Welcome to the fourth edition of my ignored news blog, coming hard on the heels of the last one, as the media seem to be overwhelmed right now keeping up with more important trivia. Recently, a new study mapped the effects of high fuel prices in Europe over the winter of 2022-23 and the effects this had on the rate of excess death. However, this short-term effect, generically blamed on Putin's war in Ukraine, is a distraction from the data also emerging right now in the crisis of life expectancy in Britain as our national economy collapses. A rising out of the economic crisis high fuel prices created, the government is, again, threatening to raise the retirement age without any discussion of how multi-dimensional poverty in Britain is affected by this decision. As a geriatric genexer, it's fairly noticeable that the boomers, five or 10 years older than me, are getting their pensions while the rest of us get further and further delayed. This is a political choice based not upon evidence for how this affects different demographics in society, but seemingly upon ensuring the security of one largely right-voting generation over the others. So, just like Little Orphan Oliver, we have to ask. Please, sir, I want some more. What? As wealth becomes more concentrated, the problems of global inequality are increasingly mirrored within the affluent European nations and Britain especially. The media debate focuses on a difference between the middle class and the elite 1%. For that reason, they miss the growing divide between the middle class and the poorest 10%. An article popped up recently in The Economist. Expensive energy may have killed more Europeans than COVID-19 last winter. Last November, The Economist's article, Russia is using energy as a weapon, had caught my attention. It projected that rising energy prices would lead to a corresponding increase in excess winter deaths. Despite the exceptionally mild winter across much of Europe, they seem to have been correct. Are these the civilian casualties of the quite literal new Cold War? Across Europe, the price of energy and the level of non-COVID excess winter deaths correlate. That's unsurprising. With high energy prices, people don't heat their homes as much and the lower temperatures can exacerbate many pre-existing health conditions as well as making it difficult to recover from seasonal diseases like flu. What this data doesn't encompass is the role of endemic poverty within this process as that pattern is buried within the national data sets. Do these excess deaths spread across the population evenly or is it one small section of the population, the poor, providing the majority of these cases? And if so, rather than just energy prices alone, do high energy prices build upon poor health, poor diet and poor housing to increase the likelihood of excess death? These more exacting questions were not answered by the Economist's article. These are critical distinctions though, because if this is the case, then Russia didn't weaponize energy prices. Any global disruption to energy supply would have caused a similar response as energy prices rose higher. In Britain, the picture is even more complicated due to our national economic woes. It was already known that excess deaths were still 6% above the five-year average after the end of the pandemic and that this was related to problems with the NHS. A recent study in the British Medical Journal noted that an additional 1 in 82 of those experiencing delays in medical treatment died. After the pandemic, it might be assumed with the weakest eliminated by COVID that the excess death rate should have fallen. Instead, excess deaths are running at almost 1,000 per week above the five-year average and many people are dying at home rather than in hospitals. The problems in the NHS are a function of a failing national economy driven by over a decade of near-liberal austerity. That's even begun to attract the attention of international news agencies who are running articles on Britain as a failing state. In the meantime, as Labour and the Tories both desperately try to differentiate themselves from one another while pursuing much the same economic policy, our domestic political dialogue continues to fiddle the figures on targets or spending rather than looking at the underlying structural problems driving the crisis. In particular, the role of Britain as the preeminent Western state in acting near-liberal economic policies and how this has progressively asset-stripped the nation. A few weeks ago, to seemingly little response, the World Compensions Minister let it be known that after the next election, the pension age in Britain would have to rise to 68. Of course, people in Britain don't react in the same way as our French neighbours. Well, except perhaps for a vague Gallic shrug. Again, coming back to who makes up the excess death figure for both rising energy prices and NHS failings, the Office for National Statistics data paints a very clear picture of where the burden lies. In England, men living in the most deprived areas die almost 10 years earlier than those in the most affluent areas. For women, the difference is almost eight years. Currently, male life expectancy at birth in the most deprived areas of England is 73.5 years, compared with 83.2 years in the least deprived areas. For women, the equivalent estimates were 78.3 years and 86.3 years, respectively. Life expectancy is an absolute endpoint. What's more interesting is healthy life expectancy, the time people live before chronic health conditions worsen their quality of life. Those living in the most deprived areas can expect to have a healthy lifespan around 18 years less than those in the most affluent areas. This creates a significant disparity when we look at setting the retirement age. Currently, the retirement age in Britain is 66. That will increase to 67 after April 2026. The government had ruled out any further changes before 2044, but it's noted that's already back on the table after the next election. This means those living in the bottom half of the affluent spectrum can expect to continue to work while experiencing chronic ill health. With those in the poorest 10% work in an average 14 years with chronic health conditions before retirement. If the pension age increases to 68, then the least affluent 70% of the population can expect to work to retirement with some form of chronic health condition. Of course, these figures are averages. While a few living in deprived areas will get to retirement age without any health issues, others will experience chronic ill health well before the age stated in these statistics. This is where we hit another element of Britain's poverty trap. Just as depicted in the 2016 Ken Lutch film I, Daniel Blake, which was ridiculed by Tory MPs at the time, thousands of people who replied for ill health benefits and were judged fit to work by the government's work capability assessment died soon after. A fifth of disabled people whose deaths were reviewed found that benefits sanctions contributed to their death. For a number of years after this, the Department of Work and Pensions tried to suppress information about the operation of the scheme. While being forced to disclose some details by the Information Commissioner, government reports showing how benefits sanctions and cuts which allegedly led to hundreds of deaths are still being suppressed. And recently, the harsh work assessment from the early 2010s, known to have caused many deaths, was reimposed in late 2022 despite the cost of living crisis. Last year I made a video focusing on fuel poverty and the historic economic inequalities in my hometown, applying the data on chronic ill health. People in the most deprived parts of my town can expect to work the last 10 years before retirement with poor health. While a short distance away, in the affluent villages on the edge of the town, they can expect to reach retirement age and good health. This demonstrates how economic decline does not affect everyone equally, even across small areas like my hometown. As economic decline hits health services, while that affects everyone across society, it hits the poorest of the worst health conditions the hardest. At the same time, the poor hit harder by higher food prices because they spend the great proportion of their income on food, so they eat a less nutritious diet, exacerbating their health problems. Finally, higher fuel prices preferentially push those with chronic health conditions into less healthy housing conditions, which are more likely to trigger a decline in their health. We have to stop treating this as a Russia or energy prices issue. It's clearly a neoliberalism and inequality issue. The media have recently begun to explore how the COVID crisis and the cost of living crisis affect women the hardest. Yet due to the complexity these relationships create, seldom do the media explore the related effects of poverty across communities and the pre-existing trends in health within certain communities that correlate to long-term economic disadvantage and how food and energy price rises make those health issues worse. Traditional stereotypes about poverty based on the establishment's 400-year-old principle of the deserving and undeserving poor cannot encompass these complex relationships. Therein lies a deeper truth. A recently published study evaluated how real wages had changed since the 16th century, demonstrating that not only did most people become relatively poorer as wealth inequality increased under capitalism and especially colonialism, but that health and life expectancy and inequalities increased in parallel. Poverty in Britain arose as a national issue at the turn of the 17th century in response to changes in agricultural land practices which arose in parallel to mercantilism, in turn creating the social punishment which informally continues to this day of the workhouse. Another study published early in 2023 found that the pursuit of economic growth which all political parties seem fixated upon at present does not affect multidimensional poverty in the same way as income poverty. Clearly, growth creates more work and more pay but that only filters through to those who are capable of work. Those for whom structural multidimensional inequalities limit their ability to access or perform work are not able to access the benefits of growth to the same extent, meaning they benefit between five and eight times less compared to income poverty. And as countries get richer, those benefits flow further as the nature of those inequalities become more entrenched. Poverty is intersectional. It's impacts compounded by an individual's age, gender, affluence, education, ethnicity and health status. Poverty is not monolithic. It represents a diversity of experiences very little of which gets any serious coverage in the national media beyond the generic stereotypes and certainly not within the discussions regarding the retirement age. That's why we have to have regard to the complexity of increasing the retirement age and how that measures with benefits for those deemed unfit to work. Otherwise, current policies will just further immiserate those already subject to the harshest, most unequal quality of life in Britain today.


