Hi this is Julia and welcome back to my channel. And for today's video I'm just going to be talking about some of the books that I've read recently. I did film like a January TV, not TVR, a January wrap up. However I didn't upload a February one or March one because of reasons. I don't know, there's an update video if you want to check that out which I really appreciate all the support I've gotten on that video by the way. Thank you. But for today's video I want to talk about some books that I've read in the past few months. So this year I'm probably just going to do recent reads just because who knows how much I'm going to read a month. Like it could be nothing. Like there was a month, I think it was April, I read nothing. So February and March, let's talk about what we read. First here I have Homebody by Ruby Kor. I have read all of her books so far, all of her poetry collections. This one does have some content warnings for depression, sexual assault, rape, anxiety, and abuse. So please keep that in mind before picking it up if any of those topics are sensitive to you or you don't want to read about them, then fair enough. So keep that in mind. This talked a lot about like the past and the present of oneself and then like the potential to grow as a person is what this kind of collection focused on. It was also illustrated by the author and it had some good themes in it as well. Those themes were like nature versus nurture and like light and dark kind of imagery. So that was pretty cool. I did enjoy that about this. Favorite poetry collection by her is Still the Sun and Her Flowers. I would rate like I rated that one the highest. I recommend that one the most. But this one was still good, just not as good as that one. I still enjoyed it but I'm glad I didn't buy like a physical copy just because I don't feel the need like to have a physical copy of it. It wasn't like my favorite or anything so like I'm okay with not having a physical copy but I ended up still giving it a 3.5 so it was still good. So if you're interested in it I would suggest checking it out. Next is a short story. So this is a Poe story. So this is the cast of Amantelito. I think is how you say it. Like I sometimes am not great with pronunciation but I try.  Like I love the atmosphere. I love being in the crypts and in the caves. It oof. Like in terms of atmosphere this story just kills it. So this was like more of a gothic tale. There was like a revenge plot. Also like just a really gullible character and someone who takes advantage of that and gets their revenge and it was it was really good. I really really enjoyed it. Plus I really love Poe's writing. I'm definitely like a big Poe fan so this was I was really happy to read this. I've read it before but I got to like study it for class which was nice. I ended up giving this one like a 4 out of 5. I still loved it so yeah. Next we have a few plays. So Oedipus at Colonus is one of them. I read Oedipus Rex in high school and thought it like I really liked it. Like if any of you were around or like you could probably find the video of me saying how much I really liked Oedipus Rex. I thought it was really interesting and I you know the Oedipus complex is very interesting in general and interesting to study but also cool to read about. So I and I really like Oedipus as a character. So this is the sequel to Oedipus Rex. I thought it was pretty solid. I don't think it was good as the first play but it was still like enjoyable. So I gave that one a 3.5. Antigon was the next one that I read which this is the third and final book in that series or play in that series. So this one does have some trigger warnings for suicide so keep that in mind. I thought this was an amazing end to the trilogy. I loved reading these plays. I love the author's like writing style and like I really enjoy reading Greek tragedies. I think they're really interesting and a lot of fun. I also just really loved Antigon's character. I thought she was phenomenal and I love seeing her grow and I I loved reading about all of the family dynamics and it was just really interesting. So I gave that one a 4 to 5. So I would recommend checking out the Oedipus series I guess. I don't know exactly what the series is called but I would recommend it. It was great. Next here I read Seven Against Thebes. So I much prefer this author's other series as opposed to like this book but this was still about Antigon and Isman and like them getting introduced to seven new people and so of course I really like Antigon so I was down for it. It was still enjoyable. It just wasn't like my favorite so like definitely preferred like the Oedipus Rex and that kind of thing. I still thought it was good. I ended up giving it a 3 out of five stars. Next up here I have Shine Your Icy Crown by Amanda Lovelace. I did get sent this by Andrews McMill Publishing. Thank you so much. They sent me a finished copy. I was very happy about that. I've read this twice now so this was a reread for me. I just really love Amanda Lovelace's work. This is the sequel to Shine the Glass Slipper one but this just has beautiful illustrations, beautiful poems. It's very feminist. It does have some trigger warnings so I'm going to just read those off to you quick but it's abuse, toxic relationships, sexual assault, eating disorders, self-harm, suicide and trauma so please keep that in mind but so deals with a lot of heavy issues but I absolutely adore Amanda Lovelace's collections like I love the little illustrations throughout them and I love like they're so simple but they're so cute. I love the illustrations. I love her poems. I like that she's a feminist and a witch and it's... I love it. It's like I will buy anything that she writes and read it so yes I give this one four to five. Next here I have Later by Stephen King so I finally read a king. I haven't read one in a little while so finally but I do have a whole review video up for this so I'll link that. I would love it if you guys would check that out. Basically this book follows a boy named Jamie who can see dead people and his mom's girlfriend is a cop so they kind of drag him along sometimes to like talk to the dead people and find out why they died and the story just kind of goes from there. It's very interesting. Again there's a lot of trigger warnings. I will... I mentioned it in the review video but I want to say it again just for people to know but it was death, bombing, self-harm, suicide, alcoholism, substance abuse, rape, abortion, motor vehicle accidents, and incest so keep that in mind. Like I find it really important to say trigger warnings or content warnings just so people are aware of what they're getting into and you know kind of what the book's gonna be about so yes I did quite like this one. I ended up giving it a four to five and I'll have that review video linked up on the screen. Okay lastly here we have The Truth of You by INS Thomas so I really like this author's poetry collections. I've read quite a few of them but this one was good. It mostly talked about like anxiety, death, and depression so that was like kind of the main focus. So just talked about relationships, romance, and mental health. I just thought these poems were kind of simple but they had a good message so like I'll read one of them. The one of them that I liked was the people who need your help are often too tired to ask for it offered anyway drowning doesn't always look like drowning. Like that's simple but just the last line that's like drowning doesn't always look like drowning I love that and that is so true with like depression and so many other mental health um and so many other mental illnesses so I really like that one and then the sunlight says sometimes you fall in love like snow falls at night gently quietly and gently covering every part of you and the next day the world looks completely different and that is how I fell in love like snow. I thought that was so cute. I dropped my phone. Well that's the end of the video anyway so that was all the books but thank you guys so much for watching. If you're new here be sure to subscribe and like this video I really appreciate it. I also have a bunch of other social media if you want to check me out on any other platform. I have been streaming a lot on Twitch, I have Instagram, Twitter, Goodreads all of that so that will all be linked down below as well including like world issue cards and Black Lives Matter information so that is all linked as well but thank you guys so much again for watching and I'll see you super soon. Bye!


