Hello. Welcome to the session. We are live and I am Harpet Jasper here. Let me just double check with everybody. I'm getting a thumbs up. Excellent. All right. So we're going to talk about uniting the edge ecosystem. And as you can see, there's a Q&A tab. You can ask the questions while I'm speaking. I'll try and answer it. If you want to wait till the end, I'm going to be around. This is a deeper dive to my keynote. And let's go ahead and get started. There are really three things that I want to cover today. The first is the edge market killer apps. It's very important for all of us to understand where the money is going to come from. And then we have to also understand how we define the community. And then finally, the most of the time will be spent on the projects themselves. Let's go to the use cases and deployments. So the first thing we want to realize is the reason edge computing has become so much in demand is because there are five technologies that converged all at the same time. And that was starting 2018-19. 5G, which gave you low latency. Microservices, which is really allowing applications to be written in a portable manner. The use of AI at the edge, allowing for more predictive maintenance. All the hardware acceleration that you can get from the TPU-GPU-NPU. And then the last piece, which is the telecom side of things bringing compute and storage close to the application is on demand, network function virtualization. So with this layer of technology that was very mature, a whole new set of applications emerged. Everything from IoT, immersive, autonomous devices, infrastructure, etc. And nobody knew what the killer apps were. But the good news was it all started coming together. And so this year, and I showed this at my keynote yesterday, 5G and Edge are now extremely critical in the battle where we are all locked here in the pandemic. And I'm coming live, obviously. But more importantly, the latency, the application. I mean, we are not time sensitive. I mean, VR, but IoT applications, connected cars, vehicles, they're all latency and time sensitive.  And so what has happened is a whole bunch of analysts have started quantifying where the killer markets are. The great news is that the top five markets is what LF Edge as a community is focusing on. And this is, you know, sizing of those. But if you read on the right-hand side, manufacturing, industrial manufacturing in particular is the top user of Edge computing. Followed by all in gas, followed by commerce retail, followed by home Edge automotive fleet transportation logistics. The other, you know, smart cities, governments, healthcare, etc. Extremely critical for Edge, but slow moving markets because of regulations and policies. If you look at it from an IoT only perspective, and remember, I have maintained the following. Not all IoT requires an Edge and not all Edge is IoT. So you could have a sensor wake up and dump data once a week. That's not an Edge application. So I just wanted to sort of separate those things out. And then so if you look at it from an IoT perspective, the same markets, manufacturing, industrial transportation, energy, retail, etc. are the top growing markets. You know, smart cities, buildings, etc. are not growing primarily because of the pandemic concerns. So that's a different view of the market and the industry. Then if you look at more of the bandwidth latency equation. The slide on the right or the two by two on the right, the X axis is really bandwidth. Y axis is latency, low to high high. And so if you look at it, either an application requires high bandwidth and low latency. Or it requires low bandwidth and low latency and you can sort of think of the combinations thereby. A lot of these are actually use cases that are right here right now. And they are either growing or the size of the bubble obviously represents the details. I know you cannot see this very clearly, but this is from 451 research and obviously, you know, the papers are out there. So with that said, and with narrowing down the focus on the markets. Let's make sure that, you know, if you missed my keynote yesterday, I want to go through a little bit detail on what the edges and define it for the audience. On the bottom is a set of terms that are defined properly for the edge. Lots of people called it distributed edge, far edge, thin edge, thick edge, near edge, etc. They are all relative terms. They're relative to where you are. And what we want to do is we want to define terms in an absolute manner. So LF edge at the Linux foundation hosts a project called state of the edge. It's a Wikipedia style definition of terminologies and terms. And we have come up with a white paper that you can download that describes each of these terms in very much detail. So let me explain the details of the edge. There are really at the macro level, only two types of edges. There's a user edge and a service provider edge. A user edge is really in your control, dedicated and operated by the user. And it could be any user in any vertical. A service provider edge is really shared and operated as a service. That's the main difference. It's separated by the last mile, which is the black bar in the middle. Not quite a hard cut, but a little bit sort of blending in. And then if you double click inside a user edge, you have three real scenarios of deployment. You have on the very left, an extremely constrained edge. And what that means is you don't have enough compute power. You don't have enough memory. You don't have enough footprints, right? These are microcontroller based devices. From there, you can get into embedded compute and things like that. And then those are edge devices. You move up a layer into the smart edge device, which is our smart device as we call it, which is really around gateways. And they are in the semi-secure area. And then you can move up into an enterprise or a larger enterprise where you have a stack of one or more servers, which are in a secure location, but they're all still under your control.   If you move to the right, you have the access edge. And this really comes in two shapes. One is below a base station, where you can actually under telco control or under a cloud shared infrastructure. You can have storage and compute one to about six servers. And then you can have smart central offices where you have a data center that is fully protected. Everything on the right hand side of that geographically is not an edge, because it's hundreds of kilometers away, hundreds of miles away sometimes, and it will not provide latency within five to 20 milliseconds. So really, this is the definition of the edge. I want to make sure I spend a little bit more time on that because it's important to understand that proximity, responsiveness, and mobility are the factors here. So in this session, I'm going to go into details of each of these projects and where they are located. There are about nine projects under LF edge. Each of them provide a specific value to our community and they're all growing projects inside LF edge. I showed this slide yesterday at the keynote, but it is important to understand that a Crano as kind of an integration project is really the end-to-end declarative configuration into what is called a blueprint. A blueprint could be a connected car or a cloud gaming or a micro Mac implementation. And so these are all the blueprints. There's about 20 blueprints already in the Crano and growing by the day. And I'll get into that. When you put all of the open source projects into perspective, edge is not isolated. We work on both sides with multiple consortiums and other open source projects, particularly relevant to the edge in terms of SDOs and consortiums are Etsy Mac. We have a liaison agreement with that at the Linux Foundation level, automotive edge computing consortium, a big partner of ours through to obviously. And then I see that defines the device specs and things like that. And then if you look at the other open source projects, we have the RAN or O-RAN on the left-hand side, projects like ONAP, which is the network automation project, or Kubernetes or some of the other projects that also form the basis of the overall open source step. If you overlay the use cases on top, they're mostly around anomaly detection, surveillance, et cetera. I'll get into each of these projects, so I don't need to spend too much time on to that. So let me give you a quick overview of the community before going deeper into each of these projects. The first thing I want to mention is the LFH community is extremely interested in creating the market, accelerating the adoption and increasing the collaboration. This is a very unique open source project umbrella. It does not value, I mean it does, but it is not important for people to save costs. This is not a cost thing. This is really a value thing. So we've seen almost 25% year-over-year member growth. I think we're over 90 members now globally. Our projects have constantly increased since the formation of the umbrella. We had two new projects, an IBM project called Open Horizon, an Intel project called Secure Device on Boarding. They all got added recently, more coming. We've seen a lot of deployments, downloads, and just straight 160% growth in terms of developers year-over-year and commits. Clearly, one of the very important Linux Foundation initiatives. Now I forgot to mention that the goal of LFH is to unify the various edge communities, so whether it's the cloud edge, the enterprise edge, the IoT edge, or the telco edge from a market perspective. That way, we are not seeing different people implement different things. Obviously, there's a landscape, landscape.lfh.org. And in terms of premium members, you can see that we have a wide variety of cross-section entity being on the board as well. But more importantly, we have hardware vendors that cut across the silicon. So you've got Intel, Qualcomm, et cetera. You've got NRM. And then you have service providers on this. You have cable companies. You have IoT gears. You have this many graders, et cetera, et cetera. So very wide variety. And that's what's the beauty of bringing it all together. And obviously, the general members in terms of consortiums and others. OK. Any questions so far? Feel free to type them in the chat on the right. I will. Bear knocks and see if that's OK with everybody. All right. No questions so far. All