Hey, this isn't a show. This is what we do.  Is this your profession? Yes or no? Do you want to master this? Did Kobe Bryant master basketball? Yeah.  Yeah. Damn right, he sucked. But guess what?  Remember Michael Jordan? When he started coming up, he wrote the top 50 guys that were picked ahead of him and he picked him off one at a time. What do you think he worked from? A state of madness? A state of competitiveness? A state of wanting to be the best?  Do you think he wanted to break records? Yes, even when he didn't, he knew one day he was. Give everyone your best. We're not a skill set change away. We're also a skill set, an attitude, and a perspective shift away. Only the winners, only the winners today will change. Who's got the courage to change? Focus on what you can control. Don't focus on what you can't control. Guys, can you control the interest rates? No, but you can control your attitude. You can control having a great relationship with the client. You can control showing up to work early. You can control making the calls. You can control being good to people and showing them love. You can control training. You can control your own reality and you can create your own economy right here. It's called a mindset. All that other stuff you can't control. And if you can control you, everything else will come to you. If you believe and you'll cut out everyone else in your life that something can happen, here's what I'll tell you. It will happen.  The better you get, the more time you spend with your family.  Put everything else to the side. Take good care of your family. Take good care of your customers. Take good care of yourself. And I want you guys to attack relentlessly. Do me a favor. Take the next 365 days of your life. Go through a season of saying no to every effing distraction in your life right now. Say no to it.  Eat good food. Be around positive people. Don't listen to complainers. Master your mouth.  Protect your mind. Guard your mind. Get close to God. Be good to your family.  Give more effort, okay? And be open-minded.  Some of you guys, you want to keep doing it the way that you're doing it? That's getting you the life you're currently getting. Like it's right now, the life you want to just wait. Everybody go, man, that's what I got. Or would you like to have more? And be more and become more. Dude, I want more. So like you've got to make a decision, man. And guess what? No one can make this decision for you. This is bigger. Who believes in God? Raise your hand. Hey, listen, listen, listen. I'm going to tell you right now, okay?  Who's never lost a war? Who's DNA do you have? Come on, man, are you kidding me?  Here's what I've learned. My kids are healthy, they're beautiful, they're amazing. I've married up to a wife. I can't even believe I got my wife. I can't believe the way I feel about myself. You know what I'm talking about, right?  This is the foundation. These are the pillars right here. God, a lot of people don't talk about God anymore. This is the answer. I swear. Hey, love, don't lie. I tell people the truth. I am the example. You're going to be the example for now on.  Do we need more examples to start going out there and showing people how to be great? Yes, it is your turn.  It's time to play. I want you to get hated on. I want your friends to not understand what you're doing. I don't care. Listen to me.  You're going to need him. Okay? You're going to need him.  I'm a very talented guy when it comes to sales and closing. Money fell through my fingers my whole life. I found God. Boom! With humans, it's impossible. And with God, it's possible.


So how many guys have heard about Prakto? Awesome. How many guys have tried online consultation at Prakto? Nice. So for those who don't, consult is an online consultation platform where a user can post a health query and we as a platform connected to a doctor who replies back with a medical advice. So in such a platform if you want to optimize for a user, you would often assign all the questions to a set of doctors who are performing, who are answering a lot. But over a period of time, this set of doctors who usually are a very small fraction will get a lot of questions assigned and they won't be able to answer all of them. They would feel burned out and at the same time the rest of the doctors would get very less questions assigned and they won't be engaged with the platform. So if you try to optimize for the users, you would end up with a bad experience for the doctors. On the other hand, if you try to optimize for the doctors, you would randomly assign the same set of same number of questions to each doctor. But not all doctors are equally active. Some don't even open their app to see the questions that were assigned. So those questions would obviously end up being unanswered and would impact our user experience. So if you optimize for your doctors, you would have a bad experience for your users. So usually you come across such kind of trade-off at any marketplace, even for e-commerce sites like Flipkart or Amazon. If you try to optimize for buyers, you would have a bad experience for sellers and vice versa. So in this talk, I present how we use Bandit algorithms to actually solve for such a trade-off problem. The key takeaways from this presentation are to understand how multi-amp Bandits work and how you can use at any of the problems that you work on and how we used at Pratt or Concert. So let's begin. So let's say we have a guy who just came to Bangalore and he wanted to have a good dining experience. But he has no idea which restaurants are good and which are bad. So he asked a couple of his friends and they said a couple of restaurants. So he went there and he liked them. So he made two buckets, one bucket where the restaurants he liked, the other bucket which he never explored. So the next time he wants to go to a restaurant, he wants to play safe and goes to those restaurants which he liked. So more often than not, he wants to visit such restaurants, let's say out of ten times. But he gets bored and he wants to occasionally explore new restaurants. So he goes to one of those unexplored bucket and if he likes them, he adds it to the like butter, like restaurants. So what we are seeing here is a multi-armed bandit setting. The restaurants on the right side are your multiple arms and the reward this guy is trying to optimize is to have a good dining experience. And the way he's trying to do that is through an exploited explore strategy. So he's exploiting nine out of ten times and exploring one out of ten times so that he would not miss out on those new restaurants. This is the simplest form of multi-armed bandit algorithms. It's called as epsilon greedy. Where epsilon stands for a probability. Here epsilon is 0.1 means that 10% of the times he explores and 90% exploits. So this guy really liked this strategy. He started exploring all the restaurants after experimentation. He was no more a stick man. So coming back to has a lot of use cases for patients like you can search for doctors clinics or hospitals. You can even book an appointment or you can consult online or also you can order medicines. So the one use case where you're looking here is consultation online. A usual consultation form looks like this where you post your problem. Also you select a problem area in which specialty you want this question to be asked. So we only send this question to those doctors in from that specialty. Once the question gets posted we now have the task of assigning to a doctors that we have in our list. So let's say today we have with us doctors Shyam, Alexa, Rahman, Bob. So if you can see Bob looks like a surgeon looks like to be more busier than other doctors. So somehow we have figured out their timings. And if you look at the availabilities Shyam is available on weekends, Alexa on evenings, Rahman mornings, but Bob has a lot of surgeries scheduled for that week. He's just busy for that week. So let's say this question comes on a Wednesday morning. Even if let's say hypothetical Shyam is the most performing doctor we won't assign it to Shyam because he is not available on the weekdays. So we assign this question to Rahman instead because he is available on mornings. So this kind of using the context is what we call as contextual assignments. So how we do that for every question we build what we call as a contextual feature vector. The features can be the number of words, the time the question came in, the age and gender, etc. And we multiply that contextual feature vector with parameter vector of each doctor to get what we call as a predator reward. A predator reward is nothing but how likely this doctor would be giving you a fastest response, the higher the reward, the faster the response you can get from the doctor. So it's straightforward. You can just pick the doctor who has the highest reward, assign the question to him. So here in our case, Rahman has the highest reward, so you assign the question to him. But the important question is how do you estimate the parameter vectors? We look at the previous assignments made to the doctor. And we look at what are the true rewards we got from the doctor. We then apply simple ridge regression to estimate the parameter vectors. So all is good and we made few assignments over a period of time. We observed that Bob has got very few assignments compared to other doctors. So if you see, Shama has got 30, Alexa 50 and 45 and Bob has got only 9 because he was not answering a lot of them. So this doesn't mean that Bob won't be answering questions at all. He's just busy for the week, so we should give him more chances. So let's give Bob few more chances. How do we do that? So let's say you got a new question on a Sunday afternoon. So using the contextual feature vectors, we have calculated the contextual predator rewards here. Now along with that, we calculate now what we call as a regret bound.  