Jonathan Gluck from the Heritage Provider Network. So, Jonathan, let's start with you. What is the Heritage Provider Network? And what did you guys announce today? Can you get real close to the mic so we can hear you here? Can we block out all this crazy background noise? And so welcome to theCUBE, first of all. Thanks for coming in. Thanks for having us. The Heritage Provider Network is a physicians group that's based in Southern California. We provide healthcare for approximately 700,000 members, making us one of the largest groups in California, the largest bi-geographic scope. And I'm sorry. And that's really what our nuts and bolts businesses is providing healthcare for our membership. So you announced a prize today, right? A competition, right? What is that all about? We have decided to launch a $3 million prize for a team that creates an algorithm that predicts the hospitalization of the members of a given patient population. The theory is that if we can create an algorithm that predicts the hospitalization, we can then talk to the doctors for that patient who can attempt to provide care to keep the patient from needing to be hospitalized. Unnecessary hospitalizations are both a drain on resources as well as simply not good for an individual. Hospitals have their place when care is necessary, but are also dangerous places to be if you don't need to be there. So we're talking about an application here of data science that actually has social implications and implications in terms of making lives better. Now we've talked about in the past about, well, maybe it's predicting traffic patterns and things like that that are relatively trivial compared to saving lives. So Jeremy, talk a little bit about Kaggle and what it is you guys do and where you fit in this whole competition. Well, hey, don't knock traffic patterns. Nobody wants to be late to work. But yeah, sure. I mean, there are even more important ways we can use data. So one of the interesting things about this question of how do you actually leverage data is it often takes some pretty serious number crunching algorithms, right? And it's really hard to work out who's the guy that's got that number crunching algorithm I can use. I could pay enormous amounts of money to big name guys. They could come up with an algorithm and then I don't even know if it's any good. So what we do at Kaggle is we run data mining competitions.  And then it's data mining as a sport. They put that answer back up on the web just by clicking another button and they appear with a score on the leaderboard. So there's teams, there's scores, there's competitors. And so this approach of running competitions, we're finding and Netflix founders as well is a really great way to get the most out of data. So that's why Kaggle is running this HPN Prize. Now, we were talking off camera. This isn't so-called big data, is it? It's pretty small data. Yeah, well, I mean, maybe it's medium-sized data, Dave. I don't know. To me, big data gets big when you need multiple machines to look after it. This data set easily fits into memory, very easily fits into memory. And I've competed in a lot of these competitions. I can tell you that the algorithms I see or that I use that do well are generally ones that I run on my laptop, normally using free open-source software. So if I think back to 20 years ago when I started in the world of data science, we needed big multi-machine setups to run what today we'd think of as simple 100,000 road problems. But today we can run these problems on our laptops. And HPN's data set is a few million records once it's all put together, easily small enough to fit in pretty much any software you can think of. So Jonathan, we talk a lot these days about health care costs, obviously new health care bill passed, and there's a lot of finger-pointing going on. The insurance companies point at the doctors, the doctors say, hey, you've got to have accountability and patients need to get more involved in decision-making. Is HPN through data and data science actually attacking that problem? Is that part of what we're seeing here? Yes it is. What we believe firmly at HPN is we need to move from what we think of today as a sick care system, meaning we provide most of the health care to individuals after they become ill. We attack the problem after the horse is out of the barn. We need to move to a system that focuses on keeping people healthy. It's really when you think about it the only way as a society that we can provide all of the health care needs that people have. We just simply do not have the resources as a society to attack the problem after people have become sick.  So how's the prize work? Is it winner-take-all? There's got some alpha geeks out there that are sort of, I think, chomping at the bit here to get involved. The prize is going to have two components. There will be one component which is the person who creates the algorithm that predicts the given percentage of hospitalizations. And that final figure is yet to be set, but the rules will obviously be out before the April 4th launch date. We'll win the $3 million prize. In addition, we intend to have milestone prizes along the way, meaning the leading team after presumably six months, one year, a year and a half, however long it takes for someone to win the prize, we'll win also a significant sum of money. So the duration is open-ended? The duration is open-ended. Someone will need to come up with a algorithm that meets the minimum requirement. And when you come up with that algorithm, you win the prize. So the first one over the finish line wins. First one over the finish line wins. Dave, can I give some suggestions to the alpha geeks? Yeah, please, absolutely. I mean, I love competing in these competitions and I know a lot of people who say to me, Jeremy, I've so much been thinking about competing in one of your competitions and I say, why don't you? And it's kind of like, oh, I don't know, I haven't got time or I'm worried I might not do very well. There's all kinds of excuses, right? My advice to people is to start getting involved in competing right now, okay? This huge $3 million prize is gonna be around for a while. It's not easy. If you can imagine working out who's gonna end up in hospital and who's not, it's gonna require lots of great model building, looking at things like everything from drug effectiveness to number of interactions, to people's characteristics and so forth. So I really think people need to start actually getting involved in competitions. So they can go to the heritagehealthprize.com website, register now. Then they can, on April the 4th, they can download the data set. Pretty straightforward. But in the meantime, go along to Kaggle.com and have a look at one of the other competitions and start learning how to compete. Meet some people out there, build some teams, find out about how this all works. I think it'd be really great experience. Maybe try it out on some other competitions and sharpen your skillsets. And look, if you give it a go and you come last the first time you put a submission in, that's okay. Think about it overnight, come back the next day, try something a bit better, you'll keep moving up the leaderboard, you'll have a lot of fun doing it. Now that data set is a fixed data set, right? At time zero, whatever, right now I guess, right? Or is it April 4th, you said? April 4th will be the launch date. Okay, so you'll launch date and the data set will be fixed at that point in time. Yes. Is that right? Yes, it will be. It won't be any changes made to the data set. Okay, so Jim, that's good advice for people out there. So where do they find these resources? To Kaggle.com is the best place to start. Kaggle.com is to check out some other competitions. And they all have basically the same kind of framework. So there's always a file that you download which has got information about outcomes in it. In the health prize example, it'll be hospitalization outcomes. There'll be another file that contains all of the same information, but without the outcomes included. And that's the thing where you've got to send back a column of numbers saying, this is what I reckon the outcomes will be. Then on our site on the Kaggle website, we split the result into two pieces. There's one which is used to build the public leaderboard where you see how you're going. And then there's another piece that we actually keep separate, even again, hidden from everybody until the last day of the competition to actually double check that the leaderboard result can actually be applied to a whole new fresh piece of data as well. So there's this standard framework that we use that works really well and you can try using it now. All right, so definitely go check that out for those of you who are inclined to attempt to go win $3 million. That's a serious chunk of change. And so that URL is Kaggle.com. Mark, in case you want to bring it up and show people, as you can see guys, we got a lot of people watching here online. So this is obviously an interesting segment. So while I have you two, I wonder if we can talk about the conference here. We're at Strata, the big data conference, right? We were talking, well, let me ask you. It's a data conference, it's big. It's a data conference as big as a lot of people here. It's in the heart of Silicon Valley. Is big data, Jeremy? Is big data overhyped in your opinion? Some people, Dave, have big data problems. Google has big data problems. Bitly has big data problems. You need to come closer to the mic. Most people, I think, have medium-sized data problems. They have the kind of data problems that if you use a good algorithm on your laptop, you'll get great results. So if you've got less than a few gigabytes of data, then you probably don't need to be sending it out into clusters. You can probably use a fairly simple streaming algorithm using open source and free tools. And the great thing about doing it that way is that rather than spending hours waiting for the algorithm to finish, you can use much more powerful algorithms, try many more algorithms, spend a lot more time crunching the data and getting better answers. So Dave, I compete in a lot of these competitions, right? And I've had some good results. And the times I've had good results is when I turn the big data problem into a little data problem and I find a way to run lots of algorithms really quickly and try lots of things. So you're saying in that instance, it's about speed and getting the most efficient application of those algorithms as possible? It's about speed of development. This is all about prototyping, okay? Now, when this is all said and done, for example, in the Netflix prize, the guys that won that had 300 submodels. Now you can't build 300 submodels on a billion rows of data, right? So what they did is they built these things on smaller amounts and then at the end, at the very last phase, then it's a big data problem. That's where Netflix goes and say, oh, how do we do this in real time for a billion customers every day? That's the big data problem, but the prototyping, the algorithm development is not about big data tools in my opinion. Right, now, of course, a lot of people, again, we were talking offline, indicated that you said a lot of people using Hadoop. They don't necessarily have to use Hadoop. Why do you think they do that? Just because it's such a hot area, they want to be part of the next big thing? You're going to say controversial things now. Oh, come on, you might as well. Why do they use Hadoop? You think that's overkill? I mean, it's important. The reason I'm asking is that we have a lot of practitioners in the audience that are trying to figure out, should I be applying Hadoop to my business and where should I be applying it? So help the audience understand where they should be applying it and where they shouldn't. Hadoop is big because Java is a terrible platform to be working on these kind of solutions. Because it's so damn hard to do in Java, you've got to have incredibly clever tools to do it more easily or even to do it at all. Hadoop works on an incredibly difficult engineering problem which is making Java work on these kinds of problems. The people that work on the hardest engineering problems and gets traction on it are the coolest people in any industry. That's why Hadoop is cool. So for some people, it's fantastic. For a lot of people, it's the wrong tool for the wrong job. So it's the pain reliever for Java. Now Jonathan, now having said all that in the medical and particularly in research communities, big data has a lot of applications, doesn't it? I mean, what are you seeing there? Can you talk about that a little bit? I would probably defer on a question like that, something because that's not my area of expertise. There clearly is a ton of data in the medical field. There may be more data in the medical field than anywhere else. The question really is how well do we use that data today to allow us to do what medical care really should do which is take care of patients? And I don't think we do a particularly good job of that and that is something we're trying to begin to solve with a prize like this. Right, right, so how long you guys here for? Are you gonna be here tomorrow as well?