right. So let me move to the projects themselves. The goal of all these projects is to abstract data application and domain knowledge from the underlying infrastructure. So what I call the plumbing layer. It's also to separate data from cloud services and allow for simplified API and apply principles as possible on all these projects. So this is simple, high-level principles that all of our projects work on. And as I said, the Kranos project. So these are stage three projects. So these are really mature projects deployed, larger community. And so Kranos gives you a set of new prints that allow for use cases and deployments. And I'll talk about that. And then EdgeX is an IoT framework. If I move to stage two projects, you have EVE project EVE, which is an open abstraction engine or an edge virtualization engine that can be put on-prem at the equipment with a very secure onboarding facility. And then Fledge is really an IIoT framework that allows for very constrained deployments to have life cycle management. And the equivalent of that is obviously the home edge. That is really an interoperable edge for all your unique needs at the home. And then State of the Edge obviously brings you the definition, the analysts, the research reports, et cetera. The incubating projects are at large, as we call it, Betel, Open Horizons, SDO, or Secured Device Onboarding. Betel is a cloud edge type of a project where it's kind of more facing towards the applications on the cloud that are running. Open Horizon is really managing the containerization and the deployments and the scale at which these edge deployments happen. And then obviously SDO is like the zero touch for the onboarding, right? Lots of different projects, but they have one simple goal, which is to unify frameworks and life cycle management of edge deployments. Okay? With that said, let me go into the impact projects. So the first one is a Crano. A Crano is really looking at two main things. One is looking at the telecom use cases. And they could be anywhere from a radio base station to a smart central office. They could be VNF, CNFs. They could be any of those applications, et cetera. Radio resource controller, RIC, et cetera. And the second thing they are looking at in terms of a Crano is a blueprint or end-to-end integration. So a Crano does the system integration for the entire LF edge umbrella. It includes blueprints, which are really declarative configurations based on use cases. It could be cloud gaming. It could be connected vehicle. It could be AIML applications. It could be integrated factory. It could be with open stack. It could be without open stack. Kubernetes based. Arm based. Very specific use case implementation, hardware software, and pulling the entire ecosystem. They're here for download. Go to a Crano, Vicky, and download this. You can actually use it. Some of the more interesting use cases like connected cars are being used in a lot of places in China. Plus, there's a lot of new use cases coming up in the Crano, which are focusing on the public cloud and telco interfaces. And these are very important because you don't want to reinvent every time you hit a new cloud. And as we said, some of the blueprints all the way from, say, a provider edge to, you know, if I look at the connected vehicle on the service provider side. So there are blueprints that are on the service provider edge side. And then there's blueprints that you can download as an enterprise if you are inside the gateway or inside a micro Mac kind of a facility. If I go to the second project called edgex foundry, it is really a framework for IoT lifecycle management. It's a loose collection of microservices.  And I know you can't see the diagram completely on the right, but, you know, what's on the bottom is really a whole bunch of IoT devices. And there's thousands and thousands of those. They come in different form factors, different hardening requirements, and different messaging requirements. There's 170 ways you can message, right? You know, from low power ran to Wi-Fi to Bluetooth to MQTT and there's like a whole bunch of messaging protocols like that. So what edgex foundry does is it abstracts all that and allows lifecycle management to simplify and do it once, right? So that's, so it runs, this runs on an IoT gateway and on the northbound it's really connectivity to the cloud or the next hop-up. The three main target use cases or verticals are manufacturing, retail, and building automation. And there have been deployments on this. There have been distributions by a lot of companies that use edgex and it is kind of growing significantly. There's 6 million downloads as a first half and they just keep on growing. And we just really want to make sure that people who want hardware agnostic, OS-independent, protocol-independent frameworks that for, you know, cloud and enterprise endpoints, this is the place to go, okay? Let me move on to some of the Stage 2 projects. So let's look at Eve. If you look at the diagram here, now we are moving into sort of the on-prem side of the edge.  In here, if you want a consolidation of say a container and a VM-based workload that's running on a on-premise that virtualizes your frameworks, right? And connectivity in a very secure manner because remember the parameter is not there, right? You could lose connectivity. Security is not there. So this is a very secured way to get an IoT device and it's connectivity virtualized and you have, you know, VMs, you have Docker containers, you have all of the security built in with, you know, root trust and things like that. So keep in mind this is, you know, going through a lot of trials and implementation, several support from a lot of ecosystem players, companies like, you know, there are behind it and it's an open orchestration API. So, you know, if you're not looked at it, it's a very cool project. Then if you move to Fledge, this is one of the other stage 2 projects where then I showed you the diagram in an extremely constrained environment like an IIoT, right? So these are sensors, turbines, transformers, you know, things like that where you need a framework and you need to connect IIoT gear up and all, including, you need to perform predictive maintenance, right? So you need some sort of an AI engine inside. So the Fledge is connected to Google's TensorFlow integrated with the APIs and plugins. And it's pretty cool on how a lot of these IIoT management systems are using Fledge. And if you look at it, it's kind of a similar framework for the energy side of things as well, right? Where you have, you know, transformers and turbines and things like that. So that's kind of where this project fits in. Home edge is the equivalent of Eve, but it's inside the home. So today you have the classic device problem at home with their own APIs.  But more and more you have, you know, security challenges. More and more you have surveillance. You have a lot of other devices that you better off with a single framework, right? Rather than, you know, your thermostat and your fridge and your TV and your video cameras, they are all using different APIs, different connectivity, different predictive maintenance things. So what this project is doing is it is basically called service offloading in a home environment where all the devices may not have the, either the connectivity or the power or the capabilities to do processing. And you can actually do it all in the central place inside your home under your control. And you can provide a lot of more features that makes your house even better. And then you get to the state of the edge. And this is a very cool project. So it's a Wikipedia style project, which is defining it. It's non-code. It does the landscape. It does publish research reports like the one you're seeing on the right. The next one is due later this year, early next year, where definitions, terminologies, terms, right, that are confusing are being utilized. And it just provides a framework for people to sort of talk about clarifying this market. Then Beetle is an interesting cloud project where there is application processing and application integration with the infrastructure using AIML and a lot of zero touch. So how does that work and where does that go? This project is in the early stage, but we are starting to see good traction early next year on using Beetle. They are supported by some of the largest public cloud players in China, obviously. And then Open Horizon is a new project that just got inducted into LF Edge. It's really to manage the machine learning models and the containerized workloads that are sitting in the constrained environment with a large proliferation of devices. And so this project was seeded by IBM and then we're starting to see a lot of good use cases on that, so if you're not read it, please do. And then finally, secure device onboarding. This is really accelerating the build-to-plan model. So ODMs can build IoT devices very, very quickly, onboard them faster. And this was kind of an Intel ARM joint project and now we have it at LF Edge. So with that said, I want to sort of close by saying LF Edge is a place where we are unifying the open source edge communities across IoT, Enterprise and Cloud and Telecom so that not everybody sort of views Edge as different pieces, but it's a cohesive puzzle. And our driving principles in LF Edge is we will keep it open. We'll keep it interoperable. It'll be hardware, silicon, cloud, OS and protocol independent where we can bring the best of Telecom, cloud and Enterprise, which is really location, latency and mobility all in one place in collaboration with the SDOs. And of course it's an umbrella similar to CNCF or LFN where a lot more projects are hosted. So with that, I would like to open it up for questions. I believe the question and answer button is on the right. Give it a second or so. Yeah, I know I don't speak Japanese and it's tough to answer or ask questions, but feel free to do that if not. Thank you very much. I had spent, I'd reserved a lot of time for questions primarily because it was coming live. Any last minute? Okay, doesn't look like. All right, well, then I'll wrap up and stop this broadcast. If you need to get to me, it's ajoshipuraiatlenixfoundation.org. Feel free to send me a note or an email. And thank you for joining. Edge is again four times the market of cloud, so don't miss the revolution. Thank you.