The lesser the information, the higher the regret bound. So in this case, Bob has a very few assignments. So we don't know much information about Bob, so the regret bound is higher. So we add this regret bound, the contextual rewards that we already predicted, sum it up and pick the best. So now Bob would get a chance to answer. This is a second algorithm in multi-arm bandits. It's called as upper confidence bond. If you have a mirror with statistics, it's very similar to you giving an estimate of a parameter u plus or minus sigma. The lesser the information you know, the lesser the sample size, the higher the confidence interval you have, because you're not sure of the parameter value. Similarly here, you're not sure of Bob, so you have a higher regret bound. So we have implemented this algorithm and some of the results we've seen was with the random installation of weights, we were able to see a 60% reduction in response times within 10 days. Also interestingly, we were seeing a 25% reduction, sorry, 25% increase in engagement of the doctors, which means that we are able to tap into those doctors who are actually participating with the platform and we are able to send more questions to them. That's for the console. Let's see some of the use cases where you can apply bandit algorithms. So one of the common use cases people refer is with AB testing. So user comes to your platform, you have two versions of your website. Let's say you know the true conversions, which is like 0.3 and 0.2. Let's compare AB testing with the simplest multi-arm bandit algorithm, which is epsilon greedy. So here the blue curve represents the average conversion of epsilon greedy and the green curve represents average conversion of AB testing.  You randomly show one version to each user after you finish the X trials, you pick the version which has the most conversion rate. So as AB testing was doing a random assignments, the average conversion was lower than epsilon greedy.  But after a thousand trials, AB testing starts to perform better because it picks the best version, which was version A and shows that to every user. However, our epsilon greedy is still exploring 10% of the traffic randomly to each one of the versions. So there are two points that we can take from here. If the behavior in the versions changes, it's good that we explore and the bandit algorithms would be able to adapt to those changes in the version. On the other hand, there are methods in bandit algorithms like annealing, which will make you explore less as you collect more data so that you can optimize this graph over AB testing. Some of the other applications are if you want to send user notifications and you don't know what time to send. So you can apply bandit algorithms to come up with a personalized time for each user. And similarly, news recommendations. So you might be like pretty much across most of the data science journals. So you would be seeing a lot of data science articles. But what if if you're interested in bitcoins and never come across such articles? So bandit algorithms would occasionally explore your interest in new areas which you have never seen before. And if you liked them, you would be like exploring new areas. So if anyone from the audience are from in shorts, you have a cool new project to try. So these are the two references. But if anyone who wants to get into multi-unbanded, bandit algorithms for website optimization is a very good start-up book written by John Miles White. And the paper, a contextual bandit approach to personal news article is the one that we have implemented. So lastly, when to use or not use bandits? So if you have a short experiment like a Facebook campaign or a marketing campaign and you have a couple of different versions to experiment, let's say you have a message like sign up and get 30% off or please sign up and get a discount of 30%. And you don't know which of these versions would give you a maximum conversion. You don't have time to run an A-B test and come up with a better version. So instead, if you put bandit algorithms there, it automatically optimizes for the better version and redirects the traffic to that version. Also, if you have continuous experiments like in console, we have doctors who keep adding, who keep moving away from the platform or their behavior keep changing as we were looking at Bob. So if such a case is there, you can use bandit algorithm which continuously adapts and tries to optimize for the conversion. So when not to use for bandits is when you want to run any test and want to infer which version is better than the other. So usually bandit doesn't care about what is the version, just cares about the conversion. So A-B testing should be preferred there. And like one example is clinical trials. Yeah, that's it. Questions? So you mentioned about the bandit algorithm and the A-B testing where the trials where A-B testing started improving after a certain number of time frame in one of the slides. So when do you think or what do you tell as a cutoff that you will say that I want to switch from A-B to bandit or what is the time frame that I'm looking at? Usually it's used as an alternative to A-B testing. So A-B test, all it does is it finds out that version which has highest conversion and just shows that to 100% of your traffic. But do we need to show that same version for every user? Maybe the other version is close enough or maybe if you use more traffic, the second version might be better than the first version. So bandit algorithm tries to adapt to that. So instead of A-B testing, it's an alternative to implement. It's not like after A-B you can use bandit. Yes, that's a random example. But the number of samples depends on the power of the test you want to have. The difference between the options, if both the versions of your website are roughly the same, then you need to look at more data to make a conclusion which version is better. So bandit would be better because it automatically tries to maximize for the conversions. It doesn't look at which version is coming out to be good. But if you want to have a stable website where you just want to have one stable version, then you can choose A-B testing and have that version. We'll take another question. You can take this offline. Any questions? So basically when the algorithm converges, it's more or less like an A-B testing, right? I'm sorry? No, when the algorithm converges. So then it's essentially the performance would be like A-B testing. So when the algorithm converges, you would still be having some amount of exploration happening in bandit algorithms. Always you would have some percentage of traffic which would be assigned a random page or a lesser efficient page just in case the behavior changes. You can actually set that in the bandit algorithms to explore less as you collect more data. More questions? Hi. My question is about the exploration probability. Two points. One is how do you determine the rate that you reduce the exploration probability? How do you determine that over time? And the second, how does the context of the problem influence the size of the exploration probability? For instance, if it's about news, in some ways people might have more of an inclination to explore more. So they'll tolerate. I didn't get your second question. So the size of the exploration probability, how do you think about the influence of the context of the problem on that size? So if you take news for instance, news are probably more open. Say one out of five to have a random article. It doesn't hurt me that much. And there's every chance that it's something that's exciting. But say in something like getting medical advice. One out of five cases if you give me a doctor who's not the best qualified to answer that problem, I'm probably going to be more upset about that. I'm just going to do more damage. So how do you think about the context influencing the exploration probability? So if I understand it right, how do we define the exploration probability based on the context? So how does it shrink our time? So if you are sure that all the arms, all the multiple arms has got enough samples, so you can have simple measure like you can use any log of N or anything very simple like entropy, anything, which can give you some measure of how much data has already been seen. If you have like 1000 samples or 2000 samples already collected, that can give you a measure of how much exploration has already done. Based on that, you can reduce the exploration probability. So it's a 0.1 divided by log of N, something like that. It keeps decreasing as the N keeps increasing. So the second question, it depends on the context and the conversion probability. So how critical is your conversion probability? In a health case scenario, the conversion probability might be super critical than other contexts. So it depends on the use case. Last question, anyone? Cool. I guess that's it then. Thank you, Santosh. That was a great talk.