This Wednesday afternoon, and if you have a young person with learning difficulties or a disability, then you'll know that it can be really tough getting them a start for them to get work after they finish education. There's a lot of places that shy away from taking on anyone with a disability. In some places it just doesn't suit, which is fine. In some places they don't think that it might suit, or they don't think that it would work, in reality it could. And in some other places, well, they're just not interested in trying to make it work. But the inaugural Disability Employer Equality Awards, and this is for employers who do make it work. And there's going to be an awards night, and this is great to see because the awards ceremony is happening this month. In fact, at the Central Hotel in Donegal Town. It's the Bluestack Foundation, Donegal Local Development Company, the LDC, and the Donegal Children and Young People's Service Committee that are coming together to recognise and celebrate businesses that are championing inclusivity and diversity in the workplace. And from the committee I'm joined by the coordinator, Ann Timoney-Mehan in studio. And thanks for coming in. Great idea, these awards. It's important that businesses and employers be recognised when they go the extra distance, if you like. It is indeed. And first of all, thank you for giving me the opportunity to tell people about this award. This award is one part of a larger project on disability equality that we are running for the next couple of years in Donegal. It's funded by Healthy Ireland, so we're very grateful for that. But we've been working around supporting the agencies in the disability sector to work with young people for a number of years. And it's been an ongoing priority for Donegal Children and Young People's Services Committee. That's a bit of a mouthful, so we call it Donegal Sipsy. It's easier to deal with. So it's an ongoing priority because it's something we're always afraid that supports through the disability sector will slide off the radar. There's so much happening, so much going on, so it is one of our priorities to keep it front and centre. So this event is about acknowledging and encouraging businesses to make that extra effort to engage with young people who have a disability. They have a lot to offer. And to engage with the agencies that are acting on their behalf. Exactly, exactly. So it's also about raising profile. I think what happens sometimes is that we want to be helpful, we want to be supportive, but sometimes we're afraid that we'll use the wrong terminology or we'll say the wrong thing or we might offend someone. And if we feel uncomfortable, we then do nothing. And what we're saying is we need to become much more comfortable around disability, like any other difference. I need NASA's, you know, someone else needs hearing support. We're all human, we all have stuff to offer. And this is about really trying to make it easier for businesses to look at what can they do? What can they do that is both beneficial to their business, that is enriching for their workforce and also ensures that a young person with a disability gets the opportunity that their peer group do. So it's all about that equality piece. Yes, about getting that opportunity. I mean, it's hard for many people to get work, but can you imagine for those with a disability what they're up against, but just to give them a chance to give them an opportunity. And it's amazing what they can achieve. So our awards right now, it looks at two categories. We know in Donegal we have some businesses who are absolutely shining stars in the work and the inclusive way they work with their workforce. So we have an award for the businesses actively employing people with disabilities. So this category really applauds businesses that have already demonstrated a long-term and steadfast commitment to integrating people with a disability into their workforce. And also because they foster an environment where that diversity is both celebrated and valued. So we're enriched by our difference is what we're saying. So that's one category. We also know that a lot of businesses are very early on their journey in terms of embracing that disability and they're learning how do we go about it and need support to do so. So this award looks at businesses who provide work experience to those with disabilities. So you may have a small business that can't take on anyone new for a lot of reasons, but maybe they can offer work experience. They're willing to do that? Yes, and that is really important because we have to recognize the significance and the importance of direct hands-on experiences in the workplace to see what it feels like to be in a workforce, to have colleagues. So this category acknowledges businesses that offer work experience opportunities, even if they're short-term to individuals with a disability. And because that contributes not only to their personal growth as an individual, but also to their professional development. And it means that if you have a young person who is doing a training course, maybe through the ETB, Adult Education, maybe through the Progressive Training Program run by the Blue Stack Foundation in Donegal, that if they're doing that training, they're also getting an opportunity to see themselves in the workplace. Even if initially it's just a few weeks or a few months to try it out and get that experience and be part of the workforce. A first step. And all too often young people can just then fall down between the cracks because they've done the training courses or they've completed their education and then there's so few opportunities out there for them that they then get stuck on a rut or they lose interest in going into the workforce and that's a shame. That's very difficult. We have on the night, and I'm glad you've gone down that route, because we have on the night definitely one guest speaker, possibly two, who are young people who have gone down this route, who have a disability, who were doing well in school, did some training and then were finding it difficult to make that step. One of them is a young man who's agreed to tell his story at this event on the 19th of October. His name is Jack Campbell. You probably know him because his big break came when the business across the road gave him a chance. And that's the way he put it. So the Kellys' Mike and Top Centre. And this is what Jack told me a number of years ago when we launched the first disability directory of services. He said that he had, he got good grades, he had applied for jobs all over the place, he got loads of interviews, but when he did the interview he never heard anything again, because people obviously were then aware of his disability and thought, oh goodness, how's that going to work? But he said to me it was Martin who gave him a chance. And from then he did supervisory management. I know he was a supervisor in the Centre for a period. He has now completed his degree. He is now moving on in his life. Brilliant. Because a local business thought, well we'll give it a go. Yeah, yeah. You know, for all of this. Give him a chance. Give someone a chance. That's all young people like anybody else are looking for. It's just a chance. And he talks very passionately about the importance of that chance and what that local business did for him. So he is our guest speaker on the night which I'm delighted about. How we have found these businesses I suppose, we asked the public to nominate businesses in their area. So anyone in their area if they noticed a business that was active in any way around. And can people still nominate? It's closed for this year, but next year. Next year. Hopefully. So we asked the public to nominate businesses and we have 30 nominations across those two categories. And we also have an independent working group who are the adjudicators. So we have sent out some questions to each of those nominated businesses, just five short questions. And that's about us getting a wee bit more information on their business profile and what this has been like for them. And we will supply all that information to our adjudicators. And that's how we will pick the award. So it's completely transparent and everything. I think the biggest thing is to be nominated first of all. So to ensure that all the nominated businesses are applauded and feel embraced and celebrated, we also will have a window sticker for each nominated business. So that they can let their clean tell. They can display it on their window and people go, OK, that is an equality conscious business. I like that. And that makes a difference to your business profile. So all of those nominated will receive that. And we hope this will be an annual event. This is the inaugural event on the 19th in the Central Hotel. And we're very fortunate in that we know the event will go with a swing because Noel Cunningham, who is a patron of the Blue Stack Foundation, has agreed to emcee on the night. No better man. He'll make it fly. So that is what we're planning. And you're also going to launch a booklet that you mentioned off air on the night. We have three items to launch on the night. This is part of a wider disability equality project that is part of the Economic Well-being section of Donegal Sipsy. And the first thing we want to do is launch the updated edition of the Directory of Disability Services for Donegal. We launched this originally in 2018. This was from 2019. COVID sort of prevented a lot of work happening. So now we will have a new version. They will be available from Donegal Sipsy and from the Blue Stack Foundation. It'll also be on both websites free to download. So it's there, but we will have hard copies available either in the Blue Stack or Sipsy. I'll give you the web address in a minute. The other thing we want to launch is in past years, Sipsy brought in trainers from Dublin. We used to work with a wonderful organization called the Disability Equality Specialist Support Agency in Dublin. They would come in to do disability awareness training for us in County Donegal. We've moved from there and we are now able to celebrate the expertise that we have built up in the county. So the Blue Stack Foundation, fantastic organization, very passionate about the work they do. They have developed disability equality training. And this is our flyer. So we will launch this training on the 19th as well. We're training the trainers. Well, it's more for any organization, any club, any business that wants to have their members learn more about how we can be more equitable in the way we do things. So it's everything from, it's an interactive workshop. First of all, it's not very text heavy, very in-person. It's about improving our communication skills with people who have a disability and how we make small but very meaningful changes in how we do things. Now, there is a fee attached to that. It can be a half day training or a full day. And that fee goes straight back into the social enterprise part of the Blue Stack Foundation. So it's going back to support young people with a disability. And the third one we have, this is very new. This is a resource for young people with a disability who are 16 plus. This is developed by Sipsate. We've been working on it all summer. I like that word on the cover, pathways. Pathways, progressive pathways information. How do we move on from here? And there's lots of ways of doing it. And this is for young people, 16 plus, and their families, obviously, who have a physical, sensory, developmental or psychological disability. And all the agencies that have something to offer from the HSC, the ETB, community and volunteers, all our disability sector. There's lots of small and large opportunities. That's all in there. So we will launch that on the night as well. And finally, Annie mentioned the website. You're going to give me some addresses. There's two I want to give you. The first one is the Sipsy website. And that is www.cypsc.ie. Forward slash Donegal. So that's a national website. Go to the Donegal page. And under local resources, you find all the resources we develop. Anything that has to do with not just disability, but anything that is to support children and young people from 0 up to 25 across mental health, sports, everything we've got. The other one is the Blue Stack Foundation. And the Blue Stack Foundation is www.bluestackfoundation.com. So all the resources we've mentioned today will be available from both. Inherit copy are also free to download. OK, well, all good, all valuable information and all ahead of what hopefully will be very successful. And it's important that it's the night of recognition and inspiration on the 19th in the Central Hotel in Donegal Town.  And she is 100% right about what she's saying. I stopped what I was doing just to listen to her. There you are. Oh, well, whoever that was, thank you very, very much. Praise be to God. Thank you.


