What's going on everybody? Got Kyle here with Layton Sports Cards and we're ripping two boxes for Mr. Dustin C. Got two boxes of WWE Revolution. Let's see a kaboom for you Dustin, good luck. Let's see a kaboom. Yes, yes, very good prices guys. Dustin took us up on it. Let's see a kaboom for you sir on the WWE Revo 200 on the F1 Sapphire and $79.99 on the Revo. Good luck sir. Also got Sam over on Fanatics Live ripping with Jay. You guys are interested? They're giving away the Red Sox body to the Roman Chrome Breaks today. Alexa Bliss lift off. We got an Astro, that one's going to be Scarlett. The Astro. Rich, you think the Dolphins are going to beat the bills today? Got Shady saying the bills are going to win. I said Sean's not going to like that. Got a Revolution of Brock. Brock on the Fractal. Brock Lesnar. I don't know why anybody want to fight Brock Lesnar man. I don't know why anybody want to fight Brock. Say Nikki Cross. This one is going to be Finn Balor. Finn Balor on the insert. There we got a Apollo Crews. Apollo Crews to $149 Cosmic and that one is Damien Priest. There's an Astro of Mansour. Base Base. Bobby Lashley. The Almighty. There's a Star Gazing Mankind. Mick Foley. Cactus Jack. Mankind Star Gazing. Mansour on the Astro. Come on, come on, let's see a kaboom for you. Good luck Dustin. Got a White. That one is Otis and Chad Gable. The Alpha Academy. Insert. We got a Impact. That one is Solo Sokoa. Solo Sokoa to $199. Here you go Mark. I saw Timor Hamlin was playing. He was at least suited up. I think that's awesome. There's a Revolutionary Finisher of Braun Strowman. And a Groove of Santos Escobar. Groove. Come on, kaboom. Come on, kaboom. And guys we got Forest Rippin tonight as well. He's got a 40 box Sunday Funday mixer at the end of the night. Definitely check that one out. That's for week three and four. Had no breaks last Sunday. Goonser. So we got week three and four Sunday Funday tonight. And entries into that will be a random at the end of the year for regular season every entry. You guys get in Sunday Funday. There'll be a random at the end of the year for a big regular season for all of our regular season Sunday Funday breaks. Here's a Shawn Michaels, little HBK. Stargazing. Got a Groove of Santos Escobar. Come on, kaboom. True Mark. True Mark. You got the jersey on today. Got the pads. Here's a Ilya Dragunov on the Astro. Mark's ready to go today. Who's your team, Mark? Who's your team, Mark? I'm a Bucks fan. We lost to the Eagles last week. Got the Saints this week. I think we can do it. Revolutionary of Matt Riddle on the Shockweight. Riddle. Got a Astro Kit Wilson. Luck Dawson. Let's see a kaboom, man. Let's see a kaboom. That one is Kevin Owens and Sammy Zane, the team there. And this one is going to be a, is that a Cubic to 49. Cubic of Core Jade. Core Jade to 49. Come on, Dawson. Again, guys, not a long queue for Personals. If anyone wants to get one, go and get it going for you in a few minutes here. Stargazing is Leta. Got a Groove of Carrion Cross. Again, got discounts on F1 Sapphire down to 200 and this WWE Revolution down to 79.99. Some great prices on those. There's the Rock. Got a Astro. That one is going to be Blair Davenport. Apollo Crews. Rex Ryan picked the Dolphins. Oh boy. Oh boy. Mark's getting going today. There's a Rey Mysterio liftoff. Mark's having a day today. And a Astro. That one is a Alundra Blaze. Alundra Blaze. And Otto. Whoa, that was unexpected. Roman Reigns to 99. There we go, Dustin. We'll take that. The head of the table. Very nice. Roman Reigns to 99 on the auto. Was not expecting that. He's been redemptioning a lot of stuff. Very nice one there, Dustin. We will take that. Roman Reigns to 99 on the auto. Sunburst. Very nice one there, Mr. Dustin. And one pack to go. And guys, these boxes are cheap today. We got them down to 79.99, chasing some kabooms. And there is a Undertaker. Another Sunburst. That one is to 99 as well.  Really turned up there. Very nice couple boxes. Undertaker to 99. And Roman Reigns auto to 99. All righty there, Dustin. That will do it for your two boxes of WWE Revolution. Some nice ones there. We will get it right out to you, Sir. Thank you.


Ever heard the old phrase, strike when the iron is hot? Well, it's never been hotter than now. All this month at Appalachian Wireless, you can get the Samsung GS6 or the GS6 Edge for just one penny when you send them for service. The Galaxy S6, normally $49.99, now just a penny. The Galaxy S6 Edge, normally $99.99, now just a penny. Strike now while supplies last. Better service, bigger savings, serving you for the last 25 years at Appalachian Wireless. Fifty-year-old Monroe Jackson was set to go to trial today before Pike circuit judge Eddie Coleman on a felony count of theft by deception, but that we'll have to wait. He's accused of collecting hundreds of dollars in donations for veterans by claiming to represent the Veterans Transitional Housing Center at Robinson Creek, but then not giving the money to the center. He was initially charged with misrepresenting military service for wearing decorations indicating he was a colonel in the armed forces, but he was eventually indicted by Pike County Grand Jury on the theft by deception charge. Now, some 10 months after he was initially charged, Jackson's ability to stand trial has been brought into question by his own lawyer, Pikeville attorney Bill Sloan. Neither Sloan nor the Pike Commonwealth's attorney's office wanted to go on camera to talk about the case, but both sides indicated they were working well together to come to a resolution. But just last week, according to court documents, Sloan filed a motion asking for Jackson to be evaluated to see if he is competent to stand trial. As a result, Jackson's trial has been continued for an unspecified amount of time. Jackson's been named as a person of interest in a case in Letcher County that's similar to the one against him in Pike County. He's accused of going into several businesses in the Isom area to collect donations for veterans. He hasn't been charged in Letcher County yet with any crimes. Police in Letcher County said Jackson has not been reported collecting donations there since EKB News reported he was believed to be in the county doing so. In his criminal case in Pike County, Jackson is facing up to five years in prison if convicted on the felony theft by deception charge. Attorney Bill Sloan said his motion is likely being taken under advisement right now by Pike Circuit Judge Eddie Coleman, and he anticipates it will take about 30 days to clear up the matter. Reporting in Pikeville for EKB News, I'm Chris Anderson.


Hey, coach, welcome back to the channel. If you haven't yet, make sure you subscribe to the up-to-date with all the latest content, explain pretty much what content going on every 24 to 48 hours. So don't stay stuck with your business. And if you need more help, reach out to me below this video, number of ways you can do that. You can either apply for Accelerator program or you can jump on a 15 to 20 minute call with me where we can talk about your business. Now today I wanna touch on something that I've been getting asked on a regular basis. A lot of coaches reach out to me via WhatsApp, via email, and they asked me about starting a private soccer club. All right, so private soccer slash football club. So I wanted to make this very short video to kind of give you a eight step process in which you can build your own private soccer club. Now this is something I did myself. So run away with it, implement it. And if you need more help, we have a number of ways we can help you to get to the next level. But essentially this comes down to the first point which I'm gonna make here, which is opening a training academy business first. So most of the coaches that we work with that go on to open soccer clubs or basketball clubs or clubs in their sport, which have teams that play in local leagues, all of them start off by opening a coaching business or a training academy. Now why is that important? It's important because you can't just simply open a club, right? I mean, you could, but opening a training academy, number one, it gives you that experience of opening a training environment for kids, right? So once you open a training academy, then you can start to build your academy to the point where getting, taking the business to the next level is what I'm gonna talk about in the second point. So the first one, if you want to open a private soccer club, something we would recommend is first, look to open a training academy where you're working with players of different abilities, different ages, male, female, right? So that way your training academy can start to get exposure and people start to like trust your training. Now the next one is grow your academy until there is a demand for club. So something we speak to coaches about is once your academy gets to a point where you have 100, 200 kids, now you can have a look to see, right? Is there demand for teams? Is there demand for parents to be traveling every weekend to go and play in different places against different teams? Okay, one thing is bringing your child to the same place every week for just training. Another commitment is committing to a local league where you might have to travel one or two hours for just a game, okay? So first of all, you have to see whether there is demand for it, okay? Now the third one kind of touches on the second point but the third one is create a survey by asking parents if they would join your club. So something I did is once I got to a point where I knew there was demand because a lot of parents were asking me about it, players were asking me about it. I sat down and I put together a survey which I later on sent out to parents. And based on the feedback I got, if there was a majority that said, right, yes, we would be committed to joining your club, then the next bit we did, which is number four, we ran tryouts. So one day during the week, before, well, once all the teams or the leagues finished training, we then held a tryout session where we had different age groups at different hours come and tryout to try to put together teams. Now we managed to put together several teams for the club, okay? And that goes on to the next point which is once you have teams, once your parents committed, right, the next bit is you've got to try and now look for coaches to run your teams, okay? You are one person, so you're not going to be able to be everywhere, every weekend when your teams are playing in different places. So you're going to need to hire assistant coaches to be able to run your teams and to be able to do that. So the next one was hire coaches to run your club teams. Now the sixth one is before the start of the season, make sure that you register your teams into local leagues, okay? So once your tryouts are done, once you've got demand, the next bit is right. Now can we then look at hiring out, so hiring out coaches and then looking to register them in to a local league? So the next bit is to look at the local league. Something I would recommend though, before you do anything else and before you run a tryout is look at the local leagues in your area and what are the requirements for them to join? In my area, there were some leagues that you had to have minimum three teams in order to join. Some leagues, which are private leagues, they might say, right, you're okay with only one team. They might have their own requirements for you to join, but just make sure that you do your research and you read up on what are the terms and conditions for each league and what are the requirements for players in order to register onto them, okay? So the seventh one is now structure a payment system, membership type of model. So the way I did it was our league run from September through to May. So essentially that was about seven months or so, six, seven months, because we did have a break in between for Christmas. So I think it worked out to about seven months. So what I did is I had a subscription, which was a monthly subscription every month to come for the training, and that essentially paid for all the weekly training sessions. And then there was an upfront registration fee to that essentially was to register for the league. So parents made two payments. The first one at the start of the season, which was a bigger type of payment, but that was towards the league and the league fees. And then it was a month to month direct debit, which would come out every single month. And that would cover the costs for training. It will cover the costs for facility rentals. It would cover the costs for the coaches, okay? And then the eighth one is now add extra training services to your teams, okay? So once you have a number of teams in your private soccer club, now what you can do is you can run holiday camps, you can offer one-to-one training for them, and you can essentially do what you would do with your training academy. You can offer that as an extra on top of your team training. So if any parents want to hire one of your coaches or want extra one-on-one training, you can add that as an extra service. If there's holidays coming up, where, for example, Easter, Christmas, you could run holiday camps for all your teams where parents can register, they bring their kids, could be a half day, it could be a full day camp, right? You can run clinics. So these might be position-specific clinics. You might run goalkeeper clinics for all the goalkeepers at your club. You might run defender clinics for all the defenders at your club. Striking clinics, right? There's a number of different things. You might decide to do workshops, tours taking your teams to different countries, or different cities across the country you're in to play in tournaments, right? There's so many things that you can do as an extra bonus and added on as an extra service to your soccer club, okay? So thank you for watching. And if you have any questions, number of ways you can reach out to me, visit the description below. And we can speak over a Zoom call, or if you want to learn more about our Sports Accelerator program, you can do that by applying through the link in my description below. It takes you to a landing page. You'll be able to see all the results for the coaches that we've worked with who have built training academies. And if you want to take the next step and apply for it, then you can do that through the link in there. Thank you for watching, and I'll see you on the next video.


