Good evening. This is Peter Lawrence. See, were you being kind as to give your attention tonight to the subject of murder? Were you going to have to do anything? No, really. Just follow the side of story. Just surrender yourselves to strange tales of murder. Listen, my friends, to the mistrained playhouse. For tonight's lesson in murder, we take you backstage to one of those little secondary boulders of theaters. The home of one-night stands, stave grief-pained, half-hearted audiences, and struggling performers. The world of comic street men, chorus girls, and tennis. A world of make-believe, yes, but with a hearty reality of the everyday world in the background. And it's a company in chorus of jealousies, passions, and disappointments. Yes, disappointment. One opens the door of her dressing room, then comes quickly down the iron stairs and crosses to the stage door. The manager of the theater calls out to her. May, May, where are you going? What's with the street clothes? You're on in about 15 minutes. Got me. Oh, wait a minute. Look, Mr. Walton, just let me get out of here. Now, hold on, baby. Take it easy. But I just want— Just calm down. May. What's the trouble? I'm through. I'm done. Oh, another fight with her, huh? Just don't mention that guy to me. May, you've got to use your head. You can't walk out like this. It ain't professional. Oh, please don't hand me that show must go on routine. Is that what you're leaving? Makes no difference. Wait. Hey, Pop. Yeah? Wapped out of Graham's dressing room. I'm gonna ask him to come down here. Well, yeah. Okay. Now, look, there ain't no point in having him— It's gonna be this time, May. I just can't stand working with the guy. He's your husband. He's also a comic. That I can't put up with. Honey, will you tell me what happened? It's just dripping water. Step on his lasty, sis. I see more keys, sis. Oh, every comic's that way. You can't walk out for a little thing like that. I'm not anywhere I'm walking out. Oh, what's that? Guy. Huh? He's got a new thing now. Guy. You think I'm flirting with a tenor in the flashback? Well, are you? Are you kidding? What would I want with a tenor? Well, what makes you think you're flirting with him? This tenor thinks I got talent. He tells all that. By the way, Al thinks the guy's new up with me. Oh, and the whole fight is on account of that? Yeah. But it was just one fight too many. If you think I'm going to let any blackface comic, even if he's my husband's husband... Excuse me, Mr. Walton. Oh, yeah, pop. And Mr. Graham can't come down. Well, why not? He just ain't able to. How come? He's got a knife in his back. He's dead. Uh, Chief? Yes, Mr. Newman? You said you wanted me to take a description of the body here? Oh, yes.  Can I come in, Chief? Oh, yes. Come right ahead, Hank. Have you finished the examination? More or less. Yeah? Any prints on that knife? No. Well, they called off the performance tonight. I told him you'd be questioning a lot of people. Yeah. What's the story on this man here? He was a vote-a-villain, blackface comic. Well, of course you can see that. He did an act with his wife. Where is she? Downstairs. She did kind of a break-up when she heard about the guy getting it. I told him to send her up when she found it a little better. I got a few witnesses outside the dressing room here. Do you want to see him, Chief? Well, who's there? Well, the doorman. He found the body. A guy named Walton. He's the manager and the wife when she's ready. All right. Have the doorman. All right. I'll finish that description later. Okay. My name's Pop. Yes, sir. This is the doorman, Chief. Pop Bradford. You do, sir. How do you do? You discover the body here? Yes, sir. Can you tell us how that happened? Sure. Mr. Walton, he's our manager, asked me to come up here until I'm dead from there to come down on stage. Yes, sir. I knocked on the door. When he didn't answer, I opened it and come in and I see him lying on the floor. Pull back that covering, Harry. Would it please? All right, Chief. There we are. Was he in the same position when you found him? Yes, sir. Have you seen that knife before? Sure. You did? Of course. When? In the pop room. I've been there every year. That's the room where they keep stage pop. That's right. Have you got any idea pop who might have done this to him? Nope. Did you hear any disturbance or argument before the stabbing? Nope. And any strangers enter or leave your stage door? Nope. In other words, you don't know nothing about it. Yeah, that's right. All right, Walton. And then we open up. Yes, sir. Don't leave the theater, Pop. Don't leave the theater again. Why should I leave the theater? I haven't paid to work all night here. Yeah, okay. How about manager come in, Harry? Yeah, sure. I'm taking all the testimony down here. That's fine. Would you come in, Mr. Walton? Yeah. Yeah, sure. This is Mr. Walton, Chief. All right. How do you do? You're the manager here. That's that, sir. Did you know this man who was killed? Yeah. Sure. Good little comic. Yeah, when did you see him last? Alive, you mean? What else? You told me downstairs you didn't see the body. That's right. Well, when did you see him last? During the supper show. How long have you known this man? Oh, on and off for years. They played here three or four times. How'd you get along with him? Fine, but... Hey, look, you don't think that I... Wait, I just asked you a question. You were very good friends. And he had any trouble with any of the other performers or anyone who worked backstage? Well... Thanks a good question. Yes, he did. Who worked? His wife. What was it about? Well, they've always fought more or less, all two acts, too, especially when they're married. What was this fine amount? Well, he was jealous of his wife. She was walking out of him. I found her leaving the theater. That's why I sent for him. What was this jealousy based on? Well, he thought she was going for a guy who sings one of the other acts on the bill. What's his name? Tom Kent, he's a tenor. How'd he do know about this? His wife told me. You say you found her when she was leaving this theater? Yes. Did she use this dressing room, too? No, she was in a room on the other side of the stage. Well, ain't it customary for a man and wife to dress together? Yeah, but like I told you, the grand couldn't get along so good, so I separated. Come in. Um, do you want to see me? Are you Mrs. Graham? Yes, ma'am. Ask him in, please. I'm on the District Attorney. Uh-huh. You know what happened, of course. Yeah. Well, just when asking Mr. Walton here some questions, he, uh, he happened to tell us about a fight you and your husband had. Could you give us a film? I guess so. Well? He thought I was flirting with a tenor. Were you? No. What made him think you were? I don't think they're liable to think anything. They ain't like normal people. And you were walking out on him on a kind of disfine? I guess so. Then why did you... What's the matter? That body on the floor. Well? It's supposed to be my husband? Yes. Why? It isn't him. Huh? Take that wig off him. I told him. There you are, lady. There we are. My husband doesn't have blonde hair? Come here. Go ahead for a cheek. I had him checked to send out an alarm on that guy Graham. Oh, that's fine. I also checked again with the dormant. He swears Graham didn't leave this theater. Well, there are other exits. The stage door is the only way out. That's true, but I sent a couple of detectives searching around backstage here anyway. What did you do with Mrs. Graham? He downstairs in the green room with that Walton guy. I got a cop with him. Chief, why do you suppose a dormant didn't recognize a body here as not being Graham? Well, it was quite a natural mistake when it's like there's makeup on with a wager. Sure. And he's laying on his stomach his face is only partly exposed. Why did Graham bother to put a makeup on this man? Well, it was very simple, Miss Miller. He figured it would take time before the real identity of the guy was found. And that gives him a head start for a getaway. Well, all we got to do is nail Graham and this case is sewed up. We got motive and all. You mean the jealousy angle? Sure, the jealousy. Chief, what are you doing there? I'm just figuring out something. Yeah, what? Well, first of all, I believe the makeup and the dressing gown were put on this man after he died. Huh? You noticed the tear in the back of the robe? Yeah. It was made to slip over the handle of the knife. Oh, yeah. It would also seem that he died immediately. And there's just this one blood stain here on the floor indicating the man didn't move. Yeah. Well, here's what puzzles me. Hey, look over here. And we got a side of the robe. What is it, Chief? Blood stains on the floor and also on the windowsill. Hey, that must be how the guy got out. Graham? Sure. The bees aren't the cursory stains that a man would have on his hands after killing. They're quite pronounced. Yeah, like maybe he was bleeding. Yeah. I don't see how that would be possible. This man was stabbed from behind. No sign of the struggle. Oh, yeah? It could be just a... Hey, what? Hello? What was that? Hey, sounds like the old dormant. Come on. There. That is the dormant. Yeah. Hey. Wait. All the stage lights are out. There's some glass lab hearings here. Sure. Here we are. Here we are. The steps are right here. What do you suppose is happening? I don't know, but it ain't good. Here. Watch your step on these stairs. Yes, we're all right. Let's keep going. All right. Are you okay, Pop? He doesn't answer. Yeah. Stage door's right over this way. I'm just leaving away. Hey. Hey, look. What is it? The old boy's stretched out on the floor. Hey. The time right now just don't try to move. Stop him. What's wrong? What happened? He hit me on the head. He got away. Who? Al