And the Intelligent Data Summit continues, this time with Red Hat and another great view on the state of AIML trends. And to bring that to us, let me introduce Abhinav Joshi, Senior Manager, OpenShift Product Marketing at Red Hat. Abhinav, welcome. Hey, thanks a lot, Vance. I'm glad to be here. Yeah, we're really glad to have Abhinav with us here too. He focuses on workloads for OpenShift Kubernetes platform, which more and more of these days includes support for AIML. He has almost 20 years experience in hybrid cloud, machine learning, and data analytics. So it's all a terrifically holistic view we're going to get of cloud Kubernetes and AIML this morning. Abhinav Session is entitled Fast Track AI from pilot to production with a Kubernetes-powered platform. We're going to hear how Red Hat will let you fast track AIML projects with Kubernetes and DevOps powered by the OpenShift hybrid cloud platform. We're going to get a look at the ISV ecosystem. Red Hat is built as well as some great customer examples to show you how real this is. So just a quick reminder, you can download Abhinav Slides. Just hit the big red button right under the view screen. You'll also see that he and his team have brought some other great valuable resources. We know you'll gobble them up, so just click the link there and you'll be able to download right away. And any questions, just let us know. Type in the submitted question box. Abhinav, after all that, it's time to hear from you about fast tracking AI. Thanks again for coming. Thanks a lot, Vance. Let's do it. That was a great introduction from Vance. So I'll directly jump into the agenda for today. So these are the key topics that we will talk about. We'll start by talking about the overview of the AI market. And then after that, we'll jump into the desired architecture and the execution challenges that we are seeing out there. Then after that, we'll briefly touch upon the value of containers, Kubernetes, and DevOps for taking AI from pilot to production. And then after that, we'll dive into the value of Red Hat OpenShift, broader portfolio, as well as our ISV ecosystem to help operationalize AIML. And then finally, we'll talk about the success that we are seeing out there and the key reasons as to why some of these customers have been successful with AIML. First things first, data has been extremely important. It's a critical business asset. And being able to leverage the power of AIML can actually help you achieve the data-driven insights and be able to achieve the key business goals, like being able to improve the customer experience, being able to gain the competitive advantage, being able to increase the revenue, save cost, and also being able to automate the business processes. And all these are extremely important these days, especially with the COVID-19 situation going on. And the more customers that we talk to, the more we hear that AIML is top of mind for them. And they are looking at ways, say, to fast-track AI from pilot to production. And that's the key topic for today. These are some of the ways in which the businesses are using AIML. And you can see a lot of good examples all across different industry verticals. They beat health care, like extremely important these days. They beat financial services, telcos, insurance, like automotive, as well as we see a lot of fraction in the federal government sector as well, and a few other key verticals. And one thing to notice that AIML has been there since 1956, but it was not until recently that the momentum has actually picked up. And the key reasons have been the compute power, like a lot of data is getting generated, and the availability of open source machine learning and deep learning tools and frameworks. All these three things are actually helping make the AIML more real now as compared to before. One more concept I want to go over is the AIML lifecycle and the key personas, because it's not a project where a single team is going to execute. It actually involves a full lifecycle and a lot of different personas as well. And all the people have to collaborate. So what you see on the top of the screen is the typical lifecycle in the AIML project where at the business level, so the goals are set. And then after that, you have to gather and prepare the data. And then after that, you have to build out the machine learning models, so you have to test them and train them, make sure that they are going to make the right recommendations when you deploy the models in a production environment. And then after that, once you build out the models, the next key step in the process is you have to integrate the models into your software development process because these models get consumed in real world as part of a software application. And then once you've built your application, then you have to deploy the application in real world and then begins the task of the inferencing where your model is going to see the new data and start making the predictions. And then as the last step is you have to continuously monitor and manage the machine learning, the deep learning models to make sure that they are making the right recommendations. And the key reason here is as the models start to see the new data, they may not make the right predictions. So you have to continuously retrain your models so that way the right business decisions can be made and you can continue to keep your customers happy with the right kind of recommendations. And throughout the process, you have all kinds of persona that are involved in this life cycle. So you have the business leadership that is mainly responsible for setting the goals and then the data engineers whose key job is to gather the right data and prepare the right data, do the visualization, make sure the data is all good before passing it on to the data scientist who are mainly responsible for building, training and testing the machine learning models and also being able to monitor the models into production. And then you have software developers whose main job is to work with data scientists to take the machine learning models and then integrate that as part of their application development and roll these out into production. And then IT operations is gonna be involved throughout the process here because they have to work with all these different personas to make sure that they have all the resources that they need as and when they need them and it's extremely important to be able to speed up the entire process. And we are starting to see a new kind of persona called the machine learning engineer. Think of this as a person between the data scientist as well as the app developer and the IT operations with main focus to help roll AI into production because the data scientist may be focused on perfecting the machine learning model but at the same time, they may not be as skilled or it may not be part of their job to make sure that the models are getting rolled out into production at a fast pace and also to continuously monitor the models in production, so that's where the job of the machine learning engineer comes into play as a glue between the data scientist and the software developers to make sure that the whole lifecycle is actually operating in a nice way and the models are getting deployed into production at a fast pace as well as the retraining of the model is also happening on an as needed basis. To be able to realize on this lifecycle you have to operationalize a solution architecture and there are certain set of capabilities that you need to make all this real. At the top here is all the tooling that your data scientist, your data engineers and your software developers need to be able to gather the data, prepare the data, build the machine learning models to do the software development and so on. And these days there is a concept of machine learning operations being able to apply say DevOps to the ML ops and this helps to kind of speed up this entire lifecycle. So these are the different tools that you need and then the next key thing is your data, right? So data has a big role to play and what you need to be able to do is build out your data pipeline as well as the key data services that you need in each of these different phases of the project. So that's where things like Apache Kafka comes into play to do the stream ingestion and you may have databases like a SQL database, no SQL database and you may build out a data lake say to store all your data and then be able to serve the data as and when needed in the different phases of the project. The next key layer in this whole solution is being able to host all these tool chains that the different persona need on a hybrid multi-cloud platform that has the self-service capabilities because the last thing that you want is the different persona to be waiting on IT to provision the resources and the applications that they need to get their job done. And also they should be able to get the consistent experience with these different software tools be it like on-premises, be it in the cloud or be it on the edge location. And a lot of these tasks are very compute intensive. So that's where the value of the hardware-based compute acceleration comes into play and an example of that is the NVIDIA GPUs. So this hybrid multi-cloud platform with the self-service capabilities, it should have seamless integrations with the compute acceleration like hardware. So that way, all these different tasks can be speed up as and when needed in a seamless way without the data scientist and the data engineers having to spend a lot of time to be able to make this work. So all this has to be seamless. And then at the bottom, what you see here is the infrastructure stack. As I mentioned, a couple of minutes back, all these tool chain as well as the platform, the software-defined hybrid cloud platform with the self-service capabilities, it should work in a seamless way, regardless of if you are doing all this on the bare metal servers on-premises, be it on the VMs like virtual machines, be it a private cloud or be any of the public cloud or it could be edge location as well. So at a very high level, these are the key capabilities that you need to make the AI ML real and the necessary people and process transformation. So that part is extremely key as well. And one of the reasons that we highlight this is because at the end of the day, what do the data scientists care about? And data scientists are one of the key persona in the AI ML lifecycle. So what they really need is a self-service capability like a portal where they can access all the tools that they need to get their job done, to be able to build out the models and be able to access the different data sources. And then once they're done with their modeling job, they should be able to share the work and collaborate with the colleagues as well as the software developers to be able to roll these models out into production as well as they should be able to make the tweaks at a fast pace and build out the new models because as I mentioned earlier, it's a very iterative and a compute intensive task as well to be able to build out the models and roll them out into production. So at the end of the day, they want to care less on the infrastructure as long as it provides the flexibility, portability, scalability and the agility. So that's all they care about. So to them, the infrastructure, it should be an invisible thing at the bottom that just works. So all this is good, but at the end of the day, there are several of the execution challenges that all the organizations have to solve for. And all this is based on what we are seeing in our customer base. The first one is the shortage of the skills, the talent shortage, like the lack of the key skills that you need to make all this real. And what we believe is the automation can actually partly help you solve the challenges on the talent. And the next key thing is lack of readily usable data because you have to collect a lot of data, but yeah, it has to be good data, meaningful data should have all the key things that you need. And this can be a daunting task as well because the machine learning models are going to be as good as the data that you use to build and train those models. Next key thing is the lack of collaboration between the various teams that we saw on one of the slides earlier, because if these teams are not collaborating, it can be a challenge as well and stall the various projects. And there are statistics out there that show that more than 80% of the models that get built out don't actually show up into a production