Hi everyone, this is George Cow and I'm happy to be here with Tessa Stowe We're gonna be talking about happiness today. And so let me first say hi to Tessa. Hi Tessa. Hi George Thanks for doing this. Good to have you here. Hi everybody. And what we are hearing is you might hear some birds because Tessa lives in the beautiful place. She lives in Bali, Indonesia, but let me kind of read you her bio So you all can can have a sense of her background and we'll go from here So a Tessa Stowe calls herself the happiness pathfinder She teaches people the essential life skill of happiness so that they're healthier less stressed and live longer and She got to be doing this because she was She considered herself the complete opposite and she realized that she needed to take action before it was too late and Tessa you're originally from New Zealand and now you live in Bali Yes, do you want to say a little bit about how it got you there? The abbreviated version is January of last year I just had this voice in my head telling me that I need to go away somewhere and think So I listened to my voice and I decided Bali is the perfect place to go and think So I went online found a place to stay which is the resort actually opposite to this one and I arrived and then the next morning I got up and I thought well I'll just walk into town or village and I came out and there was a road to the left Which we which was the obvious road because it went up to the main road And then there was a road to the right wasn't really a road path to the right and there were chickens Wandering around so I thought I think I'll follow those chickens So I thought the chickens down what I then you thought was maybe this is someone's driveway and I thought well they can always show me out, but I kept following these chickens and Sure enough, it was a driveway to someone's house but it was also the back of the resort next door and to cut a long story short I then met the owner of the resort and Within a few minutes of meeting him. He said to me oh Tessa, you might want to come and live here I'll show you an apartment and I thought this guy is Weird we've only just met he wants to show me an apartment, but I'm not rude so I thought I'll humor him So I went and looked at his apartment With just being polite Anyway, the long the short of it here. I am now living in that apartment It's amazing Worked out. Yeah, fantastic. I love it. I really really love it And I always enjoy seeing the photos you post of your life there on your Facebook So be sure to check it out as we're watching. I will link to Tessa's Websites and Facebook and things like that Oh, right. Yes. So let's get into this topic of happiness You know, this is something that you're you're passionate to help others with and and why do you think it's so important? I mean, yes, we all want to be happy But you're looking at it beyond just oh, I want to be happy. But what why is it important in your perspective? Well, it's it's essential for your survival. I you know from personal experience after my husband died from brain cancer and And And it was just first of all having the brain cancer was horrendous, but then after he died my You know any happiness I had just got sucked right out of me. I was I Was stressed out. Oh, yeah, I didn't want to get out of bed. I my health deteriorated I got shingles on my face on my eye specifically which can make you go blind I got severe birthsitis in my shoulder. I could hardly move I was drinking too much wine. I had all these thoughts of guilt I mean, I was just on a downward spiral to not a good place and Essentially I was I Was tricked into happiness back into happiness, but I realized I realized now from from From where I've been and where I am now I've realized if you know happiness is essential for your survival if I kept going the way I was going I Would have ended up with some terminal illness because it's it's interesting because we just not taught this stuff at all in fact that's I've become passionate about happiness and What it is and why and the way for that how we can actually Become happy because I actually think happy well I know actually happiness is a skill It's actually the central life skill that you can learn and the more you practice it the the better you get at it so but basically why I say it's essential is because We basically have George we have two sets of emotions or two buckets we can lump our emotions into so we've got like Positive emotions negative emotions or we've got safe emotions unsafe Stressed unstressed happy not happy. So if I gave you a list of emotions It would be really easy to pop them into a bucket which bucket. So and the thing is what's important though and it's not really well known is And we're not educated on is that these emotions in our minds Convert into chemicals in our body Okay, it's that whole mind-body connection George, which I know that you've you know very focused on yourself But the thing is they they are converted into chemicals into into your body and then the chemicals Caused specific things to happen. So if you're feeling And what are what like what's one or two of those chemicals that that we might recognize? Oh Well, it's more so you can go down in fact, I've actually sort of put in together a list of particular emotion What are the some of the chemicals you know like oxytocin? You know the the hug one Is is you know, that's a really Well-known one for example, but then it's more at a high level at a high level, which is easy to understand is that When you've got when you're having negative or unsafe emotions It converts into chemicals which say to your body Switch your body into survival mode and what that means is take away energy. That's focused on building your immune system Take away energy that's focused on growth and repair and put it all into Surviving the threat put it all into running away and surviving the side the saber-tooth tiger So your heart beats goes your heart beat goes faster your blood pressure goes up, you know Blood rushes to your extremities You get a flush of cortisol so that you you know, you don't feel a pain when you're running away from the saber-tooth tiger So all your energy the key point is that energy is taken away From growth and repair taken away from your immune system and let's face it that makes sense because What's the point in having growth and repair and what's the point in having? Your immune system if you don't survive the saber-tooth tiger so So on the other side of it when you have positive Emotions or emotions of feeling safe The chemicals that it produces in your body are ones that basically say to your body. Okay It's time to thrive and be happy. It's time to To repair, you know, it's time to build your immune system So you'll have all those really good things happen to your body and they're all triggered because of these You know happy positive emotions. So now um The thing is if you're stressed if you're in that stress state for too long and I was in that after my husband died I was in that stress state for you know A long time. I mean I was living in bed at one stage. I mean why get up, you know, no reason No energy either So if you're in that stress state for too long, obviously Because your immune system your immune system Breaks down because there's no energy put on it or very little you all can start to break down and and you get sick so I guess so it's interesting because stress-related diseases now are the number one cause of death and and So some say 70 percent some say 90 percent but between 70 and 90 percent In other words a big number of doctors visits are related to stress So in other words, we seem to be living in this stress state most of the time Which is really not good for your survival So the alternative is instead of living in a stress state. How about living in a Happiness state and the really good news is that You can learn The skill of happiness you can learn how to generate those Happy emotions so you can learn how to generate happy emotions so that your immune system is Is building up and functioning and you're in growth and repairs. So that's why I say You know, it's an essential light happiness is an essential life skill if you want to be happier first of all If you want to be healthier if you want to be less stressed and live longer because they're all They're all connected. That's so it's this whole mind body, so yeah, I'm I'm curious if somebody says well I'm not happy. I'm stressed out because there are things to be stressed out about in my life. Yes. Yes. Yeah, I'm I have income issues. I have health issues. I have maybe family issues I have time not enough time. What do you say? What do you say to that? Yes. Oh, absolutely. Oh my goodness I could I could I could sit here in barley And if I wanted to I'd get myself really stressed out What about stuff? So Absolutely, we all have issues in life. Yeah, uh, which cause us stress and and and I and Don't and it's not it's important. It's really important actually not to deny them Or suppress them but to acknowledge them And then Do what I call even though you're feeling stressed anxious Uh, even even depressed You can still do happiness exercises, which are like a um, I call they're like a brain hack because They're so easy to do and they're so they don't require much energy because let's face it if you're really stressed out And if you're depressed you don't your energy just goes as well So but the cool thing about these happiness exercises They don't take much energy. They don't take much time and they're kind of like a they're like a trick they're like a brain hack because they'll actually Just you'll do them you'll generate a uh, a happy happy emotion And and you'll generate that um that chemical reaction in your body and and if you do these happiness exercises correctly You'll you'll actually just feel good and you want to keep doing them. It's kind of like Yeah, they're they're exercises if you keep doing them. It's just amazingly impact they have So yes, acknowledge the fact that you're feeling stressed acknowledge the fact that you're anxious just but Do that do the exercises and most of the happiness exercises that I that I coach and teach are 60 seconds or less But even though they're 60 seconds or less they have a huge roll-on effect because Once you do some of it some of them and you think wow, that is great It sort of builds a momentum all of its own. You just have to be you just have to get started And you know, that's why I make them so they're so easy and in fact I work with pj fog who's the world's Uh leading expert in creating habits and I use his methodology for all for all the happiness exercises And then specifically when you want to turn some of the happiness exercises into habits so that you just do them Automatically, um, yeah, that's amazing. Yeah, that's it is and maybe you can give us One or two examples of what a happiness exercise looks like Well one that I do every day And this is it's this one is actually a habit as opposed to it Well, when I say difference between an exercise and a habit is Exercise you may just do once or four But a habit is that you just do it automatically. One of the ones I do is Is basically when I wake up in the morning, I put my feet on the ground I stand up and say Today is going to be a great day Yeah So I start that's how I start how I start my day You know, it's amazing. So that's that's a habit A great exercise a great ha a quick happiness exercise Is well, I when I teach happiness exercises, george, I start off There's three distinct camps or stages The first is happiness in the moment then there's happiness in self and then there's happiness in relationships and I start off in happiness in the moment because That's the easiest place to start you're going to get results really quickly And the thing is, you know, we have we're here in a body To the reason we're in a body is so that we can experience the world so that we can like experience Ice cream for example. That's why we're here in a body. And yet We seem to have become disconnected We're we know we're on our we're on our Bones You know on social media. We're everywhere except here in the moment and in the moment. There is just so much Joy and excitement. I mean where I live. Oh my goodness. I go out of my apartment.  