I'm not sure what happened. I suspect an internal dropout, but then my OBS dropped out as well. Okay, let's jump back into development. We have this tool here, it's just an example of neural network training with the default parameters. This is what it does. I suspect the default parameters will have to be changed. And we'll leave it with the description at the bottom of the page. See how we go. Just loading the model, just loading the HTML and JavaScript. And just the Python. The Python currently is empty. There's nothing happening at the back. And so if we increase the training size and hit the train button again, we'll see if it's doing any better. No, the first hidden layer. Yeah, that three, let's put it at the level five train again. See how it goes. Yeah, not much better. Put the second hidden layer size to five. Train again. Yeah, now it starts, no. So it started starting converging. So noise level is fine as the noise level in the input. Now learning rate, actually restart this. While I'm playing this, I'll just read what GPT-4 got to say. Okay, we'll go over these comments, probably one, one by one. We have a good thing about GPT-4 is that you can do this. So we are changing parameters. I don't think the default parameters are very good. Can you suggest how to change the default parameters in the HTML? Now, every time we train, the training loss function is being added to the existing chart, which is a bit odd where I want the first prediction chart to be having the same way where waveforms are added to the chart or refresh the chart every time we train any suggestions? Welcome. Yep, it's making quite a few suggestions. We have another problem. We are currently not using CSS. Could you, well, because the one we have is not working correctly, can you regenerate or generate the CSS script? A couple of things, well, actually, two things. The CSS is okay. I just want to make sure that on the bigger screen, the controls should be on the side of the two charts. And on the smaller screen, they can be below the charts. A second thing, we want to default the values for the controls to be specified inside the HTML. So any changes that you are suggesting, please change the HTML with that. Yep, cannot suggesting to check the media. Some reason it's going for 768 pixels as a cutoff between large and small screen. It's like, okay, set the bottom quickly. Can we regenerate the whole CSS script? Also, when you use any colors, can you use the following template? So start the CSS script with these five colors defined. Yeah, that's probably helping adding more epochs. If can I get reduced? No. It's starting so hard, starting from around four. They're not really reducing. We changed the learning rate. Really know what that does. A number of epochs. No, there might be, it was a bit odd. They reduced the noise level. All right, that's work. Yeah, something's not right. When you change parameters, the noise level should have went away. It's not reducing, is it? All right, this is very, it's a bit odd. Yeah, let's sort out the CSS first. Have the port making this for us. It's not looking at the screen size. Let's control F5 this. Yeah, it's not very great, is it? Yeah, the idea for it was, the previous version was better. Let's go back for the first prompt. Could you give a description for this application? How it could be used? What the different controls are really struggling with that CSS? They meant to be displayed side by side, are they? But it doesn't fit. Can we fix the CSS? There's a problem that on the, can we make sure that on the larger screen, the charts, the two charts are displayed on the side of the controllers. And on the smaller charts, smaller screens, they, everything is displayed top to bottom. So, yep, let me read the description in the meantime. It's positive, it's also fixed the CSS and refresh it for, yeah, I'm checking the media now. The display size does, yeah, it seemed to be doing the right thing, but currently the problem with the CSS is that on a larger screen, the controllers don't seem to fit on the side of the charts. Can we fix this? Yep, it's giving them some space, but it meant to be going over there. It should maybe relate to the fixed width set in the media, so we can try using flex properties instead of allocating space dynamically. Okay, sounds good, sounds good, still generating. And we also need to change in the HTML. Can we go over each control and change the default value so that the prediction converges better? Yes, CSS is not happening at the mean max, a width for controls that we might need to change. We're actually changing the controls, changing where the position is on the screen a bit, which regenerated the parts of the HTML code. Okay, what's reading that code? I wish the text to speech was better in the sense that you could have more control over it. Otherwise, it's a pretty good voice, except hidden layer. Okay, so the hidden layer we suggest instead of 1 to 6, do 1 to 10 with a default value of 5. Try that straight away. Okay, let's quickly turn off the CSS for the moment, so we can actually see what's going on. Hidden layer 2 size at the robot, suggesting 0 to 10 with a default value of 3, which is kind of what we had at the noise level, because it's defaulting to zero noise. Well, actually, maybe it's not such a bad idea to zero noise for a second. Now learning rate. Learning rate is a bit odd. Instead of maximum 0.3, suggesting a maximum of 0.1, okay, I think this will make much difference with, is it code verging? Going down, so I could possibly add more air box, but I don't think it's going down sharply enough. Okay, air box. Instead of 100, suggesting a default of 200. I think we can leave with that. Nothing converges. That's not cool. A batch size, suggesting default of 10. Okay, let's try that. Don't understand it jumping around like this. Don't get it. What's the point? Why is it doing that? Activation function will have the same ones. And the default will be the first one, is it? Yeah, that remains the same. Optimizer. Optimize default. That remains the same. A loss function. Okay, that doesn't converge. Does it read that quickly again? No, actually, do something else. To a print screen quickly. Hey, without a prompt. See what it says? It's not why it's jumping like this. It's essentially going through 0.1 to 0.5. It keeps jumping around. Jumping around. That's not cool. I don't know why it does that. There must be something wrong. We don't need these comments. I don't know, do we need any comments now that we have LLMs explaining you the code? I don't think you need any comments. Well, nobody will be reading code anymore as well. What do you think of that? Should you be scared as a software developer? Probably yes. Just doing it while the bot is generating stuff. Yes, it's also doing better with 0.03 learning rate. It's not going any lower. Going down to 0.1 with 0.35. With some sort of table for the sessions. A question is how can I monitor performance of different sessions or between different sessions? No, sorry, between different trainings of the one session using the application. Yep, 0.45 getting down to 0.2. It's worse. 0.35 was better, about 0.29. Yeah, so there's this magic number. It's not cool. I don't think this is what a lot of people are trying to do. They're just manually fine-tuning stuff endlessly. So this is what you see with a lot of the Kaggle competitions where competitors are allowed to submit, I don't know, like 100 submissions per day. Just worried that it's jumping around like that as well. Can this image also wind this image? The training loss function is jumping between 0.5 to 0.165 and still generating. Generating, okay, so yeah, we'll need to... Yeah, I like the console logging. Need the run number. I have GitHub, Koopa are trying to fix this. Apparently, they don't have these variables. Okay, how do you do the hypothesis-driven experimentation? In this case, it should have been a simple task. Patch size of 20 doesn't do any better. Yeah, it's behaving a bit oddly. This one 0.25 does get to 0.1. Sorry, I have to remember to edit this out if I'm turning this into a video. I don't think this will be a good video, but we shall see later. Yeah, obviously, adding more epochs doesn't help in this case. How about increasing the training set size? I don't think that will do much, but we can try it. That's what we have this tool for. Yeah, it was performing better before. Yeah, this is a bit odd. So then Ctrl F5, 0 to 5, give me 0.1. That's not converging anymore. But yeah, something isn't right. So don't get it. Shouldn't be deterministic. Okay, if I go at learning rate and 0, if this is my learning rate, yeah, now I'm getting 0.14 to 0.23. I'll try to understand. Yeah, probably someone is shouting at me. Why for the same waveform? So the waveform doesn't have any noise in it. The noise is 0. Why am I getting different results? Yeah, the input, the training data is the same. So if the training data is the same and the noise level is 0, why am I getting different results every time? Doing 200 epochs is randomness in the process. Random initialization, data shuffling, stochastic gradient descent, hardware, hardware, what? Parallelism and concurrency. It's really weird. Yeah, right. We might have to continue this next time.  It will have some sort of table that will monitor its performance over time. We need some more changes to the code to be able to do that. And yes, do we need to change the model or something? Sure. We have all that stuff in the description of the HTML. Just actually have to turn it into HTML text. And we will continue from there. Hopefully we'll make this tool available for you soon on the website somewhere. Probably be, you know, the first at the top. And if you haven't checked the Barney Chaos yet, please go do so. There's a lot of interesting tools. You will be supporting the project this way by watching some advertisement. And hopefully you find something useful. And let me know how you go. Don't forget to leave comments with your feedback. I'll see you in a bit. Bye.


This study examined the potential reuse of thermoset glass fiber reinforced polymer, GFRP, waste and concrete mixtures. It found that increasing the proportion of GFRP waste in concrete mixtures decreased the strength of both normal strength concrete, NSE, and controlled low strength materials, CLSM, but that adding 5% GFRP waste to CLSM resulted in a 7% increase in strength compared to concrete without GFRP. Additionally, the addition of superplasticizer allowed for the production of CLSM with sufficient workability to meet engineering standards. Furthermore, the use of recycled GFRP waste as a replacement for traditional aggregates in CLSM was found to be a more sustainable alternative to the current CLSM mixtures used in the market. This article was authored by Yufeng Li, Iwei Su, Jinyuan Su, and others.


