Here we are. This is the patreon patrons of Geek Nights Sib 5 game Probably not uploading these videos right away. So these were probably recorded Long ago when the game was begun because I can't tell my enemies what's going on but I'm playing the Sib 5 game with seven other admittedly dangerous humans and civilization 5 and Many of them are most of them if not all of them are fans of Geek Nights or at least listen to the show So they know my tricks they know what I'm up to and they're probably really looking forward to the opportunity to take me the fuck down so I've got to be extra cautious and extra candy in this game so I'm playing the Shoshone, which is something I normally don't do and That means one that I'm kind of more land bound than you might otherwise see and We'll see what that means. I usually play an expansive Navy-driven game So I very rarely play in the land, but I'm going to expand quickly and one of the main powers of the Shoshone As you see when I found my city I get extra territory a lot of extra territory And that means that I want to expand rapidly and cover a bunch of this territory because my other powers is that my units Get a combat bonus inside of my own territory So it'll be actually moderately hard to conquer me if I build a great wall later It'll be even better. So my plan is to expand rapidly to build multiple cities and to Basically make this nice land empire I'll get one little edge into the sea so I can start sending boats out and Probably conquer one of my neighbors early and quick if I can But it's gonna depend on what I actually find now normally when I play this game. I would build a monument first That's pretty much a hundred percent like that is my strategy I build a monument I go Liberty or tradition depending on who I'm playing and I go go go pop up off But this time I'm actually gonna build a pathfinder you play to your sieve strength and the pathfinder one you can see strength eight Strength eight. It's just a better warrior and it only costs five more. It moves better It's got all these powers. It also lets me pick what I find in the huts within reason So I'm gonna build another pathfinder. I might even build a third one Maybe a worker and then a third one, but I got it because these are humans I can't take any chances. I got a scout out as much as possible.  Oh marble. I could after wonders early and stone So I'm gonna have a pretty high production capability here. So I Definitely want to go down the mining route and either Bronze working or more likely masonry to start so I can build these quarries. We'll see how it goes I'm gonna just build mining first and not actually pick my path, but it'll probably be masonry next I won't mining. I want to get this luxurious or is right away.  This is gonna be a heavy industry city So I'm gonna need another city that can grow a bigger population So we'll see how this goes Best of luck to all of you. May the odds be ever in your favor and Maybe I'll go for the great library. I got plenty of forest. I could chop down and I'll have the marble.  This is not my typical game of Civ


Live from San Diego, California, it's theCUBE. Covering KubeCon and CloudNativeCon, brought to you by Red Hat, the CloudNative Computing Foundation and its ecosystem partners. Welcome back to San Diego. It's KubeCon, CloudNativeCon 2019. You're watching theCUBE. I'm Stu Minim, my co-host for three days of live coverage is John Troyer and happy to welcome Fresh off the keynote stage. To my right is Azhar Syed, who's the chief architect for Telco at Red Hat, and the man that was behind the scenes for a lot of it, Han and Garcia, Telco Solutions Manager at Red Hat. Gentlemen, thanks so much for joining us. And thank you very much for inviting us here. Thank you for having us. Interesting keynote, so 5G, my background's networking, we all watch it. Let's say my Telco provider already says that I have something related to 5G on my phone that we grumble a little bit about, but we're not going to talk about that. What we are going to talk about is the keynote. We had China Mobile up on stage. Maybe, I love a little bit behind the scenes, as you were saying, CloudNative enabled not just the keynote and what it's living, but gives a little bit of what happened. Sure, look, when we took on this particular project to build a CloudNative environment for 5G, we spent a lot of time planning. And in fact, this is the guy who actually did most of that work to do a lot of planning in terms of picking different components and getting that together. One of the things that CloudNative environment allows us to do is bring things up quickly, the resilience part of it, and the scale part of it. Those are the two important components and attributes of CloudNative. In fact, what happened last night was obviously one of the circuit breakers tripped and we actually lost power to that particular entire part that you saw on stage.  I didn't talk about it as part of the keynote, but guess what? Through, because it was CloudNative, because it was built in an automated fashion, people were able to work. Yes, they spent about three hours or so to actually get that back up, but we got it back up and running and we showed it live today. But I'm not trying to stress on how it failed, or why it failed. I'm trying to stress on how quickly things came back up and more importantly, the only CloudNative way of doing things could have done that. Otherwise, it wouldn't have been possible. All right, so Hananez, as the man behind the scenes there, it's great when we have, here's actually the largest telco provider in the world, showing what it's happened. So the title, Kubernetes Everywhere, the telco edge, gives a little bit of a behind the scenes as to kind of the mission of building the solution and how you got your customers, your partners, engaged and excited to participate in this. Well, it was a very interesting enterprise to realize, actually. We took four months, around 15 partners and I would say partners, because in that case, I'm taking Bel-Canada and China Mobile as a partners. They are part of the project. They were giving us a requirement, helping us all the way through it. And together, other more commercial partners and of course, as well as our alliance, like the team in Eurocome and Open Interface Alliance is well working with us. It was about 80 to 100 people working behind the scenes to get this work, to have a lab directly completely set up with a full 4G containerized mobile network in France. I have the same in Montreal, 4G and 5G course, directly in Montreal as well, in one of our partners, Calum Labs. And then bringing here the 5G pop and have everything connected through the public cloud. So we have everything in there. So all the technology, all the mobile technology was there. We have enterprise technology that we're using to connect all the labs and the pop here with the public cloud through SD1 technology. And we have, of course, deployed, as was mentioned, we deployed Kubernetes on the public cloud and we have, as well, Kubernetes, Rehat OpenShift container platform running on all the prems in the lab in France, the lab in Montreal and the pop here. As I say, it was kind of an interesting enterprise. We have some hiccups last night, but we were able to put that out. The world of telco, very specialized, very high service level agreements. I always want my phone to work. And so a little bit uses different terminology than the rest of IT sometimes, right? That's true, yes. NFV and VNF and VCO. But so maybe let's tell people a little bit, like, what are we actually talking about here? I mean, people also may not be following Edge and telco and what's actually sitting in their hometown or it used to be embedded chips and then it was like Linux, but we're actually talking about installing Kubernetes clusters in a lot of different, really interesting topologies. That's absolutely true. Actually, with the way Hanan described it was perfect in the sense that we actually had Kubernetes clusters sitting in a data center environment in France, in Montreal, and a remote pop that's sitting here on stage. So it was not just independent clusters, but it was also stretch clusters where we actually had some worker nodes here that were attached back to the Montreal cluster. So the flexibility that it gave us was just awesome. We can't achieve that in Jambo. But you brought up an interesting topic around carrier or the telcos operated in an environment which is different and cloud-native principles are a little bit different, where they want very high availability, they want very high reliability, a good amount of redundancy. Well, cloud-native environment actually provides those attributes to you, but the operational model is very different. You have to almost use code as throwaway, hardware as throwaway, and do a horizontal scale model to be able to build that. Whereas in the older environment, hardware was a premium, switches and routers were a premium, and you couldn't have a failure. So you needed all of those compliance of high availability and upgradability and so on. Here I'm upgrading processes in Linux. I'm upgrading applications. I can go deploy anytime, tear them down anytime. I'm monitoring the infrastructure using metrics, using telemetry. That wasn't the case before. So a different operating environment, but it provides actually better residency models than what telcos are actually used to. It's a complicated ecosystem to put all these pieces together. Gives a little insight as to Red Hat's leadership and the partners that help you put it together. I would let him answer that. I'd say it's not our first rodeo. We have been working on the virtual central office project with the Lino Foundation Networking and OPNFV community for