Graham. How do you feel now, Mr. Bradford? I guess I'm a bit better at things. Good. Chief, could I have some more water? Yeah, sure. Here you are. That's fine. Thank you. You're sure you don't want to talk to him, Mr. Bradford? Nope. I'll be all right. I'm just done, that's all. Oh, you put the lights on. Mr. Harrington did that. They've been turned off on the switchboard over there. That's a great figure, yeah. Okay. Yes. We're over here about a certain time. Oh. Okay. Well, how's the old boy? He's much better. I just done a checkup around backstage here to see where Graham might have been hiding. Yeah. I stopped by in the green room. What do you think? What? Mr. Walton, the manager, ain't there. What happened? Oh, he conned that cop I left in charge to let him go out to the bus office. When was this? Well, Chief, as near as I can figure, it's right before the lights go out. That's sweet. Did he go to the bus office? Oh, yeah, yeah, sure. I just called him out there. Well, what about Graham's wife? Oh, she's still in the green room. Oh. They're popped. Yes, sir. Yeah, tell me, Pop, did you actually see the guy who assaulted you? Well, I thought I did. Were the lights on or off at the time? They were off, I guess. Were dark here then when you were slugging? Yeah. Well, Pop, then you don't know for sure if it was Graham or not. Oh, my, sure. But what's your point, Harry? My point, Miss Miller, is that I talked to a cop who stationed out in the alley, and someone did come out there. It was Mr. Walton. Now, I understand Graham was the little guy, and Walton was the little guy, too. Now, Pop, you put a mixed them up, couldn't you? No, but I still say it was Graham. All right. I picked up a pillar on Walton from one of the stage hands, Chief. Yes, what was that? He had a fight with Tom Kent, that tenor guy yesterday, right here on the stage. You sure? Yeah, sure, I'm sure. Three or four people hurt him. Well, I think you should go out the box office and have a talk with him. There's something I want to look into back here, and check with me when you're finished. What's that? It's me. Oh, what's the idea, Walton? Of what? Of sneaking out here. What do you mean? I told you to stay back down to Green Room. That's a business to attend to. Yeah? Like what? To cancel the show tonight. That's not a lot of refunds. I have to take a look through the seats. Why didn't you ask permission? I did, McCut. I mean from the district attorney. Well, I didn't figure we were playing school. Oh, oh, you didn't figure we were playing? Well, well, this ain't playing school. Playing school, Walton? There's a murder mixed up in this. I know that. Now, how did you come out here? To backstage, of course. To the stage door? No, one of the fire exits on the side of the theater. Would the lights on backstage when you left? Yeah. I suppose you don't know nothing about what happened to the dormant. Oh. But what's wrong? Well, the lights were turned out and he was slugged. Oh, is he all right? Oh, yeah, yeah, yeah, yeah. He's all right now. Oh, how did that happen? That's what I'm asking you. Whoever gave a tomb got out the stage door into the alley. Look, are you trying to finish on me? You were seen in the alley? I didn't touch. Well, who did? I don't know. Look, if I was feeding it out of here like you're suggesting, I wouldn't be here now. Would I? Oh, yeah. If you would. How do you figure that? That cop in the alley would have kept you here. There's another cop in front of the theater. He'd have stopped if you tried to leave by the front. You chased that out and decided to stay. Stop talking like a straight man. You're very funny, Walton. Look, what about this fight you had with the tenor? The guy who was killed? Who told you that? One at the stagehand. Okay, so I had a fight with him. Well, that don't make very good character for you. Listen, I had nothing to do with that killing. Well, let us be the judge of that. Come on backstage. I want to finish it. Look, Walton, there's another straight man who'd like to talk to you. The district attorney. The chief is up here in the dressing room, Harry. Oh, that's good. I left Walton down there with that cop. Oh, how'd you make out with him? Well, I think he knows something. Really? Yeah, I'll let the chief pick up from there. Wait a minute. Isn't this the room here? Yeah, sure. That's it. Go ahead. Thank you. Okay. Where's the body? Oh, the chief had it taken to the morgue. Yeah. Where's the chief? I saw him come in here. Huh? You sure? Sure. I was walking him down on the street. Hey. Hey, what is this? I don't know. There's no other way out of here except... Hey, look, that window's open. Yeah. Something's screwy here. Chief. Oh, chief! Yes, hang on. What? Where are you? I'm on the road here on the fire. What in the world is he doing down there? I'm on the road. Oh, okay. See, I got a scare for him. Me too. I've been doing a bit of exploring. Well, we didn't know what happened here. Let me give you a hand. Oh, it's all right. I'll make it. There we are. Chief, look at you. You're covered with dust. That's all right. It shouldn't rush right off. Yeah, I'll help you. All right, thanks. I got walked from downstairs, chief. I think you'll want to talk with him. I'm sorry, Harrington, but I don't believe I will. Huh? Where? I'm releasing all the suspects in the case. What? Chief, did you find the murderer? No. I believe I can. Oh, chief, I don't get you. You will. Let's get downstairs. I'm right over here, Mr. Graham. The, uh, the attorney wants to talk to you. Small question? No, I don't think so. Uh, chief, uh, here's Mr. Graham. Oh, that's fine. Oh, chief, that brings them all together. Yes. Well, what happens now? I'm letting you go home, Mr. Paul. What's about time? Can it go for me? Yes, Mr. Graham. You can grab a powder too, Pop. These are my working hours. Nice day. Oh, OK. We're going to concentrate on our search for Graham. Oh, I must ask you all to be available for questioning again in the morning, though. Sure. OK. If you should find Al, I wish he'd let me know. I won't sleep much, anyhow. Yes, we will, Mr. Graham. Now, come on, Mr. Miller. Harrington. All right. Well, can I go back out to the bus stop? Yes, go right ahead, Walton. Here, I'll get the door. Thanks. Go ahead. Yeah. After you, chief. Well, now where do we go? Back into the theater. Huh? But this time we're using a fire escape. The level we want. OK, chief. Now we get in through this window right here. Are you up to window climbing, Mr. Miller? Sure. What's in here, chief? Isn't Graham's dressing room right above this? That's right. I'd better go in first. Here we are. Um, give me your hand, Mr. Miller. Right. You making a hand? Yeah. Sure. How about a light in this place? No. No, we don't have to go in. We'll stand over here and hide in this pit. All right. Oh, I better call in the window first. OK. Why are we here, chief? You looking for something? No. No, we're waiting for something. I don't know. Uh-huh. Look, what is this thing? I honestly don't know. But whoever it is shouldn't be alone, chief. Where'd they come in from? That door leading out of this thing. Huh? Have your flashlight in there. It will be easy.  Really? I miss you. Well, thank you. Watching Tom Bradford in the act of removing Graham's body enabled us to obtain a confession from him to the killing of both Graham and Tom Kent. Because of a mental condition, he wasn't sent to the electric chair. However, he will spend the rest of his life in a state institution. You know, chief, that old guy sure had me fooled. Why don't you tell her you're pregnant? Well, Harrington, as I told you in the property room when we were waiting for someone to appear, I didn't know for certain who that person would be. But you did know Graham's body was there. Yes, Mr. Graham. I discovered that earlier as a result of examining those bloodstains I'd noticed near the window in Graham's dressing room. I followed that trail out of the window and down the fire escape and into the prop room. And there I found Graham's body stuffed into a large covered box. Oh, the body had been dragged there. Yes. But I wasn't certain who had done it. And that's why I suggested our leaving the theater. I knew this hiding place could have best be a temporary one. And I felt that, given the opportunity, the killer would attempt to move it to a more permanent spot. Which he tried to do. Yes. How about the motive for the killing, Chief? Well, we learned that when the old man confessed. It seems that in his younger days the Norman had been a singer. Quite well known, as a matter of fact. In Tom Kent, the tenor, knew this and used to kid the old man about the croaking attempts he made to sing that stage. Bradford developed a great hatred for Kent. And in his warped mental state, he conspired to get revenge. He overheard in the argument that Graham would leave the theater earlier that evening after the supper show over Tom Kent. And so, Bob Bradford decided to take advantage of it. He saw Al Graham leave the theater. They then told Kent that Graham wished to see him in his dressing room. And when Kent came there, the old man let him have it. Yes. But while he was fixing up the body, Graham came back. That's right. So the old man killed him as well. And that, pupils, Christopher Hyde's murder listener in a mystery playhouse. Thank you, Mr. District Attorney for presiding over our cast this evening. Thank you.


I got so involved on campus because I'm a friend believer of taking advantage of all the opportunities that the campus and the school had to offer, ranging from clinic involvement to clubs on campus to work study opportunities to student body positions. Each and every single thing that I've been involved in has really been a catalyst for growth during my time here.