Just like you're going to see tonight, when there's girls who are feeling good, who are feeling open, who are signaling to everyone in the world, even though they don't even realize they're doing it, that they feel amazing, looking around, smiling, moving around, doing that like loud girl laugh that girls sometimes do, we're like, why is she laughing so loudly and obnoxiously? She's signaling to every single guy, hey, I feel good, I feel sexy. Come talk to me, please. And if you go and talk to that girl, as long as you're not just reeking of closeness and discomfort and, I don't know, a stack of, I don't know, as long as you're not just radiating that you're very, very close, she is going to give you a positive response. She is going to, you know, light up a little story just to highlight this. I was working with some guys, Bar in New York City, and corner of the bar, there were three girls, by far the most attractive girls at the bar, hands down. Every other guy there saw them, every other guy there noticed them.  So the thing about all of this is that, you know, if a girl is closed to start out, she's not going to stay that way.  But most girls, most people, what happens? They spend a little bit of time at the bar, they start having fun with their friends, they start to loosen up a little bit. Maybe she has another drink, maybe a song comes on that she really likes, maybe she starts getting, talking to some good friends and they have some laughs. People open up over time, if you just give them a couple minutes, depending on the circumstances. So, this guy said, I want to go approach them, I said, not yet, not yet. And sure enough, we watched some other guy, saw the exact same girls, just like all the rest of the guys did. You know, he was the one guy who had the balls to approach him first, so he walked up and I said, give him 15 seconds, girls will be rejecting him. Sure enough, about 12 seconds later, he was walking away with his head down, the girls were, you know, closing in the ranks, and every other guy saw that.   But, me and my guys continue to observe, we continue to keep an eye on them. And sure enough, because those guys, because that one guy went and talked to them, they started to loosen up a little bit. Maybe they said to themselves, you know what, we came out to the bar to have fun, maybe we shouldn't be so stiff, maybe we should have a little bit more fun. Maybe that guy, I don't know, kicked something loose, like inside of them. And they started to smile more, they started to laugh more, they started to turn out more, they started to look around a little bit more, so I said to my guy, okay, now go, perfect, talk to him now. And he went up, made sure that we'd already gone over, open, close, I made sure he was expressive, eyeing, moving the body around nice and loosely. He went up and go and talked to them, simple line. I think it was something just like, guys look like you're having a great time, I had to come say hi, nothing to it. And sure enough, the girls blew wide open. They were all laughing, they were all touching him. They were all like, you know, he was part of the circle. And every other guy in there was looking at him like, oh my God, this guy's some kind of master. This guy is, oh my, I wanna learn from him. He is, he knows something that I don't. He, what is he doing? What did he say? No secret to it, no secret to it, observation, paying attention, not just having the gear spin a million miles an hour up here, focused, watching, observing. Most important thing when it comes to opening, by far. Five minutes, really?  Okay, really, really quickly, I wanna give you something for conversations, something for that, you're in a conversation, things are going. That'll change your life and every conversation you'll ever have. I don't say that lightly. Conversations, even as Fran was talking about earlier, they always tend to happen where the guy talks way too much, way too much. Girls love to talk, right? We all know that, we've all heard it before. Girls love to talk. Why is it that we seem to be dominating the conversation a lot when we're talking? Well, because we're nervous, well, because we don't wanna lose the conversation, we're afraid that if we keep quiet for too long that things are gonna drop. What? Oh, that was a question?  Oh, I'll slow down then. What's that? Oh, yeah, yeah, yeah, I was like, hold on, questions at the end. I'll save, I'll save a couple of minutes. Oh, jeez, oh, it happens. It was fun. All right, what am I supposed to do with this Anthony? What am I? Okay, so we're a conversation, change your life, yeah, yeah. Guys talk way too much when it comes to conversation. So they're afraid that if they stop talking, if there's any silence, the girl's gonna lose interest, so they have to keep her attention. That is some of the most self-destructive thinking that you can possibly be doing. You really should not be talking that much. It's the truth. Girls really do like to talk. So what's a technique that you can keep in mind to swing things back in your favor? Everything that I teach, all the techniques I use, they're simple as possible. They're very, very, very simple. And why? Because when you're nervous, when you're going haywire, I want to give you just a simple objective that you can focus on, concentrate on, and do that's going to have the most positive result. Why? Because everything that I teach is very simple, but at the same time it's very difficult. Why is it very difficult? Because you're actually looking that fear, you're looking that anxiety, you're looking that doubt right in the eye, and you're stepping through it. You're not trying to circumvent it, you're not trying to look around it, you're overcoming it. So the simple thing to focus on when you're in those situations, you're talking, and you just want to do one thing. I like to call it the buffer, it's beautiful. Why? How do most conversations go that a lot of guys have, a lot of guys that I work with, how do their conversations go? Well, they start the conversation because that's what a guy does. He says something, and then she says something. And then he says something, and then she says something, and then he says something, and then she says something, and then he says something, and maybe she doesn't always say something. So he says something again, and then she says something, and then he says something. She says something, and he says something. Back forth, back forth, and sometimes if there's a silence, he'll say something to try to get her back into it, right? Has this happened to anyone? Yeah? Yeah? Yeah? How would my conversations look?  But it's always at a time when you don't really know each other and you want her to be more into you and you're not sure how it's going to go yet, then you're trying probably way too hard. How do my conversations look at that time? Well, of course, I say something first, I'm the guy, more often than not, unless I'm wearing silly bands and she starts a conversation with me. I say something first, and then, of course, she's going to say something more often than not, she's going to respond, and then I pause. Silence is your most powerful verbal weapon. I know that doesn't exactly make sense on a literal level because silence isn't verbal, but silence is your most powerful verbal weapon.