Hi everybody, this is Dave Vellante, and we're here at wikibond.org headquarters. This is theCUBE, SiliconANGLE.tv's continuous coverage of all things tech, enterprise tech. We bring you the smartest people we can find, the smartest nodes out there. We extract the signal from the noise and package it and provide it to you, our audience. Thanks very much for watching. We're here today with Ed Walsh, who's the vice president within IBM's storage division. He runs the portfolio strategy. Ed, welcome back to theCUBE. Great to be back. So Ed, we hear a lot about Smarter at IBM, Smarter Planet. We saw the integrated systems announcement that Steve Mills made several weeks ago. What's Smarter Storage? We're hearing a lot about that these days. Right, so it's a natural extension from Smarter Planet to Smarter Computing. It's really our approach to designing and deploying storage infrastructure. So what is it? Yeah, so basically it's three core tenants that you can look to IBM in their storage that we're putting into all of our products, but also where we're investing, where we're announcing, in fact, with Smarter Storage and also where we're going with it. We're doing a lot of statement and direction showing you where we're going with the portfolio. But the three tenants are Smarter Storage is efficient by design, Smarter Storage is self-optimizing, and Smarter Storage is clad agile. Okay, so efficient by design. Sounds good. What does it mean? Yeah, pretty straightforward and simple. But basically it's a, you know, Smarter Storage should help you deal with the cost and complexity and also just the capacity that you're seeing in your environment. Are there products associated with efficient by design? So again, I can talk about the entire portfolio. So if you talk about it from disc or tape, I can talk about the V7000, DS8000, XIV, specifically one product that would be high level, which is something that only IBM has would be our real-time compression. What we've done is actually taken compression technology and allow it to not have the downsides or the optimization issues with performance. So what you can do is a software key, turn on compression, get two to five times more storage out of your production, fiber-general environment, without any performance degradation. That's something that's 35 patents, a lot of engineering, and no one else in the industry has. So, okay, how about self-optimizing? Let's talk about that a little bit. Drill down, give us again examples if you could. Again, I could go across the different technologies, but the biggest trend right now is Flash Optimize Infrastructure. So Flash is everywhere in the storage infrastructure. It's at the server level, the network level, or end of the storage level. And across our storage portfolio, they're all Flash Optimize from a disparate perspective. Of course, we have tape products as well that would be optimized, but when we're talking about Flash Optimization, our entire disk portfolio. The key thing is how to coordinate the cache. So if you're caching a server, the network and the storage simultaneously, how do you coordinate that for efficiency? You don't wanna double-cache things, but more importantly for actually data availability. What you don't wanna do is have a virtual machine fail from one server to another, and you're caching locally in the server, and literally you have either a stale read or a stale write, which is a corruption. So it's a coordination that we're doing. So what you saw us is actually show how we've extended Easy Tier, which is our tiering up to Saucid Disk on the storage layer, and extend it into both using either network or PCI cards, standard PCI cards in any server platform. Okay, now in Cloud Agile, is there infrastructure associated with Cloud Agile? Good one. So again, I could go through our portfolio and show you where we're working in cloud technologies and robustness that we're talking about with Cloud Agile. But I would put in general, the best one would be PureFlex. You mentioned the Expert Integrated System announcement we made in April. The industry is looking for private cloud, and basically what they're asking for is, can you help me consolidate my environment, have a single way to manage it, give me agility across my environment? What the industry responded with, which I'll call private cloud version 1.0, was how to consolidate your VMware and only VMware infrastructure. And typically when I'm in environment, I said, well, how many people have VMware and everyone raises their hand, almost everyone? And I said, but how many people only have VMware? And literally it's crickets, almost nobody. So what IBM has done, which is smarter, but it's our PureFlex system, is basically integrate that you can integrate your environment, which is, yes, VMware, but also other virtualization platforms like Hyper-V, Zen, and PowerVM, but also physical workloads like Linux or AIX, as well as Microsoft platforms. It also can do both x86, but also power platforms. And bring that together with systems, virtualization, applications, a network, and storage all together. And smarter storage is part of that package built in. But what you're able to get is what you're really vast for. Help me get my private cloud, I'll call it private cloud 2.0. How do you bring it together under one roof, one management, and only IBM's delivering that right now? It's actually very difficult to do, but again, only IBM is able to deliver that. Well, it's hard because there's a lot of diversity. And my understanding is you guys have codified a lot of the knowledge that you've had in your field engagements. All right, well, good. Well, thanks for taking us through smarter storage. It kind of completes the picture at IBM. It sure does.  We've got a bunch of analysis on these and other announcements, so please do that. And Ed, thanks for coming on theCUBE. Thank you. Okay, everybody. That's it for now. Keep watching for more information from SiliconANGLE.tv and Wikibon. Thank you.


