trap slash black markets. Appreciate it, baby. Man, we got some representatives for one of my favorite places on earth. Yeah. And I don't mean Disneyland, Disney World. I am talking about none other than Magic City. We've been taking you so long to get over here bringing my updated VIP pass. Man, we didn't wait for you to pull up. What's the matter? I was waiting on you to pull up with my passes first. Oh, they had to do it. You know, every year I supposed to get a new pass. Well, you pull up, they already know who you is. All right, babe. 20 man's waiting. Word. Yeah. Sheesh. You look like a slide of the card, though. Yeah. 20. 20. That's life for you. Man, that's a lot. That's a black market. Yeah, I know, but that's heavy handed, man. That's a lot. I'm not a rapper. I'm a comedian. 16. Nah, bro, I'm more like two. I seen DC do like 14. You got, you know, one time. Right here in the movie with Monique now. That's pre-paid, that's pre-paid, isn't it? I'm living in a black market. Yeah. It's levels. Don't do me like that. Don't do me like that. Nah, bro, every time I go to Madison, I spend way more money than I ever planned to.  You trying to go out to do? Being that four hours. Trying to go out to do? We might as well, I mean, we ain't working. Ain't no better place to relax. They call, that's the streets calling right there. We might as well go, what's the day? I ain't say magic. Nah, I don't know this. Stephanie from Magic? Hell no. What are you doing? That's who I thought it said. Who is Stephanie? You know the real name. That's who I thought it said. I don't even know their real name. I like to keep it mysterious. Yeah. Yeah, stage name on. I'm all up in Cumberland Mall. Sparkle. I'll be having to see the paperwork. Word. Man, what's been going on down there now? Man, too much. We got this Miss Magic City campaign, man. What you mean? Looking for the best bartender, bottle girl, merchandise girl, and dance in the country. In the country? The Ed Magic City? Nah, it's this new talent. This new talent. We're looking for the best in the country. Giving away $5,000 per piece, $20,000 altogether. Ooh, I like how that sounds. So the best bottle girl, how do you prove you the best? What you got to do is, well, in the beginning, there's some videos you got to submit. OK. Had a little $50 application fee. OK. Then you going to show us your little twerk or your dance. Or how you move to your music. Right. From there, you also going to show me your hustle video. You going to tell me how you can expand our brand, take the merch, take the wings. Tell us how good our brand is and how you can make it better. OK. And the last one is just the interview. Just tell me about yourself. Submit the videos. And then from there, we going to take the top seven. Bring them to the A. We going to shoot a little week show to figure out, do some contests, figure out who the best, and crown the next Miss Magic City. Or the first Miss Magic City? Oh, the first Miss Magic City. I think y'all need my input on that. You want to help me judge? I probably can help you find. Yeah. Yeah. Stephanie? Maybe. I don't know what category she want to be in. Y'all going to do the best dancer? Dancer. So you know, the dancers are wide range. We can go tricks. We can go thick. We can go skinny. Or petite. We can go, you know, middle. You know, it's just. I think you should have them torque to some gospel music. I don't know if we got enough bass in it. You do. But it's got enough rhythms in there where it's like she could catch any one of them. That's what the talent is. Like some Kirk Franklin? Just saying. It ain't necessarily got to be limited to Kirk Franklin. Yeah. What song would you choose? I would probably choose something like, like, Beethoven Symphony 5 or something.  Yeah. Yeah, I don't know if they're going to be to catch that. A little Moonlight Sonata or something. Yeah. Yeah. Not like, not no little baby? No, not no little. Because that's so, she could do that without even thinking. I'm going to challenge upstairs. What I mean show necessarily got to think on this one. I want her to give us, we want her to give us the best of what she got. I know. You know what I mean?  Laser focus. You got people who would have made $20,000 a week, $15,000 in a day, you know what I'm saying?  I want to make sure I got the best of the best presented for you. That ain't going to even go in interest me. What's going on? I'm telling you what, when I'm living in a strip club and I'm looking for a dancer, it really just depends on the mood. So, so, so tell me about it. Some of the dancers, I always like them look weird as dancers.  So you're like a pole girl?   But she look. Yeah. Yeah. She be doing the backflip. She be doing push-ups on you? Yeah, you know them ones. Yeah, them crazy ones. So how you picking? First, I'm going in there, I'm walking around. I just want to see who at work right now. Because I know that, like, depending on what time you got there, these girls might be about to get off. OK. And then you don't want the fine strippers to walk upstairs and see you dancing with what that ain't fine. Yeah. And then you be like, hey, swap it out. She be like, now go ahead. So how many dancers you like to give you? It depends on what's going on. Yeah. Song. Like, if the DJ keeps stopping to interrupt that, it's going to throw the vibe off. No, no, but just imagine, we won't do all that. I know, but I'm just speaking in general. When you talk about the Mississippi clubs. Yeah. Ain't no gun. Don't do it in a Mississippi like that. Ain't no clubs. Ain't no strip clubs in Mississippi. We got our own freaks. We got to be chosen. Ain't no. Everybody don't get a fast shake in Mississippi. All right. Yeah. Sorry. You get a fast shake? I get it, I get it, I get it. I'm good at this. Proud of you. Shit. Sorry, but, bro, you don't matter the city, that's on most grown men's bucket list. It should be. Things to see. Yeah. And you saying y'all about to do a whole rebrand and find the first Miss Magic City. What's been going on down there since the pandemic? Pandemic went crazy. Yeah. Once ATL opened back up, I mean, you know, the thing about Magic City that makes it so, I don't want to say so different because I haven't experienced every club in the city. Why not? It's just a bit of magic. Oh, yeah. You know what I'm saying? It's kind of hard. We never know who we're going to pull up. Yeah. You know what I'm saying? It's not a club that's promoting celebrities that are being booked or paid to come. You know what I'm saying? Yeah. Then the club is so small that when you were bossing and you paid that 100 to park and that 100 to get in and you politic and it's like everybody's family. You know what I'm saying? It's none of that super, super hood vibe, none of that street vibe. It's one of them places where you can get some business done. Yeah. You can have a good time. Get something to eat. You ain't going, you definitely got to get the food. You know what I'm saying? You know, we've never gone online. So since pandemic, it's just been crazy. The other night on Monday, we had a gunner came, pulled up. A couple other people pulled up. Who else pulled those up? An early chopper. Yeah. It's just one of those things like putting on the date. On a Wednesday, you might crash, Neil, us just sliding through that thing. Yeah. Whoever basketball team, football team, the sound they're sliding through. It's just one of those places that you just got to experience, you know what I'm saying? You got to experience it. And the food is damn sure amazing. You know what I mean? Right, the food line be so damn long. I saw you gotta be there for a while. You got to. You gotta spend a little money while you there.  Yeah, yeah, nah. They do this every night. Yeah. 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Man, what do you need to tell the fellas who ain't been to Magic City before watching this with World Wide? You gotta get the experience. You know what I'm saying? It's something for everybody in there. If you went to throwin' money in the air with a place, if you wanna find a nice woman, it's a place. That's the thing people be overlooking. It's a lot of women that just bein' there. It's it. And we ain't talkin' about just dancers. Exactly. It's a club, but also even with our dancers. Like our background of our dancers, we got people who used to work in the government. We got girls who went to college, who had degrees. We have girls who, like, it's not one of those places where you just gettin' the, I'm broke, I want a dance type of thing. These are higher, upper S-A-Line type of women. They come from different backgrounds. They come from all over the world. Literally, all over the world, we recruit. So it's just one of those things, like when you come to the aid, you gotta come past Magic. Like, it's, it's not wrong about it.  You should. Damn, I had so many good nights in Magic. When are we goin' back? Ooh. So nice. Who's your best, who's your best night in Magic? Man, I think probably one of the most fun nights that I had in Magic, hosting the Amateur Night. I know you used to do that. How long ago was that? Shit, that's been a minute. You know how long ago this been since they had the Amateur Night. They ain't amateurs no more. Nah. That was always dope. You know, just fallin' in Magic after the comedy show.  