性格我們很快如宿班即在此他倆會買擯燈可以做 這個這個這個這個這是這是這是這這個這個它是那你下午休息哦因为我马至巴罢了别ます别谢谢虎唐人兴旺但包括?!"可差点 但是我还没ERO�我們會用手機轉移到中間你會經常聽到原來的聲音從而轉移到韓國和英文所以在中間的中間你會聽到我們在中間的聲音在中間的聲音你會聽到製作人的聲音從另一邊的房間所以她是能夠轉移到另一邊所以這是技術的部分不然我覺得我們可以開始所以因為我們是一個國際節目所以我會開始在英文再次祝福大家我很高興你來這裡會有很重要的討論在DUNA Part 5我的名字是Mate Gaspar我是一位學者在大學的大學還有一位學生在英文學院的DUNA Part 5所以這兩隻手機在我手上希望能夠讓我有很好的討論和我們的 guest在這裡我確定你們所見的兩位在DUNA Part 5最後一天有一位是寫的特別的 guest我會解釋我問她為什麼DUNA Part為何我問她為什麼DUNA Part為何DUNA Part為何DUNA Part為何DUNA Part為何DUNA Part為何DUNA Part為何DUNA Part為何DUNA Part為何DUNA Part為何DUNA Part為何DUNA Part為何DUNA Part為何DUNA Part為何DUNA Part為何DUNA Part為何DUNA Part為何DUNA Part為何DUNA Part為何DUNA Part給我們我不願意給你們機會去談論表演所以就開始跟藝術人跟創作人和表演的創作人我也想請你們作為一位這個討論的同學來反映在大部分的什麼時候我們嘗試做一個適合跟創作人來反映表演還有這一點我也想作為一位這個討論的關於表演在香港或是兩位不一樣不夠雖然我們有兩位創作人所以我們作為這個討論好好早安我叫康奈然我自己有點不完全但是比較有趣的歷史我開始我們叫我們叫我們叫我們叫我們叫我們叫我們叫我們叫我們叫我們叫我們叫我們叫我們叫我們叫我們叫我們叫我們叫我們叫我們叫我們叫我們叫我們叫我們叫我們叫我們叫我自己的工作和相似的問題我本來有幾個產品關於羅馬的問題讓我先解釋在英國語言如果你說Gypsy不一定是OK的但可能Grystoff會說其他話我寫了很多因為我寫了很少在香港的羅馬還有很久的推介但我也有很多介紹在羅馬社區我發現有些人也比較喜歡Gypsy因為他們覺得羅馬總是不外出一般另外當然的意思是如果只要你認為羅馬是人A Gypsy character is an exotic character is a comic character is a joyful, a gleeful musician for the most part, not only but for the most part.當然,他們最主要的演員是白色演員他們在中華白色人的意見中說話他們的意見中說話他們想要演繹那種智慧的演繹他們不必令他們 critical or in a bad way或是沒有意見的他們只是一個 funny exotic character你可以看到這在大部分的香港人的工作在香港人的大部分的香港人我不會確定你會知道我們沒有這個數字因為很好的原因不夠正確的問問你你不可以問你在香港人的數字所以那些數字沒有這個數字我們有幾百萬人在地區可能是7%至10%的人但是一個大大的社會我從我的個人體驗上我可以告訴你我從中年開始從中九年開始也许这是我们的最兴趣的一部分我认为只有两位企业企业企业企业企业在香港的企业企业继续地代表了中国文化 rather than the exotic character一位是余安os Monachy他作为最大的企业企业企业还有他作为企业企业企业有两对企业企业只能认为余安os Monachy fooling very no Boss历史这里上浴和然后陆建永裁判 comic剨是他倆吃了本 slackby theonroha try to say but we didn'tand then finally trained a crown witnessso to say this guy has to diehis dog and said yesI am the crown witnessand I test the fact thatthat they ate me up他們指出我們這次的聖誕禮,我們只收了一隻鵝鵝 from Christ when we crucified Him because that's why the two legs are crucified by the single nail because the other one was stolen by the Roma which is a board from them but you will be able to tell us more from the so-called Roma anthem.Anyway, it was a musical piece and then being up in here you might be familiar with the show Muck in some translation called Scum but anyway the original actual translation is Muck which is a very complex story and I won't tell you about it but it shows you a sort of the birth of the right wing extreme mason in Hungary but it's about two girls who get adoptedand one of the girls is a Roma girl and I can't I don't have the time to now tell you about the whole piece and I think this might sound strange taken out of the whole context of the piece but to me the highlights of that show wasor one highlight let's say many highlights but one highlight of the show was when the Roma girl who was adopted stole a purse from the communityand I found it extremely brave on the theatre maker side to show this kind of extreme cliché about the Roma that they steal in a piece which showed the whole issue in a very complex mannerso I think in a way I don't know if I'm making myself clear enough but in a way the fact that you don't refrain from that kind of criticism or that kind of color or that kind of anecdotal thingit shows you that your shows me at least that this particular theatre maker who both writes and shows is very free of prejudice but I don't know if any of you have seen it but it's a very complex playI can send it to you the text as well in German and in English. I have to translate it into German. Anyway, so for me I would argue that these two theatre makers were the most white theatre makers with the most complex attitude toward the Roma communitythe representation of the Roma on stage and then in 2000 although the lecture show was after that but that's hardly the point in 2008 and nine I don't know if you were if you are aware but there was a series of attacks against the Romathere was a completely random nine villages were attacked randomly nine houses and nine different villages where the attackers were simply put neonazis didn't know who lived there the only thing they knew was that it was Roma who lived there and in one case they were mistakenwhat they did was they threw a lot of cocktails on the rooftop and when the house was a place and the family started to flee they were waiting in ambush and they shot themand this went on and on and you know you read in the paper one day after the other yet again one other incident and yet another incident and to me this was shocking that the Hungarian theatre for the first few yearsnot many but still didn't react to this that they were you know the Hungarian theatre which historically is very social critical so before the wall came downHungarian theatre was very well known for social criticism for you know reading between lines so winking at the audience and understanding that actually staging whatever classic play is actually about todayand it's a critical view of our present society that suddenly in a free society where you were free to put whatever you wanted on stage for a few years there was no reaction to thisso we were playing our you know city and state theatres were performing their Chekhovs and Fidoz and Shakespeare's on a very high level many of thembut hello do you read the papers like what's going on anyway and so but then in 2010 there started a reaction to this thankfully and one famous show was called the Chipsi on the stage of the Katronijac theatre which is the most renowned most established stage of the theatre I would argue in Budapestand it was a very problematic show and I don't have the time to go into the details now but I'm happy to send you my article again but I still applaud the effort to deal with the issuesso what they did they took classic play which constituted Act 1 and then commissioned a new play about the attacks which constituted Act 2and I would like to emphasize one element that there was in the show there was a suspicion that the Roma family that the father of the family impregnated his daughterand the problem was that it was never made clear whether this was truewhich was you know one of the worst cliches that you can think ofand the independent theatre which is an actual theatre within the independent scene they call them the independent theatrethey are a Roma theatre mostlythey started a correspondence with the director of the Katronijac theatre who is also the director of the showGabor Matipublicizing him for thatand for not having a Roma expert or asking about a Roma expertwhether a Roma expert was involved in the showfootnote we have unfortunately very few Roma actors although the numbers are growingso many might first ask the questionhow come Roma characters are not played by Roma actors to begin withand you can correct me if I am wrongbut I am afraid that would be unrealisticbecause we don't have enough Roma actors by farbut I would like to point to the fact that now the Academy of Theatre and Filmis choosing just in these weeks is electing a new deancorrectivelyand there are two candidatesand I would just like to point to the fact that there was a debate on Tuesdayabout this issue or about the two applicationsand I ask them a question about quotaswhether they have anyI said beforeI know the word quota drives you up the wallso I realize thatbut ok maybe it's not the best ideabut anything that would sort of start a trend toward more womenbeing admitted to this institutionbecause the problem begins with not nearly enough applicantsand sadly enough both candidatessort of said no not reallyand one of the candidates said well actually we would have to start withsecondary school studentswhich is I agree that's why I want to startbut I just wanted to point out thatwhereas the independent theatrealso has a program of traininga rumour actorsvery successfulsomehow many of them would never apply to this institutionwhich is the only one of the twobut anyway the longestthe one with the longest tradition of actingactors training and directorsso anyway I don't want to waste more timethere areI was trying to focus on the whiterepresentation of citymostly city and state theatresbut independent theatres as wellof the Roma characters on stagevery briefly my own company Panodramabut up a show after the attackscalled word for word which was a verbatim showwe interviewedsurvivors, neighboursmany other peopleand it became sort of ashow in a waythe first of its kindwhich I think was not uninterestingbut obviously I'm very partialbut I would like to just mention one more thingand because the others will talk about the Romapeople making the Roma theatreso I don't think that's my placeI would like to point to Constanza Mákraswho I don't know if you're familiar with herI'm sure you areso she's an Argentinianchoreographer who works in Germany a lotand she made a showcalled open for everythingwith checksif I'm not mistakenchecks,Sovakian,Polish and HungarianRoma communitiesand what happened wasshe conducted interviewswith the Roma peopleobviously through translationand she involved some of those peopleon the stagealong with her dancersand I'm not saying there weren'tthere weren't some points of interestingexchange between thembut she took the libertyof rewriting the materialsfrom the interviewseven though she didn't understandthe language obviouslyand she also didn't understandwhat was being said on her stageso the owners of these storiesRoma people were forcedto tell their own storiesin different wordswhich I thought was very problematicand she camethis became open for everythingbecame a huge successit traveled all around Europeshe was outside of Europe as welland with a friend I saw itI saw an open rehearsal in Budapestand then I saw the final productin Zurichand the Swiss loved itand it seemed to offer youan explanation of how the Roma arethey can dance and they can singthey are jollyand there are these issuesand now I understandthat was the feeling we hadthe Swiss audienceand we were shockedand we were outragedbecause it was completely superficialit had nothing to do with the very essenceof what we were talking aboutso finally just to finish upI think we have