Nine expert hacks to create in a video that blows away the competition. Hey guys, you're interested in doing videos for your business and I get it, man. They say that in 2022 that 87% of the search results will be videos, so you wanna have great videos and I get it, but there are certain hacks that you need to do to make your video work. In this video here, I'm gonna walk you through each and every one of these nine top hacks and by the end of this video, you're gonna know how to make videos that will blow away your competition. Hey, thanks for tuning in and do me a favor. Hit that subscribe button down there. Turn it from red to gray, don't forget. Turn all bell notifications so you're notified each and every single time I go live or I upload a video. Okay guys, I'm gonna go through each one of these steps with you. Number one, create a video that addresses the pain point. Create a video that addresses the pain point. That's so important to do. I think so many businesses don't do that. They just wanna talk about their business and how great they are and how wonderful they are, but that's not why they're contacting you. They're contacting you because they have a pain point, whatever that pain point is. Now I own a company called Survival K Food and we sell canned meats. We sell a lot of canned meats and we do this for long-term food storage. So the first thing we start off with in any type of promotion is, do you wanna be standing in line waiting for food and get some type of government handout or do you wanna have your own food supply? What do you want to do? And the majority of the people will say, well, no, I wanna have my own food supply. I don't wanna stand in any government line, okay? So we hit the pain point right up front and you've gotta figure out what that pain point is for your particular customer. And then number two in your video, you want to present the solution, okay? So once we were to say to somebody, hey, do you wanna stand in line for government handouts or do you wanna have your own food supply? They said, well, I wanna have my own food supply and then we say, great, we have the best food supply. We have beef and chicken and turkey and pork and it's canned and it's like your grandma's pot roast and it's delicious and it's low sodium, it's low fat, low cholesterol. We talk about our product. So step two is talk about your product being the solution to their problem. Now the next thing we may choose to do is and you should too, is demonstrate your product. Demonstrate your product. Show how your product can help them. Now in our case, it may be opening a can of meat. It may be showing how you can make a sandwich out of it. It may be showing a happy family, sitting at a table, enjoying a great meal when everybody else is pretty much starving because they didn't save up for this disaster. But whatever you wanna do, make sure that you demonstrate the product and they can see somebody using this product. The next thing you wanna do is have a call to action. Once they've heard about their problem and they know that you're the solution and you've demonstrated it, the next step is to buy the product, okay? So you're gonna wanna have some type of button there, call to action, click here to find our inventory, click here to take advantage of our sale, whatever it is, but make sure you have a call to action. So that's gonna be your step number four. That is a must. Number five, this is the easy part. Keep it short. Keep it short. A New York Times survey found they abandoned the video in 19.4 seconds after only watching 10 seconds of the video. So if you're not getting to the point in the first 10 seconds of the video, you're gonna lose your audience. And the next scary statistic is, if you're thinking about making a half hour video, don't do it. 44% of the people left the video before one minute. One minute. So you've gotta really get on it. You've got to really get their interest in the beginning and don't make the video too long. Those of you who were worried you had to make a half hour video, well, there you go. You don't have to do it.  You wanna consider the platform you're putting them on. YouTube likes eight to 10 minute videos because they can put ads on your videos, okay? I don't know if people actually watch them for eight to 10 minutes. When I go into my statistics, I see about three to four minutes average watch time, which is almost half of my video, which is not bad. However, you've gotta think about where your video is. If it's on your website, they're not gonna be staying there for eight to 10 minutes. I can promise you that. Number six, you wanna make sure within the first, say, 30 seconds. And of course, we wanna talk about their pain point and our product and all, but talk about your company within the first 30 seconds. It's very important that you get your company's name out there. If they do leave the video, if nothing else, they heard your company name, they know who you are. So get that in there within the first 30 seconds. Number seven, make good quality videos. Now, I understand that you may not be a professional videographer. However, you gotta have good quality.  