Well, good afternoon all. First of all, welcome to Intel's AI launch. My name is Bhushan Desam. I am the Senior Business Development Manager at Lenovo's data center group. My primary focus is in artificial intelligence business. So, I mean, there's a lot of excitement around artificial intelligence in recent years. But at the same time, there are several fears too, especially when it comes to automating everything that we do today. And also, we're losing control on some of the things that we're good at. But there are also a lot of social benefits that comes from artificial intelligence. And I'm going to make a case for that, the AI and computer vision for social good. So, if you take that picture, what do you see there? A man and a passenger van. It's very simple, isn't it? Quickly. So, what is the context here? So, a man talking on the phone is about to hit by a van while crossing the road. And what would you do? You run away to avoid the van, and you just want to make sure that there's no traffic on the other side. That's the common thing that you would do. Instantaneously, without thinking much. And what is e-power 91.2345 plus 25 power 2.2? This is a hard problem to solve for humans. So, until recent past, if you ask the same thing to computer, if you say, what do you see? Doesn't have a clue. What is the context? No clue. What would you advise? No clue. But if you ask the same math problem, it's saying, now we are talking. This one is easy. 4.1941 times 10 power 39. So, who are intelligent here? Humans are computers. The computers who could actually solve the hard math problems are the humans who could instantaneously analyze the situation, reason it, and take a quick decision. Who is intelligent? So, let's just, you know, quickly look at what is intelligence? So, if you look into a Webster dictionary of intelligence, it says the ability to learn or understand or to deal with newer trying situations, reason also the skill use of reason. So, that's what intelligence means. So, if you take this and apply between humans and computers, so, if you see humans are very good at sensing, you know, so when we look at the picture, we could immediately sense there is a man and, you know, like there's a car. So, and then we learn. We learn from our experiences or we learn from others' experiences. You don't have to hit by a car to know that it's a dangerous situation.  So, it's basically humans possess and, you know, like exhibit intelligence. You know, that's what intelligence is. Whereas, until recent past, what the computers are good at is basically doing complex math operations, data storage and retrieval, you know. So, it can store terabytes to petaflops, you know, like petabytes of data and it can retrieve it in milliseconds. So, that's what the computers are good at. So, there is a clear division of flavor until now, you know. So, humans do some things, computers do some things, you know, the things that computers could do, we can't do, you know, the things we could do computers can't do. So, that's actually a very nice division of labor between us and computers. So, what are computers good at? You know, so the computers are good at following rules created by humans. So, if you take in traditional way the way we interact with computers, you actually write a computer program that has some logic and what the logic does is it codifies rules.  And when you give those rules through a program, the computer executes that program and it gives you, you know, whatever the desired output. But the real thing is what computers are good at is they're very good at following rules created by humans at incredible speeds, you know, like so that they can just execute, so you know, millions and billions of operations, you know, like in a second. So, that's what computers were good at, you know. So, that's just what we've been used to. But that's about to change. So, this is a very popular visual recognition challenge in the computer vision community. It's called ImageNet. So, ImageNet is a large database of images. It has about 1.2 million images that belong to thousand different categories. So, the challenge is if you, if the computer actually can look at 100,000 samples and test what that image is, you know, be able to tell what that image is and at what accuracy, so that is a challenge. So, if you look at the error rate, the error rate in doing these 100,000 tests, if you historically track that, in 2010, the error rate from computers were about 28%. So, that is the error rate, you know. So, they were getting, you know, 72, you know, like right, but the 28 wrong. And then, you know, the error went down, it keep going down until 2014, but something happened in 2015. So, this is when Microsoft researchers could actually bring the error rate even below the human level, slightly below the human level. So, the human error rate is if I show you that thousand, you know, 100,000 pictures, you could get 95% right and 5% wrong. But this is the first time the computers did this at 4.9%. So, the slightly exceeded the human capability and in 2016, it went further down. So, basically, that in 2015, you see this whole thing turning around. So, there is the press went wild. Sorry, I'm another person who's blaming press, but they're just saying, hey, A is here, is going to take over the job, is going to change everything. So, that was the main theme of the story. But also, keep in mind, it's only doing one task. It's not doing everything, isn't it? So, all it is saying is, hey, this is a picture of a cat, this is a picture of a dog. And that too, in those thousand categories. So, if you find something that's outside the category, it says, I don't know what it is. Simply, I don't know. So, it's very, very narrow. You know, keep in mind that. You know, it's not solving every problem. It's just taking one data set of images and kind of trying to find what this image belongs to. So, is this artificial intelligence, really? So, let's look at some definitions of artificial intelligence now. So, we look at intelligence, but what is artificial intelligence, isn't it? So, artificial intelligence is basically the intelligence exhibited by machines or software. So, this is the Wikipedia definition, very general definition. And we already reviewed what intelligence is, you know? So, the ability to sense, learn, act, reason, you know, like adapt and all of them are considered intelligence. But another technical definition of artificial intelligence is the study and design of intelligent agents, where an intelligent agent is a system that perceives. You know, that's the key thing, you know? So, it actually perceives the environment. You know, it tries to look through, it perceives the environment and takes some action that maximizes its chance of success, you know? So, perceiving and acting, those are the two things. So, let's take this and try to apply to this ImageNet Challenge. Is this really artificial intelligence? There is actually, in fact, there are two kinds of artificial intelligence. One is narrow artificial intelligence. The other is general artificial intelligence. There is a big gap between these two. So, I think, you know, one had to realize that. So, the narrow AI is today is driven by industry, you know, like both large, you know, like large industries. And also, there's a lot of startups working on it. What it does is, it tries to address one specific, you know, like area, such as either it recognizes images, it recognizes voice, or it tries to, you know, like, you know, translate the language and run business analytics. So, that's the kind of, you know, like typical use cases for the narrow AI. So, today it's already deployed by hyperscale companies like Facebook, Google, you know, like Microsoft. So, when you upload an image, you know, Facebook image to Facebook, I mean, an image to Facebook, it suggests some tagging. That's how it is doing.  And, but now it's actually finding very good, you know, applications in medical diagnosis, education, finance and manufacturing. So, I'm going to review some of those applications. And it's poised to create some good societal and economic benefits. So, that's the narrow AI scope is. That's what actually happening today, you know, like in overall. But there is this general AI. It's kind of mainly driven by academia and scientists, you know. So, what they want to do is they want to, they want computers or, you know, artificial intelligence to come to a level where it really exhibits the intelligent behavior that we exhibit. You know, taking multiple things together, like, you know, sensing, acting, learning, reasoning and all of them and how we react to a situation. We want the AI system to react to a situation. But this is where, you know, if you track the history of AI, it went to booms and busts, you know, like twice. Probably perhaps, you know, we are not there yet. And there's still, you know, like little progress and it needs decades and decades of research in this area. And, but it has important and complex implications to society. But we are not there at least, you know, couple of decades. But even if we get there, there are always, technology always poses several challenges, isn't it? So if you go back very, very early days, isn't it? So the steam engine actually revolutionized the way, you know, like the muscle power is replaced by, you know, like mechanical power. So that has huge implications in productivity in the society. But at the same time, the steam engine was actually powered by coal. Coal was actually putting carbon dioxide into atmosphere and it's believed to, you know, cause this global warming. But then, if you compare to, let's say, 20 years ago from, you know, coal power plant versus today, it's only putting 1% bad stuff out. So 99% is clean. That is again driven by policy. And the policy, you know, like, in fact, drove environmental technologies to clean the bad stuff that's coming from the coal power plant. So it's going to be an evolving field. There will be lots of technological progress. But at the same time, there's going to be policy changes. And they all have to come together really to deliver the right benefit. Either it's the general AI or the narrow AI. So let's come back to the ImageNet challenge, isn't it? So this is the first time computers beat us in recognizing images. But what made it possible? So there are three key factors in there. One is the computing power itself. Today's computers are very fast. There's tons of data that's available today compared to, you know, like five years ago or 10 years ago. And there are several algorithmic advances, too.  