because of the lack of collaboration between the various teams and the siloed operation. So you have to solve for this challenge as well. And that's where we believe that the DevOps capabilities and a Kubernetes based platform can actually help you solve some of the collaboration challenges. And the last one is the unavailability of the infrastructure as well as the software tools that the data scientists, data engineers and the software developers need. If they're able to access all this in a self-service way, that is great for them to be able to speed up the whole process. What we believe is the cloud native technologies like containers and Kubernetes and operation best practices like DevOps can actually help solve a lot of these challenges. And that brings me to the value of containers, Kubernetes and DevOps for fast tracking AI projects from pilot to production. And the values that you see on the slide here are not very different than what we see in a typical software development project. So the same values are directly applicable to the whole machine learning lifecycle as well to be able to provide the necessary agility that you need, the portability, build once and deploy anywhere, be able to share with the team as well as the software developers and so on and then the flexibility and the scalability because so you have to be able to respond quickly by being able to automate the computer source management and also being able to use all the self-service capabilities that you get with the containers and Kubernetes. And then the portability is extremely important because you should be able to develop the model once and be able to deploy them say anywhere be it on-premises at the edge or in public clouds and then being able to provision and be able to scale up and down in and out as and when needed and where you need it because all the flexibility and the scalability that you get are going to help speed up the AIML lifecycle. But at the end of the day, these are the concepts and this is not gonna be enough. What you really need is a Kubernetes platform that has all these capabilities and a lot more. So that's where the value of Red Hat OpenShift Kubernetes platform comes into play. It provides all the key capabilities that you get with containers and Kubernetes and also DevOps best practices. But on top of that, we have done a lot of the value add integration to can further speed up the AIML projects. And first key thing is we've done a lot of integrations with the key ISVs on the AIML site and that actually helps you speed up the deployment and the lifecycle management of the key tools that your data engineers, your software developers, your data scientists and so on may be doing. And all this is done with the concept of the Kubernetes operators. So to be able to automate the deployment and the lifecycle management of the AIML tool chain. Next key value proposition that we bring to the table is the portability and the consistency that you can achieve by running your AIML tool chain and the intelligent app dev on top of OpenShift because it's got both the cloud hosted as well as the self-managed options that can be consumed in a data center, in a public cloud or Edge locations of your choice in a seamless way. The third key thing is OpenShift also includes the DevOps capabilities with the pipelines like say Tecton and it supports Jenkins and also Kafka and so on. The serverless with Knative. So basically what you're able to do is it gives you a complete platform. It helps you extend your DevOps for the entire machine learning lifecycle and helps aid enablement of the collaboration across the different teams that are participating in the process. And the last one is we've added a lot of different capabilities that you desire from a typical enterprise platform in terms of being able to do the monitoring, the automation and the host tours that you need on a Kubernetes platform, the log analytics, being able to provide a serverless capability, a VM capability, a container capability, like all these key things that you typically desire in a Kubernetes platform. So all these things are part of OpenShift and all this is actually based on the open source we have our projects. So that way you don't have a vendor lock-in. If you go with Red Hat OpenShift Kubernetes platform, say to run your AI ML workloads and the intelligent applications. And on top of OpenShift, we have capabilities in the rest of our portfolio that help you with the other pieces of the architecture. As I'm showing on the slide here for helping you build out the data pipeline and the services, that's where we have the Red Hat AMQ. Think of this as the Kafka on Kubernetes. And then for helping you with being able to roll out the AI ML models into production. So that's where we have the solutions like BAM as well as the decision manager, and to be able to help you build out the software applications that have the AI ML capabilities. That's where we have the Red Hat OpenShift application runtime capabilities. And then at the bottom of the stack for the software defined infrastructure. So that's where for your container host we have the secure and the trusted Red Hat Enterprise Linux. And also for the storage capabilities, that's where we have the OpenShift container storage as well as the self storage that can help you with the data lake, as well as providing the container storage capability that you need for the entire life cycle. So what you see here is the depth and the breadth of all the ISVs that we are working with to have the integrations as well as the reference architectures. Like a lot of the software ISVs on the AI ML side. So they've already have the software integrations onto OpenShift using the operator framework. So to be able to speed up the deployment and the life cycle management of the key AI ML tool chain that your data scientist, the data engineers may be using. And also we have done the integrations and we have reference architectures with the infrastructure partners that shows prescriptive guidance on how to design and deploy a full AI ML solution at a fast pace because we've done a lot of work in building out the best practices and have captured the best practices as part of the reference architectures. And on top of that, we participate in the community projects as well on the open source side because open source is at the heart of Red Hat. So all the things we do are in the open on the AI ML side. So we work on a lot of projects that you see here. We do TensorFlow, Jupyter Notebooks, Kubeflow, PyTor, Spark and so on. So these are some of the key upstream projects that we participate on. And then based on these, we've actually built some of the downstream projects as well. And the one I want to highlight here is the OpenData Hub. Think of it as machine learning as a service platform based on OpenShift, Chef Storage, Kafka, Jupyter Hub, Spark and so on. What this actually showcases is that using all the open source tools and technologies, how you can build a complete AI ML architecture. And also we have the contributions into operatorhub.io. So it's the home for a lot of the community operators that have been coordinated with a lot of different ISVs to be able to build out. So both the OpenData Hub and the operatorhub.io help kind of show that using the open source technologies, you can do a lot of things and speed up your projects on the AI ML front. So the last thing I want to touch upon is the key wins that we have had in the space. We work with a lot of different organizations across different industry verticals and help make them successful. And a lot of these that you see in here are the public success stories that we've had on the AI ML front. If you look at on the healthcare side, we have HCA healthcare who built a data platform to help save lives. And now they are using this platform to be able to fight sepsis as well as COVID-19. And it was not too long back that they did a public webinar where they talked about on how they used OpenShift say to build AI ML and the data platform say to help fight sepsis and also COVID-19. And then on the automotive side, so we have customers like BMW Group that have used OpenShift to build a data platform to be able to improve the customer experience and also this data platform is actually helping them to speed up the autonomous driving projects. And all these links are actually real and you can learn more by clicking on these links. And then on the financial services side, so we have RBC Bank, we have discovered financial groups that have spoken at say KubeCon and also at OpenShift Commons gatherings on the use case and how they've used OpenShift to be able to speed up the AI ML projects. And then the last one is on the slide in the oil and gas space. It's a famous company, ExxonMobil. So they have been able to democratize data science for all the phases of the oil and gas exploration. And we have a lot more deployments in the field but these are some of the ones I wanted to show on the screen here as some of the key wins and examples that we have in the real world. And all these organizations have been able to achieve the real world business benefits. Okay, and the last key thing is the resources that we have that can help you get started to be able to fast-track the AI projects from pilot to production. So we have a website that has a link to all these things that I talked about. Then we have an e-book as well. So if you are in the beginning phases of your project, there's an e-book that I just built out on the top considerations for building a production-ready AI. So go through the e-book. It's a short e-book like eight pages and we have a lot of videos as well. High-level videos as well as the detailed solution architecture level videos that show you how to take the AI from pilot production using that had OpenShift and the broader portfolio. Then to summarize, as we saw at the beginning of the talk that AI can bring in a lot of benefits to the businesses but then you can get a lot of the execution challenges that can stall the projects and that can have a business impact. Then the cloud-native technologies like containers and Kubernetes and DevOps can actually help you achieve or they have the potential to get you the desired agility, the self-service capabilities, the portability, flexibility, scalability and automation that you need say to be able to speed up the AI ML projects but then at the end of the day, what you really need is a full Kubernetes-powered platform as in say that had OpenShift that can help you leverage all the key benefits that you get with containers, Kubernetes, DevOps and the key ISP ecosystem to help you speed up the AI ML project and the delivery of AI-powered applications into a production deployment. So that way you can get all the benefits that you expect out of the AI ML solution. With that, thanks a lot for listening to me. Please feel free to kind of see all the things that we have listed at the bottom of the screen here as the key resources that can help you get started. And if you have any questions, feel free to reach out to me. That's my email on the slide here. So that concludes my slides and let me turn it back to Vance and see if we have any questions. Fantastic, fantastic. Avanava really great and well thought out discussion on this whole AI ML ecosystem, let's call it, with so many different stakeholders and technologies that can help us a really, really great look. Thanks a lot, Vance. I truly appreciate it. You know, you make a good point, especially toward the middle and end of your session, Avanava, about the idea that the whole goal of an AI ML project should be that it's production ready and actually gets deployed and helps the business. And a lot of folks these days spend an awful lot of time talking about the front end development or the model design. Let's extend this to production ready. What are some best practices that Red Hat has learned to help make sure all that front end work and investigation actually does turn out to be production ready? Yeah, that's a very good question. And based on all the deployments we've learned is the following. The first key thing is that you need to have the sponsorship from the top levels in the organization. Because if you don't have a sponsorship and they don't see the value in the project, it can stall your projects. And the next key thing is that you have to identify the key stakeholders on the business side that can provide you a lot of feedback as you execute on the project and are part of project as well. Because the end of the day, they