Well good afternoon Wolverines, it's Command Mass Team P-100, if you'll be here with EMF in Orlando from Chief of Weapons Department and we're here to talk about shock trials. So my first question to you is what were your thoughts about shock trials and your experiences? Well CMC, I just got out into the fleet a week ago and I was told that I could go straight to the fleet So when they told me that I was gonna be straight going right into shock number two I was out of my mind. I was like all right, you know straight getting into training Start getting into my job and everything and as soon as it happened it was not what I expected at all I thought you know it was going to be not as boom as I thought it was going to be But I'm really happy that I got to experience something great and something for our nation. Oh, that's good. That's good. Especially you work in Weapons Department So boom happens in weapons, you know, so the experience overall you say it was good bad It was surprising. Surprising? I expected it was very suspenseful from the count to down going from 10 to... Yeah, yeah. Oh, cool. Trust me. I felt the same thing you felt. Good stuff. All right All right, Wolverines, we're back with IS-1 Grahawar from Intel Department We're here to get IS-1 experiences doing shock trials. So how was it for you IS-1? The experience was surreal. The first one was definitely I felt more intense than the second one Not knowing what to expect and the countdown I was expecting the pause and then the boom But there was no pause, very immediate, very intense and it shook me to the soul so It was great to be a part of such a point in history Since we haven't done this since 87. Were you born? Oh, I was. 90s, 90s babies. But you know to be able to For someone in my job We don't really get to do a whole lot of things that we can go home and share with our family So to be able to be a part of such a historical event and be able to go home and talk to my family about it and show them All the amazing things that I get to be a part of within the U.S. Navy is amazing. So very thankful for it, but Don't want to do it. All right. Well, IS-1, unfortunately you will have to do another one but for me I was On the bridge so it was a little different experience been on the bridge but it was fun to see and To hear all the things that went on to know how great our sailors are to go into action and get us back You know to operate, you know right after the shock. So good to this stuff with that being said, you know You got CMC here with IS-1 and we're ready for shock three This is Ford class


So, again, for a frustrating afternoon of watching football, but I'll add to what your initial thoughts have to do after that. Yeah, we started slowly, and we spoke this morning at the hotel, two of us, and the start was going to be today, because we knew you were going to start quick, because the position you were in, and obviously they knew we'd had a game on Wednesday. That first 20 minutes, we weren't quite out of the races, and we got punished for that. And there on in, I thought we played well, without really showing enough quality in and around the final third, but we dominated possession. And we deserved something out of the game, just that slow start has cost us today. So the main frustration is, really, you look at the second half, it was, certainly for the vast majority, all of us, you know, attacking in their half, it was most of us pushing to get that goal. Yeah, I can't remember them. I think there's one call on the second half, completely dominated. We just didn't quite produce that real quality cross today, a real moment of quality in and around the box, which a lot of our play deserved really. And that can be the difference, you know, in the balance of play, in so many good positions, and we haven't worked the opposition key quite enough today for the dominance we have. And I suppose it can't be the easiest when you've got these teams, as you say, fighting for their lives, they get that goal, and obviously they're naturally just going to sit in and make it as hard as possible for you to try and get that goal. Yeah, obviously, you know, the whole place lifted when they got the goal, and the teams were at the conference, getting a goal, and Gary's talked about getting that first goal, and they got it, and obviously he gave them that extra bit of belief, which happens in the game of football. Well, the way that's responded here, back in the first half of the second half, it was terrific, they've given absolutely everything for the show. It was a tough week in terms of the games we've had, and the travelling we've had today. And we move on together, get on with coach, and we'll have a review of the game, and we'll be ready for a great week coming up for the football club. Yeah, so we are trying to make excuses, obviously this week has been a very long, a good week as well. Obviously in Ajax we had the 80 minutes, 10 minutes of Rocksdale, obviously the efforts of Wednesday, the travelling down yesterday. It's been quite a testing week for the lads. Yeah, no, it has, you know, there's no doubt about that. And we still found it in ourselves to push ourselves after the first goal, and that was the annoying thing. So we had it in our legs to do that. And I felt we'd done it from the start, we'd have gone on to win the game. But yeah, no, it's been a good week for us in terms of the cup game. It was a fantastic night on Wednesday, and like I say, it was a good week to enjoy now, the field up to the Chelsea game and our aim is going to the league win. We haven't been able to do that, but unfortunately that's the way it is. Credit to Joghul, brought two strikers in, one of them's Nick Tadol, and that'll give them a lift. And Andy Halladay, Frantoz Oko, the loan's ending after today. I understand you're keen to thank people of those two, if possible. Yeah, I'd like to keep them both if we can. I think the Zokes did well today when he got on, and he's been unlucky. Not to score when he's gone, and not to figure more. But since he's come to the club, lights Billy Clark, John Stead, and James Anson, and Billy Knott, those attacking musicians have done really well, and it's been difficult for him to force his way in the team. So I'll have a chat with him on the way back, or so I spoke to him briefly earlier in the week, and I'm a bit of a black baller out there for us to keep him. Andrew Halladay, we'll have to wait and see on that one as well. And one player could feature in the Chelsea game next week, Ben Williams, you might be sticking around a bit longer as well. Yeah, we had an agreement with Ben that he was going to stay with us for the rest of the season, and another year. Like I said before, Ben's a good character to have, a good goalkeeper, but a good character as well, and he's been great for us. And I'm pleased he's got the opportunity to play such a high profile game next week.


Today I have the pleasure of speaking with Chris Reed of NeoMetals. How are you today, Chris? I'm very well, Tracy, enjoying the move to summer. Well, of course, Chris, you've had a lot of news lately and you've had, you know, a terrific update for your shareholders. And you recently announced an MOU with mineral resources to further progress the development of a downstream lithium chemical plant that would produce battery quality lithium hydroxide product suitable for direct sales to the lithium ion battery industry. Okay, I wanted to make sure I got that right. Can you please provide investor intel audience with an update on this MOU, please? Absolutely, Tracy. Part of the transaction with Gamfeng last year was that they were obligated to purchase the first three years production from the Mount Marion mine. After the third production year, mineral resources and NeoMetals had the option then to buy back 51% of the offtake so that we could have a feed for our own downstream processing ambitions. You know, we've long wanted to become an integrated producer of lithium. The first step was to get the mine up and running as a concentrate producer and then a couple of years time because it actually takes that long to once you've completed the construction of the concentrate operation. It takes you a number of years to permit and build a lithium downstreaming plant. So it is really just an affirmation now that the mine is moving into commercial production, that we are now in the final stages of the evaluation of downstream production of lithium hydroxide. And it's a commitment for both of us to advance that with a view that we can make a final investment decision in early Q3, a calendar year 2017. Well, I think you've kind of answered this question but I want to step you sideways for just a second. Christopher Eccleston was describing this as a superb deal and that shareholders may not truly appreciate the nuances of this deal. I think what he was trying to say is number one, you have proven your extraction technology process works. And perhaps you'd like to comment a little further about what Christopher was saying in his analysis on this deal. I think Chris is certainly one of the few analysts that gets our overarching strategy and that's one to get the mine up and running, de-risk that at the same time develop the flow sheet for downstream processing. The deal with Ganfeng and I think one of the misunderstood barriers to entry into the lithium market is actually having a large customer to sell your product to. If you want to start up a good size mine, you need to move below concentrate, there you go, you need a large partner. And Ganfeng are the largest, most diverse lithium producer in China, certainly the fastest growing. Multi-billion dollar US market cap and the ability over, certainly over the last year that we've been dealing with them to raise large amounts of equity. They're actually moving beyond lithium conversion into cathode production and batteries. And as you get further down the chain, the lithium value in that end product becomes less. So they become a stronger partner. We're able to get the mine up. They take a lot of production. And then after three full years of production, we're then able to take some of the feed and put it into our own downstreaming plant. So we're doing it in two stages rather than having a half a billion dollar roll of the dice to start off with. We've done a deal with Australia's largest processor of minerals where we've developed the world. Within 12 months, we've constructed the world's biggest hard rock lithium concentrator. It's twice as big as the plant down at Greenbushes that's owned by Tianji and Albemarle. We've done that with no dilution, no need to raise capital and no need to contribute capital. And now we're more concentrated on where we want to be. I think if you have a look at the, should we say the normalized pricing for lithium concentrates and lithium chemicals, that the chemical production is normally about twice as financially attractive as producing the concentrates. So now we have a very strong balance sheet through selling down some equity in the mine, and we're able to finance our share of a downstreaming plant should we make that final investment decision next year. Okay, you always leave me with more questions that I want to ask than we have time for. So one of the reasons we have you speaking at the Clean Tech and Technology Metal Summit is because of your understanding of the lithium market. And there's a lot of debate right now about whether or not there is a shortage of lithium. Clearly, I think your stats, you'll be supplying 18% of the global supply of lithium in the future. Is that correct? Did I read that right? Yeah, I mean, Matt Marion will contribute. Once the Fine Circuits Commission in the first quarter of calendar next year will contribute about 50,000 tons of LCE as lithium concentrates into the Chinese market. Now that will take the Chinese from a utilization rate of just over 60 up into the 90s. So it is the most significant addition of lithium units into the market. I would suggest probably since the last expansion at Greenbushes a number of years ago. I think if you have a look at, we get back to just basic mineral economics. The lithium carbonate and hydroxide prices for 2017 from the Western producers. So the Brian producers looks to going off global lithium looking to be well into the double digits in terms of thousands of dollars per ton. And then a couple of thousand dollar premium for lithium hydroxide on top of that.  