All right, sorry for the bad sound here, but I'm intentionally picking up sound from the external camera So you can hear or rather not hear the keys of the keyboard clicking versus not So just to rehash before this is a brand new MacBook Pro That's now on its third keyboard and still doesn't work The original keyboard had a problem with the arrow up key that I tried to get by with for a few months But when it kept getting worse, I eventually had that replaced by my local service provider lawnmaster in Stockholm They're awesome by the way And then when I got that computer back after a week I realized after a couple of hours of using it that the upper left side of the P key and the upper left part of the spacebar They only worked kind of 50% of the time. I have a separate video for that. I can provide a link to and Then I then they happily replaced the keyboard once again, and they apologized for Apple's lack of quality control And now I'm on the third keyboard of this machine. So when I picked this machine up literally four hours ago I haven't typed anything on this at all really. I Decided to test all the keys this time and look and behold when I get the spacebar. It's still a faulty key So it's not entirely obvious because I guess that it's something with this new butterfly mechanism that once you hit the key a couple of times They don't really push back far enough. So you don't get a good click I'll try to show this on the spacebar on the right hand side of the spacebar here I have a fairly nice click. It usually works. Sometimes it misses. I could even try another key You might hear it better So you hear that it's faint But you have a very nice and clear click no matter where you push on the key on the spacebar If I push the left hand side at first it works So it seems perfectly fine And they push it a couple of times and you realize in particular you rest your finger after a while It doesn't really work that well anymore. So let's try So it's now starting to happen here once I push the spacebar a couple of times the left hand side is almost flush There is definitely no clear click and in particular the lower side here You see I keep pushing it. Nothing happens You can probably even hear my fingers strike the spacebar. I can do this slower as you can hear So I'm not even doing this. I'm not doing this in an extreme way of resting my finger I'm I'm hitting it fairly hard here and I sometimes get a space and I sometimes not The right hand side still has a really beautiful click. Let's try the left hand side again The upper part now not even the upper part doesn't work anymore and the lower left side The lower left corner, it doesn't really react at all So I'm sorry Apple. This is not even pathetic anymore, but it's just sad I have no idea how a computer company of your size can even get to the point where you realized Where you serve where you release hardware with this bad quality control This is going back a third time If you need a computer you want to trust you might want to consider a different brand


Thank you all very much for coming today. My name is Alan Dengore. I'm the director of Climate and Health at the Welcome Trust in London. I'm really pleased to be here today to kick off this discussion about data and digital innovation and really pleased to be doing this in collaboration with our colleagues in the Rockefeller Foundation. So we're here to discuss how data and digital innovation can really transform this field of climate and health. And before we get into that, I just want to say just a few comments about what we mean by the field of climate and health, because it's a new field. And it falls at the sort of borderlands, really, between existing fields of public health and climate science. And that makes it a particularly challenging field to work in, because doctors and public health people want to talk to public health people and other doctors. And climate scientists are very happy talking to climate scientists. So the difficulty of getting those two communities together, those two disciplines, those various skill sets together, is really quite large. And the difficulty is particularly acute in the area of data, where we have completely different understanding of data. So the climate scientists understand what's going to happen over the next 50 years at a stratospheric level. And their health people understand what happens to an individual over the course of their lifetime. So we have different types of data collected in very different ways. But before we get into the data and what we're trying to do, welcome, and others, let me just talk about those interconnections between climate change and health. So we typically think of three different pathways. The direct pathway, it's getting hotter. There are more extreme events. And this obviously has immediate impacts on health. The pathway is mediated by nature, by the environment. So as the environment changes, so things happen in nature, which in the environment, which, for example, the distribution of vectors like mosquitoes, disease vectors like mosquitoes, those mosquitoes can live in different parts of the world and are. And we know the evidence is very clear that the distribution of mosquitoes is increasing enormously, which will lead to a change in the distribution of infectious diseases. The ability to grow foods as the environment changes, of course, is another example of how the environment interacts with human health. And of course, we know very little about some of these interactions. And mental health is also part of that discussion. And then the socially mediated pathways. So that's as the environment changes. So society has significant challenges. And that can lead to, for example, migration, can lead to social breakdown, can lead to conflict, and at the extreme event, and at the extreme ends. And that's obviously going to have significant impacts on health as well. So those are the pathways. And those are the sorts of pathways we need to interrogate with data and with innovations in digital science. And what are we doing in welcome? Well, in welcome, we are seeking to put health at the heart of climate action, to really use health as an argument, as a fundamental argument to driving action on climate change. So understanding the impacts of climate change on health, understanding the benefits to health of rapid climate change mitigation, understanding the adaptation actions that countries or cities can take to protect the health of their population. But underpinning all of that is a much better understanding of the analysis, the approach, the methods, the tools, the data sets that we really need in order to conduct high quality analysis in this area. And at welcome, we split that into three things. The first is the data sets. How do we bring together the climate data sets and the health data sets in a way that enables us to do the sorts of analysis that's required? The second area are the sort of the tools, the methods, and the metrics. How do you, what is the best metric for an impact of climate change on health? How do you attribute, actually attribute the impact of climate change itself on health as opposed to poverty or as opposed to other things? And then finally, the third area, of course, is we need to think about equity. And we really must think about it is not appropriate. It's not at all that very few people, largely sitting in universities in the global north, are able to do this analysis. So building, strengthening, the cohort of data scientists and data professionals who understand and who work with and who innovate in this area is going to be absolutely critical and something we welcome are totally committed to. So with those introductory remarks, you don't really want to hear from me. You want to hear from our fantastic panel. And I'm absolutely delighted to hand over to Raj Kumar, the president and editor-in-chief of DevEx to take us through this session. Thank you very much. Thank you so much, Alan. And thank you for not scaring us to death. So Alan and I met in Charmel Shake a couple of months ago. And I walked out of that conversation a lot more shaken than all of you look from his introductory remarks. And I think for good reason, but you presented it academically, dispassionately, and objectively. But there are some scary facts here when we think about the nexus between climate and health. I also think of this session as the quintessential Davos session, partly for the reason you described. We're talking about two leading themes in the world, climate and health, and adding in a third, which is digital, and layering them together. It's a kind of thing that only happens here in these ice villages. So I'm excited to have this conversation. And the folks at Welcome in Rockfeller put together a great group for us. So let me welcome to the stage Carl Brinton, who is the managing director of strategy for the Rockfeller Foundation. Please have a seat. Joanna Shields, the CEO of Benevolent AI. Please. And Tallulah Oni, who is the public health physician scientist at the University of Cambridge. Tallulah, please have a seat. And we're going to have a conversation about these three themes and how they connect and where we go from here. And hopefully include a number of you in the room. So if you would like to jump in, take some notes, think about what you want to say, provoke us if you would, tell us what we're missing. Maybe we just start at the high level here. So it seems like the time frame is a key point. Alan brought it up that we often hear about climate targets as 2050 or 2100. And we just wrote a story in DevEx in which Alan has heavily quoted that does do a bit of the scaring I just talked about, where it says, look, there's actually quite a bit of this could be happening right now and within the next decade. We may see many of the effects here, whether it's extreme heat or the movement of disease vectors like mosquitoes. I guess we'll be just starting at this high level. Are we at this point where if you're in government, if you're at the WHO, if you're in a not-for-profit working on health, you need this data right now to be tracking? Is this an immediate issue? Or is this sort of like a let's look at into the future as we often do at WEF? And imagine what it's going to be like a decade or two decades from now. Maybe we just get a quick take from all of you, and then we're going to dive into many more details on this theme. Carl, go ahead. So in short, it's now. And a little bit more on that. If you don't just ask people at Davos, if you ask people in Nigeria, if you ask people in Pakistan, if you ask people in Queens, so I'm in New York City. And a year and a half ago, we had torrential rain. And people died. People died in New York City, in the global north, a number of people. This is real for every single person on the planet. It doesn't matter where you are, and it's real right now. Joanna, what's your feeling? Is this on OK? Yeah, I think the first time I got exposed to this, I was a government minister in the UK and bringing digital into various government departments at the time. You were a UK minister of internet safety and security. This is another topic. But we had a flood event in the UK. And we had once in a generation type flood event in 2015. And it was like we called in people from the technology community and said, we've got to be able to warn farmers to move their livestock. And how can we do that with ordinance data and all kinds of other things? So it was like a big data science project. And it became clear to me that the work that we do in technology development and the use of AI and machine learning for data for all kinds of solving big problems, that it was going to become a real issue relating to climate. So ever since then, I've been focused on these things. And I was the co-chair of the Global Partnership on AI for the last two years. And we did a big work stream, brought experts together, and did a big work stream on responsible AI for climate change. The health angle, we didn't bring those two together. And I think that's a really good next project for G-Pay. Sure. Well, what I like about your example of the livestock is it shows if you had the data in advance, maybe using predictive analytics. If you knew something's