They came together to, you know, like make that kind of progress. So let's review each of them. First of all, I mean, we are coming from Lenovo, so we want to talk about computing power, isn't it? So we always say you need more. And if you really, what's showing on the right-hand side here is, on my right-hand side is, the top 500 supercomputers in the world. So the curve on the middle one here is actually showing how individual computers, you know, like the number one is actually growing at the capacity. And the one in the bottom one is the 500th computer. What is the power of the 500th computer? And the top one is, if you sum up all the computing power from top one to top 500th computer, if you sum up all of them, that's what the capacity is. But keep in mind that, keep in mind that this is a logarithmic scale. It's not a linear scale, isn't it? So do not take this advances lightly. The reason I'm saying is, if you take today's CPU, if you take just one Intel CPU, that one Intel CPU can almost deliver one teraflops per second. So it can do one tera, you know, like flops of work in one second. And interestingly, if you tracked back 25 years ago, if you take all top 500 supercomputers, I'm talking about supercomputers, not the, you know, like consumer computers, in the world, if you combine all of them together, that was only delivering one teraflop. And if you take the same one teraflop, 20 years ago, the fastest supercomputer on earth was delivering what your one CPU today is delivering, the astonishing progress in technology. And see where we are at today. We are today, the fastest supercomputer in the world is actually in China. So it's a national supercomputing center in Wuxi, China. That delivers about 100 teraflops of computing power, per second. And the US is actually building the next gen supercomputers called Summit and Aurora. So they are going to be in that kind of 100 teraflops per second of computing power. And the one here is actually the Marconi supercomputer that Lenovo and Intel built together for the Italian research institutions. It's about 11 teraflops per second, this machine. So this is in Bologna, Italy. And this is the world's number 12. You know, so if you look in the 500 list, it's the world's top, you know, 12th computer here. So if you can see, just to sum up the computing power, it's going up and up. But at the same time, the prices are also going down. You know, for the same dollar, you can actually afford more and more computing power. So the next one, the advance is the computational side or algorithmic side of it. So there are these deep neural networks. So the neural networks are actually inspired by human brain.   So this is not exactly, it doesn't act like a neuron, but, you know, it mimics a neuron. And when you feed something to the neural network, you know, so you take one, some image.  And then, you know, like it tries to put some features in there. And then, you know, it try to capture our face. And by actually creating these abstracted layers, it can actually keep a lot of information in there. So what happens typically in a, you know, like when you develop an application is you build a neural network and you keep training with the data. So the best example, you know, like everybody says in deep neural network is a cat, it's not identifying a cat, you know. How it came, nobody knows.  You don't know why, isn't it? So, but imagine you are feeding a million cat images here. So, when you keep actually throwing that image there, then it kind of creates an abstraction, saying, oh, this is how a cat looks like. This is exactly how we learn when we grow up.   So we kind of start creating an abstraction.   So the key part here is learning, and also learning by example. So that's the key part. So that's how the deep neural networks actually work. We talked about computing power. We talked about algorithmic advances. So the next one is the big data explosion. So there is a lot of data today. So typically when you talk about big data, there are three elements, the volume of the data, the variety of the data, and the velocity of the data. Look at these numbers, 44 trillion gigabytes of data by 2020. 3.31.25 million messages, 7,200 hours of video, hundreds of sensors in cars, 500 million tweets, 6 billion devices, these are like huge numbers. And that's the volume.  And also the velocity. When these hundred sensors are pumping up, it's pumping up very fast. And also look at, all of these numbers are in minutes, in hours, in days, you know, that's a huge amount of data coming. So the pace of the data, it's also very high. So the real, you know, like challenges, how do you actually make sense of this data? But at the same time, there's an opportunity because these deep neural networks, they get better with the data. So it's really a challenge, you know, like this one challenge is actually creating an opportunity. I mean, that's how nature is, isn't it? So, okay, so I briefly mentioned in the beginning that there are two common fears when we talk about AI, isn't it? One is AI replacing humans by automating tasks that we handle well today. And also, AI exceeding human capabilities and taking over the world.  So this is a very good report, you know, like if you guys are interested in that, you know, I highly recommend you to read this. You know, it's a very easy to read report. So this came out in October of 2016. This was published by a committee from National Science and Technology Council.  So it's a typical body that advises the president. So basically, what the report notes is that the best way to attack some of the jobs in the future is actually to pay humans with machines and situations where the human partner cannot compensate for weakness and the computer are vice versa. So let's see if we can actually work together, you know, like with the humans with artificially intelligence-powered computers to kind of, you know, like better the outcome. Would this work? So let me work to an example.  So this is a research done by some of the MIT scientists and also Harvard Medical School folks. And what basically is they're trying to address a challenge.  So they take these biopsies, you know, they do the imaging and then you want to see if there's a tumor or not. And when they did that with artificial intelligence to just classify whether there is a tumor or not is a classification problem. The computers were doing about 92.5% accurate. So that's the result from the computer. But when a pathologist actually looks at the same image, he was actually giving a 96.6% accuracy on that. And the next category is the localization accuracy. That means is you take the image, you need to identify in which part of the image is the tumor in. So you need to actually, you know, identify the tumor. So again, AI is doing about 70.5% whereas the pathologist is doing about 73.3%.  But even the most interesting part is when the pathologist was assisted by AI, the whole accuracy improved to 99.5%. That's a damn good accuracy. I mean, that's actually 85% reduction in human error rate. So how good is 96.6%? In some cases, it's not. You don't want to be diagnosed, you know, you could be that 3.5%, you know, like you may fall into there, you may be wrong diagnosed, wrongly treated. That has a lot of implications on human lives. But look at that accuracy, 99.5%. That's very good accuracy.  So that's the message. So it makes sense. So we talked about healthcare, how we can improve healthcare. So let's look at a social issue. I'm not sure this is the right place to talk about it, you know, because we are in a bar, okay? But this is coming from national issues on alcohol abuse and alcoholism. And what it is saying is underage drinking is a serious problem in the United States. It's actually not just in the United States, it's throughout the world.  I'm originally from India, you know, like I see this problem too. You know, it's not just one country's problem. And if you see the data that was actually shown by them, it's saying the eighth graders are consuming 9.7% of them are consuming alcohol at some point, 21.5% in 10th grade and 35.3% in 12th grade. So they don't drink frequently, but once they drink, they drink in excess and they have, you know, they pose serious health and safety risks. So this is a serious problem. So let's see if we can apply the artificial intelligence to identify or solve this problem. So I don't know, like if any of you have a chance to check out the demo that Lenovo is showing there, it's basically a demo to identify your age. So keep in mind that, you know, that's a work in progress. So we only fed 20,000 images to that. You know, so it's not complete demo yet, but we are just showing the early potential for that.  And basically you can use that to identify, you know, persons like that are under 21 years in an image or video. So that's one example of, you know, like, identifying that. So the second one is, Intel is actually demonstrating a demo right there. So what this does is it looks at a scene, it localizes things, and then immediately it identifies people, it identifies objects like bottles.  So you can easily use them to identify those kinds of users. And then there is a startup that's an MIT startup in Boston, it's called Netra. What Netra does is it uses computer vision to identify brands and pictures and videos. So when you show this picture, it's actually picking up that Ray-Ban and saying it's 100% accurate that's a Ray-Ban, you know, like so imagine that.  So that's how you can actually identify those brands. So imagine you can actually put all of these three together, you know, so age identification, container detection, brand recognition, and if you build a tool, this can actually help some of the enforcement, you know, like agencies, especially public places, you know, in large crowds and large public places to identify underage drinking. So just an example, you know, like how, you know, all of these technologies can be, you know, like put together to solve some of the problems. So going back now, you know, like we look at a healthcare, we look at, you know, how it can address a social challenge, but let's also look at how it can deliver the overall benefit to the society, you know, like at large. So this is where we need to review the history, you know. So if you review the history, and if you look at really track the GDP per capita, and it stayed almost the same, you know, like until, you know, like 17, you know, late 1700s, but then it just ticked off. It took the hearty stick, you know, the hectic stick pattern. So it is actually the steam engine that was invented in 1775 was actually credited for this industrial revolution that gave us like massive productivity and human prosperity by basically replacing the muscle power with energy from natural resources.  So that power actually gave a lot of ways to improve our lives and also it helped the economy. I'm not saying it doesn't have, you know, like bad implications. Again, at the time, there's a lot of people like who are doing human labor work and they were displaced, but then they kind of, you know, transformed and found work somewhere else. So a lot of muscle work now we actually move to knowledge work. And the next generation could be, you know, something else, knowledge work powered by AI, you know. So that could be a trend, you know, like so going into the future. So the key thing there is, it's actually the steam engine took the natural resources and actually delivered the value.  So that's the one that I was talking about. So you are creating lots and lots of data. This is a man-made resource. So is there a value in that? So if you see the data, you know, like and how you can actually extract value, you can put into four different buckets. So there are descriptive analytics so you can take all of the data or you can take a sub-sample of the data. You can see, hey, how did something happen? You know, what happened? You know, so you can always see it. This is what typically you do in monitoring, isn't it?  And then there is a diagnostic analytics where you take the data and you try to create a hypothesis saying, why did it happen? You know, so you're trying to come up with, you know, like ways to find out why it happened. But actually compared to that, there are other analytics that can even provide more value, but they are very difficult, you know, so it's very difficult to do that kind of, you know, analysis. Those two are predictive analytics and prescriptly analytics. So in predictive analytics, you want to predict, you know, ahead of the data. So you don't have the data, but you take what you have today, but you want to be able to predict what's going to happen tomorrow. And that's the predictive analytics. And the prescriptly analytics is, it should be actually, sorry, it should be a prescriptly analytics. What prescriptly analytics is, you want to actually change the outcome. So you want to do some kind of scenario analysis and see how can I force that scenario if I know what the outcome is?  