three last years, let's say. And the interesting part of this one is that even though we typically was working with what the technology that they are using now, we decided it's time to go with the technology that we'll be using from now on. But of course, there is a set of partners that we need. We need to build the infrastructure from scratch. So for example, we have Lenovo was bringing all the servers for the setup in Montreal and here in San Diego, which actually the San Diego pub was built originally in Raleigh, in Lenovo facilities and cheap all over the country to here for the show. And then we have the fabric part, so the network part that's this column. This was bringing us the software-defined fabric to connect all the infrastructure. And then we start building the software layers on top. So we have Rehab OpenShift Container Platform for the two, completely deployed on the metal servers. And then we start adding all the rest of the component like the 4G core from Ultran, like the 4G and 5G radio from Ultran, together with Intel, ComScope. That is building, start building the mobile part of it. In Montreal, in San Diego. And then we add on top of that, then we start adding the IMS core in the public cloud. And then we connect everything through the SD1 by tuning. So a couple of things that I'd like to highlight in terms of coordinating partners, getting to know when they're ready. Figuring out an onboarding process that gives them a sandbox to play with their configurations first, before you connect them back into the main environment.   Every morning, just a daily stand up from 8.30 to 9.30. And we continued that. All over the day, actually. So, Azhar, one of the things I really like to China mobile when they talked about in the keynote, first of all, they said, you know the promise, 20 by 2026, you know, it's rainbows and unicorns and 5G will help enable so much around the planet, seriously. But today, Shifu talked about major challenge in the rollout and infrastructure and service and capability, so help us understand a little bit the hype from reality of where we are with 5G, what we could expect the next couple of years. Absolutely, we are going through the hype phase right now, right? We are absolutely, all the operators want a 5G service to be delivered, for sure. The reason why they want it to be delivered is they don't want to be left behind. Now, there are some operators who are moving more opportunistic and looking at 5G as a way to insert themselves into different conversations, IoT conversation, smart city conversation, right? Edge compute conversation. So they're being very strategic about how they pick the set of technologies, how they go deploy in that particular infrastructure and strategically offer capabilities and build partnerships. Nobody is going to rip out their existing 3G, 4G network and replace that with 5G by 2026, it's not going to happen. But what will happen by 2026 is an incremental phase of services that will be continued to offer. As an example, I'll give you, cable providers are looking at 5G as a way to get into homes because they can deploy a millimeter wave band radio closer to the house and get a very high speed, multi-gigabit high speed connection into the home without having to worry about what's your copper look like? Do I have fiber to the home? Do I have fiber to the business? And so on and so forth. So that's actually an interesting way. Okay, so you're solving the last mile issue in a very targeted use case. Absolutely, so that's one. The other area might be running a partnership with BMW, Toyota and some of these car companies to provide telemetry back from cars into their own operating environment so that they know what's going on, what's being used, how's it being used, how can we do pro-diagnosis before the car actually begins to fail? Pick private environments like oil and gas, mining. They're going to go deploy public safety and security where all of these policemen and safety personnel are required to now use body camps. Now you have video feeds coming from hundreds of people that are deployed on incidents. Now you can take that information, you need high speed broadband, you need the ability to analyze data and do analytics and provide feedback immediately so that they can actually act. So literally this specific targeted use case, even a country like India, where they're talking about using 5G for very specific use cases, not replacing your phone calling. I love that point and it kind of ties back into some of the other things you were saying about the agility in the operational model and I relate it back to IT. Again, my perception of some telco may be 20 years old and that they had a tendency to do very monolithic projects when you're rolling out infrastructure across a country, there's a certain monolithic nature to it but you're talking about rolling out one, rolling out individual projects, rolling out, that's also the advice we give to IT, try it with one thing, try OpenShift with one application. Absolutely, absolutely. And then also though, it takes the upskilling and the cultural model. So, with your telco managers who were on Slack there with you and I don't know if there's any relation, any other kind of things to pull out about the mirror of the IT transformation with telco transformation and culture. That's actually a good point that you bring up, right? Look, the cost of building a 5G infrastructure from ground up is extremely high. If they want to completely revamp that, you're talking about replacing every single radio, you're talking about adding capacity, you're talking about adding, you know, backhaul capacity and so on. So, that isn't going to happen overnight, it's going to happen, it may take even more 10 years. I mean, the most interesting thing that I saw was even LTE is going to grow, LTE subscriber count is going to grow for the next two years before it flattens. So, we're talking about LTE 4G that's been around for a decade almost, right? And it's going to still grow for the next two years, then it's going to flatten and then you'll start to see more 5G subscribers. Now, back to the point that you were bringing up in terms of operational model change and in terms of how things would be IT principles, applying IT principles to telco. There are still some challenges that we need to solve in Kubernetes environment in particular to address the telco side of the house. And in fact, through this particular proof of concept, that was one of the things we were really attempting to highlight and shine a light on. But in terms of operational models, what's used applicable in IT will now be totally applicable on the telco network. The CICD pipeline, the delivery of applications and software, the testing and integration methodology, the operational models, absolutely those. In fact, I actually have a number of service providers of telcos that I talked to who are actually thinking about a common platform for IT and telco network. And they are now saying, okay, Red Hat, can you help us in terms of designing this type of a system? So I think what Hanan could speak to you a little bit about in this context is how the same infrastructure can be used for any kind of application. So you want to talk about how the Kubernetes platform can be used to deploy CNFs and then to deploy applications and how you've shown that. Yeah, well, this is what we have been doing, right? So we have the Kubernetes platform that is actually deploying the services. We have all these partners that are bringing their cloud-native applications on top of that, that what we are calling the CNF, the cloud-native network functions. And basically what we were doing as well during the whole process is that we have those partners that are still developing, they're still finishing the software. So we were building and deploying at the same time and testing at the same time during the last four months. And even, I can tell you, even just to the night. Even last night. So the full CI-CD pipeline that we deploy in IT side, here it is in operation on the network side. Well, yeah, so I want to give you the final word because John was talking about IT cycles. You know, if you think about enterprises, how long they used to take to deploy things and what cloud data is doing for them, it sounds like we're going through a similar transformation at Telcos. Absolutely, in a big way. Telcos are actually deploying private cloud environment and they're also leveraging public cloud environment. In fact, sometimes they're using public cloud as a sandbox for their development to be completed until they get deployed in a private cloud environment. They still need the private cloud environment for their own purposes like security, data sovereignty, and their own operational needs. So, but they want to make it as transparent as possible. And in fact, that was one of the things we want to also attempt it to show which is a public cloud today, a private cloud and bare metal, a private cloud and open stack. And it was like, and you know, it came together, it worked, but it is real, that's more important. And for enterprise and for Telcos to be literally going down the same path with respect to their applications, their services, and their operational models, I think this is really a dream come true. Well, congratulations on the demo, but even more importantly, congratulations on the progress. Great to see the global impact this is going to have in the telecommunications market. Definitely look forward to hearing more in the future. Thank you very much. Thank you for the opportunity to actually be here. All right, for John Troyer, I'm Stu Miniman, back with lots more here from KubeCon, CloudNativeCon 2019 in San Diego, California. Thanks for watching theCUBE.