You've gotta have good lighting. So if you're gonna film outdoors, those, the golden hours, which is like a couple hours after sunrise and a couple hours before sunset when the sun is just right. If you're gonna film indoors, you wanna make sure that you have a lot of artificial lighting or natural lighting coming through the windows. I have a bunch of windows in my office right here and those windows provide a lot of light, but I also add to it all the artificial light so that it is lit well. If you're gonna use visuals, make sure the visuals look good. Don't use stock images. Use good visuals of your products or videos of your products that you've shot so they can see that that is actually your product. Don't use stock images.  Number eight, use good audio. Use good audio. Guys, if you have bad audio, if it's a tinny sounding audio or it's echoey or whatever it may be, it's not gonna work out for you. Now, right now you see, I have this little microphone on right here and that goes to a microphone on my camera there to give me better sound. Is it perfect sound? No, but if it's bad sound, guys, people will not listen to a video. If the video visual quality is not great but the sound is okay, they'll probably watch your video, but if the sound is bad, it just something, it grates on your nerves. You can't watch a video that sounds horrible, so make sure you have good audio. If you're using a cell phone, something like that, about arm's length is about as far away as you want that cell phone. Because it's an omnidirectional microphone and it picks up all sounds from around the room, that means it's gonna pick up echoes or any exterior noises or people mow in the grass, so make sure if you're gonna have it further than arm's length that you have an external microphone. Number nine, try to be funny. Try to be funny. I know this is the craziest thing in the world. I used to speak and I used to travel around the country and I had done a day-long presentation at one point and somebody came up to me after I had spoke that day and they said, hey, you were really funny up there. I really like what you did and I think I would like to be a speaker too. I said, great, you know, you should do it. And the person said, well, do I have to be funny to be a speaker? And I looked at him and I thought about it for a second. I said, well, only if you wanna get paid. See, people want to be entertained. They wanna feel good. You can make them smile. If you can make them feel good about listening to you, you're gonna do a whole lot better than if you're monotone and you're just regurgitating information. And I do wanna, I guess, kinda give you a bonus. You're never gonna feel totally comfortable. You're just never gonna feel totally comfortable. And you shouldn't because you should always want to improve. That doesn't mean that because you feel uncomfortable, you shouldn't do it, okay? You should still shoot videos. They're so very important nowadays. It's one of the best ways to communicate with your audience out there. Now, it just really is.  So it's so important you should do it. But I think a lot of people don't do it because of that fear factor. They're worried about, you know, what somebody's gonna think of them or if they're gonna make a mistake or if somebody's gonna make fun of them. It doesn't matter. It doesn't matter, okay? There's always gonna be somebody out there trying to tear you down. There's always gonna be somebody out there that's going to say something negative about you. But overall, you're gonna benefit a whole lot more if you do make these videos. You're gonna benefit a whole lot more as far as building your audience, as far as building your customer base, as far as building your loyalty if you do make videos. And my final thoughts on getting good at this is do it. The more you do something, generally the better you get at it. Study your competitors out there that are good at this. Study people that you watch videos of and ask yourself, why am I watching this video? Why do I like this video? What parts about it do I like? Is it the lighting? Is it the way he's talking or she's talking? Is it the subject matter? What is it about this video? Ask yourself those questions. Make a list of all those things and try to incorporate it into your video. And always, always, always keep learning. And you can learn more on this channel, by the way. I have over 1,100 videos at the time of this filming. You can learn a lot on this channel about your marketing. And don't forget to put comments below. Maybe you've got questions about this. Maybe you don't know what kind of camera to get. Maybe you don't know what type of lighting to do. I also have a lot of links below on some of the equipment I use. So you can look down in those links there, click on those links and look at some of the equipment. I really appreciate you watching this video. I do have a video course series that's $97, but I'm giving it away for free for a limited time. You'll also find that in the description below. So click and grab that while you're here. Thank you so much for watching this video. I really appreciate it. If you haven't subscribed yet, please do so. Hit that subscribe button, turn it from red to gray. And don't forget, ring the bell. Once you subscribe, a bell will pop up next to the subscribe button. You want to click it and turn it to all notifications. That way you're notified every time I go live or I upload a video. Thanks so much for watching this video. I really appreciate it and I'll see you in the next one. Hey, thanks for watching my video. Don't forget to subscribe to my channel and click that little bell right there so you can be notified every time I do a new video. Also, click on one of those videos there. Keep watching on my channel.