So that's where there is a high value and that's where the high productivity is. And ironically, we're collecting a lot of data and 90% of the data is never analyzed because there are no good methods to do this kind of high value analytics in every scenario. So can AI get us there? You know, that's the fundamental question. So if you really see the two axes that I'm plotting is the volume velocity and the variety of the data. So there's a lot of data coming in. And you want to somehow create value from that. You know, so that's what I'm showing on the Y axis.  But that traditional programming is mainly driven by humans, you know. So this is where humans are adequate. So to some extent we are good, you know. So that's how we've been dealing with computers, isn't it?  And especially if you know what to program, if you can actually codify the logic into rules, this really works.  This is when then you move to a classical machine learning. You know, machine learning is a kind of, it's a broader umbrella. That's where the deep learning fits. But still in machine learning, it still needs some human input because you have to define the high level features and ask the machine to learn low level features in order to, you know, like make the estimation. So, but again, the classical machine learning, it doesn't scale very well. You know, at some point, you know, it also saturates. And then the deep learning that is based on the neural networks, the very deep neural network, that's when they can come and actually, you know, like help us with creating, you know, high value. So they can analyze video. They can analyze image. They can analyze text, you know, whatnot. And also, look at here, the human skills are not adequate. If I give you one petaflop of video, can we analyze it? You cannot.  And also, we cannot look at patterns.  So you cannot actually extract patterns there. So let AI handle this. So this is where the AI can help us in the digital age to get higher productivity, like how Steam Engine did this during the industrial, you know, revolution. So that digital age is really in need of AI to extract high value from data. So I just want to give you one quick example. You know, like how Lenovo is using machine learning, you know, to extract value.  This is bad. You know, I like this. I didn't like this. There are lots of images, you know, like videos and all like, and those kinds of data. But they're very difficult to analyze it. So this is where we use it, actually, machine learning to understand what customers are talking about our product, took that feedback, and actually build this yoga product, which is very successful. The Lenovo yoga brand is very successful. So that's what we created.  That's what the computers and the AI is doing and helping us with. So another thing is, AI is going to impact every industry, nearly every industry, you know. So I'm just highlighting some examples here. So the investment industry, the healthcare, you know, like as I gave you an example, new automation, self-driving cars, you know, like marketing, oil and gas, manufacturing, security or defense, media. So there are lots of, you know, lots of, you know, like industries that can get impacted by AI. And again, there's always opportunities to leverage the artificial intelligence in the right way in all of these. So I'm just, you know, like kind of, you know, switching the topic and say, how do you build an AI system? If you want to really build an AI system, isn't that so? You have to store a lot of data.  So you collect all of that data in a big data system. This is where you store, you prepare the data and you feed the data. And then this is where the neural network actually gets trained, you know. So this is where you need a lot of computing power. That's why I was actually talking about the importance of the computing power. So it's even common to see a lot of these super computing centers are now moving towards AI direction, they're pivoting towards AI because they know how to do this problem. You know, like this high performance computing kind of problem. Once you train a neural network, then you want to deploy the neural network.  So that's where you deploy this. And then, so for each of them, you need, like, you know, different kinds of, you know, like technologies. So for example, the big data systems are typically run by Intel Xeon processor and Intel is now planning to release this Xeon 5 which is ideal for machine learning. And also the Altera, the FPGAs and Intel Xeon for inference. So that's how you can actually build a system by taking these technologies that Intel is developing. And we do actually provide those systems to, you know, like AI customers or AI users. So our mission at Lenovo is to democratize AI. So how we are doing, we are actually taking this three prong strategy. So we are building an innovation center. The purpose of the innovation center is to demonstrate what AI could do, you know, like to you. You know, like, so this is where we want to take and, you know, like these image diagnosis kind of thing. You know, so we want to actually create some of those applications to demonstrate the value of AI. And we also want to create a development platform where our customers can immediately take it and immediately start building an application rather than worrying about what kind of hardware I need to bring, what kind of software I need to bring, and all other stuff. And then finally, the deployment. So this is where we are kind of planning to deliver scalable systems. Because in enterprise context, if you are to deal with, you know, catapflops of data and, you know, like you want an end-to-end solution, so this is where we want to deliver an end-to-end system. So that's what Lenovo is actually doing to democratize AI. But thank you all. Thank you for your attention. Appreciate your time. Any questions? So this is one I came across. But definitely, you know, there are other areas. One is, again, they're trying to, they're all around this image-based ones. Because if you see a lot of AI today, it's all around image-based AI. So they did this in healthcare. They're doing this in oil and gas. So in oil and gas, when they are doing this seismic processing, they collect a lot of data. You know, like, so they send these sonar signals and then they do the imaging, isn't it? So again, this is where there is somebody who is actually looking at these images and try to estimate the oil reserves there, what says AI is doing that. And there is, by looking at these kinds of examples, they're looking at, you know, like, can these be linked together? It's one. So the second one is, again, in healthcare, Mass General Hospitals is doing a lot of work.  Again, they are actually training all these radiology images with AI systems. And the whole idea is, before even a radiologist come and see that, the AI is able to predict something. And then it's going to have the radiologist. So the radiologist doesn't have to spend 15 minutes there. So he can actually spend only five minutes to verify the diagnosis. And also, some of the information that's, again, giving is actually helping, you know. So it's very similar to what, you know, like the other example that I quoted, but it's kind of, but they're trying to bring that into production, you know. So the example that I gave is more kind of research. You know, it's a research paper from MIT and Harvard, but actually what Mass General is doing is trying to put this into production to bring it to patient care. But oil and gas is looking at this. Perhaps, you know, there are other examples, but there's something I could come across. Other than that, what are you going to do? So imaging is one, but I see a lot of analytics, you know, so a lot of big data analytics, isn't it? So today, a lot of those analytics are based on statistical packages. So this is where, again, they're moving not directly to deep learning, but actually machine learning at least. So the next steps seem to be machine learning.  Like, so the three or four parameters the financial companies look to underwrite your loan is, you know, basically your salary, your age, you know, like, and all of them. But now imagine if you want to look at 20 variables to get better accuracy, okay? The statistical base, you know, package they cannot correlate among 20 variables. So this is where you got to do this predictive analytics using machine learning. So what machine learning does is, it now takes all these 20 variables and try to correlate, you know, like basically that's what it is doing and underneath. So that means you are putting fine granularity into your data. So enterprises are very interested in doing that kind of work, you know. How can I apply the machine learning as a next step taking from the traditional statistical techniques? And the second trend is what I'm seeing is, you have typically the structured data that was living in databases. And then now we have this image data. Actually what a lot of companies are doing is they're trying to bring them together. So they're aggregating it, you know. So for example, you as a customer, you know.  But again, you may be posting something in the social, you know, like media. The one example I gave you is like, there's no linkage between, you know, like the customer and the data. We're just looking at the data in there. But now I want to actually really understand your preferences. So I want to treat you as a segment of one, basically. I want to see, you know, what you want to buy. I want to know what your preferences are. But this is where it's actually trying to bring these two together, you know. It's not just looking at the image data by itself, you know. So those are some of the emerging trends. That's where enterprise are finding value. Yeah, of course. So the two areas that we see in higher education is in two different areas. One is the academy is trying to push the neural network research itself, you know. So they want to actually do better algorithms. So out of those three, you know, like if you talk about computing power data and algorithmic advances, they're making a lot of algorithmic advances. So that's one area. The second area is a lot of enterprises, they cannot adapt to some of the methods because they don't know, you know, how to do this. This is where, again, the academy, you know, like is taking and trying to kind of show some way of doing it, isn't it? So they want to show, hey, this is possible. And then the next step is, you know, like for the enterprises to pick it up and see, you know, like how they can use the application.  So that's what the academy is doing. Any questions? Okay. So, well, thank you very much, you know, for listening to my talk and appreciate the time.  Thank you.


