One day, Rajinda Joha, the senior occupational therapist at King George's Medical College in Lucknow, Utah, Uttar Pradesh, in India, returned from work to meet the disaster that changed his life forever. He was shot by three men who demanded money from him during a home invasion robbery. After the incident, he was quickly rushed to the hospital for immediate treatment. Unfortunately, on arrival at the hospital, his family and friends were devastated that one of the robbers' bullets had irreparably damaged his spine, which meant that he will become paralyzed forever and bed-readen. This was a sad time for Joha's family.  Even though that meant that he was going to depend on people a lot to achieve the things he wanted, seeing that he was now bed-readen. At this time, his family moved him to Delhi in order to take care of him. According to Joha, I wanted to work in the field of disability as I was an expert in it. However, he couldn't write or walk but he had a special writing pen developed for him. He decided to start a trust called Family of Disabled, FOD. Initially, many said, what are you doing? You can't do anything. How will you work? You are totally dependent on others for everything. Eventually, a few people came around to be of help, saying, you can't walk. You can't sit. So what can be worse than this? You will only gain. Today, FOD helps the physically challenged and financially disadvantaged seek employment. Richard Branson, British business magnate and founder of the Virgin Group, said something profound about people who are brave. Today, in this video, I'll share with you one of Richard Branson's advice about bravery. This I believe will help you become a successful person. If you are new here, consider subscribing so you won't miss other interesting videos like this. According to Branson, the brave may not leave forever but the cautious do not leave at all. Lana Del Rey said, being brave means knowing that when you fail, you don't fail forever. TS Eliot added that, only those who will risk going too far can possibly find out how far one can go. Oftentimes, we're restrained from acts of bravery and courage because we concern ourselves with what people say. We get so conscious that we do not want to be seen as failures should it not work out. Hence, the fear of failure restrains us from unressing our potentials and making a difference in the world. American songwriter and poet Jim Morrison said, Expose yourself to your deepest fear. After that, fear has no power and the fear of freedom shrinks and vanishes. You are free. When asked how we made it becoming South Africa's first blackhead of state, the first elected in a fully representative democratic election, Nelson Mandela said, I learned that courage was not the absence of fear but the triumph over it. The brave man is not he who does not feel afraid, but he who conquers that fear. According to El Cordubes, bravery is believing in yourself and that thing nobody can teach you.  It doesn't mean that you're not scared, it just means you are courageous enough to act in the face of fear rather than chickening out. If you fail to give it a try even in the face, you might spend your life living in fear. Little wonder, Branson said, the brave may not live forever, but the cautious do not live at all. How to become brave and build courage 1. Examine the situation Fear of the unknown often keeps us from taking those brave steps, right? Well, maybe we should start reducing our uncertainty level in order to be able to launch out. Information is key, knowledge is par. When you are aware of every possible outcome of an action, you can easily decide the best course of action, which is why you should examine a situation as much as you can. That way, your actions will be considered a brave one. Also, bravery doesn't mean taking random risks, it can mean taking calculated risks. To do so, it's important to collect data and expose yourself to anxiety-provoking situations. For instance, say you are scared of heights. You can start by taking small actions every day that will help you overcome the situation. You can start by climbing a ladder and then gradually increase the challenge until you can easily find yourself on a mountain top. 2. Think of yourself less The more you think about yourself, the more you will get scared to act.  Shift your focus to others. For instance, rather than focusing on how you are perceived by someone new, focus on how they can enjoy a nice time. In the case of a career, business or any other project, focus on the purpose and on the sense of service. It's easier to be courageous on behalf of others than it is for your own sake. Don't forget that. Remember, you feel a sense of value when you have a sense of service to others. 3. Find role models who are brave and courageous Think of people that you admire so much for their bravery and courage and make them your role models because when you are trying to stretch yourself beyond your limit, there is a part of you that will question the possibility. Seeing people who have done or who are doing it will motivate you to keep at it because if they could or can do it, then you can do it too. A role model is a constant reminder that you can achieve what you want to achieve. And when you want to give up, always remember Branson's words, the brave may not live forever but the cautious do not leave at all. Thank you very much for watching our videos. We will see you in the next one. We'll like to give you another interesting video for you to enjoy next but before then, our team will be very happy if you can like this video and share it with your friends on social media. If you're new here, don't forget to subscribe so you won't miss other interesting videos like this. Look at your screen now to see two other videos we handpicked for you to enjoy next. We love you.


There's just one way to handle the killers and the spoilers, and that's with a U.S. Marshal and the smell of gun smoke. Gun smoke, starring William Conrad, the story of the violence that moved west with young America, the story of a man who moved with it, Matt Dillon, United States Marshal. It's another one, Mr. Dillon. Laying near his wagon, the horse was still hitched and was grazing. Another stabbing. Yes, Mr. Dillon. Two buffalo hunters found him earlier this morning on the road leading out to Simron Crossing. We just brought him back. Who was he? His name was Jones, Les Jones. Been in town a couple of days, buying supplies, food. Where'd he come from? Well, some of the boys told me he's got a little farm on up there, Kansas, peace. Got a wife, too. For a little thing, they tell me. Yeah. You know anything more about him? He was a tab-flayed saloon last night, playing pharaoh. Drunk? Oh, he'd had a bell or two, but not drunk. Did all right at the pharaoh table. He must have had $20,000. $3,000? With a woman on the Arkansas River. Big pardon, Mr. Dillon? Yeah, nothing, Chester. The money's gone, of course. Yes, sir.  Right. Doc, come down a minute. Mr. Dillon wants you. Coming! Did Jones have a gun on him, Chester? We found a sharp special in his spring wagon. He wasn't carrying anything on him. It's outside. You want to see it? Had it been fired, no, sir? Good morning, my child. Want to see me? I want to ask you a question, Doc. Yeah? There have been two stabbings in two months. Jones makes a third. You think the same person killed the other two? Well, there's no way to be sure, but from the position of the wound and the body, and from the angle of the knife thrust, I fear the killer or killers use the same... Doc, I just wanted a simple answer. Yes, I think the same person murdered all three men. Anyway, are you telling how long Jones has been dead? I'm not a picketed man, but I'd say sometime after midnight. Between three or four in the morning, maybe. And I'd also say from the amount of bleeding... Okay, Doc. Chester, get my horse. I'm going to ride out to the Jones place. I figure Ms. Jones will want to know. Howdy, bub. I live here. Where are you from? I'm from Dodge. Dodge? Need to you ride all the way from Dodge? Sure. Get down and I'll water your horse. All right. What's your name, son? Alvin Jones. My dad is Les Jones. I guess you know him, huh? Yeah, sure. I guess most everybody knows him.  