Certainly the prices have continued to rise. Yes, the spot market or the small volume market in China has come back a little bit, but you would expect that they would converge over time. I think pricing and history would say at the moment that there is a shortage. Now what it will be this time next year with our production coming in, it would be less than a shortage, perhaps approaching balance. I think what we've consistently undershot as an industry is the demand growth for lithium. Certainly the Chinese with their EV forecasts is a real shot in the arm. Probably not purely economic versus the internal combustion engine at the moment, but rapidly approaching it. Whereas renewable energy storage is now past grid parity in terms of solar plus storage in quite a number of jurisdictions, the U.S., Australia, Great Britain, Germany. So I mean that's economic now and they are larger format batteries than the car. The underlying thematics are the batteries, there's more and more batteries and the batteries are getting bigger and we have consistently undershot demand. It is for the batteries, the lithium going into the casso, it's well into the double digit growth. Well, I can't tell you how delighted I am with your analysis of the market. I could not agree with you more, Chris. And speaking of following you, Chris, in respect in the industry for understanding the global supply of lithium, I mean I've watched you take neometals since Christopher Eccleston first told me to take a look at neometals from being a small cap or a really small cap player. You're over 200 million plus market cap now. You've got 66 million Australian in the bank and you've given your shareholders dividends twice now which is just unheard of for a company in your market cap range. Can you tell us what we as shareholders should anticipate in the next couple of quarters other than I can't wait to see what you're going to announce next? It's certainly a question that we get asked reasonably often by shareholders. Look, we're going into the commissioning phase of the mine with a significant amount of cash on the balance sheet once the mine reaches steady state production in the first quarter of next calendar year. Obviously we can then look at how we're going to deploy that. One of our main focuses is obviously for the downstream lithium plant. We are cognizant that we have started returning some money to the shareholders. It is our ambition that we would maintain that. The size and timing and quantum obviously still to be decided by the board. When you start a dividend policy and paying dividends it's not something you would start if you thought you had to stop it quickly. Certainly the cash flows coming from... Marin will enable us to sustain a return of something to the shareholders. On top of the significant amount of cash we have been able to monetize our nickel assets. We've exchanged those for about 13 or 14 million Australian dollars worth of stock in another listed Australian company. So I think our concept there is we create the value, we share with partners to make it real, and then we share with our shareholders. I think the true mark of a company is that you can return more capital than you invest. Certainly if I was asking you to invest in a business Tracer you would be wanting to make a return more than I took off you to start off with. That is correct. Well Chris, as usual it's a pleasure getting an update on NeoMetals and we can't wait to see you in Toronto for the Clean Technology Metal Summit. Thank you so much. You're very welcome. Good night.


So how many of you are space geeks? Anybody? I see lots of hands. Yeah, I was, I was, I never actually intended to get into the space industry. The only thing I ever knew I wanted to do in life was race cars. But then in college, I got into it. And it was because of all the inspiring things that I saw when I was a kid, like people going to the moon. I sat on my grandfather's lap when I watched the first man step on the moon. And because of happenstance in college, I ended up going to the space industry. And I watched over the years as things degraded and we weren't doing as much. And I left it about 10 years ago. I went back into automotive. But guess what? Things have changed and I'm back because things are different now. And it's different because of aren't forneurs like you. And it's different because of the new thinking and new generation of people that are getting involved with it and the kind of money that's coming into it. So I like to say that a new space race is here. And we're at Vector, is a company I formed a couple of years ago to start participating in what I think is a massive revolution in space. So what we're really fueled by is something that I call the microsatellite disruption. So it used to be in the old days when I first started in the business, the satellites were the size of a car and they cost hundreds of millions of dollars, took years to build. In your career, you could hope to build maybe one or two and if you had a failure, it was a dismal part of your career. Now satellites are something you can hold in the size of your hand and they might cost tens of thousands of dollars that could be developed in months. So that's fundamentally changed the dynamics of the space industry. It's drawn in investor dollars who are willing to take risks on new ideas that people have and it's drawn in a whole new generation of people. So it's made really the space race come true. The problem is though that how to get those satellites into orbit is really a problem right now. The existing options are dismal. What people who build these microsatellites are reduced to is putting them essentially in the trunk of a car. In other words, on a big rocket and launching along with a whole bunch of other satellites. If you could imagine sharing your house with 10 other families, that's kind of the equivalent of what happens. So at Vector what we've set out to do is to lower the barriers to space access so that people like yourselves can get both your hardware up there and eventually get your ideas up there. We have a two-part business strategy to start with a high launch rate using very, very small launch vehicles and then eventually we're building a constellation of software-defined satellites that you can program like you would a cell phone with apps and that instead of having to go through all the trouble of building the satellite and launching it, you'll just have to get on your terminal and program it and you can get access to it. So let's think of it as an applications cloud in space.  And so on the left you can see our little vector H and vector R that we're building compared to the rockets that went to the moon and you can see what some of Jeff Bezos is building there. The thing that happens is we reduce the cost by orders of magnitude. So we've gone from about a billion and a half dollars of launch for some of the moon rockets down to about one and a half million for our rockets. So it's a fundamental change of the way we can access space once we do these sort of things. To do this, we've got two launch vehicles. We have what we call the vector R for rapid and the vector H for heavy. That's a tongue in cheek. Everybody's got to have a heavy rocket. So these are about 11, 12 meters tall. They're very small rockets. But the key is that they can launch micro satellites and they're built to address that market. So the thing that's very different about these rockets is we're going to build them in large numbers. I mentioned that I went into automotive for about a decade. And what we're doing is we're bringing that technology and that basic industrial capability to rockets. Somebody else very famous said recently, he's taking rocket technology to cars. We're doing the opposite to mass produce these rockets. So this has never been done before. And we'll be producing and flying these rockets by hundreds. What that allows us to do is make the rockets cheaper and we actually make more profit. So everybody wins on this. And there's nothing new you have to invent here. In 1923, Henry Ford made the Model T just like this. So we like to think of this as the Model T of rockets.  We can franchise out this manufacturing to places all over the United States and eventually all over the world. So we can have assembly plants closer to where the customers are. And that's exactly our plan is to start in the United States and eventually franchise some of these manufacturing operations to places like Europe. So we really don't have to invent anything new. If you look at how rockets are made today, SpaceX, I'm sure all of you are familiar with the Falcon, marvelous vehicle, but they're only made by the 10s a year. And the reason that they're reusable is so that they can get the flight rates up because they can't produce enough of them in their factory. They don't have enough people and the factory's not big enough. They sort of cap out at about 10 a year. So if you reuse the first stage, that gets your flight rates up to about 20 a year. We're talking about emulating the modern automotive manufacturing techniques to get our launch rates up into the hundreds a year. So this is a little bit like the transition, for example, of Maserati. In the 60s they were hand built where Luigi would take and take the parts to the vehicle. And it was one person, one vehicle. And now in modern days, if you go through some of those factories, they're on a moving machine and the vehicles are assembled in a assembly line kind of way. So what this will do if we can accomplish hundreds of years is it creates a transportation model that's different than it's been. At 10 or 20 a year, anybody that fly satellites will tell you that's one of their biggest risks is getting a ride to space. But if you had to do the same thing with flying on airplanes, you wouldn't go very far. I wouldn't be here in Finland. So what we're trying to do is develop a transportation model that's a much more reliable schedule, a reliable cost that the entrepreneurs can count on, that they know that we'll be there in the years ahead and the decades ahead to be able to put their assets in space. And so in the two years since we've been in business, we start off by purchasing a company that had been