You can probably hear the birds chirping But the only way you can Get happiness from that is actually if you are present to it Because those birds could be chirping all they like but if I'm on my phone or I'm thinking in my head or doing other stuff Just completely miss it. So that's why I start with happiness in the moment because you get huge You know huge bang for your buck and in a great way to be happiness in the moment. Oh my goodness ice cream Actually ice cream is not that easy to get here, but uh, it's like take ice cream You can eat ice cream Or you can experience ice cream It's a big difference In fact, I did a very funny video on Experiencing ice cream and this would be any food actually right any food any food, you know, it's like the very next food we eat Yes, you can just sit there eating it And just eating and not even looking on your phone and not even and somebody said I'm at the end of it What did that taste like and you've got no idea?  So it was all fine food But you had to you had you experienced it like suddenly food had sound food had like You know you're feeling with your fingers to try and work out because you try to work out what the food was and then the taste and the sounds and Oh my goodness. It was just amazing because that's what I call experiencing food And the guy that I was with um Who I met on this trip. I mean we still to this day Talk about that amazing experience And it's so it's there's so many of these experiences You just we just need to learn and it's a skill.   This is all about training your mind really training your mind to look at all the white space While acknowledging there still is the black dot Yeah, it's a way of life really wow So so that's interesting you you have these three kind of Segments of in the moment kind of like you start there Yes, and then you learn happiness in the self Yes, and what is that itself? Ah happiness itself. Oh my goodness Happiness in self. Well, see what is self and happiness is self in self is Basically two parts because we have this body. We have a fabulous body that we are here You know our body is our best friend. I mean it goes everywhere with us It's with us from birth until death It's with us through thick and thin I mean it does all these amazing things with us for us without us even asking So yeah, so that's a really important part of us is our body. So we have our body And we have our our self or our spirit our essence our personality that lives in the body So happiness in self is basically There's two subcategories. It's happiness in your body In other words loving your body Unconditionally loving your body And when you think of your body thinking feeling joy and gratitude And then with yourself, it's just happiness in yourself. So when you think of yourself You just think wow, I am enough. I'm amazing. I'm special So it's really just transforming Happiness and self is about transforming that relationship you have with your body And yourself into one of unconditional Love and just gratefulness and or and I am enough and I am amazing Yeah, I love I love happiness and self and then of course that makes sense to then happiness and relationship because yes Oh my goodness. You have to have the foundation of happiness within yourself before you can You can be happy in a relationship in a sustainable way precisely and also the other thing is By going through happiness in self and that the orders are very There's a you know, as you're saying to see the the reason Once you build up happiness in self Which builds up your self-esteem your self-confidence By the time you get to happiness in relationships Certain people are going to be really attracted to you certain people aren't But you're going to be more selective about who you hang out with Because let's face it. Georgia.  You become a lot more Selective because you are aware of what it's doing to to to you your body chemically You know people can chemically make you sick, you know, once you understand all these things so it just changes the dynamics and And when I say relationships, it's not just Relationships with like you're in a circle. It's the relationship you have with people you Interact with every day people. I have a lady. I buy fish from on the side of the road And we have she doesn't speak a word of English and I'm trying to learn some Bahasa But you know, we have a laughing joking Relationship I talk to everybody. I've got a friend who's just come to live here recently Um Sandra and she came back the other day and she said oh tesla. I went walked into town Village and she said I found myself talking to everybody like you do She said I never used to do that, but it's so much fun You kind of like because you've been through happiness and self and happiness at the moment You kind of get over yourself, you know And you just start to see the wonder and everything and the wonder and everybody and And you just chat to people and they chat back and it's just like amazing. Yeah, that's that's sounds like Sounds like heaven, you know So so how can people work with you people are watching this or like wow Um, what is it that you can offer to somebody watching this kind of um? Yeah, what do you what do you have? Take the next step. Yep. Absolutely. Well in january of next year. I'm going to be running a mini Clear it's january 2020. Yeah. Oh, yes. Oh, yeah, sorry january 2020 in fact about three times a year I do I run a mini class series on the essential life skill of happiness So you can be happier healthier less stressed and live longer So yeah, that's about three times a year next one is in january 2020 so the best way to be notified of that is to Uh, get on my email list, but what I suggest you do though is download my Yes to happiness roadmap, which basically gives you a high level roadmap for happiness and That way we'll you'll have the roadmap plus we'll get connected. So the way to get that roadmap is to go to Yes to happiness roadmap dot com So yes to happiness roadmap dot com great get that roadmap and that way we it's got all my contact info And there as well that way we'll we'll we won't stay connected Sounds great. Yeah, I'll put the link in the notes of the video, but yes to to yes to happiness roadmap dot com Precisely. Thank you. Thank you tessa for your work and for Being an embodiment of what you talk about Yeah, oh no, it's makes me very happy Yeah Yes, I hope this video brings happiness to others as well. Thank you. Thanks for thanks for inviting me. Yeah. Thanks for being here All right. Talk to you soon. Okay


You break it, you own it. That's what I was told as a child, but for today's billionaires, it seems like the opposite is true. You own it, you can break it, at least if you're rich enough. Just look at Elon Musk. He paid a fortune for Twitter and he's now busily destroying it, firing half its employees and driving out even more, causing chaos on the platform, making advertisers flee and threatening bankruptcy. Or consider Sam Bankman-Fried, who became a billionaire after founding the popular cryptocurrency exchange FTX until he drove the company into bankruptcy. Seems FTX was a Ponzi scheme that got out of hand. At least a $1 billion in customer funds is reportedly missing, probably billions more. These billionaires are presumed to be free from responsibility because they own what they've had a hand at destroying. So under the rules of capitalism, they have a right to do whatever they want with their money, right? Wrong. Millions have come to rely on Twitter as a vital source of information and connection. Investors put their money and trust in FTX.  You own it, you can break it, is a careless norm for a complex society. Do we really think that the super wealthy should be allowed to control so much wealth and wield so much influence and power? Absolutely not. We need stronger laws protecting the rest of us from the recklessness of these so-called disruptors.


Well, I'm delighted to visit North Carolina to pay tribute to one of the Tar Heel State's most distinguished residents, a lady who is very dear to Americans everywhere. Kate Smith is one of America's great singers of this or any other century. Her splendid voice alone has earned her a place in the history books, but it's also won her a place in our hearts because of the extra something that reaches out to the spirit as well as to the ear of the listener. Kate always sang from her heart, and so we always listened with our hearts. It's been truly said that one of the most inspiring things that our GIs in World War II, Europe and the Pacific and later in Korea and Vietnam ever heard was the voice of Kate Smith. The same is true for all of us, but I think the citation for Kate's Medal of Freedom, the highest civilian honor that our nation can bestow, says it all. The voice of Kate Smith is known and loved by millions of Americans, young and old. Those simple but deeply moving words, God bless America, have taken on added meaning for all of us because of the way Kate Smith sang them. Thanks to her, they have become a cherished part of all our lives, an undying reminder of the beauty, the courage and the heart of this great land of ours. In giving us a magnificent, selfless talent like Kate Smith, God has truly blessed America. So Kate, on behalf of a grateful nation, it's my privilege to present to you the Presidential Medal of Freedom, an honor that you have earned many times over through your singing, charitable work, love of country, and by being an inspiration to all of us. To some place else, but Kate.


Brother David, I don't know what to do. Open your Bible. Brother David, I want to hear from God. Open your Bible. Brother David, I want to grow spiritually. Open your Bible. How are you supposed to be rooted? How are you supposed to identify the lives of the enemy if you don't know the truth of the Word? It's time to get into the Word.