Blanket, the HP Moonshot Announcement, a disruption in the data center, a revolution in servers, a game changer in this new modern era of software-led infrastructure, big data, mobile apps, cloud mobile social. I'm John Furrier, the founder of SiliconANGLE.com, and I'm joined by my co-host. I'm Dave Vellante of Wikibon.org, and Carl Freund is here. We've had Carl on before, and November, Carl, in 2011, you were on. Carl's the Vice President of Marketing at CalZeta. We were here at the original Moonshot. Actually, we were out in front of Bill and Dave's office, and that's a welcome back. And lots been going on since then. CalZeta's been out finding new markets, so give us an update. Yeah, it's been really exciting. I mean, that was a momentous event, sitting outside of Bill and Dave's office, and it was also a momentous event just because of what HP stood up to do, which was to really change the way servers are designed, the way the software is envisioned for these very efficient new processors that are just coming onto the market. Since then, we've been working very closely with HP in the Discovery Center and with customers to really understand what these new arm-based SOCs are good for in the data center. Real processors that can significantly reduce power, significantly save money and acquisition costs, and reduce the amount of footprint and space in CO2 emitted in the atmosphere to run the data centers needed for internet of things, for social media, and for the explosion of big data that everyone's experiencing. Bottom line is people are starting to realize those benefits. Still early days, with the HP Moonshot system being announced today, we look forward to getting these out in larger quantity and to more customers around the world who have both environmental power and space and budgetary constraints. So you mentioned SOC stands for System on a Chip. What makes it a system on a chip? That's a good question. So traditionally, when you design a server, any kind of computer, you start with a processor around which you add other chips, sort of a core electronics complex, as it's often referred to, and each one of those chips consumes power, each one takes space, each one costs money. And SOC takes those chips, both digital and analog, and puts them on a single chip, and that's really the uniqueness, is the combining of digital and analog circuitry of an entire server, which normally takes a big board like this and consents it down to a single chip. And what that allows us to do with the SOC is to literally put four of these on a board and be able to say, this is not a server, this is four servers on space that's less than you would normally have for a single server. Now, this doesn't have the same performance characteristics, but it has much better power characteristics. So for certain workloads, a processor like this could save a lot of money, save a lot of space and a lot of power. Carl, we were at HP's at labs, we were right in front of Bill and Dave's office last time when Moonshot was first launched, and we were blown away, because obviously we were covering the big data market and we saw the need for massive scale out industry standard or commodity servers, whatever people want to call it, but really proliferation of servers, but what that does is it brings up the issue of utilization. So when we talk to some of our friends and customers of you guys in HP, they don't have a utilization problem, you have massive scale out open source environments. So having this ARM-based architecture really makes a difference. So can you elaborate on that specific use case and then also the ecosystem around this massive sea change of servers? Because this is a significant change over when you're talking about server functionality at a high level of compute and this utilization issue where you can have all this massive scale, but then the utilization everything's not 100% utilized, so there's a lot of management involved. So talk about that and then the ecosystem. No, it really starts with software. I mean, the hardware gets all the headlines because there's shiny objects, but the fact of the matter is it starts with a software revolution. This has been going on for a long time, over 10 years, where software can be written to take advantage of a scale out infrastructure that's been enabled by better networking technology and so forth, but it is really the software that changes things. Whether you're talking about software-defined storage, software-defined networking, or software-defined servers, it's all about having software that can scale out very efficiently, avoiding the so-called Amdahl's law of being bottleneck in setting up the scale out, okay? And so this was all pioneered by the Googles and Facebooks of the world, but is finding its way into really data center. Let's drill down on that because this software is kind of an ambiguous term now because most people think of software engineering. You go to school before it's computer science, but also there's a lot of double E, electrical engineering software that goes on at the chip set level. So let's take us through some of the use cases that you see that's enabling because if what I see happening here, what you're saying is this new innovation layer at the low level of the stack is going to enable software paradigms on top of it. We're seeing it already with software as a service, but in the cloud game, it's platform as a service and infrastructure as a service. You see open stack is getting a lot of traction. Can you explain that software dynamic and why it's different now? So I think what's different is that you're dealing with code that is built on interpretive languages like PHP that is extremely efficient and scales out very, very well. So it doesn't assume it's got a big vertical stack. It's not bound to an OS. It's not bound to an OS. It's not even bound to a processor. And so it's very portable. It's very efficient and more importantly, perhaps it makes the programmers more efficient, right? So if you look at PHP code, it just almost reads like English. And that's the whole concept is to make it simple for the programmers to deliver new innovation. Now it just so happens that those programming models are very efficient at scaling out. So the current model is you scale up with virtualization. So you take your virtualization layer. I'm gonna put lots and lots and lots of images of these PHP or Java or whatever you're running. And I'm gonna stack them up on a virtualized processor. Problem with that is several, full. One is that you're paying a, what's called a virtualization tax, right? Secondly, you've got a pretty big node there. And if it fails, your failure domain's quite large. So in terms of the user impact of failure of a processor or a storage device, it could impact hundreds of thousands of people. Whereas if I have a smaller processor, a finer-grained compute element, then the failure domain can be quite small. You can imagine the impact on this on something like cable on demand, movies on demand, or you can imagine this things like websites or analytics on Wall Street, where if one little element of the analysis fails, it's not a problem because it's a small impact. So failure domain as well as the efficiency of the programmer, I think what's really driving this attractiveness of the scale out infrastructure. And when you look at what the web scale folks have done, people call the hyperscale space, they really, they've tried to eliminate almost completely human involvement when something fails. They just sort of ignore it and keep going. And then eventually every component in the rack fails and they just throw the whole darn thing out, right? So that's a completely different mindset. But the specific question I have is, so originally we were thinking, okay, this class of system is gonna go into that hyperscale space. And it clearly is. I know you've got probably 100 plus systems out there, but what else have you found in terms of other potential markets for your product? You know, one of them caught us by surprise.  And we're like, eh, nah, I don't think so. We really focused on the web tier in terms of running applications and websites. And you didn't think so because storage is spinning disk and it's not this consuming power. Exactly. But it's not a power playing storage. Exactly, exactly. So you look at how much power the spindle consumes and you look at how much, you could take the power of the processor to zero and nobody's gonna care.  