doing this for a while and we've been making a lot of progress. In fact, I think we've made more rapid progress than anybody else in the industry if you plot it out. This is our first flight, which we did in the desert of a full scale vector R in May. And just for fun, we parked a drone over the top of it. Oh, sorry, I'll go back to that one. Let's see if it goes. We parked a drone over this. Ah, it's not working. Forget it. So, yeah, that's the second flight that we did in Camden, Georgia. You can see it. There you go. Everybody's got to have a little bit of rocket noise there. So we flew this to a suborbital altitude and recovered it out of the jungle. It was proof that our systems could work and that we had a gradual incremental development approach. So we've continued to build these vehicles. Our first flights to orbit are gonna be this July coming up and this is that wall of flight facility where our first flights will be in Virginia. Right now they're sending payloads to both of the moon and the space station from here and we'll be joining the ranks of people that fly out of there. We're gonna launch in a lot of different places in the U.S., out of Alaska, out of Florida, out of Wallops, and probably out of California. Eventually we might even be launching off of barges. This is essentially key to us being able to launch hundreds of years to have multiple launch sites. And when we go there, we don't have an infrastructure we have to drag behind us. We go there, we set up on a concrete pad, as you can see in the desert. We did it from a dirt pad that worked out very well. We're building a factory in Tucson, Arizona. We just awarded a contract this last week to a contractor and this will be a factory that's capable of building hundreds of these a year. We'll break ground very soon and have that up and running by early 2018, 2019. So what we're looking at then, if we can accomplish this, is transforming today what is a very hardware-centric, expensive industry into something that's software-centric. If you look around here at Slush, what you see is an innovation that's based a lot on software. And so a lot of you who are sitting in the audience and a lot of people here at Slush understand how to make software and understand how to be innovative with it. That's really what we have to do to the space industry to make it more innovative, to bring it to more people is to do that. And between our launch vehicles and our galactic sky constellation, we believe that we can make that happen. So thank you very much. I appreciate the opportunity to talk to you.


So there's some more dad humor. The live stream is up, so you're welcome to kick it off. All right, thanks everyone for joining. Quick housekeeping items before we start. Our speaker of the day, Ken, has 35 years working in the insurance industry as an architect. And he's worked with large carriers, vendors, and now EIS, an advisory organization. It's a pleasure to be hosting this session today. Ken welcomes questions during these sessions. So if you would like to pause and ask, please unmute yourself. But if you're not speaking, please mute yourself through the session. And just a bit of introduction for this session today is so every time there's a natural catastrophe or a pandemic or a hurricane on an earthquake or something like this strikes, straight regulators require or ask for insurance carriers to send them the data about the effective properties. This data helps regulators model risk, monitor the market activity, predict consumers, and plan for future emergencies. And God knows that from since 1980, United States has suffered more than 250 extreme weather disasters, causing more than $1 billion in damage each. And but these are ad hoc reports that come on top of routine, quarterly, and annual statistics. And there is no good way to present that data. So when AIIS invited carriers and regulators to brainstorm, a solution was created. It's the blockchain network. So since 2018, AIIS has operated the first blockchain system used to gather the data from across the US insurance industry. So Ken, with that, I open the floor for you takeaway. OK, so as David mentioned, questions are welcome. We're an insurance industry. This is not just an insurance solution at the end of the day. So if I'm going in the wrong direction, just ask a question, get me going in the direction, answer any questions, make sure you're getting what you like out of this. I'm going to try to focus on we're using hyperledger fabric for solving a very important problem. So if I steer away from that, just let me know. OK, so OpenIDL, why are we doing OpenIDL? There's a couple of main use cases that you pointed out. There's this thing called a data call where a regulator in the insurance industry needs to get some information about what's going on out there, how many people are covered for different things so that they can put the proper precautions in place ahead of catastrophes, et cetera. So that's kind of an ad hoc need for data where a regulator, a Department of Insurance, is trying to find out what's out there. And they're asking the carriers to provide them information. Another one is statistical reporting. AIS, as you'll see in a minute, is a statistical reporter. We provide data to the departments of insurance about how much insurance is out there for different carriers. So as I mentioned, there's other applications that can be used. You'll see it's from at its base. It's hypoallergic fabric and can be used for a lot of different purposes. It's mainly, it's probably the biggest win to get out of OpenIDL is improved data privacy. And I'll hopefully that will be illustrated over the course of this discussion. So I'm going to show you from a high level what we're doing, why, do you a quick demo just to give you a sense of how it hangs together. And then we'll go in all to the bubble charts and things to show you some of the architecture decisions made and how hypoallergic fabric fits into this picture. So here's a couple of places that you can see what's going on with OpenIDL. There's the www.openidl.org. There's also the Wiki, where we're slowly putting more and more documentation on the system out there. And we have a GitHub repository on OpenIDL.org. So we are currently, we just transitioned to a Linux Foundation project. So it's all open source. While we're working with carriers, et cetera, the software itself is open source. The data is very much important to these customers and they don't want to share it. And that's why we're doing OpenIDL. We're using very stable technologies underneath and hypoallergic fabric is one of those. And the biggest takeaway from here is this true data privacy is what we're enabling is for carriers to provide data in a standard way as efficiently as possible without sharing data they don't want to share. So what you'll see today is set in the insurance world. The first scenario, I talked about the data call where a regulator is trying to look for information. So that's the backdrop for all of the demo, et cetera. I'm not sure you can see. So let's give you a quick introduction to AIS. A-A-I-S, which Jody, as everybody first trying to say it, stumbles a little bit. It's the American Association of Insurance Services. We're 80 years old, not probably 90 years old now. Been around for a while. We're a nationwide agency that does three main things. Statistical reporting, when carriers have to report their information to the regulators of how many claims and how much premium they've gathered for different lines of business. We do that on the behalf of also our rating bureau. We provide loss costs, which are the base number used to come up with premiums. We do that with data that we are given by our carriers and so that we can understand the kind of actuarial, we use the actuarial data to provide these loss costs. And then the advisory organization will provide forms and manuals and other information about these products. So for example, for an auto product, we provide all the forms, et cetera, that you might get on your auto policy. And the reason we do that is because some small carriers or brokers or whatever might not have the desire to do all of that work. We do that in a standard form. We have a lot of experience. We provide forms that they can use or they can change them, et cetera. So these are the three main things that we do. We're not for profit. Unlike our largest competitor, we're doing it on behalf of the insurance industry and we do it very efficiently. So what are the challenges that we're trying to solve with Open ADL? The recurring problems, the data owner, in this case, an insurance carrier wants to keep their data private and in their control. But if they have to constantly send data across to regulators, then they're losing control of that data. And they don't want to do that. They want to know that the data that they are sharing is managed properly. It's an auditable process. And they want to make sure that they understand what's going to be done with the data when they provide it. So why Open IDL ensures get that data privacy? Regulators get reliable and timely information that they're looking for. And AIS, we also get a big win. We're doing this for a reason, too. We currently store a bunch of this data in order to record it to the department's insurance. With this Open IDL model, the raw data never leaves the carrier. And so we don't have to have all of it on hand all the time. And that saves us a lot of cloud bills. So the Open IDL network, it is a network of four main purposes. There's the owner and a carrier will have data that is used as the source for something like a data call. There's the government agency that needs to get the information. And then the AIS, who provides support in this interaction. And so you can see, if you know Hyperledger Fabric, there's a couple of different connectivities between these different organizations. And as we mentioned later, we use channels for that. The Hyperledger Fabric is a really good fit for what we're trying to do. This was all stuff that Drummond would have done. We'll take in a little more time because he can go into more depth. But so let's talk quickly about the architecture of the system. And then I'll do a demo and then we'll get in some more details. Any questions of interest yet? Joati or David? No questions yet. But I have one Ken really quick from in a standard point of view, so every industry is struggling towards standardizing whether it is tokens or protocols or for interoperability for free production. So is the open idea also get towards implementing any standards? Or are you? Yes, I do have a slide for that. You're giving me a sort of a four slide segue, about four or five slides from now. I'll get to that. There is a data format that we rely upon and will become a standard over time because at some point you've got to agree on the format of the data in order to have multiple carriers participate in a common extraction of that data. So yeah, so the standard that we're developing right now, it's a data standard for what we call the harmonized data store and that'll become more apparent as we go. So absolutely those standards. And Dina, I don't know if Dina is on this call, but Dina is our person inside AIS responsible for that. And her information, I think, is later on in the presentation. OK, so let me give