we usually, you know, use to be going to the library, and change the application function, to see all the parameters of the model, and then it's just a parameter you can control it, you can have this all over the area, so I can't handle that by repeating it, of any of my parameters, but how it's equal to what I need to respect, it's not that much. So that's great, okay? Good on you, this is twice the amount, I know the gas, I know it was a problem, the problem is, how it's going to be one-on-together, but the next problem we had was, you know, it is, and if my money was worth, basically, of my model, everybody's offered, no shoulders, probably had this, so I was a little bit of a six, and then any type of interest is the right thing to do, we've gone over the path, the other years, we've tried this, we've already had it, so we started off as a little bit of a six, one of us, one of us, now we have nine months, in the years we've worked on this, four years, four years, we've done this, and we've worked on this, and we've used, the role of the economic model. So it came down to, when we actually had the, we also set aside the same, it's okay, we've already done some six months, we've tried, we've actually had the machine learning model in fact, this should be my biggest, so we've used the basic model, the machine learning base model, and of course, when you're looking at the, the obvious, and you're looking at it, it's a small field of work there, the national books, what about, you know, the model needs to be right, it's how good it is, it's how good it is, you know where it was, how good it is, show me how good it is, you guys have mentioned on this, quite a lot of the outside, you guys have mentioned on this, you guys have mentioned on that, which I'll call this model, it really does sound bad, maybe it's because we, each is not far out of our DSA, this is usually the case, but actually it won't matter, just trying to create it right, I mean, I need to like, believe this, that I, by the way, I was looking at the site, you need to start with that, 100 upon 100 basis, the fact that it loses two basis, which is the other one, it was not moving, there it has to be something wrong, and yeah, so the past seven weeks straight, there are several reasons, I mean, sometimes the video was on digital, it's not that easy, what models do you have to learn, as well as it would happen, so we didn't tell it to anyone out there, some days the models hadn't come to me, so the line, you know, I basically was going to be, I experimented with you guys, and it was about 100% for the other 100 weeks, to get your shit done, going down that path, please tell you, there was something to happen, so yeah, we did that, we did that for you, we used to go and do it by hand, not because it was a brand new group, we had one for this, the long time, I had to reverse it, to show you what's going on, I said, wait a minute here, you got this, can't do it, it's easy, you need to do it, there's a lot of choices, I don't know, I've been in one of the videos that we were doing, I'm sure you've seen that, yeah, and which, so, before we get into what was in the project, and I was going to talk about this now, which is, we used to call these, hand-publishers, and for those of you who weren't really in the product, it's a brand new company, because it's easy to just, I had to do, so I gave you a tradition, it's a tradition, it's the word, it's this, it's capitalization, it's like, you can't do this, it's the way you face models, and if you have a question, please raise your hand, we'd like to ask you to stand, while you're giving your question, we've had some comments from the livestream, they can't tell who's, Andri is here in the middle of, what... one of the people I can't tell who is there, when you are using this, it's the recipe, very possible, Can you have a question? When you are using artificial teaching, when you are using auto-info data, that's like I did, it's kind of difficult to find a way. So sometimes we actually are filtering you in the embedding, otherwise there is a lot of other metadata that you are done for. So when you have a lot of other metadata which is being used in embedding, you can use auto-info data. Another question? Now it's working now. Question first is, how do you learn the knowledge of this type of teaching? The question is that especially in the education, when you are learning, what they did was like, they had one model for a different type of teaching. Auto-info rules were popular, but similar models were like, learning the different. So what they did was, they had the same model, they had to make it the same, the paper, the name of the paper was one model to learn them. This question is not here? So most of the time with the deep learning, what happens is it's kind of a black box, you don't really know what the model is. You could find out issues why you learn something, right? Is there any work or a mechanism for you to understand what the model is learning to cover these things? I think we have to include one more function and stay over there for a while though, so I'll talk to you after this one. Hi Govind, I would like to ask you how do you go about collating multiple representations of the same attribute? Let's say dimension can be expressed with multiple sides. Same attribute might be represented in different ways. How do you go about collating? You say one, two, three. The attribute name might be the same, but the way it's represented might be different. Thank you Govind. That was a really interesting talk. I had no idea that the machine facilitators set up for our UTR. I'd like you all to see