Okay, so why the interest? And so what's happening is that the, by moving processing power out of the server and actually embedding it in the storage array, you're gonna be able to have a much more efficient platform than buying a traditional NAS or SAN. NAS or SAN is basically 10 and 100 time, respectively, more expensive than a spindle. So if it costs $500 for a spindle, it costs $5,000 for a SAN and it costs NAS and it costs, you know, $50,000 for a SAN. It's expensive. It's gonna roll a thumb there. And so building them yourself, sort of like do-it-yourself NAS, for these hyperscale guys can make a lot of sense. And what we're finding is that because our chip has five SATA ports, it's kind of geeky here for a minute, but it's got five channels to disks and it's got five 10 gigabit links to the network, it's basically a big, fat pipe to all that data you're storing on your disk drives, whether it's family photos, whether it's video on demand, whether it's imagery or seismic data, whatever it is, data you probably write once and read periodically. And so what you need is a big, fat pipe to write and that's what our chip provides. Okay, so it's not the power, it's the ability to get data on fast and the bit bucket, as they call it.  Carl, talk about the folks out there who aren't in the weeds on the technology side who are just general observers of our program. What this really means to the marketplace, I mean, there's a lot of them, and I was just commenting last week about the Goldman Sachs analyst who downgraded HP, kind of called him out on Twitter, kind of didn't, kind of said he didn't really know what he was talking about, because he really downgraded him because of the shipments of the PCs and the sort of Windows 8, the normal stuff that, you know, the one level deep, but this announcement really speaks to an arc that goes out five, 10 years, maybe decades in terms of a seed change. It's kind of an inflection point, as Dave Donatelli pointed out. So can you explain to the folks that are out there that you might see at a cocktail party or general people who aren't in the weeds technically, what this means? You bet.  Nobody did, but if you look at what's available now from a Samsung, HTC, or Apple today, those are amazing computing devices and they're generating a lot of data. Now take that to the next level and look at your automobile and all the information that is being collected by the sensors around your automobile. Now, connect everything together, including sensors on the highway, sensors in your car, sensors in your phone, all of that data is incredibly useful and can really help the world be a better place to live, quite frankly. But also the power requirement too.  Well, and what's happening is all that data has to reside someplace. All the analysis of data has to be done someplace and so the demand, while the revolution appears to be happening in the mobile space and in the internet of things, it's causing a revolution in the data center because you've got to deal with a lot of data. Where are you going to put it? How are you going to, how are you going to have to get in the car? Which is storage, right? You got to rack and stack. The old way was just build some, you know, new racks. They'll do racks. Well, guess what? You run out of space. You run out of power. You run out of money. So the revolution on the mobile side is causing a revolution, has to cause a revolution in the data center. There's no choice. It's got to change. And that's what this is really all about. So when you think about Calzada and it's sort of mission, a lot of people just immediately go to, okay, it's going to completely change the existing x86 market. And now we've been talking about these new markets. So how do you see that shaking out? Is it the case where the new markets are sort of interesting and growing faster and then eventually these systems on a chip become more capable? Or do you see this as more just incremental? No, I think it's not incremental. It's pretty radical. And so as a result, it's probably not for your run of the mill application stack. You're not going to put Oracle Rack or IBM WebSphere on an ARM chip anytime soon. What will happen instead is you have these areas of the market that are growing extremely fast. Big data, media serving, web serving. And they are kind of green field and the code moves over pretty quick and easy. So that's where we'll see traction first. Meanwhile, as you say, the technology will mature and it will intercept the point of sufficient performance and software ecosystem maturity that it can become a viable platform for increasing number of applications. So it'll start out kind of nichey, right? And over time, those niches get big and then there's adjacent niches as we deliver more performance, as we deliver more memory, as we deliver more bandwidth. But when that happens, what you'll see happening is the software around it matures at the same pace. And so I think most industry analysts predict that by, let's say, 2016, this could be 20% of the server market, roughly. And there are plenty of ARM vendors out there that will be vying forward competing for that market each with their own unique approach. The ARM business model enables a lot of this innovation because you start with readily available intellectual property that allows you to build a specialized product, okay? So if you look at what TI's doing with their DSPs on ARM, if you look at what we're doing with our fabric switch on ARM, they're very different solutions targeting very different markets. And yet they're all built on the same common code base which is standard Linux, thanks to the compatibility of the ARM instruction set. Yeah, so part of your job is figuring out customer requirements. And as you said, some of the stuff is in Nitchie and there are a lot of different Nitches we were just talking about. I mean, obviously power is one theme, but as you just gave in the storage example, it really wasn't, power wasn't the driver. So what patterns are you seeing? And maybe it doesn't matter because you've got that common code base. I wonder if you could talk about that a little bit. Well, the main patterns right now are just experimentation, right? So universities around the world get access to our hardware and they find new things and potentially open new markets. So a good example is in the life sciences field where the genomic codes are exploding because genomic sequencing has become relatively inexpensive. You can go to your doctor now or a local clinic and have your DNA analyzed. And now the question is, now that I know what your sequence is, what does that tell me about your health or likelihood of health situations down the road? That analysis takes a lot of compute power and it turns out that compute power scales out really well. It's primarily integer based, not floating point, doesn't need a lot of memory. So it's a perfect example of a new kind of application that is a good fit for these small fine grain processing elements with ARM. How about the big data crowd?  