you a quick overview of what the architecture of the system is, what the different nodes are. So we use the term node to represent the full package of open IDL for a particular purpose. The three main purposes are for, I'll go through them individually, but you can see that there'll be many carrier nodes. There's usually one sort of what we call an AIS node and an analytics node. Multi-carrier node or the AIS node is used by possible multiple insurance companies who don't want to step up to having their own node. And also, AIS and regulators use this node to create and manage data calls and other requests for information. The carrier nodes are, and this is one of the most important aspects of the open IDL, this is a distributed network in that this node here is hosted by the carrier themselves. We provide them all of the scripts and start up to get a node started and connected into the network, provide them, create certificates, et cetera, necessary to create the organization and connect into the different channels. So they host that. And that's why data privacy is one of the main things we've accomplished here is the data that is there. Raw data is in their cloud, not visible outside of their cloud. And so each carrier will have their own node, and therefore their raw data will be managed only on their node. I got to speed this up a little bit. And then the last thing is in a data call, as we create data calls and the carriers respond to those data calls saying you can use their data, the regulator can see the results of their data. It all goes into this analytics node where it's combined and the report is created and sent out. That's all part of the standard data call process that we do today, where we aggregate all the information from across multiple carriers. So this is a very high level view of the system. Let me go into each type of node, give you a little sense of what's in there. So this is the diagram as it stands in IBM cloud right now. We're moving all of our stuff into the Amazon web services cloud with use of, I saw Isaac on the call. We're utilizing ChainYard to help us migrate this application and it's going really well so far. So thanks Isaac for joining. And just a quick call out that ChainYard and other vendors are part of the community and are available for carriers to utilize as they start to connect into this network as well. So carrier node is gonna have a couple of different things, a carrier and this multi-carrier node. They're gonna have a database where we capture the raw data. In the multi-carrier node, that's not nearly as private as when you have your own node but for small carriers, it might be fine. They put enough data here to get the job done. And then of course there's some cloud specific services that are required to make this all work. Like when you're connecting into an application and you need to be authenticated, you need some sort of authentication for that cloud. And because there's applications involved in here, right? These applications are running in a Kubernetes cluster. There's an application for loading data and there's an application for managing and responding to data calls. And that's all inside of Kubernetes cluster, as I mentioned, just JavaScript, Angular UI and JavaScript backend. And these applications, the API specifically have access to the network through the Hyperledger Fabric API. So Hyperledger Fabric is running its components for this particular node in another cluster and these APIs call to create transactions, put stuff into the ledger and get things out of the ledger, et cetera, and also interact with the private data collection. So the carrier node is very similar apps and data loading capabilities and local services for that cloud provider and jacking in again to the network through a cluster that's hosting the Hyperledger Fabric components. So the last type of node, which is different than the other ones is the recipient of the process when all the data is consented and I'll discuss that in a minute, the details of that. It's aggregated here. There's some listeners to the ledger that say, we just got some data in, you can do your report, it runs the report, puts it somewhere. This is representative, not, each analytics node may work this slightly differently. So we've talked about the three kinds of nodes, one for multiple carriers and one used by AIS and the regulators. That's the multi carrier node, a carrier node, which is one per carrier and those are all hosted by the carriers themselves, carrier being an insurance company, sorry, use the insurance lingo. And then the analytics node, which can be hosted by anybody and for now, we're hosting that AISs. So the life of a data call is a regulator creates a data call and puts it out onto the ledger, the different carriers see that call and then do something to say, I'm good with responding to that data call, I'll provide you an extract of my data and that'll be put onto the analytics node and reported. So, okay. So let me go through the scenario real quick, then I'll do a demo and then we'll get into the architecture, how we support this functionality. So the first step in a data call is for the carriers to load their data, right? So this harmonized data store inside their cloud is gonna hold all of the raw data that includes much more than they really want to share, but it's put into a format, as you mentioned, Joachim, this is the format for this is the standard that we are established, so that when a carrier puts their data in there, it's in a format that when we need to report on it, we know exactly how it's organized. So we describe this standard and there's a working group in the Linux Foundation for managing that standard. And if anybody wants to contribute to the standard, more than welcome to participate in that, some contact information at the end. So regularly, weekly, monthly, quarterly, the carriers are loading their data from the transactions that happen, right? New people create new policies, they have planes, et cetera, this gets loaded into here over time. And then a regulator using the AIS node will come in and create a data call, says I'd like to see how much insurance we have for hurricanes in South Carolina, creates the data call and that gets put on to the ledger, right? So we hadn't put anything on the ledger except the knowledge that data was loaded in this first step, but in the second step, the data call itself is just a simple JSON document that has text about what we want to get out of this data call that gets put on to the ledger. So I'll show later, it's hard to show too much on the right amount on a single screen, but there's two channels happening here. Right now we're using what's called the default channel where all of the carriers and the AIS node are all in the same default channel and therefore they're sharing a ledger and the data call is put onto that ledger. So at this point, once it's on here, this UI, which is very similar, will show all of the data calls that are currently in the ledger and the carriers have a chance to read, what is it you're asking me for? You're asking me for hurricane coverage and such and such, what data fields do you want me to share, those kinds of things? And at that point they'll say they like or unlike it, meaning that this is something they could respond to or if they unlike it, they might send a note over to the regulator and say, that's crazy, we can't reshare that data, we need to change this a little bit and we'll go through this process of creating new drafts of the data call until everybody's happy. So once there's enough carriers that say this is a good data call, we'll do an issue of the data call, the regulator will see that everybody likes it, they'll issue the data call and now the request itself is not changeable, but it's not quite executable yet, because what a data call in the end of the day wants to do is access this data base, this private data, turn it into some sort of shareable data that we can get over to the analytics node. So let's see how that happens. When the regulator issues the data call, it goes into a state where we can create an extraction pattern and attach it to that. The extraction pattern right now is a MapReduce, right? So in Mongo, there's the ability to do a MapReduce, you give it two JavaScript functions, it converts it into a different collection and the results are there's some aggregation and some keys created. So all the extraction pattern is two functions, a map and a reduce. That, those two functions are stringified and put into the data call and put onto the ledger. So now on the ledger, we not only have the data call verbiage, but we have the actual code that will execute should a carrier consent. So this is a real important aspect of this that the carrier when they consent will have a chance to see the data call, what's gonna be executed against the database. They have actual code, the MapReduce functions, they can pull that, download that, they can test it, make sure it will do everything they expect and nothing they don't expect. And if it's true that they're happy with it, it represents the data call, they can then consent. That's an event that goes onto the ledger so that there's evidence of who's consented. And as a result, a listener will say, how they consented, I'm gonna now run the extraction pattern. And the execution of the extraction pattern is basically on every consenting node, that extraction pattern is loaded up into a program, sent to Mongo, it runs the extraction pattern and the results of the extraction pattern are put into the private data collection. So we're again, depending on hyperledger fabric, share the data. The private data collection is on a channel that's connected to the analytics node for each carrier node. And so the data shows up here and then the report can run. I have diagrams in a little while that illustrate all this again. So hopefully that's not too vague, but that's what's going on from a high level. So the actual extraction pattern itself, when the carrier consents to the data call, the extraction pattern is retrieved from the ledger, I would put it in the ledger and then the extraction pattern is run against the harmonized data store. And at the time this is run, it has the ability because it's on the carrier node to correlate private data with other data. So in this case, we did a proof of concept to show some carriers how this could be done. We wanted to correlate the address with PPP data so that we could tell how many of these policies are for people who need a PPP data. So do they need coverage? If they've got a loan, they may not need coverage. So this is possible with this architecture because the time we're correlating this data, we have access to the address because we're on the carrier node. If we were to run this in some other part of the architecture, we wouldn't have access to the address because carriers will not share the address. So because we're running it on their node, we have access to the address, we can correlate the PPP data and we can share the PPP correlated data in an aggregated fashion and the address is never shared. So we're able to use the address without sharing it. So that's a superpower that this provides. Previous to this, every carrier would have to do all that themselves in their own way, right? But now we're providing a consistent way to