Are you going to stay for dinner? Well, I don't think so. Is she in the house? Yeah, she's there. Just go on up. Don't worry about your horse. Thank you, bub. It's not Alvin, Ms. Jones. Sorry, I thought it was my son. My name is Dylan, Ms. Jones. Marshall, Dylan at Dodge. Come in, Marshall. Thank you, ma'am. Get care for some buttermilk or maybe out here men don't drink buttermilk like they do at home. Thank you, but nothing for me. Ms. Jones, I got some unpleasantness for you. Yes? It's about your husband. He's in trouble? I left Dodge four hours ago. I thought I should be the one to tell you. He's hurt bad. More than bad, Ms. Jones. Nice to meet you. Good one. Well, thank you, son. Alvin, this is Marshall Dylan from Dodge. From Marshall? Uh, Alvin, your paul won't be home for a while than Marshall did. Well, not for how long? Well, not for how long, ma'am? Not for quite a time, son, so you will have to run things a while longer. Makes sense. I can take care of them all, right? Sure, you can. We'll just date him. No, no, no. Thank you, ma'am. I gotta get back to Dodge. Uh, Ms. Jones, could I? Yes, Mr. Dylan. Talk to the boy, Ms. Jones, explain it so. He won't be bitter. Too many gunfighters got their start for him. Make sure you've got enough whiskey to finish the night this thirsty weather. Oh, we've got plenty, Mr. Slade. If no fight starts then... Oh, Mr. Slade, huh? There's a company coming. Marshall Dylan just walked in. I'll set the bottle of rye up on the bar. Howdy, Matt. Join me in a drink? Thank you, I will. What kind of drive? Been traveling? Yeah, been up to the river a bit, to the Jones place. Jones? Tell his wife she's a widow. Oh, yeah, I heard about that. It's too bad. He was in here last night, wasn't he? Oh, yes. Matter of fact, he was. You wouldn't know anything about his being killed. Who are you asking me, Dylan? Straight question. You saying I killed him? I asked a question, Slade. I don't know anything. Someone left that man after he left here. He was taking a lot of money out of your place. You had a reason. I wasn't even here last night. The partner of Ben and the Mirrors has run the place. Where were you? I was with his sister. Oh, even there? Still late enough? Where's Ben and his sister now? I don't know. Home, I guess. And Slade, if you have any big winners tonight, make sure they get home safe. He's not at home. Or his sister. What's the matter? You don't believe me? You thought I would be in the house? Why, when the night is so beautiful? You want to talk with me? Talk with your brother. But he's not here. So why not talk with me? My name's Matt Dylan. I'm Marshal of Dodge. I know. I've been wanting to meet you. I've come on business. I like business. Talk with me. Last night, maybe after midnight, a man was killed on the river road. Killed? By a knife stabbed in the chest. Why do you tell me this? He was carrying $3,000. He wanted Slade's. And so? Tab Slade told me he spent last evening with you. He came for dinner. He often does. He thinks he loves me. Your brother was he here? Tab Slade and Ben owned the saloon together. They're partners. They think at least one should be there all the time. Ben went down after we ate. Did Slade... was he here long? Slade? He's my fiance. So it's all right. Isn't this Marshal? Well, that's your business, Miss Ramirez. My name is Avalita. You could call me Eve. Well, when do you expect your brother? I don't know what my brother does. He may be home soon. He may be late. I've seen you when I've been in town, Matt Dylan. Yeah?  He and my brother don't like me to come to town. Well, Dodge is rough, Miss Ramirez. Always he has to protect me. He may not touch fools. But Matt Dylan is not so. You wouldn't keep me out of town? Well, that's not my affair, Miss Ramirez. It'd be for your brother and Tab Slade to say. Tab Slade thinks we will marry. Well, we won't. Because I don't love him. I don't love anybody. Then, Miss Ramirez... Don't you find me attractive? Well, yes, I'm...  Well, no, I didn't mean to do... Dylan? I've got a gun pointed at the back of your head. Ben, I want to... Morning with another man's fiancé and smart Dylan. Ben, please. He's gone inside. All right. Say anything, Dylan? What do you want me to say, Ben? By this time, most men are be crawling. You're a hard one, Dylan.  Is that better? You can see me now. It takes a small man to make love to another man's woman. You can't haze me into a draw. I'm not trying to. I don't want a gunfight. I just want to talk, Dylan. What are you calling me? I saw Slade just a few minutes after you left this place. Told me you were trying to tie us with a medic. I said he was wrong and came up here to get the straighter things. From what I saw a minute ago, you might have been right after all. You would like his woman, so it would be handy to have him out of the way. Is that the way you figure it, Romero? Yeah, that's the way I figure it. The only reason I came to your place was to talk to you. I want to find the killer of Mr. Jones and thought you might be able to help. Well, you're not going to get any information sniffing around Eve. What's your plan, Merid? I'll give you some advice, Marshall. Tab Slade's been a good friend of me and I'll help him protect anything that's his. Eve's here, so stay away. I'm not going to find a killer while you're saying pretty things. Are you through? All right, then listen to me. You say Slade had nothing to do with those killings. I won't say he did because I don't know, but I'm going to find out who did it. And if it was Slade, I'll get it. How do I ride back to town? Yeah, ride back to Dodge, Marshall. And between here and where your horse is tied, it's not so much as a twitch up the finger.  Don't tie to the wrong brand. It's easy to do. Just walk away, Marshall, to your horse, and walk easy. Yeah. If you find out who killed Jones, let me know. I'll do that, Ben, I sure will. We will return for the second act of gun smoke in just a moment. But first... Hello, I'm Kathy Lewis, the girl who plays Jane on My Friend Irma. Irma, tie this string around your finger to remind you that starting Sunday, we go on the air at 9.30 p.m. instead of 6 p.m. Eastern daylight savings time. All right, Jane. Good girl, now what's that string to remind you of? To buy some more strings. That's My Friend Irma, whom you can now hear on Sunday's 9.30 p.m. Eastern daylight savings time. Remember, My Friend Irma is now heard at a new later time on Sunday's. Check your local schedules. Now, the second act of gun smoke. Most foreign to morning, Mr. Dillon. Four. Yes, sir, I'm sorry to have to wake you, but you better get dressed and come right away. They've got tabs laid. Slaid? Who's got slaves? Some of the ranchers. They're going to lynch them right in front of his saloon. Well, go and try and hold them for a minute. I'll be right along. Yes, sir. We're going to hang tabs.  I kiss and I'll tell your men to burk it up and go home. I'm sorry, Marshall. I can't do that. Slaid killed another man tonight, and he's going to pay for it. All right, now listen to me. All of you. If tab slave killed a man tonight, I'm going to say it. If you can prove tab slave killed a man tonight, I'll take him to jail and hold him there for trial. Marshall, tab ain't going to be alive to stand trial. Do you know Slaid killed anybody? If one of these men lays a hand on tab slave, I'll start shooting. There'll be a lot of men dead. How about you, Marshall? You might be able to get hurt, too. That's right, John. You could kill me, all right. But what you use is going to shoot first.  Huh? Well, which one? Chester? Yes, sir.   Yes, Mr. Dillon. And you men. Don't anybody make a mistake. Don't you move a shadow. All right. All right, Slaid, get on off there. Thank you, sir. All right, Chester. Give me a chance. Now you and Slaid walk back to the far side of the street. Slow. Yes. I tried to tell him I had nothing to do with it. The rest of you just stand where you are, looking right here at me. First man so much as moves his eyes will be in real trouble. We're across the street and it's killing me. Good, Chester. Now walk Slaid down to the jail and put him in a cell for safe keeping.  And if you got any sense at all, forget to tell your families what you were almost a party to. Now, good night, gentlemen. Man named Olson, a rancher, was at Slaid's place. Gamblin? Yes, sir. And he did pretty fair. He left around midnight and was found about two hours later. He'd been stabbed. His money was gone. You talk with him? Yes, sir. He just mumbled about having tried to be friendly. He said that several times, Mr. Dillon. Just being friendly. Then he said, I fired a couple of times. I think it hit. You mean he hit whoever stabbed him? I think that's what he meant. You say anything else? Nothing. Well, that's not much help than just that. He can't tell us anymore. And I'll talk with Slaid and I'll bring him out. Yes, sir. Man, you gotta believe me. I don't know anything about the killing. This one or any of the other. I don't have to believe anything, Slaid. I'll find out for myself. But I didn't do it, man. Why is everybody sure you did? Why are they so sure that they're trying to lynch him? Does a lynch mob have to be sure of anything?  You had a bad reputation. You were in with the Kansas Raiders, too. That's right. The Raiders were killers and thieves. Some work? Now, when a man with your background goes straight, he's always suspect. Man, I didn't have anything to do with the killing. What about this partner of yours? It's Ramirez. I met up with Kansas. Him and his sister. We joined up and came out here. So we make good a team. Where's Ramirez now? I don't know. Matt, please listen to me.  