Welcome back to this CUBE special presentation here on the 62nd floor in the Mandalay Bay in Las Vegas for Google Cloud Next. I'm John Furrier, host of theCUBE. We're here with a program accelerating innovation with Persistent. We've got two great guests here. I do John Gerhard, who's the Vice President of Cloud Technology and Product at DeepEalth. Thanks for coming on, appreciate you. Thank you. And Kamal Puri, but Cloud runs Google Cloud at Persistent.  Thank you, John. Thanks for coming on. This is a customer segment. DeepEalth, you've got the keys to the kingdom. You've got the cloud. You've got the product and technology. You get a lot of responsibility. We're here at Google Cloud, and the announcements are pretty impressive. It's for major change over to this transformation with the cloud. It's a huge opportunity. You guys in the middle of it. Take a minute to explain what you guys do, and then we'll get into the use case. Yeah, I think so. Before, let's say, we get into technology aspects. I want to give a little bit of insight into what we do at DeepEalth. DeepEalth is a fully-owned subsidiary of a company, RedNet. So I think so. RedNet, maybe a lot of people are aware of what it is, but it's still, like, a couple of seconds. RedNet is one of the largest diagnostic imaging chain in the United States. And we do over 10 million scans across the United States. And one of the vision from our CEO, Dr. Berger, was how can we make the healthcare accessible to people and also use technology as a means to drive health and wellness across the populations what we see. And principally, we are focused on imaging, but also we work with a lot of hospitals where we do JV with them to run their imaging business. So in a sense, like, say, we are like co-partners with them in the industry. But in a sense, the RedNet sole focus is imaging as a core part. And DeepEalth is a technology arm of RedNet, what we call informatics business. So I think so a few years back, Dr. Berger had this vision saying that we need to control our destiny in technology to drive value to our customers. So we had a lot of informatics assets, what we acquired. We acquired Pax assets. We acquired the radiology information system to the EMR essentially for radiology business. And also we had acquired few AI assets in the area of MAMO, area of lung and then prostate cancer, right? So these were all the assets within the RedNet. But our CEO, Dr. Berger, saw that the vision where if, let's say, technology could be an arm which is actually helping our core business imaging, which we can actually add more value to our customers and also improve care to patients. So recently, like say, a year back, they had the vision of creating an informatics arm of RedNet. So now essentially I think so a couple of months back, we announced to our public market that DeepEalth would be a fully separate business unit having separate P&L. So DeepEalth is actually an informatics business where the focus is to bring in a lot of value to our customers in the area of, let's say, cancer screening, precision medicine, diagnostics, and also oral health care. So that's kind of what we do with DeepEalth. So you see a lot of images. So images is a big part of the multimodal AI models. I'm connecting the dots here in real time. I'm almost imagining that there's real efficiencies in there. Can you explain some of the things that's going on as AI comes in? What are some of those efficiencies? Obviously, going through all the data scans, which is images, AI is helping there, I'm assuming. Can you take a little bit of color commentary on some of the things you guys are doing that drives that value? Yeah, so I think so I'll talk about four trends that's happening and one is efficiency. The other one, what we're looking at is how can we improve patient lives? That's the biggest part of our vision and what's our vision from our CEO, Dr. Berger, or our CEO, CTO, or Sham Soka, right? Is that how can we improve lives of our patients? Now, in terms of how AI is helping us, so if we look at our population, there are four major trends what we see in the industry.  Now you see a lot of people smoking and now we are vaping and all of that, we see that there's a big trend in the industry where people have a lot of lung cancer. And then you have the brain degenerative diseases that's also kind of been there. So in these areas, we actually have a lot of AI assets and a lot of them were built on cloud native Google, right? So I'll take it in point in case in terms of the breast cancer. So we have a product called the Deep Health and now we call it Sage Breast. So ideally what it does it, it actually gives you a scoring of the potential likelihood that the patient might have actually management tumor or not. And today with the lack of, let's say radiologists in the industry, a general radiologist can use this AI and actually read it as good as an expert radiologist in mammal. Now what we're doing is, this is the efficiency in what I'm talking about. I'm taking a general radiologist, I'm making it as much efficient as a radiologist training in mammal, huge efficiency because otherwise very, very few people, general radiologist wants to read mammal because it's very complex in itself. So there we are. Kind of an art to it too, if the experts know what to look for in the data that a general person might not know. So the screening is just, you're creating essentially the ability to. Yeah, we are assisting a radiologist to become much more efficient like an expert radiologist. That's one part of efficiency. The second part is we're actually removing large, when radiologists read it, they're seeing a lot of blacks. There are opportunities where they might miss something. The AI comes in handy there, but it actually can help you to not miss anything which can cause harm to patients. In fact, we have a study, probably we can share a link, but there are a lot of studies on how we are improving the breast cancer screening and the efficiency of it and effectiveness of it in the industry. And probably we are the largest, by the way, in the United States to do the breast cancer screening. I love how AI pulls forward these benefits, just saving lives, identifying screening early, early detection. I mean, this is exactly what we want. A pull forward, but it's not going to go away, unlike other examples. So one important distinction between us and probably the rest of the AI players in the market is the following, right? So there are a lot of AI companies building a lot of AI algorithms, but what probably they miss the market is AI should solve end-to-end workflow problem in healthcare, not just give you algorithm, because if I go and look at the Mamo or breast cancer algorithm, you might find a dozen of them in the industry. But what RedNet has done is taken these algorithms built in-house with our experts and actually have made what we call a flagship program today called Enhanced Breast Cancer Detection. It's actually the flagship program where you can do, by the way, now in Walmart. You go to Walmart, you can go and get the screening done for $40. So, but we control end-to-end, by the way. We enroll patients. We have them come and get a scan or screen for cancer. We then use AI now to basically make sure that we're detecting the right outcomes. And then we are actually also tracking patients with the comeback. So it's an end-to-end program. It's just AI is a part of it, but ideally this is what customers are looking at, of how AI is able to drive end-to-end outcome to our customers. That also adds to a lot of value into how patients are taking advantage of these solutions and making it really cost effective, right? So that expands the reach that patients can have and get those... I think that point is huge. And I think it's a great call-out because this gets back down to the cloud side of it and the data center because, okay, great. You get the screen and it's a point solution in the workflow. At GDC, at NVIDIA's conference, we were there. One thing that became clear to the world at that point is that if you have the horsepower and then you have all this resource called cloud, you now can then bring workflows and data to that workflow end-to-end and do all kinds of things, build a custom, maybe even custom silicon, because maybe you have images that require a certain kind of SLA for compute and horsepower. So you start to see this end-to-end workflow not be a one-off, but a standard in AI. And also the other case, also I'll tell you in terms of a real practical use case of how Google, assets are actually very purely in this transformation. So if you take a RedNet or a deep health as Informatics ARM, so we have a lot of our assets in the data center, right? And moving all of these images to the cloud is an exercise that we have to do. But in terms of the business outcomes, we have to do it immediately. So what it means is that if I have to run a business operation with this concept, what I just said for the screening, we might not have an opportunity like to do more later to the cloud flush and then you do all of this, right? It is about how you connect cloud and on-prem together so that we can drive an outcome as we transform the cloud. It's not like I want to go to cloud and then transform. It's about how I can transform to go to cloud but still be on-prem. So Google has lots of assets like Google and Anthos or other assets through which I can connect with on-prem and cloud and skills still can drive the efficiencies because especially for, let's say, Mamo cancer screening, what you do is you take a current image, also you can look at the five prior images, at least in writing that's what we do. So let's say you find today go to cloud, I want to get my five priors that are on-prem. Now if the expectation is that I want to move all those images to cloud, it might be a non-practical thing but Google now provides ability using Google Kubernetes Enterprise Edition to basically connect on-prem Kubernetes and cloud Kubernetes and then drive the same outcome regardless of where the data is, which is kind of a nice- So they got the hybrid and multi-cloud and was a key point but I got to ask about DevSecOps. So SecOps security, obviously confidential, I mean healthcare, you got to maintain security. Can you talk about the DevSecOps automation piece of this? How does that work? What's Google bringing into the table there? So I think so we can add to it but I'll tell you the importance of cybersecurity, right? I think so recently in our industry we have seen few incidents because of which in fact, a lot of insurance claims were held up because of the recent incident that happened in the healthcare. So and also especially in the healthcare segment where the customer is still transforming the talent to secure infrastructure