Alright, so welcome, on this lecture we're going to cover Uroboros proof of stake. We're going to start from the origins of the protocol and then take it to Uroboros Prouse, which we present this here, and then to the next evolution of the protocol, which is Uroboros Genesis. Let me start with the concept of a robust transaction ledger, which is the problem that Bitcoin protocol solves. So the formalization of the problem was itself like an interesting question, which was something that we tackled a few years ago already now. This was work that was with Juan Caray and Nicos Leonardo's. That was the first formal definition of the objective of a robust transaction ledger. Now this work, which you can find it here, basically set out a crisp model and an objective that a ledger protocol has to meet. And after that work, there was a number of other papers that took that model and improved it in a number of ways, and I'll just mention a few of them that came out of the years afterwards. Mainly the definitions were further refined. Partial synchrony was considered, and finally there was a composable definition that was presented just in the summer last year at crypto. So what does it take to realize the ledger? So realizing the ledger is something that, as you can expect, it can be achieved by Bitcoin. Bitcoin itself, the Bitcoin protocol, was the inspiration for defining this problem. Nevertheless, this solution also, I should say, came out, in retrospectively, somewhat unexpected. The problem itself was never considered in this setting. The problem of consensus or the problem of having a ledger of that type that Bitcoin solves. So you might actually, if you look at the theoretical results in the distributed systems literature, you might even consider that result as impossible given though that we knew that the barrier without using some type of public infrastructure was one third of the number of parties that might be malicious and Bitcoin was achieving that with a threshold that was going close to 50%. So despite this remarkable feature of the protocol to provide us consensus in this setting, there are significant scalability and energy efficiency disadvantages that the protocol has. So the clear question came to be, once we have this and we understand the problem that the protocol solves, is it possible to realize it in a more efficient way without fundamentally compromising any of the basic assumptions and the features of the objective that the protocol tries to solve? So proof of stake was an idea that early on by the Bitcoin community was considered as an alternative and in the Bitcoin forum were many discussions about how it might be feasible to use that in place of proof of work, which is in the case of Bitcoin. So a little bit of background about what is proof of stake. Now if you look at the way that the blockchain protocol works in Bitcoin, it's a little bit like an election. So what happens is that the next entity that is going to produce the block that is going to be added to the blockchain is elected and in some sense is elected with probability proportional to its hashing power. So basically the more hashing power you have, the more chances you have of being elected and produce the next block. Now collisions so to speak may occur, the system is decentralized, there is no coordination between the parties that are running the protocol and the protocol itself is designed to be able to absorb such collisions, to absorb such momentary, temporary disagreements between the parties. So all of that is fundamentally based on this concept of proof of work. So modifying this is what proof of stake is about. So using stake, which is a virtual resource instead of hashing power, which is a physical resource is at the core of the POS idea. So while in POW and proof of work, we're going to have the set of stakeholders being the miners in some sense, this will be substituted by those that have stake, which is reported in the ledger. Once we have this, then we have to come up with a randomized process that is capable of emulating basically the same election idea that naturally comes in the proof of work domain. So this idea proved to be very powerful and motivated a number of people to propose a lot of protocols for implementing the proof of stake concept. So there are two fundamental approaches here that you can consider proof of stake. The one is a proof of stake blockchain, where basically what you try to do is think in the same way that the Bitcoin blockchain works and use proof of stake in a way that does not jeopardize any of the features that the proof of work protocol provides. An example of these protocols is Uroboros, the protocol I'll be talking about today, Snow White Next and a number of others. The other approach is based on classical bidding for tolerant protocols, which can somehow be upgraded to operate in the POS setting. And an example of that is Algorand. So both approaches are POS, since what is important here is that participation in the protocol is based on your stake, as that stake is reported in the ledger. So POS didn't come without its controversy. There was an early folklore idea that was discussed among many people that were working in the Bitcoin space that POS blockchains are fundamentally impossible to work. And the main deficiencies or vulnerabilities that people were pointing at, where it can be pointed, can be summarized in these costless simulation vulnerability that the POS has intrinsically and the ability of attackers to mount what's called long range attacks. Let me just explain in a bit more detail what are those. So a costless simulation basically suggests that in the absence of proof of work, proof of stake, after all, is a proof of a virtual resource. There is nothing that prevents you from doing this over and over, perhaps in parallel multiple times. So a fundamental difference between a proof of work based blockchain and a POS based blockchain is the fact that in the proof of work case, all parties they have to commit to a certain protocol execution and advance that execution using their proof of work algorithm. That's not the case in POS. And it's not the case because it's really costless or nearly costless to execute the POS protocol. Thus, in principle, the adversary has really literally nothing at stake and is capable of advancing multiple different executions of the protocol so that it finds one that is the most favorable. And that could lead to what has been called like a long range attack. So basically in a long range attack you have a victim node that tries to distinguish between two alternative histories which are furnished by the network without having access to recent information. And recent information here is key. I mean, if you are constantly online, you have a good understanding about what is communicated in the network. Nevertheless, imagine that you join the network after a big hiatus or basically you're just a new node that doesn't have any other information about what was going on. And in that case, you face the bootstrapping problem. How are you able to synchronize with the right blockchain without having any recent information? So just to understand how this comes like in practice. So here is just a visual depiction of this bootstrapping from Genesis problem where you have the Genesis block, let's say, you have a new party. And the new party now tries to find what is the right history. He doesn't have any information about the protocol until at least the, for instance, you can even say the Genesis block. And now you have the honest parties which are providing this blockchain that you see at the top and then you have the adversary that is providing the blockchain at the bottom. So what we want is that the new party, presumably honest, would like to join the top version of the history. The only information the new party has is the Genesis block and he's faced with that decision. So how does this work in the proof of work world? So what happens in the proof of work world is that what you can prove is that the main chain, the chain that is maintained by the honest parties, is going to have the most blocks. Or to be more precise is gonna have an aggregate difficulty, which is higher than the alternative chain. So basically, the adversarial version will be substantially shorter, counting difficulty as length, and that will enable the party to just connect to the correct blockchain. So this is a very powerful idea and you see in some sense, realize intrinsically on the fact that this is a long blockchain represents aggregate proof of work and the resources of the honest parties are assumed to be in a strict majority. So basically, the majority of the housing power is with the parties that follow the protocol, while the adversary is in the minority of the housing power. Clearly, this argument would not work if things were otherwise, and there was an adversary that has the majority of housing power. So this setting suggests the following model for understanding how blocks and protocols operate, which we call dynamic availability. So dynamic availability is a setting where we analyze a blockchain protocol that suggests an environment that parties will join and live at will. There is a number of offline and online parties that dynamically change over time. They may lose clock synchronization. They may lose their network connection and there will be no a priori knowledge of the participation at any given time. So the blockchain setting is dynamic availability is what people would like these protocols to work on. So we have to see how proof of work compares to proof of stake with respect to dynamic availability. So let's see, compare for example, the Bitcoin protocol with a POS blockchain or a POS BFT protocol. Now, in terms of setup assumptions, there is a first difference. Bitcoin protocol uses a common random string, as it is frequently called in the cryptographic literature, or basically the Genesis block contains a random string, and that is common and shared by all the parties that run the protocol. On the other hand, in the case of a POS blockchain or a POS BFT protocol, what we assume as a starting point is a public directory. So in both cases, this is a shared