are the key stakeholders, especially the people that will be using the solution. Like say the example I want to highlight is the HCA Healthcare. So what they did was they partnered with doctors and nursing staff right from the beginning of the project because at the end of the day, the software app that they built out that had AI capabilities is being used by the nursing staff. So they have to have the confidence in the solution that they are building out. The third key thing is being able to train the people and get them comfortable with the solution because the people are not trained and you may have the best software in the world, but at the end of the day, if you have not done the people and process transformation, yeah, it can stall your project as well. And the good news is that based on all these key things that we have learned from a number of deployments in the field, we have a very mature, insulting services that can help on the AIML projects to help you take the AIML from a pilot to a production phase at a very fast phase. So we have the capabilities in terms of both the software, the ecosystem, as well as the key consulting services that you need to make AIML real. Great answer. In fact, I think another question here wants to also tap into your brain on the idea of how to do AI projects better. And it says, I'm really impressed with how Red Hat has studied how AIML projects move across different personas. What are some of the best features in OpenShift that support a handoff in the AIML lifecycle? Yeah, so we have a lot of key capabilities in OpenShift. So it's a lot more than the basic containers and Kubernetes. And what all we've done is we've also integrated a lot of DevOps capabilities. Like we provide Tecton natively in OpenShift as a CI CD pipeline. So that can actually like help you make it easy to roll the AIML models into production. At the same time, we have the integrations with the software with the app dev capabilities. That way, using OpenShift runtimes, you can build the software apps in a seamless way. And then also we provide the integrated automation capabilities, we have monitoring capabilities, the log analytics. So these are the key capabilities we have that can help you with the entire machine learning lifecycle and speed up your process of taking the AI models into production. And we have a lot of capabilities on the services side. We also have data scientists and the folks that have been there done that. And also the folks that actually support the internal AI ML solution we have at Red Hat. So that way as part of the service, the services, we are able to bring in the right people that speak the right language and can talk to all the key persona in the organization. And the different success stories I showed on a slide. So our consulting team has been a key part of all the projects. Bottom line is that we are able to bring in all the key capabilities that the customers need to make AI ML solution real. So beyond simply getting from pre-production and production, you actually have support services or consulting services, Abhinav, that will actually help people design a pilot correctly? Yeah, pilot and also be able to roll out the pilot into a production environment. And we have a number of success stories there as well, where our team actually help the customers make AI ML solution like real, right from the concept phase all the way to the deployment and also the management, if need be. Fantastic, fantastic. I know that some of these steps, depending on how well people are staffed or how well they know about DevOps can be a bit intimidating. So this has been great so far. Abhinav, I know that you've given a lot of thought this whole, not just across the life cycle, but the stack of technology you showed. And a question here about that, can the speaker talk a little bit about their roadmap for the future to fast-track AI projects? Yeah, that's a very good question. And there are different aspects to this. So our goal is to continue to drive the innovation in the open source community with the projects like say Qflow, the Open Data Hub and so on. So that way the organizations can use the power of open source to be able to make the AI ML real. The next key part of our strategy is to continue to work with the ISV ecosystem and be able to build out the key software integration that we need, simplify the deployment and the life cycle management of the key tool chain that your software developers, the data scientists and the data engineers need to make AI ML real. And then the third key thing is to continue to add the capabilities in our products and also make sure that the whole DevOps thing we talked about. So all the value DevOps can be extended to the entire ML life cycle. So to have all the key capabilities, that's a key part of our strategy. And the fourth one is to continue to mature our services offerings because as we said, it's not just products, you have to bring the capabilities as well. To help the different organizations throughout the project. And the last one is we continue to work with our hardware partners as well. So that way we can bring the complete end-to-end solutions to the market that have the software as well as the hardware at a very high level. These are the key things that we are focused on. That's a lot to be focused on. So thank you for up enough for that. That was really fantastic. You know, I see time is just about up. But before you go, Avinav, we'd like to have you give folks a next step opportunity here. I can tell from the job titles here that are attending with us today that we've got a great group of folks that are AI, ML professionals. And then we've also got folks that have been working in DevOps and Kubernetes and maybe even OpenShift for years. Maybe give us a suggestion of how we could bring these two professionals together with a next step from Red Hat. Yeah, what I would say in terms of the next step is to check out the resources that are listed at the bottom of the page because there we have a lot of good examples. And based on the various phases of the journey that you may be in, there is a link there that can help you. And then feel free to reach out to us. Like I have my email on the slide as well. So if you have any questions, feel free to reach out and we can help you make the AI, ML real in your environment at a fast pace. Yeah, we love that. We love that. And in fact, you mentioned partners before just a quick note here. We're proud to say Abenov that we've got several of your partners here with us this morning. We have folks from H2O and Cladera and MMSQL as well. So another great way to kind of bring all that knowledge together that you presented for us this morning, I think. Yeah, absolutely. Definitely agree on that. Excellent, excellent. Abenov Josh, you've seen your manager OpenShift product marketing at Red Hat. We're really glad to have you here. Thanks again for bringing together the worlds of AI, ML and cloud native development. It's really been a fantastic eye-opening session actually for me. And I could tell from the questions that really tapped into a big news trend going on in the sector for intelligent data. And we appreciate your time. Thanks again. Yeah, thanks a lot, Benz. Abenov mentioned quite a number of resources. Let me quickly review. We've got some of them here locally right in the breakout room. Click the link that you see below and you'll be able to get directly to that asset. We've also got a copy of the slides. That big red button will get you that. And so much going on at Red Hat in the AI, ML, intelligent data space. We didn't have room for everything. Here's a slide that'll take you directly to the Red Hat website. Click on any of these links. Once you download Abenov slides and you'll go directly to that asset because the links will be live. Thanks again, everybody.


Welcome to my maximum volume hypertrophy routine where I will go over the training principles I've developed from over 15 years of experience in the gym. The actual routine we're doing is focused on my personal goals, just how you can use these principles to alter your training regimen to be more effective whether you're a bodybuilder, powerlifter, or even athlete. There are 10 of these principles. One is intensity, the frequency, volume, rest time, and rep range. The training frequency in volume is substantially higher than average. It's why it's called maximum volume hypertrophy. You're doing every muscle group, full body, every single day, 7 days per week, no rest days. To fit that volume realistically every day, the rest time between sets is 20 to 30 seconds as opposed to 2 to 3 minutes. That is typical to most bodybuilding routines, the average grow in the gym. Since you're working the muscles so much every day, you can't go super heavy as it will just stress your ligaments, joints, tendons, central nervous system. Therefore, the rep range is slightly higher to accommodate moderate weight 10 to 15 reps. Number two, no downtime. We superset contrasting muscle groups to minimize the time spent in the gym, doing calf raises in between sets of back, maybe some glute bridges or squats during your bench press rest time. No need to stand around for 30 seconds and do nothing. Most people spend more time on their phone in the gym than actually working out. Number three, muscle size versus volume. Making sure the volume corresponds to the size of the muscle. Obviously, your back is going to need more work than your biceps, same thing with legs requiring more volume than triceps. You simply need more exercises, more sets to take that muscle to failure. For being failure, definitely make sure you're going as close as possible to failure without risking injury, which is number five. Do not strain yourself. It's important to go near failure, however, you don't want to risk injury by doing so. Doing a few less reps per session is insignificant compared to missing several months of working out from a busted shoulder. Number six, mind muscle connection. When you perform an exercise, the most important factor is that you feel it in the muscle you're intending to work. With back, you don't want to be doing a bicep workout. Number seven, ideal repetition tempo. That means moving the weight in a manner that stimulates the muscle as effectively as possible. For instance, on a chest fly, really squeezing the chest, stretching out on the negative, and then squeezing again. Bringing us to number eight, drop the ego. No point A to point B lifting. Moving the weight for the sake of moving it. Since our goal is to feel that muscle, have the proper mind muscle connection and repetition tempo. The weight is not the end all be all factor. It's certainly relevant, just not necessarily better to go heavier. Just because you move 200 pounds from here to here, does not equate to automatic results. Number nine, be goal oriented. As we mentioned earlier, these principles can be applied to any routine. So if you don't want muscular legs, don't work out your legs. If you want bigger arms, incorporate more volume for your biceps and triceps. Same thing goes for a lagging muscle group.  Number 10 is consistency.  I find that it's much easier to become bored on a full body routine. So if you do miss one or two training sessions per month taking a rest day here and there, the effective volume of this routine is still multiple times higher than any other routine. Now you can structure this routine in three different ways. One is finishing each muscle group on its own, which is what I've been doing, like chest, then back, then shoulders, and you superset it with non-invasive muscle groups like doing some calf raises between bench press sets. Number two is alternating exercises, like doing a barbell bench press for three or four sets, then dumbbell rows for three or four sets, dumbbell flys for three sets, and lat pulldown for three sets. Instead of just blasting your back first, you're doing a back exercise, a chest exercise, spreading the energy evenly between muscle groups, which brings us to number three. If you're strapped for time, you can alternate sets and really superset and try to get through the workout.  So it's definitely not the most effective way to do this, but you'll spend 40 minutes in the gym as opposed to over an hour. That's the introduction of my training routine for you guys. If you'd like to obtain the full routine, you can do so on frank-tepano.com. You'll receive a write-up of it as well as access to the video course where I explain all aspects in depth and take you through a real-time workout.