So next locks and egg tech field days. We've got Diane Stewart down. Would you like to introduce yourself please and what your product is today? Sure. Thanks Matt Our company is called aka primary solutions. We're not a startup We're actually an agent for a New Zealand technology called harvest electronics. So they've been around for about 20 years. They have a bunch of cellular Sensors that connect through a base station and upload data live. They can collect data from everything from a rain gauge right through to Fire service data. We've got some work here with the fire service departments in Victoria They run signal monitoring on tracks railway tracks in Tasmania But I'm here today to talk a lot about the orchard and vineyard monitoring that we do And what benefits are there from what you're doing for our orchard and vineyards around the Riverland? Sure, there's a lot of benefits with efficient resource use so things like your irrigation water We can monitor soil moisture, which we've seen a lot of today. We also have pump monitoring So you can monitor pressure water flows And give you alerts on all of the parameters that are monitored So if you've got one of those leaks you'd get a phone alert that says Pressures it's not where it should be or flow rates not where it should be So you have that ability to improve your decision-making improve that use of time We've heard a lot about today But also be very efficient with your resources something that's worked very well in New Zealand a lot of the regional councils have water meters on Systems as well as individual off-takes from water systems and that data is uploaded automatically to the regional councils For your water bills. It's also shown on screen so you can see it live when you turn your pump on What's happening and it all tallies. It's all electronic saves. It's one job. You don't have to do now So what would be the standout feature of your product compared to with some others? Well, we've got a pretty good range so we can have up to two kilometers between Loggers back to the base station. So when you're on big properties like we typically have in Australia You're not looking at necessarily a lot of the expensive base stations to jump the signal back So that's pretty handy. It gives you really good economies and also from what I've seen We can fit the most sensors on those systems so much greater range of data points the site the Base stations are fully customizable and there's also a free app that's fully customized to To the user


Hello, President. Mr. President? Do you remember Dana? I'm sure you remember her. And we have a few people here that I'd like to meet. Steve Matthews is my second researcher. He's been a very instrumental grad, you know, of course. And Mark Levin has been my Chief of Staff. And he's done a great job. And you remember Carol Helene when she worked here? Hi. And of course, Cassie. Oh, Mr. President. Well. Want to do a family photo? All right. And then incorporate everyone? That would be very nice. Good. You're always pretty. You're red and green today. Okay. Thank you. And then maybe we can get one group shot with everybody? Yeah. Wouldn't be the entire thing. You want the family in that team? No? Sure. Sure. And you want to put the two reds on either side? Well, why don't you stand there? I'll stand like this there. All right. And the rest of you guys, get some of you over here. Basic black. And we're going to get everybody to smile, right? Okay. Thank you, Mr. President. And now, just a few souvenirs. Well, thank you. Thank you very much. Oh, that's very nice. Five individuals here that have a souvenir of that. These are key rings with the seal on them. Thank you, sir. Everybody has keys. Thank you. Well, thank you very much, Mr. President. I guess we'll have a few things to say in there, but I just want you to know how much I appreciate this chance for these people to be with you. Ed, for more than two decades, I've known you as an ally and advisor, and most of all, as a trusted friend. And you've served your country and our administration well and faithfully from its first day. I should say even before our first day, because it was you, Ed, who made possible the best-planned presidential transition our country has known. And in your tenure here at the White House, you provided leadership to our policy team as we set out on a momentous voyage to put this country back on the right course after a period of malaise. That was the word for it. Over the past three years, we've looked to