assumption and the difference lies on the fact that the common random string is basically can just be a string in the sky, something that is random and predictable till the moment it appears and everybody agrees on that. Whereas a public directory basically it is what it is. It's basically a set of keys that have been accumulated and they provide the root of trust of the system. So this is a fundamental difference between POW and POS and this is something that is going to remain. So this is like something that typifies, let's say, the proof-of-work concept and the POS concept. Nevertheless, in both cases this is a setup assumption and as long as we are capable of somehow ensuring that this setup assumption is there, then the whole protocol is going to be hopefully safe after that. Long-range attacks, though. How do they deal with long-range attacks? So in the case of Bitcoin and the proof-of-work-based blockchain, the longest-chain rule is sufficient for the same reason I told you. So basically a party that is bootstrapping from Genesis and potentially faces a long-range attack will just follow the blockchain that has the biggest number of blocks or the highest difficulty and it can be shown that it connects to the right blockchain. Unfortunately, what happens in the POS domain things are not as simple. So there are a number of assumptions that people have used to deal with long-range attacks and you will see that many of them are not quite satisfactory. Mostly is that the longest-chain rule, for example, that is used in POS blockchain, is not sufficient and what is frequently assumed by these papers, essentially all POS papers in one way or another, is what we can call a local moving checkpoint. So essentially the parties are assumed to not diverge to any alternative blockchain compared to the history they have. So they have basically a recent block that they trust and they just remain that. Perhaps that's a block that has naturally evolved because they are online all the time or it's a block that they heard from another node. In a sense, that requirement means that if you are not online all the time you will have no way of having such recent information at the local checkpoint and thus someone will have to provide you that. And that would be basically an assumption that when you're initialized in such a POS blockchain you will have to get some trusted information. Now this is fundamentally different from the POW setting, at least in all these previous protocols. Another requirement is to have key evolving signatures which is something that doesn't exist in POW but it is something that we know cryptographically how to design. If we look at the same question at the POS BFT case we also want key evolving signatures but now a different requirement comes up. What happens in the BFT case is essentially that the nodes that are running the protocol agree on all blocks. That means that you do not have forks because every time that you produce a block there is sufficient agreement about that so you do not have to resolve such type of collisions or disagreements that we were having to deal with in the blockchain setting. Nevertheless, in order to run such protocols you will have to have a good understanding of what is the level of participation. So this points to these deficiencies that you see are described in this slide. So while dynamic availability is feasible in the POW setting using only the Genesis block in the POS blockchain setting rejoining parties would need a somewhat recent block whereas in the POS BFT setting parties will need to know participation level at all times in history. And these are both strong requirements that hurt this general setting of dynamic availability that a POW proof of work based blockchain like Bitcoin works. So in summary, POS blockchain so far they need to have additional advice to do this bootstrapping from Genesis information the longest chain rule in itself is insufficient while POS BFT, the level of actual participation needs to be known throughout. So and the reason as I mentioned is that these BFT protocols they have to rely on these counting decisions that need accurate estimates of participation. So all this is the motivation for the new version of the Uroboros protocols that we call Uroboros Genesis. So the new version of the protocol is exactly dealing with this problem of dynamic availability and the intention is to see whether it is feasible to produce a POS blockchain that is capable of natively working in the same setting as Bitcoin. So the Uroboros Genesis protocol is a new milestone in the line of Uroboros POS based blockchains the protocol is based on Uroboros PROS that will be presented in Eurocrypt 2018 and the Uroboros protocol which was presented in 2017 at crypto. The main novel feature is that we design a new chain selection rule that enables parties to bootstrap from Genesis. So let's take a step back and see how the Uroboros protocol works. So the protocol was designed together with a formal proof that realized the functionality of robust transaction ledger and that's one of the fundamental distinctions that was put in place in the design of the protocol. So we designed the protocol together with a proof that the protocol is capable of realizing a distributed ledger. The proof strategy involves properties of the underlying blockchain data structure and these are properties that are shared with the Bitcoin proof of work analysis. Common prefix, chain quality and chain growth these are the fundamental properties that previous work has identified as the important building blocks for arguing security for blockchain protocols. The analysis pairs the honest parties against an adversary that controls a malicious coalition of parties that is in the minority in terms of stake and furthermore the adversary has complete network dominance in the sense that it's capable of delaying messages and acting after all the honest parties. One of the things that is done for the network is that messages are eventually delivered. So there is an upper bound in the delay of the network which is not known to the participants that within which parties will actually receive messages so it's not possible to stifle information from being propagated in the network. So the design of Uroboros protocols was this structured stage approach that comes as follows. First, the protocol is an initial analysis is performed in a setting where state is static so it's basically like we freeze the stake of everyone and we see how the protocol works. Then we use randomness, a randomness source which you can think otherwise unspecified like a randomness beacon that at regular intervals emits a unpredictable value and we show how we can basically bootstrap the protocol the stage one protocol that is static stake to a setting where stake is allowed to move and evolve over time. And then finally at stage three we show how it's possible to remove the assumption of the beacon and using cryptography or some type of cryptographic implementation show how the protocol itself can emulate the beacon. So here's an example of the static stake segment or how it might advance. So what you see here is a sequence of blocks that are produced in time starting from the leftmost side where we have a genesis block let's say that's B0 that you see on the left side of this of the screen and then moving on the time moves forward to the right. What happens is that every moment in time every heartbeat of the protocol there is a certain party that is identified and is capable of producing one of the blocks. So that assumption is happening for most of the moments in the protocol but there will be also moments by design that will also be silent. For example, this is what you see in the fourth in the fourth time moment in that sequence in that time window here. What happens when you are elected to produce a block then you issue a block using essentially just a digital signature and that is distributed to the other parties that are running the protocol. Now that block is connected to the previous block that an entity knows. If there are points in time which in the protocol model we call them slots so if there are slots where nobody is elected then this by definition is going to be a silent slot but there might be also slots where multiple parties are elected at the same time. The election process itself is based on a randomized process which initially it is seeded by the Genesis block. So this is how the first segment of the protocol let's say advance and if you also want to think about it this is the first few moments of protocol execution. Conflicts are bound to occur and the protocol uses a simple longest chain rule to choose what is the correct history. So once we have this in place here is how the evolving stage of the protocol will take place which uses a randomness beacon that regularly emits unpredictable values. So what you have is a sequence of blocks that are produced in the static setting where basically state is assumed to be frozen and then what happens is that the beacon value is emerged and in some sense refreshes the randomness that will produce the next segment. Now what happens is that during that time the stake that is assumed to provide the safety of the protocol is assumed to be frozen but the real stake is not assumed to be frozen and it's possible to flow arbitrarily between the participants. What we do is that when we are about to go to the onset of the next stage of the protocol which in our technology is called an epoch the protocol looks at the stake as it has evolved in the first epoch and then creates the stake that will provide the safety of the second epoch and this is what happens and now you go into that recursively in an indefinite cycle. There is another basically state called a distribution that has evolved from the first epoch guaranteeing the safety of the second epoch and the protocol advances in this fashion. When we are ready for the next epoch again we look at the ledger, collect the stakeholders produce the next randomness value and so forth. Finally, in some cryptographic fashion we need to show how the beacon itself can be implemented. So let's take a dive a bit deeper and see how the security