No. Matt, I don't know. Why isn't Ramirez around now that you're in trouble? Please. Maybe he doesn't know. I don't know. He'd know by now. The news is all over Dutch. Chester? Yes, Mr. Dillon? Put Slaid back in his cell, then load your shotgun and keep a close watch on it. Well, where are you going, Mr. Dillon? I'm gonna take a ride out to the Ramirez place. I want to have a talk with Mr. Ben Ramirez and his sister Eve. I talked to you. I had a gun in my back. Now your gun's on your hip and I'd be smart to keep it there. Keep it there. I'll be no reason for anyone to draw. I just want the answer to some questions. What questions? Where's Eve? What do you want her for? I ask you a question, Ramirez. I want an answer. Where's Eve? She's in bed. Where was she around three this morning? Here, I suppose, asleep. I think you better get her out here, Ramirez. What's so important about Eve? A man was killed this morning and I think she might have done it. You know what you're saying. Yeah, I know.  That's right. And if you're going for your gun, Ramirez, make sure you're ready to die.  But if I can trick you out, kid, don't try, Ramirez. Why do you say my sister killed a man? No hand around these parts and stop for anyone on the road at night. Not unless it was someone they knew or someone they didn't have to fear, like a woman. Like your sister. You don't know anything, Dylan. You're guessing wrong. I didn't know when I got here, but now I'm sure. What do you mean? The man who died the night shot at and hit the person who stabbed him. There's no blood on you, but there's blood on the floor over there by the door. Blood that could have come from a gunshot wound. That doesn't prove anything. And there's blood on the table by you there. It's not blood, it's just a shuttle for the lamp. Does it hurt, Ramirez? Inside. Bad. She do it, Ramirez. Your sister killed those men. She's not my sister. She's my wife. That's why she didn't marry Slade. She's the other Rome. One of the money they had. Is he dead, man? Did you kill him? He went for his gun, Eva. He was kind to me. I tried to love him. I didn't love anything. But, man, he did not. I could have loved very lonely. Would you do something for me? Sure. Would you hold my hand? It's unwarmed the soft behavior of Ramirez and her husband were buried on the outskirts of Dodge City. The city was alive with saddle bums, ranchers, cattlemen. Searching the dark of a Kansas night for excitement and life. Unsmoke under the direction of Norman MacDonald stars William Conrad as Matt Dillon, U.S. Marshal. The new music for tonight's story was composed and conducted by Rex Corey. Featured in our cast were Georgia Ellis, Hiaverback and Jack Krushen with Richard Beals and Morrison and Herb Ellis. Polly Bear is Chester and Howard McNair is Doc. Join us again next week as Matt Dillon, U.S. Marshal fights to bring law and order out of the wild violence of the West in Gun Smoke. Don't Miss Gangbusters and the Case of the Variable Blonde will be available later tonight on most of the same CBS radio stations. This is Roy Rowan speaking and this is the CBS Radio Network.


The organizers were inviting me and also whoever canceled the last minute and gave me a spot. And I should say before I begin, everything I'm going to tell today is work done in Venki's lab with other people involved. I'll mention them later. And what we're interested in is figuring out how mice extract information about things they may care about in a cluttered environment. And the idea is that we all assume that mice depend on the sense of smell for many daily functions and we believe that the objects that they care about are never presented alone. So somehow to be able to identify the cheese here, this mouse will have to segregate the odor of the cheese from other odors in the environment. And the way we think about this is in a factory version of the cocktail party problem, the auditory cocktail party problem, in the sense that signals from different objects activate overlapping sensory neurons in the sensory organ. So the brain now needs to take this mixed signal and kind of recover the original sources that gave rise to this mixed signal. So before going into the neuron mechanisms, we wanted to have an idea of how well we understand the behavior itself. And by understanding behavior, often we take this approach, the psychophysical approach, that basically says if you can predict the performance of an animal based on some parameter of the stimulus, then you have some good understanding of behavioral ability. This is what we wanted to do here. And to do this, you need two different things. You need to be able to measure performance and you need to be able to define and vary some parameter of the stimulus and see how that relates with performance. So let me start by describing this arm here. So to do this, we devise a task in which mice are required to report detecting a target odor. And the target odor is presented with random sets of background mixtures, pseudo-random. In more detail a little bit, the world for these mice was composed of 16 monomolecular odors, odorants, of these two for each mouse were selected as the targets. We varied the targets for different mice. And by targets, I mean if you can detect these odors, you can get a reward. The other 14 odors are distractors in the sense that they may be present, they carry no information about a possible reward. And then in every trial, we present a mixture of these odors. And these mixtures are binary in the sense that each odor is a binary variable. So odors are either present or not present. We do not play with concentrations. And the job for the mouse is to just tell us whether one of these two is present within this mixture. And they do so by licking a water spout, if they do it correctly, they get a water reward. So we trained a set of mice on this task using this kind of training scheme in which we start with, so what I'm showing here is the distribution of the number of components in the mixture. So we start with sessions in which most trials actually had just one single odor. But some trials had more than one, still low numbers. And then as mice learn the task, we progress until we reach this flat distribution in which the number of component, the chance of getting a seven component mixture is the same as six or 40. Yeah, so we go through two different distributions. These are four defined distributions. This is one mouse. This is, I think, the fastest learning mouse. And this is already the flat distribution. So one of the nice features of this task, because I mean, everyone here uses olfaction and many people use mice, but one of the really nice things is that this is really quick. So in about 800 trials, they get this distribution. And of course, they mostly need to learn the test rules. Everything is arbitrary to begin with. And then as you increase the complexity of the task by having larger mixtures, the drop is not huge in performance. All the data I'm gonna show you is from sessions in which we use flat distributions. So one more nice feature of this task is that just because of the combinatorics of 16 different odors, you can produce a huge number of different mixtures, way more than the number of trials that any mouse ever does. So that most of the trials that a mouse encounters are novel mixtures he's never seen before. So there's no way to make a lookup table and just remember combinations. You have to perform the actual test. So we trained 13 mice collected a little over 30,000 trials. And when you look at the performance, first thing to ask is whether it's even sensitive to the background. And you can see that increasing the number of, I'm gonna move this. Increasing the number of components in the mixture reduces the performance of the animals, which is kind of expected. Another thing you can see here is that because the task is asymmetric in the reward, there's not much information in the go trials. They basically never miss the target. And the way we interpret this is that when mice have any doubt that the mixture may be there, sorry, that the target may be there, they just go for it. So going doesn't tell us that they knew that the target is there. But when they decide not to go, then that really tells us they knew that the target was not present. So if we want to understand what makes them fail, which is really what the psychophysical function is about, we really need to analyze the no-go trials and that's what I'm gonna show. So, I mean, you could think of this as some sort of psychophysical function. You have a parameter of the stimulus, that's the number of components. And that relates nicely to performance. But it's not extremely pleasing because you don't think that all ten component mixtures are equally difficult, right? So there needs to be something that depends on the specific target and background molecules that will make a certain ten mixture component more difficult than another. And we wanted to figure out what this is. I'll just take a small digression. We tried to do this with humans and we did not complete the project. So I'm not gonna make any extremely strong claims. But one of the reasons we could not complete the project is that we basically could not get humans to perform at any reasonable level. So maybe we didn't do it. We did it basically the same way as we did with mice, same designable spectrometer, just less odors. And we didn't just ate in the spectrometer, we just couldn't