It's just, I miss it. We open. I know. Six days a week. Oh no, man. I'm gonna make it through there. Sooner than later. It's time to go back. Man, you gotta help me figure out who gonna be the next Miss Magic city. I mean, I'm all in. You know. I'm all in. I wanna be a part of this media. Yeah. We're grown men. We gotta have something to watch. We get a hundred videos. You know, the craziest part is when you got a girl who, I don't know, they from Kansas and they hear one of the dancers' story. Like, all right, my first week, I made 10K. Then on my birthday, I made 30,000. Like, shit, I gotta get in there. You know what I'm saying? So the way people are applying and looking at it, you know what I mean? And back in the day, I think it was looked at a little differently. Yeah. But understanding like, this is a female's hustle. And it's a legal hustle. You know what I'm saying? Magic city, one of the things is that keeps us understanding is one had a boom-boom room. You know what I mean? One had a champagne room. You know what I'm saying? It's for the ladies. It's a safe environment for them to come, make their money, and do whatever they wanna do next in life. Right. It's a step of stone. And it's not just about dancing, the bartenders, the bottle girls or waitresses, the merchandise girls, the girls who sell the clothes, the merch. Yeah. What's up with the merch? What we doing? Oh, we got you, baby. We asked you about it, Doug. Well, you know, we about to do the collab. I was just going to do the collab. I was just going to do the collab, everybody. You hear, I was going to let you tell me. You know, we working on some of this magic city. Got some hoodies with the titties that. Yeah. You feel it, man, feel it. Got them sweat panties, man. That booty, that ball, all that type. She come for the women and they're like, nah, I was just going to say. We definitely working on some dope shit, dope men just to put on, you know. And we want to make Atlanta the brand. You get what I'm saying?  Nah, it's for real. That's what the bigger brand is, man. And we want everybody to be a part of that. That's why we had to make sure we reached out and get everybody in the black market or at least in the trap or, you know, just where we can all exchange information. For sure. Who you brought with you today? I see a few magic city hoodies over there, man. You see none of the ladies over there? I see they want to have them on camera. Oh, they shy? Come on, man. You shy?  You got a mic?  Who you got over here? That's Zarya. She runs the merchandise box. Oh, word? Yeah. Let's see what she talking about. Let's see what she on. Look, I know that this would be a great opportunity to tell you, but I don't have to tell you to go to magic city.  Yeah, if you ain't been, you gotta go. This is one of those things. Yeah. You know what I'm saying is that it has to happen. Now, Zarya, also, she gonna be able to tell you a lot about that collab with the clothes. That's what I really want to hear about. Try to see what type of work I need to be putting in right now. Yeah. Because we definitely gonna need something that, you know, when you do finally find Miss Magic City, she gonna need something to wear. You gonna design the female line? Yeah. How you doing? I'm doing good. How you doing? Good, good. How's the black market? Oh, thank you. How's everything over there at the merch box? No, everything is wonderful. The brand's gonna sell itself, of course. Of course. That's how you just tell him. He don't listen to it. But, with y'all, it's a bigger place for show. Yeah. Because y'all represent Atlanta as a whole. What do you represent Atlanta? We're a staple of Atlanta for show. Most definitely. A landmark for show. I love the throwback Thursday pictures of Magic City. Yeah. You ever see them pop up on the ground? Who's show? Oh, those are the best. We're gonna do a documentary. Back in the day. Well, we try and figure that out, you know. You talking to the man. We need some help, you know what I'm saying? It's the time, and it's the time of the day. We got everything to do it. Y'all be taking a while, you know what I'm saying? Is it us? It is. Say less. Nah, but we definitely can do some cool shit, man, and put it on for the city. But, you know, we just launched our streaming app. We got the same thing. So you definitely, we know your show. The shows I got on the app. We got a couple shows, man. We got Project Carlos. That's me and the old school. Yeah, yeah. We got some exclusive shows from all of us. Michiko, DC, Clayton, everybody's gonna be dropping a little stand-up comedy on there. We got our live shows on there that haven't been seen. And we got a lot of exclusive, you know, behind the scenes footage. And you get early access to the shows. We got links to tickets, merch. Everything that's 85 South is streaming through the app. So can we get something on that? Yeah. We definitely want to do something. We was going to talk to y'all about doing this show that I got. It's inspired by the Japanese game show type. What we're going to, it's just going to be like, let me draw this for you. So it's just like, it's a man sitting in a box. And all you can see is from the neck up. And then a lady on this side, and a lady on this side. And they just slapping him with titties. With titties. Whatever side do the most damage to his face, that's a one. What size of titties gotta be? They don't, it's not necessarily about the size of the titties. It's about the damage that the titties can do. And the thing about it is, when the titties hit the face, slow motion. Nipple nerd. So isn't the brawls out? It's totally up to the competitors. But I feel like if you're going to use your raw titties, we got to line you up with somebody with a raw tittie. OK. These just ideas, we're just going to build these out how we need to. So I think that'll go. It's a tittie thing. What's it called? In your face. My name. I like that.  Would you be on there? I wouldn't be on there. I feel like she wouldn't be able to do it. She came from a two-parent household and said, she a good girl. Gotcha. We want some savages. Right. We can't do magic. Got good girls. I know. But what I'm saying is, that's the concept of the show. But you know what? For the ones who don't win this Miss Magic City, I can plug them in for that. What you think? Because I'm all about tapping into these untapped markets. You feel it?  Oh, what the? That don't even require nudity.  Yeah. Watching motherfuckers eat. Mukbangs.  Just eat. I ain't got to be doing that so long. That's what I'm saying.   I could do that. Mic'd up, though. They don't want to hear all the crunches, don't wipe nothing. We're all headed. Sloppy like that.  Let's do the magic city. Right at the bar.  We'll just be like, we can put two dancers on the team and a dude, and then there's two dancers and a dude on this team. Whoever eats the 100 wings first, win $1,000. Let me pepper stuff. That's it. You want to hunt my mom? Thousand.  You going to put the money up?  She went at least $1,000. Doesn't that part of the $1,000 you about to throw at the club tonight, though, right? Mm-mm. Oh, god. You got to make sure we keep going. Mm-mm. I like that. We can do that. I like that idea. You got any ideas you want to throw out there? I got this one show that I think going to really hit with black women, though. What show is it? Just women getting they hair done. Like a hair salon show? Yep. Or like, smiley? It's not even the hair salon drama. It's a tutorial. It's just like, she walk in, hair ain't done. Spirit low. You can see it all in her face. She don't know where she going. She don't feel bad enough to get her hair done. Brand new person. New person? Hey, no lie. I like that idea. That's something that I probably would watch. I know. I know. And then we're going to be throwing little tees on the stage, too. You got to see what she do to her updo. You're like, ah, I got to see it. That's all I do is just come over and show it, bro. Hell, yeah. And we need to get done. Hell, yeah. I was thinking of, I was thinking of having like, you know how they got little women of Atlanta, right? I'm thinking of doing tall women of Atlanta.  But six feet is the minimum. I'm looking for women who like six, six, six, seven. What you going to do with them?  What they doing with the little women? What you going to take them? Whatever. Everywhere I take them, I'm going to make sure that it's too small for them. North house. All right. OK. This is just how I be, you know. They got a little women. You want to see what the tall women do? What race is the tall women going to be? Who you going to get them from? Black. They from the hood? How you going to find these marketer people? It's easy. They just need to know that you're looking for them. How you think they found the little ones? All right. All right. No for real. Is that a point?  Who would love to get up out of it? Yeah, yeah. You got that one. I ain't seen no tall ones. It ain't a lot of tall ones, but like when you see one, they stick with you. I ain't going to waste all my good ideas on y'all, because y'all not even in the media industry like that. I'm just saying. Just know, it's a well spring in this.  I just come up with shows. Yeah. Hell yeah. Well yeah. I need you to tell us which ones going to win this contest. That's easy. I can spot a winner. Can we bring, like, the top ones back here? Let's not even start with the top. Let's not start with the top. Let's not start with the top. Let's pick randoms. Just randoms. Randoms. Who we believe in. OK. Right. I ain't seen you do nothing, but there's something about you.  We can do a pre-show. Intuition. The N.C. how they do. Like, oh man, yours pick. She got 13 plays, you pick losers. Yeah, I'll bring them through. We just do the categories. Do it like the basketball tournament, like NCAA finals. Yeah. Oh yeah. You know what I'm saying? The bracket. The bracket. Yeah, the bracket.  