So actually, I was 84 the year I was there with Josh Glenn Russell. This was one of the moves that he got himself third in the country with. And then the following year I left, and he basically pinned his way through the season with this exact move. I think he had over 20 pins, something crazy like that. You know, he was the guy that came in and worked a couple of things. And I hear he was good on top, naturally, and then developed something even more offensive. This gets real dangerous if you're comfortable with it. Where's my partner? Just to find two and one thing, I don't want to go to a near side. There's a thousand ways to do table. You've got to find what works for you. There's no wrong, I'm going to tell you there's no wrong way to win a wrestling match at the end of the day. As long as you're working hard and doing nothing illegal. There's no wrong way to win. So everybody's techniques can be a little different. Whether you're under hooks, two on one, whatever it is you're trying to score with. We're all going to hit it in a different way. Body types, just at the end of the day, if you have confidence in something, nine times in a ten is good work. If you believe, if we're wrestling each other and you believe that this move is going to work, a lot of times, mind over matter is going to happen. You're going to have a technique behind it. But I'm telling you right now, that's why it's top top guys. That's some of the difference is the leaf. Going back to this real quick, I want to find two and a couple of things. Going up high. And coach asks you on the elbow, there's no wrong way. If I go on the elbow, that's fine too, whatever you like better. I just like, I've got longer arms so it tightens this up. And what I do in my elbow helps a lot by keeping this elbow out. If I'm just here, no, it won't work. There's no wrong way where to grab. Go over your elbow. Fine too, go ahead, step this out real quick. From here, I'm holding this ankle. I'm basically pretending like I'm falling over. That's what I'm trying to do. And then when I run this over, I get my lock, jump inside. He grabs another guy to ask too. He grabs my leg. All I'm going to do is sprawl. I'm just going to sprawl that out. Probably he'll fall at this position.  So again, don't worry what this guy is doing to you. You worry about what you're doing to this guy. Let's get that mindset. I'm in control of this position. I'm on top. I'm dominating the position. You can give me a couple things, but I'm going to wrestle through it. Step that up here. I get this lock. I jump sides. Right here. Knee block. Knee block and pull this up to me here. And then all I'm going to do now is take my knee to the ceiling right here. Keeping this tight. I'm pulling my chest into him too. I'm putting a lot of pressure on his knee actually. I'm pulling it to me. I'm pulling that up. I'm dropping my chest on him so I stay tight to him. That's not loose. I'm not sitting here where he can fish out. I keep the pressure all the way through this. Some tricky little things going on here. Let me ask you this. How many guys actually have a journal? How many guys have a journal? One, two. How many guys want to stay tight in this room? One, two. Look at the numbers. The guys that have a journal. And the guys that are winning state titles. They're all doing that. I know this right now. College level. The guys in our team that were in the national finals. The guys that won NCAAs. The guys that are on the world team now. There's a journal. I can go in and I can actually read what they're putting down. And I can go back to three months ago. From their training, to their technique, to their nutrition, to their weight management, to their mindset, maybe during the season or practice. All the stuff's written out. So I strongly encourage you to start with some kind of journal. Diary, I don't know, whatever you want to call it. Your phones. The Fortnite games you guys play. You can play in your phones. Take five minutes away from Fortnite. And let's write down some thoughts. Like after every session, you guys had some of the best clinicians in the world come through. Coach Ryan, Coach Dresser. They were in here. I don't know if there are other people who have come through. If you guys didn't take notes on what those guys taught you, then I think you made a mistake there. So we can correct that. Alright? After this session, you can write my notes down. I hope you do. Alright, but let's fine tune this a little bit and we'll get back into some other things. Alright, one, two.


Well, good afternoon all First of all welcome to Intel's AI launch My name is Bushan Desam. I am the senior business development manager at Lenovo's data center group My primary focus is in artificial intelligence business So, I mean there's a lot of excitement around artificial intelligence in recent years But at the same time there are several fears too especially when it comes to automating everything that we do today and also We're losing control on some other things that we're good at But on the but there are also a lot of social benefits that comes from artificial intelligence And I'm going to make a case for that the AI and computer vision for social good so If you take that picture, you know, what do you see there? A man and a passenger van. It's very simple. It's not quickly, you know So what is the context here? so a man talking on the phone is about to hit by a van while crossing the road and What would you do you run away to avoid the van and you just want to make sure that there's no traffic on the other side You know, that's the common thing that you do, you know instantaneously, you know without thinking much and What is e-power 91.2345 plus 25 power 2.2? This is a hard problem to solve for humans so We will ask the same questions to a computers that were Until until recent past, you know, like if you ask the same thing to computer if you say, what do you see? Doesn't have a clue. What is the context? No clue What would you advise? No clue But if you ask the same math problem is saying now we are talking this one is easy, you know 4.1941 times 10 power 39 so Who are intelligent here humans are computers the computers who could actually solve the hot math problems? Are the humans who could instantaneously analyze the situation? Reason it and take a quick decision. Who is intelligent? So let's just you know quickly look at what is intelligence first of all sorry the The slight thing, you know, so it's a very sensitive so the if you if you look into the Webster Webster dictionary of intelligence What it says is Apologies for this. It's not working.  So if you if you look into a Webster dictionary of intelligence It says the ability to learn or understand or to deal with newer trying situations Reason also the skill use of reason. So that's what intelligence means So if you take this and apply between humans and computers So if you see humans are very good at sensing, you know, so you when we look at the picture We could immediately sense there is a man and you know, like is the car So and then we learn we learn from our experiences or we learn from others experiences You don't have to hit by a car to know that it's a dangerous situation You know that, you know, like so you somehow saw this you read somewhere somebody got killed, you know by when They were run by a car run over by a car So and then you reason it and then you acted no So you immediately run and then you adopt a situation that you don't want to run into another car But you want to run away from that so that you know you are safe So it's basically humans possess and you know, like exhibit intelligence, you know, that's what intelligence is Whereas until recent past what the computers are good at is basically doing complex math operations data storage and retrieval, you know, so it can Store terabytes to pedophiles of you know, like petabytes of data and it can retrieve it in milliseconds So that's what the computers are good at So there is a clear division of labor until now, you know So humans do some things computers do some things, you know, the things that computers could do we can't do You know, the things we could do computers can't do so that's actually a very nice division of labor between us and computers So what are computers good at, you know, so the computers are good at following rules created by humans so if you take in traditional way the way we interact with computers you actually Write a computer program that has some logic and what the logic does is it codifies the rules You know what rules you want the computer to follow and When you give those rules through a program the computer executes that program and it gives you, you know, whatever the desired output But the the real thing is what computers are good at is that very good at following rules created by humans at incredible speeds You know, like so that they can just execute So, you know millions and billions of operations, you know, like in the in the in a second So that's what computers were good at, you know, so that's just What what we've been used to But that's about to change. So this is a very popular visual recognition challenge in the computer vision community It's called image net. So image net is a large database of images. It has about 1.2 million images that belong to thousand different categories so the challenge is if you if the computer actually can Look at 100,000 samples and test what that images, you know, be able to tell what that images and at what accuracy So that is a challenge So if you look at the error rate the error rate in in doing these hundred thousand tests if you historically track that In 2010 the error rate from computers were about 28 percent. So that is the error rate, you know So they were getting, you know, 72, you know, like right, but the 28 wrong and then, you know, the error went down it keep going down until 2014 but something happened in 2015. So this is when Microsoft researchers could actually Bring the error rate Even below the human level slightly below the human level. So the human error rate is if I show you that thousand, you know 100,000 pictures you could get 95 percent right and 5 percent wrong But this is the first time the computers Did this at 4.9 percent. So the slightly exceeded the human capability and in 2016 it went further down so basically That in 2015 you see this whole thing turning around. So there is the press went wild Sorry, I'm the another person who's blaming press but They're just saying hey a is here is going to take over a job is going to change everything So that was the the main theme of the story But also keep in mind It's only doing one task. It's not doing everything isn't it? So all it is saying is hey, this is a picture of a cat. This is a picture of dog and that too In the in those thousand categories, so if you find something that's outside the category It says I don't know what it is Simply I don't know so it's very very narrow. You know keep in mind that you know, it's not solving every problem It's just taking one data set of images and kind of trying to find what this image belongs to So is this artificial intelligence really so let's let's look at some definitions of artificial intelligence now So we looked at intelligence, but no what is artificial intelligence in that so Artificial intelligence is basically the intelligence Exhibited by machines or software. So this is a Wikipedia definition very general definition and we already reviewed what intelligence is You know, so they built you to Samsung act reason, you know, like adopt and all of them are considered intelligence But another technical definition of artificial intelligence is the study and design of intelligent agents Where an intelligent agent is a system that perceives? You know, that's the key thing, you know, so it actually perceives the environment You know, it tries to look through it perceives the environment and take some action that maximize its chance of success, you know, so Perceiving and acting those are the two things So let's take this and try to apply to this image image net challenge. Is this really artificial intelligence? there's actually in fact there are two kinds of artificial intelligence one is narrow artificial intelligence The other is general artificial intelligence.  