do it. We tried to train ourselves to do it. It still didn't work. If any human or faction person is interested in figuring out whether we can actually get humans to do that, I think that would be fantastic. Yeah, so the number is very low. So in Lang's papers, they basically did a very similar task. And there were two things that I looked at that I thought may be the difference. One of them is that the performance on a single component is kind of low for the humans. So I thought, get good performance at least on the single component before you say anything about the mixtures. And the second was that they don't train the subjects and we train our mice. So what if we train people? And we try training ourselves, but it didn't seem to work. Yeah, right, they tried to make the task as easy as possible by letting people rate familiarity and similarity and choosing the ones that are not similar and familiar. So and we didn't do that. So I'm not saying that humans can never do this. That's not a claim. It's always cinnamon that people use for. So I don't want to make any strong claims that I think one claim is that at least humans are far away from mouse behavior in this respect. So we use the same, less odors, kind of the same odors. Most of them are the same. We train mice easily. We don't train humans easily. So that's the only real claim I have here. Okay, going back to mice. Yeah, so we can debate about the synthetic maybe later. But I think in my view, once you've formed an object in your memory, it may not be synthetic anymore when this object is presented with other things. I agree that if you, and in a way, maybe these targets become an object for the mouse, right? I don't know. But if you just mix unknown molecules and smell this, you won't know that it's a mixture, that I agree. But I think we can have this debate later. Okay, so we want to move beyond the number of components and figure out some other parameter, right? That will describe the relationship maybe between target odors and background odors. And of course, the difficulty that was discussed here by multiple people is this issue of chemical space. If this is the target, these are two different background odors. How would I say which one is more similar? How do I define parameters that relate them? So one way would be to take one of the spaces that people here work on. And other ways would be, you know, you can take it in just a complete intuitive view. And let me explain what I mean by this. So these are the 16 odors that we used. And half of them were to Glake assets, okay? This structure here within the red frame. And the others were not. So obviously, I can't really say that these two are more similar than these two, but intuitively maybe they are. So the first thing we wanted to ask is whether, if you are searching for a TIG-Late target, do you care about whether the background has TIG-Lates or not? And when you do this, let me walk you through this in this plot. Each pixel is a specific number of combination of the number of TIG-Lates and the number of non-TIG-Lates, okay? So the column is the number of TIG-Lates, the rows number of non-TIG-Lates in the mixture, and then the color codes for the performance. Okay, yellow is good performance blue, not good performance. And what you can see here is that really the color depends on the left-right, right? So just on the column. So if you have a small number of TIG-Lates, these are mice searching for TIG-Late targets. If you have a small number of TIG-Lates in the mixture, you do well. If you have a large number of TIG-Lates in the mixture, you don't do well. And it doesn't depend much on the rows. So the number of non-TIG-Lates in the mixture doesn't matter that much. Not in a way. So I guess, depending on the, they all have some common functional groups, right? So you could find, I'll get to that in a second, but you could probably find a different group that relates somehow. These were not chosen to be related. But maybe relating to your question is you could ask whether this is specific to TIG-Lates or could you find a better group? So to do this, you need to kind of quantify the performance of this group. And the way we do it is by fitting just linear fits to each row, okay? Which tell you what the decrease in performance is when increasing the number of TIG-Lates, but holding the number of non-TIG-Lates constant, okay? So you get one fit for each row and these are all the fits. And then just take the slope, the mean slope of these fits. That's the overall TIG-Late effect in this test. And then, yeah, I didn't have it here. So what we then did is you basically split this group of 16 molecules to all possible eight and eight and ask what is the slope that we get. And we found that TIG-Lates are among the highest few percent. And if you look at the other groups, it's seven TIG-Lates and one replaced. So within this group of molecules that we chose, TIG-Lates have a very strong effect. Now, I still think this is kind of striking, but on the other hand, it's not clear how you can generalize this to some other functional group. It probably depends on the size of the functional group on which receptors relate to this function group, and we don't know. So it would be nice if we could find some other parameter that is maybe less intuitive and more easy to generalize. So the way we wanted to do this is by thinking, so it doesn't matter really whether a chemical structure is similar or not. If we can tell whether they activate receptors in a similar way, then that would be a good measure of similarity. So basically use receptor space. And the idea is that you can, so basically to do this, we imaged OMP G-Camp-3 mice, these we got from the Dulac lab. The OESA guy made them, so we can image glomerular activity shown here. And then look at the ROYs for a different glomeruli and get up for each molecule, you can get a vector that is just a list of activation by different glomeruli. Okay? And you do this for different molecules and now you have the representations of these different molecules and these we can relate in some mathematical way that does not rely on our intuition. And once you have these, you can ask, so what is the parameter that you want to define, what relationship? And obviously one of them would be similarity. You expect that if two molecules activate very similar sets of receptors, then probably one is a difficult background for the other. But that maybe doesn't have to be the case. And let me walk you through two different parameters that we looked at. So, assume that this is your target. This is the activation pattern of the receptors that you're searching for, okay? And now you're presented with a mixture that looks like this. So it's not exactly the same, but you've been trained a lot and you know that there's some noise in the system and you think this could be just one version of the target. So, if I were a mouse, I would probably lick for this, I want to get the reward. But another option you could think of is a mixture that looks more like this. This is, clearly you don't think that this is the target, but this is not what you were asked, right? The task is to figure out whether the target is included in this mixture. So potentially this mixture also includes the target. So, we call this kind of a mixture, a masking mixture for the target. Now, what we would want to do is be able to, for each trial, okay, for each mixture that we ever presented, be able to quantify how similar it is to the target and how much it's masking the target. And then ask which one of these parameters correlates with performance. And so for similarity, we just use the Pearson correlation coefficient. We're masking what we did is threshold the target response. So, we only take the mirror light that are activated by the target. And then basically take the ratio between the mixture and the target and average across the mirror light. And these ratios we bound between zero and one. So, it's basically like asking what percent of the target activity is present in the mixture activity, okay? These are all fixed concentrations, yeah. So, when we look at the performance, it seems much more, much better explained by the masking index than by similarity. And you could think of this as telling you that when there's a background, it doesn't need to be, similarity is a very strict criterion. So you don't need to be similar. It's enough that you activate the right receptors. What you do with other receptors doesn't matter, right, that's the idea. So we now basically think that we have some parameter that we can define that explains performance on a figure background segregation test, okay? So going back to this psychophysical curve, we basically think now we found a parameter that can produce such a curve. Okay, so the next question is, so obviously there's this debate of synthetic and elementary processing. And at least in some people's minds, just the fact that animals can solve the task maybe is difficult. So the question is, should we be surprised? How difficult is this task really? Or in a different way of asking the same question, if you read these