Okay, so at this point, we've gone over a bunch of standard library collection functions. And we'll put some of them to use here in an example program. We've previously done rock paper scissors on the command line. Here's an implementation of a tic-tac-toe game on the command line. This one's a little bit more substantial. It's 10 functions long instead of just three functions long. But it's still quite simple, so let's walk through it function by function. So here are three of the first four functions. I'll start on the bottom one here. This function triples, which accepts an argument of the board. In our program, we're going to represent the board, the tic-tac-toe board, as a vector of nine elements where each element of the vector is one of the slots on the board starting from the top left and then going to the right row by row. So the first element of the vector is the top left element of the board. The second element of the vector is the top middle element of the board. The third element of the vector is the top right. And then the fourth element, we go down to the next row. And that's the left slot of the middle row. So we get to the last element of the vector, and that's the bottom right element of our tic-tac-toe board. And in this vector, an empty slot will have the value nil, and an x occupying a slot will be represented as the keyword x, and an o occupying a slot will be represented as keyword o. So in any case, what this triples function does is take our board, our vector of nine elements, and it returns a sequence of the triples of the board, the triples being the rows, columns, and the two diagonals of the board. Ignoring for now the call to concat and list, ignoring that part, look first just at this call to partition all. This call is where we get the rows, the three rows of our board. The partition all sequence function recall will return a sequence of sequences each made up of that number of elements, a specified number of elements in this case three. So it takes the first three elements of board, then the next three elements of board, and then the next three elements of board after that, and so on until it exhausts the entire sequence. And in this case, our board is made up of nine elements. So we get three sequences, each with three values. And of course, the first three elements of the board represent the first row, the next three elements of the board represent the middle row, and the last three elements of the board represent the last row. looking at these calls to take nth. First off, we get the first column with take nth3 board. The first column is made up of the values at index zero of the board index three of the board and index six. And the take nth function will return a sequence made up of the item at index zero, and then it gets the subsequent values by adding three each time until it exhausts a sequence. So we get the values at index zero three and six. Now to get the second column, what we want are the values at index one, four and seven. And we can do that if we first take our board and we drop the first element. So this call to drop one board that returns a new sequence, which is just like our board vector, except without the first element. So its index zero is index one of the board and so forth. So now when we call take nth three, we're again getting the elements at index zero, three and six, except now zero, three and six referred to index as one, four and seven of the original board. And then to get the last column, the third column, it's the same idea, we just dropped two. So now that what was index two of the board is index zero of this return sequence, which we passed to take nth. Now to get the diagonal, which runs from top left to bottom right, we want the values at index zero, four and eight. So we can get those values with a call to take nth, where the argument is four. But to get the diagonal that runs from top right to bottom left, we want the values at index two, four and six. And so once again, we drop two from the original board, yielding a sequence that starts at index two. And we could call take nth two on that new sequence and get back the values at two, four and six. But then we'd also have the value eight and we don't want that last value. So here after dropping the first two values of the board, we use the drop last function to drop the last two values of that returned sequence. And we end up with a sequence that runs from the original vectors index two up to index seven. We didn't need to drop last two, we could have just dropped lasted one, and gotten the same effect, it doesn't matter really. In any case, we end up with the top right to bottom left diagonal, the index is two, four and six of the original board vector. So now you should understand the call to partition all and the calls to take nth. What we need to do now is combine all of these sequences representing the rows columns and diagonals into one big sequence of these things. So first, we need to take all the individual columns and the diagonals and combine them into one sequence and a list in this case. And now that we have a list of all that stuff, and we have our sequence of the rows, we can combine them together into one big sequence with the concat function. So the concat function will return a sequence of all of our triples the rows columns and the diagonals. And so that's what this triples function will return the result of concat. So now that we can get a sequence of all the so called triples of our board, let's look at this top function triple winner question mark, which is passed in a triple. And if all the elements of the triple are X's, then it returns an X a keyword X value. If all the elements are O's, it returns a keyword O. And if it's neither case, if it's a mix of X's or O's or nils, then this triple winner function returns nil. First looking at the outer if form in this function, its condition is a call to every question mark. And the every question mark function is a sequence function that first takes a predicate, a function that returns out of true or false, and then the sequence in this case triple, every will invoke its predicate function once with each element from the sequence. And only if all of these calls to the predicate function return true will the every function return true. Otherwise, if any of the calls to the predicate function return false, then every will return false. So here the predicate function is actually a set. And as we discussed, a set can be invoked like a function. And when you call a set as a function, you pass in some value. And if that value is contained within the set, then the set will return that value. Otherwise, it returns nil. We could instead of this set, we could have a predicate function that takes in a parameter. And we test whether that parameter is equal to the value we're looking for, colon x, a keyword x. But this form, as you can see, is more compact to write. So this is the preferred idiom. And so in this condition, this call to every, if all the elements of the triple sequence are the value keyword x, then every invocation of the predicate function here, the set will return keyword x, which is considered truthy. And so that every function will return true. And when this call to every returns true, then this if will return the value keyword x. And so that's what gets returned from the triple winner function. If however, this condition tests false, if not all the values are equal to keyword x, then we reach this other if form, which has a very similar condition, except now we're testing whether every value in the triple sequence is the value keyword Oh, and if so, then we return keyword Oh, otherwise we return nil. So as we said, if all the elements of the triple sequence are the keyword x value, then this function returns keyword x, if all the values are the keyword Oh value, then this function returns keyword Oh. And if neither of these cases are true, then this function returns nil.