Okay, my name is Bruce Mamjan. I started the conference day with a two-hour workshop about wind and comment table expressions to SQL capabilities and Postgres. I am from Foto Pennsylvania. I work for EDB and I am one of the Postgres core team members. Like my morning talks, this slides are online. If you go to this website, you will see that there are, there's this talk plus probably 25 more talks that also include the slides and the videos of many of those talks. So I'm going to finish the day by talking about Postgres and the artificial intelligence landscape. Artificial intelligence is a hot topic, one of the sort of named topics for today. And I wrote this talk because I wanted to understand fundamentally what is artificial intelligence and specifically learning and how do those relate to a use in the database. So this is not going to be a super practical talk. What it is going to be is to show you exactly how machine learning works, show you how the works, and then finally talk about how artificial intelligence and machine learning can be used in various fields. So we're going to talk about what is artificial intelligence. That's actually a question. I'm going to talk about machine learning and the deep learning branch of machine learning. I'm going to demonstrate machine learning using Postgres and it should be a very even if you don't know Postgres, that's okay. You'll you'll be able to understand the code very easily. Then we're going to talk about how artificial intelligence is normally implemented and then talk about some tasks and why use a database. So what is artificial intelligence? This comes from a Wikipedia article and the quote that I like the best are artificial intelligence are machines that cognitive functions that humans associate with the human mind such as learning and problem-solving. I want to highlight the word mimic because it's not totally clear if artificial intelligence is the same as the human mind or if it's sort of mimicking it and that's kind of a fundamental issue in trying to understand the limits of artificial intelligence. So if it's whether it's the same it just is mimicking something that looks like it. This is my one philosophical slide and I think it's worth considering. I've considered it myself. The question is if the physical world exists, if nothing exists, they can't be measured or isn't physical. The human intelligence really only differs from machine intelligence because it is not naturally developed. It differs only in how the intelligence is created. So if only the physical world exists, then human intelligence, artificial intelligence may be exactly the same thing. It only means that one was developed and one was developed by humans. The interesting caveat to that and correlate to that is that if that is true, then human free will effectively becomes an illusion. The free will that you think you have are really just the neurons in your brain sort of calculating things and that the things you're calculating are predetermined in a way. Inputs based on, you know, your past experience. Anyway, I'm not going to get into that. There's an article there at the bottom of URL right here that talks about the concepts of what the Orleans but it's I think it's worth something worth thinking about. Here's the history of artificial intelligence. Artificial intelligence basically started before the computer was invented. It started in sort of this pre-computer philosophy concept and there are a number of philosophers who were kind of big on this. Then you have robotics as a part of artificial intelligence, the idea that if you mimic human you're intelligent or you're sort of artificial intelligent. That's kind of formed by the wayside, but it was an area of artificial intelligence years ago. Then we had the Turing test around the fifties sort of tried to test intelligence. Our Robert Turing obviously was the margin of that. Then we have expert systems in the eighties where they tried to take the experts would use to solve a problem and codify those into a into sort of a program that could be run. Then we had those didn't work too well either. Then we had what's called AI wind where there wasn't a whole lot of AI going on at all and then sort of back in you know the two early 2000s you have artificial intelligence and machine learning, which actually has some application. It can be quite a success. For me having been around this field for a long time, artificial intelligence is like fusion energy. It's always ten years away. So it's it's something that's always always there but it's the same thing you know dozens of decades ago. Let's get more specific. Let's talk about artificial intelligence and specific implementations. What we're going to talk about today is a specific brick intelligence called machine learning. As you can see machine learning is inside the larger artificial intelligence field and then inside of machine learning there's this thing called deep learning and I'll show you some examples of exactly what deep learning is. There are other branch of artificial intelligence but these two machine learning and deep learning are the ones that are most popular today and the ones that are getting the most activity. See this slide pretty repeatedly. Oh, I'm sorry by the way if you're curious, there's a the URL here at the bottom again has a very good explanation of those fields. So you're gonna see this slide for a couple versions and it takes make sense to spend our time kind of looking at this and breaking it down. This is typically what a machine three stages of a machine learning. The first stage is called the initial stage where you have a machine called a tensor with three neurons inside of it. That's what we call these neurons. Then we have a training stage and then we have an action stage where we're making requests and we're getting results. Okay, so there's typically an initial stage, a training stage and then an active stage where we're making requests and getting results. So here is the the first example of unsupervised machine learning. So in unsupervised machine learning, we're going to walk through exactly what these stages are. Okay, in the initial stage you have this thing called a tensor. A tensor is technically a vector. It's like if you're familiar with a vector in in in math and math. A tensor has a specific meaning to it, which really does apply here, but they just call it tensors. Okay, and inside this particular tensor, we have three neurons. So this is one neuron one, neuron two, neuron three and all neurons, but again, they aren't the same as the neurons in our brain. They're basically just levels. Okay, and I know that doesn't make a lot of sense, but once we kind of get going it will. Then we have our training stage and here what we're doing is we're increasing and decreasing the tensors based on the training data. So you notice that I've got an arrow that has an up and down here. It's kind of got a slash. So this neuron is going up and down based on the training data that has come in that we're feeding into the machine. So that the typical flow is you start with empty to empty neurons. You set the training data and finally you actually use the tensors and the neurons to produce results. Now in unsupervised machine learning, we're feeding in training that has no results to it. All right, so all you basically have is the training data. All right. So effectively, you just have this thing. It's going up and down, but there's no results to it. It's just it's just it's just we saw a lot if you have, for example, pictures of cats and dogs and you just feed them into the system and you ask the system, distinguish between these two sets. I'm not going to tell you anything about the sets. I'm not going to tell you which is a cat or dog. Feed this data in and it's the job of the machine to identify which one is which. Okay. Now, I'm not recommending this mechanism, but this is a mechanism for doing machine learning. The next one is called supervised machine learning. Now this one is a little easier to understand. What we're doing here is where you have a state again starts empty, but now our training data has some results to it. So the example I typically have with this would be if doing let's say fraud detection.  Right? And you basically feed in 100, 200, or whatever you feed in false and true, false and true, and you basically are then getting some results back on that. Okay? Hold on a second. Hold on a second. I'm going to have to update my story about that. Let me just stop here and I'm going to get some of my I'm going to get some new slides. Because for some reason it has some new slides. There we go. I'm going to switch over to a new slide deck. Okay. Okay. Never mind. I guess I'm okay. Hold on. I'm just checking here. I was just concerned about something. Okay. So So unsupervised learning has has us feeding the data in with no with no results and supervised learning actually has results attached to the individual entries. Okay. Now reinforcement a little different. In reinforcement learning, we're effectively doing the same thing we're doing with machine learning but we're reinforcing it. So what we're doing is we're starting with empty neuron training data in with results. Okay, but then once we get once we start working, we actually where we feed the results back in to the to the tensors the neurons again. Okay. Hold on a second. Okay. So we're actually feeding results back in. So what we're doing is we're adjusting the neurons once we've gone live. Okay, so we've had the initial state between the training with results. Okay. We're setting the neurons, but now we're feeding the output back in. So why would we do this? The reason we would do this is if we find a case the neuron where we want it to improve itself while it's running. Okay, because if we go back to supervised machine learning, all we're getting here is a case where once we go live, once we're done the training that the neurons don't change the they don't go up and down. But with super reinforcement, we can actually have it learning and improving as it goes. Okay, that's a very complicated topic. I'm not going to really be able to get into it in a lot of detail, but effectively this is sort of the case where you're you've got this thing getting better. Now the problem is that if you if you get too much feed, you can overwrite the test data and the system starts to go wacky. So there's a very interesting sort of philosophy of when you want to do this, when it makes sense to do this reinforcement. This may not, you know, this might be kind of an unusual case, but I think I think you kind of get the idea when you would use this. Okay? What about that? I have to take a break. Okay. For some reason the number of slides was wrong, so I'm going to fix in that. Kind of weird. Okay. So that's the reinforcement one. Okay. Deep learning. Remember I talked about deep learning. I promised we'd talk about that. In deep learning, you have multiple machines stacked together. So here's our here's our here's our original case of machine learning where we've got three neurons and in deep learning, these three neurons feed into another tensor for another stage. Another stage after that. So you've actually have one, two, three stages of machine learning and you basically, you