Hey everyone welcome back to gamedevacademy and I just wanted to make a quick video to give you an update on what I've been doing with the channel, some changes I've been making in the background and how you can keep up with gamedevacademy going forward. So I have noticed that the algorithm hates me and it's my own fault I've been releasing videos on the main channel that I know they're not designed to get lots of views these are the short videos that I like to make but they don't do well they are tanking me so I need to make some changes so what I've done is I've created a new channel called gamedevacademy bite size where I'm going to put these shorter videos and it means that the longer videos the finished ones will go on to the main gamedevacademy channel the one that you're watching me on right now. So what that means is that if you want to keep up with the tutorials as I'm making them and I will still put them out step by step as I'm making them then you need to be subscribed to the gamedevacademy bite size channel and I will have that linked below and probably on screen so that's the first part of the update. The other part is that whenever I go live I lose subscribers so I'm going to stop doing that for the most part on this channel but I am going to create a new one I've created one actually it's called gamedevacademy live at the moment but I'm considering changing it to gamedevacademy after hours if you think that's a better name let me know and that's where I'm going to go live so I'll be doing things like prepping for tutorials on there just messing around I think I might recreate my house in Unreal Engine just for poops and giggles and that's still going to happen live and I'll be doing that far more regularly because it won't cost me subscribers to do that in theory and that's again good currently called gamedevacademy live the name of the channel might change but it sure will won't and that's linked below as well so if you want to keep up with the changes that are happening to gamedevacademy make sure you subscribe to the main channel which you probably are if you've seen this and then subscribe to gamedevacademy bite size and gamedevacademy live and that way we can continue this little relationship we've got going on there may also be some small changes to content I'm going to try out some new things as well just to keep the creative juices flowing you know how it is lockdown 18 months going a bit crazy the creativity just appears out so I'm going to try some new stuff so you will see how that goes but basically I needed to make this video because this was next on my list to make and it's just not been getting made so I've made it it's ready I'm going to just put it up I might even edit it because I'm a badass like that and then you'll know what to do and then I think my next video is going to be a day and night cycling on the in on meal engine five so stick around for that thanks for watching and I'll see you in the next one later


d see what teams you got. That's why he's the man. Rams and the Giants and that should be our 32 teams. It is.  All right, with that video, right? All right, let's do it. What's good, everybody? Chris from jassescasebrakes.com doing 2022 Panini Phoenix football four box break, PYT number 12. This is the last four boxes from a break, all-card ship, lots of parallels, some nice autos. Here we go. Our four boxes right here. Did one team random for it, 17 teams given away, so if you have that little slash TR next to your name, it means you won that spot, and that team random going all the way down to Patrick with the commanders, sort it off with Howard and the cards. Let's do it. Last four boxes from my case, and I think that's it for now. If you want to grab some personals, I know Teddy still has some, and I know he has the H2s as well. I don't remember if I saw any archetypes, any metropolises, metropolises, so we will see. If there's any left. Joe and Joe versus the volcano. Joe versus Joe versus Joe versus Joe versus the volcano. Lots of parallels, usually a patch auto per box, some nice stuff. And we'll look out for some donuts too. Little non-numbered, little rare case hit type parallel. World baseball classic game over USA, wins six for one. Our first inning home run to British superstar, Trace Thompson. The US cruise over Joe Rassock Park, the Joller Express. All right, here we go, box one. Good luck, everyone. Travis Kelsey Teal, that's to 150. Tealaw, little crack dice, that's to 50. First hit gonna be John Metsch of the Third, dual relic and rookie auto for the Houston Texans. That's gonna go to Howard Zeke, got that one straight up. Nice start, little RPA. John Metsch of the Third, RPA for Howard and the Texans, 100 to 149. The Tealaw go into the Jags and Evan, team random spot and the Kansas City Chiefs, Travis Kelsey, gonna Sean Robinson. Matt Corral, contours for the Panthers. Dalvin Cook Red for the Vikings. Here at Wilson, Pinkershaw Bateman for the Ravens. 74 out of 149, jumbo patch for Drake London, Atlanta Falcons, it's gonna go to Marquet. Got that one straight up. Not kidding what the jumbo patch is. Jumbo and Teal, but a nice rookie patch, Drake London. Rashad Bateman, Pink going to the Ravens and Simon from the team random, Dalvin Cook Red going to the Vikings and Ryan, team random. And the contours, Purple, Matt Corral, it's out of 175 Panthers, the Dalvin Cook out of 250, Rashad Bateman out of 199. All right, let's keep moving. Non-numbered Zeke Elliott, Purple Joe Burrow to the Bengals. Second auto, gonna be Jalen Petrae. One more for the Texans and Howard. Pink auto, 64 out of 99. Darien Mathis, silver. The Purple Joe Burrow contours going to Ryan from the team random. Kobe Myers, Pink for the Patriots. Got a lava, George Pickens, rookie for the Pittsburgh Steelers going to Howard. That's that one straight up. 143 out of 175. Nice Fireforge goal, a yellow, Randall Cunningham. 27 out of 75 for the Eagles Fly, Eagles Fly going to Roberts from the team. Kobe Myers, Pink. Patriots and Karen. Altman, Lefty, Lefty. Bombski, yes, sir. Box two. Belis Jones, year 25 of 25. Jumbo Patch going to the Chicago Bears and James from the team random. Let me get to it. Kyle Pitts, Yellow Hot Routes for the Falcons. Kyle Pitts out of 75, Falcons and Mark. 400 points, randomized at the very end to one person. I will not forget it this time. It's a South Gardner Silver, give that one a sleeve. A Tua Yellow out of 75 for the Dolphins. Red and a Kobe Dean. Excuse me, Red and a Kobe Dean for the Eagles. Five, four, Terry Bradshaw. Kobe Dean going to Robert out of 250. The Tua going to the Dolphins, to Eric out of 75. It's a straight up one. And there's the Silver South Gardner going to the Jets and Howard. Can you pick it, Rookie? And the George Pickens. Ooh, rising Rookie, six out of 10. Beautiful patch and Rookie Otto going to the Steelers and Howard. Six out of 10, very nice for the Steelers. Is that my coin? What's it going, man? Traylance, Orange Hot Rookie's Hot Wheels. Flamethrowers, got the Rising, got the Rookie. Traylance at a 99, going to the Niners and Joseph K from the Team Random. Bondale Robinson, pink to the Giants. Mike Evans Lava to the Buccaneers. Romeo Dobbs, Teal. That's to 150, going to the Packers. Packers going to Joseph K, Mike Evans and the Bucs, going to Joe Anderson. That's out of 175, by the way. Pink Wondale going to the Jima, another Team Random spot, going to Evan. Little one at 99. Tuva and the Orange at a 99. And the Randy Moss. Randy Moss Contour, this is the red, looks red to me. Out of 199, Vikings going to Ryan. Team Random spot, one more for Eric and the Dolphins. Tuva, out of 99. Third box, obviously Roman and Cody are going to be made at a 92. I think Knight one should probably be Charlotte and Rhea. My personal feelings aside for Charlotte, she is a star. I'm sure Logan and Cody, Logan and Seth will be a deceptively good match because unfortunately, Logan Paul is a good wrestler. Sammy and Kay almost likely versus the Usos from the tag titles, man, Monday. Another killer segment, yesterday another killer segment. Jay Lusso has just been on fire for the last couple months. It's going to be a good card, man. It's going to be a good show, two good shows. All right, box three, starting off, Breece Hall, RPA for the Jets going to Howard. Once again, J-E-T-S, Jets, Jets, Jets. Hey, thanks for stopping by, Charles. Thanks for the donuts too. You brought those donuts, yeah? Thanks, brother. It's very nice of you. C.B. Landpink for the Cowboys, out of 199. Trent McDuffie-Rookie for the Chiefs, out of 175. Greg Dulsich, out of 125 for the Broncos. Broncos going to Anthony. Chiefs, Sean Robinson. Cowboys, how about them boys going to Nate? So good. That was busy, busy, busy, man. That was crazy. Kyle Hamilton on the patch. Baltimore Ravens and Simon. 123 out of 149. Troy Anderson, 900 parallel there. Green Garrett Lusso on the contours, out of 150. One more for Howard. And the Jets. Troy Anderson numbered the Purple this time, out of Pierce Silver-Rookie. Matt Corral, out of 150 for the Panthers. Skye Moore, to 75. Got a Chris O'Lave, hot routes, green. New Orleans Saints going to Joseph K. That's 149 out of 150. Skye Moore out of 75 to the Chiefs and Sean. Matt Corral in the Panthers, Sean Duke. And the Falcons, Purple. Going to Mark K. That's like a 125. Elijah Moore, blue. Out of 35. And there was DC. Dalvin Cook, red. And the hot routes, Dalvin Schultz. One more, red. Going to Ryan with the Dalvin Cook. And Elijah Moore out of 35 going to Howard. All right, and then there was one last box of this case and last box at the moment. Like I said, for now, just personal, if you want to grab a Phoenix football, they're $2.79. Jaspys.com, Ted Drippin' until 11-ish. May stay a little later. May leave a little early. Who knows, but you want to keep them busy. Jaspys.com for all your personal needs. Last box, box four. Thanks again, y'all. Good luck. Quick and easy, just like that. Orange, Garrett Wilson on the rookie auto. Nice box for Howard and the Jets 17 out of 25. Little number match, too. Very nice. Your offensive rookie of the year, Garrett Wilson, very nice. Jalen Hertz, pink to $1.99. And a donut, yum, yum me. Deontay Johnson on the donut, non-numbered parallel, short print case hit type. Going to the Pittsburgh Steelers, going to Howard. Going to break for Howard. And a rookie QB, Desmond Ritter, Lava. Atlanta Falcons, going to Mark. 36 to 175. Patrick Mahomes on the flamethrower, got a 175. One more Troy Anderson on the teal this time for the Falcons. One more for Mark. One more for Sean R. Cracked Ice Jared Goff out of 50. Jalen Tolbert, nice rising rookie patch and rookie auto. Little bit of the pig skin there. Dallas Cowboys and Nate. Jalen Tolbert, 137 out of 149 on the RPA. Another Garrett Wilson, green hot routes, out of 150. I haven't really called the lines, named Matt. Gets to the Jared Goff, cracked ice. Charlie Kolar for the Ravens, red out of 250. Snoop Connor Pink out of 199. Jackson Hill, out of 150 for the Bangles.  And Flago. Very, very nice. And Flago, Josh Allen, Buffalo Bills. Another name that we have not really called. Going to CJ. Non-numbered, but another little short print case hit type, they're super sweet. Got a purple Rashad Penny for the Seahawks and a beige silver Joe Burrow contours to end it. Nice. Rashad Penny and the Seahawks, going to go to Mark. The Daxon Hill go into the Bangles and Ryan. Snoop Connor Pink go into Evan and the Jags. And the Charlie Kolar, red, going to the Ravens. All right, we'll do a recap and then our points. So the Enflago, Josh Allen, Jalen Tolbert RPA, Deontay Johnson on the donut. Garrett Wilson, Rookie Otto out of 25. Breez Hall, George Pickens out of 10 on the RPA. Some nice stuff all around, John. Thanks again. But we do got a little something to deal with here. 400 points being given away to one person. We'll switch screens. Grab this real quick, all of our friends. Put them into a fresh dice, a fresh list. We'll roll our dice two and four, six times. That's what we are going to randomize our list. Top name gets those 400 points. One, two, three, four, five, and six, six, the final time. Boom. Top name, Eric. Eric, we'll get those points. Goli fight, minor league Goli fight. Pretty sweet. All right, Eric, congrats. 