do it and in a private way where they don't have to share data that they don't wanna share. Okay, so the results of the extraction pattern are put into the private data collection and that's on the channel that's connected to the analytics node and because the way your hyper legit fabric works, it automatically gets put onto the analytics node in a private data collection over there and analytics tools have access to that data to run reports. Any questions so far? We're halfway through when we go through a quick demo and then we can go into the details. Hey Ken, there are a few questions in the chat if you're gonna, I'll read them out to you. Okay, let's see. Winston asks, are there any smart contract examples published standards such as ERC-20 tokens kind of look? So are, yeah. Okay, so let me just answer the smart contracts are all crud here, right? Most of the secret sauce to this application is outside the smart contracts. We update and read from the ledger and push to the private data collection but those are just very simple smart contracts. We haven't actually changed the smart contracts in a year and a half. All right, Sanket asks, is there only one channel and you're using private data collections or multi channels and then private data collections? Yeah, there's two kinds of channels. There's the default channel that connects pretty much everybody on the network and then there's a peer to peer channel for every carrier to the analytics node so that when they actually get their data extracted it's on a private channel with the analytics node. Sounds good. And is managed service being used for this? So we're moving over to Amazon. We're not using a managed service. We are considering creating some managed service for these things. We know that the Amazon Kubernetes managed service doesn't go cross-cloud and they need to do multi-cloud. So right now we're not doing any managed services. We probably will provide a marketplace component as we make this available in a more open way. As of now, we've been building with infrastructure as code but it's more sort of as we go building each node in its own way without managed services but we've looked to get there and ChainYard will be helping with that among other vendors as we go. Great. And the next question comes from Vladin. Have you considered IPFS interplanetary file system? I would have to say that's a question for ChainYard. As of now, we don't, I don't know where we would use that so I'm happy to have a follow-up conversation. We've used S3 a little bit but that's a work, so one of the tenets of this architecture is to be cloud, multi-cloud and therefore as agnostic of the cloud as possible. And so we're trying not to use S3 per se as a place for files but IPFS, where that would fit in your architecture? I'm not sure. I'd have to have a separate discussion so sorry I couldn't answer that one. Okay, this question from Isaac. He asks, did you harmonize data standards get developed as part of the OpenIDL project or did they exist prior to the project? Yeah, prior to this project we had something called statistical plans which is how we would capture data from our customers that are very old. We wanted to modernize those and as part of the modernization we're defining those standards as we go as part of the OpenIDL. Yeah, we do try to base it on other standards and point to other standards but there's all kinds of things that are outside my sort of expertise as to why and what for is it how those standards will be developed? That's good. Shashank asks, what's the compelling reason for using hyperliter? Or could we use something like EOS as Vincent? It's a follow-up. Yeah, so I had an earlier call where one of the carrier architects like why are we using blockchain? We just wanna use the term, right? And the answer is it looks funny I was that person inside AIS, right? And I was like, why are we using blockchain? We could do this ourselves. So blockchain has some inherent trust associated with it that if a carrier comes along and then here's the word blockchain they know there's something going on there about privacy and trust, et cetera. Hyperledge of fabric is an open source, well-known thing that it does this pretty well. I'm not gonna tell you that there's not a better option for what we're doing here, it fits well and we're not, I don't think we're overusing it and as we get into the diagrams I'd be happy to get feedback on all what you didn't really need hyperledge of fabric for that. And the answer is clearly we didn't need to use hyperledge of fabric but if we built it ourselves then that would be a red flag for a lot of people but some of the other technologies there's a history to any project to the people who initially built the system they use hyperledge of fabric and we haven't seen a reason to change course on that. But so if that's in the chat I'll wanna look up that particular I don't think I'm familiar with that particular framework mentioned there. There are a few more questions but maybe we can table that towards the end. Yeah, we'll get into more detailed pictures which we don't like to see. So let me do a quick walkthrough. I'm not gonna do the full flow it's like a 10, 15 minutes flow but I'll give you a sense of what's going on at any one time, right? So the first step in the process is for a regulator to create a data call. So this is the AIS node we mentioned before. Sorry, AIS node, right? So the name is indicative up here. So I'm gonna log in as a regulator, right? So I have credentials, part of an organization I'm looking in the default channel now and I see these data calls these are the draft data calls and then there's some issued data calls. These are all things that are on the ledger, right? So I am getting these from the ledger there's some room for some UI improvement here but basically a regulator comes along creates a draft data call and now this is visible to all the carriers, right? So when a carrier logs in, I will log in as this carrier here. Again, carrier is an insurance company. I just use that. So they see these same draft data calls they can go in, take a peek at it and say, yeah, I see what you're trying to do, right? This is all verbiage at this point, right? There's no code to run but this is something that's been put onto the blockchain and I as a carrier can say, I do or don't like it I can put this like to like the data call and then when the regulator comes back in and looks at that, I know there's a little bug here they'll see that lights increase and eventually they'll say that's good and the regulator at one point will say, I'm gonna issue this, right? So there's a few functions here available to the regulator because of their role that are available to a carrier that can create new data calls they can issue, et cetera, right? So this here, while we're running it in our own cloud right now for a proper concept purpose this is the code and the whole node that would be running inside a carrier's cloud, right? This would be on Amazon or Google or Azure and maybe the IBM cloud we don't get a lot of calls for IBM cloud but this will be running on their cloud. So once enough carriers are happy with it the data call is issued and once issued, the data calls are available for consent, right? So up to now a carrier hasn't done anything to say, I want you to use my data they have to look at the extraction pattern so here's the extraction pattern I can look at what the code was written to implement the language that we saw let me just say this real quick so the code it can be very complex but it's really just like I said a map reduce function so this is just a way to share as a string so a carrier can take this and say I see the map function I see the reduce function is down in here too and take this code, put it into their development environment point to their database, run it, see what would result because it's just a standard way of executing something against Mongo and they can basically test it and verify that it is what they expect and once they agree that it is what they expect then there's a consent, I've already consented to this there's a consent button over here that says I consented that and at that point the consent will run the extraction pattern against their Mongo database so let me give you a quick look at what the Mongo database looks like so this is just a little test database that I used but so this is the insurance carrier's data this is what they actually have provided in here so let me just expand this guy so here's information that is private for the carrier for this particular policy they have provided an address that they don't want to share with the regulators but when we run the data call we do want to use that address to see if there's a PPP loan associated with that so when the extraction pattern ran it results in another collection and that's what this collection is it's basically taking that input data running that code you just saw and then giving results and the results of those are there's no address in these results there's some some key information and some aggregated information but there is evidence of what the address was used for which is this PPP information you can see here that we were able to use the address we don't share the address itself now on the output but we use the address to find the PPP loan we can get the loan number etc but we can get how much the loan was for some race information all kinds of extra information that the address was used for but didn't share the address and so it's really important so then what happens is one of the components in the carrier node will then take this collection and put that into a private data collection private data collection goes over to the analytics node and then the report can be executed and then after a certain period of time we throw away the data on the analytics node so I know that's not the full fledged start to finish demo it's too long for this purpose but you get the idea that there's different roles there's carriers there's AIS role there's a regulator role so the AIS role was we were the ones who put the data call the extraction pattern into the data call and then the carrier's consent and like and the regulator creates and issues the data call okay so sorry about the quick demo but I would do want to show why we're using hypoallergic fabric because I know that is the main purpose we're together so you know it's not all about hypoallergic fabric this has got a lot of basic requirements architectural requirements that we need to cover off so I'm just going to throw those out real quick and try to answer how we hit these after I go through the diagrams so trust immutability right it's very important that the the carriers trust that their data is not going to be messed with and if they if they do something that nobody can change that like when they consent to something somebody can't say they didn't consent it needs to be performed of course needs to be secured it's got to be permission we need to know who's in this blockchain network that's why we're using hypoallergic fabric permission network needs to be distributed we're going to have nodes that are not running in our cloud and that other cloud could be AWS could be Azure could be Google it might not even be a cloud we're not sure we want to support the non-cloud sort of running on on-prem model