know, feed from one to the other. Okay. Now, why would you want to do this? What's the matter with the where? Well, imagine you're analyzing an image, you're analyzing audio, which is what we do a lot if you're if you're having using something like Google Assistant or Alexa or, you know, Siri, Siri from you talk to it and it has to first identify, okay, what is the pitch of the voice and what's the volume and, you know, what's what are the words there? And then once you have the words, what should I do with the words? So you can imagine when you're doing that, if you're analyzing an image, you have to identify the foreground from the background and then the colors and then maybe the tail or the head and then you sort of work your way through and each phase is doing a different okay, so that's exactly what this is designed to do is to give you different phases where you can kind of walk through the system and get different sort of results through the phases. So it's very popular when you're doing complex things again, where you're using each machine once and you're feeding it in and what you can see here is that each neuron is going into each the next so I have three neurons here, I have three neurons here, I have nine connections here and then I have nine connections here and so forth and sometimes these deep learnings can have thousands or millions of neurons and thousands or hundreds of stages and of course deep learning that big, you're looking at a huge amount of computation that's why AI is known to have to require a lot of computation. We'll talk about that later okay, so what we've really talked about is the concept of machine the concept of unsupervised machine learning supervised machine learning reinforcement learning and finally deep learning, okay so, uh, let's talk let's use a demonstration of exactly what uh, thing is I was going to ask for questions between each section, but I know uh, this the uh, the conference is running 10 minutes late, so I'm going to wait for questions at the end um, and again feel free to type those in and we'll answer as many questions as we have uh, but again, I don't want to take time because we are running a couple minutes late in the conference so with uh, with here's an example of of machine learning that is very silly, okay I'm not recommending this to anybody, uh, but this is a very simple example of uh, of machine learning so what I'm doing here is I am demonstrating uh, how to use machine learning to identify what number has a non-leading zero in it okay, so the first number here 31903 has a non-leading zero, uh, right here okay, um, and this number does not now this is a very stupid example you'd never use machine learning for this because you can figure it out pretty quickly using other methods, uh, that don't need machine learning, but the reason I use this example is because it's very easy to understand it very quickly, okay so, um, first we're going to install pl pearl, uh, pl pearl is a server-side language for postgres I'm going to do this entire presentation with AI using postgres if you'd like to follow along you'd like to run it yourself this URL right here, uh, ai.sql if you download that you can actually run the entire presentation uh, in psql just just run the sql and you can see exactly what I'm going to show 10 slides or so okay, so uh, what are we how are we going to do this we're first going to create a function which creates a tensor we know what a tensor is a tensor is those three neurons that in this case we actually have 11 so we're actually going to have 11 neurons in this particular test the first 10 neurons indicate how long the how many digits are in the number and the last one whether it's divisible by zero so we have basically two, uh, two types of 10, uh, neurons we have number digits and then whether it's divisible by zero or not, okay then we have a, uh, we have to create a training set because of course as we saw before, uh, we the neurons start empty so we have to put some training data in there we're going to run a training set we're going to create 10,000 the numbers aren't technically random they have a random number of digits so, uh, a typical random number generator most of the digits would have eight or nine you know digits in this case we're going to have a lot of short a lot a lot along a lot of different ones so we're going to insert those 10,000 random numbers into a table called training set that's where our training data is going to live so what is our training set look like? this is an example of the first 10, uh, training numbers that I generated as you can see on the left there's a train there's the number and you can see their random number of digits uh, we have a training output which is whether it has a zero or not in it this is supervised machine learning because we actually are telling the computer exactly how many um, how many of the zeros are there how many you know whether it has a zero or not we're telling it so it can learn from that and finally, uh, the last row on the right is the tensor 11 true and false values okay and that's how that's the tensor is going to be generated for this particular when we feed it into the machine learning um, machine learning you have to sort of play with the math I'm going to say uh, we basically have to generate weights for these tensors so I'm going to create a function this function is going to be three pages so this is just the function again in pl pearl, uh, which effectively just creates the weights for us so it allows us to adjust the weights up and down in the tensors based on the data that comes in this is the body of the function takes a particular value and, um, it's tensors and then and then it it's neuron weights and it kind of calculates weighted um, average that tensors and then it computes a, uh, weighted percentage of the basically the counts of how probable how how predictive is this neuron of the result I'm looking for so when you have a machine case you're going to create a lot of different neurons some of the neurons be maybe very predictive some of them may not be predictive at all so what we effectively have here is a way of controlling sort of how how weighted those particular neurons are okay okay we also have a tensor mask here we have to mask out the neurons when we're doing our our learning we have to mask out the ones that match and the ones that don't this is kind of a you know straightforward function another thing that's pretty common in artificial intelligence is that you have to you have to weight the neurons when you run your initial training set some values are going to be really high and some of them are going to be low but what you need to do is you need to even out the tensors so they all add up to one so you're going to kind of take you know maybe you have a 300 there and 500 over there and you want to balance those so it's like 0.3 0.5 0.1 so that they add up to 100 or to one so you have a balanced set of of neurons you know technically adds up to one and that's what that does there's also called softmax this prevents particular tensors from getting too high so again there's a there's a video link in this presentation that our video link that's probably the best one I've seen that walks through exactly how artificial intelligence works with an example I think of identifying dogs in a picture but again it does go through this map and need these particular aspects of the to-do machine learning so we actually have to store the the training data the tensor weights so we created table for the tense it's table for the false tensor weights and now once we once we run our training data through you can see the weights there hold on let me back sorry I missed something here um yep yep so uh what we're doing here is we're um we're what we're but what we're doing in this slide is we are taking our training okay the 10 000 numbers and we're running it through the generate weight function and we're inserting them into the true table weights and the false table weight in separate tables which is fine okay uh so here's where this is our actual training set this is our actual uh uh training process where we take our 10 000 numbers with we feed them in and we generate weights and here are the weights they generate again I said it has to it should add up to one if you add up all these numbers do they add up to one and what it's trying to tell me is how selective are each of the 10 okay so now we have our training data we've run our training data through we've set the weights on the tensors and the neurons we can now use the program so now I'm going to feed the number 100 into the machine I'm going to run it through my tensor weight table and I'm going to generate a tensor I'm going to use max some weight and soft max and it comes back and it tells me the only chance that it has a zero in it well it already has it has a zero in it but it only thinks there's a 20 percent chance of that um is that good no not great all right you you'd like to have a higher percentage uh but again machine learning is not precise okay it's it's fuzzy kind of and you get these probabilities if I feed 101 into it notice the percentage in it for 100 was 22 percent when I go to 100 it's 11 percent so it clearly knows that when I've used 101 the percentage drops dramatically and that's because there's no zero in the first right here's another number a very long number um and you can see it thinks there's a 68 percent chance that there's a zero in there there isn't but because it's a long number there's a lot of a lot of digits it thinks there must be a zero here somewhere there's hopefully a zero somewhere again it's only using the training data we we set in to figure that let's walk through a test of values so what I'm going to do is I'm going to take a thousand values and I'm going to run it through the machine okay I'm going to run it through and generate my probabilities for those 1000 values and I'm also going to add those thousand values for those of you who saw my common table expression this morning that's actually exactly what I'm using here I'm using common table expressions and I get this result this row right here tells me the system is 15 percent better than random it's not great but again it's it's kind of the best we're going to be able to do with this simplistic example the 72 percent likely it was to kind of pick the right number but again not a great result but that's what it is we we didn't tell anything about how to find a zero we just gave it some rules and then it ran the training data now you'd never do this in real life this is just for a demo so you can kind of see it walking through so let's let's talk about what we would do in real life in real life there's really some very popular tools to do machine learning tensor flow is probably the biggest one that I've heard there's also matlab which is open source no I'm sorry mad lib is open source matlab is not matlab and then there's weka and those are all client side tools meaning that they run on the client and they can potentially use a database to store all this server side you can use pl python with some libraries you can use matlab or you can even use something called scikit which is very popular in open source which again runs at the client and the server side it is