assets is probably one of the most challenging feat. That you're probably afraid of ransomware too. I mean tons of ransomware tax. Correct, correct. So now cybersecurity on cloud is an essential component of how we can succeed in cloud, right? And Kamal can add in terms of the assets what we use in Google Cloud but we are working with our ISVs to actually transform that as well. And the same thing applies here. So we want to not just take security for one cloud, we want to expand it to all the clouds that we are operating in take the best of the breed, whether it is a model from working on Azure or Google Cloud or on-prem on a data center. We want to have a same security posture across the board making sure all of our healthcare data, all PHIs is safeguarded and we are compliant with all the best products. And Google is a good job on that. Google has great products which is now expanding to all the clouds. And you've got all the announcements here. Google next, obviously gonna be more generally embedded in. All right, cool. So I want to get into the deep health, fascinated by the innovation strategy you just laid out that's really compelling. You got a cloud native environment. Okay, you mentioned Kubernetes. We'll have hand Goldberg on the cube tomorrow. You have this clinical AI operating model. Can you explain how that's working and where are you going to take it? Yeah, yeah. So I think so, maybe I'll send to the base before I go to clinical AI. So what we are doing in deep health we're building what we call an operating system for ideology. We call deep health OS. Essentially what it is is a platform or ecosystem through which we're able to drive some of the efficiency to all of our, let's say, end user base. For example, today, take an example of radiologist. Radiology uses viewers, they use packs, they use different tools to actually read a particular image. And actually these are sold by multiple vendors, by the way. Viewer is by different vendor, advanced imaging is by different vendor, Pax is by different vendor, AI is from different vendor. I mean if you name it, radiologist uses five or six different vendors to actually read a particular image to get to an outcome, clinical outcome. Now same with the technologist who actually scans you, he uses another, let's say, dozen tools to actually scan a patient. Now you have a friend desk who also is a dozen technologist to actually schedule a patient. And same thing after the patient goes home. The silos are just ridiculous. So what we're doing here is that we are saying that hey customers and users, we at Deep Health, we want to transform that particular, let's say, area. But we are saying that you don't need 20 different tools. We'll give you one back one at which you'll get one user experience to be able to drive an outcome. For our radiologist, he'll have access to all of his AI, all of his viewer, all of the advanced imaging, all of the work list under one platform called diagnostic workspace which is built on top of this Deep Health OS. Same thing for technologists who scan a patient. You want remote connectivity. You want to scan a patient effectively. You want to like say, make sure that the patient is, you know, patient engagement is there for him. It comes all under one platform called technology workspace, right? So- All cloud-based. All for the cloud-based. And you know, cloud-based, but of course we work in hybrid because of the- Because the workflows are there, the machines are on the edge. So the way we're doing it is we're actually using Anthos as a means to connect or on-prem. And I'm using GraphQL as one of the technology engine to API or on-prem, like I say, database. And I'm connecting and building a new workflows now without actually migrating stuff to cloud yet. But eventually they- And on-prem is Graph, what? The GraphQL. GraphQL, yes. It's lightweight, high-performance, works well with the cloud. Exactly. Yeah, good edge in this case here. Correct. Yeah. So, the D-PAL has essentially like say, an ecosystem on which we're building all of the workspaces to drive now radiology efficiency, clinical outcome for radiologists, efficiency for technologists, and also for front desk, contact center and all of that. So ideally this becomes a one-stop shop for entire radiology business. What's the ease of use on their side? Give me a taste of what it's like on their side. What's changed now? Sounds like the back end's nice. Congratulations, good job guys. Now, the user, they got a device, they got people's faces coming in. They're probably concerned. They want to make sure that they get detection if there's a cancer. Look, I'll give you a specific use case. So for example, let's assume that you go ahead for stomach pain. So a technologist is scanning you. And you find something which is not normal, right? So typically what happens is when you are getting scanned, you basically have to get what we call order from a physician. And it has set up protocols that you need to use to scan a patient. But now a technologist has found something which is he feels that it's not normal. So you need to now call up a radiologist to say that, hey, you want to add even more sequences to it so that I can get a better shot or better image of the patient. So typically in this scenario, let's say pre-OS, what we're building, it just will literally, they have to make a call to support, they call radiologist. Radiologist that doesn't know what is happening. He might, if they can get ahold of them. Exactly. One. Yeah, and then if they are basically on-site radiologist within the hospital, then they can come in and look at it. But that is basically lots of moving around things. Now take a look, let's look at OS, how we can solve it. Let's assume a technologist is basically find something which is, he doesn't feel something is right. He can click a phone button. I need to actually give you a first available radiologist. He can click a button. He can call radiologist. Radiologist, it'll pick up a call. Green, yellow indicators out there for him to know they are there or not. And then radiologist, when he calls the technologist, he can remotely see entire scanning process that is happening with the full view of patient and the previous scans. Yes, all of them. It's one click by the way. So instantly up to speed, he's, you get real-time response. And no recall, no repeats. Because imagine this, you have to send a patient back. I can bring him back again to scan it. So the user experience is phenomenal. Modern user experience, the patient experience. What happens to patient that, you know, he's fully taken care on the table. Not something that he goes back home and comes back again.  Exactly. The cycle times involve the waste and cost too on the facility. Yes. All right, so now all the scans go into the cloud. Yes. And that's where everything happens. Great, so you got a great solution. Yeah, and the user experience is the same across the product. Why isn't everyone doing this? Yeah, the reason is because, like I said, there are- Bureaucracy? No, it's a good question. I mean, there are startups, like we feel that we are a startup. Startups are the one who have the DNA to basically go and say, this is not how you look at it. This is how you change your perspective. And we are, I think so, the trailblazers and building one stack for the entire radiology. That's why we call Deep Health as a Radiology Operating System. So- And this to your point, by the way, just for our other concentration, the anti-an workload's playing here. Because you look at it as an anti-an workload, you can innovate the entire process and make those claims and deliver them. And on top of it, it's just not a technology play. RedNet runs imaging business. Imagine now I can bring in service capability to deliver that. For example, if I would today go to, let's say, an imaging chain, which is beyond RedNet, and say, I can offer you Mamo screening, turnkey, with technology, with people, with services, with clinical discipline, they'll love it. Yeah, I mean, lock-in is not about bad for the customer because the lock-in's a success because it's so good that the switching costs, why would I switch to a worse solution? Correct. And there's more images, and that's the business there. It's a very sticky solution. Yeah, and it's a little bit turnkey in the sense that if you want to buy a certain specific piece of the product, you can buy it. Now you can upgrade your subscription to another part of the products. So we sell different offerings, but ideally, people can upgrade whenever they want at their like because it's cloud native, it becomes easier for them to do it. Because we are touching almost all part of patient interaction, the whole modern end-to-end modernization becomes really key, whether it is on the web, on the phone, everything is in a single platform, a turnkey solution. This is a great example of how you guys are adding value and how innovators can innovate with Google Cloud. Quickly, before we end the segment, talk about the Google relationship of persistence. This is the kind of transformation and change management and innovation strategy, frankly, that people are looking at. Complete rethinking of how they approach the market end-to-end, data workflows, intellectual property, their business model, advancements as well. So I mean, across the board innovation, this is all enabled by what you guys do with Google. Take a minute to explain the relationship. Yeah, so for Google and even for the other Google Cloud provider, whether it is Microsoft or AWS, we have deep partnership with all of them, working directly with their product owners. What products are coming up down the line for 2024 or 2125, how does that impact our customers and how can we bring all of that to the table as a turnkey solution to accelerate the transformation that our customers are thinking about and that's what we're doing here with the Cloud. Madhu, congratulations on a great product and operating system and solution. It's changing lives, certainly. It's a game-changer and people, they get one early detection.   If you don't have that, you have to then come in, you got to get your health.  I would say this was a vision of for, let's say, CEO Dr. Berger, Howard Berger and also our vision of for CEO and CTO Shamsoka. I think so, they put up this vision of let's simplify things for our end users, of our, let's say, customers, of our operators and let's take care of these things for them where they can focus on improving patient life and then we are here with partners at Google to kind of innovate and bring the value to our customers. That's good business. Yep. Good job, guys. Madhu, thanks so much. Kamal, thanks for coming on. Thank you. Thank you for watching the queue here. Special presentation, I'm John Furrier. Thanks for watching.