Or is it? It is. I think our second generation platform called Midway, which will be out later this year, that's probably the target for big data. Our current product, it's great for web serving, it's great for media serving, it's great for storage. It doesn't really have enough memory for big data in the large. So we'll be getting that second generation product out and that should be a really good big data platform. Why? Because it's low power, it's low cost, but it's also got a lot of IO. Big data is all about moving a lot of data around. And if you can do that more cost effectively, you're gonna save a lot of money. Yeah, okay, and maybe I said before you got 100 plus systems out there and sort of rough estimates, but can you give us an update on where CalZeta is at as a company, what kind of progress you're making, what you're shipping and when you're shipping? Yeah, we're Austin based. We do have offices now in China and in Silicon Valley. We're at about 115 people right now, mostly hardware and software engineers. We've had this product in market, it's been shipping in volume now for about six months and our second generation product is in the engineering labs now and we'll be shipping that out in the second half of the year. That'll basically double performance, increase memory capacity, bigger, better, faster, but in this case not bigger and not more power hungry. And then we have a roadmap that goes beyond there to our 64 bit product will be available in the first half of next year. So we're pretty much in the pack, in the middle of the pack of the 64 bit players. We do have the ARM A57 license. Our strategy is to use standard ARM cores, not create new cores, which may or may not be compatible with the ARM standard. And therefore we focus on everything but the core. We think the core is a commodity and so we focus on things like our fabric and our management, which helps reduce costs, not only of the processor, but really of the entire electronic complex and even the top of the rack. And so your products at the second half of this year and the 64 bit will ultimately find their way into moonshot cartridges, is that right? We're working closely with HP to make sure that the HP customers have access to the latest ARM technology from Calzada. Carl, my final question, we're coming up on the time limit here, but it really has to do with kind of the future of the business side of this, which is obviously success and penetration and actually putting it. I think it's going to be a big game changer. Both just on the energy side alone is a winner to the point you mentioned about telematics and what's going on around the world with data and obviously the footprint issues and performance. But in terms of the ecosystem, always when you've seen these seed changes from the early days, you had an ecosystem that's floating at all the boats underneath the technology.  No, the ecosystem is primarily an open source. This is really about open systems. You look at things like open compute sponsored by Facebook. You look at the kind of software people are running on our platform today. It's all open source and people that can build on top of that open source platform are the ones that are changing the environment. This is not about replacing your current SAP stack. This is about hosting what's going to replace your SAP stack in the future. And so I think that's what has to happen here is that as people's applications move to the cloud, as the Internet of Thing is hosted, basically its data sits in the cloud, then it reduces the complexity of the overall and breadth of the overall software ecosystem to something that's more manageable and actually can be lifted up, put over on a new platform, in this case ARM. And the software paradigms are changing to support this new kind of revolutionist modern era that we're living in. Amazing announcement, I'm really bullish on it. I think it's going to be a boon for entrepreneurs out there as well as the ecosystem. I think it's early, not even the first inning, it's warm up. It's like spring training for this market. So Carl, thanks for coming on theCUBE. My pleasure. We'll be right back to SiliconANGLE Wikibon's exclusive coverage of HP's moonshot announcement on the ground, getting all the action. Here in New York City, Times Square, I'm John Furrier with Dave Vellante. We'll be right back with our next guest.


Good morning from beautiful sunny Budapest. I'm here until the 22nd, including the 22nd of a month. And those of you who want to have a private counseling session, please write to me on my email address, Samvaknin, that's one word, Samvaknin, at gmail.com, that's Samvaknin at gmail.com. Today, as usual, I'm going to disambiguate. I'm going to help you make sense of the nonsense spewed out by self-styled experts on YouTube and elsewhere. And who am I? My name is Samvaknin, and I'm the author of Malignant Self-Love, Narcissism, Revisited in many other books. I'm also a professor of psychology. So let's get right to it, shall we?       Don't keep us suspended. Let us have the real goods. Sexual identity comprises self-perception. It's how you perceive yourself sexually. Usually, it sits well with gender roles. So you would perceive yourself as a male or a female or one of the other seven sexual identities that have been identified. If you perceive yourself as a male, you could still have a sexual orientation that is, for example, homosexual. You could be a man who sleeps with men or a woman who sleeps with women. That is distinguished from your sexual identity.   Sexual identity is about yourself. Sexual orientation is about others. It involves object relations.  So this is the chain.   Sexual identity forms in five stages. Stage number one, in projection, internalization and in projection, also known formally as identification. You interject as a child, primary objects, usually mother and father, but you also interject role models, including siblings and peers. At the end of the day, you have a library of intrajects, representations of other people in your mind, and they all speak to you. They all advise you. They all accompany you in life. These voices can be healthy. These voices can be unhealthy, partially critical and cause dysfunction. Intrajection is a very important stage. The child intrajects his parents when he starts to separate from them and individuate. It's the child's way of maintaining a merger or a fusion with the parental figures and with other meaningful others, even as the child realizes his or her separateness from these people. Separating from people on the psychological level even is traumatizing. It's very frightening. Intrajection is a defense against this. You separate from mother, you take on the world, you become an individual, you develop a modicum of grandiosity known as primary narcissism, but you want to keep mother with you for life. So you introject her.  This is intrajection.  They become identifiable sexually and gender-wise. The second stage is emulation. Emulation of a parent. It could be a same sex parent or an other sex parent. Emulation is affected by the parent's behaviors.     Emulation is the imitation of a parent. It is a layer, a layer added to the intraject. The intraject becomes fully fledged, fully fleshed, three-dimensional. Now the intraject resembles a person very much. Indeed, it is at this stage that the child begins to realize the separateness of other people. The child begins to develop object relations, relations with other people. This stage gives rise to interactions with other people, some of which have a sexual dimension or a sexual nature. Everything is infused with sex and erotism. At the initial stage, the child is auto-erotic. He has auto-erotism. He is sexually interested in himself.  Gradually, as he separates from mother and father, he redirects his sex drive at them. He becomes infatuated with or he develops limerence with mother and father. This is the Oedipal complex. Every child, male or female, becomes emotionally attracted to both parents of the same sex and the opposite sex. At this early stage of life, prior to age three, the child is pansexual. He is not a boy. She is not a girl. The child is just a child.  He also has an identity which is not yet fully formed. There's no core. The child redirects this energy which is not focus-like. It's not laser-like. It's more like a light bulb. He redirects this energy at anyone within range, father and mother included. The stage of emulation is the initial choice to focus on one of the parents as a model. This process is known as modally. A parent is a good enough parent if the parent is not a dead parent. For example, a dead mother if the parent is not narcissistic, not psychopathic, not absent, not rejecting, not harshly critical. If the parent is there for the child catering to all the child's psychological needs, the child is likely to emulate this specific parent. So emulation leads the child to develop an introject of the parent which is also sexual in nature and which allows the child to progress into the next phase. The next phase is exploration. The first two phases, introjection and emulation are critical to the formation of sexual identity. The next three stages are about other people. Introjection and emulation are internal processes. Entirely within the mind of the child.  All the other stages in sexual development are other-oriented. They go hand in hand, as I said, with object relations.  