coming, it's going to affect this area. And if you had the ability to reach and pinpoint the farmers affected, you could actually make a change right away, a change in advance of a disaster. Maybe we'll just go to Totaloo quickly if we could. Your take on, is this an immediate issue? Is this a bit off in the future? So yeah, so you've kind of spoken about something like flooding, which is an immediate and very acute. So I want to kind of flip to the other side and talk about the more slow-motion issues, because part of the issue, I mean, the answer is yes. Of course, it's immediate. But our risk perception for the more slow-motion health emergencies that emerge as a result of climate and are accelerated by climate mean that we don't have the level of action. So if you take, for example, something like air pollution, you don't get the same level of action you get for flooding or that you should get for flooding for a high air pollution event. But if you take it from the side of the health care sector, and you look at, you would find actually spikes in hospital episodes in terms of admissions, in terms of outpatient exacerbations of existing conditions during those times. So it's urgent, but you often don't, it is not perceived as urgent, because there's a little bit of separation in time and sometimes space from the very acute health implications. And your expertise in particular is looking at urban health and studying how, especially rapidly growing mega cities, particularly in the continent of Africa, how can they design policies. So let's take the air pollution example. If we did have the data, if we did have the digital tools, what might a city, a mayor, do differently knowing that air pollution is going to get worse in a particular area? So I mean, in the cities I work, I work predominantly in, as I mentioned, large cities, primarily in Africa. So I work in, it's like Lagos, and you don't shy away from what you mentioned. Somewhere between 20 and 30 million people and rapidly growing, Accra, et cetera. One of the most striking things is how little is measured to start off with. So you say, what does the policy act to do with air pollution? Well, firstly, it needs to be unearthed. Something like air pollution is also normalized.  So it's normalized. So the first thing is kind of making it obvious that it's not normal, right? We shouldn't accept accepting it. And a really critical part of that is measurement, but not just measurement. Measurement and having data in the hands of people. So one of the things that we've done, for example, is I mean, this same cities that I work in, the median age is 18, 19, right? So three quarters of people under the age of 30, 35, right? And who are really passionate, actually, about environmental justice, about health justice, about climate justice. So one of the things we did, for example, was work with young people 18 to 35 to say, can we be part of unearthing? Why it's important to measure? Because that's the first thing. You have lots of emergencies. Why should they prioritize measurement of something that no one's obviously dying from right now? Quote unquote. And what we did was worked with the young people, so we did have this running intervention. Okay, pun alert, we ran an experiment. So they designed running routes and they ran through the city with wearable air quality sensors. Firstly, highlighting the difference in air quality within the same city, so they designed the running routes to capture what they thought would be different parts, different levels of air pollution. The second thing was to highlight how little air pollution monitoring was happening in the city. So you could argue, well, what is the effectiveness or what is the accuracy of the wearables? Like, well, let's calibrate it then. Oh, that's right, there isn't any, so let's do that. And then the third thing was understanding how that could drive action. So with that majority demographic, I don't like calling them young because that sounds like a niche group, they're just everyone. So with that majority demographic, they then designed and say, okay, these are what we see as the priorities, bringing them together then with the policy actors to say, okay, what are they doing and how can they be part of the decision-making in shaping the decisions around the cities? But critically, so I mentioned, measuring having data in the hands of people and then evaluating. I say health doesn't trickle down from good intentions, right? You do the thing, you identify it needs to, something needs to change, has it changed? And they play a really critical role of a majority demographic in saying, we wanna work with you to be part of understanding what this climate and health risk is, but we will also keep you on your toes and we will also celebrate when it works and come back to you when it doesn't work and really being part of that solution. And that's the power of data that is generated in close collaboration with both the beneficiaries and the people effecting change and the people that would be getting buy-in in terms of effecting more community level change, whether that's around waste management practices, et cetera. So really underlining the public in public health, really using data to engage the public in a way to change the environment for their health. I guess, Joanna, you're unique because you have this perspective as an expert in AI and the technology side as someone who leads business, but also who knows the government side of this. So just taking this example a bit further, imagine it's in Lagos and temperatures are rising, extreme heat events are rising. What can we do today if we had the right data, if we had the right digital tools, what could we do today that might change the trajectory for health during extreme heat events in a city like that? I think the most important thing is to look at how do we scale solutions? Like every country, every city is coming up with their own plan. And that's why, I'm a big believer, I know everyone talks about multi-stakeholder organizations and some of them are really effective and they're all well intended, but some of them are less effective than others. And part of it is that we have to organize for action. So one of the things that I've found that works really well is coming up with a model national response to a problem and making sure that there's contributions from everybody in the data community and the technology community to bring their solutions to bear on that particular problem in a model national response that can be scaled out to everyone. And that's the only way, because otherwise everyone comes and recreates something themselves. And there's just no, I mean, whilst I'm creating one thing, someone can be doing something else, you see, we need to share the load.  But that model really works. And so when you have a multi-stakeholder problem like this, if you bring people together and you can invite technologists and experts to contribute projects and code and ideas and then get that out to everybody, that's the most important thing. All the talking shops we have are great and they make us feel a bit better. But, you know, and everyone's got great intentions, but we really need to translate it into action. So zeroing in on a specific problem is the key here. Yeah, I think so. And Carl, I guess from a philanthropy standpoint, you're a Rockefeller, obviously you have been welcome for putting this event together. There's a lot of ways you might approach an issue like climate and health. You might approach it, let's say, by thinking about how the policy environment should shift, right? But it seems like you're particularly focused on this idea of data and digital. And I guess the question is why? Why is that the barrier or the opportunity if we could unlock it? What might it do for us that would be different? Anna, you're right. We are very focused on data. Part of that comes from our long history. So we've been around about 110 years back before data was data, data was a thing. But at the time, we talked about science-led philanthropy. And that then naturally led us to data-led philanthropy. And so we see data as a strategic function that plugs into and supports all of our programs across the different topics that we do on clean energy, food, health, finance, all of those.  And we hear a lot about the poly crisis, right? And maybe it's a funny word, but it's true, right? The reason why people are using it is because it's true. We see it in climate, we see it in health, we see it in war, we see it in food. What we haven't heard yet about is the poly solution. And again, apologies for the cute word. You can call it whatever you want. You can call it the Davos sandwich. You know, you need all the layers and then it's good. But the fact is we need integrated solutions. We need to be applying data solutions to climate in the context of health. We then need to be bringing food into it. We need to finance the whole thing, right? And then we need to collaborate. And this is a point that I love whenever I interact with our data team or any of our partners in data.org or data kind or a number of different places that we've been working in the tech field, tech for good. One of the things that tech has done particularly well is collaboration. Think of what field has a GitHub equivalent where you can go and you can see someone's code. You can see, you can verify the code. You can say this person can do this job well, right? It's competency-based, that it's open source, that it's then a collaboration engine. We need more of these types of things in other fields. We talk about collaboration and philanthropy and we're really trying to do it. But at the end of the day, we've got a long way to go. And so one of the things that we've started doing recently, Rockefeller, is implementing some sort of agile structures to some of our teams. So using what tech has done to collaborate more effectively to get things done. So that's really where we come from and why we are seeing data as something that is not just a fundados buzzword, but is absolutely core to everything that we do. Maybe we can come back later in the discussion to just at what level do we want to engage on this issue? Because it does seem like where you and welcome likely engage is at this very global level. And what Joanna is saying is, hey, we got to get really specific. We got to narrow down to this particular location and problem. And let's see if we can play with that tension during the discussion today. But totally, maybe going back to your example of the wearables. So you said, in a lot of cases, we just don't have the data. And obviously, we're not going to get to the point where we can use advanced tools if we don't have basic data. It's all based on how good the data is. So what's the state of play right now with the data available at this climate health nexus? Where do we stand? Start with you. I just want to hear your sense of where we are. I maybe want to take a step back and deconstruct data a little bit, right? Because that play between what is sense and what is nonsense. And what is, if you think about a matrix of sense, nonsense, scientific, and lived, or practical, there's some things that, in the scientific community, because it was rigorous and because it was done using validated methods, et cetera, it's scientifically sense. But it's so disconnected from the reality that, in terms of the ability to inform action, it is practical nonsense, right? So I want to make that distinction, because when then we talk about how we collect data and what exists, there's quite a lot of innovative tools that we can use now in terms of understanding the environment in terms of remote sensing, satellite imagery. And so at least get a sense of variations in that could be green space, that could be heat, it could be air pollution, even remote sensing, it could be land use. So that gives us some clue as to the variations within and between cities. When it then comes to making sense of that, and what it actually means, my experience is that often the tools that we have available to capture this information are not necessarily adequately tailored to the environments where they're most needed, right? So you can have a lot of data on the food environment. Let's