As dawn strikes the capital dome, a city of 540,000 souls stirs into light. It is the beginning of another day, another day to be torn from the calendar time, but a day not to be forgotten, for the camera has recorded that day's drama and the beauty and the comedy of the streets. The camera lends its focus to capture the spirit, the personality of the city of Washington and so through the day from dawn to darkness to interpret in the form of shadows that movement that restless stirring which reflects the character of the city as the face of an individual is said to reflect the soul. Washington has again become a war capital, as in the hectic days of 1917-1918, but today there are no colorful uniforms to be seen upon the streets, no tramp of marching feet to be heard or rumble of caissons. For this gigantic conflict is not with the armed forces of an alien foe, but a war on depression and the forces are economic. It is a battle to put men to work, as in every county of the 48 states, evidence of the government's vast works program is seen. But now the weapons are the weapons of industry, and when the war is over, as all signs now indicated soon will be, Washington will emerge a happier and more beautiful city than ever in bygone days. Meanwhile, the populace proceeds calmly about its business, mainly the business of government, the civilian forces behind the battlefront, carrying on to the victorious conclusion of the war. The capital of the mightiest nation on earth, a capital city the beauty of which foresight of the fathers, a testimony to the valor with which they fought the good fight against the forces of depression, as against the forces of an alien foe. Another day passes into history, bringing America one step nearer to that goal which must fell victory, economic victory, and a happy and prosperous people guarded in its right to economic and social justice.


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control and somehow picking up my research where I left it many years ago. And my interests, obviously, are in two decent place controls, we say robustness, performance, and so on. So, what I'd like to do today is give you an introduction about a new approach that I have developed and that presented gradually for CISO problems, single input to the output, a very simple case, and we build up on the table and in vertical, then on stable. And from there, we show an example of an application and then we go to multi-variable extension, multi-input input, and we show that this method applies very well and allows you to have decent place control. What do I mean by decent place control? Imagine you have this building, you have 50 rooms and 50 thermostats. Each one set the thermostat at a given temperature, but obviously there is a link in the room somewhere 20, somewhere 60. Obviously 16 can get hopper, 20 can get cooler because of the interaction. What you would like to get is a situation in which someone at 16 you feel 16 and vice versa for the other one in spite of the interaction. That's simply said, what is decent place control? So it so happened that this stabilization method gives you as a bonus decent place control and here again we just finished the experiment on a drone, a cheap drone, but it was done. You can see simply how our method works and how. And with that I'll hand over it and I'll speak about some possible direction of research, some of which maybe we'll lay out. Okay, what do we want in control? We would like eventually to have a system to stabilize, but stability is not enough. Stability is that we want also fast rovers tracking. If we want to say to a radar, go to 30 degrees it's likely to go from 0 to 30 degrees and stop there the fastest possible. But if I want to go too fast it might simply start to oscillate and maybe get unstable. So you want stability, but you want stability, the problem with stability is not enough. A plane is stable, but the plane burns gas. Therefore it's mass decreases. Therefore the dynamics, all the interactions, if the system changes, the model is changing. The model is changing. The model is changing. You want it to keep being stable and perform. So you'd like eventually to have somewhere for ensuring adaptations of the word adapted here and might have a difference in the electrical control. With large 70 margin I would like eventually to feel safe enough so that if I have changes in gains and changes in faith, I still maybe perform. Robbersness can be seen in two ways. Robbersness, if I have external disturbances, let's say you have a plane, you have a wing, so the wing can be reflected in direction, so you would like simply to resist that reflection, and also parametric uncertainties, because the model is changing. The model, you know, it's a fiction, we linearize the world because it makes it easier for us. We would like a method which is easy to design in demand, and if possible to apply it to a family of non-linear systems, and as I said before, decentralized control can be the context. Many years ago I did some work on optimal sensitivity where H infinity started at the time. H infinity basically is an optimal method of control, trying to minimize the gains of the operators that are involved. Much later I met the poster Kellerman who showed me that he had a second method, second order system. He had a method in order to make sure that it both runs in good time performance, and he told me that he did something. I get something parallel on second order, so we work together on the key to any order, and we find the magic solution. There's a paper for the pages in international job control where basically we show that not only we get stability, but we get time domain convergence in no time, like zero rise time and zero settling time. We thought we found, you know, the Alibaba key, we said it's fantastic, we almost patented it and then we realized, but in fact the theorem that we worked worked only when the game goes to infinity, like it's some kind of a symmetrical value, which means it's a beautiful result there to go, but not practically. It doesn't translate into practice. So I have to think back at the end of different way, unfortunately the Kellerman died, and I decided to start completely different approach, trying to get some of these concepts of real or poisonary control, and maybe make it with practical gains. And that's what I'm going to try to show. I'll consider very simple system. We have a plant, and you have the output, you have the compensator, and you need to feedback, and you need to feedback it's simple, okay? So this is the input, this is the other system. Obviously what that looks like, you're tracking, you have wide equal parts, that means the output for exactly the input, we need to have a zero error. This one should be zero. So this translates into very simple situations, that the transmission T and the sensitivity. E was the error signal, R is the input, P is the output, P is the input, and you have T, and as you can see from this definition here, you can very well check that S is equal to 1, which means that the sensitivity operator and the closed loop operator, we add them with new unity. And what do we want to achieve? We want to achieve G equals 1. That means output follows the input, okay? I'm trying to make sure my writing doesn't work. But it's impossible, because we know that practical system, causal systems, have more poles than zeros, otherwise it wouldn't be realized. So those systems, T of S, when S equals anything equals to zero. So if it goes to zero, it contradicts the first one. I want S equals zero, but obviously when T is equal to zero, S becomes one. So there is some kind of contradiction. How do we solve it? We go that way. This is the sensitivity curve here. And remember, S was equal to 1 plus PC. If I'm using high gain, C, S becomes small. So I'm going to use sensitivity and using high gain, lower sensitivity, but that's the advantage of interest. Very good danger. And the danger is that when you decrease sensitivity here, it increases, this will increase. I believe the chamber bill experiment was something related to this conflict, but they had increased the gain to make tests, reducing sensitivity here and then creating problems and resonances that simply went unstable. Because this area is equal to this area. How to design a system when I look at this area is equal to this area. So I said to myself, just follow me. I'm going to design a system so that I have minimum sensitivity on the bandwidth of interest. But I'm going also to limit this one, this time to maximum value of sensitivity, limit it, and then the area will be the curve and I'll have the bigger boundary. What allows me to do that? I can do that because today, sensors are so performing, they are very precise. For $5 I can buy a meter at home that measures to the depth of a meter or so distances between walls and so on. So things have changed. The sensors that we used to work with many years ago were normal. So they are very accurate high bandwidth and we have processing, digital processing also very fast. So based on that, I'm saying to myself, I'm going to lower this value and it will increase my bandwidth maybe, but at the same time, at the same time I have tools that will make this possible. So this is the ideal sensitivity, zero and one. Practical, I'm going to limit to m2, m2, m4, maximum value, and m2. We need to have interest. I'm going to explain this curve by taking a simplest case, something which is both stable and both the system and the inverse of state. This is a linguist. And what I would like to do is having sensitivity smaller than epsilon. So I will show in a moment how because I have some graphic tools. But this circle centered at minus one, if it is smaller than epsilon, it guarantees u, but sensitivity is smaller than epsilon. And the total linguist, if it's outside the circle of m, or 3 to the power of m, guarantees u, but it won't go beyond, but you have m in sensitivity. So the design of stability is based on sensitivity side. So this is my aim. Low frequency outside the circle and at all frequency outside the circle. How to do that? Do that by having a compensator which is basically does three things. Number one, I have a low pass here. Compensator with high gain. I have a high gain of low pass. So, and I have also something in high frequency which makes the system realizable. That means that to make sure we