Hello, everybody. Andrea Terevsky here with Dental Oil Tutoring. We had just moved, so I'm sorry about the boxes behind you. It looks a little bit messy, but soon they will be gone. In the video today, I would like to talk to you guys about how to start studying for the board exam. So this is for dental hygiene and dental assisting students. This is literally when it's hopefully at least a few months before the board exam and you're thinking, okay, I have to start studying, but I don't know how or where or what to get started with. So this is for you who just has no where or you do not know where to start, okay? And remember, I was a student once too. I had the same thing. I took the board exam actually before I had graduated. So I took the board exam a few months before. So I was studying for the board exam while I had clinic, tests, assignments, projects, you name it, it was crazy, but I started studying about three months before the board exam. And I've been tutoring now for about 13 years, and I have a course called the Board Exam Prep Academy where students are able to sign up a year even before they take the board exam because I like saying that, because I actually want you all to study a little bit over time, because what I've learned over my 13 years of teaching experience is if you study a month before the board exam, you will not be as successful as somebody who has studied at least three months before. Even a year before the board exam can't hurt anything, because everything that you're learning for the board exam, you have to know for the real world also. So even if you're technically studying a year before, all of that information that you're learning, you have to know it anyway. Whereas three months before the board exam, you pretty much have to study specific to the board exam because you don't want to study too much, you don't want to study not enough, and there is such a thing as studying too much, and there is such a thing as studying not enough. So if you're not a part of my board exam prep academy, I will tell you guys where to start, and I say this because within the board exam prep academy, I will actually show you guys exactly where to start depending on when you had signed up. So it is outlined actually by modules, modules and topics. So it makes it easy to study. So you go through module one, study all of that, go through module two, study all of that, and then move on, right? Plus I have mock exams, I love mock exams because that's the best way to test your knowledge. I have lecture videos that are about an hour long because we do our live tutoring sessions online about twice a month. So there's lots in there for you, so you will never be thinking to yourself, what do I study because it's all right there. But if you're not part of the course yet because you should be, but if you're not, what I want you guys to start off with is your notes from school. I say your notes because if you start off with your textbook first, you get overwhelmed very, very fast. I've known so many students who pretty much try to start off with chapter one of their textbook and then they realize, oh my god, like this is boring. We learn this first semester, I've been studying this for four hours and I know nothing, oh my god, this is horrible. So that's what happens and then students stop studying, students get angry, they get upset, they get discouraged and then they just don't study until a couple months before the board exam. But then at that point, they feel like they have to hurry, hurry, hurry and rush, rush, rush to get everything in there. So I do highly suggest not looking at the textbook, but look at your notes in school because they are typically easier to read and look at each topic. Now if you're not sure where to start, I suggest starting with radiography and if you haven't seen them yet, I do have a few videos on YouTube about x-rays, processing errors, the history of radiography and there's another one too, I just can't think of it now, but I have lots of them. So do a search on YouTube, I have tons that you don't have to pay for. So have a look you guys. So that's a topic that I do suggest starting with. My puppies have decided to play behind me, so I'm sorry if you can hear that too. But another one is oral pathology. Now oral pathology though is one topic where I do suggest looking in your textbook because the notes you took in school probably aren't quite up to par as what's in the textbook, but it depends on your instructor. Your notes might be amazing, right? So have a look at that, but you do need to know the lesions, know all of the lesions. I'm talking like the macules, the papuels, the lesions inside the mouth, all of that. So again, if you're not sure where to start, start with radiography and oral pathology and go from there. And you should be studying at least two hours a day up to six hours a day. Don't study more than that and don't study less. If you don't feel like you have the time to study two hours a day, make the time. And if you feel like six hours isn't enough, it is enough. Trust me, I've been doing this now for 13 years. I have seen students who study 10 hours a day and don't pass. It's because there is such a thing as studying too much. You need to take it easy sometimes. You need to take a break. You need to get enough sleep. So there is such a thing as studying too much. And if you need help, I am always here to help, especially if you have taken the exam once before and you were not successful, you should be asking for my help. Because if you didn't pass the first time, it's not likely because you don't know the material, but it's because you did not study properly. You need help with multiple choice type questions. So that's where mock exams are excellent too. I have tons of them because I find after you study a topic, even if you think to yourself, okay, I know that pretty well, you don't know, you know it until you take some mock exam questions to test your knowledge. And you don't know what types of questions to ask yourself. So even if you're studying with a partner, you don't know sort of what questions that they should be asking you. But I have all of them down there so that you know that if you do a good job on the mock exam package that you will pass. Because how could you not? So if you need help, let me know. And I should say too, with the mock exams and questions, look at them after you study a topic. Because if you look at a mock exam package first, you will look at the questions and get easily overwhelmed because they will be hard for you. Because if you haven't studied it yet, then you don't know the information because you have to remember that I was a student once too. So I had to learn something for the first time. The first time I learned something, I didn't know it. I had no clue. In fact, I remember learning the teeth numbers for the first time in dental assisting and I was kind of like, what? I'm never going to know this, you know, but it's easy, right? So we all learned something for the first time. It's a matter of keeping up with it and keeping in mind that the first couple of times we learn something, we don't know it. Like we just, it's, you know, in one year out the other, that's normal. But after the 10th, 11th time of doing something, you're like, okay, we're starting to get the hang of this. So that's very, very normal. So start there first. Start with your notes from school and then if you must have a look in the textbook afterwards and do a topic a day, I'd say. So two hours of study minimum up to six hours every single day. Thank you guys for watching and I'll see you guys in the next video.


The setting ablaze of a bus rapid-chancit BRT vehicle in Ogu Dwarlong, the old shocky expressway in Lagos, last Wednesday was a sad reminder of the evil effects of the takeover of Lagos State by touts and street urchins who take the laws into their hands at every opportunity. Now, according to a statement by the Director of Public Affairs and Enlightenment Department of the Lagos State Traffic Management Agency, or authority last month, a day by your Taufiq, hoodlums in the area set ablaze the bus belonging to the Lagos State bus. Recall that also, one of the fallouts of the October 2020 ensarse protest was the takeover of Lagos State by touts and hoodlums who blocked highways to extort commercial vehicles, heavy-duty trucks and other road users hiding under the cover of the ensarse protest. These hoodlums had set ablaze 80 BRT buses in Ogu Dwarlong and Odu Bergar areas of Lagos State, joining us to discuss the mainness of Thaigri and Taps in Lagos, is Dheji Awabide. He's a legal practitioner. Thank you so much, Dheji, for joining us. Good evening. Good evening, Maryam. Thank you for having me. Great. Dheji, as a lawyer and also a legosian, obviously, it's not news that Lagos has a thug-stroke-tout problem.  But the one that's more of a problem is how these people continually extort from unknown, you know, rather normal citizens, passers-by. Some of them would claim that they are working at the behest of a certain government agency or paristata and their tax collectors as opposed to the normal people who are expected to come and collect taxes. Let's start from there. Dheji Awabide Lagosian is facing some trauma because of touts in Lagos. Have you experienced it, and why do you think that this has grown over time? Thank you for the question. Are they related to Thogre in Lagos? I'm sure it's not new to anybody who's lived in Lagos who's had any business doing Lagos, that these thugs are a part and parcel of the two-tile package of Lagos. And regrettably, they've done more harm than good to the economic well-being of the states and also to the citizens of the country who are resident in the states and also to Lagosians. I've also had my own personal encounters with them. Sometimes I've had a running with them twice. The first time I was coming back from port on the island, heading to my chambers on the mainland, and then I had a flat tire somewhere on the Adeculi descent, heading into Yaba, Alagumeji area. And then, from nowhere, six different guys appeared from that bridge. When you pass through the bridge, you don't see anybody. So within a minute, I was accosted by six different guys who, yes, tried to render assistance, but for a fee. Of course, I was lucky that it happened in the afternoon, so I wasn't really scared for my safety. I knew that even if push comes to show, I would probably disappear from the scene. But there are several instances where people are just accosted by these thugs in every corner of the states. I mean, no part of Lagos is immune from this menace of Thogre. And I think that it has thrived because the government has enabled it. And I think if we're going to be brutally honest with ourselves, Thogre in Lagos has thrived because there's an ecosystem that is funded and is supported by the government.  