Right now, one of the differences between Secretary Clinton and myself is how we raise money for our campaigns. Secretary Clinton has several super PACs. She has raised just in the last reporting period alone $15 million from Wall Street. Not to mention giving speeches behind closed doors to Wall Street firms for hundreds of thousands of dollars. Now, what I kind of think is if somebody is going to be paid $225,000 a speech, it's probably a great, wonderful speech that she would want to share with the American people. I am very proud to tell you, we do not have a super PAC. We don't want the money from Wall Street or corporate America. What we have done in this campaign in terms of fundraising is literally revolutionary. We have received, this is really unbelievable, something I never in a million years would have thought could have happened. We have received up to this point in the campaign, four million individual contributions. That is more contributions than any candidate in the history of this country at this point in a campaign. Our phrase, Abraham Lincoln at Gettysburg, this campaign, which by the way, do you know what the average campaign contribution is? $27 indebted to Wall Street, the drug companies, the fossil fuel industry. I'm indebted to millions of people for that $27. Abraham Lincoln at Gettysburg, this campaign truly is a campaign of the people, by the people, and for the people.


Hello and welcome to NewsClick. Today, we will be discussing the $5 billion fine imposed on Facebook by the U.S. Federal Trade Commission. To talk more about this, we have with us Prabir Prakash. Hello, Prabir. Prabir, so the $5 billion amount is actually a settlement between the FTC and Facebook. For violations of a 2011 settlement, they had reached regarding users' privacy. And while the full details are not out yet, it's largely understood that it's a response to the Cambridge Analytica scandal. So do you see this almost record fine? To be a meaningful step in actually regulating what Facebook is doing? You know, it might appear to be a very hefty fine. $5 billion is not chicken feet. So that is there. But it's interesting how the markets have reacted since every day now, every time anything happens today. We look at the markets for what is supposed to be good or bad. Well, if it was hefty fine, it should have depressed the stock of Facebook shares. Instead of that, it has actually gone up. So the Facebook is supposed to have had a revenue, profits, not revenue, profits of $22 billion in the last quarter. Now, that would simply indicate that they have, they're really earning a very high income from the kind of business that they do. And this $5 billion, therefore, is a wrap on the knuckles for 10 years of violating, essentially, the user, shall we say, privacy. Now, we have commented on this earlier extensively that whether it's Google or it's Facebook, the essential model of these companies come from the fact that they have access to private data, our private data. They can dice us up as consumers in any way you want. And if we see what the leak of the documents which took place in the one British Parliamentary and sees some documents from a traveling person who had these documents of Facebook with him, it is very clear that the way Facebook operates, it essentially extorts money from its, not money from its partners, but extorts data from its partners. And if they don't give that data access to the data for any app on Facebook, then they are generally treated differently, shall we say. And after the Cambridge Analytica scandal, the issue is not that Cambridge Analytica had access to this data, but Facebook's anger was how is it they could use the data which should have been exclusively used by us for marketing. So, it's futile to talk about violating users' privacy if the entire business model of Facebook is built on violation of user privacy, which is what it is. So, either you look at the market power that Facebook has, or you decide that certain principles you put in place, what constitutes data and how it can be used, neither of which is the direction which either the US government wants to travel, and the FTC at the moment is really much weaker as a regulatory agency. The monopolies that Facebook and Google have built up in the last 10, 15 years are the most powerful monopolies the world has ever seen, and has also been built up over not 50, 60, 70 years as happened with earlier monopolies, but the shortest possible time. So, I think the challenge that all governments and US government, because it's a home country for Facebook and Google, but it's also true that Alibaba and WeChat and all are also there, Baidu are also there in China. So, the question is how do the home governments regulate what are essentially global monopolies, and what does the rest of the world do? There are some presidential candidates in the US who have been talking about breaking up these companies. Yes, that's one possible answer. What are the other regulatory answers or what other answers we can take? Ideally, you don't know at this stage. And right now, the strategy generally seems to be imposing fines because recently the Italian government also imposed a one million fine, and Ireland is conducting at least 10 enquiries right now. But there seems to be a general lack of clarity on what else can be done in addition to just imposing fines, which like you said is actually peanuts as far as Facebook is concerned. You see, this has been also the European Union's attempt to force Google to do certain set of things, and what are called anti-competitive practices. Impose on them fines for violations of anti-competitive practices. They have also put hefty fines on Google. You know, the problem that is there in this fine model is if violating this anti-competitive practices, say for five years, gets you 20 billion dollars of profits say over a period of five years, then paying one billion seems to be an entry price. So it becomes instead of buying a license say on telecom, you buy a fine. So in order to violate the privacy, and this is the buying the license to misuse private data is essentially what Facebook and Google are doing in terms of accepting fines in lieu of what this particular order calls the egregious violation of privacy. So I think we are in that model that violating privacy for Google and Facebook and a host of others is the basic business model. And since violating of the privacy has a price, they are willing to pay the prices fine, because otherwise there is no shall we say regulatory barrier to getting access to users private data. Now if you remember what was to be said the airwaves, you had to buy the airwaves license. So you are buying private data through fines seems to be the business model of Google and Facebook. So privacy protection, we must be very clear, privacy protection in a model which is based on user surveillance is actually meaningless. And therefore, to think that we can protect it in any one other way except by either law, which enforces certain things or by regulatory creating regulatory barriers and breaking up monopolies one of them is at the moment only two things to do. Otherwise, I am afraid that we are, we have a really shall we say a fine filled future, but it will not harm Google and Facebook's growth. This is a price they are willing to pay for in lieu of shall we say untrammel access to your and my data or our viewers data. And this is of course not even considering the Facebook currency Libra that is coming out which is also faced hearings and similar criticism. And we also have a situation the global south where most countries actually have not enacted including India for that matter have not enacted very strong provisions to stop these companies and Facebook is actually involved in promoting the internet with free basic schemes in many of these countries. So again, that's a lot. There's another question there as to what these kind of countries will also be able in a position to do. So just passing two separate things out of it. One is the privacy angle that you've talked about and the fact that Google and Facebook are equally guilty about it, but Facebook has the other aspect which is that it pretends to be the internet and says everything should happen in my ecosystem. So in that sense while Google also has similar policy in terms of giving you more and more tools in lieu of which you part with data, Facebook in that sense also tries to create ecosystems which you don't leave then and that becomes effectively your internet world. So that's the net neutrality versus free basics debate which of course India Facebook failed to get it through but in a lot of other places they have successfully got it and the zero rating issue is still an issue in different parts of the world and zero rating when it comes to even European Union it's not that clear they have got a clear policy against zero rating while India we do. So I think those are some of the issues that we have to take into account when you talk about how to regulate this company but when you come to currency and this is an area where Google obviously has not gone this particular way. All the other digital platforms we know create what are called wallets. Wallets are not outside the currency zones you put in currency money over there and that stays in the wallet it could also be essentially adding to the reserve of these companies because the wallet if you don't take out the money that stays with the company you don't get any interest but they could park it in a bank and get interest or invest in other things get interest. So if you create a wallet you are actually creating free cash for yourselves and you are not a banker so you don't have any obligations what a bank has if you put in money in a bank but at the same time you can use the money in all kinds of ways. This the wallet is what we have seen till now digital platforms do Facebook has gone one step ahead he said not only am I providing you a wallet I'm actually providing you a currency and I'm also going to provide you a currency which will be recognized by other people as well they've got a set of friends with who are also backing the Leibler currency and they have said for verification as a first step we will verify but behind our verification is a bit coin equivalent stuff that we will do which is that we will also have a verification which will be algorithmically done. Now how this algorithmic verification will be done blockchain what variant of blockchain etc we don't know but it's also clear that blockchain kind of methods as you know the number of transactions globally you can do with blockchain is about seven or eight based on current computing power and internet speeds but this obviously is not sufficient for normal transfers a normal accounting purposes so while credit card companies can do up to 7000 per second this supposed to be 1000 per second kind of speeds so the first layer of verification is going to act like a credit card company that some companies verify whether this is money or not the blockchain method is supposed to be the second tier we don't know what the hell it is so is it a cryptocurrency as it is claiming liberalized cryptocurrency or we don't know what it really means is that is it outside the currencies of governments because companies have not guaranteed money governments have states have is it with are we seeing private money creation instead of wallets are we really seeing private money creation and do they lie outside all currency regulations therefore bypassing all the capital controls other currency controls that are there so large amount of money can it flow out of the country through this route these are all open questions I don't think it's going to be that easy the regulatory barriers are not going to given up by other countries so we might find the liberalized declare illegal so all those possibilities exist but I do think that facebook now is moving into an issue area where it says not only do I use your private data but I'll create an ecosystem within which you always are going to live and you don't need to have anything else to go out of it so can it actually envelope amazon can it actually envelope all other shall we say companies which deal with merchandising basically buying and selling can they develop that system as well within it and become a ecosystem from which you get from internet to goods all and services all of it we'll do and on top of that also we give you a currency within which if you stay in the ecosystem you don't have to change this currency I think that's the threat of Libra and it's really a much bigger regulatory threat where it that it comes to private data because money is what every company every human being in the world and governments use therefore its control going to this kind of ends is a is a shall we say a very daunting prospect given the fact that the way Facebook has dealt with our privacy I think dealing with Facebook whose model is an extortionist and predatory model I think is very dangerous for all of us thank you that's all we have time for today keep watching us click