have a power k here, is in k, but make sure that this compensator is realizable. I'm taking all the case of p being invertible. Imagine you take c is equal to p minus 1j. That means this is the operator. But the j is my choice. I'm choosing to j. Doesn't matter which p is. So if I have a system, I take the middle of p, build the p invertible. So in that case, this energy appears. I'm leading with a low frequency filter, but make sure that I'm outside the sensitive circle. I'm having here something which converges faster to make sure that I'm outside the circle of 1 over m. And in between, I have three circles. And that is the tuning part. Because we're not going to implement this and this, I'm going to ensure stability, performance. And here, I'm going to be tuning in the time domain. That's the strength of this system. We appeared in a paper in automatic gravity 2015. Let me explain this. There's a nice geometric method to show that. Okay. Can you see? Can you close the light for me? Okay. This is the night waste. As you can see here, this is the night waste. This is the p. Yeah. This is the open loop. So what I have here is a vector of 1. This is p. This vector is 1 plus l. That's the 1 plus l. The inverse is going to be the overall sensitivity. Sensitivity plus t is combined. This vector is t. This vector is s. And when I'm changing the frequencies, I can see the vector t and s very simple. And I can also play with the value of m, having the circle m and the circle epsilon. That's the basics. So what do we do with that? This is my night waste system. And I'm going to tune my filter so that what is the idea that I want to achieve? If this is a night waste plane, I'm going to take minus a half.  So we would like to have a p-scurve. And we can play with all the parameters of the inverse to get as close as possible to p-scurve. At the same time, we can see the transmittance. We can see sensitivity. And also we can see u, which is the input of the process because, again, you might have a beautiful design, but if the input of the process is too high, you saturate and it doesn't have. Therefore, what you'd like to do is play on those to make sure you see here this is the value of m, the sensitivity of m, and this is u.  And you don't want at the same time to go beyond a maximum value of u so that you do not saturate. So this can be done by playing, by tuning the filters. It's done very easily and it doesn't matter which model it is, it works beautifully. So what I have here, as you can see, is like this. It closes later on. But g2, remember this filter g2, which is intermediate frequencies? g2 allows me to act in this region to make phase circuits so that even here, I'll get something more vertical, as much as possible. Which means g2 is a tuning which means make sure that all frequencies u, t equals one. That means you have an orchestra, you would like to have the bass and the flute and violin and everybody arriving at the same time period. So that's what I would do. But I would like to have this as vertical as possible. And this can be done with g2. G2 allows you to focus on this. So we continue. So that's the design, as I say. I count on two things. Having, again, high precision sensors, 30 no processing, g1, g2, g3. This one, a low pass filter will do. And k1 is a high gain. This one is also low pass filter, with a given power. And again, to make sure that c is realizable. And here, this is a tuning circuit. And it so happened that when you maintain this epsilon value at low frequency and m value at high frequencies, it so happened that this curve, remember the curve that we had on the sensitivity curve, we said we would like to have something smaller than m everywhere and epsilon in small frequencies. But there's many ways to go there. I can go like that. I can go like that, like that. And here, that's where g2. So g2, tuning g2 allows you to change this profile. But without touching the epsilon, and again. And g2 usually, when you do it, when you tune it, it basically allows you to tune the time response. Now there's a trick. The trick here is that I can use high gain, and I'm not afraid. Usually, when you have high gain, too much gain, then you get unstable. You'll see it, and maybe you'll get unstable. If I'm asked you to take your hand like that, I go 30 degrees, you're going to do it. Do it fast, you're going to swing. Faster, you're going to swing more, and eventually you're going to fly. So, when you increase the gain, you'll see it in the studies. Now I'm going to show you exactly the opposite effect. When you have, let's say, on the nickel curve, when you increase the gain, but you'll see it because you're getting closer to the particular point, minus the 1, 0, if you want 0, you'll have a very t-degree here. But this design has a given secret, which is that the gain is related to the frequencies, which means that the poles and the gains are related, so that when I'm increasing the gain, my poles are changing at the same time. Which means, instead of getting closer and closer to 0, I always keep avoiding the 0, because the pole is changing. And what happens when I'm increasing the gain? You see, I'm getting closer and closer. So if it's countered, it will continue, because usually you want to go fast, you'll see it. Here, no. The faster you go, the stronger your compensator, and the more ideal value you have. And this is what I called, as I said at the beginning, some different meaning of adaptation. Okay. How to do it not for unstable systems? This stage we consider unstable but invertible systems, but have some slope bound at the end. So the compensator might be complicated, but in fact, it goes that way. And we do consider my plant P, decomposed in two parts. One part, which is minimum phase, that means stable invertible. And the second one is unstable. So the first one, I can invert no problem. I cannot invert the second one, you know, when you have a right half length pole, okay, if you want to invert it to 0, it doesn't work.  We cannot cancel the right pole. So here, I invert what's invertible. And I replace the non-invertible by something equivalent to its behavior at infinity only. That means I'm building this tension H so that it behaves like P at infinity. High frequencies, P becomes like H. That's all I need. When I do that, then I have my new compensator, P, H minus 1, J. And this will still solve the problem. When we do it, I'll apply it. So this is an experiment we did in McGill. And you have here a ball. This is a standard experiment on which we had many students working on the PID tuning for many pieces and so on. So we know that they got the best PID possible. And we wanted to compare it to what they have. So we have a PC for supply. We have a pole setup for LabVIEW and circuitry. And this is what we got. We got a system which is unstable. And we increase the gain. And as you see, as we increase the gain, it becomes, we ask the ball to jump. And we increase the gain. It jumps faster. And the ball increases again. And the faster it converges. I'll show you a very short video on that. But before, let's just do on this. What you see here is S-curve for different gains. You know, it's a sensitivity curve. This is a Nikols plan, a Nikols plane. And in this one, the four sides of the M-circles, that means you would like to have close to, that means if this is zero dB, you would like to place the gains, but you get simply tangent to zero dB curve most of the time. That's what you do on a Nikols chart in order to make sure that you have T equals one over one. But if we pay attention, a Nikols chart is PC over one plus PC is equal to MT, the transmission. Or if you want L over one plus L is equal to MT. If I replace L by one over L, what I'm getting is simply one over one plus L, which become what? Sensitivity. That means if instead of showing the gain, I'm showing the inverse of the gain, so both curves that you have here do not become T equal one curve, but S equal one curve. Or zero dB curve. So we can play on both planes. You can play with the Nyquist too. And with Nyquist, you get the answer to that, so you have a different gain. Like for instance, you have to see a possible region where you have stability. We're going to work with the Nyquist and apply it to the Nyquist region. And the theorem always works. But with Nyquist, we can always add different ones. Two more than Nyquist in different directions, but as long as the number of Nyquist is fine, I don't mind really. But there's no point adding. I'm set to go. This is what we got. It's a new computer. The control button has moved. Contacting the server. I got it. So there is here the pieces on the neck. You can see it, but I just said. And let's look at this. It's a three minute video. So the method that increases the steam stability and precision of the control system by an order of magnitude compared to today's leading technologies. As you will see in the video, the impact of this method is meaningful enough to be observable to the naked eye. But it's even more impressive when measured with a high degree of precision. We will demonstrate the method using a very simple magnetic limitation control system. But the applications are significantly broader. In general, systems react faster when the gain is increased. However, a gain increased to a high enough extent in service systems can lead to oscillatory behavior and instability. With this algorithm, we get improved tire response when we increase the gain while maintaining stability and robustness. At least the algorithmic detected advantage of the new sensors, which are much more precise, have higher resolution and higher battery. As well, we also have EGLC analysis units, which work much faster. Our component signature is shown here in the background. We invert the vertical part of the component signature and add some corrections as well as three-dealters. G1 acts on low frequencies and GV acts on high frequencies. And both indicators guarantee the reliability and stability in the robustness system while G2 acts mainly on the tire response reducing the second time at the most time. We are affecting the really robustness margins. That's the strength of the algorithmic test. Our magnetic levitation device, we have a diagram here that describes the components of the system. And mostly what we want to do is to levitate this spherical metallic ball here with using an intellectual