You have to come in there briefly. What do you mean by it's supported and funded by government? You're saying that government gives monies to these people who are a threat in quotes to society. So what I'm saying is that the environment for them to try is provided for by the government. How do I mean? So if you go around Lagos, you'll find a lot of these polls, of course some of them will say that they belong to the ANWU or what you now know as the Lagos State Parks and Garages and they're collecting revenue or collecting funds for the government. But aside from that, that platform that has been given to them by the government is also what has enabled them to try. The funds that it gets from all of these things that they do, of course some part of that fund will go to the government who also end up in government coffers. So when I say government gives a platform for them to try or support the ecosystem, that's what I mean. Now apart from that economic angle to the entire debate, there's also the political angle. Let me take a step back. You know that if you go to every major jump, every major bus stop in Lagos, you would find who are existing with these talks, policemen and last man officials. Right? Now you also find that most of those policemen and last man officials do not even have a say. I must even control these talks. So in other words, they allow these talks free reign at every opportunity. Can you really blame these law enforcement agents who allow these talks free reign? You can't because their safety at a particular judgment area is not guaranteed unless those talks give approval or give them some form of protection. So you see that there's some form of systemic arrangement between the law enforcement officers, either at the federal level as you have by the police that are standing at those points, at the state level as you have with last man officials, and then by those talks who by themselves are also there along with their friends and their cronies. Now another thing you also notice in Lagos is that there are several spots in Lagos where you have one way signs. Now these one way signs are not particularly very obvious. And even where they are obvious, you find some people who are standing in one corner hiding behind a particular signpost or hiding somewhere under a canopy ready to operate anybody who flots the law. Now the job of arresting offenders is the job that is reserved for the police. It's not a job that is reserved for talks and talks, but if you encounter these people, they will tell you that they work for the local government or their collection of money for the local government. And if you go a step further, by engaging them and calling them to the local government, you will find that they also have a space within the local government system that enables them access into that place. And they even give you tickets. I had a case in court against one local government of a very similar scenario where you have people who are not even stuck with the local government, accosting road users and obtaining them, exerting them on funds. Now, all of these instances have tried because the government has enabled them. The government is not unaware, mind you, they're not unaware of these events or these people. They know where they are, they know who they are. But the other reason I was getting out is that the government realizes or the politicians realize that in holding on to power, they require some level of individuals who can display some level of dogry. And of course they won't send their children to display this dogry. Neither would they send you or send myself or anybody else who is gayfully employed. So they need these people who they have groomed in this ecosystem to go ahead and do these jobs for them. So when I say they are funded and they are sustained by the government, this is what I mean, because they cannot try. If the local government decides today that there will no longer be dogry in Lagos, that will not have unemployed youths, unemployed people roaming the streets of Lagos at every junction accosting road users, vanishing vehicles and obtaining road users, you will not see them in Lagos anymore. Let's talk about the role of people, because I mean if you live in Lagos, you pay your taxes obviously in Lagos and so that means you have a right. But let's just do a little flashback to what happened during the elections. We saw a lot of videos and pictures and some of us even experienced it in real time, how Thogs prevented some people who were non-indigins from voting on election day. We saw how some of them were literally at polling units telling people what parties to vote for. We saw them destroying ballot boxes. We saw them beating up people at polling units. And you said something about the fact that politicians have discovered that the only way they can hold on to power is to keep these so-called touts happy. And if you look at statistics from what I've done in my research, I've realized that these guys are growing astronomically and most of them street urchins empowered day by day. I've had a guest in the studio who said that they're becoming some sort of sleeper cell and they all have responsibilities given to them and targets. But then my question is to you and every other person who lives in Lagos who knows what is right or who has seen these people in action. Why are we quiet about this issue and why is this not one way or the other become a front-burdant issue every single day of our lives? Well, thank you for the question. I think the number one reason why a lot of negotiations are not particularly going all out to confront the government is because they've realized that it's very difficult to wage a war against the government and win. How do you mean wage a war against the government and win? The government is just a percentage of us and then we're the people who they are supposedly serving. So what do you mean by that? The government controls almost every major institution. For instance in Lagos, you have the judiciary, you have the police, you have every other agency of government. Now if you decide to go against, say you apprehend one of those towns and you take them to the police station because as a private citizen you have the powers to also arrest an individual if you witness the commission of your crime. And you then decide to apprehend one of them and take them to the police station. What are the chances that that individual you've arrested will not be out of the station before the close of day? What are the chances that the DPU will not be called by some higher person asking that DPU to release the individual? Now, so what happens to you? What's the safety measure that's in place for you to put yourself, your life on the line for everybody else's sanity and safety? So if I understand you, you're saying the arts are stacked against us and we're all just going to fall our arms because we believe that the executive controls everything and then the voice of the common man cannot be heard. What's that? What's that? I mean, what's the fate of the average devotion then? The voice of the common man can't be heard and as indeed has been heard. But what do you find? You find suppression. You find suppression at every turn. So the opportunity to change the government usually avails the citizens at every election cycle. And when you have, so what did you have the last election cycle? The people spoke against the candidates of the ruling party in Lagos and the candidate lost the election. So indeed the common people have also lent their voice to show that they're satisfaction with the government. So, but again, you know, that's lending your voice to the ballot boxes wanting having the overall impetus and mechanism to achieve the objective of replacing the entire government is another thing entirely. And that's what you find with the largest society of Nigeria, that even where you find people who are willing to go the extra mile, who are willing to fight their neighbors, willing to stick out their neck for a better government, a better society, you always find these roadblocks that have been deliberately put in place by these people who want to retain and hold on to power. So it's not as if we don't know that it's wrong, but how do you effect that change? You can do that. You can effect that unless you have political power. That political power does not lie with the common man. It lies with the few people who are controlling it. Who gave these people this political power that you make reference to? Because again, I wonder how much information do we have in terms of how a democracy works?  And so what can we do?  But this thing is growing. It's becoming a big problem. And it's not going to be at the dust depths of just one person, whether the guy who lives on the mainland or on the island is going to spread. And how long are we going to keep thinking this way as opposed to looking at ways that we can democratically get the government to see our side of things? OK. Well, thank you for the question. Now, when you look at it from the perspective of trying to allow the government to see this side that we are all saying, I made the point earlier that it is not as if the government is not aware of what's going on. So, I mean, what they have, what you have going on is that the government is playing the ostrich, right? So, the picture that you flashed on the screen, the end source protest of a guy who came to disrupt the peaceful rally, holding the cutlass, right? That guy has not been found till today. People have, NGOs have used the pictures. Is that not a failure on the part of our law enforcement and the judiciary? Because I mean, what have we made of all of that? So, this picture is everywhere. So, are you telling me that the government does not know who this guy is? Of course they do. But of course it was, it didn't just come to that rally to disrupt the rally by himself. It was an engineer to do so, right? So, that's what I say. I mean, when I say that it is, it is an ecosystem that is, it is very organized and supported actively by the government. That's what I mean. Because you can't tell me that in almost two years of the end source protest, that we've not been able to locate this guy. And people have learned their voices through it. Celebrities have done so, media houses have done so, and up till now, we've not found this person. So, that's what I mean by the people being helpless in this situation. The only way we can do that, we can get control of political power is by peaceful elections. Now, the question is, can we have peaceful elections? Well, that's a big question that we might have to answer the whole of this year. We'll have to think about it. And Tinka, maybe in the whole of, I mean, this first tenor after May 29. But did you have to go because our time is up. Didja Wobiyide is a legal practitioner and always a pleasure to have you here on Plus Politics. Unfortunately, today's conversation does not give us a glimmer of hope in the direction that we were hoping. But thank you so much for being here. Thank you for having me. All right. And that's the show tonight everyone. Thank you for being part of the conversation. If you want to catch up on all of our shows previously, you can go to our YouTube, which is Plus TV Africa and Plus Africa Lifestyle. Don't forget to subscribe and follow us on all our social media handles. Be part of all our conversations. I'll be back tomorrow talking for development. I'm Mary Annacon. Do you have a good evening?