receptor activation maps, how easy is it for you to tell whether the target is present or not? And this is work largely done by Alexander Mathis, who is still, I think, in the next slide. So what Alex did is first asked, what if we can just find for each target a single glomerulus that if you just listen to that glomerulus, he's specific enough to the target, then you can solve the test. And the way he did this is by looking at the distribution of the activity level of this glomerulus, so we don't, I should say, go back and say something. We only used the activation maps at this level of individual glomerulus. And we modeled what the mixture actually looks like. I'll get back to this in a second. So what we do here is take each glomerulus for a specific target. Look at the sum of the activity of this glomerulus for different trials. And ask, do the distributions of the activity levels for the trials, go trials, differ from the ones that are no go trials? Okay, can you put a threshold somewhere that will easily tell you whether the target is present or not? And obviously in this case, you'd do pretty bad. So we did this for all glomeruli target odors, pairs, and the accuracy levels that we found were far from mass performing. So it doesn't seem to be, there's always the question whether we, if we recorded all glomeruli, we could have found one. But it doesn't seem to be a good explanation for mass behavior in this case. So we then asked whether if you read combination of glomeruli, not just a single one, can you easily tell whether the target is present or not, and Alex used a linear class part to do this. And first pass, we took the linear sum of these glomerular maps as a mixture representation, and the classifier is just perfect. Not a single mistake ever. We then realized that that's kind of unfair, the linear sum doesn't make much sense, it should saturate at some point. And there's probably some noise that you should add. So we did some experiments to try and come up with a better model of what a mixture is. So these mixtures were again going back to imaging these glomerular responses, repeating each odor multiple times to get some estimate of the variance. And also looking at the mixtures and seeing whether we can come up with a model that explains the mixtures based on the single components. And I'll just do this a little bit more quickly. What we saw is that you could typically fit a kind of saturating curve. So what I'm showing here is the linear prediction. Each dot is one mixture, the whole figure is one glomerulus. Each dot is one specific mixture. The x-axis is what you predict linearly to be the response. The y-axis is the actual response. And it kind of nicely fits some saturating curve. So we plugged into the linear classifier model that takes into account the 10% variability that we measured in the saturating curve. And the linear classifier still does pretty well. And this is with a way smaller number of glomerulite than the mouse actually has, probably not as accurate measurement of glomerular activity as the mouse has, and still relatively easy to solve. So I have no claims that this is how the mouse solves it. I don't believe that's the case. But what this tells me is that the input layer is so rich that you could probably solve way more complex tests. So we shouldn't be too surprised that it's possible to solve this test. Okay, so now what actually drove me to this whole thing is an interest in feedback connections. The reason I started doing this task is because I was trying to think about what feedback may do, and you could think of attention-like mechanisms, and I thought this is the task. So the next thing to do is really to try to figure out whether feedback is involved. Haven't done this yet. What I'm just showing here is, I think something we should remember is that there's about 10 times, we don't know the exact number, but there's about 10 times more feedback projections from cortex to bulb than the other way around. So you probably do something interesting. But I'll just go through what we have done with the feedback projections. And that was to look at, basically ask what does the circuit that receives the feedback information look like? Which neurons in the olfactory bulb receive it, and what's the effect on mitral cell output? And we did this, this is with FIVOS that was in Benki's lab then. We did this looking at the AON. We injected a virus to express general adoption in the AON, and recorded in the olfactory bulb, shine light, and see core from different cell types, see which cells respond. I don't need to go over the bulb circuitry. So the first thing we looked at is where these axons actually reach. This was also described yesterday. And we saw that AON axons don't only go to the granulosa layer, but also reach the glomerular layer. And then in slices, FIVOS recorded from each of these cell types. And we'll start with the mitral cells. So if you look at inhibitory currents, then you see very strong inputs from these AON fibers not directly, but via inhibitory intern neurons. This was kind of expected. What was a little bit surprising is that if you look at excitatory currents, you also see inputs from the AON. And we did some experiments to show that these are direct AON to mitral cell synapses. Now, you can see that the scale bar here is very different. So the excitation is way, way weaker than the inhibition. And it's also a little bit prior to inhibition because it doesn't go through another intern neuron. We then recorded from inhibitory neurons both granule cells and PG cells to ask which ones contribute to the inhibitory input that we see. The short story is that basically every cell type that FIVOS could identify and record from in slice receives direct input from the AON. So they all seem to contribute. And he did some experiments by trying to locally block GABA. And it seems like a substantial amount from each of these involved in this inhibition. So we then asked, what is the effect of this input on mitral cell firing? This is just a conclusion that all cell types seem to receive direct AON. So what is the AON input due to mitral cell firing? We went in vivo to record single units. This is probably the way it's all working rats, the AON stuff. And you can see when you shine light, there's very, very strong inhibition. Basically silences mitral cells. And we asked, it took us a while to figure out why we never see the excitation until David Geyer said, maybe you're not looking at the right temporal resolution. So if you use one millisecond bin, PSDHs, then you start seeing the excitation. I'm seeing this stuff even more. And the excitation has a small effect of producing one spike, with not an extremely high probability. But it's just one spike, and then the inhibition kicks in. So this doesn't tell us exactly what the AON does and how it's involved in figure background segregation. But the one thing that it does tell us is that the input from the cortex is not a weak modulation, at least has the potential to very strongly modulate mitral selectivity, in a way much easier to see strong responses with AON activation than with odors. Okay, so I'll summarize. Yeah, this is going to be much faster. So the first thing is we found that mice can easily detect odors that are embedded in the background mixtures. If you look at the psychophysical curve, we don't reach much far on a curve. We didn't really challenge them with 16 odors. We think that we have a parameter that describes the difficulty of this task, and that's this masking, which is basically the overlap and the representation of the target odors and the background odors. You could solve this task with a simple linear readout. So we shouldn't be surprised that the task is solvable. And feedback is a very powerful or potentially very powerful modulator of olfactory bulb output. So I'll just mention what the burning questions for me are right now. And the first two that are very related is this really an attention task. Can we show that attention is involved by showing that when a mouse is searching for one target, he's going to miss the other target, something of that sort. And very related are top down signals involved. So can we show that they actually do something and figure out what? Another question that I find really interesting is, is there anywhere in the olfactory system where you really see background invariant responses? It's like the IT and visual cortex where the exact features don't matter. It's just the object matters. So we want to search for that. So the question about the masking experiment, so can you- Can I just thank people and then you'll ask? Yeah. Okay. So I- There's a second for the talk now. No, there is no second. So I just want to thank, first of all, Venki, all this work was done in his lab, all these people were involved in it. Kiosk-Bedke was involved in the classifier. And this, I think is, did you draw this, Tim? No, I was a artist, so. So. Maybe if I drew it, you might- I see. Because Andras already showed the cover, so I showed this one. Tim's version of the olfactory cocktail party problem. Yeah, thank you.