to be extremely carefulwe white people when makingtheater about Roma issuesand I'm sure you can tell mehow I fucked upbut at any rateI have to be extra sensitiveand extra cautious about thisthere is a lot more I could tell youbut I have completely abused my timeso I'm happy to send you my articlethank you very much謝謝謝謝現在我會繼續因為這幾個問題I have a very simple questionI hope that the Romacan't be fasterand after thisI would like to askChristopheryou broughtthe tableit was very goodthe beginning questionbut from the bottom of my heartI would like to talkabout where at leastif there are not manythe first scene of the Romawe are doingI think we have totell youthat you talked aboutyour own thoughtsand if you are the samethen you will take usin your thoughtsand we will talk about itbut in this presentationI hear myselfand I don't knowhow can I talk about itthe question I would like to askat the beginningthis tea and whatit's in a very dramaticthe atmosphereit sounds very strongI thinkand then I will takethe questionthe wholethe wholeand it's verydiscoveryand it's verydiscoveryand it's verydiscoveryand it's verydiscoveryand I would like在你對於新加坡的文化,你能夠在其他地方,例如在新加坡,在新加坡,在新加坡,在新加坡,在新加坡,在新加坡,在新加坡,在新加坡,在新加坡,在新加坡,在新加坡,在新加坡,在新加坡,在新加坡,在新加坡,在新加坡,在新加坡,在新加坡,在新加坡,在新加坡,在新加坡,在新加坡,在新加坡,在新加坡,然後他就開始找到他很多年多的Sterotipia他就開始承認他有的想法他從他的心裡不知道他也不知道那種訓練不懂如果我對他講了如果他訓練了我對他講了我對他的想法不懂你根本就不懂我對他講了我對他講了我對他講了所以 once in Berlin I was invited to write a rap songand it was about hatred that am I allowed to be angry?Now this is a big topic for methat I should be having nice so that I would look whitethough I think my anger might be legalit's okay to feel anger about this and thatand once it happened that I will finish in like 3 minutesso I was going with the very same groupyou have seen to the countrysideand in the morning we went to the McDonald'sbefore getting on stage we were travellingand one of the kids asked for a shakethe shake fell offand everything was shaking all overso I turn overI turn back and I see that my fellowis given a bunch of napkins in his handsand he starts to wipe itand then I get so madhow dare youyou are giving him the napkinsI mean it's a fucking accidentwe have paid for itwe gonna pay for itit's your job to...you can't humiliate himand then it turned outmy friend was asking for the napkinfor himselfand then he made his choice to start to wipe upso he wasn't forced to do sobut still you know Iyou know I reacted very shittyand now you can see that I was too paranoidand I'm always wonderingcan you say that okay sirwe understand why you are so paranoidI mean it's a misunderstandingbut I understand why you felt like thatit's all cooland can you calm me downthere is a village in netherlandswhere there are all thesewhat do you...dimensiondimensionthe old people in dimensionbut they don't know about themselvesthey don't know that they are livingin a secret villagebecause there are all thepeople in the shopsthey are apollonursesand this makes me think ofhow great that isthat I think there is some problem with youbut I'm not saying that you should do thisor why don't you try thatI give you a chanceand you can try to live like mebut then I saywhat I can dowhat I can changehow I can approachI can't tell you what to dohow to bewhat to thinkI can change my own approachand I think this is thetheare you with meoh nono no noyou wellyes I'm trying tookI used to be a poker player as wellthey say that it's aknowledge basedme as it's a renciatelike a gameso you can't say thatwhy didn't you raisewhy didn't you throw your cards awayyou can't tell the dealerwhy didn't you put the six downon the riveryou just don't have the power to change thatI only can say why didn'tI raisewhy didn't I fold my cardsso the only thing youyou really have somethingthat you can changeis what you doyour own actsand how I approachso I can't tell the old peopleto remember your nameor remember that I am your sonor I can't tell my owndemand auntienot to be scared of beingwhat about meyes she's scared of the governmentit's amirebut why can't I justmake her feel happyby approaching to herinvadedI share youryou knowvaries and they mightfire at yourwhateverokI knew Istart speakingand then I justdon't know whatI startedwhere I feel ityesyesyes at the moment yesyes thank you forbeing hereand actually my fatheris a Hungarianso my motheris a real gypsyso I can alwaysfeel a bityes but careful with the HungarianRoma are Hungarian tooyes this is alwayswhat we teach to the kidsI mean noit's not something you teachI can offer them thingsthat you will have to be thinkingwho are gypsieswho are the Hungariansare you Hungarianbecause in yourID cardyou are Hungarian tooalso nowyou were using the word gypsyin Hungarianit isgypsygypsygypsygypsygypsygypsygypsygypsygypsygypsygypsygypsygypsygypsygypsygypsygypsygypsygypsygypsygypsygypsyRomaor somebodyhow would you call yourselfthat you should call yourselfbut it's okay to let you knowabout the fact thatAtsinganos comes from thisRoma is in the language gypsiesthey say that gypsies is somethingmisunderstanding because thetraveling Roma families weremasking them so they saidthat we are Egyptian kingswho were sent awayand now pleaselet us just wander aroundand that's how it becamethe Egyptian gypsiesbutyes maybe like thatit's in the Roma communityvery often the Roma saycall themselves Romaand call the white peopleHungariansbut that'sthey are entitledto do thatit's likeAmerican black peoplekidding aroundand saying hey nigger what's upthat doesn't give us the rightto call them niggersso it's like thatyesokkosanamany of you get pizzaand fast and most likemake itI haven't finished this oneand I promiseI won't say a sentencebut with the girlsthere were the twosisters of minein Berlin when I waswriting this rap songabout hatredand I saidmy muse is a whoresome say I shouldhank herand they were looking at mejust like thatwhat did you saythat wordI mean how can youand I waswhat I'm talkingsome otherstalk about my museand they saythat she's a whoreit's notbut it's aboutmy hatredand gypsythey would not listen meand I was so madand I'm the poetand I haveI'm not choosing a wordjust becauseI chose that wordon purposebut then I changedbecause if theyif it hurts themI just can't tell themthat I is stupid girlthen I had to changeand then I saidmy muse is unacceptablesome say I shouldhank herit's either angerconsuming meor I consume angerand then theywould listen to the rap songand then I learnedthatI will have tobe listening to it煉魚然後可以說MudeCut藝滟酥我找到這是圜桑這大說判明是這個這是臺語我們這欺拒這什麼也就是這感覺但這個這個我們也想利用自己的資源去提供我們這次的議題和盡情調查而且我想要和大家分享這次的議題我們應該會在這層面和這堆人之間和這群人之間在領導中留下的主動向我們說明自己的意思這件事完全跟我們說明我們這次的議題和我們在這層面我們沒有想過會聽到更多的詛咒所以現在我們想不斷地回到原本的歷史所以我們要先請教大家當這故事開始變得更好然後要發揮更好的歷史在那裡的時候我已經跟他們的建築物和設計師們相處在那裡的時候我已經跟他們的建築物和設計師們相處我在那裡的時候我已經跟他們的建築物和設計師們相處在那裡的時候我已經跟他們的建築物和設計師們相處所以我應該知道如何可以將這些建築物和設計師們進入藝術的卡通如何能做出這些工程你有沒有用到的體驗技術和使用技術在一方面,我曾經接觸過不同的角色,因為我最初是攝影剪輯師,我曾經在短片中發佈過這個問題當我準備了這個短片當我進入了這個問題我問了問題我看到了這個問題然後你開始學習然後你進入了這個問題然後你看到實際的實驗然後你開始學習然後你開始研究然後你開始學習然後你開始學習然後你開始學習然後你開始學習從最後之徵然後是從哪裡變 race我開始搞歷程後來就開始學習因為我懂得從哪裡我居上想燃起這個問題所以我做了一段短短的故事但我並沒有在過程中做過這件事因為我們創造了這件事我們一起表達了這件事所以我在訪問這些問題這些問題我並沒有在這件事中討論我們訪問這些問題我們訪問這些問題我們訪問這些問題我們訪問這些問題我們訪問這些問題所以這條路比我們更簡單因為我永遠沒有這個外面的眼睛我沒有必要做決定的事情因為我沒有必要給這些難題的問題答應但當你創造了一件事當你創造了一件事我認為當中有多少種風格但我認為當中有多少種風格但當中有多少種風格但當我相信這個原因當你現在回到這條路當你現在回到這條路當你現在回到這條路如果這個行動對於自己的身體這個行動對於自己的身體如果這個行動對於自己的身體或一邊有其他問題,會否有更大的要求?會否有更大的要求?會否有更大的要求?在一個方面,我不明白,我甚至不知道如果我能回到這個問題,或者在此跟大家分享我必須要說一下,Longer than it's important to involve people who can relate to this topicwho have experience of having what I have self confidence in here Here, I've made the instructions and the, and I was shooting the film我對這部電影的影響對我來說有很大的影響,但在我最愛的電影中,我曾經在我兒童的家庭中,回到我的小朋友,他曾經是我們最強烈的兒童,但是他非常好歷史,所以我今天也有這個想法,他卻不斷地答答我,他卻不斷地答我,我認為他的意義很重要,因為他在世上也有經驗,我也有談論過他的父親,當他看到的,他也有談論過他的父親,所以我們變成了一些大眾,之後的談論,我們在這個世界中,我認為我們在這個世界中,我們有在這個世界中,我們是在世界中的一種情緒我們相信我們的想法是可以的然後有一個人從我的觀點然後有一個不同的觀點然後讓我們改變我看著我的表演然後我必須意見那些人是沒有辦法的所以我們必須要向上衝因為我們從來沒有想過我想要把這個觀點帶進去因為我想這點在這裏我們每一個社藝的觀點每一個社藝的觀點都要感謝大家但是我真的也是我也是在這裏我和小朋友在這裏我們找到其實我們在這裏我們都很高興我們在這裏我們都很高興我們在這裏我們都很高興我們在這裏我們也很高興我們都很高興我們還可以因為我們都很高興但是我們在這裏我們都很高興我們都很高興我們都很高興我們從來沒有想過我們將可以認識楊詠欔的大陸感到我們可以認識那些人我們可以看過其他人我們可以看見市民的這一點的干預把市民的乳冷從民主地區的城區到民主地區的城區然後我們可以看見民主地區是個一個他想要藉這個事情用而為代表這是一個次數的選舉我們已經有棉花我們 我們已經有棉花所以這是一個意思要是我們決定我們可以選擇我們可以在這代表我們也是選擇這次的訪問是由我們來講的因為這次的訪問不只是訪問而是由我們來講的這次的訪問不只是訪問而是由我們來講的因為這次的訪問如果你可以看見在香港當中最多的一群人在其他地方我們想要這樣做當然你可以問這種問題所以我們想要這樣做因為你同樣的演員也有兩種不同的性格我們可以做什麼我們可以保持這個性格我們要保持這個性格所以我們要保持這個性格我們可以保持這個性格主要會肯定談論這個性格他在討論我們香港人太多,我們都很希望香港人這是我所展示的一種基礎所以我不認為這個禮拜應該存在在我們的生活中這是一個特別的禮拜這些我要用另一個演繹我對你們來說我對別人來說我對不用這句的我對夥伴自己來說我自然知道但是我都可以說各位想知道這個很有意思的動作這是我非常的喜歡的如果你和 제가想可能你為什麼只是夾夾夾夾夾夾因為當你不夠好的夾夾夾你不應該只夾夾因為沒有人知道我們是在愛術中說的我們是不侵犯的這也是一個非常重要的事因為我們有很多人在我們學校很喜歡我們很喜歡我們因為他們說不可以不可以在那裡瘋狂我們有些人瘋狂的事他們說我們要瘋狂因為我們有別人在那裡瘋狂我們是瘋狂我們是瘋狂我們�地洋空要瘋狂我們是瘋狂我們瘋狂我們瘋狂我們都不願意中國人我們未能在這處拥挽這就是利用我們瘋狂的理由現在我們要瘋狂我們都是瘋狂的那這是不是瘋狂在看待這是目前的成分因這個十字城我已經工作了十多年了,我已經工作了十多年,我已經工作了十多年,我已經做了幾部短片,我已經在這兩年期間做了幾部短片,在我的公司,我已經做了幾部短片,在我的公司,我是要好好地問,但是在這兩年期間,我們要說,我們沒必要說在這兩年期間我已經做了幾部短片,我已經做了幾部短片,我已經做了幾部短片,所以至於我們有幾個個人獲得了不同的經歷,我們一直都能透過也有不同的觀點,我們也有自己的經歷,例如我們也有自己的生活,我們也一起歇一樣的過程,像我們也曾經歇一樣的經歷,而且我們也曾經歇相關的過程,他給了我這道書他在這裏寫了一塊書這些肉依然太輕鬆了他的筋都用了它很容易讓人無所遮掩他就用了這些肉依然太輕鬆了在這裏會有應用的而且他還把這道書這就是我的對比我對這行動不利的,所以我認為,在這一次的進行,你得到的因素是,是做一個保留在手上的實際性的衝擊,而不是一個優先,而是一個優先的行動,因為在地面上,我認為,這不是一個優先,而是一個優先的行動,因為在這個地面上,我認為,沒有什麼不重要的負責人物,我認為,這不重要的負責人物,因為在這個地面上,我認為,而我心裡還有一段時間我心裡還有一段時間對 你們也知道我的意思是我心肺的驚喜是在我的心肺上我心裡有一個方法在我心裡的驚喜是在我的心肺上我心裡有一個方法我在這個程度上我心裡有一個方法這時候我會來過然後在我的心肺上這個社群並非代表行動,而是屬於一個很重要的政策。這個社群並非代表行動,而是屬於一個很重要的政策。這個社群必須對著提出的意見,如果你想清楚這個意見,這會令人變得更豪爽更豪爽這會令人變得更豪爽加上我比較喜歡我比較喜歡我比較喜歡我比較喜歡我比較喜歡我經常在很多時候提供這個問題給孩子們現在沒有真的他們的多數當他們看到他們的腦袋腦袋腦袋腦袋腦袋然後當然他們會死當然當然我也很多時候會想當然因為我可能會覺得我的腦袋真的真的我真的我也很想但我腦袋對不起其實我認為他們的小腸腿袋最有趣的是他們要在他們的腦袋腦袋腦袋他們要腦袋腦袋對,這次的訣竅已經成功了所以我們也要想清楚我們也要想清楚這不是一個自信的訣竅不是一個自信的訣竅是我們要找到的而是我們要想清楚我們也要想清楚我們也要想清楚我們也要想清楚我們也要想清楚我們也要想清楚我們也要想清楚我們也要想清楚我們也要想清楚我們也要想清楚我們也要想清楚我們也要想清楚謝謝謝謝我們只能順便找到這次的訣竅你們也要想清楚是不是還是我們要做你在嗎?哪裡會有朝鮮藍色?在哪裡會有國家呢?在西方和英國所有的國家都要在這裡對,這些國家還有很多英文也很好,而英文中的國語不算是雜語雜語兩個字雖然是雜語但是哪一個life是雜語我只是想說,對不起,但有一個很厲害的故事我不知道你是否 aware of the huge anti-Jewish sentiment in this country而且,這故事是非常深刻的Portian vests, which they, the state forbade to perform in the National Opera of Hungary因為他們沒有做這件事 with black performers當然,你可以說,在這個國家,他們很少在美國生活,所以讓我們有這個機會但是,他們做的是,完全白白的香港歌手他們自己是 African-American這個類似的對象,真的,這完全是對象,這個類似的對象會告訴我,我覺得這很重要,很高興,但在另一邊,我們也有這個機會,謝謝你