Okay, so welcome back to this second lecture where we're gonna describe more machine learning methods for many body physics, quantum physics, classical physics. So I didn't mention it at the beginning, but my lecture notes will be available or are already available, I don't know on the website of the ICTP. So I think that you can access them only if you are logged in or something. If you have attended this conference. And tomorrow I'm also going to give you some codes, computer codes, which will be public available. For example, to simulate some quantum systems using these ideas. So you might want also to be here tomorrow morning just to have a look at that. Oh, in the previous lecture we've seen the rule, one of the paradigms of machine learning which is the supervised learning. And we've seen some applications to, for example, classifying phases of matter. I've shown you before, right, this example here. Now what I want to discuss is another paradigm which is called unsupervised learning. So what is this unsupervised learning? It's conceptually a bit harder than the previous one. So the previous one I've told you that it's basically just fitting. In this case, plus a clever method for optimizing. In this case, unsupervised learning deals with this problem. So we have again, a lot of data, my data set, like the one I wrote before. But I don't have the labels. So I don't know what the answer to my problem should be on those selected examples, right? So it's not like before that I already know. So I already know that this phase is disordered, the one on the left are ordered or the one on the right are disordered. I don't know that in advance. So I want to find it by myself, in a sense. So this is much more powerful application if you want of artificial intelligence. So to do that, we need a couple of tools and conceptual and practical tools to realize this goal. So in particular, these unsupervised learning can be used to find the probability distribution according to which those samples are generated. So we assume that underlying these samples that I've been given by somebody, there is some unknown probability distribution that I want to determine. So the goal of supervised learning, unsupervised learning is then to devise to find, for example, an artificial neural network that I call F again, such that as much as possible, so all of these are just high dimensional vectors, such that this probability distribution is well approximated and I will specify in what sense by my neural network. So I imagine, again, that my high dimensional function or my artificial neural network depends on some parameters and here I'm also specifying a normalization. So this is a probability density. So I have to specify also a normalization for this neural network. So in particular, if I want this to be a proper probability density, I have to divide everything by its normalization, which is basically in this case just, so imagine that X is just a discrete variable, so it would be the sum of all possible values of X of F of X and P, right? It's just the normalization. Okay, so now how can I do that? Well, to do that, first of all, like before I have to define some function which measures how far we are from the target probability distribution and how well we are approximating our data. So it turns out that we cannot use the measure we were using before, which is L2 basically distance, simply because we don't know the value of P of pi. We just know samples from pi. So we just want to use this information, not the value that pi takes on those points because simply we cannot estimate and we don't know it in advance. So the quantity that is said is much more meaningful. In this case, it's called the cool back divergence. So I always cool back libler divergence, which is a measure or how similar or how far two probability distributions are. So for example, imagine that my target distribution is this one and my approximate is this one. So then in this case, the cool back libler divergence is defined in this way. So it's, so basically it's the probability of pi given basically sort of distance between pi and f. So my approximate probability distribution and it's defined for a continuous, for a discrete valued function, like the one that I'm using here, like the sum over all the possible values of x of basically pi of x. So my unknown probability distribution times the log of, so my pi of x. So the exact probability distribution divided by the approximate probability distribution. So which in this case is f of x and P. So divided by z of P, right? So I can put z of P here, okay? So there are two things that we should remark about this cool back libler divergence. So first of all, you see that if the target probability distribution pi is equal to the approximate probability distribution, okay? Then this ratio is equal to one and log of one is zero. So which means that this quantity has a minimum at zero. And the other thing is that it is not a proper distance in the sense of the metrics. Because if you invert the role of pi and f, you see that this quantity changes. So it's not a proper metrics. But still it has a right clear interpretation in terms of information theory. In particular, it can be interpreted as the quantity of information that you miss when you represent your data with this approximate probability distribution instead of the original probability distribution. So it's the information you are missing in compressing if you want your data with another probability distribution. So we want to minimize this quantity and find the minimum which corresponds to the best possible approximation for my probability. Right? So to do that, okay, I mean it's, we have to basically, so this quantity, this object depends on the parameters of my neural network. So, and this will also show you why this quantity is important. Because if we do the derivative, for example, with respect to some one of my parameters, so this dkl, then you can see, I mean that this can be written immediately. So you see, you find this. So the only thing that depends on p here is f and z. Okay, so you have a first term which is just minus pi of x. Then we have the derivative pk of the log basically of f and p. So this is the first term that is basically deriving this object here, the denominator. And then we have another term which is plus. So the derivative with respect to pk of the log of z of p, okay? But then, I mean, we can rewrite this eventually just as the difference between two expectation values. So we have the, so the first expectation value is with respect to this probability distribution pi. So you can already see it here. So it's the expectation value over pi of the derivative of log of f, okay? So this is an expectation value over this probability distribution of pi of x, right? With a minus. And then I have a plus. So you can also do the derivative of this object. I mean, I'm not going to do it explicitly, but it's very easy and but you can show that when you do this derivative, you find that the basically the same object. So the derivative of the log of f again. But the average now is not done on pi but on my approximate probability distribution. So it's done if you want on f over z, okay? So the gradient of this quantity has a very transparent physical meaning or I mean, probabilistic meaning in the sense that it is zero only for the expectation value of these objects are equal on both distributions. So you attain a minimum when you basically have that those expectation values are equal. So for all these, all those derivatives have the same expectation value. And it is also important that it tells you why we choose these objects in the first place. Because as you can see, those expectation values, for example, this one. So if we rewrite it, it can be written like the sum over x, so all the possible values of x of pi x and these objects, which typically I call the decay of x and p, okay? So this is this expectation value. So pi is already normalized, but this can be approximated like the sum over my samples. So the sum over i, if you want to ns of this decay because of the law of the large numbers. So one over ns. So it's the simple average over my sample of xi, okay? So I take my samples and I estimate this expectation value again as just the simple average of this object, which is a function I can compute very easily on those samples. So this means that never during my optimization when I need the gradient, so I can use the gradient descent, so Gaussian gradient descent as before, I need to compute or to know the actual value of pi on those points. I just need the points themselves and to compute some averages over those points, right? So the other ingredient that we need is this one. So this one, so this thing is really easy to compute. Once you know the artificial network, you just need to do the derivatives and compute them on some given points. So this other one is a bit trickier because you have to be able to sample from your neural network. So these expectation values, in other words, would be, so the idea is that you generate some other samples, so let's call them x prime of i, x prime, so x prime of one, x prime of two, okay? So in these samples, these high dimensional data are generated according with a probability which is given by the neural network probability, okay? So in order to compute those expectation values and so to compute the simple average over these x primes, you need to be able to generate those points. So you need to be able to sample efficiently if you want from the machine. So in order to do so, we need to introduce some practical scheme to do that. It turns out that the networks that I introduced before, so in particular, these deep networks, these feed forward networks are not particularly suitable for this purpose. And instead, I mean, there are others, yes. No, I didn't, yes, they are too far. The first term here, I don't have the probability pi, no, but I have the points which are distributed according to pi. So which means that computing expectation values over pi can be estimated averaging the function I want to average over those points. But this is the low large numbers, right? It's the principle of some Monte Carlo sampling. So I generate a lot of samples from a given distribution and then I compute the value of the function I want to average over those points. Yes, but I'm never using this distribution explicitly. I'm just using the points which are drawn implicitly from that distribution. So to compute this sum, I don't need the pi, that was my point before. To compute this sum, I don't need pi, I just need to know the value of x, that's it. Okay, so now let's introduce an architecture, so let's introduce like a network which allows me to compute this quantity efficiently. To sample from this network efficiently. So what I'm going to talk about are called restricted Bosnian machines. And these are somehow the, for many reasons, I believe these are the natural entry point in the world on neural networks if you come from the statistical physics or in general from physics, because they have a strong connection as you can imagine from the name with classical and statistical thermodynamics. So what's the idea of these machines? So I've told you before that we need the high dimensional function, right? So we need the sum f of, so we have this f of x everywhere, so this high dimensional vector. So let's assume for a moment that my vector, so my x takes the form of a spin variable. For example, you can imagine that you have a binary variable which can take values zero and one, which can be mapped onto itself on a spin variable which can take values plus one and minus one, okay? So we call these variables as you expect