At some point in the future, please subscribe to 1000 subscribers by the end of the year and we are so so so close but we've been peering the last couple of weeks I think because honestly I haven't been uploading as many vlogs as I usually do and I haven't been doing my intros I've just been getting right into the mix and forgetting to ask people to subscribe so please subscribe please leave me a comment if you're also a teacher to college student because I have been meeting so many of you guys on campus I absolutely love it like if you ever see me on campus please feel free to stop me I would love to chat with you in person and yeah so let's get this day started I it's 8 30 let me check what time the interview is I feel like I should know when my interview is I also told myself I wanted to practice my commute did not have time to do that so I should probably leave a bit early oh okay it's at 10 15 which if I know me it's probably at 10 30 I just put 10 15 because I know if I put 10 30 then I will be late so it's at 10 15 and it's actually not too bad let me see how long it'll take me to get there oh 30 minutes okay perfect it's 8 30 now interviews at like 10 so we have a couple of hours maybe we'll start packing for London that will probably be a really good use of our time but I was doing big Scott to pay my rent yesterday and that's important like I've been looking at like student loans because teachers college now that I know a lot of you guys go there it's like they're really messing with me my second year like they did not renew my scholarship and now I'm kind of freaking out because it's October and I still haven't got my refund and it's like girl the bills are due like I've been in school for a month like where is my refund check anyway that's like a whole another story and maybe even potentially a whole another video but like that has been only stressing me out and I'm in four classes which has have been like also stressing me out and now I'm so happy I listened to my friend Danny because she was like when I got my oh my god I haven't told you guys yet oh okay I have to be like sit down catch up like we need to we need to chat because there's a lot of things you don't know and I'm really excited to tell you but when I got my offer from like last week Danny was like don't cancel your other interviews like just see where it goes so I took an interview yesterday and now I have the interview today and now I'm so happy I did both of those things because clearly teachers college is playing with my money and my refund check so anyway listen to Danny we all need a friend like Danny in our lives let me see what the weather is looking like in London I literally just started typing London into my phone oh my god weather because I don't even know what I should pack oh it's raining what a surprise okay it's not looking too too bad it's like oh my god it's gonna be 70 this weekend so I am super excited gonna pack some dresses and stuff since it's gonna be 70 a couple of days so this which you guys have probably seen like five million times because truly like once I start to like something that's like all I will wear I don't know if you notice but I do wear a lot of blue so hard I love this one and it's like nice and like kind of foley colors and then I do want to bring the stress from rent the runway but I canceled my subscription because we need to save money clearly and I don't know if I have them for like any longer like I think I might have to return the stuff let me see oh my god I don't have the app on my phone okay I'm gonna check that later ah I already feel a little stressed and chaotic let me put some jeans in here I definitely need to wear those also I was watching my old vlogs and I realized that I completely like filmed me building this like temporary um drawer situation but I never actually posted the video or edited it so that'll be coming soon a couple of dates well now I'm gonna give it for five days how many outfits and honestly I do want to pack some workout stuff because I don't know if I can go five days without working out so definitely have to throw some of that in there and then I think I just need to do shoes and like honestly y'all that was kind of easy peasy lemon squeezy I'll probably wear sneakers there and I'll probably also bring another pair of sneakers just in case but I honestly feel pretty good about the packing situation this is going to be my carry on that I'm going to pack later tonight because I'm not ready to do right now but yeah like wow I don't know if you guys if you've been watching for a while I have a pack with me from Mexico in which I literally have a full meltdown this is so much better than that you know that's that's gross I'm 12 now it's gonna take me about 30 minutes to get to the interview and I want to pack in like a little extra time just in case I get lost because I have no idea where I'm going because I did not practice my commute also I even know people like did that like practice commutes like that's actually really smart I should probably do that in the future I need to go ahead and get ready I mean I guess I am ready I just need to pack my bag I feel I feel good I feel a little nervous I haven't even really prepared anything it's honestly because I didn't even think I was gonna go on this interview like I wanted to cancel it last week all right let's go to the interview I'm sweating and I feel kind of because I'd be working with like an at-risk youth population in New York City and I just think that could be like a really cool experience because right now I do work with the youth but more so like depression but like not at like risk like my population I would work with if I were to get this job would just be facing a lot more stuff than you know the problems of an ideally student whose parents probably pay for everything for them it's so funny I say that and I'm like I need money my parents do not pay for everything for me anyway I'm like right next to FIT isn't that pretty cool I like really wanted to go here like tbt for undergrad my parents were like you know but anyway I probably be at the airport at like seven ish probably like six ish because my flight is at nine but you know we're just flowing international growth you should come with me next time really fun time I honestly have an exam that I need to take this week and I haven't studied too much for it but my brain is like so dead because I was up to like 1 a.m. last night so I'm just going to edit tomorrow's youtube video instead and I have a cookie that I got from Whole Foods earlier so I'll probably snack on that on the plane and yeah I'm really excited to take you guys with me to London I'll see you there


Hey guys welcome back to my YouTube channel this is Daniel Rossell here bringing you another video today on camera accessories and I wanted to show today an interesting audio setup I'm currently using to record this video with. Firstly the microphone I'll start with that a Audio Technica 8875R which is a short shotgun microphone it's not really intended to be used as I'm using it like an interview mic but it is a terrific short shotgun there aren't that many of them on the market I could find I recently bought the Canon XA40 which is a really compact camcorder and so I wanted this shotgun microphone that wouldn't kind of overwhelm the camcorder so it was really really great microphone really happy with it but that's what I want to make the video about. I recently picked up an XLR wireless pair from Lakato and I went for a pretty budget XLR wireless system that's what the top part you can see here that's the transmitter and the receiver is going into a direct plug-in XLR to my XA40 about 60 centimeters in front of me here. Now what I noticed about the XLR systems is you can get ones that can deliver phantom power in fact UVive which is the intermediate component do actually make one the UVive U3C C standing for condenser that's a XLR plug-in wireless system capable of delivering phantom but the cheaper ones on the market and I went for this Lakato MK5 thing is definitely on the cheaper end of the XLR wireless systems they typically won't deliver phantom so what happens if you want to use a microphone that needs phantom such as the audio technique I'm recording speaking into which needs 48-fold of phantom power but your XLR pair doesn't support phantom and that is where the intermediate piece in this rig comes in it's the UVive and I'm going to actually just kind of turn around so you can see I'm using a really short XLR cable to keep this all tight together it's the UVive P1 and what this is I did not know that these things existed until I googled on Amazon something like phantom power injector in other words some kind of an electronic that its only purpose was to add phantom power into the receiving end and then pass through the audio signal and this is just basically all it does so basically it's a battery it has an input and output side XLR on either side so I'm connecting you can see the XLR transmitter directly into the output terminal here on the on the P1 and then on the input side it's just that short shotgun microphone leading up into the into the into the microphone I'm speaking into you've got 12 volts and 48 volts of phantom power and an on off toggle on off toggle and that's all it does it's a simple piece of gear no need to make it complicated it charges up over usb type c and there you go so basically the the point of this video is if you have a microphone that needs phantom power and you own a XLR wireless system that doesn't deliver phantom power you're not out of luck you can buy yourself a UVive P1 and that can go straight into your transmitter and that will inject phantom power into the microphone coming into the wireless transmitter and then you'll be able to get it working perfectly recording into your camcorder hope that was helpful if you've if you're in that sort of dilemma or that's your situation this was about $50 on amazon.com and definitely I think a super useful thing to have in my audio bag going forward thank you guys for watching if you'd like to get more videos from me please feel free to subscribe to this youtube channel