analysis works. If you look at the protocol execution as I described it you can think of a string that we call a characteristic string. Every heartbeat, every moment in time can be assigned one of three values. Zero means if the parties that follow the protocol own that slot. One is when the adversarial coalition controls that slot and the next signed bottom means that nobody controls that slot. So this is a type of a random sequence that is produced based on this election process that the protocol is utilizing. So let's see how a possible protocol execution can go. What happens is that little graphs like the one that I'm presenting here and we call them forks naturally emerge from such characteristic strings. So let's examine how this comes to be. So here on the left we have the genesis block and that's in a sense the root of this tree-like graph. What happened at time one? There was an honest party that produced block one. Now observe that the next is silent and now let's examine what happens when the next honest party is activated which is at slot number five. So the adversary served block three to that honest party and thus the honest party at slot five abandoned the block that was produced by party one and adopted and extended a different one. That happens because the adversary enjoying network dominance and delivering blocks in advance is capable of delivering its own blockchain which extends from slot three, the genesis block, to the party at slot five. Let's see what happened at seven. Here is another honest party at seven that is served by the adversary block number six. That block number six extends one and thus the chain extended by party five is abandoned. Party eight, observe here what happens. Here is a situation where you have honest parties at five and seven but party eight chooses to extend the block one. What happened here is that the adversary could exploit network delay and have party eight receive the block produced by party one without seeing the blocks produced by parties five and seven. So here is a situation where network delay puts the adversary at an advantage. Finally, as the adversary moves on we have an extension of the block from party eight by an adversarial party at position 12 and finally adversary serves block 12 to the honest party that extends it at slot 14. What happens now in this picture is the following. We've managed to take a protocol execution and substitute it with this tree-like graph. This tree-like graph emerges naturally from the randomized process that produces the characteristic string. The nice feature of this is that we can study now the properties of these discrete structures in order to understand the security of the protocol. How can we show eventually that the protocol converges to a single history? Because this is what is the fundamental question. So drawing from Bitcoin analysis what happens is that random walks play a fundamental role. A one-dimensional random walk actually is what you can find at the core of the security analysis of Bitcoin. So this random walk you can think of it as follows. Imagine that there are the bad guys and the good guys. Every time that the bad guys find a proof of work you can imagine that there is this random walk advancing upwards. Let's say they're equal at the beginning starting for instance from the Genesis block and then the parties that follow the adversary the adversarial coalition will advance this proof of work upwards with a certain probability which is how much they can find the proof of work. At the same time the good guys will pull the random walk downwards whenever hopefully they produce a block. It turns out that this is not quite the case. What happens is that indeed there is a pool of this random walk downwards every time almost every time the honest parties find a proof of work. The reason that this probability is gamma and not equal to alpha which the probability that honest parties found the proof of work is the fact that honest parties pay the price of being decentralized. And here this paying the price of being decentralized you can see by the fact that gamma is approximately alpha minus alpha square is the probability of two or more honest parties colliding around the same time and producing a block together. So we cannot use these moments if you want. Still, if we make the assumption and that's the fundamental assumption that is done in papers that analyze the security of Bitcoin is that if gamma is strictly bigger than beta this random walk is going to get a strong pool downwards and the honest parties will win. So here is an inspiration from the analysis of Bitcoin. So now the question is can we apply this logic to the POS setting? Unfortunately, winning a block for the honest parties does not necessarily constitute a move to the left in the random walk and the main reason actually is what we cited at the beginning, costless simulation. So the adversary may reuse an opportunity to issue a block in multiple paths of the fork exactly because this costs him nothing. So you can have bad situations like the one demonstrated in this graph below and that's like a pathological execution of the protocol where basically see what happens. The adversary just owns three out of the total eight blocks which are shown here and still is capable in producing a situation that completely forks the blockchain despite the fact that he is in the minority in terms of ownership of slots. What happens is the reason of costless simulation slot three and four can be used in two separate blockchains without the adversary actually having to pay twice for that and that's the fundamental issue of how costless simulation comes here and spoils the analysis or if you want the proof logic that was used in the case of Bitcoin. Nevertheless, what we've shown is that it's possible to overcome that. But we need a more complex analysis. The complex analysis that we did in the Ouroboros lines of papers uses the following features and studies these graph structures that represent the protocol execution. There are three important quantities that are relevant when you study a protocol execution like this one. Every path that you see in the execution will have these quantities measured. The first one we call it gap. The gap is we look at a certain path in the execution and we say how far it is from the leading one. That's the gap concept. The reserve concept on the other hand says if we look at a certain path how many slots are still at the full control of the adversary and the adversary might use them to advance that path forward. In that particular case for the top path you see there is three slots. And finally, if you see you subtract gap from reserve to say what is the reach of that specific path. So that path here has reach minus one which represents the fact that it's a bit too short by one to win over the leading path. So if we are in such a situation and you are an honest node that follows longest chain rule you will not be able to fool by the adversary and adopt the top branch. You will follow using longest chain rule the branch that was extended by player at slot nine. A similar situation happens with the lowest branch. And then at the end if we look at the whole execution we look at two concepts. The first one we call it reach which is the maximum reach across all times and the other we call it margin which basically says what is the second best disjoint reach? What we would like is that the margin is always below zero. That would be a setting like in this particular one that the adversary will not be able to fool an honest party that tries to connect in this protocol execution and any honest party that connects in that particular execution is going to go and extend the block that was produced by the party at slot nine. So reach and margin are the two fundamental quantities that are interesting when we analyze these protocol executions and we can show that the adversary will win if and only if the margin which is the second best disjoint reach of a certain protocol execution is at least zero. What is interesting now is that these two quantities together define a random walk which is even though more complex than the random walk we analyzed in the case of Bitcoin it still has some good features that we can exploit to prove security. So here's how the random walk is defined. So it's a two-dimensional random walk and what happens is the following reach and margin when the adversary wins a slot they advance forward so basically both of the quantities they go plus one. So that's like the standard one-dimensional behavior the adversary wins a slot or wins a block then things are towards its favor. On the other hand when the adversary loses a slot things are more nuanced. The one-dimensional behavior would suggest that would just go minus one so what you see in the third branch here of this equation but that's not what happens in fact reach and margin decrements only in one of the possible cases but what happens is that first of all reach will never drop below zero and that's by definition but what happens very interestingly from the adversarial point of view is that the adversary can prevent the margin from going negative by sacrificing reach. So essentially what happens is that the adversary can make margin stick to zero by using the quota if you want that he has accumulated in terms of reach. This completely describes the random walk and here is a picture of it. So what you see in this picture is how now the probabilistic process that the characteristics thing suggests in the protocol will induce a random walk in this reach and margin quantities. What happens is that the adversary wins when you go when he gets a slot and when the adversary wins you see a move of the random walk towards the upper right of this diagram that's the beta moves that you see here and now you see what happens with the gamma moves. The gamma moves is when basically the honest part is win a slot and this gamma is a similar parameter to the one we've seen in the Bitcoin analysis. Now when you are in the upper right and you get a gamma transition what happens is that initially you can follow the same one-dimensional behavior. So beta goes upwards to the right and