She be ranked number 15 or something. We can do it. If you're going to go through, I will. Yeah, we can do it. If you want me to do the pre-screening interviews, I don't have no problem with that. We're going to send you all about what? About 300-something videos that we got to go through now? Yeah. Yeah. Minimum. The dance videos be a little off sometimes, though. That's the ones I'm picking. The off ones? Yeah. Them the ones I want to bring first. Yeah. We got one. Because we got to point out, like, hey, you know this was off, right? We got one, but she, I don't even want to say it, because I ain't discriminating. Yeah, because she going to feel like she's saying it out. She actually did good. And then the first time we seen them, we didn't think she was going to go this far. But she climbing up the ladder, though. But she really, really, really, really, really, really bow-legged. For real? She listen? I mean, she talk. I don't know if she listen. I ain't nothing to hear. She real bow-legged? She's real bow-legged. So I'm about it. I already, I ain't even seen her. It's both the legs. Parentheses.   It was cool. It was cool. She just wild boiled a little bit. Ain't nothing wrong with that. And it might be another reason why she wild boiled.  See? See? Yeah, baby. Brother. What's the social media? Is it just at Magic City? Magic City Atlanta. Magic City Atlanta. And then you going to know, all right? Yeah, Magic City underscore lifestyle. Magic City life. What's that like? The lifestyle of Magic City is showing you smarter in the club, it's more to it than just strippers, dancers, fool. You know, it's the legacy, history. We actually tell stories with our brand. So that's what Magic City Lifestyle Facts is. Bad. She said. You apply at lifestylemagiccity.com for Miss Magic City. You can apply to be in the exclusive groups. Well, you can actually sign up for our newsletters on the same website as well at lifestylemagiccity.com. But for Miss Magic City, you can sign up at lifestylemagiccity.com too. OK. And it's in the bio of both Instagrams. Where can they grab the merch? Online or at Magic City. Website again, lifestylemagiccity.com. Magic City, we have a store in the back of the parking lot. It's called our merch shop. That's what you be? Yep. OK, bet.  Yep. And we got $100 stuff back there too. What's back there? Pressure. It's a pressure? We got everything for anybody. What are the sodas? Mill. Oh, you got exotic snacks. Like what? Canadian sodas, Canadian candy, European candy, Asian chips. Asian chips. Goodies. My Asian partner going to come check y'all out. And we close at 3 AM. For real? And they got hair. And we got hair. Y'all selling hair out of there? And lashes. I was just about to ask you. We don't got a waystern. That's what we're going to take you to. Take you to right there. All the hair is still open at 3 in the morning. Now, that's a selling point. More people should definitely know about that. Breaking news, ladies. Magic City got a hair store that's open till 3 o'clock in the morning. You can get lashes, all that. Y'all sell the edge of blue. Edge of blue. That's blue for the ladies. The lace front is blue. Y'all got that up there too? We working on it. OK. But I am. Flat irons? Nah, you ain't. That story ain't that big, though. It ain't that big. It's on, like, two, three parking spots. We might have to add another level. We got a few ideas.  It done with it. Y'all pull up. Bro, you keep telling me that, like, that's easy, man. I just ain't seen you. I definitely ain't seen you. You ain't seen him, then? I haven't seen you. I ain't been since pre-pandemic. I've been in the house. It's definitely been two years. I don't remember to this trap. Both jangles, chicken sandwiches here, dear. And the bank. Ain't nothing wrong with that. And that's it. And you know where I'm gonna show it. My list real short, though. Oh, gosh. We got any questions from the floor? Another lot of Magic City fans out here amongst the staff. Don't get shot now. Look at the Asian chips. See? The Asian chips? Asian chips? The chips. The chips. We have different kinds. We have, like, a chai lime. You got fish chips? Yeah. We actually do. The fried crab. The fried crab chips. They actually sell first. They like a fried crab.  He know that. He grew up on that chip. Yeah, those actually sell out first. We got some Doritos, roasted chicken. Roasted chili Doritos. No, roasted chicken. Roasted chicken. Roasted chicken. Roasted chicken.  Roasted chicken. Over the seas. It's exotic.  It doesn't sound good. It's a motherfucker. We have Oreo wafer rolls with strawberry filling. Skittle cookies, tweets cookies. Skittle cookies. You was holding out.  Skittle cookies. What'd you say? Yeah, double chocolate M&M cookies. What else we had? They're loaded. We got an old Western. The Skittles. The Unshelled Skittles. Oh, we got the Unshelled Skittles. We got the Chewy Skittles. The Giant Skittles. It's like a Japanese jasmine flower. I had some freeze-dried Skittles that hit different, too. Who? Freeze-dried. Like the astronaut seat. Freeze-dried? Yeah. Just look into that. You put your own name. Look into that. Yeah, right now. I'm telling you. Look into that. Yeah. Yeah. What's the address again? 249-463. 249-463. Right across from the Greyhound station. Yeah. Kat, what is it? So, yeah, somebody's coming for me. Please. Ironically, I think when the last man was running, they had did a poll. And out of, it was Coca-Cola in Magic City, the two-top tour spots in Atlanta. Then the Martin Luther King Center was number three. But I mean, you got to. It's the baddest women in the world. Some of your top bosses in the world. So, if you get money, you want to be around people who get money. If you're not getting money, you want to be around people who get money. You always want to be around beautiful women. And you want to be around some banging-ass food. Yeah, yeah. I thought you were about to say they about to make Magic City a voting poll. They should have made it so you could vote in Magic City. The bulls. We didn't do the stripper boat. That was QC. They talked to us about it, but as big as they were making it, we couldn't have hosted it at Magic. It's not big enough. I think Nancy did it at, like, Gold Room or something. Yeah, you know what I'm saying? Tries who can't. The city permits. We're growing 5 and N. So the moment you hit a wall, take something down, you lose it. So that's why it's going to be historic at that side of it. Yeah. You know what's crazy? In the last four or five years, the crowd has somewhat changed. I can't just always credit it to the Louisville, but I mean, it's definitely beginning to become more diverse, to say the least, especially on your Thursday nights, your Saturday nights. You know what I'm saying? It's definitely people from everywhere. It was a game in town, the fans, out of country. Like, last month, a lot of people from Canada just kept pulling up. You know what I'm saying? So it's just like, it's not one of those spots you're going to get. Everybody black. It's definitely a diverse crowd. Let me ask you this. You're a lady there. You work at Magic City, right? Mm-hmm. What would you say to all the ladies who don't want their men going to Magic City? Who don't want their men to go to Magic City? Come with them. It's an experience for everybody. It's not necessarily going to be like, oh, I don't want my men to go here because for whatever reason, it's a strip club. Like, it's more to it than that. It's a lot of customers I've met who didn't even know it was a strip club. And they'll be like, I literally came here because they said Magic City Kitchen. When my wife finds out that I came to a strip club, like, she's going to be upset. But I literally didn't know. She should have brought us some wings. That's what I tell people. If y'all bring back something, it'll be a little bit more. Because it's nothing you can't, like, every time you bring something back from Magic, it's going to be something that's worth it. Oh, it's some of them fish chips. Plus the acrobatics, the people flying in the air, the girls stabbing on each other in the air. What's the team called? They do the tricks. It's a lot of them. It's a lot of different ones. Oh, OK.  It's nothing like that in Magic. You know what I'm saying? It's the entertainment. It's the erotic circus soleil. It's the appreciation for the acrobatics.   It's the 10th circus soleil. 10th is that. It's the whole vibe of the appreciation of seeing somebody contort their body in a way that you couldn't even think it could happen. And then start twerking. That's crazy. Or do a backflip in heels. You know what I'm saying? It's that level of professionalism that keeps Magic City at this time. Maybe the Snake Packs did it out there? No. They kind of retired. It's been a long time since you guys.  They can't do it. For your friend, the Snake Packs. They can't do it. For a long time. He's talking about 2010. Right. No. For a freak. I was going for a freak. I was going for a freak. I'm like, yeah. Yeah. It's like, roll the pot still. Yeah. It's just a little bit of a mess. Yeah. We ain't gonna keep y'all here all night, because you know, I tell you, this is one of my favorite places. Hopefully we got enough information to last until we do this Merch Collab. Where it's coming soon. I'm saying this should hold them over until we finish what we doing, right? For sure. Alright then, with that being said, cheers to Black Money.


In regard to being a SWIC, we're driving speedboats. We're driving, it's a beautiful job. And you know, in regard to the community that we're in, at the end of the day, we're boat guys. So you put us on any boat with the team so we can make it happen. Everybody who's gotten out, that's done their time from this community, the one thing that they have, the general consensus is that they miss the brotherhood. Again, this isn't your high school, the cross team.   And it's a different type of family. There's something that feels good about working with a group of guys and having a group of guys on your back to where you know what the drop of a dime these dudes are ready to hold it down for you. And they have the skill sets necessary to get whatever is needed done. That's a different type of community. That's the NSW community. Whether you want to be a SEAL or a SWIC, you say those names and enough set. People know what that comes with. And that's what we expect to anybody that's pursuing this community.