kind of take another that has been collected using validated tools. But then when you try to make sense of it, in the context of a city that is changing very rapidly, where the food environment is predominantly actually most people get their food from the informal sector, which is actually, again, the majority sector, how does the data that you generated using very scientific methods, how do you translate that into action when it's not contextually relevant? So I think it's important that we are, and this is why participation and co-design is so critical. From the perspective of air pollution, for example, there is very little measurement. On the city like Lagos, there was essentially the US Embassy. In the city that size, there was a World Bank project a couple of years ago that partnered with the government and installed six equity sensors, which kind of increased the number of sensors by a factor of five or two, six. And so what is powerful is that through the wearable, so again, coming back to this particular is just an anecdote, but with this particular project, it caught so much attention because the majority demographic made a lot of noise about it that it actually led to a collaboration with the government and a grant that then resulted in 30 sensors being deployed. So we can talk about where things are now, but actually one of the really overwhelming experiences I find is how much appetite there is at the city level anyway. A lot of the governments are working for change, and what the key resource challenge is often is hardware and is capacity strengthening to monitor over time. So you have environmental sectors that have that capacity to monitor, but because they've not had sensors in place over time, that skill set to do that over time is. So when you actually listen, we wrote this grant together because we said, OK, listen, what do you need? And they said, listen, you academics, you come in, you swan in, you do a bit of research, we go. You don't do what we actually need. And so, OK, what do you actually need? So we need sensors on the ground so we can actually measure. We need to stay, and we need to train our people to monitor that over time. So then we were able to say yes, but we would only collaborate with you if you bring in the young people as well, and that data needs to be available real time because that's not government data. That's public data. It's your air that you breathe. So it can be really powerful understanding, unearthing the gaps in the data. And in a very short space of time, actually being able to do something about it. So it seems to me like the tech exists, right? Remote sensing capabilities, satellite imagery, where it's advanced tremendously. And it's getting cheaper and cheaper. It's more available. But the key point you're underlining is we might have the technology, and we might have institutions that could potentially deploy that technology, but if it's not connected to what Joanna earlier talks about, that solving a specific problem, like getting very narrow, then we end up with plenty of data, but it's not actually useful to us. Yeah, exactly. And I would highlight two other aspects of data that would make things more useful. One is the integrated data, integrating the health data with the environment data, with the climate risk data. Because again, really powerful, just being able to see the, we're not talking about long-term impacts of climate and health in the next 20 years. Just over a month or two period, how, actually, just a few weeks period, how changes in air pollution can actually impact on health care. So integrating that and also integrating different types of data. So I talked about the wearables, but the other type of data that the citizen scientists collected was qualitative data. So they collected photo, audio, video that contextualized the numbers. So you could actually say, OK, why is it high there? OK, that's a busy road, there's construction there. And you can actually, it brings in the lived environment component, too. So I think it's really critical that we're looking at mixed methods, qualitative and quantitative integration. Fantastic point, Joanna. I'd love to get your take on this. Yeah, I'm going to riff on a different sort of vector here. During the COVID crisis, the very early days, we got the sequence of the virus, and that was really helpful for data science and some solutions. But what we realized very early on is there was no viral ontology available. Like no one had really done the work to map out viruses and the interaction of various medicines with viruses. And there was no way to train the AI models to look for potentially other antivirals that might be effective. It was very strange, like dynamic. I was like, I couldn't believe it. We were looking everywhere, there was nothing. So we looked for existing drugs, but not in the context of the virus. We looked in the context of other existing drugs that might be useful to combat COVID-19. And we found just by luck that there was an antiviral effect of a rheumatoid arthritis drug that meant that it would be an antiviral. And we proposed this and published it in the Lancet. And the company on the drug literally said, you people are crazy, you're going to ruin your company. But there's no way that there's a soft target effect in this drug. But we saw that signal in the data using our models, just running across all the available medicines. But it was just striking to me that there was no infrastructure in place. I mean, how many pandemics do we have to have before we have an infrastructure in place? We have all this technology. But to make a long story short, this discovery led to a drug that, for hospitalized patients, reduced mortality 38% on oxygenated patients. It was 46%.  So finally, we got full FDA approval, like Q4 of last year. That took a long time.  I just feel like we need to learn the lessons of that. And we need to look at this nexus between climate and public health. And what do we need to do? Like, I was reading the report in the Lancet about dengue, for instance, and the increase in prevalence of dengue going up. I think it was 12% increase in airborne transmission due to climate issues. We're working with drugs for neglected diseases here in Switzerland on drugs that can be repurposed for dengue because it's a disease that just has no treatment at the moment. So we need to mobilize, but we need to learn from what happened to all of us in the past couple of years. We can't just go flat footed into the next crisis, or poly crisis, as you say. Just quickly, staying on this theme of dengue is a great specific example. Do you feel like we have the data? It exists. It just hasn't been put together so that we can use it against machine learning models and be able to, with some accuracy, say, this is where it's going. These are the places that need to be on alert. We need to preposition medicine. We need to train health care workers. Is that the case, or is it more? Actually, we don't even have the data today. I think we have the data. I think if we, I mean, the challenge is normalizing across no one collects data in the same way. It's very difficult to make sense of it.  And one thing we've done really well over the last four and a half years since I've been leading the business is run machine learning models across all the world's peer-reviewed research and mine that data for insights that might be useful for scientists. Now, the big thing for us now is how do we take this, you know, this vast corpus of knowledge across not just peer-reviewed papers, but we use like 85 other different data sources that we normalize across. And how do we get the power of those models into the hands of tens of thousands of scientists? Because I do think the data is there. I just think it's about mobilizing and getting... So what we do is try to illuminate disease biology, give scientists, I say superpowers, to see into what's happening in a disease and understand the network around it and how there are other sort of novel targets you can modulate with a drug that aren't being thought of and to come up with new ideas because as we all know, like drugs don't work that well for the patients who take them. They're like a blunt instrument. And, you know, especially for complex multifactorial diseases, I mean, we're making a lot of progress on gene therapy and, you know, there's a lot of cell therapies and things that are really quite precise, but for a lot of multifactorial diseases like TakeNash or something which is, you know, a lot of it is environmental, by the way. These are diseases that people have that are really complicated, diabetes, you know, and there's not great solutions today. Carl, when you think about the space, you're trying to kind of create the climate health nexus space. Are you aligned with Joanne on this?  You're gonna fund more coalitions to take existing data, put it against existing problems using AI tools? Is that where we are at this point? Well, so I loved the example you used, Dengue, and I'm looking at my friends over at Welcome Trust because I'm sure they lit up when you said it as well, because Welcome Trust and Rockefeller and others have been working specifically on Dengue using existing data, building the models, building the tools to then do the predictions of where it's going to happen at this point, pretty high degree of confidence. And, you know, we're doing this across Brazil, Vietnam, you know, and building the evidence base, right? The randomized controlled trial that Welcome Trust is doing in Vietnam on Dengue is a great example of you've gotta show that it works, and then when you show that it works, then people will be picking it up, they'll be using it, because at the end of the day, that's what it's about. It's no longer about collect the data or analyze the data, it's about giving it to the people who need it and then supporting them in acting upon it. And so I think this was a great insight that you shared, which is that it's about the end user. This is another thing we learned from tech, right? It's the end user design that matters.  The user is never wrong, and that's a difficult thing when you're doing product development because users will do crazy stuff with your prototype, but you have to be really humble in product development and you have to be really humble in human development as well. I wanna bring in the audience, and we have a lot of people in the room, a lot of expertise on this to tell us what we're missing. If anyone already has a burning question, feel free to raise your hand. Otherwise we're gonna keep going a little bit while you think of one. It seems like there is a little bit of a tension here, though, between the way you're just describing this, and what we're hearing from Carl and Joanna, and the tension that if I'm hearing it right, is there's an idea of liberating the data, but you wanna make sure, even from the data collection standpoint, it's originally being collected with the end users in mind, and they're part of that process. Yeah, because data is not just data. You know, who informs, what informs what sensor you used, who decided where it was going to be placed. So what intelligence did you use to say, okay, we're going to measure this part versus that part? That's not scientific knowledge. That's kind of lived environment knowledge, right? So if you're not engaging with people, and you design something really fancy that is scientific sense, you have to engage them from the start because getting a sense of knowing what you don't know, like on the other thing, what you may not know because you're not in that environment is really critical. I wanted to pick up, if I may, on a point that you raised, which I really like the example you use of COVID, actually, something different, but what you were talking about in terms of the infrastructure that we actually have, reminded me of the importance of having this in place for early warning systems as well. So even for preparedness and early, say, outbreak, early notification of outbreak. So an example in the UK was that app that we were all using, when it first started to say, okay, report whether or not you have symptoms, right? And it had so many people using it that you were able to actually make sense of and spot patterns before they were actually confirmed. The only reason that was possible is because it existed before the pandemic hit, right? So it was something that was in place that was repurposed for COVID. So I think for me, it really underscores the importance of having that agile, you used the word agile earlier, agile data infrastructure that you have in place for whatever that you know that you can pivot to say, okay, this something, we having an indication of something or other, we can pivot it and say, tell us about this and get early indications from a general population perspective. The same applies by the way in terms of preparedness for the healthcare response, right? So we haven't talked about healthcare when we're talking about health and climate here, but really critical for that. If you get early indications of an outbreak coming up, what implication does that have on the components of your health system, of your personnel, getting the beds ready, getting the products ready, because sometimes that procurement takes time. And you can get those and again, and we have those data in that people know, but we're not asking people, we're not seeing people as important sources of data, but I don't like how I just phrased it because that sounds very transactional, but if you talk about citizen science, I like to think of it as redefining what it means to be a scientist, as well as what it means to do science or contribute to science, as well as what it means to be a citizen, right? So there's a civic engagement.  