sigma one, sigma two, sigma n, okay? So again, sigma one, so this is my high dimensional vector which I'm now taking in the form of a spin vector. So each of those can take plus or minus one. So it's a spin one half, classical spin one half. Now, the idea of this RBM, so restricted Bosnian machine, is to write this high dimensional function, so this network if you want, as the partition function of a classical object. So in particular, it's written, so let me write it like that, as the sum of some over some auxiliary spins which are called hidden units. And I'm going to tell you more about this in a second, of the Boltzmann weight, so the exponential of, so we have in this classical energy, some interaction term which is equivalent to the network terms I was discussing before, which drives an interaction between my spin and those artificial spins, so hidden variables. So this is in exponential, so this is really just the classical interaction energy. Plus, then I also have some bias terms for those spins, so both for the, let's say for the hidden spins, so these H variables, and for the physical spins. Let's say physical variables, sigma i ai. So let's see what does it mean. So in this function, so in this partition function, the parameters to be determined are the following. So first of all, the interaction matrix, so this weights w, then this biases, and then this bias for the, this fields if you want for the classical, for the spins, for the spin variables, sigma. So in general, what we do here is that we take a fixed number of hidden variables. So for example, in this case, we take m hidden variables, so m hidden variables, so which are these Hj's which take the value of plus or minus one, so they are themselves spins. Okay, and m is also like a parameter that I can tune, right?  So you can imagine that the more of those artificial neurons I have, the more complex my network would be because I will have more connections and the more accurate the approximations of the functions I want to determine will be, right? So this is the basic idea. So there are also representation theorems for those machines. Now, so the idea now is that so you can represent this graphically where those are your sigma spins. So sigma one, sigma two, sigma three, sigma four, for example, and then you have a couple of hidden units. In this case, I have just two, so H1 and H2. And these hidden units, as you can see here, interact. So these are classical spins. They interact with all the physical spins in this way. So why are these machines called restricted bosom machines? So they are called restricted because as you can see, we don't have any direct interaction between the spins. You see? So the only interactions which are allowed are between the sigma and the H. We don't have direct connections between the sigmas. And the reason why this is the case would be apparent in a moment. So indeed, since we don't have any interactions between the CDH, we can perform explicitly this sum, okay? Because you see, we can basically factorize this object in the form of, so we can write it as, again, so the exponential of sigma, so the sum of i of sigma i ai, which is just a common factor, which does not depend on H. And then we have the sum of all the possible values of H of the product of J, where now J, again, is the index of the hidden unit of the exponential of the sum of i, w, i, j. i, j, so h sigma i j plus hj vj, okay? So I can do that just in manipulation. And then I can sum individually each hj because these are non-interacting. So it's basically if you want a mean fit model in those variables. So at the end, you can write explicitly this function as, again, the exponential of this first object times the product. So here for each hj, we have to sum the two possible values. So we have the exp of plus one and the exp of the same object with a minus. So at the end, we have the exp of something plus the exp of minus that something so that we get twice the hyperbolic cosine of the argument. So basically, of sum over i, of w ij sigma i plus vj, okay? So we've been able to perform this summation which is in practice on the inverse space of this variable. So it's exponential analytically because of the structure of those connections. Now, we have this structure. So we know the explicit form for the F and we want to use it to sample from, we want to use to generate samples which are distributed according to this network. So how do we do that? So how many of you know about the metropolis as things, algorithm? Now, let me ask again the question. How many of you know about the metropolis method? Okay, that's better. So the idea is that we have a probability distribution probability density at least and an unnormalized probability density which has an explicit form, okay? This one. So in principle, what we can do is that we can devise a Markov chain. So I don't have unfortunately time to explain how this works in practice but let's assume that you know already what a Markov chain is and how it works. So which generates a chain of samples. So for example, it's a stochastic process which starts let's say from a given many body configuration. So let's say S1 and then transits to a sequence of other configurations, sigma two, et cetera, through some transition probability. So I have a probability of going from this to the next element through a transition probability which I call capital T of let's say in this case would be of sigma one of going to sigma two. So this is a stochastic process. So with some probability, so given sigma one, I will generate stochastically one possible sigma two with some probability and then given sigma two, I will go on and I will advance my Markov chain. So this will give a way to generate my probability, samples from my probability distribution provided that I satisfy the so-called detailed balance condition, right? So this is what these askings, these askings metropolis approach comes in. In particular, you can show that you will sample. So those configurations are distributed according to my original probability distribution. So let's say let's call it pi of sigma. If I accept my for next configuration with a probability which is given by, so let's say the probability of accepting my configuration be equal to the minimum of one and the value of my probability distribution over the new configuration. So here this is the accept of the probability of accepting a transition from sigma to sigma prime, okay? So we are here and we want to go there, for example. So this is equal to pi of sigma prime over pi of sigma times this transition probability. So this transition probability, for example, of going from sigma prime to sigma and that's a transition probability of going from sigma to sigma prime. Okay? So this is the metropolis asking the criterion. So in practice what you can do then to sample from this machine, so one simple thing that you can do is that, for example, you have your current configurations of spins, so N variables which can take plus or minus one, you pick and run on one of them, you flip it and you compute that thing, that object on the new flip configuration, compute this ratio. In that case, the transition probability is symmetric. So you compute just the ratio of the two probabilities and decide whether to accept or not this change in the configuration according to this probability, right? So you might have used this approach, for example, to sample from the classical partition function of the Ising model, right? That's something people see. Okay, so this is one possibility. The other possibility which is, however, used a little bit is more popular in this field, at least in this field of restricted Boltzmann machines. It's not the metropolis sampling, but the Gibbs sampling. So Gibbs sampling is a different idea, which is particularly useful in this case. So the idea of the Gibbs sampling is that instead of considering just this probability distribution as a function of sigma, and I assume now that also these weights are just real numbers, so that this object is just a probability distribution as it should be. And what I can do then is that I define, so I want to sample from a space, a joint space, so if you want now my x, so my configurations that I want to sample now will be the ensemble of the sigma and of the h variables, right? So now I really have an ensemble of sigma and h variables that I want to sample from. And in particular, this Gibbs sampling does the following. So it takes as a transition probability. So again, now the transition probability needs to drive me to a different configuration, x prime, where one of both, the configuration of sigma and h are changed. So typically we do this in this way. So the first type of moves, the first type of sampling that we can do is where we only change, for example, sigma. And we left h unchanged, so this is one possibility. And the other set of moves that we instead is that we fix sigma and we sample only h, we change just h. So these are the two possibilities that we can do. So in the first case, so in the case where we just sample sigma and fix h, we use as a transition probability basically, so the probability of going in this case, so max to x prime, we use the probability of, so this is basically f of sigma given h, okay? So in this case, what I do is that I basically just, so I use, sorry, this is the probability of going, so sigma prime given h, okay? So which is defined basically as the, if we normalize everything correctly, this is just this divided by, so it's just, so okay, so this is the definition of the probability of sigma prime given h, which if we write, if we do this thing in x piece, if we write this thing, x piece is just f of sigma prime and h, so the quantity that I've written over there divided by the sum over sigma prime, so all the possible sigma prime of h, okay? So the old point of this is that in this case you can compute exactly this quantity for this restricted Boltzmann machine architecture and you can do this sampling using basically this transition probability. So the big advantage of this Gibbs sampling strategy is that at the end you don't have to perform an extra acceptance step as you have in the Metropolis sampling, but you accept all the moves you are generating. So this is the great advantage. So let me, so you can find all the details in the notes so now I don't want to go too much into the details, but the idea is the following, I mean, graphically. So you have your set of spin variables and you have your set of hidden units, okay? So what you do is that, for example, in the first step you fix the hidden unit so you don't change them and you sample, so you generate values for those using this probability which you can determine and easily compute. So you freeze those and you update at once all the spins that you have down here. So it's like a multi-spin move so it's very particularly efficient. And then you have another move where you froze the hidden and you sampled the spins and you go back and forth sampling from this machine in this way. So once you've done that you can also measure then the second term that we had in the KL divergence which was this difference which was this expectation value over the my approximate probability distribution. So over my neural network which in this case is this restricted boson machine. So you are not obliged to do this. I mean, you can just do metropolis if you like. Yes. Yeah, I mean the historical reason is that because people wanted to give sampling. So if you don't, if you have a different architecture where for example you have interactions here you cannot do give sampling. But you can still do metropolis, yes. The important thing however is that you do not want to have