Keir Starmer's talent for getting his opinion pieces in Tory newspapers continued this weekend with an article on vaccinating teachers. The piece was headlined. So Keir Starmer, let's harness the spirit that has made us the envy of the world to get every child back to school. So in the piece he argued or he said, I share the government's ambition to make it a national mission to reopen our schools. I will do everything in my power as leader of the Labour Party to make that happen. I have offered to work with the Prime Minister on this, including calling for the opening of Nightingale-style classrooms and I renew that commitment today. He says, I believe we can take a further step towards reopening our schools by getting our teachers and school staff vaccinated as soon as possible as the mail on Sunday has called for. Now this proposal which Labour have had since about last week, they've said, to get our kids back into school quickly, get to vaccinate the teachers, has had some pushback from the Conservatives and some people in the media because it is seen as going against the proposals from the JCVI, I'll talk a bit more about those in them in a moment, and potentially it would mean pushing down more vulnerable people down the priority list. We'll talk about the details of that in one moment. First of all, this is Starmer's response because he tries to preempt that argument. He writes, this is not about deprioritizing existing groups. That is not what I'm calling for. It is about having the ambition to do both. I've met the staff at the vaccine centres and I know they are up for this challenge. We can capture that spirit by going further, faster and smarter too. For example, we should be looking at how we can use our supply more efficiently. It's estimated that 5% of vaccines are wasted. That could mean more than 120,000 a week based on recent numbers or the equivalent of more than 10% of school staff in England. That last part of the argument I don't find particularly convincing is Kierstarmer essentially saying, look, Britain has the fastest vaccination rollout of any major country in the world, any non-very small country in the world, but I think it can get faster. We don't have to make tough decisions because let's just make the fastest rollout even faster. I do, however, think there are some good arguments for vaccinating teachers first. So what people have been pointing out, which is very true, is the key reason that schools are closed right now is not because teachers are at risk of getting coronavirus, it's because of their impact on community transmission. Clearly with COVID-19 so widespread in the country and with so many people in vulnerable categories, not vaccinated, it would not be safe to open schools at this point in time and vaccinating teachers wouldn't change that. However, I can imagine a time in the near future where there is a non-negligible risk of community transmission by kids going back to schools, but as deciding collectively as society that it's worth it. It's worth kids going back to school while there's still some risk of them getting COVID-19 and taking it back to their families, presumably by that point in time you'd have the most vulnerable people have been vaccinated but still there is a risk of it spreading. Now in that situation, I would say it does actually, there's a good argument for vaccinating teachers because if we are asking people to go into a situation where they are at more risk of catching it than anyone else and remember there's long COVID, it's not just dying from COVID, which is a problem. Then I think fairness says we should probably give them more protection. And so if we do open schools before everyone has a vaccine, which most people are suggesting we will, then I think we should probably give that extra protection to teachers. Ash, what do you think about this particular call from Keir Starmer that teachers should be vaccinated as soon as possible? I mean, he specifically said in the February half term. So the reason why I don't think it will necessarily happen during the February half term is because the vaccination priority list has been designed so that you get the biggest impact on hospitalizations and deaths possible which frees up your vaccination capacity because the more you have a health service which is overburdened with hospitalizations and treating COVID cases, the less efficient it is at handling a nationwide vaccination rollout. So that's one of the reasons why it's been designed as it is. So if you want to get teachers higher up that list, say ahead of clinically vulnerable under 50s, then you start slowing down the vaccination of those groups and you increase the risk of undermining the vaccination rollout itself and then it becomes counterproductive. So that's one issue that I have there. I don't object to the idea that once you have made your way through that rollout, that vaccination rollout priority list that you then move based on exposure. So you've got teachers, you've got bus drivers, you've got security guards, you've got cleaners, you've got people who work in roles where you cannot work from home and you are exposed to other people. I think absolutely you prioritize those who are most exposed. I think that you're right, there is a case to take that risk a little bit more with schools than you would with other settings and vaccinating teachers would be a huge part of it. One of the problems with how Keir Starmer's framing it is that, well, as I've just said about undermining the vaccine rollout priority list that risks undermining the vaccine rollout overall.  It's not necessarily that you can then requisition that 5% capacity in redirect elsewhere. Usually it's from things like misappointments and so you've got vaccines which have been transported, they've been taken out of the cold storage where they've got to be and then they've got to be used very, very quickly. So maybe you then have some kind of local system based on local authority records about who works in which key worker role and you can informally start ringing people up in the area and say, hey, come get this vaccine. But the idea of simply redirecting that 5%, I don't think it's as simple as Keir Starmer's making out. But it sounds good, doesn't it? Sounds good where you go, look, 5% goes to waste, it can go anywhere. Logistically, that would be really difficult. Yeah, I mean, it's unlikely that no one thought of that already. You know, you can imagine Keir Starmer going around putting a sort of teacher's jar in every vaccination booth. It's like, if there's anything left in the bottle, pour it in the teacher's jar and then we can give it to everyone at halftime. I feel like, you know, the vaccination program is going quite well. I think they've probably done a lot of this troubleshooting. And we're going to go to Rachel Reeves being challenged on this by Andrew Ma. Before we do that, if you're new to Navarra Media, new to Tiskey's Hour, make sure you do hit that subscribe button. We go live every Monday, Wednesday and Friday at 7 p.m. and put out videos every day on the issues that matter in British and world politics. On the Andrew Ma show yesterday morning, so on Sunday morning, Rachel Reeves who leads on Labour's response to the pandemic was challenged on whether the call to vaccinate teachers was backed by the science. So in his question here, Ma quoted statements from the JCVI, which is the Joint Committee on Vaccination and Immunisation. That's a sort of government panel of scientists who advise the government on this and the ONS, which is the Office for National Statistics. The JCVI have looked at this and I'm going to quote from their report. They say they do not advise further prioritization by occupation during the first phase of this program. And you quoted the ONS just now. Again, quoting the ONS, rates of death they say, including COVID-19 and men and women, who worked as teaching and educational professionals such as secondary school teachers were not statistically significantly raised when compared with the rates seen in the general population. In other words, on both sides, the JCVI and the ONS have both looked at this and they have both concluded that your policy of vaccinating teachers first or vaccinating teachers during the half-term is wrong. No, well, Andrew, first of all, we're not saying in phase one. Phase one should be completed by the middle of February. We're not saying that teachers should be vaccinated in phase one. We're saying in the second phase, can we bring teachers in? And second phase starts at half-term. So we're not suggesting in phase one. We're suggesting in phase two. And second, when schools went back in the beginning of September, within a couple of weeks, 25,000 teachers were out of the classroom having to self-isolate. The only way we're going to get kids back to school is not banding around dates as the government are doing. Putting in place a proper plan and that involves vaccinations. And Liz Trosser said that this morning is vaccinations and testing in schools and the better wearing of masks. Rachel Reeves makes one good point there about sort of absences and teachers having to take time off. I'll talk about that in one moment. First of all, I've often said throughout this pandemic, Andrew Maher isn't particularly good at political interviews because he doesn't actually know enough about COVID-19 to hold anyone to account because Rachel Reeves there did tell a bit of a porkey because she said the Labour Party will not be proposing that teachers are vaccinated in the period of phase one of the rollout. Andrew Maher there read out a quote saying, we as the JCVI do not think that people should be vaccinated according to occupation in phase one. Rachel Reeves suggested, well, phase one will be over in mid-February. That's not true. She is making out there that phase one is the government imposed target, which is vaccinating the four most vulnerable groups of people before mid-February. Actually, the JCVI includes in phase one, everyone who's considered in any way vulnerable. So everyone over 50 and anyone with existing conditions. Let's get up from the government website what the JCVI counts as phase one. Just to prove to you, I'm not telling porkeys. So say phase one, direct prevention of mortality and supporting the NHS and COVID and social care system. It says the JVCI advises that the first priorities for the COVID vaccination program should be the prevention of mortality and the maintenance of the health and social care systems as the risk of mortality. From COVID-19 increases with age prioritization is primarily based on age. The order of priority for each group in the population corresponds with data on the number of individuals who would need to be vaccinated to prevent one death. And then you can see in groups one to nine, you have everyone from residents of care homes to everyone over 50 and on the way you have people who are between 16 and 64 with underlying health conditions. So labor are encouraging the government to go against JCVI guidelines. So you can argue that they are arguing for a position which there could be scientific arguments for it but it is going against the advice of the scientific body that have been set up to provide this advice. So Rachel Reeves was wrong there even if Andrew Marl didn't realize it. On the point about absences, I do think she has a good point. What I potentially would have said if I was Rachel Reeves is that the quote that Andrew Marl wrote about the ONS is quite misleading. So he was saying, the ONS have said, look, there's no extra risk to teachers in terms of deaths, they have said that. So in terms of deaths, the ONS have said, if you look at teachers compared to other essential workers, they've got no more likelihood of dying than people in similar jobs. Infections though is potentially quite different. So when it comes to infections, teachers seem to, especially in November and December, get infected at up to three times the rate of the rest of the population and it makes sense. You've got lots of people in a room, very little protection, mask wearing, et cetera. So she could have pushed back on that particular point. Ash, do you think it is dangerous for the Labour Party to be seen to be contradicting the JCVI, which is the fairly objective and independent scientific body advising the government on who should be vaccinated first? No, I don't think it is simply because nothing the Labour Party does matters all that much at this juncture. So all that they would do is sort of undermine Keir Starmer's position at Prime Minister's questions. What's more dangerous, I think, is that where the Labour Party departs from the demands of the Labour movement, i.e. the teachers unions, workers unions and other sectors as well. So then you start asking the question of, well, who are the Labour Party for? Who are they supposed to be politically representing and how are they going to go about doing that? So that, for me, is where the danger is and it's one of sort of political affinity. But no, more generally, it's not dangerous for Labour to do that at all. It's just that they sort of sacrifice their moral authority to talk about things later down the lines. If they start questioning aspects of the government's vaccine rollout, then Boris Johnson can say, well, I put a user that you rejected the priority list as put out by the... Sorry, I cannot remember the acronym, the JCWI. JCVI. JCVI on vaccinations and immunisations. Brilliant. See, I'm so bad at remembering letters, Vs and Ws, I get really mixed up. But yeah, it's a political problem, I think, more than a public health problem. Yeah. Well, I mean, I think again, this is similar to the question we discussed earlier about travel restrictions. The reason Labour are going hard on this is because they see there is some political advantage for them. So the level at which I think potentially this is quite irresponsible in terms of public health is this is all really part of a manoeuvre by the Labour Party to put loads of pressure on the Conservatives to get everyone back in school by the 9th of March.  Now, even if we vaccinated all the teachers, that wouldn't be safe. So, you know, I don't think Keir Starmer should be rushing schools to reopen, especially not without rotors, for example. If you're saying vaccinate the teachers and everyone can go back, that's not good public health. But what people at home are going to hear, and I think this is what Keir Starmer is hoping, you've got lots of people, lots of parents who are like, I want my kid to go back to school. One, maybe they're going to get annoyed by their kid at home. Also, they're seeing that potentially they're falling behind or they're struggling. And then they hear on the radio that the Labour Party is saying, we support this one neat trick that means your kids can go back to school. And they're like, well, that sounds good. I quite like the idea that there's this one neat trick to send my kids back to school. So that's where I think potentially you've got opportunism over public health more so than on them disagreeing with the JCVI. And I think there are lots of good arguments to disagree with the JCVI on this, because I think fairness comes into it how much risk are people putting themselves in if you're a 50-year-old office worker, you have the option of staying at home and avoiding risking COVID-19 before you get your vaccination. If you are a teacher and schools are reopened and you're 49, you don't get that choice. So it seems to me that fairness suggests that we should consider vaccinating not only teachers but bus drivers and anyone who we're asking to go into harm's way to give them extra protection.