you in the words of the oath of office many of us here have taken to see that the laws are faithfully executed. Your record at the helm of the Justice Department is one in which we can all take pride. Whether it is fighting illegal drug trafficking or international terrorism or recruiting sound judges to the bench, you set new standards of accomplishment. And of course, we haven't missed seeing you over here at the White House. You've chaired over 125 meetings of the Domestic Policy Council. And if you weren't busy enough already, you took on the chairmanship of the National Drug Policy Board. Ed, I'll miss you across from me at the cabinet table, but I don't plan to say farewell because I know you won't be far away and I intend to continue relying on your counsel. I can't stop without saying something that I know all of you are aware of. You've been through a hell on wheels here recently, and I always go to bed feeling guilty about that for you and the others because ever since we've been here there's been a lynch mob in action, and they pick somebody out and they go after them, and I know it's in an effort to get here at the whole administration that they're doing that, so I feel guilty about it and that you have to take that punishment. But it follows on. There was a very fine secretary of labor, a gentleman, and all of a sudden he was gone and then found totally innocent of every charge that had been raised. And then there was Jim Beggs at NASA, the same thing, and found totally innocent. But then I've always figured that most lynch mobs aren't hanging the right people anyway. I'm just in for the hanging. You've got a record there in the Justice Department that tops anything that the Justice Department has been able to boast about for many, many years and in a number of different fields, whether it was going, declaring war on the mafia as you did and bringing them up, more settling of cases of discrimination than any other administration or any other Department of Justice under other administrations has ever done. And it goes on down the line with all of those. So I was glad last night on television to see you and some of your folks getting together yesterday morning, and I think it was wonderful and it was due to you. And I just want to thank you and Ursula from the bottom of my heart.  I can't think of a better way to end my service to the federal government right now than those kind words of yours to be here in the Roosevelt Room with this group of friends. You talk about the things that happened in the Justice Department. The people who really made it happen are the people that are here, the heads of our various components who have done such a great job. And with us are our friends. Very pleased to see the Vice President and so many of my fellow cabinet members here later on. But I just want you to know that as always it's been an honor and a privilege to serve you. You came to this office with a very definite idea of what you wanted to do. The campaign, the transition, and then what you've achieved over seven and a half going into eight years, you never wavered from what you wanted to do and you let us know what it was. And I think all of us who had a hand feel extremely grateful for that opportunity. The best part about it, quite frankly, was the people I got to work with. Starting with you but extending through this group, my fellow cabinet members, the people I worked with here in the White House, the friends on the outside who were so supportive. But you mentioned the tough times that we have gone through. Admittedly there were occasional slings and arrows that have been thrown my way. But the one thing that enabled Ursula and Dana, Mike and Ramona, and me to get through it, was your unwavering support. I don't think anybody has ever had a more loyal and a more supportive friend. For that I'm very grateful. So to you and to all of these friends, I just want to say thank you. I think this is a party now and they're at all the makings for pretty good. And then I never had any problem about who was right and who was wrong. There's one thing about Hollywood. You always knew who played the bet, guys knew who played the bet. George, what are you doing back there? I can't stay and say hello to so many of you and to old friends as I would like to. But I'm going to turn you loose now to get at that. That's the only part I shouldn't partition. Thanks again, Mr. President. Thank you. Thank you. I got over there with that. Nice to see you. Have fun. How are you? Thank you.