400 points heading your way. Just like that. And we're all set. Thanks again. We'll come back with more breaks very, very soon, jassescasebreaks.com. Bye-bye. What's up, Chen Sutter? How's it going, man? Nice to see ya. Some little break, some nice stuff. Good, man. It's been a long day. You know, lots of people in here for this PSA event. But, you know, some fun stuff. Saw some cool cards coming for grading. Very successful day with the PSA event. Oh, hell yeah, man. Very nice.  A little stone-cold, a little Steve Wiser. Stone-cold has his brewery there. The Broken Skull Ranch Brewery. Of course I know. What kind of Southern California wrestling fan would I be if I didn't know about old stone-cold and his little beer? What? Old tequila? What?  Some whiskey. I'm sure he'd love to make a tequila. Hey, gave my guy Stone Cold Steve Austin some tequila to put his name on, you know? He needs it. He's, you know, I'm sure desperate for money. Not like he's one of the more popular, famous, biggest drawing wrestlers of all time. Damn. You know what, get him out of here. No more Stone Cold tequila. No tequila for Stone Cold. Fresh, fresh.  I think it's so crazy that he wrestled last year at Mania, and they asked him like, hey, do you want to wrestle again? And he was like, I don't think about it. Like, legitimately thinking about it. All right, nice. I got one order, Chad grabs the first two spots, and that next, clearly Don Russ. That's random team three. Okay, so thank you, sir. I think it's the only order I got. It's around 9.36.  Tequila's your number one. I don't really drink, but I think I like tequila if I drink, especially with the drinks you can put tequila in. You know, I'm under the impression that like, the things you put in your body should taste good. A lot of people don't do that. I think that's kind of crazy. He has BC. Of course they were. How many times do you think someone's tried to buy him a drink? Like a bunch, right? Like, oh, you are my hero.  Like, kid, if you buy me another drink, I'm gonna be dead. Tough. Tough life to live for Stone Cold Steve Austin. I'm going to update some stuff on the site. Hope to see some stuff on our break sheet for Joe tomorrow. Joey back in, 3 p.m. Pacific. Taking care of you. I'm most likely doing this case of moments best right here. Mac, whatever we're rockin' with. Prism is good. Phoenix, I can take off the board, actually. Nick puts in any more. I don't have to actually make it out there for something, you know? I really shouldn't. No, we're gonna have one more filler after it. We did two already. We'll do three. Most likely Joe will do three. Hopefully even four or two. Keep him busy tomorrow. We'd love to get this immaculate done for y'all. Two fillers right here. Gets you into what, a $230 spot, $50, way of a fraction of a price. Don't make me do the math in my head. I will not be able to do it. But some very, very fun stuff on the site right now. You can get that NT going for him, too. We can start making progress to it now, obviously. But unless someone comes in and grabs a lot of teams all at once, most likely not gonna be able to get it done. Which, hey, that's okay. It really is. Let me just get some stuff going for our guy Joe. And then most likely, once 11 is done, Joe can load up 12. I got 12 right behind me. He'll have 12 right behind him, too. Easy, easy. But I'll just check the site. I'm sure Nick and Joe will cook up some kind of concoction to get going here. Another looking a little barren at the moment. But plenty of new releases coming out this week, as well as, say, a look at the new releases. That's what I've heard. I think, you know, every mania, like when I used to pre-COVID, I would go over to a friend's house. We'd have a few people over, and they would always go grab a few weeks before. Because once it gets to that March 16th date, couple weeks before mania, they're gone. You can't get any more of that beer. So, at least that's how it used to be in the past. Let's look at the new release date, let's see.  But yeah, look at just this week alone, crazy week. 2022 Bowman Heritage, based on one of the last releases from that rookie class. 2223 Don Russ Elite basketball. Some more of that current rookie class, Paulo Chet, Prism Football, already on pre-orders on the site right now. And then we have SP Game Used Hockey. And the week after that, 22 Obsidian Football, 2223 Obsidian Origins Basketball, Gold Label Baseball, which is actually the last year they're doing this. They're actually discontinuing Gold Label for 2023 class. So, it can be your last chance to get it for a while. And then Prism Premier League Soccer. So, lots of fun stuff in the next couple of weeks. So, while we wait for some big releases, like Flawless at the end of the month, Joe and Nick will cook up some fun stuff for y'all. So, you treat him well, treat him. Guess so. Jeff puts it in order. Jeff grabs the Packers in Prism number three PY team. No, it's late, you know, I get it. It's Saturday night, people are vibing. Let me grab my label real quick. Let's take this first. Jackie Robinson's name is spelled on a road sign. As I said, that got this right. But, you know, I'm only gonna be here for 20 more minutes. So, unless someone sells out something incredibly quick, I'm probably gonna have to. For real, Saturday night, people feeling good. Hey, Alan, I don't wanna be stuck inside anymore with this weather, I wanna be out. How's it by you? It was like super foggy, kinda humid over here. Oh, jeez, not me this goes.  Oh, like all day yesterday. Foggy and humid over here, and it's gonna keep on running, keep on continuing to run tomorrow. Just ugh, ugh, gonna do this for the first time, grab a couple of these stickers, make sure no one touches our cases. For Joseph. Joseph Jaspecif. March 11th, snowing, by the way. 110 a couple days ago, cloudy on day. It looks like it may be a while until we get summer, man.  This is sweet, man, but I swear to God. 30 for the highest, 20 for the lowest. Well, yeah, it's the best, you know. I wouldn't mind moving temporarily, like give me a year or two out of state somewhere, but I wanna, I'd like to live here forever. You know, I'd like to be able to move to a place that's not a 200 square foot, like, studio with half a bathroom, but, you know, would be kinda nice. Not living in a studio for my entire life. And not this studio. I live in here. I got my PC here, I can stream here. Oh my God, that'd be great. I'd be cooking in here. I really can't get this sticker off, that's crazy. That's what I'm saying, man. I'm living like, like, bender from Futurama. I'm in a storage closet for fuck's sake, man. It's tough, I'm dying out here. Sing about me, I'm dying of thirst. This isn't possible. Nice. I love no-ho, like, but there's no way I could live there. It's just too expensive. What you're working for? Nah, I'm just curious. Somebody was asking if we had any. So this is the newest one, right? That's 17, 18. Oh, Jesus. Yeah, okay. What color was the newest one? It's like orange or something? Pink. Yeah, pink is purple.  I know we have a few, but. I knocked out the last few on Monday, I thought. On Monday? Yeah, someone take this the last, whatever. I'm sure Joe would like to do this. So I'm putting this Bowman case marked. Press is gonna be 11. And then this last one could be us. Could be the IG, who knows? Yeah, it's just, you know, Southern California, it's just like so hard to just find stuff.  Or this thing that actually has space and like amenities, but it's also like 2,800 for 500 square feet. What? Someone let me live with them. I'm gonna open up a Jaspie's branch out wherever you live. I think it could work. I don't think I could never live. Well, where it is, technically. Let me see the map. This is, this is getting to like Orange County area. You know, I don't know if I can be an Orange County kid. Is this, is this, is this technically? Oh yeah, this is Orange County. Once we get past Huntington, this is where I can say Orange County. I like Newport though. Some, I've, I've met some very interesting people from the Orange County as all I'll say. Oh, it's the Tanks a lot more. Boring. But I mean, ever since I was like a teenager, I've always wanted to live in Long Beach, but same thing, like Long Beach has become expensive because all the doofuses from LA decided, oh yeah, I can't pay to live in LA, so I'll move to Long Beach and they've ruined it. I lived across the bridge in San Pedro for most of my life. So like to me, Long Beach is always the much cooler version. It's about five minutes till, I'm just gonna call it 10 p.m. You know what, that's okay, had a fun day. Pretty busy, you know, we'll call it unless someone fills out something real quick, but I don't think that's gonna happen, but that's okay.  You too, man, take it easy. I see you, I think you're kicking it. Everyone in the chat, who's lurking. I think Rex might be listening in while he's grinding away, I respect that. I'll call it some of these. You too. You know, I hate to say it, but I'm usually kind of working. I know it all the while I say it, see it. Not only kidding a little bit. I'm just filling out some stuff until I hear something that kind of includes me. I usually just lurk. It's a hybrid. I am, I've lived here all my life. I don't want to think in this. This looks like Joe and Jason, thinking the hell out of this. I don't know how they do that. Yeah, I've lived here all my life. Born and raised in Englewood, actually. Englewood, California, shouts out. Damn, no kidding. Yeah, Englewood, Marina Del Rey for a year before my parents moved to Pedro. I lived there until I was like, 24? Now I live in the Mida. So all pretty close except for Englewood, but you know. School went to school in Torrance and Harbor City. I went to Harbor College, shouts out, goes Seahawks. Woo! But no, I've lived here my whole life. Shouts out, man. I don't think I've ever stopped in Bakersfield. I've driven past it plenty of times, but I've never stopped and just enjoyed a day in Bakersfield. The closest thing I almost did was, once upon a time, Carly Ray Jepsen was opening for Katy Perry in, on some tour, but they were in Burbank, they weren't in Burbank, in Bakersfield, they weren't in LA for some reason. So I almost went to that, but I'm really gonna pay like $150 to see Carly Ray for half an hour and then Katy Perry for like an hour. I'm not gonna do that. But one of these days, if I make a little day trip, come kick it there. That's what I'm saying. That's what I'm saying. I'm gonna roll this out into the back, and when I come back, I think I'm just gonna close up the stream for the night. But like Joe always says, and it's a good point, show stops, store, always open. First person I'm talking to. I think we got it. No, what do we have for 21? Morning. Morning. Morning. I'm gonna call it a night, folks. Thanks again. It was a very fun night. Joe will be taking care of you all like he does every Sunday. 3 p.m. Pacific. Watch some world baseball classic. Most likely the final round of the players championship. All right, Trent's there. Nice talking to you, man. Nice seeing you. Take it easy. Anyone else thurking in the chat? Let's see. Rex might still be in here. I think she's kicking it, Rex. I think she's coming in. Anyone lurking? Anyone putting in orders, whatever it may be? Thanks for hanging out with me tonight. It was a busy day outside, so thanks for keeping me company in my cage. I'll see y'all later. Ikewap, hey, have a good one, man. Take it easy. I'm sure I'll pop in before Mania. We'll talk shop then about the card before it finally gets to the night. We're pointing to the sign. WrestleMania sign pointing season. All right, y'all. Take it easy. Have a good one.