magnet that will lift the ball as we're putting current through. So what we have here is the magnetic levitation device. And the ball is magnetically levitated right now. And it's under the control of a PID controller, which is proportional to the ball derivative. What we see is that the ball is staying at the position. And then we change the subpoint so that the ball can move up a notch by about one millimeter as we see here. The response time is not great and there is a little bit of overshoot, which is something that we will correct with the new controller. We apply now a new control algorithm on the levitation system and we're going to give some steps inputs and check how the ball is reacting on a vertical plane. And as you can see, there's almost no oscillation when we go from one step to the other. If we look at the time response and as only if we can see that there's almost no overshoot, most of it together, and in less than 60 milliseconds we get the maximum response. Now we also have the right time and the setting time are equal. If we do compare our compensator to a PID compensator, which has been optimized for many many trials, we observe the following. That one is quite important. The step here is small, but the setting time is quite near 60 seconds. Therefore, our compensator performs much better. To validate quality, we draw the system quickly to hang different objects, different shapes, different waves, and check that we have been found modifying the compensator. We apply to any system and we apply to a hard disk. What we would like to do is control the arm of the hard disk and make it vary from one position to another as fast as possible. So we take our hard disk model, apply our algorithm and compare the solution to the other solutions that do exist. As you can see here with PID, we get 63 milliseconds. We measure PID 1.5 milliseconds, which is PID control. With algorithm, we get 0.27 milliseconds. In fact, our primary simulation of the simulation, we see that our rate is much more interesting. Before we go to the multi-variable case, any questions so far? Convincing? I have some questions, but not now. Okay, let's go now to the multi-variable. We have here a system which is unstable, multi-variable and stable. But so far, I'm using the property that is in your system. And what I do here is I'm assuming that the system is ultimately diagonal dominant. That means that at higher frequencies, the system becomes diagonal. If you remember when we started, we spoke about room and heat exchangers and so on. So obviously when you have slow variations, they will affect the second rule. When you have swings, very fast swings, because of the galvanic mass of the walls and so on, it won't pass as fast as low. So therefore, this assumption of diagonal dominance at high frequencies makes sense in many, many times. And again, that I have some slow conditions at infinity with single-variable conditions. Before I go on, I was discussing with a professor here two days ago and I emailed him this morning a theorem of Davidson, 1973, because he showed me a design where he had the diagonal compensator for an unstable system. And I said that we can't do that in theory, because there's a theorem just taken this morning, on which we're going to spend some time maybe later. And here I said to myself, we cannot, we would like to use a diagonal compensator. It solves many problems. People trying to build c, matrix of c, to get very complicated because one affects the other. But when you have n compensator and not n square compensator, it's fine. So, Davidson said that it's impossible to stabilize the unstable multivariable plant with diagonal compensator. And here we say, okay, but we're going to use diagonal quantization at infinity only, and then we can use the compensator. So, here is the system, multivariable, and I want to use this one. So, as you can see, the compensator is relatively simple to build and to use. And I'm taking into account the interaction b, and obviously I have y equal to r. But in fact, the transmission t, this time is not one, but it's i, it's identity. Which means input one affects that input one only, input two affects that input two only, and so on. So, I have this condition at infinity of diagonal dominance, but it is infinity. This is simply a small game theorem application of diagonal dominance at infinity. But the demonstration is much more complicated than that. But let's build the system. As before, I'm going to build a function h of s, which has the same behavior as the diagonal part of the plant at high frequency. This is the unstable part of the plant. Again, the plant is divided into stable and versus unstable. And I'm building something on the diagonal part of the unstable, and we need to build a function which is function of the behavior of this diagonal part of the unstable one at infinity, just like I did in the single input case. During that, I built this intermediary function, which happens to be diagonal, stable, invertible, and I apply my system. During that, I'm getting stability and more work. Now we're in a multi-variable case, which means now instead of talking about magnitude, absolute values and so on, we're talking about singular value, which is the infinity norm, let's say, of the system. So s plus t equal 1. So the norm of s is equal to the norm of t minus 1. If I minimize the sum of t below f0 and epsilon being a number as small as possible, so t gets closer to identity. That means I got disamplified control. And local controllers. This is a two-by-two simulation example. I have interaction and I have diagonal compensators, and I would like simply that R1 controls Y1, R2 controls Y2, and it doesn't matter about the interaction. I'm using a system here, that's a solution of it. This is a stem which is unstable, but the inverse is stable. I'm building this intermediary function, and again the condition of infinity. I can check with the system because they're going to be not less than minus equals to infinity. And therefore, I can apply the compensator. I apply it using all those methods separating, taking into consideration the diagonal part and stable part, building this function which will depend on it. And then using to build h and using h to get c variable. What are the results? What we now see now if you look at t, the matrix of transmission, we have t11, t12, and you can see here that they're both 0 dB. That means t11 is 1, t22 is 1. How about t12 and t21, very 40 dB or 20 dB or 60 dB below, which means I got really diagonal transmission. What's interesting is that when I look at the time domain, okay, here I tuned only for the first element, c1. I tuned c1 and basically located an excellent time response. And this is the interaction. Interaction is basically that even in the time domain. I did not optimize the time response for g2. I kept the same t1 just to show you, even like that, having some uniform thing, I get something pretty convincing. But I can also optimize and adjust the g2 of the second compensator and get something better. So it's kind of interesting. I'm new to the drones because I never would do that. So we decided to go to which variable example. So we found a cheap drone and we did some studies. And we got the resistance model impact you all know, some of you are working in the field. And we have a controller, we need by the controller 2, translation dynamics, which are slower and orientation dynamics. And we applied there. And we decided to compare our controller to h2, h2 infinity, by giving them the same objective, that is the same objective, the same sensitivity, you know, that means the weightings of h2 and h infinity are done in such a way that in fact we're talking about the same epsilon and the same m for all the designs. And the question now is, what do we get? Okay, this is what we get, I'll correct you the reason this is. This is what you get with h2, when you want stationary. This is what you get with h infinity. And this is what you get with our control, between our control, managers to keep the drone more stable and more stationary. And this is a picture, where you see x, y. So the red inside is our compensator effect on the displacement of the drones. The green is h2, h infinity is the blue one. So it has some value. Let me go back here. We decided to apply the system to compare it to mu synthesis. And doing it in mu synthesis, we realized one thing. But number one, a compensator, the p-compensator reacts much faster. But somehow in a time limit, there's some peaks, very short peaks. And this, we didn't have the time to get just to see it, the best way to program it. And this is what we get here, when we take mu synthesis. You can see that the time here as a p-compensator, again, is the same objective. We want to keep the same game, the same objective, to compare, you know, apple tree levels. This is a mu synthesis. We introduce some noise and see how highly it goes. And the p-compensator, obviously, here we can see that b is better. But again, the swing is higher. Again, the swing is also bigger here. And there. This one is the same scale. This one, in this case, is the same scale. The b is not bad. But if I go to the next one on the angle, this is the response that I have when I'm doing a change in x and y, this is the effect it's going to have on mu on PID and mu delta. But here the scale is changing a bit. So we get something very stable and so on. But we have something that is still to be adjusted. But here's what it's encouraging to see that compared to H2, H infinity, we're doing not bad. With mu synthesis, there's something that I have way to correct. And our compensator reacts faster for sure. Now, let's look now at the variations of the angles. You have H2, H infinity, and b. And here I have to pay attention when it's the same scale and when it's not, because sometimes I have to change. It's the same scale over time. So you can see here the variations. We've got b controller much more, much smaller. Here, we get the lift what you get in H2, H infinity, and b. And again, it reacts relatively okay compared to the others. And in terms of RPM, RPM, you get b control RPM's are much smaller than what's needed elsewhere. If I go now to this part, this is the drone that we had. And we use simply the PID of the manufacturer at the suggestion. Look at the swings. We did the experiment in the same day to have the same conditions. Let's