So again, welcome to the webinar. My name's Todd Wallach. I'm spending the year as a Berkman Klein Neiman fellow at Harvard. And I've been a data journalist at the Boston Globe for about seven years, as well as an investigative reporter working both with the Boston Globe spotlight team and the rest of the newsroom. Caroline Chen covers health care for ProPublica. And she previously was a reporter at Bloomberg News. Armand Imamjoba is a graphics assignment editor for The Washington Post. And it was previously a deputy director of data visualization at the Los Angeles Times. And I was excited to have this group of people talk about the issues that journalists face dealing with data because we all have some different expertise. I'm sort of a generalist looking at data, trying to find, mine it for all sorts of types of stories. Caroline is more of a specialist in health care and will have more expertise in health care data. And Armand has lots of experience in visualizing data. And it seems like there's been tons of interest in challenges and looking at COVID-19 data. A lot of it's been people have been trying to track it by day and time to see trends, and whether it's getting worse or better, as well as geographically. But there've also been a lot of challenges, such as obstacles trying to obtain the data. So you've seen a lot of headlines about that, particularly at local level or getting more details on the data, such as the race or age or other details about people affected. In questions about the accuracy and reliability of the data, ProPublica and the New York Times and others, I've written a lot of stories raising questions about the accuracy and the challenges comparing one area to another because of difficult variances in who's tested, how accurate the tests are, how accurate death counts are, and other issues. So I'm going to start by asking a number of questions. And at about 12.35, we'll switch to questions from the audience. So feel free to start tossing in questions as we speak. And about halfway through, we'll start going to audience questions and finish at one. OK. I want to start off just by asking Armand and Caroline, what data have you seen readers most interested in? Should I hop in here? I think early on, the questions were just, where is the disease spreading? So I think, obviously, especially in the US, as the virus first started to hit, everybody just wanted to know case counts. And then I think that started to soon overlay with a concern about deaths. And so I think that continues to be of interest, cases and deaths. And then I would say, where are the tests and testing capabilities, testing capacities? And I think now there's an understanding that there are two types of tests, the diagnostic tests. Those are the PCR, the swab tests, and now the new incoming antibody tests. And I think the more sophisticated readers are starting to gain an understanding of what do the numbers mean as we're starting just right now this week, starting to see studies come out with some numbers around these antibody studies. And there are already furious debates around those study results and whether or not those are meaningful. So I see those as layering. We continue to want to care about case counts, and we continue to want to care a lot about deaths and segmentations of those. So demographics and race and who are being affected. And these are layering as we go. And Arman, what have you seen? Yeah, I completely agree. Definitely along the same lines of that pattern. It has been case counts and where there were reported cases and reported outbreaks, deaths. And now I think the one thing I can add to what Carolina said is there's been interest in the trends that are being reported by states as well. So we've made steps to show what does this data look like over time, of course, noting the caveats in the data and how it's being reported and recorded by the states. Oh, and I forgot to mention, of course, there's the whole conversation around supplies. PPEs, ventilators, it's obviously been very hard. That's always a moving target, depending on whether you're talking a local level, national level, you can never put a nail on how much PPE there is at a given hospital state. But there's always interest in that question of supplies. And I'm also curious about how easy or difficult has it been to obtain all the data for your stories and graphics? I can talk about that. I think the data at a national level is basically kind of non-existent. Everything is reported. Most things are reported at the state level. So that means you have to either rely on an aggregator or aggregate the data yourself, going to all these different state sites or figuring out where they reported, how they reported in what format. Also noting that this data, what the states are reporting, also changes over time and what platforms they're using to report it. I sent out a tweet like a few days ago that was like, what if you were reporting a live election, but you were reporting, you were building your rig for reporting the results as the election was happening and what everywhere was reporting as the results was also changing. And they were changing how they reported as well. So it has been extraordinarily difficult in that sense to build things that don't constantly break and build data flows that are actually kind of stable, given the fact that what is being reported is moving under them as well. Yeah. I would say that there are certain things that, just by the nature of the pandemic, are going to be constantly changing. So for