really kind of nice down there at the bottom I have a URL I'm sorry a video link that you can use if if you're interested uh why would you want to use a as I said before machine learning and particularly deep learning is very computationally intensive you've got maybe millions of tensors you maybe have hundreds of layers and a lot of comparisons so GPUs are very well processing so if you need to do that a lot of GPUs are designed the libraries are used to design to use GPUs to perform this sort of repetitive calculation to have with machine learning and deep learning kind of cases so that's why you hear a lot about GPUs in AI because it it really is required to sort of do that kind of work okay so what tasks would you use AI for well you would you could use it for chess you know when I was a kid we used to have chess computers a IBM used deep blue for jeopardy that was very popular voice recognition is very good anytime you're using Siri or Alexa or Google home or your phone you know you're talking to it that's voice recognition searching search results recommendations for videos for example action very popular and even weather forecasting you can imagine why you would use that I'm going to walk through what I think is the most common example that is fraud detection in fraud detection you effectively have a case where maybe you have a credit card company and they're trying to determine if a if a particular charge is fraudulent or not okay so what would we measure what would our neurons be well we would be measured charge the amount of the amount of the charge uh whether it was a magnetic swipe or a chip or an online charge the distance from the vendor to the address or the distance from the charge to the previous charge uh we could look country we can look at previous charges by the vendor okay so how would we set up AI to perform this task we would probably choose our attributes maybe the ones we would use before okay we would then create a machine to measure each of the things we want to measure then we would feed a whole lot of data into the machine which would have results here's a transaction it has these neurons and it was it was a valid transaction here's another transaction has these neuron values it was fraudulent this is machine supervised machine learning because we're giving the results with the training day right we would feed it through and then once we got it working which we loaded all these old transactions through we could then predict whether future transactions were fraud fraudulent and if we wanted to get fancy we would have some of the incorrect results or correct results back into the system so it would learn it would learn new fraudulent patterns as it was sort of tracking okay so uh database and all this why am I using Postgres and AI in this example uh well of course machine learning requires a lot of data and where is most of your data most of your important data is in the database so learning in the database is clearly a reasonable thing to do it's not super popular right now because machine learning is still a new field but as machine learning starts to become more popular you'll see more and more machine learning probably moving into the database just just like data warehouse moved in and full tech search moved in and json moved in AI event will move in uh to be part of a standard part of database but again it's still in its infancy so it's still being developed externally in most cases ages of doing machine learning in the database of course um you can use previous activity you have seamless access to your data you can take action on these particular entries um you can even check a transaction before so you could say before I commit this transaction what are the odds it's for a joint great way of sort of using that and finally uh there are a whole bunch of uses for machine artificial intelligence inside of databases user applications which is what we've covered but for performance improvement and alerting that finishes my talk for today I'm going to leave you with an interesting picture which sort of talks a little bit about artificial intelligence I think unfortunately we don't have any time I believe for questions but I'll be available uh to answer them either here or in the discussion room yeah thank you mr boost for the really informative sharing on on artificial intelligence and postgres so before we finish the talk can I just uh there was a bit of a rush introduction at the start so I'm so sorry for that and uh yeah so I'd like to rush share again that it's really an honor to have mr boost here with us he has been working on postgres for a really long time and yeah he's a co-founder and core team member of postgres global development group and yeah so he's very knowledgeable and very experienced in this topic so yeah we really appreciate having you here mr boost my pleasure yeah thank you for our share so actually I think we do have one question we can address that quickly here so it's can we have the test url out here please boost I'm not sure if it means url url you can see in the chat notes there okay I'm looking here um it's in the chat public chat okay chat notes below that okay all right okay um look and hold on can we have the test you are here oh the test okay so um so you wanted I think they wanted the url of the actual slides I think you're you're this slide it's actually right here at the bottom this this is right here so if you go to this url right there uh under under extended you'll see this presentation and then you can also download the sql that you saw so all the you saw you can download the sql and just run it and try it yourself that's what I was recommending to people um the actual url uh for the actual test is okay let's see if we can get back here find that for you that was way back at the beginning right there but again if you download the slides you just click on it it'll download for you yeah I think that answers the question great so actually now we have another question yeah I'm reading that question now so uh how do you create a progress with without billions of dollars so the way we can do that uh is because and I have another presentation about that on my website so if you're curious uh there's a there's a presentation called uh democracy and open source databases which I've given at other conferences but not at this one so if you'd like to take a look at that uh what it basically says is that because uh postgres is so efficient uh and because our development team is so efficient we're more efficient than a billion dollar company uh come software developed in big companies is very inefficient um so there's another presentation called well postgres live forever and that gets into the mechanics of open source and why open source development is so effective uh and you really have to read those and I believe there are videos of those as well understand why it's it's not postgres itself it's really the open source process is so efficient uh that uh we can develop you know with volunteers all around the world we you know I like to say we take volunteers all around the world and we we take their intelligence and we make like a lens and we focus it on the problem uh so that's uh the short answer to that any resources to learn about postgres and ai that you recommend I have put a lot of url here there is not a whole lot of information right now about postgres and ai I'll I'll say that right away um it feels like we're still in the infancy where people are still looking I need to do it outside the database uh but again uh as I said before over time as the technology matures it moves into the database there is a nice video on one of the slides if I can find uh which I would start with which is a good summary it's the slide that has all the software listed which is right here right here so this slide has a youtube video that youtube video kind of gives an overview of postgres and ai could you elaborate on sql versus no sql I'm sorry I I have a presentation on my website about that I probably doesn't fit the topic today uh those communities and companies that develop and participate in the community in Asia well uh we certainly have uh we have NTT out of Japan uh Fujitsu out of Japan and NEC out of Japan uh Alibaba out of Chicago out of China I have companies in Indonesia um I have companies in uh India and Pakistan I'm trying to remember I don't know of any in Vietnam right now uh but I will say that Postgres is growing rapidly in Southeast Asia we I was in Southeast Asia a year and a half ago for a conference in Indonesia and it was great we had hundreds of people so uh we're definitely growing quickly in that area region it's Postgres growing manifesto the MySQL yes it is because the community of Postgres is open to development so in MySQL whether it's MariaDB or MySQL from Oracle all the development's happening inside a company or inside a closed organization uh with Postgres it's community developed similar to Linux and Apache uh and that's why there's so much activity and innovation happening in the Postgres space that's I'm going to I'm going to defer to Hong on that one I don't know the answer I don't know of any Vietnamese language Postgres material yet we do have translation a lot of languages I'm Armenian and we just added some Armenian support but I don't know of any Vietnamese language material yet I'm sorry about that the good news is each country kind of develops its own ecosystem so for example China has developed they translate the manuals into Chinese there's sort of a Chinese blogger that's the Postgres blogger for China has a whole bunch of user groups related to that India has its own conferences as well as China and Japan so the good news is that you don't have to do everything in English you can create your own ecosystem inside a country where you have blogs about that are in Vietnamese where you have events that are in Vietnamese where you have materials that are in Vietnamese so one of the great things about the project is how international it is so I actually just checked when you read that question actually there are materials in Vietnamese um yes so so so if people from from Vietnam they want to to google team hill portraits there are a lot of materials written from the different community so there are I think I believe that are also um material in other languages Vietnamese for sure there are many I just google and can confirm that yeah I mean the great thing about the project is so decentralized so there isn't a central place that usually with proprietary software there's one company that's where all the activity happens but with Postgres it's happening all over the world all the time uh so that's great and you can create your own materials in your own sort of ecosystem within that environment the last question I see here we are part of google summer of code uh in fact we just got approved last week for number of code so if you're interested in that please contact the community uh do a search and we can be part of the community that way uh google summer code is not good uh I don't I actually am not part of it myself so I don't know um I know we have people from all over the world but I don't know the mechanics of google summer of code um it's just a project that that work but again there are there are