Thank you all for coming. I'm very excited to talk to you today. I would like to thank Marty and Frances for getting such a big room. It's amazing, so please give them a big round of applause too. So I'm Gilda. I talked here last year and I'm coming back this year. So I work at Le Boncoin in Paris and today I'm going to talk about AI Search with Go and TensorFlow. So, spoiler, AI is really not about intelligence at all. It's more about magic tricks, doing things that you wouldn't expect a computer to do. But nonetheless, it can do a lot of different things. It can make your phone call for you. It can beat multiple pro-gamers at StarCraft at the same time. It can make up, invent some new, some false celebrities, or it can swap faces in a very realistic manner. So today I will show you how you could use this kind of state-of-the-art model into your Go application. All right. So the plan for today, first of all, we'll review a bit some of the basics of AI and deep learning and machine learning, and we'll see how TensorFlow and Go work together. Then we'll see a first concrete example with image classification, and then we'll see how facial recognition can work too. And then we'll see how we can wrap this up to make an image search. And then this will be the conclusion. So AI and TensorFlow. So it's a very good time for us developers regarding AI, because all the big players right now have a huge focus on AI. They are all competing to get as much traction as possible into the AI product, and what the result is is a lot of different frameworks that we can use and that we can do very cool thing with. So Google released TensorFlow, which can be used with Keras, Facebook as PyTorch, Microsoft as the cognitive toolkit, and Amazon developed MXNet, which is used by other companies too. And you can also find very easily some models online. So the same Google, Facebook, Microsoft are giving away a lot of models that are ready to use for you. So let's see what are the basics of AI. So it all starts with one of this little buddy. So this guy is a cell and is getting some float as an input and releasing back a different float as an output. So the function usually looks like something like that, but we don't really need to get into the details. So that's the shape of the Sigma function most of the time. But what is important is that these guys can combine with some other ones and start to make interesting things. So after a while, you can have some nice things happening. For example, from a non-obvious picture, you can guess the breed of the dog. Maybe you can fail in some other example. It can turn a recent float into Nicolas Cage, or it can protect you from a non-safer work representation. So I want just to clarify a few terms that will certainly pop up later. So architecture is the shape of a network. So that's a very important factor in a network. And the next one, the one I will use the most is the model. So a model is basically an architecture with all the weights and bias defined. So here we see the function we've seen just before. And the pre-trained model is the kind of model that you can get from Facebook or Google that already do a function well. And then a saved model is a format to export this model and to share it with some other people. So now TensorFlow is a framework for creating, training, predicting, exporting, and importing neural networks. So it's a C++ core. Most of the time for the training part, it's used using its Python binding to its C API. And so that's using Python, you will do the, you will create the network, you will train it, and then you can export it to a saved model. And the part that interests us is the, on the other side, once the model is trained, you can import it into Go, and you can run the prediction using the Go API. So in that talk, we'll really just focus on the Go part. So now let's see how the code looks like. So here is all the code you'll need. This is all the specific TensorFlow code. So it's split in two, in two different parts.  And then the second part is actually running the session, giving the feeds, and getting the fetches at the other part. So we can split this into three parts. First one is getting the model, getting the model and loading it into TensorFlow. The second part is building an input, and then filling it into the input of the network. And then the last part is to fetch the result and to interpret it. So let's see a concrete example with image classification. So image classification... Well, image classification is basically taking an image and extracting some labels. So there was a beautiful cat, Siamese cat, there, with some scores about the fact that it's a Siamese cat. All right. So one of the most common database for this is ImageNet. Well, some of them are loaded, but not all of them. Well, just two seconds now. I'll move on to my mobile thing then. All right. Is it better? No? Not really? Yeah, yeah. Okay. Well, anyway, so basically you have an image of a cat at the input, and then you get some labels. So we'll need to add three more steps before actually running it into Go. The first one is to find the model. The next is to run it into Python and then saving the model. And then the two other ones we know about. Yeah, it's better. All right. So to find a model, there is a website I like a lot. It's called Model Depot. There's not too many models there, but they are very well documented. Another good resource for this is the one from Google, Facebook, and Microsoft. Most of these models are on GitHub, too, so you'll find a lot of interesting things. And please don't be afraid to look at some research, too. They often come with some pre-trained models. So I chose that one, which is a quite simple one and a quite light one. So now I have a model, I can download it from it. It's actually MIT licensed, so it's nice. So we'll run it into Python. First of all, we need a few imports. So this is based on Keras, so it's mostly Keras imports. And now here is the Python code. So it's quite simple, a bit like in the Go code. The first part is loading the model, then we format the input, and then we render prediction. All right. So now we can run it into Python, and we can actually have the correct labels. So the next step is to save it into an export model. So we'll add a few, a couple of more imports. And basically the only thing you need to do is just to surround your code with a few more lines to connect your session to TensorFlow and then to export it into a saved model. So now we have a folder with everything we need to use our saved model. So now one step that is usually simple is to find the input and output layer names. So if we're here with using Keras, so we can just print them straight out of the model. It's one of the functions of the model. If you're not using Keras and if it's not documented, maybe you'll need to print all the operations and to just look at all the names and find which one looks the most promising one. Or the last solution is to debug the Python code. So in our case we're using the first option and our input model is input underscore one and the output is prediction softmax. Now we need to format the input image. So here we can see the shape of the input layer. So it's a 224 times 234 image with three channels as RGB. So there's just one more function we need to apply it to these channels. It's just a simple function. Actually it's just a mapping between a smaller float range. So this is what we have and we do it for every pixel. So this is how the go code looks for that. So it's only using the standard library, the standard image library, and then creating a tensor flow tensor. All right, so now we have our tensor that we can feed into our network. Now the last part is to interpret the results. So the shape of the result is a slice of a thousand floats and this actually corresponds to the thousand classes there is in ImageNet. So it's basically all the objects that we have and they are linked to a score. So usually what you'll do is to keep the 10 best results maybe. So perfect. Now we can get the output and we can find out it's the same as cat just the same way as we did with the Python code. So to wrap it up, so now you can give any image from any source you want from your go program and we can get the 10 best labels. So face recognition. I won't go into the code for face recognition but I just want to give you the basic of how it's working. So the first step is to detect the faces. So it takes a picture of any size as an input and it will give you back some some boxes and scores about the detections. So in the exact example we can extract five different faces. The next part is landmark extraction. So the shape of the input shape is also a square image of 112 pixel wide and the output are 68 landmark points and the maps to some peculiar landmarks into the face and you can use it to straighten the face to improve the performance greatly. And then the last part is the descriptor extraction. So again it takes an input an image as an input and the output is a 128 size slice of float which actually represents a coordinate in a 128 dimension space. And what is good about that is that you can apply some acridian distance with it. So the acridian distance is the most common distance and and so this define some distance between faces. So the smaller the distance between the faces the more likely it is that it's the same person. And yeah it's especially nice for us because it's a very lightweight representation. It's very fast so it's good for search. The models I've used are from Face API.js which is using TensorFlow.js. It's a very nice one because it's only using TensorFlow as a dependency and I really wanted to keep it only TensorFlow and not using OpenCV or Delib. So I had to do a bit of extra work on this one because the code was in JS so I had to translate all the code from JS to Python which was quite simple because it's almost the same function and then I was able to save the model and