example, testing capacity. I've done a lot of reporting around testing and testing capacity. And just by the nature of what's happening, that is changing constantly. So whether that's nationally, whether that's locally, if you're trying to say, what is the testing capacity of my state, that number is going to be constantly changing. And it should be, because we have been constantly ramping up testing capacity. So for any reporter to try to get a beat on that and try to inform their local readers, they'd have to constantly update that. Is it possible to actually get an accurate number at any point in time? I think that is technically possible, but your number is going to be outdated within an hour, even at a specific lab. So I have at certain points been able to be like, I nailed the number. It's already old. Is there any point in even doing that? Yeah, I think it's a worthy exercise to try to get a ballpark and to track trends for readers. So there have been times where I've tried to do that for specific stories, but it is a frustrating exercise and I've really encouraged other reporters to really try to explain to readers like where you got this number, what has gone into this number and like how long of a shelf life the number will have and really try to like show your work to your readers more than I normally would. So I think some of that is inherent. There are other things though where you can only, your information is only as good as where you get it from. So for example, like the WHO puts out daily situation reports. That is the only way you can really get in a source for international case counts, right? But the WHO's information is only as good as the countries from which it comes from. So I keep like repeatedly explaining this to people that the WHO has a recommended way for what they count as a positive case. And they say that it is like, if you test positive with like a PCR based test. For the longest time, China just decided that they were only gonna count as positive, someone who had a positive PCR test and symptoms. They were not counting people who had a positive PCR case, but no symptoms.  There's nothing the WHO can do about that. There's nothing anybody can do about that. And then after a while, like China changed that. So you needed to know that about China. And I mean, that's deeply frustrating. You can't get everybody to report the same way. And you need to have those caveats in your reporting. And this also trickles down to like, you know, 50 states and, or 56 states and territories all doing it in their same way as well. Right. That's gotta make comparisons really tricky when everyone has a different way of reporting the data, tracking the data, there are different rules on who gets tested and what gets counted. It sure does. And again, yeah, I mean, I think we, you can only be clear about like the caveats of like, this is entirely dependent on how, on what's being reported and how it's being reported. Yeah, I've been very, very cautious about comparisons. Got it. And are there any other problems you've noted in the data that people should be aware of? I have been very careful, or I've been encouraging, you know, reporters in my newsroom and trying to explain to the public just to be aware of, you know, what the definitions are of numbers that gets thrown around. So one thing, for example, I've been trying to explain a lot to, to lead readers is like what is the, what actually is the fatality rate, right?  And what is reported as the case fatality rate, right? So the case fatality rate is the number of reported deaths divided by the number of lab-confirmed infections. So everybody knows in the US, it's really, it had been really hard, it continues to be really hard in many places to get tested in the first place. And a lot of places are not testing unless you're really, really sick. So that denominator is gonna be much smaller than the actual number of infections. So especially early on in the United States, the case fatality rate was something like 10%. Because we just weren't testing a lot of people and like that you think of it as like an iceberg model, like the deaths are usually the easiest to find and count, especially early on in a pandemic. This always happens in a pandemic. And the people who are like asymptomatically infected are the hardest to find in the first place. But again, like the average lay reader, they just wanna know if I get infected while I die. And they're looking at that number that's being reported in your headline. And they're just looking at that and be like, that's like if I get infected, like that's my chance of dying. And like we have such a huge responsibility as reporters to explain that number. And not just like throw things around in headline. So I mean, I think there are a lot of numbers like this as a science and health reporter where like, I feel like we have a lot of it responsible I take explain to people. So like that are not and are, these are like the rate of infection, like the chance you have of infecting other people, the average number of people who infect, which it's a process of understanding. And this is what I'm trying to get across to my readers, like there is so much, we are still learning that we don't know yet. And we cannot present this as set in stone. And building on that, like mentioning the, you know, the ball cases being kind of a difficult fraction to divide against, like the deaths number also is slippery that we've seen stories highlighting this in recent days. And it's been something we've been kind of