do now our same one, which is the b controller applied to the system, much less swing than what the manufacturer proposed. This is a preliminary work on drones, it's new, but we believe we have something there. So, as a conclusion, this method has shown to be stable and robust. And we get a time response with two important properties. We have no overshoot. That's important. And rise time is equal to setting time, which is an important feature also. Okay. Most of the time in the technology we compare rise times, but in fact to me the setting time is more important because setting time is where you're really going to have performance. We can apply to much available, we achieve the satellite control, and we can tune the independently the time responses. Where do we go from there? There's many, many ways to extend it. Number one, I'm going to try to do it if I have the time. I'm on sabbatical, but I'm really committed to writing two books. But I would like eventually to teach this geometric method in first grade, I mean, undergraduate, where this is a, this is the infinity circle doesn't matter. That's a Nyquist. I would like to have the Nyquist like that. And tuning, tuning the Nyquist can be done directly here. I think I'm going to, to form this there. I'll start again. We can tune the compensator. It doesn't matter which compensator we put BAD. And in order to get there, look at this green, green line. This green line is the T, the closed loop. You see here, T is equal to 1 until the frequency, and I can read the frequency here. There's a problem also in the frequencies. That means I would like to have T equals 1 all the time. Number two, if I want to limit my, my gain, C. I write C over C max. I can't afford a gain in the serial pattern much. Okay. So C over C max should be smaller than 1. So by changing the parameters, I'm changing the T circles. Okay. And if I don't overshoot, I simply make sure that this curve, T equals 1, doesn't go to T equals 1 or something. You know, then I won't have overshoots. It's ineffective domain, it has a relation to the time domain. And there's, I can introduce unstable poles and do the end supplements and then play with the games and make sure that I'm getting a good performance, like have the end supplements, and it works on any system. So most of the PID experiments that the students are into school with this system will be able to treat much, much faster than the event otherwise. That's one thing I would like to maybe add to do it. But you know, write a procedure and it simply takes all the experiments and they graduate and say, try to tune that way. And maybe you feel better tuning when you see the T, when you see the S. Always the way you have also the S curves, you have the T curve, you have the U curve, that means the input is applying and so on. Another way to apply it, but this again, I have to go with it. I was thinking of the L1 adaptive control, there is a filter there that has been added, which does not affect the relation to the input, but it affects the relations between output and disturbances, noise and so on. And we do it on the geometrical method and we can see the most transmission between the output and disturbances can be controlled to be small. And sometimes you have to make, I would say, compromise between two situations, you can do it, but in fact you can improve rejection of perturbances by tuning the filter, maybe. But again, this is very preliminary, we can do it again. Number three, the open direction to go to is basically goes nonlinear. Nonlinear can do many things. You have the classical methods, you know, the Luri problem extended to circle criteria and more on which, you know, the pop-off kind of school continue combat and adding this system, which gives you some good results, but obviously those systems are limited in the sense that you need to find a slope, that you need simply to go by the origin, I mean the non-linearity sector should go by the origin and so on. And I'd like to go more and try to come back basically up and up and to different families of non-linearity is to try to get the best. That's what I'm aiming to. I have a year or so back go to sharpen my thoughts about that. That's it. Any questions or maybe some questions? I have very many questions. Not now, not today. We need. Pleasure. Pleasure. We need. I was saying you brought the book and the song, the concept behind it contradicts some theories like I said before, Davidson, but we really need to go through it. And I was very, very interested because his work and some of his interests are more or less the same in many respects. Was it a book and so it is? Yeah. More questions in the visual circuit, okay. So if no more questions, let's then hand it to him, maybe, and put all that behind. Oh, I think I might be able to discuss this here. No, but that's his question. This is what we already think in our department. Oh, okay. And I'm here again. Thank you very much.


Welcome the man I'm sitting with today needs no introduction. You probably know I'm as Mr. Wonderful Maybe you've seen him on Shark Tank. He's a fellow Canadian businessman entrepreneur Kevin O'Leary Kevin It's so wonderful to have you today great to be here But I also want to make sure we cover my other nationalities because I don't want to diss anybody I'm also Irish and recently became an Emirati. Emirati. Wow so much happening in the Middle East in the UAE Great for business big crypto. Yeah center. That's excellent. Yeah, so Kevin yesterday I heard you speaking about Bitcoin mining and potentially its role in Creating a greener future a lot of people might not understand that because of all the fud surrounding Bitcoin mining Can you explain maybe the role of Bitcoin mining and creating a green technology or actually paving the way for more green technology? Yeah, and it's a good observation. You're making Let's start with fun flows. Okay, because what Bitcoin mining is a cap-axe. It's an infrastructure play It's a data center play so when the first companies started to build a hash here hashrates here in the United States They basically linked into the existing Electricity lines not knowing what the source of that power was it might have been flared gas It might have been coal. It could have been whatever it was. It's blended now That was fine two years ago and and why it mattered was that many large institutions like sovereign pension plans They are not allowed to own Bitcoin, but they want exposure To the assets so what they did and I know this because I'm in the indexing business So let's say you're a sovereign wealth fund in a Middle East country and you're most of your wealth came from oil But you don't want to invest more in oil. You already have that so you go to an indexer and you say, okay Index me the S&P 500 X oil X airlines. We don't want to own those and so indexers like me do that So we were exposed to fund flows by the trillions You know 24-7 we're servicing these giant entities and they are majority of wealth US pension plans sovereign funds This is where most of the money is in investors So the reason I'm walking you through this cycle is you'll understand this this massive switch that's going on in Bitcoin mining So we indexed all these companies companies like marathon hive Hut eight riot and others and that's how these large sovereign plans own Bitcoin because the price of these public stocks Goes up and down as a proxy against Bitcoin pricing. So, you know Bitcoin pricing gets cut in half They get cut in half if it doubles they double so it was a great way to own Bitcoin until The ESG mandate came out from it started with black rock Then you saw it in the POTUS executive order then the SEC just announced they were in a memo that they're contemplating carbon audits Unpublic companies doesn't matter what if you're a Bitcoin miner or any company that's in the S&P 500 And here's the problem Everybody knows and all of the Bitcoin miners were using carbon offsets. So they would say we're carbon neutral because he bought all these offsets the offset market is such a wide range of Call it target error that it's impossible to audit and here's the problem The SEC order is contemplating getting your audit firm to sign off on your carbon Neutrality in the same way they're signing off on your financials You can't get an auditor to sign that right now They're never going to do it because everybody knows that tracking error is so wide on carbon offsets in another term Carbon offsets are bullshit. That's the only way to put it. Okay, so all of a sudden we had to sell all those shares Because we know they're going to get into a lot of trouble when they try and prove their neutrality So the new mining is emerging. It's being funded primarily by sovereign wealth. And here's how it works You find a country like Norway or you find a province like Quebec or upstate New York or Montana or North Dakota Where there's excess hydroelectricity You build a facility right by the turbines It's a brand new build and you get an agreement with the miner I think this is happening right now in northern Norway with a company called bit zero. It's a private company I'm an investor in it. So it's largest investors out of the United Arab Emirates Every coin that they're awarded is staying on the balance sheet. So that becomes our proxy to Bitcoin now we own our coin now we've mined it ethically now we've mined it all green We do not have to be audited for carbon because there's no carbon. It's a hundred percent hydro And so in doing so we are not only capturing unused hydroelectricity We've got the latest state mining equipment which just uses 40% less energy We're taking the heat from the stacks and creating a hydroponic facility that grows tomatoes and a canning plant beside it Because in northern Norway, there's not that much sun for tomatoes. We're integrated into the community We are creating a new power source for all the citizens in that 3,000 person village That is the new mining. So the best way to look at this is We should applaud the early miners that you know built up capacity here in the United States But they are essentially the pioneers with the arrows in their back Because the capital is going to move away from them and it's going to go to the new generation of miner Which is the Norway model as I call it. We're building these in almost every state here in America that has hydro We're very fortunate because we have access to the latest technology A lot of people don't realize this but it's the sovereign wealth plans in the Middle East that were Neas and early on 25 years ago to buy capacity of chip manufacturing in many You know the global foundries and many of the other Taiwan semiconductors, etc So we have access to those mining chips that we need And we're going to build these new miners all over the world Brazil has hydro Georgia has hydro all of these Quebec has hydro That's the new model and I'm not against what's happening with The existing public companies u.s. And I'm getting a lot of people aren't happy for me telling the truth, but it is the truth They're screwed. They're going to face carbon audit. What can you do? And everybody knows that they're it's going to be very very hard for them to pass that and whatever happens There Stocks are going to be one alternative for an institution to buy or all these other companies that go public in the next 18 months It's obvious which ones they're going to pick.  