Hello and welcome, my name is Shannon Kemp and I'm the Executive Editor of Data Diversity. We'd like to thank you for joining the current installment of the Monthly Data Diversity Smart Data Webinar Series with host Adrienne Bolz. Today, Adrienne will discuss leverage the IoT to build a smart data ecosystem. Just a couple of points to get us started. Due to a large number of people that attend these sessions, he will be muted during the webinar. For questions, we'll be collecting them via the Q&A in the bottom right-hand corner of your screen. Or if you'd like to tweet, we encourage you to share how it's a question via Twitter using hashtag Smart Data. If you'd like to chat with us and with each other, we certainly encourage you to do so. Just click the chat icon in the top right for that feature. And as always, we will send a follow-up email within two business days containing links to the slides, the recording of this session, and additional information requested throughout the webinar. Now let me introduce to you our series speaker for today, Adrienne Bolz. Adrienne is an industry analyst and recovering academic providing research and advisory services for buyers, sellers, and investors in emerging technology markets. His coverage areas include cognitive computing, big data analytics, the Internet of Things, and cloud computing. Adrienne co-authored Cognitive Computing and Big Data Analytics, published by Wiley in 2015, and is currently writing a book on the business and societal impact of these emerging technologies. Adrienne earned his BA in psychology and MS in computer science from SUNY Binghamton and his PhD in computer science from Northwestern University. And with that, I will give the floor to Adrienne to get us started. Hello and welcome. Great. Thanks, Shannon. It's always fun to do these, and it seems like the year has gone by pretty quickly here as we do the last one for this year, anyway. What I'd like to do today, I'm going to go into here, is look at the impact of the IoT, the Internet of Things, the Internet of Everything, as it works with what I'm calling modern AI and cognitive computing to provide new business opportunities. So we're going to do things just a little bit differently today than some of the other webinars. We tend to focus a lot on specific technologies and apps companies and different markets. Today's going to be more about the business strategy implications of adopting an IoT first, or an IoT and cognitive approach to building your businesses or growing your businesses. So to do that, knowing that we have a pretty diverse audience, I mean, typically we get people from many countries in different roles and different industries. I want to do some level setting. I'm going to talk about conventional strategy and how businesses make plans for competing in their market. Look at a couple of common frameworks for strategy, and then I'm going to take a quick stroll down the memory lane. I have a series of slides that are labeled flashback. To show some things from when I was a semi-serious academic teaching strategy in an MBA program. Look at what's changed, what's stayed the same, but then take that into the actual implications when we have access to the internet or things as it exists today and as we expect it to grow over the next several years to decade. How does the combination between IoT and modern AI, and I'll explain in a little more detail what I mean by modern AI, rather than just conventional artificial intelligence, then I'm going to give what I consider to be five rules for creating business value with modern AI and the IoT. So let's start by taking a look at SWOT analysis or Strengths, Weaknesses, Opportunities and Threats. This is a pretty common model when you're trying to understand a business to decide where you go, where you invest your money, how you hedge your bets. When you think about it, pretty much all of business is about managing risks and identifying opportunities and then backing them with capital. So we look at the SWOT model, looking internally, we're looking at what are our own competitive advantages, do we have resources, capital data? I'm going to emphasize data here because at the end of the day, as we go from bricks and mortar to e-commerce to cognitive commerce to IoT-enabled cognitive commerce, if you will, it's really all about the data and data is a form of capital and it's the way we deal with people. So looking internally to understand how the business is today, assuming you're not starting from nothing, what are the opportunities? Can we create new products? Can we create partnerships, mergers and acquisitions? Do we have some strengths based on our geographical presence? Do we have some limitations on the flip side, the external focus? When we think of the weaknesses, in general, there's sort of a mirror image there where our competitor is stronger but we also have specific weaknesses that may be independent of competitors, maybe perhaps an overall dependency on a natural resource that scarce or could be disrupted, that's a weakness. And that also ties in with threats like disruption in the business, disruption in your supply chain, displacement by an alternative. So this is just one of the models. And what I want people to think about and have this kind of in the back of your mind as we go through today is for every new type of data that we will have access to through the IoT and for every way that we can add value to that data through cognitive computing and machine learning and sort of narrow it down a little bit with the different technologies for different opportunities. How does it fit back to this overall, are we in the right business? Do we have the right product mix and where do we go from here? So the next model that I want to spend just a couple of slides on, Michael Porter's Five Forces, which really kind of shaped the way business schools taught strategy for many years. A number of things have been updated, but I wanted to start with the basics here. And in the abstract, when we were thinking about what to present, we talked about creating an ecosystem. And this is really sort of the central diagram to the ecosystem when you think about it. Porter looked at strategy from an industry point of view. So you could draw a diagram like this and let's say you're studying insurance as an example of an industry. I know we have a lot of people from the insurance industry that participate in these webinars. So to look at your position, you have to look at the whole chain. You have your suppliers that are providing you with either physical goods or intellectual property or capital, whatever it is. You have the people that are buying your services. You have the threats, if you will, of substitute products or services. There can be some disruption. I'll talk about that in a second and how the IoT is really changing each of these relationships with the industry competitors. And then the idea of potential entrants, new companies that can get into it. If you think about the companies that you use all the time, for example, perhaps Google for search or Facebook for communications. These are companies that weren't around 20 years ago and now it's hard to imagine our daily life without them. So we're going to look, as this comes in, in terms of whatever industry you're in, your business may change industries. The popular term today is to pivot. We're going to develop something completely new. You may be a company like GE that makes everything from appliances to locomotives. So for each of the specific sub-industries, if you will, you want to do this sort of analysis. And the reason I'm starting with this is because as we look at the kinds of data that we can have today because of the Internet of Things, because of a sensor-based infrastructure, if you will, and what we can do with that data and what we can predict based on that data using predictive analytics and machine learning and some of the modern AI techniques, it changes the way we look at our ecosystem and companies that used to provide us with maybe physical goods now perhaps the value of the information that we're getting from them is actually more useful to us or higher value than the physical goods themselves because we can start to spot trends and that's what I want to look at. So within the Five Forces as a basic model, just given a couple of examples here for each of those relationships. So the threat of new entrants, how do you have a competitive advantage? Is it because you have economies of scale? You're already there and you've built up a network, for example? I mentioned Facebook. There was no Facebook when I started teaching in business schools. Now it's so entrenched it would be hard to create an alternative even though the technology isn't difficult to replicate, if you will, but there are these network effects of having the platform and there's switching costs when somebody is used to using it. So just to have you thinking in those terms, the traditional approach to strategy looks at relationships between these entities, your relationship between the supplier and your own business. But today, the relationship that matters the most in many cases is the data relationship. And so if we look at last slide in this section, when Porter was writing about this as a way of evaluating overall markets and he did a lot of work in specific industries and that was followed by some of the stuff that he may be familiar with in the high-tech industry, writing about creative disruption and looking at how individual companies tend to fail when they can get disrupted from the outside. Supporters' generic strategies were to lead by cost to differentiate products and to focus on a product niche. Back in the days when I was a product manager for a software product or a suite of software products, dealing with our company management, it was always, do you want it fast? Do you want it cheap or do you want it high quality? And you could sort of pick two out of three to optimize, but that meant that you were giving up on one of those. So do you want to be the cost leader? And if you look in here, the issue today is that so much of the value that our customers perceive, people are actually paying the bills or paying them indirectly in the case of users to whom our partners sell advertising, that cost leadership is a much more difficult thing to deal with unless you're dealing with manufacturing. So the sub-strategies create the barriers, and most of these have actually changed based on the availability of data and the movement of data. So a lot of what was talked about as strategic advantage in the past was based on the idea that you would have access to information or access to data that the buyer didn't have. And so for the next couple of slides, this is where I have the flashbacks, I decided to just leave them in the form that I used them back in 2000 when it was a PowerPoint, just a novice. The reason I'm doing it here is to show what was predictable, and this is going back now, 16 years, 17 years I started teaching this class at NYU in 1999, to look at some of the things that we knew were coming, but also to see how the structure of the different industries has changed and where we go further. Now that we've got a full-fledged Internet of Things, if you will. So back then, I was talking about the IP-enabled future, and IP was, I actually talked about IP over IP, it was intellectual property over Internet protocols. The idea was that going beyond the e-commerce, which was selling everything that we already sold, selling it online, there were things that were coming that we're seeing today that kind of changed the way we thought about business and our relationship between a customer and a business or between businesses that collaborated within a supply