interactions between the hidden units. If you have interactions between the hidden units then you cannot trace them out as I do here analytically. So this is the only constraint, the important constraint. Okay, so now I have all this nice stuff. I know how to sample from my machine. I know for example how to compute also those quantities that entered before. So for example, I mean I told you that I needed to compute this d of k which are basically the derivative with respect to my parameters of the log of my function. Okay, which in this case depend on sigma. So also in this case we can compute that for example, I mean just let me write down something. So for example, if you derive with respect to this visible bias, so this is just sigma i, this is very easy. If you derive with respect to the weights, W, i and j, then this is just equal to the hyperbolic tangent of the sum of, sum of j, i, j, basically W, j, sorry sigma, some more. And I mean, et cetera. So you can determine, I mean you can just do the derivatives of that quantity with respect to, so the log of that quantity with respect to all my parameters and derive analytically all these quantities, okay? Okay, so I can then use all this machinery. I can implement it also if I like to compute the second term that I had in the, in the, in the, in the cool black library divergence. So I remind you that, so the derivative, so the gradient of this KL divergence was given by, again, so the expectation value. Of this d of k of x, so again x of p over my approximate f LBM minus the same expectation value of the same object. So again, for example, this pin or this other quantity over the true probability distribution. And again, I can use my Markov chain, so the sample that I've generated from my LBM to estimate this expectation value as a simple average over those samples, right? So again, as before, I can use the stochastic gradient descent to perform the optimization of this object. And here it is even more interesting, I believe, because what people do is that, again, they define a number of, of, of elements in my, in my batch, so, so I take a smaller element than the total number of samples. Again, for example, to compute this expectation value over p over pi. And typically one takes the same number of, of elements to sample from this object. So I generate the same number of, of points distributed according to, to my approximate probability distribution, and also to estimate this difference, okay? And one of the most remarkable things is that this approach is particularly effective for NB as small as one or two, right? So this means that you can just take at random one or two samples and estimate the gradient in a way which still will, will drive you towards the minimum of the function. So this approach, which is, which is extremely noise in practice, in theory, but in practice work very well, is known as contrastive divergence. So the fact that you take NB one, for example, you have a strongly biased estimate of the gradient, but still this, this estimate will drive you, will allow you to converge relatively quickly to the, to the ground state. So the minimum of this scale divergence, okay? So at the end of everything, then you have obtained the best parameters you have in your network to estimate this unknown probability distribution. So the question is, is this useful to do something, for example, in physics or in other fields? So, of course, yes, it is extremely useful. For example, you can use it to forge handwriting, right? So this is an application which is quite fun. So let's imagine that my data set is a lot of things that I've written, like for example, I've written a lot of text and my X is the things that I've written. So again, the image is on my head writing on a lot of text. So here, for example, there, we have three examples of things that three different people have written. And you can imagine that for each people, we have a collection of letters, of written papers that each of those peoples have written. So this is my large data set, okay? And you can imagine that, for example, each of those correspond, each of those persons has a different pie, right? So for example, I have a certain probability of writing the four in a way that I normally do. I do not always write it like that. Sometimes I write it like that or like that, but so there's a probability for me to write a certain letter in a certain way, right? And this probability, of course, is a mess. We don't know it in advance. We can only estimate it through those techniques, for example. So what you can do then is that you learn this probability, which is crazy. And you can write whatever you like. Just you can have this person write, for example, that is giving a lecture in 3ST today, but probably they don't even know where 3ST is. Or you can write that they owe you a lot of money and this kind of stuff. So it's also potentially dangerous. You have to be careful. And you can go, for example, on this website where they show you other interactive examples or where you can write your own text and it will be generated in that writing style. This is a general application. The other application is, for example, if we want to learn the thermodynamics. So let's do this again. This is application to physics again. So I always switch back and forth from, let's say, more fancy, useless applications like writing things to more physical applications. So in this case, so let's assume, for example, that you have a classical system of which you can measure, for example, some, from which you can measure, for example, the spin configurations. So you have a classical system of spins and you can observe the configuration of those spins as a function of time if you are in a lab. So I have my two dimensional or n dimensional model of classical spins and I can observe a lot of those snapshots, okay? So each of those, again, is my data set. So we know from Boltzmann that those distribution, those things are distributed according to the exponential, basically, of minus beta, the energy. Oh, sorry, of this x, right? Divided again by some normalization.  So you want to find the energy. So you want to find what, somehow, what is the energy that describes this experiment. So what you can do then is that you just do unsupervised learning. So you take those samples, you use a Boltzmann machine, your model, to reconstruct this probability, a non-probability distribution and from that, you can read, basically, or you can compute the energy on an arbitrary configuration that you have not found in the experiment, okay? So this is the idea of reconstructing thermodynamics if you want from the lab. So now the question that you might ask is, okay, but what kind of interactions can I describe with my RBM, for example, which is a very simple machine? I mean, you don't even have interactions between the spins. How can you think that you can describe complex interactions?  By efficiently, I mean with a number of parameters, so with a number of hidden units, which scales basically only polynomially with the sides of the system, so with the number of spins. So to give you roughly an idea of this, for example, imagine that you wanted to describe a term, an interaction term. So I told you that at the beginning, we don't have any interactions between the spins, sigma one and sigma two, for example, but imagine that for some reason, in my Hamiltonian, I have an interaction of this form. So sigma one, sigma two, times some interaction. So how can I describe this in the form of a Boltzmann machine? So now we are talking about the representability of those interactions, right? So the idea is that we can mediate this interaction through the addition of an extra hidden unit. So this is also a very nice interpretation, if you want, of these extra hidden units. And these hidden units can be used a little bit, a la Habard-Stradonovich, if you know. So we can insert an auxiliary hidden unit, so just one, which mediates this interaction. So we do this, and we say that this is equal to a Boltzmann machine where we have an interaction between the spin sigma one and this hidden unit V with some term W one V. And then I have also another term, which is sigma two H V W two V, okay? Then this H can take just plus or minus, I can sum it again using the hyperbolic cosine and all of that. And basically, you can find, so using the two possible values of sigma one and sigma two, so in total we have four possibilities. You can just fix, you can solve this system of equations if you want, for the unknowns W one and W two. So there is a solution, and you can make it exactly this interaction, and you can make it exactly this interaction using this extra hidden unit. So this is the idea also of this hidden unit, that they made it correlations, they made it interactions in the physical spins. So, and in particular, so this tells you that, for example, if you have the short-range ising model, you have to put a number of hidden units, which is equal to the number of bonds, so the number of interactions that you have in the, yes, in your problem, which in this case would be order N, so the number of spins. Okay, so, and this is what it has been shown in these papers, so they showed that reconstructing the, for example, the thermodynamics of the ising model, when you put the number of hidden units, which in here is called MS, equal to the number of spins, you basically manage to reconstruct all thermodynamic quantities in efficient ways. They showed this also from numerics. So let me just mention very quickly our last application, which is to quantum physics. And which, I mean, you can in principle do this same reasoning, but in the quantum case. So in the quantum case, of course, you don't want to determine the energy because you don't have a classical energy, but what you want to determine is the wave function, for example, of the system. So you can imagine that now I have quantum spins, I'm in a lab, or I have a cold atoms, if you want, and I'm doing a measurement of the density of these cold atoms of my quantum spins. So when I do a measurement in my system, you know that what I will get from, for example, for these images that come from actual experiments in cold atoms, so each of those points are just single atomic densities, basically. What I get is that I get psi square. So those points, so those spins values, if you want, are distributed according to the square models of the probability by the measurement process in quantum mechanics. So what we showed is that we can use our machinery to train a network to learn psi square, basically, in some given basis, okay? So once you can learn efficiently on very large systems also this probability, you can use this machine then to, for example, reconstruct some observables that you were not able to measure directly in the experiment. So in a sense, what we can do is that we forge some quantum measurements that have never been performed in the experiment. So we use the information we get from the experiment, or we use the wave function we reconstruct from the experiment to measure other things. For example, we can measure the entanglement entropy or other things that are very hard to measure in actual experiments. So in this paper, we have shown that we can perform effectively and one in silico reconstruction of the radioentropy or other quantities. And the idea, of course, is that it's not sufficient to learn just one basis, but if you want to reconstruct the phase, you also have to look at different basis, but this is a technical detail if you want. Okay, so this is the first application to one of the physics that I will discuss and tomorrow morning I will discuss a lot more. So thank you and I'll see you tomorrow.