Everybody for us to your ripping 2022 top scrum sapphire baseball full case 10 box full case number nine Random teams. I'll show the results right here. Find these anytime a late in sports cards.com. There's a link in chat as well results quick link for the for the Results quick links bottom of the website So say results quickly is for the results, but I think that's a little redundant This is the department of redundancy department overheard Good luck everybody. Let's see that super. Let's see that super fracter. I think I've bought too many I think Saturday I had like I like seven envelopes in there Right number one. Yeah, I've seen it right number one. I see it always and everybody's like top five Yeah, can't be better than Fenway though As a non-biased Red Sox fan, I can't be better than Fenway. I've got Woodruff Brinson gold with the Cubs, Nico Horner to 50 Sub-hawk Y'all Rojas There you go, Hawk. Give you a shout out Miguel Cabrera, Cole Tucker, rookie Jeremy Pena for the Astros and a Pavan Smith Recap all color in this one. Austin Warren rookie. Herettia, Colesvari, Garrett Cole Yeah, totally the sound of a hawk too totally KC Mye's purple Classe Guardians. It's a 10, nice Good stuff for the Guardians Emmanuel Classe, Andrelton Simmons, Nico Goodrum Keeps saying I need to find some Goodrum Taiwan Walker, G-Man Choi, Ryan Felner, Kelnick, Mike Moustakis, NLE's Division Smiles We'll go 1-2-3 on the die roll for the Marlins 4-5-6 for the Mets on this card This will go Mets right side, Mets on that one Sennot and Wheeler, Nico Horner A's team card with the gold Nolan Aronado Cardinals Yeah, 150 Cardinals gold sapphire Nato Jake Rogers one of the best defensive infielders of all time Nolan Aronado. Oh Another parallel. Wow. You only get two per box on average. You got four in the first one Joey Wendell with an orange future stars Andrew Vaughn White Sox 25 nothing huge in the first box, but a lot of solid cards, Ryan O'Hernan Fadal Bruhan We'll take that. We'll take that He's got the gray goatee. He's not the kid anymore Kyle Hendricks, Kenta Mayeda. Oh, I know he's in the Hall of Fame. I'm just kidding The Hall of Pozo, Kevin Bizio got hit 600 homeruns, I guess Could have been the best ever ever ever could have been Padres, Zerpa, Elias Diaz and Harrison Bader was a Siri rookie, Piscotti Nandes and Matt Veerling No, that's not gonna happen Hawke. It's not gonna happen Jared Walsh, Clint Fraser, Yasmine E. Grandal and Peyton Henry, Patrick Sandoval Moving on Dodger's card and a gold Brett Gardner for the Yankees of 50 Austin Beto's a Brett Gardner in 2022 top scrim sapphire. That's uh, I don't know about that one Got Tristan McKenzie, Kwayto, Kendall Graven and Eddie Rosario Last stat, John Last stat, Cinder guard, Brett Phillips and Vlad Jr Now there was one in that one. Cousins, Royals, team card, Reevas and Gavin Sheets They've still got five in two boxes. That's good. One more question for the football coach How did they do that? I don't know too much about Northeastern over here I know they're in the big twooo, the big ten, I know the big ten That was the big twelve They have like 18 teams though, so How, keep the same name, big ten, 18 teams Idiots, Zack Short, Brandon Nimmo, Mike Miner and Barrero Hazing allegations. Oh, that doesn't sound very good It's like a bad thing. Eli Morgan from the freaking coach Tony Kemp, Sixthos Sanchez, Brandon Marsh The guy who's supposed to lead the young man They fired him because he's dead coach Yeah, I don't know. There's something more to it if he got fired because of it Dodgers, team card, he knew and probably didn't do anything as the issue I bet All right, so we've got um One, two, oh, this one's gonna go to the Astros overall for all the triples right now Unless there's a majority in any of these. This one's gonna go Astros regardless obviously Um, but it was one, two, three, four, five, six. They'll all go to the left One, two, three, four, five, six. I'll go to the left on the two there That's Astros obviously. Gold to 50. Charlie Morton for the Braves Finn Dunning Charlie Morton Got the Chanagher with an orange. Nathan Ovalde, Red Sox to 25 Having a fantastic year with the Rangers. Miles Straw, Trevor Larnak Anyway, Zach Efflin, Weathers, Liam Hendricks and Chas McCormick Have they announced, have the managers announced who's starting the All-Star game tomorrow?  Kyle Seeger, Caprillion, David Bodie and Everett Graham Like who is this person dressed like they got swag? I was like, oh It's Poppy. He's got, he's got swag Bailey Ober, another triple there Anandas and Kershaw Cole and, and Zallen That Gallant, Zallen sounds like he's at Zach's Bees or something Does it bother anybody that Zach's Bees calls their uh Salads, Zalads Tyler McGill, Gold Glaiber to 50 Yankees Calhoun and Para Seems fair to have Gallant and Cole start. Ooh, we got a red, we got a red There's a Teosca Hernandez, Hans Kraus, come on big red Shohei Otani and red David Fletcher, Angels Color Match That's a pretty solid one for sure, five of five Mark S with the Angels Eyes Fletcher Lizardo, Gardner, Logan Allen and Charlie Morton Manoa, Dish Lafany, Melanson and Barrios I wonder if this is live I recorded earlier because it's probably live but Poppy's at the Fox broadcast, I wonder if that's where he's going He's going to have a broadcast I'm going to roll the dice for this one, the How Far Cart Left is Blue Jays, right is Padres, one, two, three, four, five, six So three, going to go to the left, Blue Jays on this one, just Blue Jays on this card alone Yvonne Castillo, Otto Lopez, Brandon Belt Yvonne, do you think Kenley Jance is going to get the save tomorrow? Got Fraser, Torque, Crochet and Lopez Michael Chavis, Purple to 10, Carter Key Boom Nationals Kyle Gibson, Jed Lowry Kyle Muller Orange, Gene Cigarra at 25 for the Phillies Tommy Edman, Corey Seager Yeah, I won't give him any stolen bases Aziz Aguilar, Soroka, Reyes Card and Brantley Molina, Gunther, Ortega, Buxton So yeah, that Tuesday is going to be busy We're going to set up, we're going to get tattoos We're going to go to the White Sox game We're going to hit the town We're going to, I don't know, go to Milwaukee, I don't know dude I'm down Ernie Clements, Chris Archer Gold to 50, Kyle Hendricks Cubs I think Tuesday, Brad Miller Tuesday's like set up day Which shouldn't be incredibly difficult, honestly It's just a matter of like making sure we're ready to go for Wednesday Panada, but we'll have the daytime Wednesday too Michael Panada twins Allens team card A Cardinals team card in Willy Castro Right, it's like VIP preview is like 4 or 5 p.m. like 8 p.m. or somewhere around there Wednesday Juan Soto, Will Smith, Bruce Zimmerman and Taylor Weidner Louther, Patino, Orange, Trevor Story to 25 Red Sox Five France That's Rockies, I say Red Sox, Trevor Story So he's saying that Rockies, Trevor Story, Rockies, Rockies, Rockies I'm not sure what I said Ben and Tendee Royals Zach Collins, Adrienne Houser, Carlos Correa Herd Boss $50 for me? Got him What now? Oh man Cronoworth, Gold to 50, Ryan O'Hearn Royals Got the forest fund around here James O'Cann, Red's team card He's got the latent scholarship It's 50 bucks for me Okay, I wonder if he'll even pitch Guys, make sure you got less than an hour to get your predictions in For the Dinger Derby For our promo on Instagram and Facebook We're at 4 to go on select age 2 now Basketball's filling up quick A lot of football spots left too, everybody Optic football team all, select football team all There's a Optic Football Retail Full Case Divisions and Selecting Chronicles mixer as well I'm not too appreciative getting these breaks filled up so far Auto Blue Jays card, Aaron Hicks and Shane Bieber All right guys, if you had to pick your own home run derby of current players Who would it be? You can just give a few Redolfo Castro, Corbin, Gold Rookie to 50, Sammy Long Giants And Matt Brash You don't have to give eight of them You can just give a few Dude, Yorna would be sick Boz, Perez, Giants, Cardinal Elohimenez I would say Soto, but we already saw him win once I'm going to hold off on Soto But yeah Alberto Torres, liftoff for the Ashes and Pat Wisdom Jake Myers, Eurena, Louisa Reyes and Stephen Brault Christopher Morel, really Joe Ryan, Rookie for the Twins Kelly Jensen Goes Dodgers on this one Gives us two of them, Ozzy Albies De La Cruz is a good one Corbin Castro, Gold Nice How far we're going to roll a die for this one One, two, three left for the Blue Jays Four, five, six right for the Padres Gold to 50 This is a one way over here It's going to go Blue Jays Congrats, Blue Jays Nice Tigers, Cardinal Xander Bogarts Nick Madrigal No, the homerun derby will The homerun derby Not who can almost get it to the fence Carson Kelly, Connor Overton, Mitch Morland, Luke Voight It's got to go over the fence It's got to go like Bryce Harper in there Harper's a good one I'm trying to go with guys that we haven't seen before Ellie's a really good one though Same with Yordan But like we've seen Harper win Harper doing it every year would be good They just have Harper and Soto and like Judge and Yordan You know, just the Mashers Rogers, Escobar, Raspi Sin and Chapman Personal resume tonight after group breaks Grossman, Singer, Lurie Garcia and Raver San Martin God, decent amount of orders in there so far as well Yeah, a few boxes in queue so far Got Freeland, Alfaro, Philly's team card and Velaid Yeah, show him Show him he'd be good Vaughn, how about Bobby Dahlbeck? He could actually hit, he could actually hit bombs Thanks He's like super streaky but you put him in a derby He would launch him What about the dude with the shirt? It's always open He played for the Padres and went somewhere else What was his name? He played for the Yankees His shirt was always Luke Boyd Yeah dude, that guy's tanks, man Steven Strausberg Evan White Gold to 50, Mark Molanson, D-Backs Devers would be a good one No, I wasn't trying to be biased But Devers would be a good one Shane McClanahan Castellanos Yeah, Yordan Yeah, we said Yordan Like that was like the first one Sean said was Yordan, that's a good one Flaherty, Badoo, Bradley and Trout Brad Keller, Orange at 25, Joey Gallo, Yankees What's the guy on the Yankees? The little meatball Bassett, Abreu I'm not on the Yankees on the Metz Vogelbach Yeah Max Kepler It's Tank Garrett Richards, Alex Cobb, and a Braves Triple Ah, sorry to hear that, Jimmy Yon Gomes, Deakman We stream every day So you can watch us every day Yeah, come on Jackie Bradley Jr., Louis Heal We don't take days off Break every day Basically that isn't Christmas or like Thanksgiving Pretty much every day Jay Hap, Carter Keyboom, Kluber, and Cabrion Hayes Karate Tilesz Javi Baez, Dallas Keichel, MVP Meets Rookie World Die for that one And then you got Darvish So it's Ray's left is Wander 1, 2, 3 And then right is Philly's Harper 4, 5, 6 So 1, 2, 3, 4, 5, 6 That's a 5 Gonna go to the Philly's on this one Philly's Sean Murphy That's actually a pretty good one Sean Murphy Matt Olson Matt Olson Nobody said Matt Olson, did they? He would hit some tanks Got Verlander, Candelario Gold, Kelly Janssen Dodgers to 50 He's spoken in the new existence, Vaughn Kelly Janssen And Padre's Card there Vogelbach might strike out High five highway, Mike Bowman Eddie Rodriguez and Jackson Culler That would be funny Got Jesse Winker with a purple Garrett Hampson Rockies out of 10 Color match He wouldn't strike out, you're throwing pizzas Dickerson and Polanka Damn man This guy is getting roasted right now Keegan Aiken Gregory Soto Rookie Gold to 50 You'll help Pozo Rangers