So I'm going to review some of those applications And it's poised to create some good societal and economic benefits. So that's the narrow AI scope is that's what actually happening today, you know, like In overall but there is this general AI it's kind of mainly driven by academia and scientists, you know So what they want to do is they want to they want computers or you know artificial intelligence to come to a level where It really exhibits the intelligent behavior that we exhibit, you know, taking multiple things, you know Multiple things together like in a sensing, acting, learning, reasoning and all of them and how we react to a situation We want the AI system to react to a situation But this is where, you know, if you track the history of AI, it went to booms and busts, you know, like twice Probably perhaps, you know, we are not there yet and they there's Still, you know, like little progress and it needs decades and decades of research in this area and and but it has Important and complex implications to society, but we are not there at least, you know, couple of decades But even if you get there, there are always Technology always poses several challenges is not so if you if you go back Very very early days is not so the steam engine actually revolutionized the the way, you know, like the muscle power is Replaced by, you know, like mechanical power So that has huge implications in productivity in the society But at the same time the the steam engine was actually powered by coal Coal was actually putting carbon dioxide into atmosphere and it's believed to, you know, cause this global warming But then if you compare to let's say 20 years ago from in a coal power plant versus today It's only putting 1% bad stuff out. So 99% is clean That is again driven by policy and the policy, you know, like in fact draw Environmental technologies to clean the bad stuff that's coming from the coal power plant So it's it's going to be it's going to be an evolving field There will be lots of technological progress, but at the same time there's going to be policy changes And they all have to come together really to deliver the right benefit either. It's the the general AI or the narrow AI so Let's come back to the image net challenges, not so this is the first time computers beat us In recognizing images, but what made it possible? So there are three key factors in there.  So let's review each of them First of all, I mean we are coming from Lenovo, so we want to talk about computing power, and that's so we always say you need more And if you really what's showing on the on the right-hand side here is On my right-hand side is the top 500 supercomputers in the world so the the curve on the Middle one here is actually showing how individual Computers, you know, like the number one is actually growing at the capacity and the one in the bottom one is the 500th computer, what is the power of the 500th computer? And the top one is if you sum up all the computing power from top one to top 500 computer if you sum up all of them That's what the capacity is but keep in mind that keep in mind that this is a logarithmic scale It's not a linear scale, isn't it? So do not take this Advances lightly the reason I'm saying is If you take today's CPU if you take just one Intel CPU that one Intel CPU can almost deliver One teraflops per second so it can do one tera, you know, like flops of you know work in in one second and Interestingly if you track back 25 years ago If you take all top 500 supercomputers, I'm talking about supercomputers not the you know like Consumer computers in the world if you combine all of them together. That was only delivering one teraflop and If you take the same one teraflop 20 years ago the fastest supercomputer on earth was delivering what your one CPU today is delivering the astonishing progress in technology and And see where we are at today. We are today the fastest supercomputer in the world is actually in China So it's in a national supercomputing center in Wuxi, China that delivers about 100 teraflops of computing power per second and us Is actually building the next-gen supercomputers called summit and Aurora so they are going to be in that kind of you know hundred teraflops per second of computing power and The the one here is actually the Marconi supercomputer that Lenovo and Intel built together for the Italian Research institutions, it's about 11 teraflops per second this machine So this is in Bologna, Italy, and this is the world's Number 12, you know, so if you look in the 500 list, it's the world's top, you know 12th computer Here so if you can see just to sum up the computing power It's it's going up and up but at the same time the the prices are also going down You know if you for the same dollar you can actually afford more and more computing power So the next one the advance is the the computational side or algorithmic side of you know So there are these dupe neural networks So they the dupe near the the neural networks are actually Inspired by human brain so what what I mean is you can see that there are multiple of these You know like circles are you know like all these squares you think of those are yes You were neurons, you know so this is not exactly doesn't act like a neuron but you know it mimics a neuron and When you feed something to the neural network, you know, so you take one some image So the first layer is going to keep the very you know like low-level features You know it try try to you know like immediately detect your edges And then you know like it tries to put some features in there and then you know It try to capture our face and by actually creating this Abstracted layers it can actually keep a lot of information in there so What happens typically in a in a you know like when you develop an application is you build a neural network and You keep training with the data.  You don't know why is not so So but imagine you are feeding a million cat images here So when you keep actually throwing that that image there then it kind of creates an abstraction saying oh This is how a cat looks like this is exactly how we learn when we grow up So the first time you know like you we get this nice, you know like book from our parents saying hey This is a cat they don't say a cat has you know like two years, you know like it has four legs and nobody tells us So we kind of start creating an abstraction and and we learn by examples You know like after a hundred time we say hey, this is a cat and then we slowly you know We start learning you know like we learn the context, you know like and then there goes on So the key part here is learning and also learning by example So that's the key part. So that's how the deep neural neural networks actually work and Oops, I think I messed it up. Can we go back a few slides go to the big data slide? Yeah, there you go. Yeah Thank you so so the third the We talked about computing power. We talked about algorithmic advances. So the next one is The the big data explosion. So there is a lot of data today So the day typically when you talk about big data, there are three elements the volume of the data The variety of the data and the velocity of the data. Look at these numbers 44 trillion gigabytes of data by 2020 three point thirty one point twenty five million messages 7200 hours of video hundreds of sensors in cars 500 million tweets six billion devices these are like huge numbers and That's the volume when it comes to variety you have now images you have video you have you know like text You know it's so much of the variety in the data and also the velocity when these hundred sensors are pumping up It's pumping up very fast and also look at All of these numbers are in minutes in hours in days, you know, that's a huge amount of data coming So the pace of the data is also very high. So so the real, you know like Challenges, how do you actually make sense of this data? But at the same time there is an opportunity because these deep neural networks they get better with the data So it's really a challenge, you know, like this one challenge is actually creating an opportunity. I mean, that's how nature is isn't that so Okay, so the I briefly mentioned in the beginning that There are two common fears when we talk about yeah, isn't that one is a replacing Humans by automating tasks that we handle well today and also Yeah, exceeding human capabilities and taking over the world. So those are the two common, you know, like fears So this is a very good report. You know, like if you guys are interested in that, you know, I highly recommend to read this You know, it's a very easy to read report. So this came out in October of 2016 this was published by a committee of from National Science and Technology Council They advise the US president on what is coming up in the future how it has implications, you know, like for our society Whether it's you know, like employment or you know, like environment, you know, what not, you know So it's a typical body that advises the president So basically the what the report notes is that the best way to attack some of the jobs in the future Is actually to pay humans with machines in situations where the human partner cannot compensate for weakness in the computer or vice versa So let's see if we can actually work together, you know, like with the humans with artificially intelligence-powered computers to to kind of, you know, like better the outcome Would this work? So let me Work to an example So the the dilemma here is, you know, is that humans are AI, you know, so which one to pick so this is a research done by Some of the MIT scientists and also Harvard Medical School folks and what basically Is they're trying to address a challenge.  The computers were doing about ninety two five point ninety two point five percent accurate So that's the result from the computer But when a pathologist actually looks at the same image, he was actually giving a ninety six point six percent accuracy on that And then on the next category is the localization accuracy That means is you take the image you need to identify in which part of the image is the tumor in so you need to actually You know identify the tumor So again a is doing about seventy point five percent Whereas the pathologist is doing about seventy three point three percent. They're getting close You know a is getting close, you know to the to the pathologist level But even the most interesting part is when the pathologist was assisted by AI The whole accuracy improved to ninety nine point five percent. That's a damn good accuracy I mean that's actually eighty five percent reduction in human error rate So How good is ninety six point six percent in some cases? It's not you don't want to be diagnosed You know you could be that three point five percent, you know like you may fall into there You may be wrong diagnosed wrongly treated that has a lot of implications on human lives But but look at that accuracy ninety nine point five percent. That's that's very good accuracy so I mean we could work together, you know, so you can actually bring the humans and Artificial intelligence to basically say that you can augment your intelligence by using artificial intelligence So that's the that's the message. So it makes sense. It makes, you know complete sense So we talked about healthcare how we can improve healthcare. So let's look at a social issue I'm not sure this is the right place to talk about it, you know because we are in a bar, okay but this is coming from National in shoot on alcohol abuse and alcoholism and What it is saying is underage drinking is a serious problem in the United States It's actually not just in the United States is throughout the world, you know, like even in developing countries, you know, we see this I'm originally from India, you know, like I see this problem, too, you know, it's not just one country's problem and if you see the data that was actually shown By them it's saying the eighth graders are consuming ninety nine points on percent of them are consuming alcohol At some point twenty one point five percent in tenth grade and thirty five point three percent in twelve three So they don't drink frequently But once when they drink the drink in excess and and they have, you know, they pose serious health and safety risks So this is a this is a serious problem So let's see if we can apply the artificial intelligence to identify or solve this problem So I don't know like if you if any of you have a chance to check out that the demo that Lenovo is showing there It's basically a demo to identify your age So keep in mind that, you know, that's a work in progress.  