There we able to do so let's take the hands of this place that Jesus we thank you for your mercy We thank you for your grace of our life God We thank you God that you don't look in our works what we've done the wrong things we've done But because of your mercy, God is to love us today in your presence Thinking all the good things you have done we didn't hear Patiently still small voice again. I just Failed till the end No promise show your loving door My soul's secure Your promise She beautiful she beautiful she beautiful you Jesus


Hello everyone, welcome back to another session on dentistry and more Today we have a nerve block that is intra orbital nerve block So it is a maxillary nerve block, which is given just below the orbit. That is why it has got this name intra orbital Before moving on we need to understand a little bit about the Nerve Anatomy so we have the fifth cranial nerve that is trigeminal nerve has got three divisions the Ophthalmic Maxillary and mandibular so this intra orbital is a part of maxillary No, so you can see the maxillary nerve here so it has got many branches so I can see the Posterior superior alveolar nerve anterior superior alveolar now the middle superior alveolar now then the Intra orbital nerve and which has got the terminal branches which is emerging from the intra orbital foramen That is palpebral branches nasal branches superior label branches and you can see towards the posterior side It is going to the temporal region and that is a zygomatico temporal and zygomatico facial nerve So this is just the anatomy and the distribution of the nerve So understanding this will help you to get the idea about the Anesthetized area because we are going to apply the anesthetic solution here. That is the intra orbital region So it will definitely anesthetize these three parts. That is the terminal branches then the anterior superior Alveolar nerve and then the middle superior alveolar nerve because these three are the main branches which is after the intra orbital Foramen so this anterior teeth so we have One two three that is a central incisor canine and the lateral incisor which is supplied by anterior superior alveolar nerve whereas the premolars 4 5 and the meso buckle root of six supplied by middle superior alveolar nerve and And the other molars that is seven and eight that is second molar and third molar also the roots which is accepted that is a disturb buckle and palatal root of Six that is the first molar supplied by posterior superior Alveolar nerve, okay so this is how The nerve supplies teeth pulp and pyridontium Central lateral canine is supplied by anterior superior alveolar nerve Which is anterior most branch then the middle superior alveolar nerve which is supplying two premolars and the Meso buckle which is near to the premolar Meso buckle root of first molar the remaining That is second and third molar along with the other roots other than meso buckle that is a disturb buckle and palatal roots so when we apply Local anaesthetic intraction So we are going to Anacetize these many teeth so it will anacetize definitely anterior superior alveolar nerve, so Infra orbital sometimes known as anterior superior alveolar nerve, but most of the cases Middle superior alveolar nerve also will be anacetized, but in 20 percentage cases It will not be anacetized in those cases. We need to give a separate Middle superior alveolar injection for the procedure So moving on intra orbital nerve block So the aim is to deposit anaesthetic solution into the intra orbital canals through the intra orbital Foramen, okay, so in this technique as I said it will anacetize anterior superior alveolar nerve and also middle superior alveolar nerve and along with The branches, okay, so the terminal branches of intra orbital nerve They are inferior palpable the lateral nasal and superior labial terminal branches they are Lateral nasal So you can see the picture here superior labial that is above the lip then inferior palpibril Just below the eyelids So these are the nerves Anacetized when we apply intra orbital Nerve block, so what are the areas to be anacetized so the areas As as we can imagine This is being deposited just side of the nose below the eyes on one side so this will Anacetize upper lip not bilaterally only on one side the lateral aspect of nose on the injection side that is a lateral part and the lower eyelid and Also the Bacchal mucosa of the upper anterior teeth and trimolars and the pulpan Investing structures of these upper anterior teeth and primolars and mostly the meso Bacchal root of first molar So these are the areas Anacetized These are the nerves Anacetized, okay So now what is the position of the patient and dentist so patient position? As you see the picture here head neck and trunk on the same straight line Okay, and the back of the chair is tilted so that the patient is in a supine position The occlusion plane of the maxillary teeth and at a 45 degree angle to the floor So it should be a supine position the maxillary occlusion plane should be 45 degree angle to the floor and we can Start giving the procedure whereas a dentist position stands on the right side in front of the patient for a right side injection and alongside the patient for a left side injection and Mostly the armamentarium or needle commonly used is 25 to 27 gauge and it will be definitely a long needle Armamentarium 25 to 27 gauge long needle No, what are the landmarks so landmarks will guide us to give the injection at exactly The intra-orbital region or exactly where it is intended to so anatomical landmarks are The supra orbital notch can see the picture here and The intra-orbital notch then the intra-orbital depression intra-orbital depression Then the anterior teeth and Pupils of eye So we need to Visualize a imaginary straight line which is passing through the supra-orbital foramen Intra-orbital foramen and mental foramen Okay, so these three will be passing through a straight line and we need to visualize The area which is near to just below the orbit lateral to the nose at that depression, okay so First thing is we need to start Using our thumb we need to feel the depression, which is the intra-orbital depression and The line we need to visualize imagine a visualization and we need to visualize the we need to feel the Intra-orbital depression where the intra-orbital foramen is located Next a needle pathway so needle pathway of insertion So how we are going to insert the needle into the mucosa approaching the Infra-orbital foramen. So we have got two methods one is a bicuspid method and the next one is central incisor Method so bicuspid method is a commonly used one because it is Going just above the primolars that is a straight line We are keeping our needle in a straight line and we are Approaching the infra-orbital foramen We'll deposit around two ml of solution by inserting the needle five mm into the Mucosa, but we should never Go more than three fourth of the needle length Where as a central incisor approach it passes through the mucosa and a ariola tissue Okay, it is like we have the central incisor here will this is a Ankle we have two central incisor Okay, this is a lateral incisor and this is canine So we aim from the meso incisor angle to the Disto cervical ankle and we aim the sorry so we aim the needle at a 45 degree or at a Ankle towards the This is our target area Infra-orbital region Okay, so you can see the picture here This is a central incisor approach. This is two ways of approaching Sorry Next one is a parallel approach that is Through the primolars. Okay, so through through the primolars. Let it be here through the primolars It is going almost parallel without any angulation almost parallel to the primolars. This is making an angulation And approaching the infra-orbital foramen So, what are the indications of Infra-orbital nerve block that is a dental procedure involving more than two maxillarities and other buccal tissues that is central canine even primolars We can do using infra-orbital or any other inflammation or infection procedures or Or when supraperiosteal injections have been ineffective because of this dense cortical bone So the residence cortical bone is present. So this supraperiosteal injections might not be effective at times So in those times we can directly go for the block contra-indications Some procedures it requires just supraperiosteal injection in those cases. We don't need to go for intra-orbital and Any localized Indication also we can avoid intra-orbital instead. We can give for a infiltration Now we have the Symptoms so the symptoms subjective symptoms what the patient tells you patient There's tingling and numbness of the upper lip side of the nose and one part of the the inferior Eyelid area whereas objective Symptoms our patient might not feel any pain during the instrumentation. Okay, so complications involve the hematoma and Facial now paralysis So these are the complications associated with intra-orbital numb block so it's a very commonly asked essay question and oral surgery paper So we need to write about its indication contra-indication its side effect two methods of pathway of insertion that is a central incisor and bicuspid approach The amount of Solution injected and the needle The length of the needle to be inserted It should not go beyond three-fourth of the length than the landmarks Supra-orbital notch, intra-orbital notch, intra-orbital depression, anterior teeth and pupils of the eye and In the beginning part we need to explain a little bit about the intra-orbital foraminamid spranches that is anterior, middle, supia diurnal and all those things So you need to draw pictures and explain the intra-orbital nerve block So along with that you need to explain about the area and it's the taste and the nerve and it's the taste So i'll come up with the middle, supia diurnal in my next session. Thank you


I'm going to talk about something psychopaths do and it's common predatory behavior with them. When he's looking at peers, he's scan in his face. He's looking at him and he's looking for little things to tell him he's afraid. The only thing psychopaths really recognize from an emotional standpoint is fear. And when they see it, their pupils died late a little bit. This guy's like an animal. He's looking for weakness right here. This is just a body running around trying to please itself, find things to make it feel better or something to do. I think this is some classic predatory behavior we're seeing. He's looking for emotions that tell him that peers is afraid or that he's weak. He's looking for weakness.