The weird circle. In this cave, by the restless sea, we are met to call from out the past stories, strange and weird. Bellkeeper, hold the bell, so all may know we are gathered again in the weird circle. I speak again, there immortal people, a passion in the desert. Have you ever noticed how very much a woman is like a beast? The resemblance no doubt is due to the fact that they have so very much in common. Any man of us frequently has seen a woman turn into a beast or a good tax simulator of at any provocation. But there is only one man of my experience who saw a beast turn into a woman. He was a Frenchman by the name of Gaston Monet. It all started in Upper Egypt. A column of soldiers with a French foreign legion were on the march to join their eastern flank. After three months of marching and fighting, guerrilla fashion, this particular group came to a small Arab village named De La Haber.  You have no idea what a white woman looks like to a soldier who has been on the march for months. And this one, well, she was no ordinary woman. She was lithe and subtle, like a desert panther. Her skin was tanned by the sun and her hair, it was hairy and brown with gold lights. And her eyes, they were the most amazing part of her. They were large, yellow orbs, remote and fathomless. She stood in the narrow streets watching the column of soldiers march by, and then she glided out from the group of gaping natives and brushed one soldier's shoulder with her hand. Gaston Monet showed her. She turned and then... What do you want? I must talk to you. I can be caught, Marshal, for talking to you. That would be important, please. Not now. I'll meet you later. Please, please go back. Later? When? Midnight, tonight?  Don't fail me. Midnight, then. But where? At the end of the farms, where you went at the city. Goodbye. Go quickly before your scenes. Goodbye! Comrade! Hold! You may camp at the other gate of the city.  Comrade! Hold! Michelle, wasn't she a beauty? What are you talking about, Gaston? I didn't see anybody. The white girl, the one who talked to me. Gaston, have you lost your mind completely? Nobody talked to you. You are seeing things. But the woman who whispered to me. Woman? Just a group of natives in a tame panther. You've quite an eye for women, Michelle. But you missed this one. And I'm to see her again. Tonight at midnight. And so it was that night when the moon was yellow in the sky. Gaston sneaked away from camp. And walked down the street. Walked to the end of the mudbank road. He saw the girl standing under the palms and his pulse quickened. But blended with that quickening was something more. Something of fear.  In those cold, placid, yellow eyes. Something that seemed to swim under the lips. Come at last! I was worried, my friend. What is your name? Gaston Monet. And yours? Mine, your alwa. Why do you look at me that way? Oh, you're the first decent thing I've seen in months. Am I? Tell me, why did you ask me, out of all the others, to meet you here tonight? I mean, it is strange, isn't it? Perhaps it is fate. And besides, I am in trouble. I'm stranded here. I set out on an expedition with some friends. Friends? Yes, friends from France. We were attacked and they were killed. The natives won't leave the village. You know that? And I can't stand it here any longer. But how can I help you? You are brave and strong. You could take me to a coastal city and then I could sail for France. For civilization. But I can't desert my outfit and just leave suddenly into the desert. Besides, we have no provisions. I have provisions. I've saved them for a long time waiting for just such a one as you. And the journey is simple. Do east four days march. I should like to help you very much in any way I can, but... Oh, confound it, that's desertion. I just can't woman.   Gaston, please. Gaston, don't refuse me. Please don't. It was warm out that evening. Warm and the desert mean placed tricks with the men's man. And the girl, Mignon, was more important to Gaston then than his country, his army or his friends. And so they left together in the dead of the night. They stood out of Delaheba into the desert, into the lonely desert with just enough provisions to make their way to the coast. And so the night passed and turned into morning and that day passed and turned into another. And back in the village of Delaheba, Lieutenant Michel reported Monet's absence to Major Duval. The alarm was given and the barracks were searched. Lieutenant Michel reporting, sir. Yes, Lieutenant. The entire city's been searched, sir, but the design of him. I'm afraid, sir, it is desertion. I thought so. Gaston Manet never was armed in material. Weak. He'll never get away.  Please, sir. Not out there. These sandstorms, they have started, sir. Oh, forget him. Blast these young men who join the Foreign Service and expect it to be a picnic? What do they think war is? I quite agree, sir. The desert, and then they expect me to send out a searching party to look for them. Man, they're right. I'll have to. You, Michel, choose four men, get your provisions from headquarters and start toward the coast.  You can reach the coast before he does. Where will we join you, sir? Or else we'll be waiting for you on the coast. That's all, Lieutenant Michel. Thank you, sir. And so the search party started under the leadership of Lieutenant Michel, but Gaston Manet and Mignon were miles ahead of them. Camping for the night under the yellow moon, completely unaware that they were being followed, Gaston looked at the girl and breathed in the magic air of the desert. I dreamt of people like you, Mignon, and in my arms they came from nowhere, just as you came from nowhere. Are you trying to pry into my secrets again? No. No, I... I was just looking at you. You are so incredibly lovely. Oh, I'm glad. Mignon, we're about three days' march from the sea. We're only two days away from another native village. I could get work there, and we could get married. Married? Nonsense. And settle down. We'd be there five years or so, and then we'd return to France. The army would have forgotten about me by then, and I'd get a good job. I'd make you proud of me. Being buried out here for another five years, honestly, Gaston... But you wouldn't be buried. You'd be with me. Just the two of us together. I'm...I'm frightfully in love with you. If you loved me, you wouldn't treat me the way you do.  Yes, I chose you. And now, if you don't mind, let's get some sleep. We're going to be on the march tomorrow morning, aren't we? Sleep and march. Sleep and march. Three more days, and then... then give myself up to the authorities. You'll probably spend a few days in the guardhouse, nothing more for this little adventure. Stop dramatizing yourself. The army calls a desertion, Mignon. There's only one punishment for desertion. What is that? You know very well what it is. Death. You got away once, you'll get away again.  Of course I care, but what can I do? What is that? Oh, a desert lion. Don't worry. He won't come near us, not while the fire is going. Fortunately, a beast is afraid of fire, but a woman loves it. Good night, Gaston. Good night, Mignon. Well, such a hurt way to say good night. And so they slipped by the fire, and the fire played tricks with the shadows on the sand as they slept. But crouched not far from them was the panther, eyeing the boy and the girl, and it crept toward the fire unafraid, sidling up to the boy until its large yellow head was afoot from Gaston's arm. And it too fell asleep, and the fire died down. The morning sun rose over the sands, and they awoke all three at once. Gaston, look, next to you. Where is my rifle? I've got it. Don't make any fast move, whatever you do, Mignon. Any sudden action will frighten the animal, and she'll spring on us. We just can't sit here and wait. Move your left arm very slowly toward the rifle. Very slowly, and try to hand it to me. I wonder why she stands there looking at me like that. I wonder. She's coming closer. Sightling up to you like a great dabby. Here, I've got the gun. I'll push it toward you. We can't shoot at this close range. She would never give me time to take aim. Look at her. Sitting right next to you. Yes. She licked my arm. I believe she wants to make friends. What are you going to do? Pat her. I believe she'd allow me to... There. There you are. Oh, watch out.  Beautiful lady. She's purring. Just like a great house cat. Yes. Yes, she likes it. I believe we've got a pet in you. A pet indeed. She'll purr at you now and turn on you with the first opportunity. Yes. Women beasts are very much alike. They make friends of you and then they turn on you, don't they? You are not being funny, Gaston. I'm supposed to know that. Look, she's allowing me to play with her ears. What are we going to do with her? Nothing. Not now at any rate. Just be careful not to frighten her. As soon as she goes any distance from us, I'll be able to take some kind of aim and shoot her. Yes, I suppose you're right. Look at her, Mignon. Do you notice anything? Nothing except she's a lovely specimen. Lovely, yes. Lovely. And she looks like you. But like me? That's sheer nonsense. It's true.  