gamma pulls leftmost to the bottom. Nevertheless, when we have the situation that margin hits zero it's possible for the adversary to prevent the margin from going zero and as we saw in the previous slide sacrifice reach to maintain the margin to being on zero. So somehow the adversary has a little pull to prevent this random walk from escaping to minus infinity as it happened before. But nevertheless what we have still be able to show is that despite the fact that this random walk is more complex than the one that happens in Bitcoin we can still prove that the random walk will escape to minus infinity in the case of margin and that means that the longest saying rule is going to enable the parties to converge to the same protocol history. So this gives you the picture of what was happening before Uroboros Genesis was suggested. The new chain selection rule takes this idea of longest chain and deals with long range attacks. So all the analysis which we've seen so far essentially deals with short range concerns. And up to this case this analysis completely covers us. Nevertheless, for long range comparisons where chains diverge let's say from more than K blocks where K is the security parameters we have to use a different rule to select the right chain. And here is the novel chain selection rule which I will explain to you with this diagram that we have designed for Uroboros Genesis. So when you have going back to this bootstrapping from Genesis question which is the fundamental question that a node that joins the network would like to resolve you're dealing with the choice as you see on the right you have two chains that you have to deal and they are forking at a certain point. Now if this fork is kind of recent that's like a short range attack if you want or a disagreement between nodes that was just produced naturally because of network conditions then the longest chain rule will apply. On the other hand, if the fork is bigger than K blocks the following plenitude type of rule is used. We go to the moment where the chains diverge and then shortly thereafter we isolate a certain region of blocks and we see in this region which is defined in the time domain which of the two chains is more dense and that's the plenitude rule. The party is going to follow the chain that is denser in the time domain despite the fact that that chain might have a multitude number of blocks later on. So this is how the plenitude rule works. So what is interesting about this is that this rule is still quite simple to implement. It's still a quite simple rule and enhances the longest chain rule in a way that is still quite easy to program. So what's the intuition? The intuition and what we prove in the protocol is that if the majority of parties follow the protocol then at any sufficiently long time segment the corresponding chain of the honest parties will be denser especially after a fork. So adversarial blockchains will be shortly after the divergence point will exhibit a less dense block distribution and we can use that rule to determine what is the right blockchain to connect to. So going back and remembering the picture I showed you before about what was happening before POW and POS what Uroboros Genesis does changes the way that a POS protocol can operate in a dynamic availability environment and does a way with the requirement that you need to know a recent block in order to connect to the right history of the protocol. So here is the biggest advance that the Uroboros Genesis protocol offers and for the first time we can say now that POS blockchains can operate in the same dynamic availability setting that we know the Bitcoin proof of work base blockchain to operate. So this was a very quick overview of Uroboros Genesis there is more to come in Uroboros Research and I'll just mention very quickly some of the research streams that you'll be hearing soon and following up the work that we're doing now importantly the incentive structure and delegation and stake pools of the protocol is a work that we'll be releasing soon sidechain software updates and ecosystem sustainability is another important research direction that is also coming up smart contacts and domain specific languages for these POS blockchains a permissioned version of the Uroboros blockchain is also underway and finally looking at how POS blockchains can be scalable in the most general term of more general term of scalable sense is something that is also on our plate so with this thank you for your attention and I look forward to telling you more about our research in a coming video


er 15th or our meeting or our second meeting in October whenever that is. Great. I don't think there's any other business before us except for public comments on matters which are not before the board tonight. And do we have anybody and the attendees that wish to speak? Doesn't matter. And do we have any new business or the other business before the board not noticed in the last 49 hours? I guess the one we would have is Maureen, when is our next meeting and what's? I think it's, let me double check. It should be Thursday, September 8th. And we have items for the agenda already? We do. So South Point, which is called Renew Amherst located at perhaps 266 East Hadley Road. They need to amend their previously approved special permit for a variety of different items and conditions. And so that's a public hearing and there may be a public meeting but I'm unsure of that. So. All right. We have that one. Okay. Is there any other new business from any members of the board? We're gonna get done just before nine o'clock, which is our goal. Things speed up between 8.45 to nine o'clock. You know what I'm gonna do is I'm gonna set o'clock and we're gonna start this at about eight o'clock at night, we're gonna be at 8.45 and things gonna go really fast from now on. All right, folks. Thank you very much for all your effort and work and your hours put into this. I appreciate it. We all appreciate it. And I hope you have a great end of the summer and we'll see each other in a couple of weeks. Thank you. Thank you all. See you later.


My wife once asked me, why do you always want to come back to Fort Brown? I say, because I want to jump. I'm a paratrooper. Being a paratrooper sets you apart from the other part of the Army. We're all soldiers. Don't get me wrong. But we jump in, we seize the airfield, and we support the mission at 100%. My son, you know, when he told me that he was coming to the Army, I didn't believe it at the very beginning, and then he proved me wrong. He went airborne. He said he wanted to come back to Fort Brown, and you know, it's an amazing feeling to know that your son, from a little guy all the way to now, is doing what he set himself out to do, and that is to be a paratrooper. I enjoy doing what I do. I have fun. But as I near the end of my career, nothing makes me more proud to be able to serve this great country with my son.  I've watched and I've heard some of his leaders talk about his training. Where else would I want him to deploy? Nowhere, but here in 82nd. This is the only place for him to deploy. He'll PCS another day, another time, but for him to have his first deployment here in 82nd, where the standard lives, this is where the rubber meets the road. There's no greater feeling to know that, hey, my son will be left in very good hands in the 82nd Airborne.


Hello, this is LaptopMedia, and today, we will show you how to open the Acer Swift 5 Pro SF51455 GT and what's inside of it. To get inside, you need to undo 11 Torx head screws. After that, pry the bottom panel with a plastic tool and you're done. Please remember, it would be of great help to us if you just hit the like button and subscribe to our channel. That would motivate us to make even more and better videos for you. Ultimately, there is no difference in the layout, nor the cooling, when compared to the last year Swift 5 Pro. Two heat pipes are taking the heat away from the two chips, and leading it to the heat spreader. Then, an average sized fan blows it away from the back side of the chassis. Sadly, the memory is soldered to the motherboard, so you are left with the 8GB or the 16GB of LPDDR4X you purchase the device with. On the bright side, you can swap the M.2 SSD with a faster or a larger one. Our unit has a 56Wh battery pack. Interestingly, it is mounted to the chassis via double-sided tape, so if you have to remove it, make sure you have some tape at your disposal. If you'd like to see more disassembly videos of the latest notebooks we would highly appreciate if you hit the like button and subscribe to the channel.


Live from Fisherman's Wharf in San Francisco. It's theCUBE, covering IBM Chief Data Officer Strategy Summit, Spring 2017. Brought to you by IBM. Hey, welcome back everybody. Jeff Frick here with theCUBE. We're in Fisherman's Wharf in San Francisco at the IBM Chief Data Officer Strategy Summit, Spring 2017. It's a mouthful, it's 170 people here, all high level CXOs, learning about data, and it's part of an ongoing series that IBM is doing around Chief Data Officers and data. Part of a big initiative with Cognitive and Watson, I'm sure you've heard all about it watching TV, if nothing else, I'm not going to the shows, and we're really excited to have kind of the drivers behind this activity with us today. Also, Peter Burris from Wikibon Chief Strategy Officer, but we've got Caitlin Lepic who's really driving this whole show. She is the Communications and Client Engagement Executive IBM Global Chief Data Office. That's a mouthful, she's got a really big card. And Courtney Abercrombie, who I'm thrilled to see, you've seen her many, many times, I'm sure at the MIT CDO IQ. So she's been playing in this space for a long time. She is a Cognitive and Analytics Offerings Leader, IBM Global Business. So first off, welcome. Thank you, great to be here. Thanks, always a pleasure to be here, so comfortable. I forget you guys aren't just buddies hanging out. So before we jump into it, let's talk about kind of what is this series? Because it's not, you know, World of Watson, it's not Interconnect, it's a much smaller and more intimate event, but you're having a series of them, and in the keynote is a lot of talk about what's coming next, what's coming in October, so I don't know. Let me let you start, because this was, a originally- This was a long time ago. 2014. Yeah, 2014, the role was just starting, and I was tasked with, can we identify and start to build relationships with this new line of business role that's cropping up everywhere? And at that time there were only 50 Chief Data Officers worldwide, and so I- 50. 