to load it into my Go application. So search is less than as an exercise to the reader but it's actually quite simple now. Now that we can label our photos it's quite easy to just return the matching photos when the user is querying with a keyword. And about the faces. Once you have some distance between some of the faces in your database when a new faces is found you can just calculate the distance with all the other faces and when you find a face that is close enough then you can tell it's the same people. All right so the conclusion. Yeah first of all I want to point you out to my repo where you can find all the models I've talked about. Yeah you can find it quite easily I think. What I want you to do is just to try it. I made a nice docker image that can run just to try out to see the performance of this algorithm. Or you can use it as a library. It's hopefully simple and ready to use but please feel free to contact me if it's not working or if you would like something different. And also I would like to encourage you to try new models. So as we've seen the TensorFlow and Keras models are very easy to integrate into your applications and yeah you should try. It's nice. Other models, other frameworks will most likely require some conversion which is still experimental and it will result in some significant extra work. And so yeah remember the five steps to use a new model. Finding the model. Finding the model is just searching on Google so that's the kind of thing that you're already doing. Running into Python usually it's not a problem because most of these models are well documented. Saving the model is just a few lines that's simple. Maybe most of the time you'll have some troubles formatting the input. If the input is an image it's almost always the same kind of formatting so it shouldn't be too much of a problem but if you start to play with sound or this kind of thing maybe you'll spend some time because there is not much go code doing that. And interpreting the result can be can be very simple for some time and a bit harder at some other points. But yeah the last result is always to just render the Python code step by step and to see how it's done in Python. So it's not impossible to do. You should definitely try it. All right thank you that was all I had to show you today. If you have some questions. Yeah we have some time for Q&A so if you have questions raise your hand and if you want to leave please do so silently thank you. Any questions? Okay so just round of applause thank you very much.


Moving on to the travel and leisure category, let's find out who the short list of nominees are. I think that again for sure, I think more and more Indians are becoming more and more open to travel, be inside, be in the country or abroad. And it was interesting to see how certain brands kind of raised the bar when it came to creativity. I think what made a difference was how an activity that was done on ground, how that was amplified through social and through digital media. And how the consumers were engaged beyond that activation standpoint. So there was something which was done on ground, but there was a way in which consumers could be connected across different mediums. In this particular category is what really raised the bar for us. And we have two bronze medal winners, no silver and no gold in this. Let's find out who the winners are. We also have another trophy to be given away in the bronze medal itself. Let's find out who that goes to.


The narcissist cannot hear you, they don't pay attention, they don't make an effort to hear what you're saying, they don't respond to your advice or request, they are very unattentive, they are unable to attend to your comfort or wishes, very inconsiderate, they thoughtlessly cause hurt or inconvenience to you, no matter how clearly you speak to them or how valid your opinions may be, it feels like they are incapable of hearing you, when you speak to the narcissist, it may seem like they are listening, it may seem like they are taking notice of what you are saying, they might even be looking right at you, but then they respond to you with something that has nothing to do with what you just said, it's like you're having two completely separate conversations, this is a real problem for people who are in relationships with narcissists, it leaves you without any opportunity to state your case, the narcissist cannot hear you, so you feel as though you're never being heard, you feel as though you don't even exist in a relationship, existence is the ability of an entity to interact with physical or mental reality, it is the state of being known or recognised and when you are left without an opportunity to state your case with the narcissist, when you're not being heard by them, you lose that ability to interact with the physical reality, you feel as though you are not known or recognised like you don't even exist, when the narcissist is unhappy, disappointed or worried, this will determine how much they are able to listen, if they are really upset, there will really be no point in talking to them because they will be completely unable to hear you, you may think that this is done consciously and intentionally and sometimes it is, but it's also because their emotions are so overpowering that it blocks or denies everything else, sometimes it may seem like they're trying to change the conversation or they're trying to ignore what you're saying and sometimes they will do this if you're not validating their false self or the illusion that they are trying to portray, but often they are not even hearing what you are saying, they are not even capable of listening to you because their emotions are so overwhelming and this leaves you unable to state your case, this leaves you unable to be heard, if you look closely into the narcissist's eyes you may notice that they're not even paying attention, you may notice that they're not even listening to what you're saying, it can often seem as though they are listening to someone else that no one else can hear and actually they are, they are listening to their own irrational inner critic or inner dialogue and the intense emotions that come along with it, they cannot hear what you are saying, they cannot listen to you because what you're saying is an important, what you're saying doesn't have any significance to them, what does have significance or importance is their feelings, they see their feelings as facts, they cannot view their feelings in a way that is not dependent on the mind, they cannot view the situation in a way that is not influenced by their personal feelings or opinions, they cannot identify that their feelings are temporary reactions that may not be logical or reasonable and that is why they see their feelings as factual, however they feel that then becomes the truth, rather than reacting emotionally to the facts, their emotions become the facts, an emotionally balanced person would not feel unhappy, disappointed or worried until something has happened or until something has been done to them but the narcissist already feels this way, that is why they believe that you must have done something to them, you must have done something to them to cause them to feel that way, this occurs because they have spent their entire lives dissociating from their feelings, they have spent their entire lives avoiding any self-reflection or denying how they really feel, this was something they used as a copin and defense mechanism rather than owning how they really feel, they chose to project their feelings onto someone else, so now they see their feelings as coming from other people, however they feel about themselves, that's how they think you feel about them, if they feel a hatred towards themselves they see it as though you hate them, they cannot identify that their feelings were already there, they cannot identify that their feelings have nothing to do with you or the situation and this is why they become triggered so easily, this is why they are so easily offended, because any little thing you say or do could potentially arise feelings or memories that are already there, it's not because of anything you have said or done, it's because what you're saying or doing is associated with a past traumatic experience of theirs, but then they blame you for making them feel that way, although that's how they already felt, it's just that they were denying those feelings, if you bring a fault or mistake of theirs to their attention, the shame that they already feel will then force through their denial and even though their feelings of shame have nothing to do with you, it may then trigger them to lash out at you, but they don't see it as though they're attacking you, they don't see it as abuse, they believe that it is justified, they believe that they are acting in self-defense, because in their minds you were trying to hurt them, you were trying to make them feel bad, this is all going on in their minds, it has nothing to do with reality, it has nothing to do with what you said, it's because of their shame, you might have meant no harm by saying what you said, maybe you thought that by bringing it to their attention you could be helping them, but once you have brought their deep rooted shame to the surface, there is no persuading them, there isn't going to be an understanding, they're only going to listen to what their feelings are telling them, they see their feelings as facts, they're not consumed with any evidence that conflicts with their feelings, they're not interested in anything based on or in accordance with reason or logic, the only thing that has any significance or importance to them is their feelings, no different to an emotionally immature toddler, they are very weak fragile people, they don't want to deal with the truth or reality of the situation, they would rather exist in the safety and security of their own little fantasy world, where they can use their imagination to use objects, actions or ideas to represent other objects, actions or ideas and assign roles to people when