people supporting Postgres uh all over a high go out of China is a small relatively small company but they have a whole Postgres practice uh Equinix out of Indonesia has a whole Postgres uh you know practice and and support for customers uh so that's the great thing also is that you can create your own business and then you're part of the community and you can then use Postgres within your customers and provide support to them you know using your own language with your own materials and so forth uh so it's a very open system and that's one of the reasons we're so popular um okay so that's a great philosophical question why are other projects not organized this way um um I think you have to watch the two presentations I mentioned before uh democracy and open source databases and why will Postgres live for um it actually is the same question of why are all governments not democracies right like some countries are democratic some of them are authoritarian some are in the middle um and some of them are partially authoritarian partially democratic um and it's because there are some efficiencies to authoritarian governments or some efficiencies to democratic governments uh in in general democratic governments are more flexible more easily to change more easily to bring in new people and to use the talents of the population more efficiently and it's exactly the same with open source you're using the talent of thousands all over the world who are solving these database problems instead of trying to solve them inside a small company that only has a hundred or two hundred engineers um even Oracle only has a small number of engineers who work on the database who understand it so again it's that scaling of democracy is it messy yes is it hard to figure out sometimes what's happening in Postgres yes I didn't even know there was a lot of Vietnamese material right because it's all happening in the distributed way uh so I would recommend you look at that maybe next year I'll deliver that talk um but it is a very interesting talk why isn't everyone that way it's sort of like saying why is in every government a democracy it's it has to do with history it has to do with there is a general trend or democracy around the world it's slow but it's happening I would say the same thing is happening in the software world can you help to work for the project better I would say the first thing would be to look at those presentations because I've given those presentations in China okay um I've given them in Russia I've given them in Europe and and North America um and and Latin America as well so again what the way I've been doing it is try and break down why is something successful what things does it take to make something successful and how can you duplicate the things a lot of people look at open source and think chaos and they they don't want it right um so uh open source is chaos in a lot in some ways it's very hard to control but it's actually very efficient and over time what you're seeing slowly happen in the software world is a movement to that democratic open source process but it happens very slowly because there's pre-installed applications there's people are used to using big databases from billion dollars uh there's people so Linux moved away from proprietary unix um and I think over time open source databases are going to move away from proprietary databases but it's a slow process so Bruce um thank you very much uh you know we need to prepare for the next session and I want to thank you for for giving the time within the 40 minutes so we can take like some major question just now and and I see that there are a lot of questions coming but we are like need to finish up the session now and there's a final question dude they asked please come to Vietnam's I guess that uh when COVID uh over you uh you got invitation to come to not only not only will I come to Vietnam but my wife come as well because she told me I definitely want to go there that's on my list okay thank you very much Bruce for your presentation and also for the morning workshop so you've always been a great honor to have you with us thank you thank you


Will you be, uh, you're gonna be my boyfriend, yeah? Um, I mean it seems a bit soon, but sure. Oh, we look really good together. I'll put my hand on your shoulder, because this is the thing couples do. There, there. Thank you. Thank you for consoling me, boyfriend.  It's like we're all blind, and we're just hoping we don't run into a wall. Kevin, can I touch your crotch? Give me a moment. Don't get away from me. He's- I can't see Kevin. Get back from my boyfriend. Just come back. Come back, Kevin. Don't touch my boyfriend, exactly. Guys, high five. High five. Top of the morning to you. I've always wanted one. Damn. Wait, which one are you? Me. All right. Sound off. Sound off. Me. Which slim, shady is Kevin? I'm the one waving. I'm the one waving. Which one are you guys? We're all waving. Let's just get into the pubs. Let's just get into the pubs. I want to change my clothes. If surgery goes anything like our introductions were fucked. I put on a hat on. That means I'm the head surgeon. Hey, guys. I look a bit different. Dahi's very different. Me, too. Dahi made himself distinct. Kevin only made himself slightly distinct. I wear a hat. Oh, we need, we need a hacksaw so we can open the door. Where, where is it? Kevin, where are you going? I left.  You'd never even high-fived me yet.  Oh, you. Can I call the elephant? He's definitely pricing it. Sounds like it's moving something.  I'm trying to call the elephant. I think you're all fighting.  All right. We have to find a hacksaw. I found it. Kevin, where are you? I've moved it elsewhere. You in space right now. Oh, no, Kevin. Don't do this. I'm hitting the hacksaw, Kevin. And when you get along, you'll get it back.  I've gotten the saw stuck. Oh, thank God you're here. Now I can get that high five. Thank you. Now, here. This is where I've got the saw stuck. I can't pick it up. Can you help me?  I have no clue where they are. I can't. Hold on. I'll go, I'll go get the nurse. All right. Someone call the elephant. Oh, I'm up. She was just pushed me down an elevator shaft. I didn't know such thing. Guys, I'm stuck. Now that we've back up, we need help with this saw. How and why did you do this? I can't. I don't know what I'm doing. It's stuck. I brought fruit. I might need it now. This is pure chaos. Kevin, you should know that everything you do is going to break it. Can you really blame me for just being me? Is that really my fault? Yes, we can.  If we do one at a time, apparently four. More than four, apparently. Yeah, I think we need to restart the level. Yeah. But we know where to go. We know what to do. All right, good job, Kevin. Five minutes to break the game. That's OK. But we're all getting along. Did you see how we were working together there?  Look, if you guys were just more efficient and got there first, I wouldn't have been able to mess with it.  Dying is done. Wait. It's a different level. Oh, now we need to look for a heart. Different level. Awesome. Kevin can't break this. Let me add it. Give it to me. OK, I have a heart. We need a heart at the door. I like Kevin. Kevin has no heart. That is true. What does Bob need before we just start like hacking away at him? I'm going to give him a little pat on the head. No. Why did you take off his head?  Where does this path go? His arm on his leg. And his leg on his head. He got it. He looks good as new. He looks great. Oh, dude. This is going to take out his ribs. This sounds bad.  You lose. Give us a wave, guys. All right. I loved it. I want another level. Another level.   I didn't know my own strength. I didn't know I could just rip his head off. I'll just fan him. The air con's out. So you guys do the surgery. He's looking a bit pale. Can I be the one that pulls off the head? What did you? You just put a flashlight in him. Bob is sick. His left arm and his right leg. Or is it everything that's glowing? Wait, actually, the glowing things could be the best. No. Don't touch it, Dahi. I read it the wrong way around. I read it the wrong way around. It's his right arm and left leg and his head. No. Who's fine? Give him some more blood. I get the head as a souvenir. I get the head. OK. Let's take this easy now. Stop slapping him. He doesn't deserve it. His head doesn't.  Offer feck. Oh, no. Give me that feckin hammer.  And I don't mean Bob.  And I can open Kevin. Oh. Oh, sorry. You look the same. I feel like you guys just don't even want Bob. I'm trying. I love that man. OK. So his left arm, left leg. He needs a leg and an arm. We need to hit that button in there. See the button in there? So we need to get around. So if I pull off his arm and open the door. Yes. Don't pull off the arm. How about pull off the arm, go through the door, and we put the arm back on while you're in there. Yes. OK. I'll pop back on. I broke his ribs. I didn't mean to do that. OK. I'm taking off his arm. OK. So run. Kevin. I'm in. It's all up to you. Oh, no. We know what happened last time. I need a fuse. I got one. OK. I got one. We need to pass that through to me. There you go. All right, follow me. There we go. It's it. It's it. OK. We got to pop his arm back. I need you to put it in here. There you go. There you go. All right. He lost a considerable amount of blood. He lost half the blood in his body. Oh, we need his leg. No, Sean. I slapped the door. I don't know what's happening. I think he slapped it way too hard. Sean. Sean, no. You might be stuck in there forever. He's gone. No. There we go. I can't look. I can't look. OK. This one's easy. No arms or legs. Just large intestines and small intestines. We need the bad organs. Wait, wait, wait, wait. Guys, a vent. Hand me through his organs. Someone, over here. Look. What do you need? I need to find the drug so I can check this into my organs. Give me his old organs.  Pass them through this vent. Can I get some large intestines over here? Dude, even if he lives, he's going to be a vegetable. The body can survive. The body's resilient. Can I have the large intestines? Don't like small ones. Size matters. Bring me large intestines. Yes. OK. All right. Here it goes. There you go. Through the doggie door. No, I'm kind of lonely. Can you at least push your hand against the glass to me? So I feel some sort of connection. All good idea. They're so erect. They'll cut straight through the glass. You're going to have to inject this real quick. Yeah. No. I'm stuck behind the glass. I'll go get it. Oh, this is not someone. Don't let Kevin go.  Here you go. I'm coming. I'm coming.  Go away, Kevin. Go away. Get out of here, Kevin. I think. Yes, get it. I think we can get this one if we give it. Everything's still fine. It's not stuck anymore. I don't like how Kevin's picking this up. I don't like how Kevin does anything. I don't like Kevin. I don't like Kevin either. It takes two people to stop Kevin from being here. I'm going to hide it.  Get it off him. Get it off him. I'm going to push it. I'm going to do it. All right, we're in. What's wrong with him? Just his left leg. Oh, this is easy. This is easy. OK. And all we've got to do is put in his left leg and get a new one out. This is so easy. Got it. Just put it in here. Yep. Put it in over here. We're actually going to save Bob. Got a new one. Got a new one. He's losing blood. Swap his legs around. Just put the other one off it. Just put the leg in. We did it. Yes. He's safe.  