Guardian's team card Dugo, Diaz, Pluecky, Calhoun Yeah, Shohei would be a great one though You know he would hit some of like 480 He was in it last year, right? I'm just strange He needs to redo Bradley Zimmer Orange to 25 Braves team card Nice Braves card there 25, Chad Pinder and John Means Select H2 is at 1 to go We've got just 3 to go An optic choice I'll drop your link for that For some fast break He's at 5 to go now Got Rizzo, Jeff Hoffman, Stephen Matz And O'Neill Cruz Rookie That guy could hit some bombs Got Rafi Devers, Jordan Lyles, Iglesias And Andrew Vaughan Got Braves, Cart TJ Friedel, Jordan Montgomery Tyler Gilbert Got Kitel Marte, Jake Arietta Robby Ray and Charlie Blackman Ian Hap Austin Hedges Gold Rookie Jose Ciri Astros That's out of 50 44 out of 50 Got Rangers team card Simeon Kiermeyer Chris Owings and Joey Votto Brian Diller Cruz Rookie Marlins Jonathan Daza A Jonathan Diaz and Colin Moran Isaiah Conner-Felefa Kyle Farmer Going to the Dodgers on this one Because the majority And Luis Garcia Last box Come on, super Connor Sebold Rookie Nick Madrigal Ahmed Rosario Jose Ramirez Matt Olson Romy Gonzalez Daniel Bard And Rocky's theme card Wander-Based Rookie for the Rays Angels team car Will Myers and Austin Riley Emmanuel Rivera Garrett Hampson Lane Thomas And Chad Kool Craig Kimbrell as a White Sox Orange to 25 Yankees Jordan Montgomery Josh Van Meter Jonathan India Joe Musgrove Adam Duvall Gold to 50 Randy Arrozarena for Tampa Bay 36 to 50 on the Randy Tucker Barnhart Riley Adams Tyler O'Neill Alex Reyes Tim Anderson McCullers Mills Ortega Engulgement All right So we have Gold to 50 Arrozarena Jose Ciri Yoel Pozo Kenley Jensen Mark Malanson This one went to the Blue Jays on the How Far Sammy Long Ryan O'Hearn Kyle Hendricks Charlie Morton Bret Gardner Nolan Aronato Nico Horner Orange to 25 Montgomery Raves Joey Gallo Trevor Story Jean Segura Nathan Ovalde Andrew Vaughn Purple to 10 Garrett Hampson Carter Keeboom Emmanuel Clausset and right to five David Fletcher Thanks again everybody for the fill some nice cards in there for sure we're moving on to basketball


Thank you, Tara. And good morning, everyone. And I want to recognize the indigenous nations on whose traditional territory we're all gathered today. I'm joining you from the territory of the Shwepec, the Okanagan Sioux, the Sanayik and the Tanaha Nations. With me today is my colleague, Mike Farnworth, Minister of Public Safety and Solicitor General, as well Neil McLaughlin, the Superintendent of Predictive Services for the BC Wildfire Service. And he will provide us with an up-to-date report on fire conditions. We also have technical experts who will be on hand to answer any questions afterwards. And there are Rob Schweitzer, the Director of BC Wildfire Service Fire Centres, and Peter Brot, the Executive Director of Regional Operations for Emergency Management BC. We're here today to present August's wildfire outlook and to highlight how the conditions have changed throughout the province in the last month. Public safety is our ultimate priority, especially as the threat of wildfires increases, not just from a seasonal perspective, but also because of the ongoing effects from climate change. So to address these challenges, we have made one of the largest investments in the history of the BC Wildfire Service, which will help transform the organization into a year-round operation. This year-round model will allow the BC Wildfire Service to put more emphasis on all four pillars of emergency management, prevention and mitigation, preparedness and recovery. Before we get into those details, I want to acknowledge and thank all BC Wildfire Service staff, the contractors and industry partners. On Tuesday of this week, a contracted aircraft conducted a forced landing while responding to the Connell Ridge Wildfire near Cranbrook. Thankfully, the pilot was able to walk away from the plane. And this is a stark reminder of the potential dangers of fighting wildfires. I am continually amazed by the professionalism, the resilience and dedication of the people who show up to do this work daily. They've been hard at work responding to wildfires and supporting those on the fire lines in all corners of our province. And with the recent heat wave we experienced across much of BC, many of these firefighters and support staff have been working under sweltering conditions. Some of you may have seen a video of the BC Wildfire Service post it recently of 600 meters of containment lines built to reinforce various sections of the home and creek fire. Due to the difficult terrain, crews worked for three days and plus 40 degree temperatures using hand tools to create the barrier. Challenging terrain is a constant, a consistent and constant thing with wildfires burning this summer. Given BC's diverse and mountainous geographic geography, many of the challenging wildfires crews are battling this summer are in terrain that's too steep and rocky for ground crews to access and attack the wildfire directly. And in many instances, this terrain is even too steep for aircraft to safely access the wildfire, particularly when smoke obscures visibility. It's clear that there has been a significant shift in our weather from the cool wet conditions we experienced in May and June that kept wildfire activity to a minimum. As we are now well into the hot and dry weather of August, I want to stress the importance of being prepared, planning ahead and staying informed. We know that fire mitigation and prevention work and these actions must be supported at the local level. As of 10 a.m. yesterday, there were 91 active wildfires burning across the province with six wildfires of note. Those are the Nehomen Creek, most active in the Stein Valley, estimated at 3700 hectares. Carameas Creek estimated at nearly 3000 hectares. Maria Creek, which is between Cache Creek and Lillewit, which is estimated at about 1000 hectares. Watching Creek north of Camloops, which is estimated at 200 hectares. Briggs Creek up in the Caslow area, which is estimated at 1500 hectares. And the Connell Ridge, which is south of Cranbrook, which is estimated at 500 hectares. Over the last seven days, we have had 154 new fire starts, nearly three quarters of which were caused by lightning. And to date, we've had 528 fires, which have burned about 22,000 hectares. But just by comparison, at the same time last year, we had more than 1300 fires burning and nearly 25 times the hectares had already burned. That's why we're asking all British Columbians to continue to exercise extreme caution to be prepared to have a plan and to stay informed to help prevent and limit the spread of wildfires. We all play a role in supporting that work through preventative measures that can help protect people and communities, as well as wildlife and livestock from wildfires. It's so important to be prepared. Look around your home and remove any branches, leaves or pine needles from your roof, your gutters, balconies, doorways and windowsills. And you should also pay really close attention to corners or other tight spots where debris tends to gather. You could create a 1.5 meter non combustible zone around buildings by raking or sweeping down to soil, rock or concrete. And keep any movable propane tanks or wood piles 10 meters away from buildings. Check all your exterior vents to make sure they're properly screened and in good condition.  Grass shorter than 10 centimeters is less likely to burn intensely. And it's also so important to have an emergency plan. If you're planning to travel, check destination BC's know before you go webpage and campers and people using the back country should be aware of campfire burns. As of noon today, campfires are banned in Southern BC. Specifically, that is within the areas of Kamloops, Southeast and coastal fire centers. Make a point of checking out the firesmartbc.ca website to learn more about what you can do to protect your home, your neighbors and your community. And if the area you were planning to travel to is impacted by wildfires or is under an evacuation alert or order, connect with a local visitor center to rebook your trip to another part of the province. I'd now like to introduce the Minister of Public Safety and Solicitor General Mike Farnworth to tell you more about how we can stay safe during wildfire season. Thank you, Minister Conroy. I'm Mike Farnworth, the Minister of Public Safety and Solicitor General, and I'm honored to be speaking to you today from the traditional territory of the Lekwungen speaking peoples and the Songhees and the Squimal nations. As we progress through this wildfire season, I want to echo Minister Conroy's comments and urge people to stay informed and to be prepared. With the increasing fire activity, it's important to stay informed about wildfires and advisories in your region as well as any areas that you plan to travel. As of yesterday at 5pm, there are approximately 326 properties under evacuation order and another 500 properties under evacuation alert. By comparison, at this time last year, there were almost 4,300 properties on evacuation order and about 21,000 properties on evacuation alert. The province, First Nations and local authorities are providing evacuees with emergency support services. As of August 2nd, 197 wildfire evacuees have registered for emergency support services in the central region of the province. The resources being deployed by BC wildfire across the province include about 1,000 firefighters and other personnel fighting fires are on standby. Five incident management teams and 110 helicopters and planes supporting ground crews. People's lives and safety are always our number one priority. It is critical that people follow all the directions from their local government or First Nation and if you're placed under evacuation order, leave immediately. We're also asking residents to prepare yourselves, your family, home and community for any potential fires, follow all fire bans and avoid any activity that may result in a wildfire. If you've not already done so, now is the time to take 20 minutes out of your day and put together an emergency plan including packing a grab-and-go bag with all necessities so you are prepared if your situation changes suddenly. Recently, we announced that the Interact E Transfer payments are now available for people eligible for emergency support services. This means that evacuees will have quick access to supports for urgent basic needs such as food and clothing. As part of getting prepared for emergencies, I encourage everyone, especially in areas at risk of wildfire to pre-register with ESS online by using their BC services card app to log in. You can pre-register now for emergency support services at ESS.gov.bc.ca. As many COVID-19 restrictions have now been lifted, BC is also experiencing a significant increase in tourist travel and accommodation bookings. I want to encourage you to reach out to friends and families in other communities who may be able to provide shelter should you be placed under an evacuation order. This will help ensure that local commercial accommodation is available for those who have no other option. But to be clear, the province, First Nations and local authorities will be there to assist all families or individuals who need emergency care. The MBC has been working with First Nations, local governments and other partners to identify communities and venues that can support evacuees in the event of a large-scale evacuation. This advance planning will help keep evacuees as close to their home communities as possible. At this time, we're asking everyone to follow updates from their local First Nations, local government, local authority, the province and media. We need to be informed, be safe and be prepared. The more prepared people are for an emergency such as wildfire, the more quickly and safely people are able to respond if the time comes. Thank you. And now I'd like to introduce Neil McLaughlin to provide an update from BC Wildfire. Thank you, Minister. Good morning. I join you today from the First Nations lands of the Sequimac people and I'm honoured to live, work and play on these lands. Just to the host, I'm seeing that you've disabled screen sharing. If you could turn that on, please. Okay, so we're going to talk about the seasonal outlook for August. Before we get going, I'd like to cover the trends and statistics we've seen over July and how that set us up in terms of current conditions going into the month of August. And then we'll look at our latest forecasts and information in terms of what we can expect into the month of August. To start, we'll look at some climate statistics from our partners at Environment Climate Change Canada. Over the month of July, we did see hotter than normal temperatures above through, observed through the interior of BC. But what was most important was the below normal precipitation across most of the province. And this was due to a persistent upper ridge in the later portions of July and I'd like to speak to that a little bit more. That was what was associated with our hot dry spell and that ridge of high pressure took hold and delivered some of the hottest conditions we've seen yet this season.  