but it's definitely a possibility and it needs to be manageable so when I talked about the PPP aggregation there's a lot of different data sources that might be pointed to maybe it's just a call to a URI maybe it's a whole data set maybe it's a component that needs to be added we need to be able to manage nodes that are distributed we need to be able to go to that carrier and say we need to give you a new version of OpenIDL that gets installed in your cloud and runs the way we expect right so it's a very important part of this process too it needs to be transparent so that when we tell a carrier that we want to install new things they need to be able to know what's being installed they need to know what so not only on the provisioning of the infrastructure but also all of the different aspects of the interaction it's all in the ledger and nobody's hiding anything there's no secret things going on as far as the correspondence between the different people on the network and then the keeping the data in places is really one of the main reasons we started looking at OpenIDL how does how can a carrier keep their address but make it usable for connecting to other data and how can we keep that from going data from going across the network that shouldn't that doesn't need to right we don't they don't we don't want carriers to have to trust us not to use their data for other purposes or or have some sort of breach we don't want the data at all right so keep the data that's private in their world and never share it okay so the application architecture kind of gone through a little bit of this right it's it's using distributed ledger technology the hydrologic fabric so that anything we're doing with other carriers and nodes is all managed and evident on the the ledger itself and any data that goes across to the analytics node or etc we'll be using private data collection so this is not code we wrote right we're just building on top of hyperlogic fabric we know everybody on the network is has is permissioned and has the right certificates and everything the the code themselves where it is running in a Kubernetes cluster it's just an angular JavaScript UIs with some express JavaScript services those services are do have access to the hyperlogic fabric services sorry api and they also have access to the harmonized data store that's local and what other cloud services might be necessary to keep things cognito other things in amazon and the extraction pattern as i mentioned is is run by this data call processor and it's basically just a map reduced function for for mongo so how does the data flow the carrier data is something that we don't control right so this the harmonized data store is the data in the format that we understand this carrier data we don't know where that comes from where they keep it but we help them to build the mapping layer right so we're going to provide we provide the scaffolding for creating this output but we don't know what the input is so the carrier is responsible for doing the mapping into the harmonized data store from their carrier data so this is unknown format this is known format they use the the api to load that data into the harmonized data store they would the as i mentioned before the data calls etc are all captured in the ledger itself using simple crud chain code put get state in the world on the ledger and that's on the default channel and the when the extraction pattern runs it's going to access the harmonized data store put data into the private data collection which is shared across the analytics dash carrier channel what right one private channel for each carrier with the analytics node and if there's multiple analytics node there'll be multiple carrier analytics channels so that ledger is not visible to the default at all and the day private data collection pushes across to this node and then the the data whatever has to happen on that data to create a proper report it's basically different for every application the data calls it'll be one thing for start reporting it'll be another so that's the application pieces that's a bigger picture of that so technical architecture so we need to be multi-cloud we're using kubernetes and mongo and then there's some services specific to the cloud this is kind of an in-depth look a little bit in more in-depth there's an even deeper level of course when we're talking about all the different pieces of amazon web services that we're using to provide that but these are all the different nodes and the different channels that they contribute to and how that application so so the interaction between the nodes is all hyperlinked fabric right there's no no open check channel or anything like that if that what that's happening that's outside our application on slack or something else but no data is going across any other way than through hyperlinked fabric and here's a sort of a detailed picture of how the architecture is getting mapped into amazon this is one of the deliverables from chain yard helping us define how the amazon web services are used to to provide this to support the system okay so devbox is something we're working out as we go and a question earlier do we use managed services or just having a conversation earlier where we might create a we might decide to create a an amazon managed product right that is the open idl which includes the terraform etc so our current process is for all of the applications we're building them pushing them out to a github container registry the github actions we'll build those and make sure that they're there and versioned then terraform is used to set up the infrastructure pieces in whatever cloud is is being used we've got a bunch of credentials that need to be kept somewhere we're looking at vault for that purpose trying to stay cloud agnostic with the management of the secrets and then one of the things provisioned is there's two kubernetes clusters provisioned as part of terraform the publishing of the pieces into those clusters is done with helm and helm charts that you know get get those public images and put them into the kubernetes cluster and we're thinking thinking about using githops so we're in analysis right now and working with chain yard to decide what's the best way to make this manageable so that as we need to provide new components or updates to components what's the best way to provide that to our distributed nodes so that's the devops picture so how do we how are we addressing these particular things right we're using hygrologic fabric because it's kind of a trusted network kubernetes and auto scaling etc and and it's yeah it's very robust architecture right it's resilient recreates pods and they go down all those kind of things so kubernetes is we're depending on kubernetes for a lot of that a lot of security is built into the fact that we have distributed nodes right we don't we don't need to worry about cordoning off data for carrier one because we can't even see that database right all we have to do is make sure that they agree that none of that data has any way to get outside of their node permission right so everybody on the system has to have the appropriate permissions as defined by hygrologic fabric to even contribute to the network and of course hygrologic fabric is the only way we're getting data across the wires it's distributed hygrologic fabric supports that and that's one of the big decisions you know back to earlier question about managed services managed kubernetes service and amazon doesn't support other clouds maybe that's changed since we had that discussion but this is a very important aspect of the of the architecture we need to be multi-cloud as wherever possible cloud agnostic but if we have to do authentication differently on each cloud then we'll just have to do different infrastructure as code for different clouds and provide a way to deploy that correctly manageable as I mentioned we're going to be pushing changes to remote clouds so it we need to put in place something to do that and then of course transparent everybody who's going to use the systems you know exactly what's being put into their cloud because because we're not hosting it we need they need to know exactly what's going on in their cloud so the infrastructure as code is self documenting and then they keep that data in place again because it's a carrier node we never get to handle their data right now a couple more quick slides we provided a reference implementation running on mini cubes so you can kind of get the tires and get a sense of how things work without committing to the cloud and we'll also have a reference implementation in AWS as a result of our work with chain yard so that somebody using AWS can immediately get up other clouds are later on in the in the road now so as you question asked before maybe there's more than five slides right what is the standard the harmonized data store is not one standard but it's a standard for every use right if you have a data call that's for auto a data call that's for a different line of business they can probably use the same basic standard but there may be different ways to get the data in there and they may need slightly different things but if we're using this for different purposes like for regulatory reporting the format of the data could change so each use of this system could lead to a different standard and as I mentioned Dean Burgess is our director of BI or AIS and in charge of the data standards for OpenIDL and she'll be on a one of the steering committees for the the hydrologic fabric sorry for the OpenIDL project in Linux Foundation so that there'll there'll be a conversation about all of that over there so now we talked about the harmonized data store so how can you contribute the data models right we'll need and want very much the input of everybody using this network to share data to participate in these these use cases those data models are going to need help from everybody to make sure that they're robust and include everything you need the architecture right as we go into new clouds we'll need help building the different cloud footprints implementations of application components as they come along access and ppt access and census data the there is some technical debt in the system course that that will need somebody to do AIS course doesn't want to be the only one contributing to this chain yard is an example of an external vendor that's helping out and other vendors have agreed to participate as we get new components or if we get new organizations wanting to be part of the network those will need contributors contributors to help them get on board and of course support documentation testing all things that that we can use help this is a totally open source project all the codes available in in github and and available for for viewing etc so so what are we doing right now we're migrating from my bivn cloud aws and again with the chain yards help we're creating the reference implementations establishing those property and casualty hd harmonized data stores standards and we're building regulatory reporting we currently have an internal system that does regulatory reporting we're building pushing that over the open idl so that we can improve our footprint to make this more open the way that works all right uh okay i think that is yeah wait i want to go on on back well yeah there