And it starts with exploration.  Remember that the child has a series of introjects in his mind. He settled on one of the parents because that parent provided him with maximal attention and catered to his psychological needs. But the child is pansexual.  The child would let anyone in sexually or erotically, and that includes himself. There is an interim phase between narcissistic libido and object libido. In other words, there's an interim phase between directing the sex drive or the energy of sex towards oneself and then redirecting it towards others. It is in this limbo that the child finds himself or herself and the child needs to explore.  And this phase of exploration is indiscriminate and it is also pansexual. There is still no sexual differentiation. There are definitely no gender roles. Gender roles are performative. They come much later in a phase known as socialization.  Therefore, children at this age which is anywhere between 4 and let's say 9 or even later 12, children at this stage explore all forms of sexuality. It is very common for children to have homosexual experiences even if they turn out later to be rabid heterosexuals and homophones. It's very common for children to experience a variety of sexual acts with a variety of sexual partners of all sexes.  Identity diffusion is a phase in adolescence identified by or first described by Marcia, M-A-R-C-I-A, excuse me for the expression. Marcia augmented the work of Eric Erickson and so identity diffusion is when the person had not settled yet on a clear set of beliefs, views, cognitions, emotions, behaviors and reactive behaviors. The person meanders all over the place. The person attempts to try his own. These guys, the dead guys, the person becomes other people by imitating them, especially role models. So within the phase of identity diffusion sexual exploration is very common and very, very healthy. Now we have a sexual identity determined via introjection and emulation coupled with the outcomes of exploration. These three processes which follow each other chronologically begin to point at sexual orientation. But there's still one more stage to endure before one settles on a lifelong orientation and that stage is sublimation. Sublimation is the conversion of instincts including the sex drive into socially acceptable and socially condoned behavior. Sublimation is a defense offered by the ego. The ego in Freud's work is supposed to provide an interface with reality and warn the child or warn the person about the consequences of his actions. Structures similar to the ego can be found in other scholars in psychology such as Bandura, Ericsson, Marcia and others. There is a psychological construct which provides reality testing and this construct whatever its name may be also tells you this is the way you should behave and this is the way you should not behave. This is right and this is wrong. This would lead to bad outcomes this would lead to good outcomes this is self-efficaceous this is not. Sublimation includes socialization. Socialization has two components scripting and enculturation or acculturation. Socialization is teaching the child how to become a social being a social creature teaching the child how to behave socially and how to not misbehave socially. Socialization includes scripts. Scripts are recipes of behavior behavioral recipes how to date a girl how to socialize with peers what to do and what not to do in a variety of settings circumstances and environments these are the scripts in addition to the scripts there is a culture when the child is introduced to various tenets and principles of the child's dominant culture and dominant society. All these together are mediated are brought to the child by mostly the father it is the father whose role is to socialize the child acculturate the child help the child sublimate drives such as the sex drive one of the main messages in sublimation is that certain sex partners are forbidden or inappropriate for example mommy or father daddy another element of socialization is the acquisition of sexual skills and a very important element is the inculcation and incorporation of sexual scripts an example of a sexual script is dating hookups how do you do a hookup what's right and what's wrong in a hookup how do you do dating etc etc so this whole process is sublimation and finally all the above lead to orientation sexual orientation is settling on a pattern of mate selection and attendant sexual behavior behaviors so there is a process of mate selection from a pool of eligible sexual partners these eligible sexual partners will have been determined by the previous four processes their gender, their sex and other attributes will be subject to the emulated interjects of the socialization of the child turned adult and then there are the attendant sexual behaviors which go with mate selection each and every one of these stages can and very often is alas interrupted disrupted when these stages are disrupted it leads to psychological problems and dysfunctions is disrupted when the child is unable to form functioning fully full-fledged interjects with clear voices unequivocal non capricious voices just voices secure base voices when the child is unable to create such interjects this would yield this would bring on a confusion between internal and internal objects a disruption in interjection would yield to two solutions one of them is the false self grandiosity narcissism and the other solution in the case of disrupted interjection would be borderline the narcissistic solution is to confuse external objects to believe that external objects are internal the borderline solution is exactly the opposite an inability to maintain long-term stable interjects internal objects and to focus therefore exclusively on internal objects for external regulation a disruption in the phase of emulation would create a lot of sexual confusion the child would be unable to settle on a single parent in order to model himself or herself on that parent and because the parents are opposite sexes in the majority of families this would create an adherence to both sexes rather than the same sex or another sex and this of course results in bisexuality so homosexuality which is now widely considered to be a genetic hormonal state something which happens in the womb homosexuality is more responsive to the later stages bisexuality is determined during emulation during failed emulation when the child is unable to say I'm going to be like father so I'm going to be a male like mother the child ends up being both male and female a failure in the stage of exploration is exceedingly rare and it usually leads to a total suspension of the sex life these people start to masturbate very late and have sex in their 40s or 50s stalled exploration thwarted exploration means that the child's sexuality remains unexplored and as the child becomes an adult he remains an eternal explorer unable to move on to sublimation or to the implementation of sublimation and to develop a full scale sexual orientation a failure in sublimation is where homosexuality would tend to manifest again I emphasize that the current orthodoxy is that homosexuality is a hormonal residue or result of hormonal imbalance in uterus in the womb and has a genetic component this has not been the view and still is not the view as far as I know among most psychologists starting with Freud who suggested that homosexuality is the outcome of a domineering, overwinning overwhelming and over involved mother a result of castration anxiety a result of and continuing with Bieber and other scholars they suggested that an absent or rejecting father has a role to play in this and so on and so forth so psychological antecedents and etiologies for homosexuality are an integral part psychological or have been an integral part of psychological thinking for well over a century I think discarding this this cargo who discarding this legacy is stupid I do think that homosexuality has pronounced psychological dimensions I'm not saying that it's entirely psychogenic but it involves psychological processes Freud's suggestion that all homosexuals are narcissists because they make love to a mirror image of themselves in other words Freud's suggestion that all homosexuals have narcissistic libido rather than