It took 45 minutes, boys, but we saved the Bob. Here. I'll jam this broom in here to get you guys out. Thing of blood. That's working. Hold on. Is it? It's working. I'll try it again. It's working. Look. It opened it. Come in. Oh, wow. If you jam the doors, it was wrong. No, the broom did it, Duhi. The broom. Yeah, the broom. Yeah, the broom did it. What does this do? Yeah. What does this button do? Or is it just a distract Kevin button? It does nothing. Need it again. Oh, we need another fuse. Not a fuse. Kevin, drop down the fuse. Okay. Drop down the fuse. All right. Drop it down the hole. Kevin, that's a mark. All right. You'll have to describe what does a fuse look like. Small, blue.  Oh, this tastes like broom. So it can't be this. Okay. I got it. Oh, I found one. Oh, hi. No, you didn't, Kevin. I found it. It's in Jack's hand. Oh, I found a fuse. No, Kevin, that's a broom. I'm pretty sure this is a fuse. It doesn't sweep at all. Did you actually find a fuse, Kevin? Yeah, I found a fuse. Let's go. That's a leg. He found a leg. Where did you get a leg? Found it in the well. All right. Swap him out. Get ready. Pull the leg off. We're working on it. I'm taking off his leg. His leg's gone. His leg's gone. Pop it in, Kevin. I grabbed it wrong. Hold on. Kevin, come on. He's losing. All right. Hold on. There. Oh, it just popped right off. It's rejecting the hose. It's rejecting the hose. Oh, Jesus is not looking good. Okay. There you go. There you go. There you go. I got him. I got him. So you look at us. Kevin, I need you to take Bob's other head. Gladly. All right. Here's another syringe. I got Bob's head. I'm going to pull it down to you. Okay? Wait. We need two heads. Okay. Give us another head. Yeah. Great. Thank you. Can we just make up all heads? This isn't build a Bob workshop. Take off his... What are you doing, Bob? Take off his bare arm. There's a bear on him right now. Take off his bare arm. Okay, Kevin. Get ready. I'm going to take off his arm.  Okay? Oh, that is lovely. Oh, my God. They do say two heads are better than one. Hey, wait, wait. I'm going to take... Wait, Kevin. I'm going to take off his arm.  We'll put them on both sides. Yeah, yeah, yeah. Okay. You ready? Oh, my God. This is... Yeah. Brilliant. Go. We're getting good at this quick change act.  That does not... Oh, my God. Hello. This is not what's supposed to be. Oh, no. I'm going to need your head, but I can give you an arm instead. Will I still be able to talk if I have an arm in hand? No, but you can wave like a lot. Hey, I got the arm. All right.  We put the arm in. Which head?  Wait, wait, wait, wait. Which way? Is that the arm in hand? Done, apparently. He's fine. He's fine. He's great. Change your character. You can change your character here. I just became Dahi, I think. No, no. No, you can't be me. Can't be Dahi. Please throw one out. All right. I feel great now. What do you click? That's me. No, that's me. I picked this one. Just press E on the mirror. On the other mirror. Block to the other character, so we're all someone different. Wait, but you guys are the same.  In we go. In we go, boys. In we go.  I'm ready. I'm taking this pot. Oh, two hundred and one pot. Is that going to work? Hello. Probably a bad idea, but here we go. And carefully does it. I'm going to stop his blood.  No, it didn't fit. The host is rejecting it. I stopped this one. This one is taken. No, the host rejected it. I need this arm. I need this arm. Now we need it in the other leg. All right. I'll pull it off. Hold on. You ready? Go. Yeah. Very good job. I'm tired of the leg. There's more legs. There's more legs. I can hear the Spider-Man tune already.     Pull it off, though.  I got it. Gotta go quick. He's alive. He's alive. He's alive. He's dead. He's dead. He's alive. And he's dead. But that's because he pulled his arm off. That's because he pulled his arm off. Why would you do that? Do that. I'm dying. Dolly, why did you kill him? Why did you get him? Why did you get him? It was all going so well. We made a leg, man. Guys, I think I'm onto something. There's a hole here. If we drop something in this hole, you. Who are you? Who do you mean who am I? Stop getting hostile, Brian. Look, up here. Wait, why is no one paying attention to me? Kevin, rewind everything that just happened and then ask yourself that question again. The thing is, we've got to get in there to get the new arms.  Here, here, by me. Look, we need to put something through this hole and it'll fall on the button and open the door. Okie do. See? I'm helping. You know, instead of getting someone to come over and do all of that, you could have just. I'm trying to make it a cooperative game. It's a one-man job. Do you need to pop that arm? You need to hit the bottom, yeah.   I got it. My ball, my ball. I got it. Ball. Give me the ball. We're playing hoops. I'll do it. I'll do it. I'll do it. It's just slapping them. I don't like sports anyway. I got some books. Move, move, Kevin.  Are you going to get out of the way, Kevin?  I figured this whole thing out. Oh, I did it. First try. Oh, great job, Kevin. First try, Donny. You'll block the ball from going down. Give me the ball. I think we're playing in the forest. Are we breaking up, Donny? Go ahead, Kevin. Go ahead. Are we breaking up? We're here to get it. Pick off. I have a better idea. Go ahead. Yeah, you were standing. I did it. I got in. You were standing. What do you mean you were standing? What do you mean you were standing? All right, now all we just need is some arms. Look, I'm the one that opened it. I opened the door and got the arm. Oh, this is a lot of legs. I'm ready. I'm ready. All right, nurse. I'm ready. Oh, go. Put it in. And the other one? There. You did it. He looks amazing. Good job, everyone. Except dying. Yeah. I opened the door. Okay, Kevin. Jack, the fuse fell out and we're stuck. Someone put the fuse in the door. What are you guys doing in there? Put the fuse in the door. It's me, Kevin. Yes, great. Now, Kevin, get the fuse that's next to you on your left and put it in the thing. All right. Got it. Oh, funny business. Yeah, that's it. Now, put it in the fuse on your left. Not the other one. Good boy, Kevin. It's like working with a dog. Kevin, good man. This, what? Kevin, put it in the right one, Kevin. Wait, I have to see if that's the right fuse. Is this the right one? It just put the fuse. There's only one, put it in. Now he's getting very angry. Oh my God, it doesn't fit. It might be the wrong fuse. You just leave it in. I've done it. It's quite a predicament. Well done, Kevin.  Thank you. That's the nicest thing you said to me all day. We need kidneys. We need kidneys. Just give me one moment, dude. Just tell me when. I'm the only one who cares about Bob, dude. Then why aren't you here? We're the ones stopping his blood. He's doing pretty good.   You were getting angry at me, and I didn't know what to do.  I won't be mad. I won't be mad. I won't be mad. I'm putting in the fuse. Kevin, he's going to be mad. I just want to say Bob. I just want to say Bob. All right, I'm putting in the fuse. Is he going to be mad? He's saying he won't be mad, but in kind of a serial killer way. I'm putting it in. You don't need the whole thing. Bring that in here. He's Frankenstein. Okay, so we need another leg. We need his left leg. We need his head. Actually, we need two kidneys. Are you okay, shot? Two kidneys. Just two kidneys.  How did you get the fuse stuck in there? He's got the fuse. I'm actually so done, bro. I'm actually so done. How do you get the fuse stuck in there? We need the kidneys. We literally just need the kidneys and worked on. No, his leg fell off. Oh, so I'm like, I'm always so close. You have the fuse. How do I do a medical? Oh my God. As a test, I'm going to run to the bathroom and see how you get on without me. Hey, here's the door closing. Okay, we got five minutes without Kevin and we have five minutes to do this. We got to go quick, lads. Quick, quick, quick. All right, go, go, go, go. What do we need? Before Kevin gets back, before Kevin, we need all of this stuff. All right, leave it in the lobby. How's it doing without me? Kevin, go back to the toilet. Go toilet again. It's weird, there's like no progress made.  No, no, no, no. You didn't wash your hands, Kevin. Go back. I can see the feces on your hand. I like it that way. You have no idea how much better society is without you. There's you syringes on the floor. What the? What is this blue thing, huh? What is this? He's just lying around. No, he's... You kidding me? I'm locked in. Jack, can you stop him? Jack, can you stop him?  You know me so well already. There you go, Dan. It's green. Thank God. Go, go, go. Struggling with the door of his. Get out of the door. Get out. I'm minding the fuse, guys. I'm minding the fuse. I'll trade you for the fuse.  Right leg, left arm, two kidneys and a heart. What, do you need his left arm? Also, Kevin's hiding the heart in the... I took the heart and hid in the ventilation system. Jesus, you're just chucking this stuff.   I just see you scurrying in like a little rat, taking an arm. Hey, Kevin, you can contribute. I'm contributing by protecting the fuse, so Jack doesn't get stuck. Why would he get stuck? Guys, I need help. What do you need help with? Oh wait, no. I saw his arm on the wrong way, but I think it's okay. His elbow bends in your direction now. Wait, we've been sewing stuff? Okay, so... Kevin, don't touch the... I literally leave for two seconds and he tried to take the fuse around half. This is the kidney. This is the kidney.  No! No! Jesus Christ! Kevin, can you please click try again? I did. Oh, sorry. I see you're blaming me again for no reason. I'm just going to blame you for everything. Okay. To be fair, you have a pattern. Dude, he's ribs are... Let's go around. Go on. He has a problem. Dahi, you're being the problem now. Dahi, please get it together. Can I have a look at that? And... No, Kevin. Right, right. Kevin's trying to grab the fuse. I'm just... I'm grabbing your hand. He's being nice. Look. Aw, we're holding hands. One type of kidney. I walked away for a second, but I almost sinned! Good.  I'm sorry, I'm sorry! Dahi, you absolute monsoon of a doctor. Kevin, you have to cooperate, please. I am cooperating. This is me cooperating. Don't steal the fucking... I didn't steal it once.  You lost the last one. I didn't want to bring it up. Get your hand! Why is everything I do wrong with you? Just compliment me for once. I hear you. I hear you. Say I'm helping once. I hear you.  Can you praise me one time? No! Please get in my leg. Do something good and you will be fine. Give me some direction. Okay, ready? Okay, ready? You ready? Okay, take it out. You ready? You ready? Put it in. Boom, beautiful. Now we just need a heart. I got a heart. I got a heart. We all did it. Yes! Oh, baby! Yes! I think that was like literally a minute and a half. We might have A plus on that. Two and a half.  That's the world record right now. And think about it. Oh, my God. That's actually true. World record. Exclusive world record. You saw it here first. Irish people are better surgeons than anybody else. I'm so proud of us.