Yellow like yours. Deep like yours. Cruel and cold predatory like yours. Gaston, I tell you it's nonsense. Come over here, Mignon. Try to make a friend of her. What do you mean? I want you to pet her. Just as I'm doing. Well, of course. She will let me. Try it. All right. I will. She snucked at me. I said, try it again. No, I'm... I'm afraid, Gaston. It's nice to see you afraid of something. I said, try it again. No, no, no. I won't... I said, try it again. Did you hear me? My arm. She bit my arm. It's bleeding. She doesn't like you, Mignon. I wonder why. She'll allow me to stroke her. Please, Gaston, don't. Are you frightened, my dear? Why this animal won't hurt you? Beautiful creature, aren't you? You don't like me, Mignon, do you? Or perhaps you do like her to test the sharpness of your claws. Don't, don't, don't, don't. Don't talk about it, Gaston. You don't know what you're doing. You have no idea. You have no idea. Am I frightening you? Frightening me? I have only one chance to be a woman. A real woman. A chance to go to a civilized country. A chance to break away from them. For my brothers and sisters. Kill that animal or I must return to them. I lied to you before, Gaston. I lied to you when I said I came to Della Haber with an expedition. I was born here. Just as she was born here. And there is a kinship between us. Yes, a kinship you will never understand.  There in the blazing sun, Mignon begged Gaston to kill the penther. But Gaston insisted that all three traveled toward the coast together. Why he never knew. It was as if some power stronger than himself forced him on. The beautifully sleek creature followed behind Gaston and the girl. At times coming up to rub her fine sleek body against his leg and growl, burying her fangs at the girl. When night began to creep over the sands like some monstrous dark hen, they stopped not far from Mignon. We'll make camp here, Mignon. Yes, here. Here in the desert. I'm so tired, Gaston. Are you?  How about you, beautiful lady? You'd better spread out the blankets and the food. I'll get some water. Don't leave me alone with her, please, Gaston.  Oh, you don't know what you're saying. I'll be back directly. The water's only some half mile away. Call for help if you need any. Gaston, Gaston, please, please, Gaston. What are you afraid of? I said I'd be right back and the animal might follow me. But she's not following you. She's staying here. Still, she won't hurt you. You've got the gun just in case. Gaston, please. Don't tease me. You don't know what you're doing. I'd like to see you frighten Mignon. You who are so sure of yourself. I'll be back directly. Nice little lady. Lie still, little pointed gun. This won't hurt you. Why do you look at me like that? Yes, Gaston is right. We are alike. Our eyes. Our souls. And our emotions. No, little lady. Stay where you are until I get an aim. A perfect aim. No, stop it. Mignon, Mignon, I'm coming. Mignon, Mignon. Back, lady. Back. Leave her alone, lady. I said leave Mignon alone. That's a good girl. There, beautiful lady. Oh, Gaston. Mignon.   She wouldn't have hurt you if you hadn't tried to shoot her. I was frightened of her. I was so frightened. She's so like me and she knows it.  She wanted more. She wanted to absorb a human soul in that animal body. My soul. Try to lie still. Quietly, my dear. Quietly. Waiting for me. Watching and waiting. Waiting for me to die. Quiet, my dear. Quiet. Lies still if you can. Lies still.  That's why I was so anxious to get away from the desert. You don't believe me. Gaston, you will. Mignon, little lady.  Yes, I'll kill you for taking the only thing I love.  Mignon. Mignon, where are you? You're dead.  But I heard you laugh. Or was it the panther? Mignon. Your laugh came from a throat of a panther. Can it be the blending of two souls? The soul of a woman and a beast?  For you live in her, little lady. And you are Mignon. Mignon. Come over here. Come over here, my darling. Yes, Mignon. Now I never have to return to the coast. I'll return to the little native village and you'll be my pet. You'll never leave me now, will you? Will you, Mignon? My beautiful creature. You're wounded, aren't you? A bullet through your shoulder. Will you let me fix it? There, lie still, Mignon. It won't hurt you much. It's just a flesh wound in your shoulder. There.  No, no, beautiful lady, it won't hurt. It won't hurt at all. Just another minute and... There you are. It's a nice, clean wound. How does that feel, beautiful lady? How does that feel? And now... this shell of a woman which was once the soul of Mignon must be buried. A decent burial. Here in the burning sand. And so it was. The panther lay lazily in the sun dressed on Doug a sand degree for the body that had been Mignon and placed her in it. The sun slowly set in the orange desert and the panther waited sleepily for him. And not many miles back, the search party plodded on in the cool winds over the great rises of sand mountains. Lieutenant Michel and Trooper Jacques walked ahead. Come along, men. I know you are tired, but we will soon catch up with him. You know Lieutenant Michel. He might be gone ten different directions. If we miss him out here, we will catch him on the coast. Jacques, there is only one route to the coast. And that is the west. And that is the way we go. Don't you think he knows we're searching for him, sir? He left in too much of a hurry. He probably did not think about anything. I always thought Gaston liked the army. Jacques, you can never tell what a man is thinking. I always say, cherser la femme, Lieutenant. You cannot very well cherser la femme when there is not a woman within 30 to 40 miles of Della Heba. You're talking nonsense. You sound like you know more than you're saying, Lieutenant. Maybe I do, and maybe I don't. Tell me, Jacques, you made a complete search of Della Heba. Was there any record of a white woman living there? No, sir. Nobody ever seen a white woman in Della Heba. Well, was anybody else reported missing from the village? No, nobody. Just a pet panther that one of the natives owned. A pet panther? I wonder. And well, he could wonder. For miles ahead of him, tracking off to another native village, Gaston Monet and the sleep panther trekked through the sand, retracing their steps to a halfway mark, heading away from the coast, away from a world of civilization. On the fourth day of the journey, it was almost at an end, and provisions were scarce. Mignon! Mignon! We must make camp again. The sun is setting, and night comes fast over the desert. We've only a little fruit left. You're hungry, aren't you? Well, beautiful lady, here you are. How is that? It's all so strange. You and Mignon. Mignon, what were you really? Where did you come from? What happened to that peculiar, distorted mind of yours? The beast. So very much a woman. Are you laughing at me now? Yes, yes, I don't blame you.  Are you content now, darling? Content to stay with me forever? Please, please be content. Try hard to find happiness. You would never have been happy in France or in any other part of the world. And I'll take care of you the rest of your days. What is that? Another panther? Is that one of your kinsmen? Mignon.  Please, please, I take care of you. Don't leave me. Please don't leave me.  You can't leave me. No, no. Come here. I'll hold you here and you'll never get away. You won't leave me. Mignon. Mignon, don't pull away like that. Oh! Don't leave your teeth as sharp. Don't! Where's my knife? Where is it? Here it is. No.  I will. The knife. Darling, darling Mignon, this is your release. Yes, your release. Please come back to me. Please come back to me. Don't leave me alone at the dancing. Mignon. Mignon. Mignon. You'll never go back to your panther friend. Never, darling. Never, never. And the blood flowed freely from the wounded beast's heart, staining the yellow sand's crimson. And Gaston turned toward the fading sun, his face streaked by tears stained by sand and walking away from the beast, he looked toward the sky, then, falling face forward on the sand, he lay under the hot desert sun, toward evening as the sun set in the sky. The four men searching party reached the bloodstain's sand and then... Look, eh? The panther. Dead.  The body covered with knife bones. Look behind him. Gaston. Gaston! Oh, it's no use, sir. Gaston Monet's been dead for well over 12 hours. Dead? The panther's body covered with knife wounds and Gaston's body absolutely unharmed. Funny, eh? Not too funny. I wonder if that's the pet panther that disappeared. Probably. Jock, never mention that in the report, the Major Deval. Just write, desertion, due to insanity. Insanity, sir? Yes, Jock. Insanity. Paused by desert madness. From the time-worn pages of the past, we have wrought again the immortal tale of... a passion in the desert. Devil Keeper. All the bells.