So that's one example of, you know, like identifying that So the second one is Intel is actually demonstrating a demo right there So what this does is it looks at a scene it localizes things and then immediately it identifies people it identifies objects like bottles So again, you can actually train the system to identify the alcoholic beverage, you know, like containers, you know Like the the way beer comes, you know, like they're only a finite number of shapes So you can easily use them to identify those kinds of uses and then there is a startup That's an MIT startup in Boston. It's called Netra. What Netra does is it uses computer vision to identify Brands and pictures and videos. So when you show this picture, it's actually picking up that Ray-Ban And saying it's 100% accurate. That's a Ray-Ban, you know, like so, so imagine that so you can actually again Train this to identify popular brands of alcohol, you know, whether it's a Stella or in a Budweiser and all like But like so that's how you can actually identify those brands So imagine you can actually put all of these three these three together. So age identification Container detection and brand recognition and if you build a tool, this can actually help some of the enforcement You know, like agencies, especially public places, you know, in large crowds and large public places to identify underage drinking So it's just an example, you know, like how how, you know, all of these technologies can be, you know, put together to solve some of the problems so Going back now, you know, like we look at a health care We look at, you know, how it can address a social challenge But let's also look at how it can deliver the overall benefit to the society, you know, like at large So this is where we need to review the history, you know So if you review the history and if you look at really track the GDP per capita And it stayed almost the same, you know, like until, you know, like 17, you know late 1717 hundreds But then it just ticked off. It took the hot stick, you know, how to stick pattern So it is actually the steam engine that was invented in 1775 was actually credited for this Industrial revolution that gave us, you know, like massive productivity and human prosperity by basically replacing the muscle power With energy from natural resources.  I'm not saying it doesn't have, you know, bad implications again at the time There's a lot of people like who are doing human labor work and they were displaced But then they kind of, you know, transformed and found work somewhere else So a lot of muscle work now we actually move to knowledge work and the next generation could be, you know Something else knowledge work powered by AI, you know, so that could be a trend, you know, like so going into the future so The the key thing there is it's actually the the the steam engine took the natural resources and actually deliver the the value So today We, you know humans, we created a big resource here, you know, like in the digital age It's called the big data. So that's the one that I was talking about So you are creating lots of lots of data. This is a man-made resource. So is there a value in that? So if you see the the the data, you know, I can and how you can actually extract value you can put into four different buckets So there are descriptive analytics so you can take all of the data or you can take a sub-sample of the data You can see hey, how did something happen? You know, like what happened, you know So you can always see it. This is what typically you do in monitoring Isn't that so you keep in all like monitoring something and say hey, you know, like it's breaking something and Then there is a diagnostic analytics where you take the data and you try to create a hypothesis saying Why did it happen? You know, so you're trying to come up with you know, like ways to find out why why it happened But actually compared to that there are other analytics that can even provide more value But they are very difficult, you know, so it's very difficult to do that kind of you know analysis Those two are predictive analytics and prescriptly analytics. So in predictive analytics You want to predict, you know ahead of the data. So you don't have the data But you take the what what you have today, but you want to be able to predict what's going to happen tomorrow And that's the predictive analytics and the and the prescriptive analytics is it should be actually sorry There should be a prescriptive analytics What prescriptive analytics is you want to actually change the outcome So you want to do some kind of scenario analysis and see how can I force that scenario if I know what the outcome is So this is just you know, like analyzing, you know, this is able to predict something But then this is a you know able to actually predict the input conditions that can you know force an output or you know Like an outcome so that's where there is a high value and that's where the high productivity is and Ironically we're collecting a lot of data and 90% of the data is is never analyzed Because there are no good methods to do this kind of high value analytics in in every scenario So Can I get us there, you know, like so that's the fundamental question So if you if you really see The two axes that I'm plotting is the volume velocity and a variety of the data So there's a lot of data coming in and you want to somehow create value from that, you know So that's what I'm showing on the y-axis You could do a lot of traditional programming but the traditional programming, you know, for example, especially if you take unstructured data if you want to Analyze images if you want to analyze video, you know, like if you want to analyze all of this It's very difficult to program everything in a traditional programming manner But that traditional programming is mainly driven by humans, you know, so this is where humans kids are adequate So to some extent we are good, you know, so that's how we've been dealing with computers is not so Primarily to deal with you know, like numeric data, you know, like tons of tons of numeric data And especially if you know what to program if you can actually codify the logic Into rules this really works.  That's where the deep learning fits But still in machine learning it still needs some human input because you have to define the high-level features and ask the Machine to learn low-level features in order to, you know, like make the estimation So but again the classical machine learning it doesn't scale very well You know at some point, you know, it also saturates and then this the deep learning that is based on the neural networks The the very deep neural network. That's when they can come and actually, you know, like help us with creating, you know High value so they can analyze video. They can analyze image. They can analyze text, you know, what not and also Look at here. The human skills are not adequate if I give you a pet of one pet of love of video. Can we analyze it? You cannot I mean we still we cannot I mean analyze that kind of you know Like data that amounts and also We cannot look at patterns, you know, especially as the data is going, you know, like bigger and bigger you got to You know look into 20 variables 30 variables. So you cannot actually extract patterns there. So let AI handle this So this is where the AI can help us in the digital age to get higher productivity like how steam engine did this During the industrial, you know revolution So that this is really in need of AI to extract high value from from data So I just want to give you one quick example, you know, like how Lenovo is using machine learning You know to to extract value So we we get lots of this social media push, you know, like so somebody talks about our products Saying hey, you know, this is good. This is bad. You know, I like this. I didn't like this There are lots of images, you know, like videos and all I can and those kinds of Data, but they're very difficult to analyze it. So this is where we use it actually machine learning to understand what customers are Talking about our product took that feedback and actually build this yoga product, which is very successful The Lenovo yoga brand is very successful.  So there are lots of lots of you know, like Industries that can get impacted by by and and again There's always opportunities to leverage the artificial intelligence in the right way in all of these So I'm just you know, like kind of you know, switching the topic and say how do you build an AI system? If you want to really build an AI system, isn't that so you you have to store a lot of data You know, the data might be coming from somewhere, you know, like as a social media or you know some from your customers in enterprises So you collect all of that all of the data in a big data system This is where you store you prepare the data and you feed the data and then this is where the neural network actually gets trained You know, so this is where you need a lot of computing power. That's why I was actually talking about the importance of the computing power So it's even common to see a lot of these Supercomputing centers are now moving towards AI direction that they voting towards AI because they know how to do this problem You know, like this high-performance computing kind of problem Once you train a neural network, then you want to deploy the neural network So this is where you can deploy it in mobile phones or you know, like smart, you know, like Devices or you know, like your PCs or you know, like laptops, you know So that's where you deploy this and then so for each of them You need like, you know different kinds of you know, like technologies So for example, the big data systems are typically run by Intel Xeon processor and Intel is now planning to release this Xeon 5 which is ideal for machine learning and Also the alter of the FPG S and Intel Xeon for inference So that's how you can actually build a system by taking these technologies that Intel is developing and and we do actually provide those systems to You know, like AI customers or AI users So our mission at Lenovo is to democratize AI. So how we are doing we are actually taking this Three-pronged strategy. So we are building an innovation center. The purpose of the innovation center is to demonstrate what AI could do, you know, like to you to you know, like so this is where we want to Take and you know, like this image diagnosis kind of thing You know, so we want to actually create some of those applications to demonstrate the the value of AI And we also want to create a development platform where our customers can immediately take Take it and immediately start building an application Rather than worrying about what kind of hardware I need to bring what kind of software I need to bring and all other stuff and then finally the Deployment so this is where the we are kind of planning to deliver Scalable systems because in enterprise context if you are to deal with you know, catapflops of data and you know Like you want an entry and solution. So this is where we want to deliver an entry and solution You know system. So that's what the the the What Leno is actually doing to democratize AI, but thank you all. Thank you for your attention Shate of time any questions So this is this is one I came across but definitely, you know, there are others other areas One is again, they're trying to they're all around this image image-based ones because if you see a lot of AI today It's all around image based AI. So they did this in healthcare They're doing this in oil and gas. So in oil and gas when they are doing this seismic processing They collect a lot of data, you know, like so they send this sonar signals and then they do the imaging is not so again This is where there is somebody who is actually looking at these images and try to estimate the oil Reserves there what says AI is doing that and there is by looking at these kinds of examples. They're looking at you know I can these be Together is one. So the second one is again in healthcare mass general hospitals is doing a lot of work You know, so they have billions of images in their repositories again They are actually training all these radiology images with AI Systems and the whole idea is before even a radiologist come and see that It the AI is able to predict something and then it's going to help the radiologist So the radiologist doesn't have to spend 15 minutes there. So you can actually spend only five minutes to verify the diagnosis and also some of the information that's again giving is actually helping, you know So it's a very similar to what you know, like the the other example that I quoted But it's kind of