The graphics I'm showing on this slide along the top, it's our fine fuel moisture code and along the bottom our Duff moisture code. Both of them speak to the high pressure ridge and also the potential for rapid fire growth and high fire intensities. And as you can see as we moved from July 14th the middle of the month into the July 27th two weeks later, both of those indices increased significantly significantly across most of the province. Now, what's most concerning about high pressure ridges is when they break down and that's when those ridges of high pressure break down, lightning, gusty winds are typical and expected and so that's indeed what we saw. Over 35,000 lightning strokes were recorded during the last week of July in the southern and northeast portions of BC. And as a result we had 121 new fire starts in that period. 74% of them were lightning caused but also it's just as important were the strong winds that accompanied that ridge breakdown as those winds aligned with terrain that resulted in fast spreading fires and aggressive behavior that was challenging to fight fire in. I want to speak a little bit about the recent rain we're seeing in parts of the province. There have been significant amounts of rain recorded across the northern reaches of British Columbia. On this screen we're seeing our Duff moisture code as of Thursday, July 28th and then forecasting it forward to this Saturday. The rain is a really good thing in the northern reaches. As you can see the Duff moisture code is dropping down significantly across the north and as we move into August we're expecting to shift in terms of fire danger from the northern reaches of the province which was quite active in the spring to the south and so as you can see on the right we've elevated fire danger in the southern portions of the province going into August. The rain that we're expecting in the southern portion will be more isolated or smaller amounts and all it will take is a couple days of hot dry weather to bring us back up to high to extreme values. I'll also speak to the current conditions that we're seeing going into August so we do understand where our current conditions to the past years of history for two specific indices. One is the build-up index on the left and that speaks to the availability of fuel to burn and the other is the drought code shown on the right and that speaks to long-standing persistent drought conditions that take a long time to recover. From a drought perspective it's a good news because we're seeing a lot of above average drought conditions for this time of year in fact parts of the province are actually below normal for our drought code which is a good thing. However on the left we are seeing that persistent build-up of our build-up index through the southern portions of the province and again that's the fire danger shifting from the north to the south and will still be conducive to fire starts through the month of August. So let's look into the month ahead on the outlook. We are expecting above seasonal temperatures through the month of August and into early September and that's shown on the left. The signal for those above normal temperatures is certainly greater in eastern Canada but it's present over British Columbia through the month to come. There isn't a very clear signal for precipitation so we're expecting around normal precipitation amounts through the month of August and into September for BC. The other thing that I want to emphasize around the precipitation shown on the right-hand side of this slide is there's generally low confidence in the long-range precipitation forecast so we don't hang our hat on that forecast as much but the temperatures are noticeable. We also compare those forecasts to other international forecasting agencies and the trends are fairly consistent. Again above average temperatures through August into early September and then trending back towards seasonal temperatures into August. This slide is showing what you've heard earlier from the ministers around number of fires. The black-bold line going through this graph is just tracking the weekly number of fires we're seeing this season and the colors in the background are just showing the variability that has occurred over the past 20 years in terms of number of fires per week and as you can see there is a fair amount of variability as we enter the month of August anywhere from 50 fires to over 700 fires a week. This makes it difficult to assess what we're expecting going into the next week into the next month but what we do as forecasters is we look to years in the past that are trending similar, have a similar pattern to the way that this fire season is playing out and so there are two years that we're identifying those are years 2020 and 2011 are trending quite similar so we were expecting an increase in fire activity as in fact what we've seen in the past week there was about 149 new fire starts and we would expect a similar number of fire starts through the month of August and that is reinforced by those analog years. Despite that increased fire activity through August I want to remind folks that we are below normal for number of fires as we saw in the previous graphic but also in terms of area burned and so right now we're about 20% of our 20 year average for area burned and significantly less than what we saw last year at this time and this is mostly due to the fact that we had above normal over winter precipitation amounts cool temperatures in the spring, late snow melt and really a late start to the fire season we've had a two week dry spell which has increased fire but nothing compared to over a month of drying that we saw in previous years so in summary again we're expecting above average temperatures near normal precipitation amounts through most of BC in August and early September and then a gradual return to seasonal temperatures by October elevated fire dangers expect to shift from the north portions of the province down to the south and we are anticipating a similar number of fire starts through August as we observed in the later part of July when we have periods of warm weather breaking down and resulting in lightning and gusty wind conditions large fires that started in July will remain active into August and additional fire growth can be expected on these fires but when we do get intermittent periods of cooler temperatures that is very favorable for ongoing fire suppression efforts and successful initial attack on the new fire starts that we're expecting I will take questions but first I'm going to hand it back to Minister Conroy to finish this briefing Thank you Neil for that I think it's really important that we hear those numbers and recognize that as we're moving into August it is going to get hotter and it is already hotter but that we need to be prepared that even though we are so much under normal especially compared to last year we still need to be really safe but I want to thank everybody for joining us for this timely update I also want to encourage people to download the BC Wildfire Service mobile app it's a great app to have and over the long weekend the Provincial Wildfire Reporting Center supports a fire through the BC Wildfire Service mobile app and 81% included a photo and it's really really helpful to the firefighters and actually during the same period the Center received nearly 2,000 reports by phone so those photos they really help the Wildfire folks get out and increases the staff's situational awareness and really helps with the rapid operational decision-making of who and what to send to the fires and as Neil said we are going to open it up for questions now so I'll turn it back over to you Tara Thank you very much a reminder to media on the line please press star 1 to enter the queue for the opportunity to ask a question and a follow-up I will scan the room as well to see if we have any questions here today okay our first question we will actually start in the room this is for Minister Conroy if you can tell me why the government waited so long to put in this fire ban especially after the long weekend given that we knew that he was coming I don't think it was that long it was and people have been very very cautious I mean we still had almost 25% of our fires caused by human cause as opposed to lightning about 75% or lightning and about there's about 6% that we're not sure of so people have been pretty cautious and we thank them on that but we leave it up to the professionals to determine when they should bring in campfire bans and they've decided that we should now bring one in for the south and we're hoping everybody will make sure they take that very seriously follow-up Kylie given that we are expecting the heat to continue and will it be more proactively expanded to it forward? well it's expanded for the entire south part of the province and it's so it pretty much covers the entire area of the province where it needs to be right now and it appears that all the information has been distributed we have no further questions so that will conclude today's availability thank you everyone


The new designer for the UEFA 2021 is now out of there. Thank you, Amandine. It's good to all of you, many years away. I hope you have journeyed and to all the people that have come to do this, that have supported me. So, yeah!


SCP-671 is a large colony of Spanish carpenter ants, comprising approximately 30,000 individuals. When SCP-671 is faced with a complex artificial device, its constituent individuals will attempt to reduce it to its simplest components. Organisms, living or dead, do not trigger this behavior. The ants appear attracted to artificial devices as to food sources. Screws will be unscrewed, staples and nails extracted. In the process, even a single SCP-671 individual is able to exert forces several hundred times greater than a normal sea crew in Tadas. When large numbers of ants collaborate, they are capable of surprising feats.