Hey everyone, welcome back to figure fantasies. We're here now at sacred tour five dash one to five dash six Okay, guys, that is it for sacred tour level five dash one to five dash six I'll see you in the next episode for sacred tour level six. Take care guys. Stay safe That's the warden and I'm out here


They are solitary corals. These are the moulds of where the corals were. They are solitary corals. They are about the size of my little finger.  And they grow in a vertical fashion in cold water. This is very different from the tropical corals that are colonial. But around Antarctica, in the deep sea, you've got solitary corals. And they feed on the plankton that settles through the water. We call that marine snow. There is another group of organisms called bryzoids. They're colonial. They're very fragile and they're all lying down like the corals, which I'll explain in a minute. But the bryzoids again, they've got little hairs that feed on the plankton that's in the water.


All over the top, looking for the run of Sun. Head to the box, lays it off to Deli Alley, who has the easiest of side-footed finishes. All over the top from Olavira, finds Deli Alley, chess it down, one-on-one with Ramsdale. Brilliant finish from Deli Alley. It's going, it's going in the 50th minute from number 22. Here's Deli Alley, lays it off to Sun, left edge of the box. He's got support in the middle from Sosoko, finds Sosoko, who finds the back of the net. It's going to be the left foot of Harry Wilson, curling over the wall and into the back of the net. Born with break there duck, away it spurs. Here's Dan Jumer into the box, left side pulls it back. Harry Wilson is there, it's got in!


Welcome back to the School of Cascades with Tim and Jaco to celebrate another month's worth of graduates and we have had a lot of them. Before we start doing graduates, I've got to tell you something, I don't know if it's the right time but I'm massively overwhelmed. There's too much good stuff going on, I can't keep up, I'm feeling a little bit overwhelmed. There is a lot going on in a failure and I've been feeling the same thing and been working behind the scenes on a little solution for you and actually, two solutions for you and I think you're going to love them. Who's that? That's Tim 2.0. Is he better than me? Well, let's just say we've been trying to tweak things a little bit. Notice that you boys have been getting a bit slack.  Crikey a kidney. He's maybe gone a little bit too far although I do love his trainers. What's yours like? I'm a little bit worried actually about him. I'm here to party. Show me some graduates. Okay, well, roll the tape. Crikey, you've got your work cut out haven't you? I've got a question actually. If that's Tim and 2.0, what does that make me? My question is, are they here? I'm worried about this. Which one are you? I don't know, let's get this done before you get into it. They said, Jako 2.0 said roll the tape, didn't they? Let's get us up on screen, Jako. Here we are. Right, so we have a whole host of this month, a variety, let's say, of various movements and graduations. Full graduations, little steps graduations, we've got lower body. We've got it all. We're kicking off with... A smorgasbord. I think it's a Scandinavian word for a platter. A smorgasbord. A sharing platter. Cheese, meats, you know. So we have got now... This isn't the school of castellans rig, I don't think. I've not got one of those. Fortunately, it looks like it's just tied together with string here. Anyway, I love this. Jako, when I saw this one, I loved it when people just put a scaffolding rig in the background. This is proper background. Yeah, this is serious. Alright, let's get going. John Root. You've got John Root. Bar muscle up. And his scaffolding. That's hard with a fat bar as well. And jeans. Ooh! I thought he was going to skin the cat at one point. Yeah, I wonder if he's got a bar full over. Pause. So, what I loved so about was the message he sent through. He was like, super pumped. First time he's got over the bar and he went, I know I need to like... I guess he said something, something on lines. I know I need to work on my form or how smooth it is. But he gets the point of shoulders symmetrical. He's got over the bar for the first time. That's something to celebrate. Whether he hitches his knees up, whether he drives with his hip or not, whether he wants to, you know, whether he wants to change and make it sort of smoother or cleaner. I almost don't even like that word, but that's just a personal thing. But whether he wants to or not, that's then becomes... Strict. Strict is a good word to use. He's using a little hip drive towards the bias. His hips go towards the bar through actual driving off. We've talked a lot about this in muscle ups and go, move the way you want to move. A crossfit muscle up is not wrong, but as long as you know what the purpose of a crossfit muscle up is versus a strict muscle up. So you can choose how you want to move. That is entirely your choice. The only thing that I would say on this, I think I mentioned in the last month as well, is learn the way preferably that you want to practice because it is hard to change the existing movement pattern. Meet, Jack and I both learn with a bit of a knee tuck to start off with. And then trying to muscle up without a knee tuck when you've ingrained it is pretty difficult. And let's give John a little bit on this. Going from a bar like that and doing it like a thick bar like that and then doing it on a normal sort of size pull-up bar is way more difficult than what he's doing. So it's a hot job. Next one. Felix, handstand. For days. Got a push-up coming. For handstand hold. This is his first, yeah. Look at that. We talk about being, when you're proud of the movement you can do. Breathing well. Good control. Windy as well. That makes a difference. And adjust the amount of time the amount of time he's holding that for. And you know, pretty good. Like you can't like, you mean, line-wise you've got to be happy with that. Yeah, that's good. I think, well done. Good work, Felix. Right, next up. Got a little sneak preview, Jack. Owen, he's back. Yeah, he's come back from his holiday after doing a human flag with his kids and came to the social on Saturday school casting. It's live and I thought, I'm going to win it. What is this? I don't know. I don't know what's coming. And now I do. Superman push-ups. Boom. Strong guy. Look at that little bit of the pop. Nearly. He goes off the flipping screen. I love those for a little bit of, actually can be, yes, you can take a great photo. If we pause one for him. As he's in midair. There you go. On the screen. There we go. There's your pause. There's your picture. For a cool picture and something a little bit of sound better. Actually, great work as an explosive magic exercise for if you're going to prep yourself or prime yourself for a heavy or hard pushing session later in the later in the year. Top job, Owen. Good work, mate. Very smooth. KZ2K. Look at this girl. KZ is an absolute legend. We'll just flag anywhere. Anywhere, everywhere. Follow on Instagram. At a festival. Maybe at a drink. Yeah. At a flag. Good work. We're seriously the difference there of, well, come to remember she came from London. We did one of the first works. We did one of the first London ones. We did. So I've been following her progress for quite some time. And a nice little example there to show the amount of time, the difference between a single leg position like a flag in the fall. It looked like she could almost hold all day with that single leg. Look at that bit of rotation as well. We're going to some artistic flair. A little bit of hand position wise. Ah. Yes. Okay. Hi. From a little bit, from a, came to Marbella last year. So smooth. As you can see, can tell from the location. Here's the climber. Yeah. And he, and he, and they came to the Marbella workshop. The first one that we did out in Marbella at Mike's gym. The space, there's only six spaces we sold on the weekend actually. Someone in New Zealand with him. Amazing. I think there's only six spaces left. By the time this goes out there might even be less. But if you do want to get on it's super smooth on that one though. Loved it. Real nice. He's got the control. And that was impossible for him. Just because he was good at climbing he just literally, the false grip and some of the shoulder mobility stuff needed to be changed for him. So Dan, aka Beans. Coach Dan. Look at this. One, three, nine. Working on his pull-ups. And strong. This was, I think a PR or PB on total number of pull-ups and the quality and symmetrical of them as well. He took him through some, he was at the social as well this year. Make sure you look out for the social for 2020. It's going to be massive. And he was actually a legend on the day helping out with timing for some of the challenges that we did. But he also was helping him with some of the bar muscle progressions. And I said to him afterwards, yeah, his pull-up was feeling good. It looked strong and it was actually very easy to support him and spot him through a full bar muscle. The celebration is also one to... Yeah. The flex. Lauren. Again, came to a workshop in Birmingham. Yeah. So I remember the same with Halliwell. Yeah. The aka Spice Girl. Bodyweight. So she said she was really proud of clearing up that bodyweight squat which is for her. Yeah, yeah. We like, often people take these for granted in terms of bodyweight squat. But honestly, I've screened so many athletes over the years. Hundreds. And the few people that actually can do a really nice well-controlled bodyweight squat. I think it really is something which people strive for and should celebrate because it shows a continuation and maintenance of functional lower limb which is going to support you through whatever it is you want to do. So these are something like... You go ask the squat. But there's a lot of people who will get nowhere near that quality of movement because they're all jacked up and tight. We should definitely be prioritising these fundamental movements. You see that smile? Martin. Now, yes. This guy, Martin, came to a strength and play workshop in rather than years ago. Being coached by Georgie as well. Amazing work. Been working on this one for a while. And getting that muscle up in the bench. Absolutely brilliant. But apparently, what didn't know he was going to do one and then did two. Yes. So Georgie would have been... She'd been in our seminar. She'd been in our workshop and she was hopefully going to be working more closely with us. And one of her clients, Martin, who she said they'd been working on muscle ups and then weren't planning on actually doing any attempts at the full thing on this session but just things felt good. So I thought they'd have a go and then literally, they loved the celebration.  And he did it that day. Oh, obviously, they obviously did some degree to attempt it. Georgie's absolutely buzzing for him. And that's when... Nice, Martin. It's a beautiful thing, seeing somebody coach some people. Yes. Getting. Redefining impossible and the high-fives. Safe. Good boy. Right, this is going to look good.  