What's going on everyone? Sam here at late sports cards ripping 23 tops chrome baseball breakers the light 12 box full case break number 18 Ren team style so again at your end results as always find those on the websites under the results tab as well as a link in chat Yeah, Josh young any Josh young is like very good Gary very good He is right now. I would say probably either first or second rookie of the year voting And Ezekiel Durand's batting over 300 Rangers got some good guys Rangers are one of the top seed top five teams in this We pulled on a carol auto 80 refractor rookie Austin Hayes Ken Waldechuk Lindor Quan torque that's an as Waldo Cabrera to 99 Negative of Royce Lewis That is a to 299 Gerard in Carnation. Nice. There you go AJ Mazataki Yoshida base auto congrats Red Sox. There you go Uh mini Pascantino for the Royals and Bobby Whit. No until now. I did not know that was your name for your first name, too until now Solid first box right there. The Yoshida very nice See we get out of this now to me Anderson cannon Smith and jig bug JT Real Muto out to they a Rosarana Pascantino Christopher Morale to 99 Thanks for the sub there Shane Nolan Jones to 350 negative Mike Yastremsky Mini diamond auto Josh young there you go congrats on that one Gary. Hell yeah, buddy Jeffrey pick it up a division and select Corey lead 199 and Josh young youth quick. That's big Gary. That's a solid hit right there my friend. Yeah, those mini diamonds are Exclusive to this to this skew of top scrum to breakers the light I'll talk her Corey seager Bellinger Ivan Herrera There's Bobby Whit Josh young refractor rookie. That's gravy Jacob the grom to 299 orange very Pascantino to 25 purple Edwin Diaz to 250 giant that card is far out man James Outman on the black-and-white mini-diamond bow nailer cardians and Is he killed her in? Pull the red carol auto that'd be nasty That'd be so awesome Don't Chris. Oh Chris. Chris also appreciates good comedy when he wears it. Don't Don't feed into his garbage. Oh red Red judge refractor. I don't know if it's an I don't know if it's an auto though. I don't think it's an auto jury, I would say a bray you Jeter downs to 250 negative of Royce Lewis red Spencer Torkelson three of five on the youth quake. Nice one there Tigers base auto Logan Oh, hoppy Simeon Woods Richardson beep boop Going to the twins We haven't heard of what he's green Ohio Yeah, you do. Yes, you do Honor That's a nice outman right there Jokes aside, there's Nolan Jones David VR rookie E guy Rosario Oscar Gonzalez J. Rod refractor Miguel Cabrera Kate Cavalli to 99 blue sonar to 125 Anthony Rendon blue wave Ryan Nelson to 75 So I got a purple auto Bryce Johnson to 250 that is going to the Giants and gold wave Jared Schuster 31 up 50 going to the Braves That's Braves and Jeremy paying you you quick You darvish Riley green Goldschmidt hobby bias Gabriel Moreno to 99 you have a lot to catch up on that's a thing Salvador Perez to 250 Green Miguel Vargas to 99 black and white mini diamonds Josh young again Wow Congrats Gary to Josh young autos nasty They wouldn't rice terrain to 499 and Michael Harris Congrats Gary Yeah, great to be back Sean isn't it William just asked great Yeah We're also down to four to go on that divisional of What's it called we have Anthony with three and score And then we a bunch got deleted Here's the light. Thank you there Chris roll in with a finest mixer. Gary is cleaning up right now We've got verlander Wander Carlos Perez Rookie Forto KB Jordan And it's gonna be orange Jeremy Pena color match to 25 youth quick Steven Kwan gold Justin Steele gold wave to 50 Nice one there. Zeke Iltovar color match to 250 nice one there and a base auto of Louis Barlin So we had three autos in that pack nice Negative cannon Smith and jigba and a gold Francisco Lindor to 50 Can you see if that thing along the top of the tow bar? I'll come off.  Fabian Kyle stars. I don't think that's I don't I don't think that's true. There's the 299 Adley Rutchman I don't think so Simeon Woods Richardson MJ Melendez dimple on that send an empty box and wrapper that is gonna be for the Royals Royals Jared Young base auto for the Cubs purple Brian Bale. I swung there that is gonna be 250 And Spencer Troubleson Yeah, I'd love to build a canes right next to my house to be freaking awesome And yes, most likely leftover pizza from last night will be dinner tonight Think it's a red troll on me, bro Hey, my chanager show hey Hey Mike trout Him and as Carlos or as Waldo Cabrera to 50 Nice one there. The pink is Ronald Acuna at a 299 nice one there a mookie bets to 350 Joey Maness Justin Steele rookie auto and refractor rookie auto Vinny Baskin Dino Sam I did my first interview Oh, yeah, oh, yeah, that's awesome I never know I never said I never disclosed how much money I get I'm not I don't tell people how much money I never said that I could I could assure you I never said that come on, man But yeah, no, I did move down here for the job I did move down here for the job Z-Kill so far our foothills Perez Tyler O'Neill Mookie bets Gabriel Moreno Xander Bogart's There's a gold Mackenzie door to 50 Purple mini diamonds Tyler Stevenson. I Did not say that. No, that's I did not I 100 can I would bet My house that I did not say that Tyler Stevenson 299. There's Louis Varland rookie auto for the twins Got a masataka Yoshida to 299 for the Red Sox congrats Kevin. I swung there Kevin on the mini diamonds You get a you make that one and where yeah, I moved from Connecticut, but I never I never disclosed I never disclosed my time. I know I would never do that.  There's boba shit again to 399 green Francisco Alvarez to 99 We've got a kodai Senga radiating rookies nice one there for the Mets Be purple Brandon Hughes to 250 Yes, yes, it's not nearly that much Brian Servin Rookie auto for the Iraqis and hunter green. We got a redemption coming up. That is gonna be Cody Belanger And Jordan Groshan's rookie exactly McMahon albies Logan Webb Matthew Libertor to 99 Gabriel Moreno, that is a blue wave 275 the non blue blue wave That's a pink mini diamonds to 350 is that gallon purple auto mark vientos to 250 and redemption Legend autograph refractor of Greg Maddox the professor congrats Braves you go bravos and Logan Ohapi rookie Do a quick recap here Got autos That is Greg Maddox Brian Servin Louie Barlin Justin Steele mini Pascantina That is a there we go Travis Travis who where were you? Did you have the Braves on that one? Trav yeah, let's go Trav Jared young that's Louis Barlin Brice terrain Simeon Woods Richardson Logan Ohapi Bo Nailer Masataka Yoshida Vinny Pascantino say James Outman Zika Tovar Brandon Hughes mark vientos Brian Bayo Bryce Johnson Corey Lee goldwave Jared Schuster and Justin Steele That is a to Josh Young's Mini diamonds black and white and Masataka Yoshida to 299 that'll do it for the break guys. Thanks again for the fill Up next is finest