And this is part of being an active citizen. This is our power and I think this way because we have a demographic dividend on the African continent that we are not harnessing at all. You wanna make a quick point on this? I was gonna ask someone else, go ahead. I love Davos for these types of conversations, but I hope you won't mind me saying this, but I spoke yesterday to the Minister of the Economy and the UAE and they have a cooperative economy bill where if people contribute data to the government, like in a health environment, they get a piece of that. I think it's brilliant because like what you're saying is these are human beings and it's much more likely that people participate if they get a share in the company that, do you know what I mean? It was like a brilliant idea. I mean, I've heard it tried, there's agriculture cooperatives and there's cooperatives historically, but applying it in data context, I thought was absolutely brilliant. I hope he doesn't mind me sharing that. I think it's a great example, so probably not. I see a couple of hands are up. I'm just gonna ask one really quick one and then I'm gonna go to Jonathan here and see if there's any others. So you're talking about the implications, for example, for healthcare workers and health system. There's a whole global health infrastructure. Many billions of dollars spent every year by the richest donor countries, through bilateral aid agencies, through the World Bank, the WHO. To what extent do you feel like the existing global health infrastructure that's out there right now supporting public health systems is tuned into this discussion?  Are they thinking about the data they're collecting? Just a quick sense from all of you and then we're gonna jump to questions. Starting with you, Carl. Yes and no. So there are a lot of parts of the system and there are a lot of people in the system that are absolutely part of this. Yes. There are also a lot of the parts of the system that need some fundamental change and improvement. And there are some people who have not yet gotten to this discussion and I don't think for any malice or anything but I call it the day job problem. Everybody has a day job. It takes up your full day. Adding another sort of Davos sandwich on top of that is really tough. And so I think that people's intentions are in the right place and there's a lot of leaders in the space. And we're so excited to be working with several of them on a lot of these really important topics. So I'm hopeful we'll get the improvement that we need. Okay. I think I'm gonna go to quickly to questions then. I see Jonathan here, if you can bring him a mic and anybody else wanna, one in the back and one in the front. Okay, we'll take a couple at once. Go ahead. Great discussion and I heard Raj summarize his tables conversation in a different panel and you apologize for not getting the participants to get into a fight. So I like the attempt to just try my best, Jonathan, try my best. My question and it was joined a little bit to the point you were bringing up with the UAE but one of the things that we struggle a lot with my organization equips community healthcare workers so they can be responsive to core health system strengthening and then we argue pandemic preparedness. We had 100 plus thousand community healthcare workers equipped pre COVID, COVID hits. We got money from Rockefeller, many organizations to go do COVID-19 response. Zero of those community healthcare workers were able to get an updated application not because our software couldn't do it but because the government can't reprogram 50,000 workers jobs overnight. They couldn't even do it over a year though. So we stood up parallel workforces in both high income and low income settings rather than using the current ones. So at that point on the data collaborative or climate analytics or citizen engagement, these are horizontal skills we need today, tomorrow in the future for climate, for health, for all these things but the ROI of that public-private partnership is nearly impossible to articulate, and sell politicians on doing. So I'm curious as you think about this in all three of your respective roles, what can we be doing to somehow get that ROI? I think everybody hypothetically knows the ROI but it's a night job ROI, it's not a day job ROI. So how do we bring that in such that these collaboratives can really take root and you get a 10x return because it's not gonna end up just being that the climate data is useful and now you have a traffic sensor and you have all this stuff that you can keep compounding but it feels like we can't get over the hump of starting that process. We bring the mic over here to the front, we'll take a few at once.  Thank you, yeah. So Jane Burson from the Clean Air Fund. I wanted to ask Tolu a question because I was struck by something that you've told me in the past about how African students are really held back from doing the analysis and research part of their PhD because they spend 95% of the time collecting the data that they then need to do their PhD. And I wondered if any of you have heard of ways that especially with health systems, they have fast-tracked the collection of data or people have got around the issue in other ways. Great question. And if you can pass the mic back to the woman, two rows behind you. Good afternoon, my name is Bata Gilisitze and I'm a global shipper from Khabaronehab in Botswana. So I'm gonna ask more from, I think they say from the, I don't wanna say from bottom, but from more community angle. You talk about some of the needs that are identified. As you know, Tolu, you'll know that a lot of the stories, a lot of the data that's available in Botswana on the continent of Africa is qualitative data. So in a community where the data is available in the form of stories, how can they package that in a way where they are proactive to say, this is what's happening in the climate. This is what's happening to our health. We can't come and give you the numbers and so forth, but we do need this kind of response. How can we package those kind of stories and that kind of qualitative data for a response that fits those communities? Can you just pass the microphone back behind you? We'll just take this last one. Go ahead. Thank you very much. I'm Enrique Pobianco from the WHO Foundation. Thanks very much for this incredible panel. I have one fourth dimension that I wanted to add to climate health and data and its equity. A lot of communities that are severely affected by climate are the communities where data is very difficult to collect and where digital innovations are very difficult to bring in. So I just wanted to add this dimension and ask you, what is the direction of travel to make sure that we really move in this area with equity firm in our minds? Thank you. Thank you for that. So I think there's two questions here that to me seem like leapfrogging opportunities over the problem. So when we start there, maybe with you Joanna. Jonathan's about CHWs. They seem like a huge opportunity. There's hundreds of thousands of millions around the world. And if they could be equipped with a smartphone connected to the cloud and if it had tools for data collection and sensing and if you could bring AI to it, you could do so much instead of saying, well, you're actually tasked with this project for this particular disease area. And then similarly on this point about the places that are toughest that hit the hardest, least likely to have the tools or the connectivity. So how do you think about these leapfrogging opportunities? Is there a way we don't have to rebuild everything in the hardest hit places? Is there a way we don't have to build a whole new infrastructure but use the CHWs that are there now? I do go back to this model national or model city response idea and you know there's, I just think the best way to orchestrate and roll out these types of solutions is for everyone to be exposed to best practice and best ideas. And I think the only way you do that is somebody in a multi-stakeholder organization has to collect it, set up the infrastructure for sharing it so that there's an immediate transfer of knowledge to everyone simultaneously. You know, and I really feel like it's not that hard to set up, right? And you can change it all the time. As new things come on as evolutions in applications, various other things, it just needs to be a global GitHub for solutions. You're exactly right. I hadn't thought of that, that was a really good idea. But if you can put together just action on specific problems, whether it's air quality, I mean, there's so many different groupings of this, but experts can identify and prioritize these groups and then invite solutions. This is what I measured at the beginning. It's the question of what level to enter the problem, right? Do you enter it at the city with dengue layer or do you do it at a much higher level, the Davos kind of level, that global infrastructure, the global healthcare system? Do you think about CHWs as a point of entry? How do we enter that problem? Carl, maybe you can bring that up, especially again, keeping in mind, Honorary's point that if we just look at where the infrastructure is today, we might actually make the equity problem worse. Yeah, that's absolutely right on that equity point. And there's a couple of different threads here, right? But I think it does come down to this combination between global and local, right? And to be fair, this is something we've talked about for decades. So it's not a new problem, but it is still a problem and it's being exacerbated by the other problems that are out there. You know, in terms of how we handle it, the unfortunate answer is we need to do both. The question is how do we do that? And I think the answer is that we do, we show up at places like Davos, don't be too cool for Davos, use it for good. You show up at places like COP, you show up at places like WHA, and there's a bunch of different places that we should be showing up, we should be having these conversations, but it can't stop there, right? When we have these conversations, we then need to say, so what now what? What am I doing? What am I doing today? I'm not gonna put it on a to-do list because next week my day job is gonna come back and I won't get to my thousand Davos to-dos. So what can we do today to take that global piece where we're having this amazing conversation and action on it in a highly localized way? And that localized way, that's gonna look very different by definition for each of us, so I can't tell you what it is, but you'll figure out what it is. When you figure out what it is, you act on it. And I'll give you a hint, the way that you'll know what it is is by listening to the end