Next up we have Dr Fiona Kurslake who's the head of agronomy for BitWise agronomy. Thanks Fiona. Thanks Nikki. So BitWise agronomy, basically in a nutshell it's using GoPro's on tractors. So what growers can do is they can take a GoPro which you can purchase anywhere yourself, we're not dealers or distributors or anything like that. Just use a RAM mount and mount it on the tractor and off you go. So what the growers do is they take a video of the crop and then they bring it back, take out the SD card and upload it to our GreenView platform and we use Computer Vision to analyse the video and we can count and measure all the different parameters of the crop that are important. We're not all of them, most of them. So the kind of parameters that we're looking at. So after pruning if you want to look at your cane counts, cordons, node counts you can go through and take a video after pruning so that you can get some of that pruning quality assessment. It's also a lot of people like to start using it as a first point of yield estimation. So if you've got some historical fruitfulness or bunch weights, things like that you can start calculating that early season yield out just after pruning. So the next point in the season that people often use this is after the shoots are started to come out and so we can start doing early season shoot counts like bulb perc percentages, et cetera and have a look at the variability that Andy was just talking about across the vineyards. As the shoots begin to close over it's a bit harder for us to analyse them individually from a video so what we then move to doing is actually measuring the length of the shoots. Again, this is a really important component for variability so if you can see the difference in the canopy height as you move along the row and across the blocks. A lot of people have told us that they're really interested in inflorescence counts. They say they're really interested and we're like that's great if you capture the imagery then we can work with that and develop it. We haven't received a lot of data for that time point so we're still very much in an exploration phase in inflorescence counts but if people are really interested and motivated to take the videos then then that's something that we can develop but we do get a lot of imagery in for bunch counts as well so that's another point that we can do during the season. The beauty of the GoPro is that the grower owns it so they can take it and they can take the imagery whenever they like during the season. A lot of the times we get asked when do I need to take the imagery. I'm one of those people who says well what do you need the data for? What's the purpose of taking the data? What information are you going to get out of it? What decision are you going to base on it? So we're not prescriptive. The cost has come up a lot in the previous talks so we have an annual subscription so we're doing that to try and encourage people to take as much data as they like during the season and so that it's not a barrier say if you go past your five image captures during the season then you have to pay more or anything like that and so our prices start at $2,000 for an annual subscription and then there's a $3,500 and a $5,000 tier and that's just about additional features and functionality. With the data that's been brought up a few times today as well so we provide all the data back to the grower. Yes, we use the data collected to make the algorithm smarter and to make them more accurate and so that they perform better but the data all goes back to the grower. The data is provided in two forms that can come back to you in a CSV file if you already have a database or some other system that you're using that's a really simple format for the data to come to you for ingestion into those systems so someone like Hans Loder at Penley Estate is building his own system, that's all he wants. Other growers would prefer that the data comes to them in more of an information format so that's where we've been working on Power BI reports so that there's a little bit more functionality in that etc but we also map out the data as well so you've kind of got that granularity then we've got some of the aggregated data and then we've got the mapped data as well and so that all comes back to you. We generally find that people are using us generally for one of two reasons sometimes both it's really that yield estimation piece and whether that's earlier in the season or later in the season and other decisions maybe outside of the vineyard but like buying or purchasing fruit etc getting that information early is quite beneficial but then also the other cohort is really around managing variability some who just want to understand the variability and work with it but quite a few people who are trying to manage that variability out of the block at the block level so that they can start hitting those quality targets that they're after. So that's basically bitwise in a nutshell so we've got a stall down in the tent that everyone's been talking about and so we've got some of our reports up there etc that you can come and have a look at and then we also would love people who are interested we can do one-on-ones via Zoom so we can book those really easily for you if you like so just come and see Belinda Bramley or myself and we can organise those for you to run you through them. Thanks.


So again, we're going through a lot of basics here on the Linux shell. I'm using the Z shell you're supposed to be very quick tutorials, so I'm trying to keep them short and This is one that Is gonna be very useful Probably one that I probably should have shown a little bit earlier But we want to be able to view all the files and folders in your current directory Now if you're coming from a Windows environment, you might think DIR which Actually works on my system, but on a lot of Linux systems won't work. That's not the normal Linux command for for viewing files, but you can install that program or you could also link it to the actual command Which the actual command is LS So by default you can see it looks a little different than the DIR command by default It just gives you a list of the files and if your system set up to have color it will List out the files and colors you can see different types that like in my case folders are blue It looks like images and videos are pink and then regular files are green That's very helpful and then Yeah, so color coding is nice and That's just the basic view, but we want let's say we want more information on these Files I can add in different arguments to this command so I can go LS I can do dash L, which gives me a list view which is a little more like the DIR command It tells me you know the permissions about this file who can read it write it and execute it What user and group is in it created it or owns it The file size you can also see the date that it was last modified and then the name of the file now you'll notice right here that The file size is a little hard to read Let's put it in a little bit easier way to read by running the same command But with the L We're going to add to it H and I'll hit enter and you can see that the H stands for human readable Which has now changed it from bytes into kilobytes and megabytes and if need be gigabytes So it's a less accurate number. So like this is 11 megabytes. I'm sure it's not exactly 11 megabytes But it gives you a little more I easier to read idea on how big the files are also the top here It tells you for this folder the total File size now that doesn't include Subdirectories which may have a bit more in there. We'll look at a command for that in a future video another thing that you may want to look at is hidden files If you want to view hidden files, you'll want to add the aid of that that says all files And you can see that now there's a few hidden files which are highlighted here That weren't shown before They're now being shown and that a just means all and you can use a combination of these So I can use lna and that will list all in a list form with the Files size in the non-human readable. I can do a with I don't think that's really gonna help because without the L It's not going to show the file size But if I just a dash a it will show these hidden files here Which without the a you can see that those files are missing So that's it That's a quick look at the list command if you man ls It will give you a full list of commands here. There are many other Commands, little me many other options switches for it So you can go through those and look at those but those are the three that I mainly use lh and a so I Thank you for watching and as always I hope that you enjoy this video be sure to comment below Let me know what you're thinking of these videos Subscribe share like and again right now. We're going over the very basics. We'll get some more fun things I'm trying to knock these out a few each week so we can get some more fun things Thanks for watching as always. I hope that you have


Live from Berlin, Germany. It's theCUBE, covering NetApp Insight 2017. Brought to you by NetApp. Welcome back to theCUBE's live coverage of NetApp Insight 2017. We're here in Berlin, Germany. I'm your host, Rebecca Knight, along with my co-host, Peter Burris. We have two guests on the program now. We have Rory McBride, who is the technical account manager at Aero, and Brian McCloskey, who is the vice president worldwide for hyper-converged infrastructure at NetApp. Brian Rory, thanks so much for coming on the show. Oh, thanks. So I want to start with you, Brian, and just talk a little bit, tell our viewers a little bit about the value that HCI delivers to customers, and especially in terms of simplifying the data center. Okay, in a nutshell, what NetApp HCI does is it takes what would normally be hours and hours and hours to implement a solution and literally hundreds of inputs. Generally, over 400 inputs, and it simplifies it down to under 30 inputs, and an installation that'll be done within 45 minutes. Now, traditional HCI solutions have similar implementation characteristics, but you lose some of the enterprise flexibility and scale that customers of NetApp have come to expect over the years. And what we've done is we've provided that simplicity while allowing customers to have the enterprise capabilities and flexibility that they've grown accustomed to. And is this something that you were talking with customers? I mean, in terms of the simplicity, what were you hearing from customers? Well, most customers these days are challenged of, everybody has to find a way to do more with less or to do a minimally, a lot more with the same. And if you think of NetApp, we've always been wonderful about giving customers a great production experience. So when you buy a typical NetApp product, your colleague gonna own it for three, four, or five years, and it will continue. And NetApp's always been great for that three, four, and five year timeframe. And what we've done with HCI is we've really simplified the beginning part of that curve of how do you get it from the time it lands on your dock to implement it and usable by your users in a short manner? So that's what HCI's brought to the NetApp portfolio that's incremental to what was there before. So one of the advantages to third parties that work closely with NetApp is that by having a simpler approach to doing things, you can do more