Welcome to the Rich TV Live podcast. I'm your host, Richard DeSousa, and we are talking about the hottest stocks and cryptos of the day. And look at this pre-market Coinbase on a tear, which we predicted. And now at $206, the highest it's been in quite some time. If you look at all-time highs, the last time we saw Coinbase at these levels, the $200 level, was all the way in 2022. So it's been a long time for Coinbase to be up this much, and they are doing very well pre-market. Look at Riot. Riot's up 4% pre-market. Look at BTBT, BTBT up 5% pre-market. These are all Bitcoin-related stocks.  So all of these are Bitcoin-related stocks that are all ripping pre-market. Look at MicroStrategy's, $868 up 8.99% pre-market. So MicroStrategy's also ripping pre-market. All right. There's some other stocks that are doing very well pre-market, JANX up 145%. Put them on your radar. Put them on your watch list. Huge move pre-market. Now $36 and TCTM up pre-market. RML has been on the tear. They're not really moving pre-market. TAU, gold stock that we've been watching, Meta down pre-market, the S&P 500 looks like it's actually down, Dow Jones down slightly, and IFBD, look at this, up 120% pre-market for IFBD. So a big move for IFBD pre-market, Uber up slightly pre-market, NVIDIA also up slightly pre-market, PACT, P-A-C-T, one to watch that I've been talking to you guys about. They've been ripping, and we brought them to around 50 cents, so now at $1.95, PACT, put them on your radar, put them on your watch list. How about Ethereum? Beautiful move for Ethereum, excuse me. Tesla, I told you guys it wasn't going to last under $200. Now at $204, and they broke that $200 mark, which I predicted. I told you it wasn't going to stay under $200 for long, and I was 100% correct. Tesla now at $204. And the mothership Bitcoin, just killing it. The highest it's been in two years. Bitcoin is approaching all-time highs of $69,000, we're at $56,869 for the mothership Bitcoin, and obviously I love Bitcoin. I've been talking to you guys about Bitcoin since 2017, when it was at $1,000. Today, Bitcoin is almost at $57,000. So, beautiful, beautiful move for Bitcoin, and anything Bitcoin related is most likely going to be surging today on the move by Bitcoin. Alright, AULT up 98% right now. TCF, trillion energy, one that I brought you guys at 15.5 cents. Now, at 17 cents, congratulations if you got in a 15 in app, they're now at 17, and grayscale Bitcoin trust up 4% at $50. SRFM, we brought it to you at $1, they're up 18% at $1.26, congratulations to investors. Hood doing well up 2% today.  Here's MSTR, right now MicroStrategy's up 9%. Coinbase trying to hold that $200 zone where there's a lot of selling coming in, currently at $201 for Coinbase. Riot, they're also in the green today up 3%, BTBT up 4%. So, a lot of investors selling and taking profits right off the bat.  CLSK up 13%, and S-O-U-N, an AI stock that I really like, SoundHound AI, having a big day up 22%. All right, so let's just organize this from high to low. All right, so the biggest mover of the day, IFBD. Here's IFBD, you can see those big candles. And you can see this open gap, that is what was happening pre-market. So pre-market, if you're always wondering why is there's this gap, that's what is happening pre-market. It's a pre-market gap up. This gap up right here, this gap, there's this gap right there. That's not the right tool. Let's do this, let's do trend line. So that's the gap up pre-market, and then you can see it still went up a little bit more after the market opened, but it started to sell off. So where that blue is, is the gap up pre-market. You can see it's still up after the pre-market move. It actually went up even more, but it's starting to come down. You can see it's starting to come down. It's gone as high as six, it's off the screen, like 650 zone. And right now at 610, you can see that candle went up and it's come back down. So that's why it's not always a good practice to just chase stocks or crypto. It's better to invest in quality companies, buy them when they're down, sell them when they're high. Here's another huge gap up, JANX. You can see here the gap up opportunity pre-market that it did right there. That was the pre-market gap up.  So it's still up a little bit after the pre-market move, but not a lot. Now remember, past performance is not always an indication of future results. Rich TV Live is strictly for information and education purposes.  We talk about a discuss here on the Rich TV Live podcast. So the three auto stocks of the day right now, JANX up 141%. You can see the gap up in the blue that I drew. IFBD up 111%, AULT up 79%. SRFM, we brought you guys this pick out of dollar. It's now up to $1.28 and up 20% on the day. Huge move for SRFM and SON up 20% on the day. SON, CLSK, Bitcoin related, Bitcoin minor up 13% on the day. So we've been talking about a lot of these stocks and they've been just ripping. Here is MARA at $32 up 11% of the day. Microstrategies, one of the largest holders of Bitcoin in the world, up 8% at $865. Look at this move in the last five years that these guys have done. So they did make this move over a thousand during the last bull market, dropped all the way to like a hundred bucks. And now we're back to $866. Can you believe it? Absolutely on fire are these guys. So you can see it's been as low as $123. Been as high as $1200 and we're currently sitting at $868. So there's still room for this to go higher and I believe it will. I think this will be a $1,000 stock. Coinbase is absolutely ripping. I couldn't be any happier. I predicted this, talked about this, told you guys this. It was one of my top stocks and I'm super, super pleased and super happy that it's performing well. Coinbase up 7% on the day at $208. The highest has been in two years. Huge move for Coinbase. Hutt having a big day, another Bitcoin related stock. BITF also having a nice day, another Bitcoin related stock. So some of these Bitcoin related stocks having big days and BTBT up 4% GBTC now up to $51 up almost 5% and Bitcoin just broke $57,000. Unbelievable. It's up $2,500 in the last 24 hours. It was up $5,000 yesterday. So that's up $7,500 in two days. Like just absolutely beautiful move for Bitcoin. Congratulations if you got in for the win like I did. Riot, nice move for Riot, nice move for Hutt. I told you guys about Bitcoin. So many people believe me. So many didn't. All the guys that didn't are like, is it too late to buy Bitcoin? I just laugh. I'm like, man, you do what you want. Here's Trillion Energy. Another one that we've talked about that is doing well today, up 3%. Ethereum, they are also following in Bitcoin's footsteps. Tesla, $202 also doing well. Uber gaining some steam, really like Uber, TCTM, one that's been having big moves. Meta, $485 for Meta, just an absolute giant. Nvidia, $786, S&P 500, slightly green. Dow Jones is slightly red, packed, watching it closely at $1.94. TAU, thesis gold, gold company that we think is near a bottom. And HTOO, a penny stock that's been moving that's slightly down today. All right. So let's just quickly go back to see the top movers. All right. Now remember, if you like these videos, please smash the like button, comment down below, share the video everywhere and subscribe. I'm your host of the most. You're bored and rich from Rich TV Live in Thailand, trading live where it is 937 PM here in Thailand. We love trading at night here in Thailand. It's totally awesome.  And then at night, we can trade and we can make money. And right now we are making a lot of money with Bitcoin and Bitcoin related stocks. And I hope you are too, which right now I think should be your focus because they are absolutely on fire. All right. Hope you have a great day. I'm your boy, Richard, Rich TV, bringing you the winners and bringing them to you first live from Thailand. In a couple of days, we will be in Bali. Stay tuned for our segment called balling in Bali, where we will be bringing you live trading ideas from Bali that will be on Friday. Until then, thank you guys for watching. Stay tuned. More videos coming. CEO interviews, breaking news, trending topics, hot stocks, hot crypto. We even got some hot NFT projects. I told you guys about Campi Panda. That's done a 25X. I got another coin that I'm doing right now called RST, the rainy coin. We've already made over a hundred percent. We're approaching a 200% gain on that one. So want to learn about these picks? Join our community at richTV.io. Join our website, a community built by investors, for investors where you too can learn how to win. Your boy's out. Peace.


Aerial footage showed a heavy police presence and a cordon off-crime scene at Baltimore's Morgan State University campus on October 3 evening. Just after a shooting incident in which five people were injured, according to local police, the shooting occurred at around 9.30 p.m. local time, and the suspected shooter, who is yet to be identified, remains at large. The five victims, ranging from the ages of 18 to 22, are not facing life-threatening injuries. Local media has reported the shooting occurred during a homecoming week's celebration at Morgan State University, a historically black college.