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Hello everyone. Welcome to my talk. Today I'm going to talk about our new framework and realization for password-based K-Exchange code code. We'll talk about the introduction followed by the framework and finally instantiation and we show the efficiency ones. We go to the introduction. K-Exchange is a very well-known topic. It studies two or more parties and on how to establish a common security. We study password-based K-Exchange. In this case, two parties only share a short password. The challenge for this problem is that the attacker could obtain a short fixed function value of the password pi and then he can exhaust the password space to identify the correct pi that's consistent with the function value. This is called offline K-Exchange real-time. This password-based K-Exchange was first started by Baylevin and married in 1992. The first provable secure construction with regress model is due to Baylor point table and Logway in 2000. But their solution was in the London org mode. After that, there's many authors study this problem. We study the same problem, but in the lattice setting. In fact, in the literature, there are several frameworks were proposed. These frameworks are realizable in the lattice setting. Also, there's a ring LWE-based pegs that's proposed by Dean in 2017. But their solution was in the London org mode. So we study pegs only in the standard mode. So we note that the previous solutions seem to very much rely on the CCA secure encrypting. So we will propose our framework will based on the primitives that's without the CCA secure encrypting or it's a similar variance. And we prove the security in the standard mode. We also visualize our framework from LWE and LWE respectively. And we show that they are much more efficient than the previous lattice based pegs. So then we talk about our framework. The framework has three ingredients. The first one is one message K reconciliation. In this setting, the two parties Alice and Bob are initialized with a similar secret. Alice with D and Bob with D prime. They are they are short distance. So they want to share a common secret. To do this, Alice use a function to compute a message and a secret sign. The message is send to Bob, the sigma. The output sign as the common secret. So this is the Bob receives the sigma. Then he uses D prime and the sigma to recover the common secret. So this is a one message K reconciliation scheme. We will construct a very efficient scheme. But before that, we introduce one observation. Suppose D and D prime is the number in the modular ruin 401. And suppose the D prime and D are close so that the difference belongs to the interval minus eight to eight. So we consider a integer of eight bits, where the A four A three is constant zero one. So observations that F plus D prime minus D modular 401 equals the same expression without the modular. So the crucial part is that the expression has the first the highest three bits a seven a six a five. It remains the same as that of F. So why is this? The reason is that zero one a two a one a zero belongs to interval eight to 16. D prime minus D belongs to interval minus eight to eight. So added together is the number belongs to interval zero to 24. So this is five bits. So they added together, they cannot change the bit a five above our expression. This gives our motivation to construct the reconciliation. In this case, Alice has a secret D. So she can sample the number F randomly except A three A four that's a constant one zero. So she sends the sigma equals F plus D modular 401 to Bob. Bob has a secret D prime that's close to D. So when Bob receives sigma, he compute sigma minus D modular 401, which as we have observed in the previous slides is F plus D minus D is an integer domain. So this remains the same for the highest three bits a seven a six a five. So they can share the a seven a six a five, the three bits. So why is this? Why is it secure? Right? Because the only message is sigma from Alice to Bob. So the secret F in sigma is masked by the one compact D, where the D is usually for the one done over Z 401. Then we talk about the next primitive. That's K for the message authentic encode. This is a Mac that has that has a normal property for the message authentic encode where Alice and Bob share a secret key K. So Alice can compute the authentic encode by just use a function F to compute F K of M, which is eta, then send it to Bob. Bob with a secret key K can verify the eta just by recomputing the send code. So the key fuzzy is we have we require that this send code have alternative verification function. That's a fuzzy verification phi function. So in this case, Bob has a secret that's not exactly the key, which but it's close to K. And when Bob we save the same thing code, eta and M. So you can use the phi K prime to verify the syndication code under the message. The for this to this should be accept the syndicating code as well as K and K prime is close. Of course, for this fuzzy verification to be meaningful, you have to reject the attacker's forgery. In this case, we consider the attacker that has a one time security, the tackle with message and the syndicating code cannot forge a new syndicating code for some another message in prime. So we require that they Bob always reject the forged syndicating code. As long as K prime is close to K. Now, we provide a construction for this syndicating code. We will use the error correcting code C with large timing distance. And also another cleanly system has to suppose the sacred key is D vector. That's endless vector over the Q. So for message M, we consider the code word of H M, which is a subset of index set of size n. So the syndicating code is just a subvector vector of D corresponding to the code word of H M. So that's a index. There's a index code word is the index that's selected by the messages and he can code in the sacred key D. So the verification with another sacred vector D prime that's close to D. It's just simply recompute the syndicating code under the sacred key D prime and compare with the input syndicating code U to see whether they are close or not. Then we introduce the approximate smooth project hash. This is the third primitive that we require for the our framework. This primitive we build. We build this primitive over the commitment. Commitment you given the input pi, the commitment where output or commitment value y and the weightless tau. So y is the commitment of pi with weightless tau. So the decommitment is tau and pi. So the commitment scheme has high input property that requires y shouldn't decode anything about the input pi. Also it should be binding. That means no one can decommit or string y to different input pipeline and pi. So then we are going to introduce the approximate smooth projective hash. This requires to introduce we need to detail the tool function h and the authoritative hash h hat. For h given the sacred key k and input the pi under or variable y from the commitment space the projective hash h output h of k pi y. The authoritative projective hash h hat. In this case if the y is the commitment of pi with weightless tau then the projective hash can be approximated as h hat of tau and no function value alpha k. Where alpha k is the function of the sacred key k. And which is this is called a projection key. So we see an example for this ASPH. The commitment is to use a regular LWE tau board AS plus x and also use a random vector h. We set a and h as a commitment of a public key. So the commitment to an input pi is y equals AS plus x plus h pi. Where h pi is just the transformation of pi. And the weightless is s x. So this commitment is hiding y because the LWE or something. It's binding. So for this we show that if s is random then y can anyway can only be written as the commitment format for only one pi that has a short x for some s. So the projective hash with sacred O which is Gaussian matrix is O transpose times y minus the password information the input information h pi. Normally with above commitment format for the y this will be O transpose times AS plus O transpose x. Then we want to introduce we want to use O transpose AS as a torative projective hash. To do this we define the projection key as r for O equals O transpose times A. So for the weightless tau equals s x the torative hash with the input tau and r for O is equal to O transpose times AS. So this is approximately equal to the projective hash because O is Gaussian x is short. So O transpose times x is short. So this is the the similar version for the ring LWE case. Even this looks similar but is quite different in the technical argument. Then we are going to introduce our peak framework. This has three which can introduce this for three basic protocols. The first one is approximate K establishment. So in this case the Alice and Bob will try to base some password to establish your common and approximately equal secret. In this case Bob starts first. He conveyed a password and output y. Well the permittment is y and the weightless is tau 1. He sent y to Alice. Alice then sample secret K and compute the projective hash of h over h1 k pi y and send the projection