of them. But on the other hand, you want to ensure that you remain focused on the value at. So in the field, when actually sitting down with a customer and working with them to ensure that they get the value that they want in these products, how do you affect that balance as the product becomes simpler to the customer now being able to focus more on other things other than configuration implementation? I think what I've been able to get to, to doing something with your data is the key. You needed a low bar of entry, which a lot of the software and hardware providers are trying to do today. And I think HCI just helps to pull all of that together, which is great. We're hearing from those third party vendors that it's great that from day one, they've been integrated into the overall portfolio message. And I think customers are just going to be pretty excited at what they can do from day zero with this hardware. But when you think about ultimately there, how they're going to spend their time, what are they going to be doing instead of now all this configuration work? Especially what is there going to be doing now that you're not doing that value added configuration work? Well, hopefully we'll be helping them to realize the full potential of what they bought. So rather than spending a lot of time trying to make the hardware work, they're concentrating more on delivering a service or an application back to the business that's actually going to generate some sort of revenue. You know, in Arrow we're talking a lot to people about IoT. And I think it's going to be the next wave of information that people are going to have to deal with. And having a stable product that can support and provide some sort of value of information back to the business is going to be key. So Brian, HCI, as you noted, dramatically reduces the time to get to value. Not only now, but it also sustains that level of simplicity over the life of the utilization of the product. But how does it fit into the rest of the NetApp product that you said, the rest of the NetApp portfolio? What does it make better? What makes it better in addition to just the HCI product? Okay, so NetApp has a really robust portfolio of offerings that we, at a high level, categorize into our next generation offerings, which are SolidFire, FlexPod SolidFire, StorageGrid, and Hyperconverged. And then the traditional NetApp on tap-based offerings. And what the glue between the whole portfolio is the data fabric. And HCI is very tightly integrated into the data fabric. One of the innovations we are delivering is SnapMirror integration of our HCI platform into the traditional on tap family of products. So you can seamlessly move data from our Hyperconverged system to a traditional on tap-based system. And it also gives you really seamless mobility to either your own private cloud or to public cloud platforms. And as a company with a wide portfolio, it gives us the ability to be really consultative with our partners and our customers. Because what we want is, and we feel customers are best served on NetApp, and we want them to use NetApp. And if an on tap-based system is a better solution for them than Hyperconverged, then that's absolutely what we will recommend for them. And to your earlier question about the partners, one of the interesting things with HCI is it's the first time as NetApp, we're delivering an integrated system with compute and with a hypervisor. It comes pre-configured with VMware. And it's a wonderful opportunity for our partners to add incremental value through the sales cycle to what they've generally brought to NetApp in the past. Because as NetApp, we're really storage experts where our partners have a much wider and deeper understanding of the whole ecosystem than we do. So it's been interesting for us to have discussions with partners because we're learning a lot because we're now involved in layers and we're deeply involved at higher levels of the stack than we have been. I'm really interested in that because you say that you have this kind of consultative relationship with these customers. How are you able to learn from them, their best practices, and then do you then transfer what you've learned to other partners and other customers? Yeah, from the customer end, we try and disseminate the learnings as much as we can, but we're a huge, huge organization with many, many account teams. But it all starts with what the customer wants to accomplish. I mean, minimally, they need a solution that's going to plug in and do what they expect it to do today.  And it's really that vision piece that tends to drive more towards one part of the portfolio than the other. But take us through how this works. So you walk into an account, presumably ROECS has a customer. Yep. The ROECS customer says, well, we have an issue that's going to require some specialized capabilities and how we use our data. So you could look at a lot of different options, but you immediately think NetApp. What is it that leads you to NetApp, HCI versus ONTAP versus SolidFire? Is there immediate characteristic that you say, that's HCI? I would say that the driving factor was the fact that they wanted something that's simple and easy to manage. They want to get, I don't know, say a Mongo database up and running or they've got some other application that really depends on their business and the underlying hardware needs to function. Brian was saying there that it's got Element OS sitting underneath it which is in its 10th iteration and you've got VMWare version six which is probably the most adopted virtualization platform out there. These are two best-of-breed partnerships coming together and people are happy with that and can move on and manage it from a single pane of glass moving forward from day one right the way through to when they need to transition to a new platform which is seamless for them. I think that's great from any application point of view because you don't want to worry about the health of things or whatever. You want to be able to give an application back to the business and also we talked about education. I mean this event is geared towards bringing customers together with NetApp and understanding the messaging around HCI which is great. One of the things that you keep hearing from customers is this need for data simplicity, this need for huge time-saving products and services. What do you think if you can think three to five years down the road what will the next generation of concerns be and how are you, I'm going to use the word that we're hearing a lot, future-proof, what you're doing now to serve those customer needs of the future? Three to five years down the road. Well I think to the future, I can't predict three to five years out, very reliably, but the data- But you can't predict they're going to have more data. Yes. They're going to merge it in new and unseen ways and they need to do it more cheaply. But the future-proofing really comes in from the data fabric. So like you can, with the integration into the data fabric, you could have a information that started on a NetApp system that was announced eight years ago seamlessly moves into a solid-fire all-flash array which seamlessly moves to a hyper-converged system which seamlessly moves to your private cloud which eventually moves off to a public cloud and you can bring it back into any tiers and wherever you want that data in six, seven, eight years the data fabric will extend to it. So that's really where the, and there are within each individual product there are investment protection technologies within each one, but it's the data fabric that really should make customers feel comfortable that no matter where they're going to end up is taking their first step with NetApp, is the step in the right direction. So the value-added ecosystem that NetApp and others use, and obviously ROECS is a good player in that, has historically been tied back into hardware assets. How does it feel to be moving more into worrying about your customers' data assets? I think it's an exciting time to be bringing those things together. I mean, at the end of the day it's what the customer wants. They want a solution that integrates seamlessly from whether that be the rack right the way up to the application. They want something that they can get on their phone. They want something they can get on their tablet. They want the same experience regardless of whether they're in an airplane or right next to a data center. And I think the demand on data is huge and will only get bigger over the next five years. I was looking at a recent cover of I think Forrest Magazine which was said about, it's actually from a number of years ago about Nokia and how can anybody ever catch them and where are they now. So I think you need to be able to spot the changes and adapt quickly and to steal one of the comments from the keynote yesterday, it's moving from a survivor to a thriver with your data. It's going to be key to those companies. Well, just in talking about the demands on data growing, it's also true that the demands on data professionals are growing too. How is that changing the way you recruit and retain top talent? Well, for us as NetApp, if you were to look at sort of what we wanted in the CV five years ago, we wanted people that understood storage. We wanted people that knew about volumes, that knew about data layouts, that knew how to maximize performance by physical placement of data. And now what we're looking for is people that really understand the whole stack and that can talk to customers about their application needs, their business problems, can talk to developers because what we've done is we've taken, like even those people that were good at all those other things I mentioned, when you ask them like, what do you love about this product? Like none of them ever came back and said, I love the first week I spent installing it. So we've taken that away and we let them do more interesting work. And our challenge for us is, and I think us as a collective society is to make sure we bring people forward and enable from an education perspective skills enablement so they're capable of rising to that next level of demand. But we're taking a lot of the busy work out. Right, and then making sure that they have the skills to be able to take what they're seeing in the data and then take action. Yeah, but we, you know, we want our customers to look at NetApp as a data expert that can work with them on their business problem, not a storage expert that can explain how an array works. Well, Brian, Ruri, thank you so much for coming on the show. It's been a great conversation. Oh, thank you. Thank you very much. You are watching The Cube. We will have more from NetApp Insight. I'm Rebecca Knight for Peter Burris in just a little bit.