50. In 2014. And I can tell you that earnestly, because I knew every single one of them, I made it a point of my career over the last three years to get to know every single Chief Data Officer as they took their jobs. I would literally, well, hopefully I'm not a Chief Data Officer stalker, but I basically was like calling them once I'd see them on LinkedIn, or if I saw a press announcement, I would call them up and say, you got a tough job, you know, let me help connect you with each other and share best practices, and before we knew, you know, it became a whole summit. It became, you know, there were so many always asking to be connected to each other and how do we share best practices, and what do you guys know as IBM, because you're always working with different clients on this stuff, so. And Courtney and I first started working in 2014. We wrote IBM's first paper on Chief Data Officers, and at the time there was a lot of skepticism within our organization, why spend the time with data officers? There's other C-suite roles you may want to focus on instead, but we were seeing just the rise of data, external data, unstructured data, a lot of opportunity, rise in the role, and so I think we're seeing it reflected in the numbers. Again, first summit three years ago, 30 participants, we have 170 data executives, clients joining us today and tomorrow, so we're seeing. And six papers later, Caitlin. And six papers later. And we're going strong still. Exactly. Before we jump into the details, this is some of the really top level stuff that, again, you talked about with John and David at MIT CDOIQ in terms of like reporting structure. Now, where does CDO's report? What exactly are they responsible for? You covered some of that earlier in the keynote. I wonder if you can review some of those findings. Sure, I can share that and then have Courtney add. So we find about a third report directly to the CEO, a third report through the CIO's office, a sort of the traditional relationship with CIOs, and then a third in what we see growing quite a bit, or a CXO, so functional or business line function. Originally, traditionally, it was really a spin-off of CIO, a lot of technical folks coming up, and we're seeing more and more of the shift to business expertise, and the focus on making sure we're demonstrating the business impact that these data programs are driving for an organization. Yeah, it kind of started more as a data governance type of role. And so it was born out of IT to some degree, because, but IT was having problems with getting the line of business leaders to come to the table. And we knew that there had to be a shift over to the business leaders to get them to come and share their domain expertise, because as every Chief Data Officer will tell you, you can't have lineage or know anything about all of this great data unless you have the experts who have been sitting there creating all of that data through their processes. And so that's kind of how we came to have this line of business type of function. And Interpol really talked about, in terms of the strategy, if you don't start from the report strategy, yeah, on the keynote, you are really in big risk of the boiling the ocean problem. I mean, you can't just come at it from the data first. You really have to come at it from the business problem. It was interesting. So Interpol was one of our clients as a CDO three times prior to rejoining IBM a year ago. And so Courtney and I have known him. Express Scripts came, yeah. Exactly. We've interviewed him, featured him in our research prior to. So when he joined IBM in December a year ago, his first task was data strategy. And where we see a lot of our clients struggle is they make data strategy in 18 months, 24 month process, getting the strategy mapped out and implemented. We say, you don't have the time for it. You don't have 18 months to come to a data strategy and get by it and get it implemented. Nail something right away. Exactly. Get it in the door, start showing some results right away.  But what is a data strategy? Sure. So I can say what we've done internally and then I know you've worked with a lot of clients on what they're building. For us internally, it started with value proposition of the data office. And so we got very clear on what that was. And it was the ability to take internal, external data, structured, unstructured and pull that together. And if I'd summarize it, it's drive to cognitive business and it's infusing cognition across all of our business processes internally. And then we identified all of these use cases that'll help accelerate and the catalyst that will get us there faster. And so client 360, product catalog, et cetera. We took data strategy, got buy in at the highest levels at our organization, senior vice president level. And then once we had that support mandate from the top went to the implementation piece. So it was moving very quickly to specify, you know, for us it's about transforming to cognitive business. That then guides what's critical data and critical use cases for us. So before you answer that, before you get into it. So is a data strategy a means to cognitive or is it an end in itself? I would say it's a, it should be most effective. It's a succinct one page description of how you're going to get to that end. And so we always say- Of cognitive. Exactly, for us it's cognitive. So we always ask very simple question, how is your company going to make money? Not today, what's its monetization strategy for the future? For us it's coming to cognitive business. Have a lot of clients that say, we're product centric. We want to become customer, client centric. You know, that's our key piece there. So it's that key at highest level for us to become a cognitive business. Well, and data strategies are as big or as small as you want them to be, quite frankly. They're better when they have a larger vision, but let's just face it, some companies have a crisis going on and they need to know what's my data strategy to get myself through this crisis and into the next step so that I don't become the person who's cheese moved overnight. Am I giving myself away? Do y'all ever know the cheese? Absolutely, I give this. Every time a new iOS comes up, my wife's like, no, don't do that. But who cares about them? Who cares about the millennials? I do, I love the millennials though. But yeah, so cheese, you know, we don't want your cheese to move overnight. But the reason I ask the question and the reason why I think it's important is because strategy is many things to many people, but anybody who has a view on strategy ultimately concludes that the strategic process is what's important. It's the process of creating consensus amongst planners, executives, financial people about what we're going to do. And so the concept of a data strategy has to be, I presume, is crucial to getting the organization to build a consensus about the role the data's going to play in business. Absolutely. And that is the hardest, that is the hardest job. Everybody thinks of a data officer as being a technical, highly technical person when in fact the best thing you can be as a chief data officer is political. Very, very adept at politics and understanding what drives the business forward and how to bring results that the CEO will get behind and that the C-suite table will get behind. And by politics here you mean influencing others to get on board and participate in this process. Well, even just understanding sometimes leaders of business don't articulate very well in terms of data and analytics, what is it that they actually need to accomplish to get to their end goal? And you find them kind of stammering when it comes to, well, I don't really know how you as Interpol Bhandari can help me, but here's what I've got to do. And it's a crisis usually. I've got to get this done and I've got to make these numbers by this date. How can you help me do that? And that's what the chief data officer kicks into gear and is very creative and actually brings a whole new mindset to the person to understand their business and really dive in and understand, okay, this is how we're going to help you meet that sales number. This is how we're going to help you get the new revenue growth. So in certain respects it's a, there's a business strategy and then you have to resource the business strategy. And the data strategy then is how are we going to use data as a resource to achieve our business strategy? So let me test something. So the way that we at SiliconANGLE Wikibon have defined digital business is that a business, a digital business uses data as an asset to differentially create and keep customers. Right. Does that work for you guys? Yeah, sure. It's focused on, and therefore you can look at a business and say is it more or less digital? Yeah. Based on how, whether it's more or less focused on data as an asset and as a resource that's going to differentiate how its business behaves and what it does for customers. And it goes from the front office all the way through the back. Yes, because it's not just, but that's what create and keep I'm borrowing from Peter Drucker, right? Peter Drucker said the goal of business is to create and keep customers. Yeah, that's right. Absolutely. He included front end and back end. So money, you got to have customers. Exactly. You got to have customers to make money. So data becomes the differentiating asset in a digital business. And increasingly digital is becoming the differentiating approach in all business. I would argue it's not the data. It's because everybody's drowning in data. Exactly. So you use the data and how creative you can be to come up with the methods that you're going to employ. And I'll give you an example. Here's just an example that I've been using with retailers