key to Bob. Bob then uses tau 1 and the projection key to compute your torative hash. So normally these two versions are equal with this approximately equal secret key. So Alice and Bob can use one message K reconciliation to agree on common secret which is sign. So currently we still didn't talk about the identity verification. To be secure they have to syndicate each other. To do this Alice and Bob start from the common secret sign. So they need to use another smooth projective hash which has alpha 2 h hat and h. Alice goes first. She commits using the common secret sign as a random list. She commits to pi, generates a committment w and with weightless tau 2 and sends w to Bob. Bob has the same sign. So he can verify this w and recompute the tau 2 and w plan. Then they can use generate this K as a torative hash. Based on K they can syndicate each other for the traffic. The crucial part is that for this to be secure we have to assure the property of the QF MAC. In this case the secret key K has to be independent of w and weight because weight and w is known to attacker. So this is called strong smooth list property of ASPH. So this is a crucial point that allows us to use the regular commitment for this commitment to. This also can be much more efficient than the previous syndication that's based on CCA secure latency clipping. This is a complete protocol that pitch back the three protocols into one. Then we consider the intangulation. So for this LWE protocol that's basically plug all the sends together using ASAW from LWE. This is a protocol used from the ring LWE based ASPH. So this is the efficiency of our protocol. We can see that it's more efficient than the previous protocol. Finally we also conduct an experiment for our ring LWE based protocol. It's based on U-Bump operating system using C++ with NTR package. We can see that under the regular parameter it has a reasonable timing efficiency and the communication efficiency. Thank you.


Just like the running of the bulls, we wanted to see them come out of the gate fast, which they did.  And yeah, they just started working right away, so it's really fun to see wildlife like this in Riverside Park.


Two, one. Eating out this Christmas, check out the food rating of the restaurant before you go. You can do this online at this web address or you can check out at the restaurant if they're displaying their sticker. You're looking for a rating of five, that's very good, but actually a rating of four or a three, that's fine too.


That's your camera. Okay, gotcha. Okay, we're back here live at IBM's Information on Demand. This is SiliconANGLE.tv, SiliconANGLE.com's exclusive coverage, wall-to-wall coverage of Information on Demand. This is our flagship program, The Cube. We go out to the events to extract the signal from the noise. I'm John Furrier, the founder of SiliconANGLE.com, and I'm joined with my co-host, today, Volante of Wikibon.org. And we're here in Vegas at the Mandalay Bay. This is IOD, IBM's Information on Demand Conference. I'm here with Ken Bisconti. Ken Bisconti is the vice president of the enterprise content management business at IBM. He's focused on products and strategies for that part of the group. And Ken, welcome to The Cube. Great, thanks, David. Happy to be here. So, good to have you. Content management, it's a tried and true industry. It's been around forever. It's now coming together with big data, all kinds of mega trends going on. So this is your show. And this has been your show for a number of years. So, how do you feel and what's going on here? Well, we're excited, of course. We're really excited for most information professionals and businesses. Unstructured content is on average about 80% of their business information. And it's growing tremendously. It's growing on average according to an IDC, 44 times between now and 2020. People struggle with both how to manage it, how to capture it, how to put it into action in the context of business, how to achieve results, how to save money, how to deal with the growing cost and the regulatory compliance, legal discovery, how to clean inside out of it for improved business processes, fraud detection, consumer insight, et cetera. So it's an incredibly growing space and exciting. So take us through how we got here. So, post-Enron, there was obviously an increased focus on information risk. And certainly the federal rules of civil procedure in 2006 changed that. And then you had a period of time, and one could argue, you're still in that time, where the general counsel tail was wagging the dog, at the same time you have this new big data inflection point coming in. So is data and information, is it an asset or is it a liability? It's both, right? So it's data and information, it's an incredible asset. Organizations are really interested in how do I multiply the value of that data? So how do I, through insight, through putting it into action, how do I improve my customer experience? How do I improve the way I'm delivering services and products, et cetera? At the same time, it's an incredible cost, right? So information is growing, like I was saying earlier, usually double digit, often 40, 50, 60% year on year. And organizations, especially in regulated industries, financial services are storing everything. So you're not deleting anything, you see your information growth growing out of control, and organizations are afraid to delete anything because they can't get the general counsel, VP of records, RIM officer, CIO, IT group coordinated on can I delete the senior? I don't know, because we're a global organization spread across 45 different locations and 200 different departments that all have an opinion. It's hard to deal with. And it's different regulations per country. Different regulations per country, per province, per state, yes. So we've fallen back to this, all right, just keep it, just archive it, we know it's there if we need it. Why is that a risky strategy for organizations? So it's a risky strategy for several reasons. One reason is, okay, cost is growing, okay, cost is growing. Well, some would argue, yeah, but Silicon gets cheaper, it's cheaper to store, more and more megabytes, gigabytes, terabytes, et cetera. On average, storage costs are going to continue to decrease 20% year on year. But again, if your information's growing 50, 60, 70% year on year, you have not just the storage cost, but the regulatory compliance, legal risk, litigation support costs, governance fines, et cetera. It's an incredibly difficult challenge for businesses. So there was a lot of focus, Ken, on solving the problem with email. Kind of the mid part of last decade.  So my question is, you know, beyond emails, which I think the industry generally has a decent handle on that, maybe you could comment or agree or disagree. But how do organizations defensively delete information? So what's really required is a holistic solution. So we've had records management, we've had classification, we've had email archive and the tools that can delete content. Problem is that organizations don't have the coordination, don't have the decision management and policy governance in place to be comfortable about pushing that delete button. It's organizational. It's organizational and you really need a holistic approach to this. We call this defensible disposal or information lifecycle governance. And it's comprised of content sources, whether those are document management repositories, email archives, et cetera. The ability to collect and records manage and reply retention instructions across that content execute. But just as importantly, if not more importantly, is the decision management layer, where I'm actually doing value-based retention decision making. Where I'm actually coordinating a global file plan because I have a solution that all the lines of business that have some responsibility can say, this is my content. This is its important, et cetera. We acquired a company, PSS Systems, two years ago, which had this incredible technology under a product named