Hi everyone, Joe for JaspysCaseRakes.com 2016 Panini absolute football retail edition coming at you break number 11 Three different dice rolls happening here giving away absolute football spots Then we're gonna do the break with the second dice roll Then the third dice roll giving away those prism football tee mall spots very big. Thanks to this group for making it happen Let's gather all the names of the people who bought spots straight up new dice new list Top six get extra spots in this absolute break after six one two three four five and Sixth and final time we got Derek down to Derek Derek Kevin Brian Michael Michael Derek After six you're in the top six you get extra spots, which means extra odds To try to win spots in the next break, which will be prism football Now let's do the box break itself Let's roll it and randomize it Three into three six the hard way one two three four Five and six in final time Rex is asking if anyone knows if Mercari is similar to eBay. I personally do not know my Impression is that it is not My impression is that Mercari is all via a mobile device All right, here's the first half of the list right here, and there's the second half of the list right over here All right, let's print and rip Derek is saying if it's not for these fillers, I would never know what a team all version of a break would be You know say we're learning something every day That's what I think if we learn if we learn a little something every time we're here at Jaspies You know and if we maybe pull you a hit or two Think it's a nice day learn something about the hobby Derek saying Rex it's like it's like buy it now is all over the site kind of clunky, but I did use it once and had a great experience Nick says there's a lot of Nick Jaspie checking in saying there's a lot of counterfeit items on there at least hobby wise right I've heard that too I Mean it's basically for being able to sell like it's supposed to be used to sell like household items. I think You know like if you have like a cuisine art You know if you have like a cuisine art blender or a food processor or stuff like that I feel like it was that which I think It's supposed to be for that. So I think Hobby wise probably not as regulated. All right, we got rookie force Kevin Hogan that'll be for the chief.  Jilo says it's a nice looking Craigslist. All right. We got rook ink Kenny Clark Still around right? All right, so nothing too crazy here. I got everything. I heard something rattling around But it's just this little inner tray right there All right. Now, let's see who's gonna win those prism football team all-edition spots Let's grab everybody's names third and final dice roll new list again, and it's top six after Five two and a three five times one two three four and Fifth and final time after five. Good luck everybody So it's top six so from seven on down. Sorry Nick sad times But happy times for the top six because you're in you're in the prison break and a chance to win that Super Bowl mixer spot Brian you're in Brian again Michael Michael Shawn and Michael Congrats To the top six. Thanks everybody else for giving this a shot. I appreciate it. I'm Joe. I'll see you next time for the next break Bye-bye


Well, today we're gonna talk about our very first ever at least for 2024 Nintendo Direct rumor. Look, we spent a lot of time talking Switch 2. We'll have more Switch 2 stuff coming down the line, but for right now, the one thing we do have no, at least within a certain certainty is that Nintendo is highly likely to have a Nintendo Direct in February, right? They pretty much always do Nintendo Direct in February, especially during the Nintendo Switch era. So when we're gonna sit here and talk about this, I just wanna remind everyone that you really need to get your tinfoil hats on, your sheet of armor as it were. We're gonna be talking about a new source of information here who is completely, well, I don't wanna say completely unverified, mostly unverified and could just be making lucky guesses. And that's totally fine. We're just gonna talk about this for the point of discussion. But remember, if I had a, you know, enough tinfoil, I'd be literally wearing a suit of armor right now. So let's dive into this with an open mind but also realize that, hey, we don't really know anything, okay? So my, I have a question for all of you. Does Tommy Bear know? Wait, what? Yes, this is the name of a user over on X called Tommy Bear knows. And it's interesting because they've been quite a quote leaking things for over a year. I would argue they've probably mostly just been making predictions because when I went back and looked at their track record, it mostly seemed that they were pretty spot on about specific events like a Zelda event or a Nintendo Direct, but not necessarily what was contained within, right? So it didn't seem that they had a bunch of direct knowledge in this sort of new when things were gonna happen. Now this could just come from somebody knowing somebody at Nintendo PR who just given them generalized timeframes. But I don't know because today does involve something a little bit more specific than usual. First off, let's talk about how I got this stuff because it's not all publicly posted on this profile. Tommy Bear knows was DMing me and I just kind of decided I'll give him one shot here and that's that, the stuff doesn't check out. I guess then we know Tommy Bear doesn't really know. So what did he tell me in DMs? Well, first up an expected date for the Nintendo Direct he told me through direct message that a Nintendo Direct is being announced on February 6th. So that sets Wednesday the 7th as the Nintendo Direct date. That would be the first full week of February which is pretty exciting of course and very early in the month. I mean, look, that's actually exactly one month from today, January 7th. So that's really cool. Now he did make a claim for this Direct. He said that Paper Mario 1000 Year Door is gonna be in it which that's like not a big deal but he did note that this is the thing where I talked about what he got specific. The Paper Mario 1000 Year Door is going to have extra new areas shown in the Direct. So we don't know and we still don't know that Paper Mario 1000 Year Door is going to have extras or extra content and extra places to go. And he's saying that it's going to and it's going to be shown in the Direct. He wasn't telling me what the places were or any of that but hey, I'm just throwing that out there is that is like the specific thing he went into. He also said one other thing that I found to be interesting. He claimed his Switch Lite OLED is going to launch later this year. Now he added a whole bunch of other stuff onto this things like Nintendo at some point this summer is going to stop making the original light. They're going to do a bunch of price drops across the spectrum as well when the Switch 2 comes out. Yeah, the Switch 2 is going to come out this year. Yada, yada, yada. But a lot of that's some pretty safe stuff. Now the Switch Lite OLED, I don't know if that's a safe prediction. Some people think it's coming. Some people think there's like a mini coming but I'm just throwing out there what he told me as the quote unquote source on this and you saw me put all the stuff on screen directly from him because I can't link to DMs. I will link to his X account however, if you want to go ahead and check it out. And you know what the question is, does Tommy Bear really know? And I guess we'll know, we know if a Direct is announcing the sixth that is certainly enticing. If it's a thousand year dollars there showing off new content that would certainly add some credence. Now it's entirely possible like it is with a lot of these quote unquote people on Twitter leaking things that they're just making guesses and they don't actually know. They're just gotten very good at predicting when Nintendo's gonna do things and what Nintendo's gonna do. But again, this is true of everyone. Remember in the past we talked about Samus Hunter well it turned out Samus Hunter was actually stealing their leaks from places like Family Boards. And then on top of that, once the leak train ran out and there wasn't a bunch of new stuff going on then Samus Hunter started just making things up. And that's when Samus Hunter got busted. Obviously we know Pioro has a Sterling track record over and on X. Nash Weedle may or may not be pretty good over there but then we have other people like New Marco Mauro who definitely at this point it looks like he was faking it to make it. So you gotta always take this stuff with a grain of salt. I don't know if this person really knows what they're talking about but this does get me to wonder about that Nintendo Direct. And what's happening? Cause with a brand new system on the horizon and I know again, Zubitech was hearing that it might be teased or announced this month. I'm not so sure about that.  But I do wonder what this Direct is going to be. Like they're gonna have dates for the currently announced games, right? Luigi's Mansion 2 HD. I suspect a date for that. I suspect a date for the 1,000 year door. And I do think we're gonna could end up getting one, if not both of Metroid Prime 2 slash 3. And I'm just gonna say HD rather than remaster cause I don't think it's at the same level as what they did with the first Metroid Prime. Could be wrong about that. And I'd be happy to be wrong. I do wonder though, what else is going to be in this Direct? Like do we actually see our first look at Metroid Prime 4? We've all been patiently waiting for this game to be unveiled. And I just don't know if they're ready to show it right now, especially in the same Direct with Metroid Prime 2 and 3 potentially. But hey, it's out there as a possibility. They could also wait and show it over the summer or something like that after Nintendo Switch 2 was announced or even released. There's other things that I wonder if they'll show like the Wind Waker, Twilight Princess HD. We just talked about that yesterday as a possibility for this year. And this could be a Direct or they show that off. But can they really have a Nintendo Direct where everything they show is just ports and remasters outside of maybe Metroid Prime 4? That I don't know. Maybe there's a much a third party games coming. Maybe Hi-Fi Rush, you know, that is not rumored as coming to Nintendo Switch. Maybe that's a big announcement for this Direct. So maybe they feel like, hey, it might not be this Direct that has a ton of new games. But I will say this, if a February Direct comes and goes and there's not a bunch of brand new games from Nintendo announced, it's just ports and remasters for the most part besides Metroid Prime 4. I think that obviously just lines up with Nintendo waiting to reveal a system. And again, they could be revealing the system in March. And if that's the case, it's really not that far away from a Nintendo Direct. So it might feel like a bit of a stale beginning of the year and then there is that reveal in March. So I guess we'll see what happens. Of course, I don't work at Nintendo. I don't know what's going on. Does Tommy Bear know?  I'm just here to create interesting conversations and bring you the latest that's floating around on the internet, even if this one's a little bit newer, right? Maybe Tommy Bear really does know and we'll get much more interesting rumors from them in the future if this stuff ends up being correct. Also small note, Zippo is back. He got his account back. There was a nefarious actor trying to take him down. He claims it's not Nintendo or anything. I don't really know. But I guess, well, I guess Zippo's still around. So I guess we'll wait and see what he has to say next because I don't really know what to say. Thank you guys for tuning in. Go Pat, go.