user. So figure out who's your end user, what do they need, and then what's the next step that I can take today to help that end user with their need? Well, I guess to Joanna's earlier point, if we find that real world problem and actually solve it somewhere, anywhere, it becomes the kind of thing that people start to pay attention to and say, well, how do I do that where I live? That's the story that takes on its own life. Everybody likes to win. And so if you can create a win, people just flock to it. Totally, just thinking about a couple of questions posed to you. The story in part, maybe we can start there with our colleague, the young global shaper from Botswana, she's saying, a lot of times communities know what's happening. Maybe they see the riverbed is drying up or we're getting more flooding. We don't have data, but we know what's going on. How can we better package and utilize that information? Okay, I'm going to answer that question. I'm going to take a step back and talk and answer and point to the leapfrogging point that you made. Just do it briefly. Very briefly, she says. Leapfrogging, often we're so focused on leapfrogging that we forget what we can learn from the past. I just want to highlight an anecdote in the 1940s in rural South Africa, there was an experiment called the Pallela experiment was in Pallela, rural South Africa, where they piloted what is essentially community health workers, but you had people who were responsible for households and it wasn't just the health, you knew when they had a baby, you knew when the mother lost her job, you knew when, so you picked up. You knew that, okay, you knew when their crops failed and if they subsistence, that's an early warning of potential stunting, right? And then you step in rather than just waiting in the healthcare for it to occur. So they were embedded in that way. Now, if you can imagine that model with the tools that we have now, we actually, we can learn from the past and we can superimpose the tools that we have we didn't have then. So coming up to your question, I think that's really critical. What you just described is entirely why some of the citizen science we do is set up that way. So we look to integrate quantitative and qualitative data because I don't, often we talk about, we make data synonymous with quantitative, right? You can use stories and with our citizen science, we talk about generating data stories, right? Because there's the numbers, but then you can also do things like geolocating the story, right? Which allows you to be able to integrate it in ways that you weren't able to before. So I think we can play around with platforms in that way. And then the last point around the students absolutely critical. My PhD students in South Africa spend two and a half years collecting data and then, I mean, that's not PhD knowledge, right? My PhD students in Cambridge is just like, oh, which data set do you want to use? And you spend most of your time designing a project, data analyzing data. You both come up with PhDs at the end, but your skill set and your ability to where you jump into the pipeline, very different. And so it's really critical and that comes to the equity question. It's really critical when we're designing this data infrastructure we're thinking about. Who has access to those data? There's some data on African cities that African students can't access because they were designed by and they're collected by and they sit in, right? So we have to think about, I don't need to finish my sentences because of time. So really critical when we think about how we design the, how we collect data, we have to say, again, end user, if a key part of our end user is training a pipeline of the next generation of data scientists, of researchers that are able to grapple with big data, with AI, then we have to ask, do master students, does a master student in Bujumbura have access to big data? About Bujumbura or about any African city or any African country? Because if not, we need to be addressing that. It sounds like maybe the beginning of a set of principles that Welcome and Rockefeller could push out there on data collection at this nexus. Maybe that's for the next of us panel. We're nearly out of time. I just want to give Joanna and Carl a quick last chance to wrap up anything on the questions totally just addressed or anything else you think we should be taking away from this discussion. What I'm taking away is that Raj just gave me some homework so I'll go find Welcome and we'll chat. No, but really, that is how I feel. I feel that there's a lot of good energy, there's a lot of good ideas, and this actually just syncs up with, yesterday I was thinking about this as well, is what do we need to build out that we have not already built, because some of it's built, what do we need to build out to be that sort of platform for good within the climate health space and other spaces? Like what is missing? And that's the homework that I've taken away. And Joanna, last thoughts from you? I'll just leave you with the, I mean, I'm an internal optimist, silver lining person, and I look at the potential for increasing productivity and creativity of large language models. I know there's a lot of dangers and I know that they hallucinate and they predict really crazy stuff and that's crazy, but we're going to work that out and we're going to turn this into something immensely valuable for society. Like just the summation capabilities of bringing democratizing access to information to everyone is so powerful. I've been in digital since 1985. I've never been more optimistic about the opportunities and to finally deal with this equity problem because there really is no barrier, but as long as we train these things with the right data that's not biased and we have to be stewards of that, but the potential is enormous. And there's really no other way, right? Given how we started the discussion, given the immediacy and the trajectory, if we don't leverage these digital and data tools, there's no other way we're going to get ahead of this set of issues. So that gives us some hope as we close this panel. I thank all of you. I thank Welcome and Rockefeller for the pleasure and please join me in thanking our great panelists.


Man, you're talking about being blessed, man. See, sometimes you don't know what's going to happen, man, and God just give it to you. Man, you don't even know. But I pray a lot, though. Man, hey, man, I've been looking for this guy, man, because really, hey, I'm watching my cameras. I'm taking pointers today. This has been a class in session for me today, man. Check it, man. Sean Cotton's in the building, man. What up, man? How you doing, man? It's a pleasure. Man, I've been worried about if you was even, we was doing a good job. Yeah, nah. I've seen a few episodes. Love it. I'm a fan, man, of yours, bro. Yeah, I appreciate it. I'm a fan, too.  I was like, man, how the heck you end up interviewing him? Then he gave me D number, and then I was like, man, I don't know how I'm gonna get to Sean, man. Yeah.  For sure. You know that I've been in the city, been here, all these, you never hear from me. I see all them pictures. Yeah, yeah, I can run up on you.  I gotta speak on the big elephant in the room for me. Mode 3. Smart guy. Yeah, yeah, Mode 3. Yelling, all the stuff that happened with Trap Boy and all that. Every time I would hear about that, I would hear Sean Cotton in the middle of that. Okay. No, it was because I came up with all three of them. Okay. You gotta understand. Yeah, and I remember that. I remember you the only one that interviewed them that I really could think of. So, I would interview somebody and then they'd be like, no, that's not true. Let me get an interview. Let me tell my piece. And that's how I came, really came about. Okay. Yeah. We all human, bro. You know what I'm saying? We all human, man. So, I know when you started out, you started out by doing rappers. Yeah, I used to do like freestyles. Freestyles. On the street. Before you did the interviews. Rap went bars and rapping, rap-ity rap used to matter, but over time rap became more melodic like singing. So, people didn't care if you could rap good. They still don't. So, I went from doing freestyles on like, I used to pull up to Old Cliff and Pleasant Grove. I know. Yeah. Like all the Chicago and used to do freestyles, but it just became like obsolete, like nobody really checks on freestyles no more. They want to hear your story. And then you got a lot of people that's wanting to be on say cheese. This is the biggest dream. I heard, that's what one artist told me just this weekend, he was like, all I gotta do to blow up if I can get on say cheese. Yeah. Yeah. It's up. A lot of the negative things you hear about me, you never hear that I owe somebody money or you'll never hear that. Never heard that. Or you'll never hear that my artists are in bad situations. It's just the people who want to work with me, those are the people who find negative things to say about me, but I mean, you'll hear things about the mode three and travel and stuff, which I understand because I interviewed them all at around the same time. I had to ask that because that's the thing that everybody's going to want to know. Yeah. No, no, no. For sure. I don't feel no type of way, but if you ask them three artists, the same thing you ask me and they're going to be like, no, like Sean, he really tried. Like, came up with or he's just an idea.  Like this girl, she got viral fight that happened in the locker room. Her name was Jade. Was that the first viral? That was the first one in Rest in Peace mode three. That was his baby mom at the time. Yeah, but if you go back and look, her name is Jade. She got in a viral fighting on Worldstar in the locker room and I interviewed her and it did like two million views in a day. Wow. And I said, I said, I'm going to start mixing that with the freestyles and then over time the freestyles became irrelevant because niggas don't care about bars. They just want to hear something that sound good. They don't care if you could rap. If you can sing and harmonize, people will rock with that. So I mean, do you, is it anybody else that you've seen come up quick because you love rap too, you know how to deal with picking artists and stuff like that. Have anybody impressed you with the way they scaled after you had interviewed him? Roddy Rich. I didn't know he was going to blow Roddy Rich. I did Roddy Rich in Compton. Really? And he went and he went. Wow. I did Roddy Rich interview. I did a lot of people's interview. I got Rod Wave his deal. Really?   Yeah. Man, people see the nerve of these people, man. He don't owe me nothing because I mean now I have so many different, I open so many different doors for people. It's going to pay out later. And it ain't no beef with him. I wish him the most success and hopefully we'll chop it up soon. A lot of links with a lot of big labels. How did you read that? Because just like spot them, got them. I don't know if y'all know the beat box. That's my artist too. I signed him when he had 3,000 followers. So like that, just me keeping building these artists and finding them from nothing and making them something like Darrow, the artist I got now, he's fresh out of jail. I want to change his life. How many artists do you have? Like 18, but I don't own 100% of all of my partnership with some people. Let me say something. Let me say this. I look up to you, not because, because I'm scared of these rappers. Like, like far as I don't trust, man, I got trust issues. Yeah. Everybody be like, man, he over the years. And I just never, I thought the game wasn't like, I guess because I already were flipping. I ain't need it. It's like, this don't look for me. It seems like it's a lot of disloyalty. It's a lot of disloyalty, but I don't get into it. To I know they're going to, my bad. I know they're going to cross me sooner or later. That's the way you would have to look at it. I know they're going to cross me sooner or later. But I know we're not friends. This is business. Wow. I'm, we, I know we're not going to eat together when I'm cooking. So you know there's going to be a disconnect at some point. Because you don't really know nobody till they get money. Wow. Yeah, we.