lately. I can look at all kinds of digital exhausts. That's what we call it these days of, let's say you have a personal digital shopping experience that you're creating for these new millennials. We'll go with that example. And because shoppers, you know, because retailers really do need to get more millennials in the door. They're used to their Amazon.coms and their online shopping. So they're trying to get more of them on the door. When you start to combine all of that data that's underlying all of these cool things that you're doing. So personal shopping, thumbs up, thumb down. You like this dress, you like that cut, you like these heels. Yeah, yes, yes or no, yes or no. I'm getting all this rich data that I'm building with my app because you got to be opted in. No violating privacy here. But you're opting in all the way along and we're building and building. And so we even have like, for us, we have this Metropulse retail asset that we use that actually has hyper-local information. So you could, knowing that millennials, like for example, food trucks, we all like food trucks, let's just face it, but millennials really love food trucks. You could even, if you are a retailer, you could even provide a fashion truck directly to their location outside their office with equipped with things that you know they like because you've mined that digital exhaust that's coming off of the personal digital shopping experience and you've understood how they like to pair up what they've got. So you're doing a next best action type of thing where you're cross-selling up-selling and now you bring it into the actual real world for them and you take it straight to them. That's a new experience. That's a new millennial experience for retail but it's how creative you are with all that data because you could have just sat there like before and done nothing about that. You could have just looked at it and said, well let's run some reports, let's look at a dashboard but unless you actually have someone creative enough and usually it's a pairing of data scientists, chief data officers, digital officers, all working together who come up with these great ideas and it's all based if you go back to what my example was that example is how do I create a new experience that will get millennials through my doors or at least get them buying from me in a different way. If you think about that was the goal but how I combined it was data, a digital process and then I put it together in a brand new way to take action on it. That's how you get somewhere. So let me see if I can summarize very quickly and again just as a also test because this is the way we're looking at it as well that there's human beings operate and businesses operate in the analog world. So the first test is to take analog data and turn it into digital data. IOT does that. Otherwise there's no digital in the software. Otherwise there's no digital anything. Yeah, that's right, that's right. And we call it IOT and P, Internet of Things and People because of the people element is so crucial in this process. Then we have analytics, big data that's taking those data streams and turning them into models that have suggestions and predictions about what might be the right way to go about doing things. And then there's these systems of action or what we've been calling systems of enactment but we're going to lose that battle. It's probably going to be called systems of action that then take and transduce that the output of the model back into the real world and that's going to be a combination of digital and physical. And robotic process automation. We won't even introduce that yet. But that's fine. That's going to be in October. Exactly. Look at that one with Dave. But I really like the example that you gave of the fashion truck because people don't look at a truck and say, oh, that's digital business. Right, but it manifested in that. It absolutely is digital business because the data allows you to bring a more personal experience right there at that moment and it's virtually impossible to even conceive of how you can make money doing that unless you're able to intercept that person with that ensemble in a way that makes both parties happy. And wouldn't that be cheaper than having big, huge retail stores? Well, I don't know. Someone's going to take me up on this. Retailers are going to take me up on this. I'm telling you. But I think the other part right is- Right next to the taco truck comes the truck. So you can still- There could be other trucks in that. Cleaner truck and this and that. But one of the things we're going to talk about is you've got to still have a hypothesis, right? I think one of the kind of early false promises of big data in Hadoop and, you know, just that you just throw this stuff in and the answer just comes out. But that just isn't the way. You've got to be creative and you have to have a hypothesis to test. And I'm just curious from your experience how ready are people to take in the external data sources and the unstructured data sources and start to incorporate that in with the proprietary data? Because that's a really important piece of the puzzle. It's very different. They're ready to do it. It depends on who in the business you are working with. So digital offices, marketing offices, merchandising offices, medical offices, they're very interested in how can we do this? But they don't know what they need. They need guidance from a data officer or a data science head or something like this because it's all about the creativity of what can I bring together to actually reach that patient diagnostic that whatever the case may be, the right fashion truck mix or whatever. So does somebody from the Chief Data Office, if you will, get assigned to your assigned to marketing and your assigned to finance and your assigned to sales? I have somebody assigned to us. Well, to put this in kind of a common or modern parlance, there is a design element. You have to have use case design. And how are we going to get better at designing the use cases so we can go off and explore the role that data is going to play? How are we going to combine it with other things? And to your point, and it's a great point, how that turns into a new business activity. And if I can connect two points there, the single biggest question I get from clients is how do you prioritize your use cases? How can you help me select where I'm going to have the biggest impact? And it goes, I think my thing's falling again. Sorry. It's nice and quiet in here. Okay, good. It goes back to what you were saying about data strategy. We say what's your data strategy? What's your overarching mission of the organization? For us, it's becoming cognitive business. So for us, it's selecting projects so we can infuse cognition the quickest way. So client 360, for example. So we'll often say, what's your strategy? And that guides your prioritization. That's the question we get the most. What use case do I select? Where am I going to have the most impact for the business? And that's what you have to work with. But is it the most impact which just sounds scary and you could get an analysis process or what, where can I show some impact the easiest? You're going to delineate. Exactly. Interpol's got his short list and he's got his long list. Here's the long term that we need to be focused on to make sure that we are becoming holistically a cognitive company. So that we can be flexible and agile in this marketplace and respond to all kinds of different situations, whether they're HR and we need more skills and talent. Cause let's face it, we're a technology company who's rapidly evolving to fit with the marketplace. Or whether it's just, you know, good old fashioned we need more consultants. What are we going to do? Yes. I worked my business in. Well, we can go and go and go, but we're running out of time. We've got a full slate. We're just starting. I know. We were just starting these conversations. I haven't thought of a whole other conversation to have. So what should people look for? We haven't even hit the robotics yet, you know? We need to keep going. Good control. Less coffee for us. What should people think about when they think about this series? What should they look forward to? What's the next one for the people that didn't make it here today that they, you know, where should they go on the calendar and book in their calendar? So I'll speak to the summits first. It's great. We do spring in San Francisco. We'll come back, reconvene in Boston in the fall. So that'll be September, October frame. I'm seeing two other trends, which I'm quite excited about. We're also looking at more industry specific CDO summits. So for those of our friends that are in government sectors will be in June 6th and 7th at a government CDO summit in DC. So we're starting to see more of the industry specific as well as global. So we just ran our first in Rio, Brazil for that area. We're working on a South Africa summit. I know, right? And we're seeing, we actually have a CDO here with us that traveled from South Africa from a bank to see our summit here and hoping to take some of that back. Yeah, we have a lot of global. We have a lot of server from Peru and Mexico and Chile. So yeah. We'll continue to do our two flagship North America based summits, but I'm seeing a lot of growth out in our geographies, which is fantastic. And it was interesting too in your keynote, talking about people's requests for more networking time. You know, it is really a sharing of best practices amongst peers and that, you know, cannot be overstated at small events. Community is building. It's building. It really is. We all come in and hug. We all come in and hug. I don't know if you notice, but we're all hugging each other, you know? Everybody likes to hug on the cube. It's like therapy. It's like data therapy. That's what it is. Yep, yep. All right. Well, Caitlin Courtney, again, thanks for having us. Congratulations on a great event and I'm sure it's going to be a super productive day. Thank you so much. Pleasure. All right, Jeff Frick with Peter Burris. You're watching the cube from the IBM Chief Data Officer Summit, Spring 2017, San Francisco. Thanks for watching.