Atlas, which we have now evolved and integrated with our content management systems. We've syndicated instructions down to Optum for structured content, to SharePoint, to other solutions so that we can provide the soup to nuts decision and policy governance all the way to execution and content management. And you can automate those policies, right? Because humans just can't make these decisions because they won't be consistent. So can you talk about the human element? You can't eliminate the human element. So what role do humans play? What's IBM's sort of angle on that? So humans are absolutely a critical element to it. You need the tools, such as the policy and governance technology to be able to execute. But there's continuous churn and change in opinion on things like what is a record? What's the e-discovery approach for social media? So our involvement in that actually has been to, we championed an organization called the Compliance Governance Oversight Council, CGOC. PSS Systems helped found it. It's not anything that's owned or operated by IBM. There's, I think, 1700 legal professionals, records managers, general councils for Fortune 100 organizations. It is a self-managed organization, judges, federal judges, et cetera. But they're continuously talking about these topics and rendering opinion and best practice that we're using, that input, to then execute that in our own offerings. So talk about the, so let's go back to this sort of defensible disposal. And that seems to be sort of a hot trend in your world. Talk a little bit more about the ROI. So if I'm a customer, I hear, okay, yeah, I hear you. We maybe need to put in some systems to do this. What's the ROI of doing that? Is it just, is it risk avoidance? Or is there sort of a hard dollar ROI? Well, you know, there's definitely risk avoidance. Often what we run into is organizations will say, yeah, I agree that's risk avoidance. It's not my problem and it's somebody else's problem. And so that's a nice to have, but I'm not going to go spend this money to implement defensible disposal because of that. That, you know, the cost levers are storage, right? So storage is an incredible cost lever. In fact, we found, even in some of our most recent activities, organizations finding 50, 60% of their current information is of little or no business value. So you've got, you know, you've got storage costs. You've got operating and human costs around managing that information. I've got regulatory compliance costs, fines and other, you know, costs associated with being compliant in my industry. And I've got e-discovery. So I've got both litigation support. How can I reduce the e-discovery and litigation support costs, as well as what is the potential savings from not being sued or, you know, lowering my risk posture as an organization? Yeah, so of course, e-discovery is very volume driven. The more data you have, the more floors you've got to pay. And the number of active litigation cases that you have. We have customers that have on average 1,200 plus active points of litigation at any one point in time. So you can just imagine what the legal costs are associated with that and the business risk. So for a large organization, it's potentially hundreds of millions of dollars. Potentially hundreds of millions of dollars. We have some organizations we're working with now that forget the legal cost alone. They're looking at savings of hundreds of millions of dollars over the next three to five years through simply defensible disposal execution. Can talk about the role that technology is playing in defensible disposal? Because in a way, technology got us into this problem. Can technology help us get out of the problem? Talk about that a little bit. So technology can absolutely make it much more efficient to be able to coordinate and to execute those retention instructions to be able to do what we call content assessment. So we're using analytics to help assess what information do I have because I probably don't know. We're using that to execute retention instructions, both time based and event based instructions. And also, you know, using that to classify including automatically classify content by looking at using natural language processing to look at an email and say, oh, it goes in this bucket versus that. This is a record. This is junk email. This is discoverable. This is not. So technology is absolutely an assistant. But again, unless you have the holistic solution in place and unless you've sort of coupled that also with getting your GC or VP of records, your CIO coordinated, nobody pushes the button. So can obviously huge issues at risk here or on the table for CFO, General Counsel, you mentioned some of the key stakeholders. Not so much the CIO, although they're heavily involved. Oh no, no, for them too. Because they're the ones usually being challenged with you have to take tens of millions of dollars of cost out of your operating budget. The CFO and then the General Counsel saying, I don't want to get sued, store everything and be able to archive it really fast. Okay, got that. But now, let's talk about some of the dynamics in today's market, unstructured data.  Talk about what it means to be a social business around the business processes, instrumentation of processes and then the storage kind of compliance of that. So that's a great question, right? And social media, social capabilities are dramatically changing our industry. We were talking a little bit earlier about topics like, well, is ECM a space that's hot? Is it dead? It's been around for a while. We believe that many organizations use content management technologies and information for system of record. What was, this is the single source of the truth. This is the claim. This was the loan that we agreed to apply and give to this customer. But increasingly, we're now being asked to involve systems of engagement. So how do I move from just being a transactive relationship with my customer to forming a long term and high value engagement with them? How do I move from offering documents to communal content? How do I move from sort of simple tech search to all these forms of information across the universe? So we're applying social content services to customer care and insight, product and service innovation and workforce effectiveness and optimization. So bringing social content and social capabilities into the context of traditional lines of business to transform those opportunities. You know, it's interesting to hear you talk about the transition from a transactional world to an engagement-based world. I think it's, my point of view on that is it's a, or my angle on that, John. My silicon angle on that is, and I wonder if you could comment on this, is it's really, the end game is still a transaction, but it's more of an engagement-based, catalyzed transaction. Do you buy that or do you really see that the end game is the engagement? I think that we're seeing increasingly that customers recognize that transactions are a point in history in the customer conversation. So I'm more interested in managing the long-term customer conversation, you know, from this marketing interaction, this purchasing occasion, this support occasion, this service occasion. How am I providing multi-channel and end-to-end customer support and incredibly attractive customer experience to that client? The life cycle of that engagement. The life cycle of the customer. But doesn't that end in a sale? Ideally. Hopefully multiple sales. Hopefully this sale followed by that one and that cross-sale and that sales. And those sales are transactions, aren't they? They are. They are transactions. But you're saying you're wrapping around this engagement, this engagement wrapper around those sets of interactions that create a new experience. The reality is that today's consumer, let's just take a consumer experience. My expectation as a consumer is, look, I know you have a lot more, you as a provider have much more information about me than you ever did. You can comb social environments, you