typical or healthy libido this suggestion has been largely discarded but it's an example of an attempt to explain homosexuality in light of very early childhood processes when when sublimation fails the child is unable to form a full fully operational sexual orientation because sexual orientation is relational it's about other people and other people are organized in what we call society if socialization fails the person is unable to function in society properly so the person develops a sexual orientation which is either defiant negative identity formation I'm not going to be like my father I'm going to be homosexual or the child develops sexual orientation which is in essence frozen for example asexuality sexual orientation as the last phase requires the successful completion of all four previous phases one last comment about narcissists and borderlines this has nothing to do with sexual identity narcissists and borderlines are a perfect match and this is very surprising the narcissist tends to devalue and discard his partner while the borderline has abandonment anxiety ostensibly the borderline would never select a narcissist as a partner because abandonment is guaranteed rejection is a foregun conclusion why would a borderline risk that because she also have engulfment anxiety the borderline needs a partner who would at some point push her away give her personal space let her go the borderline she experiences intimacy she feels bad she feels suffocated and smothered she wants to run away approach avoidance repetition compulsion so she needs a partner who would at first idealize her and thereby ameliorate her abandonment anxiety but then when she feels engulfed and enmeshed and digested and assimilated when she wants to run away to avoid would be able to discard her by devaluing her she needs both functions and the only intimate partner who provides both functions reliably is the narcissist in the initial phases of the shared fantasy when the narcissist love bombs the borderline idealizes the borderline it reduces her abandonment anxiety and in the later stages when she needs it the narcissist the narcissist is available to push her away to get rid of her, to devalue her and discard her and then the cycle starts all over again because narcissists need to hoover and re-idealize their discarded partners I dwelt upon it in previous videos that's just a side comment to one of the questions I've been asked so here in Budapest sunny, ethereal and beautiful I wish you all an excellent day with fully formed sexual identity sexual orientation and God willing sexual behaviors galore


Hi everyone, Joe for Jasper's casebreaks.com coming at you with a three-box break of 2022 FnX under wraps autograph baseballs This is random player break number 37 one spot gets you nine players each and one person will get the any and all Multi-sign baseball spot big. Thanks to this group for making it happen. Let's multiply you by nine And then we'll randomize names and players and we'll match up with someone There should be 171 on each list copy your names put them into this list Let's copy their names Put you into another list Put the results of the randomizer right there tab one. Let's roll it randomize it five and a three eight times for names and teams Good luck names and players. Good luck everybody one two three four five Six seven and eight We got Bobby B Down to e.k. Five into three eight times for the players one two three four five six seven and eight We got yas on Dominguez down to Max Muncie All right, so I'm gonna show you the list as is just so we can get this on tape And then I'll then I'll alphabetize by your first name so you can see all of your players Gathered together There you go Bob with the any and all multi-sign baseball spot, and there's the rest No It's sort by your first names. I like your group of players right there for Alan Bobby's got a bunch Putting that group right there Eric has a lot as well Eugene and there's Matt Matt B's team Steve H Think should have the rest He does There you go. All right. I'm gonna pause the video We're gonna open up the trade window when we come back when we see if there's any trades Then we'll have the three baseball break stick around be right back Alrighty welcome back everybody No deals are done Let's pop open these boxes. Let's see who's gonna get what And put your thinking caps on we'll play a little game of a who am I I'll give you some clues Can you try to guess who the player in here is? All right retired pitcher lefty Lefty retired pitcher with a lifetime 251 wins to 161 losses lifetime three four set three seven four ERA and ended up with 3093 strikeouts played with three teams my entire career a six-time all-star a world series champion an AL Si young award winner an ALCS MVP and a two-time MLB wins leader Who am I not Steve Carleton? Oh? I'm also currently 42 years old and went to Vallejo high school and was a 20th overall pick in the late 90s Pretty well-liked player. I think does he still do I don't know if he still does a does a podcast But he was doing a podcast for a little for a little while Played for he was drafted by Cleveland drafted by Cleveland played with them for a little bit And then was traded to the Brewers for a Matt La Porta Michael Brantley Zach Johnson and Rob Bryson I'm not sure I'm Michael Brantley turned out to be pretty solid, but I Don't know if that was enough for CC looking back And then played with the Brewers for the remainder of his career And then he ended up signing a 70 or 161 million dollar contract with the Yankees Spent the rest of his time there where he knocked out a major league wins with Major League wins leader ALCS MVP World Series champion. So That was the that was the right choice. That's right Kevin see see sabathia and that's gonna go to Steve H With CC. I'm gonna say it pretty well-liked player, right? Take a good look at the Autograph right there nice should have all the there's the MLB sticker. There's the fanatic sticker Nice one Steve Next ball player. All right. I'm an active left fielder DH That's left throws right In my sort of short career I Already have 98 home runs have a lifetime 296 batting average 283 runs bad in I'm an all-star. I'm a World Series champion. I'm an ALCS MVP All MLB first team a silver slugger and a rookie of the year Nice Kevin Kevin got it right away. You ward on Alvarez With a rookie of the year inscription on there. I like that nice, and you got all the the both hologram sticker sometimes It'll just be one of the one MLB hologram sticker, but most times it'll be It'll be the hologram sticker and the MLB hologram sticker and the fanatics hologram sticker That's gonna go to It goes to Eric Klicks for the yard on Alvarez. Very nice He's he's had a Pretty strong start to his career What's that? He was a I think he was a year after that scandal They're all involved All right. We got a nice one another nice one here another active player and another superstar A left fielder bats left throws left only 24 years old And I'm already have a lifetime 287 batting average 125 home runs and 374 runs batted in Played for two teams my entire career two time all-star a world series champion two time all MLB first team I think MLB is really trying to lean into that first team second team thing three times silver slugger a Babe Ruth award winner And an NL batting champion Who am I? We should have a theme song for who am I? Not Christian Yelich I think Yelich has a little more than 125 home runs Maybe he should should have like two something. I entered the league in 2018 I'm known for my exceptional plate discipline Ranking fifth all-time in walk rate and 10th all-time in career on base percentage I'm a hitters with at least 2,000 plate appearances at the end of 2021 I also became known for my movements in the batter's box After successfully taking a pitcher pitch for a ball. That's right. It's Juan Soto He's also pretty darn good probably in line for a big contract at some point Both hologram stickers there and Juan Soto is gonna go to Scroll up my list here. It's gonna go to Bobby. There you go Bobby that Juan Soto going your way. There you go gang Yeah, three really nice baseballs. I'm Joe for Jasper's case braids calm There'll be three more in the store. Check it out. We'll see you next time for the next baseball break. Bye