That's interesting You talked about, you know, for me crypto proxy stocks are a way for these legacy financial institutions to actually participate in the market What's the limiting factor for outright ownership of bitcoin is it still the the regulatory side? Is it uncertainty on which way it's going to go from a you know from a policy perspective? What's preventing so many legacy finance institutions from actually investing outright in crypto? Or are they waiting for spot ETF for example? Well, you're 100 right. It's completely regulatory So here's here's how sovereign pension fund would work And this is pretty well the mandate state side and globally Let's say you're running a hundred billion dollar mandate You have certain parameters. You're allowed to work with and indexers like me. We work with these people every day Generally speaking, there's 11 sectors in the s&p You're allowed to hold up to 20 percent in any one sector and up to 5 percent in any one stock So those are sort of the diversification mandates and it's been that way for you know some derivative of that for forever So you get diversification within the broad hundred billion dollar mandate You have on top of you as the money manager a compliance department. They mark to market your positions by the second They know exactly what you hold whether you've breached the mandate Whether you're in an area that you're not allowed to invest in or whatever How much leverage you have on if you have leverage all of these things are in this infrastructure Build out that's been around for decades on top of the compliance department Is now an esg compliant department and an ethics department And so the reason none of these funds, you know people are so excited about bitcoin and say oh, this is amazing And we're sitting at 40 plus thousand dollars the truth is The majority of the world's managed money Trillions and trillions of dollars has this much bitcoin Zero and they're never going to put it on their balance sheets until they get the regulatory environment To give them the rules Then the compliance department to say okay, then the esg committee to say okay and the ethics committee to say okay, so They don't want coin that's mined in china for example because it burned coal That's the esg guys coming in and weighing in so This is forcing a change It's not changing the nature of a coin that's awarded But people want to know the providence of the coin Which is why these new age miners the norway project the bits zeros all of these other companies that are going with 100 percent green hydro They're very coveted their shares are going to be very coveted as they come onto the public market Because now these sovereign plans can buy the shares of these companies They pass esg mandates. They pass compliance mandates on ethics and they are an equity So they're going to be able to be held within one of the broader mandates So there's a tremendous amount of capital funding these new miners and that will be the proxy for exposure until The sec and the other regulators in the u.s. Which pretty well most regulators around the world abide by That that etf you talked about is actually it was an issue an order from the osc in canada Which is one of the most advanced they were the first to bring an etf that had the underlying being bitcoin But they also brought an etf that had the underlying ethereum And the policy up in canada is they also issued the very first crypto exchange with a broker dealer attached They're very advanced But my thesis this is a personal opinion is that the sec has a very close relationship with the osc in canada They their policies are almost identical. They're probably using ontario and canada as a guinea pig to try these policies, right? And if they work they'll use some of these maybe in the united states Which we hope but i'm very bullish that policy is coming because you've seen bills coming from Senator lumis and we've also got the same from hagerty You know there's all kinds of to me was on cnbc this morning talking about Stable coin the hagerty Bill is a two-page bill that contemplates stable coins So there's a lot of momentum on the regulatory side that i'm very excited about Well as a resident of ontario i can tell you that the sky is not falling because we have a bitcoin etf so There's really not much to be afraid of everyone, right? Um, so we talked about bitcoin and the bitcoin mining are there any other sectors within crypto that you're really excited about? Is it the defi space any other investments that you're really looking into maybe a thematic investment? Potential that you look at within crypto. Yeah, i'm very very interested in as an investment thesis And this is not only in canada but globally show me the jurisdictions that are issuing crypto exchange licenses Because that's the infrastructure of crypto forever And so ua united arid emirates particularly abu dhabi Um brazil argentina switzerland the uk I'm using a vehicle called wonder fi a company that's a canadian company They own the largest crypto exchange in canada. They bought bit by They're looking at other opportunities all over the world i assume That's what the management You know has said to the market and i'm very interested in you know investing in that because i'm hoping what they're going to Do is a roll-up of exchanges globally and what better place in canada because canada has been so Commodive on issuing the very first crypto license the other area that really intrigues me And and what i like about wonder fi is they have both centralized wallets and decentralized So where they're allowed to provide a decentralized wallet which That means when you when you acquire a customer they can keep both wallets active Maybe your nfts are in a decentralized and you're and you're keeping your coin in a centralized wallet for security reasons But the point is they have both I love infrastructure. So I also have a position in bit zero, which is you know also private but building the norway model That's infrastructure data centers in bitcoin mining They're they're looking at many opportunities around hydro stateside and then of course there's immutable holdings They're the infrastructure for nfts. They are they own nft.com I'm a shareholder jordan freed is the ceo there. These are all infrastructure plays That's that company again went public in canada. H.O.L.D. is the stock. So there you have it I mean, there's a lots of opportunity and within our operating companies portfolio now 20% is in crypto. That's the most I can hold I was going to ask you, you know, have you reached your 20%? I know you mentioned it last year. Have you reached the 20% Last year I was at seven seven So now i'm at 20 and now we and we have 32 positions on it's the most exciting part of my portfolio the most volatile I mean it's all over the map every day care to disclose what kind of assets you're holding I will I'll disclose some of them. Um, you know Part of this came through my relationship with ftx. I'm a shareholder in ftx As well as a paid spokesperson have to disclose that I I love what sam is building there You know and I use my ftx wallet obviously, but I'm I also own hbar I own polygon. I just put a position on in polygon after After you know meeting sandeep.  I love that idea Just got into helium Also avalanche Solana, I'm a big believer in that I've got I've got 32 aggregate positions. Those are some of the larger positions And so I don't know which one's going to win nobody knows But certainly, you know ethereum is too slow for me as a financial services guy So I you know, I'm looking I'm hoping that Solana ends up being something much faster It's got the backing of sam bank and freed, which is always a good thing But there's a lot of new ideas and I'll back any entrepreneur that has something of value I'm not so much on to the you know, for lack of a better word the shit coins I I want something to show economic potential to create value somewhere But it doesn't mean you can't have fun with crypto, you know, if you want to trade some dogecoin great doge along mars and stuff like that That's all fun. It's like going to las vegas. Yeah Some healthy speculation isn't too bad, right? Yeah, and I'm also very very interested in seeing policy come through on stablecoins because in many of my operating companies We have large cash positions We're making 22 basis points if I can lend out a contract for 30 days at 4% on You know usdc or whatever you there's many ways to go on stablecoins But we really need policy there because my auditors in my compliance departments will not let me put on a significant position on stable Well, you've got to think that market is really ripe for for growth given the yields that you can earn just by holding Stables compared to the yields have come down over the last three months because we're past 100 billion But it's still better and and I think why it should be Standardized as policy is it allows the u.s. Dollar to remain The you know the default currency globally and you want that and I'd like to do a lot of the work I do in Europe on usdc and just not have to go through fx transactions all the time But we need regulators to approve that but we're in neocent days. We're early days, but I'm extremely optimistic Excellent Kevin appreciate your time It's a sam with coin telegraph. It was great to talk to kevin oliri and uh, we'll continue the conversation