but they're trying to bring that into production You know, so the the example that I gave is more kind of research It's a research paper from MIT and Harvard But actually what mass general is doing is trying to put this into production to bring it to patient care But oil and gas is looking at this Perhaps, you know, there are other examples, but there's something I could come up other than sorry So Imaging is one but I see a lot of analytics, you know, so in a lot of big big data analytics, isn't that? So today a lot of those analytics are based on statistical packages So this is where again they're moving not directly to deep learning but actually machine learning at least So the next steps seem to be machine learning So so imagine before you want to You want to give a loan to somebody, you know, like so the three or four parameters The financial companies look to underwrite your loan is, you know, basically your salary or age, you know, like and all of them But now imagine if you want to look at 20 variables to get better accuracy Okay, this statistical page, you know package, they cannot correlate among 20 variables So this is where you got to do this predictive analytics using machine learning So what machine learning does is it now it takes us all these 20 variables and try to correlate, you know Like basically that's what it is doing and underneath So that means you are putting fine granularity into your data So Enterprises are very interested in doing that kind of work, you know, how can I apply The the machine learning as a next step taking from the traditional statistical techniques And the second the second Trend is what I'm seeing is You have typically the structured data that was living in databases And then now we have this image data That's what a lot of companies are doing is they're trying to bring them together So they're aggregating it, you know, so for example, you as a customer, you know, so You are in my database, you know, I'm an enterprise, you know, like I sold you something but again You may be posting something in the social, you know, like media The one example I gave you is like there's no linkage between, you know, like the customer and the on the data We're just looking at the data in it But now I want to actually really understand your preferences So I want to treat you as a segment of one basically I want to see, you know, what you want to buy, you know, what you know, what your preferences are So this is where it's actually trying to bring these two together, you know It's not just looking at the image data by itself, you know So those are some of the emerging trends That's where enterprise are finding value Yeah, of course So the two areas that we see in higher education is in two different areas One is the academia is trying to push the neural network research itself, you know So they want to actually do better algorithms So out of those three, you know, like if you talk about computing power data And algorithmic advances, they're making a lot of algorithmic advances So that's one area The second area is a lot of enterprises, they cannot adapt to some of the methods Because they don't know, you know, how to do this This is where, again, the academia, you know, like is taking And trying to kind of show some way of doing it, isn't it? So they want to show, hey, this is possible And then the next step is, you know, like for the enterprises to pick it up And see, you know, like how they can use the application So those are the two areas, you know, algorithmic advances And application advances So that's what the academia is doing Any questions? Okay So, well, thank you very much, you know, for listening to my talk And appreciate the time I hope, you know, like you enjoy the rest of the day Thank you


Hi guys, it's Hannah and today I will be reviewing a book that kind of broke me, that broke the way I see the world, and that really left me in, let's say, emotionally not stable place. And that book is Girls Burn Brighter by Chopin Rao. I don't really know how to pronounce the name. This book came out on 6th of March.  This is not that kind of book. I mean, yes, this book essentially is about two girls, Purnima and Savita, and about the friendship. But it is so much more beyond that. So, it's set in India. And actually these two girls, they belong to the lowest caste in India. So, for me, all the people from India that I know personally, both in Germany and when I lived in UK, they are all pretty rich because to get out of India, to live abroad, to study, or to own a company or to work abroad, you have to have a lot of money. And I never knew this poverty. I knew that India is a poor country and that people suffer. I never knew such level of poverty is even possible. So, you know, like we, what we think in a Western world of poverty, when we see people that are homeless or collecting bottles or looking at the trash bins for food, this in comparison to what is described in this book, what this book is about, is almost luxury because the poverty this book deals with, it, I don't know how to describe it, is the people in these hoods, they live, it's not even slums, it's beyond that. There is no fresh water. There is no electricity. They all sleep in the same room. They all cook in the same room. The food is always rice.  But she sees, spoiled some vegetables like an onion and that she knew that that was the food of the family for the whole week. And when you think about it, it's, it's incredibly crazy. So these girls, of course, they have no education and they go into this orange marriage, which so what the author did in this book really well was the fact that she wrote in the beginning that it was 2001. I was born at that time, of course, I was already, like, growing up at that time. And I can really, really, you know, I know what is happening in that time, what we had and what is described in these books, how these girls, when they start to bleed after that, like their work is to cook and clean for the men. And they are not even considered as children, as kind of children, when the father, like, so there's this point in the book. So I'm not gonna try to talk about specific characters, because I don't want to really spoil, but there's something to talk in general about, because there are different things happening, not just to these two girls. And basically, there's an accident where a girl runs little, runs to the water, and the father runs after her to save her. But the last minute when he sees that she can be taken, like taken away by the water, he stops thinking that, just let go, it's only a girl. Because girls don't have no way to give dowry, like basically sell to whoever takes them. And then they're taken by their husband, they're supposed to be grateful, but it's really, they're taken as a servant. And this story is so heartbreaking. It's not just a really low caste life of them, the culture, but it's... So the whole idea is that these girls meet as kids, like as teenagers, and then after four years they meet again. But things that happen is four years. So this book deals with slavery, with the brothers, where girls are kept drugged. So basically the way... So they can be either that the family sells the girls, the daughters, to pay off the debts, or just for food or something, to the brothers, or they abducted from the street. Basically they drugged, they taken in in a brothel, and they get addicted to drugs, so they make sure that they get addicted to the drug, and then they do anything to get that drug, because of the trouble. And of course in that time the, like I don't know, handle of the brother, they check if there's any family rich enough to care or look for them. If not, they keep these girls, and then they sell them, they do whatever. And at some point you get to these parts where these girls don't have anything, and I'm talking to no. And they start selling off the body parts, because they don't have anything. And also they eventually end up, some of them in the USA, they are imported, like bought, and to supposedly clean houses, but we both know it's not really the purpose of it only, so they become slaves, and how they can't really escape, because they have passwords, they don't even know what is the password, they don't speak English, and it's so heartbreaking. Like it's so freaking heartbreaking. And the worst part is, okay, so they assault by the parents to the husband, and then when the husband doesn't like they can just throw him out, or do even worse things to them, and they can't tell anybody. Apparently this is what is society. And the worst part is that it's actually so these girls know each other for a few years, and then they suffer unspeakable things, and they mitigate. And because the author said the time period in the beginning, it's really easy to put together what is happening in the world that you grow up at the time, and what these girls are going through. It's just heartbreaking. I haven't read the book that broke me this much. So I read this, I started last night, and I finished just a few minutes ago, because I just, I wanted to record my feelings for you, instead of, you know, putting together a nice review and encouraging you to read. I wanted to try to put out the real feelings I have for it, and I know it has been promoted by a few book to birth, but I think this book should really be read.  But what this book really taught me was that look around you, because this is happening today, and yes, you don't know, you can't really change anything that's happening in India to these girls, but they are being bought in the ship to Dubai, or to USA or other countries to slave, and you meet them in the airport, you go past them, and you go past them on a street, like when one of the girls was running away, in USA she tried at least. There were some people, like she can't talk obviously, and she just knows one place where she's going, and people don't understand her show, so they ignore her when she asks them, tries to ask them, they just think that she is like begging for money, or wants to sell herself, you know, or she's just being mean to Mexico, because apparently some people can't tell the difference between India and Mexico, but yeah, and then like these people, they are assaulted, they are treated like nothing, and not like people, and it's just horrible, it's so horrible. I haven't read anything based in India, people like, because this is literal fiction, so the author knows what she's writing about, and it was really heartbreaking, really heartbreaking to read. I know it's not really review, I don't know what this is, but I want to just put my feelings out there for you, and maybe this will convince you to read this book, because it's one of these books that will gonna stay with you forever, I think. It's like something that you're gonna reflect, yes it's heartbreaking, it's hard to read, but at the same time it really shows you a lot, and I also think it was amazing to learn about the real India, and not just the part you are shown, and told about the people you know with pools, and servants, and all that. Like the girls, they haven't seen cinema, they haven't seen it to be, they don't know about the film, they don't know what is passport, so I think this already says a lot about what kind of characters we are talking about. So yeah, even though this book is targeted to way young adults, audience, I think it should be targeted to everybody, because I know a lot of people think, oh it's way, I'm not gonna read this, it's gonna be silly, I think it should be targeted to in general people who read, instead of just making, oh it's a way book, because it felt give, it believed everything leader, just sitting there and thinking wow, what did I just read? Like it changes you, it changes on so many levels, and yeah, that's pretty much it I guess. I'm sure I'm gonna have many many thoughts after this, but as I said I wanted to record my original feelings. I don't think you can even read this book, because even though yes, it's a fictional story, I know it's a story of many girls who went through this, and I think you can't really read a life that they have to go through, so just for the sake of it, I give it 5 out of 5 stars, but in general this book is not equal to any other like entertaining fiction that I read that I gave 5 stars, I think this book is way above that, and it just leaves you feeling many things, and it changes you and shapes you and I hope you can end up reading it, so yeah, that is pretty much it, and I'll see you next video, bye!