animal objects, they live in their own little bubble where people and things can be whatever they want them to be and anything that does not validate their fantasy is bad or beneath their consideration, anything you say or do could potentially pull them out of their fantasy world, it could potentially pop their little bubble of magic and make belief and that is when they become hurt or offended, however they feel dictates their reality rather than their reality enforcing how they feel and since all they ever feel is misery and pain, that's all they ever experience in their reality, it's an ongoing cycle that never ends and they can't figure out why, everything is interpreted through their feelings and your actions and motives will be reframed to suit however they feel about themselves, this is why they always assume that you're against them, they always assume that you want to do something wrong to them, they're very suspicious of people, they have the idea or impression that someone is questionable, dishonest or dangerous because they take things so personally and they believe that everyone is out to get them, this is why they will often accuse you of hating them or wanted to hurt them, they will accuse you of things that you didn't say or do, but you cannot argue with them, you cannot give them a cause, explanation or justification for an actual event because they do not think, understand or form judgments logically, their thoughts, understanding and judgments are formed by their emotions, they cannot hear what you're saying, all they care about is their own feelings, that's all that really matters to them, most of the time they don't even understand what they're saying, so how could they possibly understand what you're trying to say, when you're arguing with a narcissist, you're trying to talk them out of their feelings, when their feelings were not based on any logical reason in any way, it is not possible to talk a narcissist out of their feelings, I did even if you try, they will only feel as though you were trying to manipulate them, they're not going to believe you, they don't see you as being reliable or truthful, they cannot put themselves in a position where they could be tricked, they cannot put themselves in a position where they might be wrong, so they have to completely block out whatever you're saying, and at times, it can seem as though they're completely delusional, you could have all of the evidence in the world, but they will still twist it around and portray you as the bad person, evidence does not matter to them, logic and reasoning does not matter to them, all they really care about is their feelings, they are very weak fragile people, who would rather live in denial and to accept that they are wrong, and that is why they cannot hear you, that is why they cannot hear anything you say that invalidates their false self or the illusion that they are trying to portray, being in a relationship with a narcissist can be very confusing, you could often feel as though your opinions and ideas do not matter, you can often feel as though it doesn't matter what you think or how you feel, it's like the only thing that matters is the narcissist's way of seeing things, and this could be very invalidating to you, you feel as though you're not being heard, you feel as though you don't even exist in a relationship, because it's not really a relationship, a relationship is the way in which two people are connected, but they're not connected to you, they're not connected to your thoughts or feelings, your opinions or ideas, they don't listen to you, they don't care about what you want or how you feel, because they're self-absorbed and they lack empathy, you feel so alone and invalidated, because they're not capable of giving you the time and attention that you crave and need, and that is why I make these videos, I would like you to feel as though I am taking notice of you, I would like you to know that regardless of how the narcissist is treating you, you are interesting and important, I am here with you, I am fully focused on and involved in what you are experiencing, and I am here to listen, I am here to pay attention to you, because I care about what you are going through, and I care about what you have to say, I am very grateful to have you here with me, you are greatly appreciated, and I am so thankful that you are here with me, peace and love, thank you for watching, I hope this video resonates with you, please like, comment, share and subscribe, click the bell icon to receive notifications for my future videos, if you liked it tonight, my PayPal link is in the video description, coaching inquiries you can email me at naksforvacoachin.com, thank you for watching and I'll talk to you soon.


Hi guys, welcome back to my channel. Today's video is my week 36 update of my fitness journey. As you can see, we changed up the locations because I just honestly didn't feel like filming in my room and Sherry's actually behind me eating lunch. Hi. And I'm just gonna quickly film this. So like normal, I'm just gonna tell you what I did this week. And then we'll jump into the updates and then the photos. So starting off on Saturday, I did nothing. Then Sunday, I did day one of week 28 on Pamela's workout home workout plan. It's not the sweaty heart, it's not the beginner. So basically I'll put everything on the screen. I'm just gonna run down like verbally what I did. And then I'll put like a little montage with like the photos of the thumbnails and my working out. Then we'll get into the updates. So when I follow her plans, it's a lot easier because I just run down the days I go. So on Sunday, I did day one, which was the sweaty abs. And that was the hardest day. And then on Monday, I did day two, which was the upper body workout. I love her upper body workout plans. They're really challenging, especially the ones with the water bottles. And then Tuesday, I don't work out because it's my day working from home and I just don't really feel like working out. And then Wednesday, I do still work but I also fit in a workout plan in that day. So on Wednesday, I did day three. I did the booty workout. So it's booty, there was three videos dedicated to the booty. And then no workout on Thursday. I was gonna work out on Thursday but then I'm like, eh, screw it. So I just didn't end up doing Thursday. And today, Friday, I ended up doing day five of the plan that I'm talking about. And it's the take it easy day. And it was really good because I had a hair appointment, like a hair consultation this morning and I came home eight and I'm actually glad that I did the take it easy after I ate or else, I think my stomach would not feel very well. Okay, so before we get into the updates, I'm just going to play you the montage and show you guys the workout. So if you don't wanna see this part, you just skip over it. The first update I'm gonna say is the 30 minutes per day. Workout plan isn't challenging like some of the other workout plans she does, like the sweaty hard. This one was pretty chill this week and that's good because I'm kind of getting back in the sling of things. We got our fit life foods this week, which is really good because it helps like with routine and I don't know, it's just more organized. So I'm glad that we're getting back on fit life foods and working that back into the schedule and with my motivation, I think doing the 30 day wasn't that bad because I'm like, you know what? It's just half an hour. I watch videos that are half an hour long and I can do a workout for that short amount of time and it helped me just say, you know, I could do this and push through and everything like that. But overall, the workouts in this plan that I did were not very difficult, meaning I didn't sweat as much, but at least I was doing something productive and staying consistent within the plans. Also, I am about to start my lady days anytime now and I'm just like bloated and it's kind of like made me a little bit more tired. I also had this like twitch thing going on in my face, which is really weird, but it slowed down a little bit, but I think it's because like, you know, I strain probably that's what we think it is. I also, this doesn't have anything to do with working out or whatever, but I had a hair consultation like I said earlier and I'm really excited because I think I'm gonna do like a really pretty ombre with my hair and I think it's gonna look really, really good. So that's coming next month. It's probably like, yeah, I think it's about a month away that I am planning on getting my hair done. I'm so excited. Oh, I also want to talk about gym shark. I know the whole controversy with that, but I'm still going to wear my gym shark because I spent a good amount of money on it and I think it flatters my body, but I'm definitely not probably gonna get anything else from them. I mean, I wasn't even planning on getting anything else from them. In the near future, I just wanted to try that specific workout set. So I have kept up with the news on that and I think that's pretty much it for the little updates. Let's move on to the photos. I love doing these videos because it helps keep me accountable too for working out even when I'm a little unmotivated. So moving on to the pictures. Alex last week is on the left and Alex this week is on the right. I'm surprised because I thought these photos were gonna be worse and they actually kind of turned out, which is surprising. And then here is the side view of that. Then going on to pre-work Alex versus now. So pre-workout Alex is on the left and current Alex is on the right. And here is the side view of that. I hope you guys enjoyed this video. Make sure to give it a thumbs up. Question of the day. Sherry, what's your question of the day be? If you're gonna dress up for Halloween, what would you be? If you are dressing up for Halloween, celebrating Halloween, anything, what are you going to be this year? That is a very good question, Sherry. Don't forget to subscribe to my channel and I'll see you guys in my next video. Bye.