Can you help me with market research, not bidding? Man, this is a loaded question, right? And the reason why is so many of us ask about bidding, bidding, bidding, bidding, bidding, bidding. However, if you were to bid in a market where there's large contractors, there's an abundance of people working, that there's 12 people that respond to each and every bid, you're probably not gonna have a good chance coming in as a newbie, as someone fresh, you know, green, they say, wet behind the ears, you're probably gonna have a very much chance of success. However, if they can help you with market research, they can analyze your situation and who you are, look at the region that you're in, space that you're operating in, and maybe they can determine where the opportunities that exist, that maybe everyone is not chasing. So there's a huge difference between market research and bidding because if you can find a space, right? A safe space in a market where there's no one showing up, there's no one actually bidding, there's a shortage of contractors, like my man John found, then guess what, he won four contracts this past week, and out of the four bids, so to speak, that he did, two of them, no one bid against him, no one. So wouldn't you like to know where is there an error in the market around where I live or in my immediate region, so to speak, depending upon how far you wanna go out, where there's a whole abundance of opportunities and no one chasing them? That's more important to me than bidding.


This episode brought to you by healthwithdronetech.com Studies show that the adult body produces 10% less collagen every decade. If you're over 40, that should terrify you. Collagen is the glue that holds our bodies together. If you're seeing more defined wrinkles or feeling lethargic, realize it has nothing to do with your diet, sleep schedule, workout routine, or whether you smoke or drink. It has all to do with your body's natural collagen production. This is why I highly recommend healthwithdronetech.com. Anyone over 40 should be taking advantage of this new opportunity in anti-aging technology. Learn more by going to www.healthwithdronetech.com.  The Democrats and their state media goons are really worried right now and are losing their minds over the fact that a popular black conservative named Larry Elder is looking to replace the corrupt Democrat governor. They're panicking and throwing every smear that they can think of at Elder as if they're part of the Newsom campaign, which we all know is actually the job of our former free press to protect and promote Democrats, smear, and demonize Republicans.  Over at CNN, they're horrified that a bad corrupt Democrat governor might get replaced by a Republican. There's a recall election looming in California, and here's why it matters if you live outside the Golden State. First, because it's another Republican end run around majoritarian democracy. Majoritarian democracy, but that's not the system that we have in this country. At least on a national level, Democrats always seem to be confused about this. Democrats seem to think that democracy just means Democrats in power. When Democrats are losing or about to be recalled, we're told that their political opposition is attacking democracy. And as far as state elections go, this isn't one. This is a recall election because the Democrat governor is so bad and destructive to the state. This is exactly why California created recall elections in the first place. Because control of the U.S. Senate could hang in the balance. Now, you might have thought this was just some doomed GOP stunt because California's reliably Democrat, right? And there you have the real reason for this propaganda onslaught against Larry Elder. He threatens Democrat power.  Recall races like this aren't the right way to correct it, because the rules are designed to empower extremes, requiring just 1.5 million signatures. Oh, just 1.5 million signatures, huh? This is the same amount of signatures that were required for past recall elections in California, some of which involved Republican representatives. I searched and found exactly zero claims that those past election results were a stunt or kooky or unconstitutional. It's only now that Democrats are in trouble that we're being told these lies. The first question is a simple yes or no. Should the governor get recalled? Now, if more than 50% of the voters say yes, Gavin Newsom is toast and with him the Democrats. All you need to know is what's in the lower thirds right there. How this recall could doom Democrats. That's all that matters to Democrat state media because that's their job. Not the news, not truth, and not facts. And to that point, where in this fake report are the actual reasons for this recall? According to the petition, quote, Laws he endorsed favoring foreign nationals in our country illegally over that of our own citizens. People in this state suffer the highest taxes in the nation, the highest homelessness rate, and the lowest quality of life as a result. He has imposed sanctuary state status and fails to enforce immigration laws. He unilaterally overruled the will of the people regarding the death penalty. He seeks to impose additional burdens on our state by the following. Removing the protections of Proposition 13, rationing our water use, increasing taxes, and restricting parental rights. These all seem like legitimate reasons that deserve to be heard. But CNN has decided that all these people are just kooky right-wing stuntmen who are trying to overturn democracy. Which again, by their definition, simply means Democrats in charge. So because Larry Elder is the prominent opponent looking to replace Gavin Newsom, the Biden regime has come out in support, saying that they'll campaign for him. And that does include the super popular cackling Camilla Harris. When Larry Elder was asked about the support from the regime by Breitbart News, he had this to say. I'll believe it when I see Joe Biden come here. I'm not sure he's going to. I understand that he's supposedly going to come here to campaign on behalf of Gavin Newsom. His popularity now is in the low 40s. I think I saw 41%. Most Americans don't even believe he's home, that somebody else is in charge. So it's going to backfire.  They know that the mass media by and large is in their corner and will carry the water for them and will not attack them for doing so. But I do think there's a good chance of backfire because as we know from even recent history, the media is not really good at picking the winners. All right, folks, that's all I have for this one. As always, if you enjoyed it, please hit that like button on your way out and leave a comment to let me know what you think.


Hello and how are you all today? Let us integrate the following function with respect to dx. We have 1 over cos x minus a cos x minus b into dx. Now multiplying the numerator and denominator by sin a minus b we have the integral sin a minus b divided by cos x minus a cos x minus b into dx. Now further we have 1 over sin a minus b integral o. Now here on adding subtracting x we have sin x minus b minus x minus a. We have added and subtracting x in this. We have the angle in numerator. So divided by x minus a cos x minus a cos x minus b into dx. Further 1 over sorry a minus b integral o. Now using the identity that is sin a minus b we have sin a cos minus sin b the whole divided by cos x minus a cos x minus b where a is x minus b and b is x minus a into dx. Now on simplifying we can write the function as integral. Now here we are left with sin x minus b divided by cos x minus b into dx minus integral of sin x minus a divided by cos x minus a dx. Now let t be equal to cos x minus b. So dt will be equal to minus sin x minus b whereas let z be cos x minus a. So dz will be equal to minus sin x minus a. Substituting we have 1 upon sin a plus b into integral of minus dt over t minus integral of minus dz over z that is further equal to 1 over sin a plus sorry sin a minus b into minus log of plus log of mod z plus which is 1 over sin a minus b into log of mod z divided by log of mod t plus c. Now on substituting the value of z and t we have the answer as 1 over sin a minus b into log mod z was taken as cos x minus a whereas t was taken as cos x minus b plus c. So this is the required answer to the session. Hope you understood it well. Have a very nice day ahead.