we go so here's some urls for um we're getting involved open idl dot org uh don't have the github the github account is to it real quick is open idl org open idl main and all of the code for all of this stuff is out there and we also have a slack channel if anybody's interested you can contact me um ken s at as online dot com and and or truman and you can go out to the open idl org to see more information about the system um and you can become a linux foundation uh part of linux foundation probably most of you guys are already so with that i'll open it up for questions last time i was hoping but um so do you have some more questions deep are you needed or uh i think she's needed we lost all right let me see if i can see the chat i'm gonna go from the bottom up hey sorry ken i'm having a bit of a technical issues here so um sorry about that um yes there were a few questions but i guess we are almost at the top of the hour so if anyone has any pressing question i would request for them to unmute themselves and ask hi uh this is raj sigamani uh excellent presentation great work um i think we are making big progress with the linux foundation slash high college fabric uh also i guess with ibm donating all of those as free open source it's really excellent i had quick question for you uh in terms of the private data collection versus channel data where and how are you using them differently right so the um the default channel actually um we don't put anything on that private data collection but i can think of the private data collection is used for the analytics channel when we're trying to share data across for reporting um so we establish a peer-to-peer channel between the the data provider and the reporting node and then the the data provider puts the results of the extraction pattern into the private data collection and that automatically goes over to the analytics node and the analytics node sees the event and then processes it that's your question uh yes it does but definitely clarify that the pdc is going on a different channel okay all right just turn the integrate uh the it's going on the the peer-to-peer channel okay thank you hi this is angel great job real quick can you just give us a little high level what what type of what does your team look like before you started this project what does it look like today and from a technical skill set or upscaling standpoint what are the areas that you had to invest in or bring in new people sure we start with ibm ibm being there i believe the originator of pipe allergic fabric can't correct me if i'm wrong i don't know the whole history i think they contributed it to the Alliance Foundation um we engaged with them before my tenure on this project to build the application uh i think they they went through a couple of iterations found out you know you don't put too much data on the on the ledger and the private data collection came on online as part during the process we started using private data collections but we had a big ibm presence for a while we've separated from ibm because certain different reasons and uh we are now um us we're it's me and a couple other folks in ais shepherding this into aws with the help of chain yard you know i mentioned them more than that i think probably could have hoped for but uh they're they're experts in and all the technologies that we're using we have also engaged with other vendors so our our model going forward is to have a very small footprint inside ais and depend on vendor partners and the open source community to build this out all right um if there are no other questions we are at the top of the hour um ken thank you so much for your presentation today um as i said in the beginning of the live um uh this session is being recorded on youtube hyperledger's youtube channel and it's available for viewing after thank you ken again for your time thank you thanks everybody for attending thanks everyone right giordi is that is that chat available um just for me to see later yes uh i have made the copy of the chat and i will be sending it to you shortly okay fantastic well thanks for hosting this was uh it was a good session yes it was a wonderful session thank you so much for your time thank you bye bye


A preview of the new season of Black Mirror. What if your car horn honked your darkest memory? Stole an identity! Black Mirror. What if your computer ran out of battery but kept running for eight days? Black Mirror. What if guns didn't exist, but bullets did, and the very strong used them to kill? Black Mirror. What if there were now computers? But computers were evil. Black Mirror. What if... The winner of American Idol Season 15 is... Senjai. Black Mirror. What if South America and Asians swapped places, and it was computers the whole time? Black Mirror. What if dinosaurs weren't made up by the government? Black Mirror.  What would do that one again? Black Mirror. What if Gandhi never existed, but computers still do? And they're evil. Black Mirror. Black Mirror.


Hi everybody, my name is Eric Joseph Lewis. I'm a forager, permaculturist and all-around plant lover from the Mid-Atlantic region and today I'll be sharing my top five wild edible nuts with you and I hope you enjoy. Thanks so much for being with us. Number one nut tree of all time is the Mighty Hazelnut. So the whole Corillus genus, I have so much love for. Corillus Americana has a small nut with a hard shell and grows abundantly in the wild and is super resistant to a disease called Eastern Filbert Blight. This one, Corillus Avelina is the European Hazelnut and this one is not so blight resistant but it has incredible huge nuts. I got a couple in my pocket here so you can see. This is a great size for a Hazelnut and the shells are pretty thin and they crack out very easily. So this is one that I feel has a tremendous viability for backyard growers as well as commercial potential and in the Willamette Valley people are growing European Hazelnuts with great success and yielding as much as two to four thousand pounds per acre and these are nuts that are twenty eight hundred calories per pound. So this is on a calorie basis almost competitive with corn and the interesting thing and one of the beautiful things about the Hazelnut to me is that being a smaller shrub it starts to yield nuts in as little as three years. So this is absolutely one of the real blessings in the nut tree world and I highly recommend everybody get into finding Hazelnuts and planting Hazelnuts wherever you can. Corillus the Hazelnut genus number one on the list. Number two on my list is the chestnut and I love this tree for so many reasons. Unfortunately the American chestnut is what we call functionally extinct because a blight called Cryfonectria parasitica came into this country and made it impossible or made it very difficult for chestnuts to reach maturity. But we do have Casanella mollissima that has been planted out by lots of people and now has been spread around by lots of squirrels since the early nineteen hundreds. So we can still find lots of semi feral and cultivated Chinese chestnuts often which are greatly underappreciated and so you can go and knock on your neighbors doors and ask them if it's okay to harvest the chestnuts. These things are super delicious. They're easy to gather. They store very well. They make an incredible flower. You can steam them up. The list goes on and on and so does my love for chestnuts. So yeah. Number two on the list is Casanella mollissima, dentata, sativa, the whole genus, the whole chestnut genus. I love them all. All right. Number four on the list is black walnut jugglins nigra and one of the things I really love about this tree is it's exceptionally reliable. On top of that, the nuts have around twenty eight hundred calories per pound and they're loaded with really amazing omega three fats. They'll grow just about anywhere. I've seen them high up on hill tops and right here you can see this one is growing right by a little creek, a little stream slash overflow of a spring house. So yeah, they tolerate a wide range of conditions. They're very tough, very reliable, very tasty, and the best part of all is that they store very well in the shells. If you dry these things out fast enough, they can last in the shell for four or five years without getting rancid at all. So that's pretty amazing. Number four on the list is pecan. So this is Karya Illinoisensis Illinoisensis and pecans are originally from the Mississippi River Basin area, but they've been spread all over the country and even into Canada. Parts of Canada do have pecans that actually produce viable nuts and these are a really tasty treat. They're absolutely delicious. They're loaded with nutrition, high in omega three fats. They're in the same family as that black walnut and that juggling day CA family is known for being high in omega three fats. It's also loaded with phenolic compounds. The main issue with pecans is that in the north they don't fill out as readily. So this is one that you'll find very prolifically in southern states like Florida, Alabama, Georgia, South Carolina, even Tennessee. As you get north, these trees become more and more rare and more and more precious. But this one here is Mohawk and we're at Parker cobbles amazing tree nursery in Pennsylvania today to introduce you to this amazing friend here. But yeah, wherever you can find them pecans are incredible and wherever they're not growing, they're worth planting. So get some pecans in your life, pecan, Karya Illinoisensis. So last but certainly not least on our list is the mighty oak tree. I chose to bring this one in last because it's the one that I have the least experience with, but it's also the one that I feel holds the most potential in it. And I say this because every species of quarkus, every species of oak tree in the world is edible. They have varying levels of a class of compounds called tannins.  But this is an incredible tree and it's found all over the world and every species in the world has been or most every species has been a food source for humans at some point or another. And so it's one that carries a tremendous amount of potential. There's two main groups and you have kind of the red oaks on one side and the white oaks on one side. And they're relatively easy to differentiate between. So the red oaks have these pointed leaves and they have egg horns that are small and round with caps that are kind of bulbous. And yeah, so that's the, with the red oak, what you'll expect from the red oak and black oak groups.  So these are the kind that we find down a lot in Florida, things like live oak and laurel oak and members of the white oak tribe. But yeah, either way, you know, whatever species you have abundantly around you, they're all edible, they're all wonderful and well worth deepening your relationship with. So yeah, definitely number five on the list is the mighty oak tree. Thanks so much for watching the video. If you enjoyed it, please hit the like and subscribe button for sure, for Rob's channel. And also follow the links below to find my personal Facebook, Instagram, website, things like that. And as well in the links, you'll find channels to get to the people who helped with all the recording, the Meta Creative Crew. So yeah, thanks so much for being with us and keep on sharing the good vibes and connecting with the plants. Peace family.