theCUBE presents HPE Discover 2022, brought to you by HPE. Good morning, live from the Venetian Expo Center, Lisa Martin, Dave Vellante, day two of theCUBE's coverage of HPE Discover 22. We've had some great conversations yesterday, today, full day of content coming your way. We've got one of our alumni back with us. Neil McDonald joins us. The executive vice president and general manager of compute at HPE. Neil, great to have you back on theCUBE. It's great to be back. And how cool is it to be able to do this face to face again instead of on Zoom, right? Oh, so great, so great. The keynote yesterday absolutely packs so refreshing to see that many people eager to hear what HPE has been doing. It's been three years since we've all gotten together in person. It is, and we've been busy, we've been busy. We got to share some great news yesterday about some of the work that we're doing with the HPE GreenLake Cloud Platform and really bringing together all the capabilities across the company in a very unified, cohesive way to enable our customers to embrace that as a service experience we committed to. Antonio, three years ago said we were going to deliver everything we do as a company as a service through GreenLake, and we've done it. And it's fantastic to see the momentum that that's really building and how it's breaking down the silos from different types of infrastructure and offer to really create integrated solutions for our customers. So that's been a lot of fun. Give us the scope of your role, your areas of responsibility, and then I'd love to hear some feedback. You've been a couple of days here around customers. What's some of the feedback? Help us understand that. So at HPE, I lead the compute business, which is our largest business, and that includes our hardware and software and services in the compute space, both what flows through the GreenLake model, but also what flows through our traditional purchase model. So that's about a $13 billion business for the company and the core of so much of what we do. And it's a real honor to be leading a business that's such a legacy and a franchise with 30 years of innovation for our customers and an ocean of followers. And it's great to be able to start to share some of the next chapters of that with our customers this week. Well, it's almost half the business in HPE, and as we've talked about, it's an awesome time to be in the compute business. What are you seeing in terms of the trends? Obviously, you're all in on as a service, but some customers say, tell me, I got a lot of capital. You know, I don't need OPEX, I'm fine with OPEX. What are you hearing from customers in that regard and presumably you're happy to sell them in a capex model? No, absolutely. In the current environment in particular with some of the economic headwinds that we're starting to stir down here, it's really important for organizations to continue to transform digitally, but to be able to match their investments with the revenues as they're building new services and new capabilities. And for some organizations, the challenge of investing all the capex up front is a big lift and there's quite a delay before they can really monetize all of that. So the power of HPE GreenLake is enabling them to match their investment in the infrastructure on a pay-as-you-go basis with the actual revenue they're gonna generate from their new capability. So for lots of people, that works, but for many other customers, it's much more palatable to continue in a capex purchase model and we're delighted to do that. A lot of my business still is in that mode. What's changing though, or what are the needs, whether you're in the GreenLake environment or in the capex environment, increasingly the edge has become a bigger and bigger part of all of our worlds, right? The edge is where we all live and work. We've all seen over the last couple of years enormous change in how that work experience and how the shape of businesses has changed and that creates some challenges for infrastructure. So one of the things that we've announced and we shared some more details of this week is HPE GreenLake for Compute Ops Management, which is a location-agnostic cloud-based management setup that enables you to automate and lifecycle manage your physical compute infrastructure wherever it lies. So that might be in a distributed environment in hotel locations or out at the edge where so much more data is now being gathered and has to be computed on. So we're really excited about that and the great thing is because it's fully integrated with HPE GreenLake cloud platform, it's in there alongside the storage, alongside the connectivity, alongside all the other capabilities and we can bring those together in a very cohesive infrastructure view for our customers and then build workloads and services on top. So that's really exciting. How have your customer conversations evolved, especially over the last couple of years as the edge has exploded that we've been living in such uncertain times? Are you seeing a change there in the stakeholders rising up the C-suite stack in terms of how do we really fine-tune this because we've got to be competitive, we've got to be a data company? Well, that's so true because everybody has to seem data as a currency and is desperately innovating and modernizing their business model and with the underlying infrastructure and how they think about development. And nowhere is that truer than in enterprises that are really becoming digital first organizations. More and more companies are doing their own in-house full-stack cloud native development and pivoting hard from a more traditional view of in-house enterprise IT. And in that regard, that starts to look a lot like a SaaS company or a service provider in terms of the needs of the infrastructure. You want linear performance scaling. You want to be very sensitive, not just to the cost as you call it, but also to the environmental cost and the power efficiency. And so yesterday we were really thrilled to announce the HP Proline RL 300 Gen11, which is the first of our Gen11 platforms. And that's in partnership with Ampere as the first of several things that we're going to go do together. We're looking forward to building out the rest of our Gen11 portfolio broadly with all of our industry partners in the coming quarters. But we're thrilled about the feedback that we're starting to get from some of our customers about the gains in power efficiency that they're getting from using this new server line that we've developed with Ampere. So, you know, this is an area that I'm very interested in. I write about this a lot. So tell us the critical aspects of Gen11. Where Ampere fits? Is it being used for primarily offloads and is it core, can you share with us? So if you look at the opportunity here is really as a core compute tool for organizations that are doing that in-house full-stack cloud native development. And in that environment, being able to do it with great power efficiency at a great cost point is the great combination. The maturity of the ecosystem is really, really improving to the point where it's much, much more accessible for those loads. And if you consider how the infrastructure evolves underneath it, the gains that you get from power efficiency multiply. It's a TCO benefit. It's obviously an environmental benefit. And we all have much, much more to do as an industry on that journey, but every little helps. And we're really excited about being able to bring that to market. The other thing that we've done is recognizing the value that we bring in the ProLiant experience. Everything with our integrated lights out management, all of the security, the hardware root of trust, the secure boot chains, all of that ProLiant family values. We brought to that platform just as we do with our others. But we've also recognized that for some of our service provider customers, there's a lot of interest in leveraging OpenBMC and being able to integrate in the management plane and control that in-house and tie it to whatever orchestration is being done on the service provider. So we have full support for OpenBMC out the box, out the gate with Jenna Living. And that's one of the ways that we're evolving our offering to meet our customers where they are, including not just the SaaS and service providers, but the enterprises who are starting to adopt more and more of those practices as they build our digital version. So tell us more about the architecture, if you would, Neil. I mean, so where does ampere and that partnership add value that's incremental to what you might think is a traditional sort of architecture? How's that evolving? Well, it's another alternative for certain workloads. In that full-stack in-house pro-native development model, it's another choice, it's another option. And some of our customers are very excited about it. That's the right course for the course, is that full-stack internal development, because it's just more efficient, it's lower power, more sustainable, all those things. Exactly, and the wonderful thing for us in this juncture in the market is there is so much architectural innovation. There are so many innovators out there in the industry creating different optimizations in technology, whether that's in Silicon or other aspects of the system, and that gives us a much broader palette to paint from as we meet our customers' needs as their businesses are evolving and the requirements are evolving, we can be much more creative as we bring this all together. And it's a real thrill to be able to bring some of these technologies into the HP-proliant space, because we've always felt that compute matters. We've always known that hardware matters, and we've been leading and innovating and meeting these needs as they've evolved over the decades, and it's really fun to be able to continue to do that. Hardware still matters. It does still matter, we know that here on theCUBE. Talk about the influence of the customer. With so much architectural innovation, there's a lot of choice for customers in every industry. When you're in customer conversations, how are you helping them make decisions? What are the key differentiators that you articulate that's going to really help them achieve outcomes that they have to achieve? Well, I think that's exactly it, as you say. It's about the outcome. Too often, I think the conversation can get down into the lower level details of componentry and technology, and our philosophy at HP has always been focused on what it is that the customer's trying to achieve. How are they trying to serve their customers? What are their needs? And then we can bring an opinionated point of view on the best way to solve that problem, whether that's recommendations on the particular CAPEX infrastructure