What's good y'all Chris from jaspercasebricks.com doing 2023 fanatics underwraps signed football jersey three box half case Five random players each number three. I got three boxes here. We got 18 spots times up by five so 90 spots for 90 different players from Aaron Rodgers all the way down To Zach Wilson what a combo your jets Quarterback core with a couple different combo spots in between But let's do it our fresh dice and our fresh list from Josh Down to Eric as our last bomb mojo just did that five different times To fill out our list and our 90 players from Rodgers down to Zach Wilson. All right, we'll throw that as a random ice Good luck y'all four and four eight times. Good luck one two three Four five six seven and eight eighth and final time Gonna be Steve Down to Steve And eight times for the players good luck one two three four five six Seven and eight So Ray Lewis at the bottom and Charles Woodson at the top. All right, I Am going to Alphabetize this by player name and if anybody wants to see who they've got please let me know obviously, there are a lot of players so I'll do like 15 hour time here So Steve with Aaron Rodgers down to Josh with Clyde Edwards Lair. That's one to 15 16 to 30 Alan with Cooper cup all the way down to Josh with Ed Reid Alan Matsui with Eddie George down to Eugene Lee with Joe Montana At 46 to 60 Arthur with Joe Thysman down to Eric with Matt Stafford How's there if you'd like Here's 61 to 75 Arthur with Micah Parsons down to Josh with Roger Stalback and Then last one Steve with Ronnie Locke down to Eric with Zach Wilson If anybody wants to see their players more time, please let me know And if anybody wants to trade we'll still throw something up. I'll also do it like this This is all of Alan's teams So I don't want to take a pause there and look you can all of Arthur's teams Teams all of Arthur's players Eric got a few different teams So here's the first half Eric Eric with been with AJ Brown down to Tim Brown and Then Brady nice one there down to Randy Moss Eugene has Alvin Kamara down to Rashad Bateman Ivan with CD-Lim down to the Troy Aikman War and Moon combo Josh with Brian Earl Acker at the Roger Stalback and Steve Herrick has Aaron Rogers down to Tula Daga Viola The last one TJ with Dan Reno the late great dick butt kiss down to Nick Chubb. I'll give it another moment I'm just gonna keep it on air just in case I don't want to see some of the tricks I've gone But if not I'm just gonna rock and roll. I'm gonna print this out. I want to rip it Next it's the second inning. It's not a good start, you know, it could be worse. We could have given up six runs in the first This is classic October does it we saw this we saw this playbook last year cold bats not enough pictures Injuries is a always a ballroom, but really I'm not too worried because really this was supposed to be a retooling Remember that is true. We were supposed to go 500 You're supposed to figure things out in the infield and then mess around in one Yeah, why I have a large room. I also would like to say by the way I'm kind of against the division or the wild card series now Because the cold team where the dogs get even colder and hot to live here. I mean even hotter I know that's the whole point is like you want to win your division or whatever So you don't have to deal with all the wild card series. Oh, like yeah, I guess it was happening All right tray window closed y'all. I'm gonna print this out on the other screen. Oh, I'm actually I'm looking for this out I'm just gonna do who hit and who didn't I know obviously write the names down and tape them to the box So 90 players Jersey break. Yeah. Nice. We'll do it like this. You can see again five random players each Half case number three. We're all good to go All right, let's uh, let's switch screens Three boxes right here. Let's do it I'm gonna stand up for this one Gonna be a little difficult to do Sitting down this box one Start off with a nice one future Hall of Famer That is E You are no, excuse me.  That's paid manning. These are a little hard to show off, obviously Here's the signature on the eights Very nice right there paid manning gonna go to Arthur Nice start right there that Colts blue Yeah, all the uh, there's right here as well all the cards all the certifications all the verifications all the Authentication all the all the patients and you can want In a fanatics random genius. So Arthur hack it first on the board here Nice start for Arthur One half of the manning cast here is box number two number 28 Nice once again back with his Indianapolis Colts It's Jonathan Taylor. Nice. Nice time to get this one. I'll show off the Jersey number 28 on Their whites with those big blue numbers Indianapolis Colts Jonathan Taylor that's going to Ivan with the Jonathan Taylor and the last one. Thanks again y'all You know, it's a cumbersome to bigger product to work obviously But our third and final Jersey Gonna be Leonard for net On the Tampa Bay Buccaneers that red Jersey the red uni another number 28 signed on the two Leonard for net Gonna go to Allen Grabbing the Leonard for net red bucket here is Jersey. All right, perfect. Thanks. Y'all appreciate it I'll see if I can go find another case of this for us to do number four Thanks again, folks. Jaspies case breaks calm. Bye. Bye


So today I bring you a quick video on the latest news surrounding Borderlands 3, including Gearbox confirming in 90 loot drops, increased in 90 loot drops will stay, more previews of new cosmetics and new spooky surprise information. How's it going guys? My name is D.P. Gen. I'd like to thank you for stopping by and checking out my video. If you do enjoy it, leaving a like really helps out and subscribe if you want to see more Borderlands. Ok so last night Gearbox tweeted out the following. This week during the Borderlands anniversary event, specific mayhem modifiers with weapon penalties were reduced and there was an increased chance for anointed gear to drop. We've been listening and will not revert him back to their previous values tomorrow, which would be later today people. So this is actually great news. It means you have further time to get all the gear you want anointed and mayhem won't be as crazy as it once was for an extended period of time. How long this will continue I ain't quite sure, but as soon as further details do drop I have you guys covered. But yeah increased anointed drops are staying for now. Ok so later today we get the week 5 celebration event of spooky surprise. Exactly what this event is at the moment we don't really know. But later today we get another stream on behalf of Gearbox, which will reveal more to come, including what the spooky surprise event will consist of and the next 3 DLC content for the game coming in December. So from 2K's website, this is what they state. Don't miss the second episode of the Borderlands show, featuring a spooky surprise and a first look at the upcoming Borderlands 3 takedown. In case you haven't heard, the free Bloody Harvest event is currently live in Borderlands 3. But if you've taken some particular heroin trips through heck and thick, you'll need a review of it soon. We've got just the thing, the second episode of the Borderlands show, our monthly web series hosted by kindofonnie's Gregs Miller and Fran Mirabella. This will air on Tuesday October 29th and it's bringing some big announcements with it. The Borderlands show is a chance to chat about all things Borderlands. Borderlands 2's developer guest is Gearbox's managing producer and narrative, Randy Vernell, who's ready to discuss Borderlands 3's story in depth and answer questions from players like you. There will be spoiler warnings for anyone tuning in, but it'd be a good idea to wrap up the story campaign if you haven't already before Greg, Fran and Randy delve into all kinds of major plot points and deep lore discussions. The Borderlands show episode 2 will air on the official Borderlands Switch channel, starting October 29th at 10am PDT. This will be 1pm EDT, 6pm BST and it will be October 30th 1am GMT plus 8 and 3am AEST. This guys for UK folks out there will be 5pm on the 29th later today. Following Bloody Harvest, the next big piece of free content being added to the game is Takedown at Malliwans Blacksite, which we first teased at PAX West. Getting into the Borderlands show episode 2 for an early look at some of its ultra-difficult end-game encounters. But wait, there's more. The big finale of the Borderlands anniversary celebration marking 10 years of Borderlands is a spooky surprise we've been teasing for weeks, and we're finally unveiling it during the Borderlands show. Without giving anything away, you'll definitely want to tune in to get the shift code needed to redeem the spooky surprise for yourself. You won't want to miss the Borderlands show episode 2, especially if you like getting free stuff and first looks at new content. We'll see you there on October 29th at 10am PDT. Links to where you can check this out will be within the video description, both YouTube and Twitch. Like I said, UK folks out there, this is 5pm as we are no longer in that British summer time. The shift codes they speak of I think may be the new skins we've seen a few content creators tweet out. This image from Joltz showcases skins for each wrought under and these have been confirmed to be coming very soon indeed. So that's pretty cool, now a better look at these skins can be seen on screen, now it's hard to make them add from the initial pic as the background swallows all their beauty. The Amor Ham looks a lot like the Ham of Saint 14, helmet from Destiny 2 for the scumbag Titans.  So yes, increased annuity loot drops continue, spooky surprise coming soon, with details of it later. I probably reckon it should probably start when the stream starts or during the stream. We also will be getting new cosmetics as well, so pretty damn cool times ahead. And that is it for another video, I just wanted to make a quick one covering the latest news. But on that note, we have come to the end, if you guys enjoyed it, leave it a like, rule out a shout out if you're new around here and want to see more Borderlands videos, be sure to subscribe and if you never want to miss a video I upload, you can turn notifications on by hitting that bell button, but guys thanks as always for stopping by, hopefully you enjoyed the video and hopefully I will see you on that next, one.