chain to serve customers. And here the one point on the slide is just that the example used here was proprietary software and home PC to control thermostats. And today, it's certainly much more advanced. You can go to Home Depot, you can go to online, Amazon, wherever, and you can buy thermostats that communicate without requiring as much in the way of proprietary signals. Because we're going into much more open standards. And so these things can be mixed and matched, which wasn't, it was certainly predictable in 2000, but it wasn't the norm. People differentiated by trying to hold on to that. Today it's going to be about the data, not about the proprietary piece. So one of the questions that I used to ask is where could you put this intellectual property? And this is before we talked about the Internet as the Internet of Things. But it's been obvious for a very long time that devices of all kinds would be Internet protocol enabled. So talking back then about real-time monitoring price control based on temperature and volume, where this has gone, and I have to laugh here because it says home heating, warm up your home from your pilot on the way home. We happen to have a family with teenagers. So the pilot I'm talking about today would be the Honda pilot. But when I wrote this slide, it was a Palm pilot, which was the latest in devices. So now almost any sort of device that you have is going to be enabled enough to be able to interact with the others. And that's a key distinction from where we were just a couple of decades ago. This one I just wanted to mention because back in, let's say, 16 or 17 years ago, the different companies were trying to have their own payment aggregators. And this has changed the balance of power in terms of the customer relationship. And we see things like PayPal, for example. My son was having trouble getting home from school yesterday and I had to transfer cash from my business account to my business PayPal to my personal PayPal to his PayPal. It's all relatively seamless. It's not necessarily as fast as you would want. There's still some frictions along the way. But all of these things have come to pass. So the relationship with the customer may have moved from the bank to PayPal in this case or to Uber. And now we have to start to look and say, well, if everybody has all these devices and they all talk to each other, how do we distinguish ourselves? And create some real business value. So I just laugh because this is what we're talking about today, how to make money by enabling devices and focusing on the data. And that's something that a lot of people are looking at this as, oh, it's a great new opportunity. It's something that we've been working on for a couple of decades. And what I'm going to show you today is that even though some of the terminology is new, even though some of the players are new, the concepts are really fundamentally evolutionary rather than revolutionary in terms of IOT enabling the business supply chain and the relationships between customers and businesses and between businesses within an ecosystem. So the point of this one, and some of these slides I'm going to skip over pretty quickly. If you have any interest, I can give you more context offline. And there's at least a dozen slides that will be included in the deck that Shannon sends you afterwards that I'm not going to get to today that have some of the specific technologies mentioned. But the point here is that when we looked at e-commerce as a change in the way businesses worked with their ecosystem, it was all information-based. The thing that was being exchanged was information. And all the red arrows here are just to indicate that today every one of these relationships can benefit from both IOT, some sensors, and I'll talk about that again in just a minute, but also about modern AI. And the simple way to look at it is the IOT gives us more data, and the modern AI and cognitive computing gives us more insights based on that data. And I'm going to skip that and just go to the idea of the missing middle now. One of the interesting things at the sort of the turn of the century, if you will, the e-commerce revolution was companies being disintermediated. And so instead of having multiple layers between the producer of a commodity or good and the person that was actually using the value, some of those could be disintermediated when the person could get more information. And the key here was that if the good wasn't something that was needed immediately, people would rather shop online and be able to disintermediate the retailer. Today, with drones and one-hour delivery service in Manhattan, it's going a step further. But what we want to do is look at how we get from where we were to where we are today to where we're going to be as we can anticipate the requirements based on some IOT and some modern AI technology. And here this one was just the idea of the new intermediaries. We got rid of the old ones, and of course we had to add some new ones. So the last of the flashbacks is that even nearly two decades ago, the emerging technologies that we now take for granted were out there, and that's just been refined, things like voice and speech and voice over IP. All of this is now at the point where we take it for granted. And some of the things I'm going to be talking about today as being leading edge and perhaps leading edge two or three years from now are just going to be as archaic in most of your minds as voice over IP. So let's look at how does the IOT, either of things, actually change strategy or how we look at strategy? It all starts with sensors and ends with insights. So I did a webinar on this a couple of months ago on just the IOT and the technologies. So we're not going to get into a lot of detail on the actual technologies, but just the key things that you need to think about in terms of strategy. So sensors, any kind of a device that detects the presence of a signal and reports a signal. It's event driven. You get something based. It is sensing, just like a human. You sense a change in your environment. And the sensor itself may be stationary or mobile. So you may have a sensor that travels with you or that travels in a vehicle. You can have your car certainly has lots of sensors. You may have sensors in your phone. It will get into more detail there. But there are also sensors that are stationary and are looking for events in that area. So a sensor that detects people or cars passing through an intersection. You could have just the one sensor, let's say at the intersection that's tracking visually cars going by, something, an easy pass lean sensor, for example, where you could have, in the case of the easy pass, you've got a sensor in your car that's communicating with the sensors as you're passing by. Stationary and mobile sensors. And this is building the huge network. That leads me to another term that we're going to have to draw out a lot, which is stream or streaming data. Any idea here is if the data is always moving and we've got great quantities, we're talking about big data. In general, I'm more concerned with fast data than the volume. The question is, are we going to divert that flow to be able to do something with it? Do we need to capture, analyze and then release the data? Or can we do it as it passes through? And this is a diagram that I've used once before. Just to get you thinking about where the analysis can happen, are we aggregating the data and then doing the analysis or with the modern infrastructure for streaming analytics, we can basically analyze data as it's passing through. So here the data is flowing on the edges, but the queries can be anywhere. Something that's as opposed to the data comes in, it's analyzed, and then it goes out. We have things now, it's working with a company recently that their client does real-time advertising decisions. And they're talking about the time from people bidding on advertising space on video sources to the actual ad being placed is so small and the transaction is happening so fast. That the data can't be captured and then run through. Okay. So there are a number of these platforms. We're not going to go into the specifics of any one of them. Just put it up here to show that you do have kind of the usual suspects, if you will, the Cisco's and Intel's and IBM's of the world. But you've also got companies that are known for building infrastructure on open platforms like Red Hat, as an example, and C3, which started out as very focused on energy monitoring and is migrated to build a general-purpose IoT platform. There are references to some of these in the supplementary slides that you'll get with the tech. Now, so that's kind of the quick overview of the pieces of the IoT that are relevant to changing your strategy. Here, I want to get into the contribution of modern AI. The reason for this slide is to show where I make the distinction. So AI for the last 50 or 60 years has been looking at opportunities to create machines that exhibit some of the characteristics of human intelligence, if you will. So it's artificial intelligence. Today, it's often AI is being used for augmented intelligence. But whether it's relatively intelligent image processing to natural language processing to gaining insights using logical reasoning, that's all pretty standard fare for AI. When we talk about it today, though, the area of machine learning has almost become synonymous with AI. But machine learning is really, I show it as something that's separate, but there's an overlap. Others have shown it as a subset of conventional AI. I don't really believe that or subset of AI. I don't believe it is. When we look at machine learning, we're really looking at systems that can improve their performance on some task without reprogramming. So it's based on the data that machine learns or the system learns based on experience with data. And it's the most typical way it's done today is with models of neural networks that are heavily statistically or probabilistic systems that learn from identifying patterns. And the third component that I include when I talk about modern AI is big data because many of the things that we had a fundamental understanding for what we wanted to do 20 or 30 years ago. We just didn't have the data or the access to the data or the ability to manage data fast enough to use it in real problem solving. And today we do. So those are the three components that I would include in modern AI. And where I just make one more distinction in cognitive computing, we're looking at sort of the subset of modern AI that is focused on four things. Understanding, which is a whole area by itself, learning, which would include machine learning. Reasoning, which can be a combination of top, down, bottom up, inductive, deductive, abductive reasoning. And then I had a fourth component. Many people ignore this and just look at the first three. But that's planning because if you're dealing with a cognitive system and you try to do something that's going to have general intelligence, if you will, as people, I would say a kind of a higher order of intelligence than others in the animal kingdom. We look at things. We have a model of the world that we would deal with and we plan ahead based on that. We're not always in reactive mode. So if you have those four things, and then in this diagram, you can see the top half is dealing with human interaction with a cognitive system. The bottom half is machines. And it's the bottom half that's most relevant for the rest of the discussion today, which is when you have machine input coming from sensors, that's where the IoT fits in here. Got to get through whatever big data management systems that we need to handle the data and put it in a form that the cognitive system can handle it. And