and architecture to build, or increasingly, the opportunity to serve that through HPE GreenLake, either as hardware as a service, or as HPE GreenLake services further up the stack. Because when you start talking about what is the outcome you're trying to achieve, you have a much, much better opportunity to focus the technology to serve the business and not get wrapped up in managing the infrastructure. And that's what we love to do. So give us the telescope vision, maybe not the telescope, but ocular vision, as to where compute is going. We're clearly seeing more diversity in silicon. It's not just the x86 CPU world anymore. There's all these other supporting components, new workloads coming in. You mentioned Edge, whole new ballgame, AI inferencing, and those kind of new workloads, offloads and things of that. Where do you see it all going in the next three to five years? I think it's going to be a really, really exciting time because more and more of our data is getting captured at the Edge. And because of the experiences that companies are trying to deliver and organizations are trying to deliver, that requires more and more storage and more and more compute at the Edge. The Edge is not just about connectivity. And again, that's why with the HPE GreenLake Cloud Platform, the power of bringing together the connectivity with the compute, with the storage, with the other capabilities in that integrated way, gives us the ability to serve that combined need at the Edge in a very, very compelling way. The room moves a lot of friction and a lot of work for our customers. But as you see that happen, you're going to see more and more combining of functionalities. The silos are going to start to break down between different classes of building block in the data center. And you've already seen shifts with more and more software defined and more and more hybrid offerings running across a computing substrate, but perhaps delivering storage services or analytic services or other workloads. And you're going to see that continue to evolve. So it's going to be very fun over the next few years to see that diversification and a much more opinionated set of offers for particular use cases and workloads. And our job and value is going to be simplifying that complexity. Because choice is great, right up to the point where you're paralyzed by too many choices. So the wonderful thing about the world that's been done here is that we're able to bring that opinionated point of view and help guide. And again, it's all about starting with what are you trying to achieve? What are the outcomes you're trying to deliver? And if you start there, we're having a great time helping our customers find the right path forward. Wow, sounds like a fun job. It is. Talk to me about maybe one of your favorite examples that you really think articulates the value of the choice and the opportunities that HPE can deliver to customers. Maybe favorite customer example where you think we really nailed it here and they're achieving some incredible outcomes. Well, one of the ones that we're really excited about this week is I was chatting with the CEO of Cloud Sigma, which is a global IaaS and PAS provider who's actually been using our new HP Proline RL 300 Gen11, our new Ampere-based server line. And their CEO was reporting to me yesterday that based on his benchmarking, they're seeing a significant improvement in power efficiency. And that's cool to an engineer, but what's even better is the next thing he said. That's enabling them to deliver better cost to the right customers and advance their sustainability goals, which is such a core part of what we as an industry and we as society are going to have to continue to make stepwise progress against over the next decade in order to confront those challenges in the environment. So that's really fulfilling, not just to see the tech, which is always interesting to an engineer, but actually see the impact that it's having in enabling that outcome for Cloud Sigma. So many customers, including Cloud Sigma and customers in every industry, ESG, is an incredibly important initiative. And so it's vital for companies that have a core focus on ESG to partner with companies like HPE who will help them facilitate that and actually demonstrate outcomes to their end users. It's such an important journey and it's going to be a journey of many steps together, but I think it's one of the most critical partnerships that as an industry and as an ecosystem, we still have a lot of work to do and we have to stay focused on it every day, continuing moving the bar. You know, to your point about ESG, you see these ESG reports now, they're unbelievable. The data that is in them and the responsibility that organizations, mid and large organizations have to actually publish that and be held accountable. Yes. It's actually kind of daunting, but there are a lot of investments going on there. You're absolutely right. Well, the accountability is key and it's necessary to have an accountability partner and an ecosystem that can facilitate that. Exactly, and we just published last week our own living progress report this year, talking about some of the steps that we're making and the commitments that we pulled in in time and we're looking forward to continue to work on that with our customers and with the industry because it's so critical that we make faster progress together on that. Last question, Neil, what's your favorite comment that you've heard the last couple of days being back in person with about 8,000 customers, partners and execs? It's not the comment. It's the sparkles in the eyes. It's the energy. It is so great to be back together face to face. I think we've soldiered through a couple of tough years. We've done a lot of things remotely together, but there's no substitute for being back together and the energy is just palpable and it's fantastic to be able to share some of what we've been up to in the interim and see the excitement about that getting adopted by our customers and partners. I agree, the energy has been fantastic. We were talking about that yesterday. You brought it today, Neil. Thank you so much for joining us. We're excited. We've got Antonio coming up next and we're going to unpack all the announcements and really get that customer's perspective from the top of HPE. For Neil and Dave Vellante, I'm Lisa Martin. Join us in just a few minutes as the CEO of HPE, Antonio Neary joins us next.


When I first went to cook them, and this is important to mention this, is there was a life as evening going on and Myra had a baby in her arms. A young baby, which I thought initially was a bit odd, but it didn't strike me as odd the fact that she was old in the baby, so much as the fact that the baby was there. Because the prison rules were, no kids under the age of 18. Having said all of that, with me, I had been given permission all the way through my career to bring my kids into prison at various ages. So I was in this bit of like, well she's having a photo taken as well, that's a bit risky really. Nothing happened to that photo by the way, apparently it didn't develop. And I believe it because it's never materialised. Why is that allowed though, from the biggest child killers in UK? Why is child allowed in? Well there shouldn't have been, basically, but there were. And the point where it went wrong for me as well, was that my, this is going back to Grendan again, I know we're on the Myra thing, but it's sort of interlinked, is that Grendan traditionally right from the start in the 60s through to, well even now they have socials, but Grendan used to have wing socials and they used to have charity concerts at Christmas, where staff and their children could come to the charity cow concerts, they could come watch the pantomimes. And staff and their children, which included my children, could come to the Christmas parties, which I ran for, oh I think I ran eight years Christmas parties at Grendan.  And to you it's sounding like Jesus Christ, that's right, that's like giving a killer a gun or a rapist, putting them in a fucking, I don't feel like it was. I tell you what was, I'm now able to say, was that I don't think the process I followed through that period was any different to anybody else's, but I think because of what happened with me and my daughter, they were right to stop it. Not because my daughter was at any risk, but because that loophole, that little bit of what was happening natural for prisons had to stop. So in a way, did I ever feel I'd done wrong by my kids and by my daughter? Now it's as no, Sophie was here now, she would say the same. That's not the point. People from outside didn't like what had happened, people from outside didn't understand it. My daughter was eight when she was taken into Cocomwood because she wanted to know where dad was working on a Sunday. And I said to the governor, you've got this life as day happening, are children allowed in? And she said, well, yeah, I suppose so, but a lot of the staff are not bringing their kids, they're bringing, they used to bring their dogs and horses and stuff in. And I said, can Sophie pop down and join in with what's going on? Yeah, she said, of course you can. So that wasn't a problem. So Sophie travelled with me on her own to Cocom and she went into the lifer unit that's down there. And she was taken under a wing by the women in the lifer unit, all of whom I knew, I'd known them all for nearly a year. And she enjoyed cooking, she went to the, she went to the hairdressing salon first and just had her nails done and what have you. And then they took her and she did a bit of cooking, she went and helped out in visits serving. But in the afternoon, she came up into the admin block where, because I've been to see Myra and Chris said, Myra's got this cardboard globe that she wants to make with Sophie. I said, OK, they can sit and make it while we have a chat. I said, yeah, of course you can. So she sat with my daughter in the governor's office, the side deputy governor's office making this cardboard globe. And then there was a knock on the door and I think it was a principal officer or somebody there said Myra's off on her way out and she's wondering if Sophie can go down to the gym. She's going down to use the gym, the trampoline. And that will have a look through around the grounds on the way through. In actual fact, Myra wasn't going with the principal officer, she was going with Nina Wilde who was her, I didn't know that at that time. But who later became her lover. Right. So Nina, who was a criminology student working in Cookham who had keys, went with Myra and my daughter to play on the trampoline, to go walks in the grounds, to look in the pond at the frogs and tadpoles and then came back up. Right.  What's happening?