can comb LinkedIn, you can comb all these different sources of information to provide better insight to me. Better support for me, better marketing, tailored marketing to me. If you're not doing that and if you're not taking advantage of the social and collaborative capabilities also available to manage my long-term interaction with you, I'm going elsewhere. What up? That's definitely the trend, a big data. That's one of the promises of big data. Absolutely. One of the business benefits of big data. Then you have the legacy. Right, right, business benefit, but challenge, right? So every one of our customers here, I think, is thinking about how do I apply big data to make my organization more efficient, more optimal, better customer service, and more competitive? I saw recently, last week maybe, the week before Senator Rockefeller sort of went on what, some people call it a witch hunt, but a lot of consumers are happy about that he put forth, and Senate put forth an inquiry to a lot of these agencies like Experian and Equifax, a lot of the information that they're collecting on us. And they're asking what information are you collecting? This is an information management problem. People think they can just push a button and tell you, okay, this is what we have. What are your customers? How are they reacting to that trend? I know it's early, but under the whole rubric of privacy. So great question, right? So five years ago, nobody was asking us about privacy. They just simply asked about encryption, security, access control. All of a sudden, privacy is like up to the top, like okay, I need to manage privacy of information as well, right? And it includes everything from how I'm redacting social security numbers and keeping customer information secure and contained to how do I discover back to the content assessment process, how do I discover that I've actually got social security numbers in collaborative team rooms or that I have unique customer IDs sitting in file shares or email? I mean, this is privacy information that customers are much more attuned to and worried about than they ever were before. We started talking, it's interesting, some themes that came out in this segment. People say is ECM dead, is it still hot? We talked about information asset and liability management. People used to talk about decision support being sort of a boring market. And now it's called big data. Sure, big data analytics. But personally, I think that the whole information management space is just becoming more complex, there's more value there, there's more risk, it's just going to escalate as data grows, so it's a very hot space. But it's changing, isn't it? Talk about how it's changing and where you see it going. It's changing tremendously. So as an example, when I started in this part of our business almost five years ago now, we thought about content management, images, document management. Now today, just four years later, we've introduced new content analytics technology based on Watson research. We've introduced e-discovery and defensible disposal. We've introduced case management, complex and rich capture services. But even more important than that, this sort of ECM world has now collided with structured data, big data, cloud, social, mobile topics. One of the things that I found most interesting in my involvement, I'm on the board of the Association of Information Management Professionals AIM. We've been talking all the time about the recognition that there's a glut in the industry of information professionals. So just the ability to find and hire people that understand what technologies do I apply to this business problem. So my business problem is I want to reduce insurance claim fraud in the UK. It's okay, so what are my sources of information to go actually do that? What are the technologies I might imply? When would I use big data? When would I use predictive analytics? When would I use media streaming? When would I use unstructured content structured? So just to be able to understand how to apply those technologies and the methodology to apply those to a business problem, incredible challenge. And you're saying there's a glut of those individuals. And so what you need to do is change your title from I.M. professional to data scientist, take a tech analytics class, take a stats class, and you'll be double your salary at the spot. Well, this is the same organization. I have some personal involvement with they introduced a new certification, the certified information professional, a whole curriculum about these kind of topics. Just recognizing that organizations are looking for how do I find somebody that has a certification much like a project management certification would say, hey, this person knows the difference between privacy and access control, knows how to apply predictive analytics or social media analytics, et cetera. Well, two areas that weren't really hot in the past in terms of people jumping down, saying, you know, I'm in this role is obviously compliant information management and databases, right? Two of the hottest areas right now on the planet, both in terms of what you're working on and also on the database side, you know, it's cool to be a database geek now.  Oh, I'm a database guy, whatever. Now it's the hottest thing, so data science is there. So my final question for you, Ken, is really more about back to the social business. I think everyone's recognizing, yeah, I'm seeing the debate tonight. I know what presidential election, governments are being overthrown by Twitter. The crowd is talking, that's measurable. I could maybe get that data, I could get the signal out of the noise and use it for my benefit. So I think there's kind of like a blue sky, like I see it, I don't know how to do it yet. There's a lot of barriers and I would agree with what you're saying. So share with us those barriers. What are the barriers for a business that says, you know, I want to instrument my business processes. I want to reduce the risk. I want to save those dollars and provide business value, hope a big data. So what are some of those key barriers right now? Well, you know, the key barrier, the key challenge that anybody has to deal with is the same kind of challenge that you deal with in any IT decision. How do I not get mired in technology and acronyms and bits and bytes and whatnot? How do I understand what business problem am I trying to solve? As I'm trying to solve that business problem, what are the goals and objectives and then what sort of technologies can be used to be applied to that business problem. And don't try to boil the ocean, try to pick a solution, try to, don't start with, hey, what I need is Hadoop. You know, and I've seen these scenarios where the customers, hey, this customer is building their own email archive solution in Hadoop. And I was, why are they doing that? That problem's been solved, right? So I would say, you know, look at the business problem you're trying to solve. Which is interesting and fun. It's not a bad thing, no, Hadoop is awesome. It's incredible for distributed processing, distributed file system capability. You know, like I was saying earlier, when you use big insights combined with our content analytics, web scale analytics, it's unbelievable. But don't get mired in the technology, start with the business problem, start by defining the goal and narrow yourself down to, this is what I need to do to solve that business problem. While I have some purview on what I might want to do in phases two, three, and four later. Ask the questions, then go for the solutions, then figure out the tech. Right, find yourself an information professional who can help you apply them insight. And then when it's really ready, get Watson to come on the case which we had earlier from you guys. So great, Ken Biscotti here inside theCUBE. We'll be right back with our next guest after this short break. This is live in Las Vegas Information on Demand, IBM's premier conference about information and big data, and we'll be right back. Great.