that's going to be, you know, how to spark all the open stack, open source stack of tools to get it in there. You can also have data coming in from the bottom, if you will, and going out through the top. So you get all this data coming in and then the system is going to analyze it and produce narratives or visualization reports for the people. I can go the other way. You can have input from people that ends up going to the machines. But that's kind of the context. So now we've got sensors and systems that are producing information. The value of that information is being identified or enhanced by modern AI. And now I want to spend a few minutes looking at where does this fit with the business models and what are the opportunities. So the combined impact, if you will, of the IoT and modern AI, three areas that I want to look at and we'll go right into the five rules after this. So when everything is connected, what happens is you have new sources of data. So there's data from my refrigerator. There's data from my phone. There's data from my car, within my car. Not my car, because I happen to drive a 40-year-old car, but that's a whole different story. But if you're driving a modern high-end car, there's a lot of systems in there that talk to each other, but they also can communicate via their infrastructure to the rest of the world. So you can have a system within your car that internally is broadcasting your speed, or you can have a sensor externally, like a police radar system. That's a sensor that's capturing your speed. But you can also have within that your engine is transmitting or could be transmitting or could be batched up for later. Information about its performance, about the last time the oil was changed, about temperatures. You could have individual tire sensors that are providing information. And some of this is going to be issues in terms of privacy and security, but all of these have the potential. Basically any physical device has the potential to be sensor-enabled and communicate. The question is, just because we can, do we want to? For the moment, I'm going to assume that we do, because it's a tech audience. So intelligence and the sensors, the intelligence can be distributed. It can be something that's local to the device, so we can have a machine learning system that's within your automobile that's looking at it. And then it may or may not communicate synchronously or asynchronously with your car's manufacturer. Let's say you're driving a Volkswagen and you take your car in for service, give them the key, and based on the key, they can get information about what you've been doing since the last time you were there. That's one approach. The other approach is you can have a system that is communicating in real time and you're getting that information is being used to either sell you new services, to predict failure, et cetera. So it may not be that important if you're driving your Volkswagen to work from back, but if you're driving your Boeing plane across the ocean, you probably don't want to wait until you get halfway across the ocean to know that the system is predicting an engine failure. And if we look at the amount of data that's being produced in real time on jet engines, Formula One cars, it's one of these things where putting them together, we have all sorts of new opportunities. And what we're going to see in the next couple of slides is that all of this translates to new opportunities, even with a much more mundane world of retail, of professional services. But it all starts with technologies that were developed in large part for very sophisticated requirements. So here's the five rules and I'm going to go through the examples of each. And although I'm putting this as all about customers, you could substitute for any of these what do the other entities in your supply chain want? What do they need? How can you work together? But I'm going to focus on it from a company to a customer because I think that's a simpler way to start today. So we're going to look at each of these, know your customers, know what they want, know when they want to change, know where they are and anticipate opportunities. But for each one, I want you to think as we go through it and say, do you have or can you capture streaming data that's going to change your relationship with your customer as you're trying to know them? Do you have information about your product, your supply chain or the customer that can improve your business performance? And if not, where can you get it? I'm going to look at some possibilities there. And finally, can you create value from new analysis of data that's available to everybody? Because one of the competitive advantages way back in the SWOT analysis, your strength, could be the proprietary data. But there's a lot of data out there that's either free or freely available, everything from news feeds to current weather to historical weather to weather prediction to economic analysis. That is certainly valuable. I mean, weather data is very valuable for a lot of industries. That's why IBM got involved in buying the weather company. But in general, something that anybody can buy, then there's going to be one or two companies that are going to get good benefit out of selling that information. But the real value comes and what do you do with it if you can combine it with other data sources or combine it with your proprietary algorithms that allow you to get better insights from it. So let's look at these five from it. So knowing your customer, and here's where I want to just kind of give this model for doing the analysis. You need to be able to track financial services, particularly in banking, there's some know your customer regulations. They're based on the idea that you have to know who's putting money through your system, if you will, and what's the source of funds, things like that. What I'm talking about here for know your customers is in order to be able to provide goods and services that will maximize the business relationship with a customer, which is generally profit oriented. You can collect demographics, standard information, your age, ethnicity, zip code and all that good stuff, psychographics, behavior. But today, those things don't tend to change very quickly from moment to moment, for example. Your age changes predictably if we have you in a database and we have your date of birth, we can calculate your age at any given time. Whether there's a life changing event, marriage, divorce, having a baby, etc., all of those things we can track. But what we're getting to today is being able to use sensors, use emotionally detecting, if you will, technology that's going to change how we interact with that customer right now. If you're looking at a customer relationship and saying, okay, somebody has just bought a car for me or a refrigerator, I know that they're going to need another one. If it's a car and they've taken a three year lease, I know that they're going to anticipate another car in three years. If they bought the car, I don't necessarily know when they're likely to replace it. But if I have access to all this other information, and if I have either based on, here I show that, you know, how is this information captured? If I have information about them based on their use of a cell phone, their credit history, their travel history, I can start to put all of that together. And these profiles that we're building on people are either from opt-in information at the time to become your customer, or it can be generated based on observation, or we can be purchasing it. We can derive this based on using the data and then using technologies like the analytics and machine learning to derive new information about that person. And so it's really kind of this combination that creates more value to you as an enterprise about your customer. But the next step in this, if you will, and this isn't really a hierarchy, but you would need to have that information before you could get into looking at what your customers want. You may actually get an indication of what your customer wants by asking them. And certainly, you know, surveys have been around for a very long time. Today, the general approach is to try and figure out what they may want based on what they've already done. And in this case, we're still dealing with things like natural language processing. And in general, I'm talking NLP, and I don't further qualify it. I'm talking about natural language understanding. So we may be looking at what they say they want, and that could be – this is less sensor-oriented. It's more looking at things like social media, for example, or looking at natural language understanding. Here, we're also getting into the same sort of profile that we're building. But if we're trying to understand what they want, part of it today is by looking at what their peers want. And I'm sure we're all familiar with getting recommendations. You know, customers who bought X also bought Y. But behind the scenes, the combination of looking at what's been expressed as an intent and the actual behavior – behaviors is a more accurate predictor of what they're going to want in the future. So now we may be looking at this together to see what they want. The next thing is to take all of that and add information when the wants change, and the wants change based on changes in the profile. This is a famous case about six years ago, I think it was, where Target started sending out special offers to women based on their prediction that they were pregnant. It was a case where these offers went to a teenage girl and her father got all upset and started going after Target and it turned out that Target was right. It was just perhaps not a very smart thing to just send these out without understanding the context of who it was going to. And that was based on different things, different behaviors, different buying patterns that indicated with high confidence that this young woman happened to be pregnant. But you get that information, you can use that information much more effectively because you have all this peer information and that allows you to make those correlations. So that's when the wants change. And that can be with or without the IoT, but with the IoT as we have more devices, we have more data, and as we have more data, all other things being equal, more data allows us to get more refinement with our machine learning with our cognitive systems. So now the next one, which to me is particularly fascinating is knowing where the customers are or where they will be and where they are is pretty easy based on a couple of technologies, in particular if you're using, if you have someone who has opted in with a mobile phone app, you have access to a lot of location information. I'm going to show some of the sensors that will enable you to do that. But if you go to a mall and you've got systems there that are looking at your phone, whether it's a Wi-Fi, you know, if you've ever logged into Wi-Fi at Starbucks or Panera or one of those, you've basically given them information about yourself that allows them to know any time that you're in the vicinity. But going to the next level, we have devices that will be sensor oriented for facial recognition. And if you take that from facial recognition and voice recognition and then the emotion analysis that I talked about a little earlier, you start to combine it. Not only do you know where they are, you know what they're experiencing, what their motivations are to some extent. But then you can start to build this profile. And based on that, it's one thing to know where they are right now, and that's important. You have things like Cisco, for example, has their enterprise mobility services platform. And so if you're a retailer, you could use that, give your customers the ability to get free Wi-Fi every time they're in the store. They come in and you have information. Now you have the rest of the profile. You can start to make special offers. You can know, based on the sensors, you know where they are, you know where they are within the store. It's just like, you know, the big thing 15 or 20 years ago, early in the days of e-commerce, was tracking your virtual progress through an online site and then looking for things like abandoned shopping carts. I got an email last night from Home Depot saying, well, we noticed you put something in your cart. You didn't buy it. What they hadn't figured out was that I actually went to the store and bought it. So, you know, you have online and offline that are not always coordinated. But here, if you have enough information about the person in their profile, you have enough information about their behavior, and you have these peer groups and you start to look at that, we can now start to predict where they're going to be, verify it, but prepare offers that are more complex than just someone shows up at the store and you see that they've been there for 20 minutes in the hardware section and now they start to leave and it's a geofenced, it's proximity-based.  