For James. Change on the background. Make sure that's set. Oh, yeah. Strong. Nice, looking stable. Now. Checking out what's going on in the gym. Boy walking past. You'd think he would... that guy would have stopped to give you some... He's pleased. He's happy with that. He wanted some high-fives from that guy walking past. Congratulate yourself on that one. Nothing else to add. Top job. Stuart, virtual class to remember. He worked on his muscle-ups. Oh, nice.  Working on bar previously. And then just all the strength work he'd done from the bar, muscle up, into the ring, once he'd sorted out his... his full-script position. So all about the full-script. There's a bit of strength in that full-script. You have more confidence of hanging lower positions and you can clean that up a little bit. But it's looking real good. If your ring starts doing a movement pattern in that one, then it's just a matter of strength. Once you know what shape you've got to make, it becomes much, much easier. Now through this one in there, Tim, because this came off the back of, and I believe you had a little...  He'd been struggling with this. Watch that video. Have you just said what it was that you were working on your tiger bend? And then he thought he'd take it up a few minutes. Went straight onto the parallel bars and watched this. If you've been... It's actually a class where he was buzzing. All he'd done is watched the feedback you were giving yourself effectively was what he needed and the strength to get out of that real nice. There's a couple... I've done four arms stands on parallel bars before. I don't do it a lot because the risk goes up a little bit. But what's nice about them is you can see how much control because you can get a little bit lower to the ground. Like you actually, the hard bit is, oh, Jack, I butchered it. The hard bit is that you've got to really be accurate with the other position. You've got to hit the right line.  But what's nice is you can get nice, head down quite low, get close to the bars because you're deeper, but it's a stable in your transition because you're in a nice form of shoulder stand. Yeah. Yeah, I was giving the Cajunas a little bit of a stroll out there on the bars and always it's using the weight to keep them stable as well. So yeah, nice work, right? It's firmly in mind possible box. You can do that. He's got some rep too. Crikey. Showing off now. Beautiful. So again, we talk about strength and abundance. You've got to have the strength to get that press out. You've got to have the strength to make up. Legend. Wait for the celebration as well. It's got a nice pushing that shoulder elevation. And then it's just working on improving. Now they're off. Now. I could it. Sure. There was a nice celebration on that one. Let's watch this in reverse quickly. A nice line. Yeah. That's a matter of time. Good shoulder flex. Exactly. And so it's a matter of time of just literally the moment the corrections aren't small enough or easy. Fo bar muscle up. Always point those out because you have to because otherwise people don't know. People don't know. Otherwise you get false sense. Yeah, like we did. Beautiful little smooth through there on the muscle ups. I can know. It's just remember you've got that little gap in between the bars. It does make it a little more like the rings.  It's like a hybrid between the ring and the bar muscle up. Exactly. And didn't you'll see what happens with it. Show me. Roll the tape. Look at that. We don't do a lot of handstand walking. It's good fun. Great for you to feel working on that elevation. What we do like to work on is we do like that. Handstand push it. Brilliant. He wasn't expecting it. He wasn't expecting it. He actually got it out. So again, talk about strength. You must have had the strength and it was actually like controlling the balance that was actually the technology of that. But it starts to think about something else. All of a sudden it's a body surprise. Rather than if you've been trying to do a handstand push it, would it have been successful? We don't know. Just turn it out. There's a philosophical question there. Robbie B. Retro Robbie B. The legend from the virtual classroom. Been working on his back lever. Oops. Nice. He looks like he means business. Good ceiling tiles. We look like the ceiling tiles on our gyms though. Squeeze squeeze squeeze squeeze. Good work Robbie. And then. Yes. That is the one. That is the one. You can't help it. On that Tiger Man video I was just editing it and I was like, what are you thinking? He looked like he's going to go with the Ali-Vee energy. I can't do that. I'll see my virtual classes up. Paul, online coach. Yes, he's been working with Seth. Well, a nice little rig in the back garden there. Great little setup. I know. I can feel it's going to be a muscle up. Yeah. Boom. It's a nice little position to be able to go into a proper deep straight bar. This is a nice little point there actually. We talked about, you know the how you can pull in a muscle up? The less hard you have to work in the transition because it's easier. Yeah. So we asked him because the speed of pull. So this one for Paul is a work on it. It's just a pull like work on that explosive pull because he does a good job. You're watching me coming up here sort of running out of speed a little bit more stress on the shoulder and also makes you have to work hard to get out of the dip. But it's bang on a muscle up. Good job. And you've obviously got options there to start to work on pulling a little bit higher and you can decide whether you want to keep that knee tuck in or not or you want to keep the leg straight. It's all part of the process. We learned the same way. You just choose how you want to move. But we celebrate those because it's a big moment when you get able to bar for the first time. It's a top job. Can you imagine we can see in front on that position there he's working hard to get into that. But if he hasn't got the shoulder range of motion there, he would have to pull even higher. I wonder if he does like what I do and do a bit of watering in between sets. I do that. He took his crocs off. He don't leave the crocs off. Have he? He's been coached by Seth. He likes to pair crocs as well.  Coach, Seth does have spaces available for those that want to join him for some one-on-one online coaching and programming. He has some awesome results from online coaching clients. It's definitely worth thinking about. There's one more. Just one impossible read fight. I think Seth tapes us 18 seconds. Can you get that? Do you want to check with the camera? Yes. At the social. We had some world record last year. Yes. We have a lot of sides near Dambidele with my wife. We have a number of connections. I don't know where Chancellor Venue is. East Europe. America. Where is Dracula from? Chancellor Venue. Why Dracula is America? Because America adopted Dracula. Pennsylvania is America, Pennsylvania. Yeah, that's right. They sound very similar. Yeah. Is it a country or is it a it's a country? Transylvania that one up on the map. Anyway, let's let's probably got great Dracula tourism opportunities, actually So what Yeah, but this was but that was good stuff people like that to me Tim I'll I want to just prep because I'm you I want to big it up in that your Tim spent probably five minutes with him And he'd never done a ring muscle up before You just worked on the things that we always talk about like a nice false grip and comped with the rings on Can I actually pull thunder chest can we do a deep in dip and then two minutes later after going through those put Them together and what do you got? Your first ring muscle up look at that and I clap from the audience as well Good crowd there. Look at their people. It's great. That's great. It brings like brings about good memories That was actually a second because he'd done the first one and then I said Jack I'd get the camera And just bash that little but he was absolutely chuffed with that one I didn't give you a little bit of feedback after this didn't see about just Trust in the process doing the work. Yeah following the guides in there and just be a put in the working It shows you I say always say the ring was was really good one to a good example of it of if you have the strength In pulling and push in we can give you or somebody a coach can give you the technique to be able to do something He didn't think you were able to do if you haven't got the strength and Just like think that there's this magic bullet that this little technique You know this little tip that you haven't got if you can't pull high enough on those rings You can't do a deep ring dip. You're not gonna be able to do a ring muscle So he's come to the table clearly with enough pulling and dip strength to be able to do that and so that's Take that just as that's take it more as an encouragement that work on your pull-ups work on your dips Make give make sure that the strength element is not a problem And then the the technique and the transition and those types things for that and take care of itself a little bit Help from a coach. So that's it. We better go and find out what 2.0 doing. Whoo. Yeah Some manners Yours needs to stop taking drugs I think Jacko 2.0 needs to be Needs to be more excited. I don't know why he's maybe to be honest He sounded like he's from Pennsylvania some dodgy American accent That's it from the graduates guys massive congratulations and thank you for sending them into as if you are have you made it to this point? 22 minutes in from the graduation well done well done You've probably got the best of the bansett towards the end but send us your graduation videos We love to celebrate and share everybody else It's not just about us making us feel good It's about making the whole community feel good because you guys are inspiring and motivating to everyone else as well So let's share some graduation love. Yeah, any questions need any help anything like that You know where to find those best places probably on Instagram, but also here on YouTube and We look forward to hearing from you helping you along your journey to redefine your own impossible Tim Till next week. Well, we're gonna go and get your necklace out so we can have a chance of various I am when we don't not then


Right now, all of our countries are dealing with the health and economic impacts of the global pandemic. But as we look to rebuilding from this crisis, we must do so in a way that will build back better for all of our citizens. And that must include an ambitious plan to take strong action against climate change. Last month, we set up a law project that will allow us to reach our goal of carbon neutrality by 2050 and to impose to the next government the responsibility to reach the same target. And yesterday, we presented our new ambitious action plan for climate, which will allow us to overcome our 2030 climate objectives. We will raise our emission reduction ambitions and in partnership with provinces and territories, we as a country will strive for the upper end of a range of 32 to 40 percent below 2005 levels by 2030. We will raise Canada's price on carbon pollution rising by $15 a ton starting in 2023 and rising to $170 Canadian dollars per ton by 2030. And we will remain committed to making significant investments in international climate finance because to effectively fight climate change at home, we must fight it everywhere. This pandemic has taught us the importance of global cooperation and we must translate that lesson to how we deal with the climate crisis because our kids and grandkids are counting on us.