Now we can start to say, you know what, I'm going to call your bluff. You're looking for something.  So, that's where it's heading. And now the last one here is anticipating opportunities. And this again is looking at an opportunity with one person largely based on a change in some of your understanding. So you have an opportunity with one person or one category of person, one category of customer based on changes around them. So you can figure based on the analytics if somebody is an early adopter, a vast follower, whatever category they fit into for a particular type of product or service, and now you can create a new opportunity. Going back to that slide I had for my year 2000 strategy class at Stern, one of the things I talked about then that wasn't really feasible but it is now is you've got a Coca-Cola machine. You know what the inventory is. You know what the going rate is for a can of Coke in your area. But you create an opportunity if you can now look at that and go, you know what, I have six cans left and I know that the temperature is rising and that when the temperature rises, a certain class of people are going to pay more. You change the opportunity for a return on that good. And maybe it's not a big difference if you're talking about six cans of Coke, but if you're talking about something at a retail chain, you've got a thousand stores and now you know that there's some event that's happening or that people like this person have done it. It's all about understanding the implications of the data and where it's going in the future. So to bring it back to reality, if we just take a sort of the givens that we want to be able to get more information. Sensors are an important part of the way we're going to get that information. We want to make smart choices based on it. That's going to be based on modern AI techniques and our learning and our reason. What data do your customers already produce? What do you have? And this is just a list of the sensors that are in the iPhone today. So you've got an accelerometer. You've got one for ambient light, a barometer, location. So I already mentioned what you can do with geolocation, whether it's Wi-Fi or cellular, you can start to push offers to them. Or maybe it's more passive. You just track for information. You start to look for patterns. So okay. If you're a sports team, for example, one of the NBA teams has started to look at season ticket holders and they scan the sites to see when a season ticket holder is selling their seats. And how often they're actually using themselves. Well, now you can look at this and say, okay, based on prior behavior, this person is not likely to renew because they're starting to sell a higher percentage of their seats. But maybe by using geolocation within the phone and the barometer as an example, you can start to look for what are the correlations. And then start to derive what are the conditions under which they're likely to, let's say, an MLB season ticket holder where you've got 81 games for your home team. Well, if you're selling half of those, maybe you would rather have – you've bought two seats, but maybe you would rather have four seats of the games that you go to. You can start to create these special offers based on looking at all this data. And again, this can be stuff that you're getting directly if you have an app. And I would venture to guess that many of you have like a flashlight app on your phone. You probably didn't read the fine print when you downloaded that free app. Well, the reason it's free is because they're getting all this information that they can turn into a profile and use for other purposes. So all of this is already out there. It's just a question of how do you get access to it? How do you leverage it in a smart way? Here's another one. I've used this one before. This is a site called thingful.net, and they're attempting to build a search engine for the Internet of Things. And so in this particular one, I did the snapshot when I was giving a talk at the Seaport Hotel in Boston. I just went online and searched, and it showed all the devices, the IoT devices that were registered or that were detected by – and in this case, one of them that's giving down information was the bike rack at the hotel where I was staying. And I could see how many spaces there are on the bike rack and how many were open. Now, if you start to combine that with knowledge that you have – maybe you have actual knowledge that your customer is a bicyclist, or maybe you can infer that based on the fact that they're averaging eight miles an hour instead of walking speed or driving speed between two points, you can start to create value for them by combining just this data and the interpretation, the analysis, the predictive power of modern AI to really look at what are they going to want next. So that's how those two fit together. So there are all these sources – and this is just taking it a little bit further out, and I think this is actually my hometown here. So the issue is there's no shortage of data. It's adding value to it. But depending on your relationship, you really need to start looking at what sorts of sensors can you add to the systems that you already have. And at each step, just as we would try and optimize the supply chain going between builder and logistics and distribution, at each step, look for places where data is being created but not captured. Maybe you need to be in that capture business. Look for opportunities to leverage the data that's already out there. There's a lot of sensor-based information that's available – freely available and available free from most municipal governments today. The question is just how do we look at it and how do we look at it differently so that we can create value where other people don't see it? And I close by just saying that my own interest in this and where I'm going is we're actually creating some case studies and use-cake archetypes for industries right now. We'll broaden it. But insurance, banking and financial services, healthcare and pharma. And if you're in those industries and you're interested in getting more information or participating in the research, talking about what you're doing, I would love to communicate with you and happy to send you some of our results. So we'll wrap it up with this is How You Can Reach Me, and this is the list of topics we have for next year. And I'll hand it over to Shannon to see if there are any questions that we can address now. Or if we don't get to you today, give them to Shannon and I'll get to you when she sends them to me. So thanks, everyone. Adrienne, thank you so much for this great presentation. And like you said earlier, I can't believe it's December already, the last one in the series for the year. So just a reminder to ask the most common question that we always receive is I will be sending a follow-up email by end of day Monday with links to the slides and links to the recording of the session. And feel free to type in any questions you have in the bottom right-hand corner in the Q&A section. Everyone's kind of quiet today. I think the holidays are upon us. And then look forward to the series next year, Adrienne, where you will be continuing it on the second Thursday of every month. And so as you mentioned, there are modern AI and cognitive computing, boundaries, and opportunities. And likewise, you'll be speaking at Smart Data, our Smart Data conference in January in San Francisco. Yes. Very excited about that. What are you talking about there? You know what? I have, as I recall, I've got two tutorials and a couple of sessions. So I don't actually have all that at hand. I know I'm doing a panel. I'm doing a couple of tutorials. There'll be something on AI in the future of work. There's definitely something on natural language processing. Okay. And give me the URL. I will, yes. We can all see what I'm doing. I will indeed. And there's a question here about a discount. And yes, indeed, we do have a discount, especially for our webinar attendees. So if you use, let me just pull it up here and make sure it's not plural. Use the webinar as a discount code. We will receive additional money off. I'm very excited about that. And then we do have a question coming in here, Adrienne. Could you explain what you meant by modern AI and how it is different from conventional AI? Sure. One of the problems that we have in this field in particular is people throw around terms and everybody nods like they know what we're talking about. For me specifically, there are three things when I talk about modern AI. It's all of the technologies for understanding, learning, reasoning, and planning, which are kind of in the core of that diagram. But what makes it different from the way we approached it in the past is the availability of two things. One is big data and the other is machine learning. And it's not to say that there wasn't a lot of data before or that there wasn't experimentation in big learning. But I noted earlier in the year that when I was getting my start in AI and writing my very first natural language processing app, I don't want to say when that was. The way we approached things was largely rule-based. It was largely statistically-based. It was, in some cases, an attempt to mimic, to do biomimicry for the way we process information. Machine learning, the way it's done today, which is based on a model of neural networks and deep learning, which is a multi-level neural network approach, was really not invoke at all. And in fact, the first dozen AI textbooks that I dug out of the basement, not one of them mentioned machine learning. And now it's almost synonymous. It shouldn't be synonymous. It's an approach that's very useful. But if I put it all together, so to me, modern AI is, all the AI topics, from vision to speech to reasoning, plus machine learning and big data. So it's those three things. Perfect. And that brings us right to the top of the hour. Adrian, thank you so much for this presentation and for the series this year. I really look forward to next year. We've got some great topics like you've got posted right there on the screen. And thanks to all of our attendees for being so engaged in everything we do. We just certainly appreciate it and look forward to hopefully you will join us next January for modern AI and cognitive computing and likewise at the Smart Data Conference, where you can meet Adrian in person. So I hope everyone has a great day and thank you very much. Thanks, Adrian. Thank you. Take care, folks. Cheers. Happy holidays.