saying for a while is like not every death is being accurately categorized to like recently, New York city added some, what was it? Like 3,700, I forget the exact number of deaths that were classified as like probably COVID-19, you know? So, and you know, if it's happening in New York city, it's probably, there is some fraction of cases that's being, that that's being, you know, miscategorized throughout or never even recorded. You know, so that number is slippery as well. And I think when talking about like fatality rates and that kind of thing, rather than just talking about one big number, we've been, you know, we've been trying to, when we have the data available, at least break it down a little bit better into like segments of the population or report the comorbidities that studies have been reporting. So it's not like, you know, a flat 3.2% or whatever it would be, you know, it depends on a lot of factors that are related to the individual. It definitely sounds challenging when there are questions about an uncertainty about both the numerator and the denominator when you're trying to calculate rates. You know, my sense is that these are problems that data journalists also, and journalists in general encounter when trying to get data. It's often hard to get one clean database at a national level or global level. We're often aggregating it from lots of different places and each place might have different ways of counting the numbers and reporting the numbers and the data can be messy. Is there anything different than you're finding in dealing with COVID-19 data or is it reflect challenges you've faced doing other types of stories? This is more philosophical, but, you know, these numbers are being reported by states and by countries and everywhere, like very precisely, but in its nature, it's a very imprecise like count. So there's this weird situation, you know, it's like inaccurate but precise is one type of like data classification. And I think that's where we are now. It's like you're throwing, you're like taking shots at a dartboard and they're all landing in a very exact same similar place, but, you know, you're off somewhere, you're not actually hitting the dartboard. It's like somewhere off in the wall because you're throwing the darts kind of blindfolded. But we have very precise counts. Yeah, one thing I've seen and I guess my, I know this is a really hard thing to do, especially, you know, if you have an editor that's pushing you is to resist the urge to write because what I do see is that health departments as they release data are refining as they go. And I think this is because they are also figuring out what they need to release. So for example, to give a very specific example, New York City started out by giving tests. They were only reporting by a borough and then a lot of people were like, well, that's not enough information and they were getting a lot of criticism and then they started releasing. It wasn't quite by neighborhood. It was by this very strange, not quite zip code, not quite neighborhood. And they released, it was percentage of positive, but they didn't have any raw numbers. There were no numerator, no denominator. It was percentage. And I was like, well, I can't do anything with that because if you say that in this zone, it was like 66% positive, like that could mean that you only did three tests there and two people tested positive, like that's meaningless. But I did see some news organizations like write a story on that. And I was like, that's a bit dangerous. And then I think like within a week, they then re-release numbers, which were by zip code and had numerator and denominator. Like they had way more information and then you could write a more meaningful story. And then like New York City has continued to update and iterate and give more and more granular information. So I do think that there is a benefit to kind of waiting because I've seen more than I've ever seen before in any other outbreak I've covered, sort of health departments iterate as they go with the data that they're releasing. And I actually see because this is happening across the country, actually reporters, I think be able to push health departments and be able to say, hey, you know, like Ohio released this information, Florida, why aren't you releasing this information? And be able to sort of like push departments off of each other. And I think in a similar theme, I think it's really sometimes dangerous to write a story off of a preprint. I do think it's really great that scientists, our researchers are moving quickly and sharing information on like meta archive and bio archive and not waiting to go through that whole process, but then it's not peer reviewed, right? So this puts you in a really dangerous position as a reporter to have to like write a story off of a non peer reviewed study. So I think one of my goals is to never let a preprint walk alone. As in you don't write a story on a preprint by itself, you try to let it go like in concert with other studies and look for a trend or at least like let lots and lots and lots of people comment on it and don't just write a story on this. So this is happening right now with all these antibody studies, right? Like Stanford put out its preprint on its antibody sero survey. And there were a lot of stories that got written really quickly. And then in the next day, there has been the critique wave of like, was it a good survey? Was it biased? You know, like all of that stuff. And I just wish that a lot of reporters might have waited a little bit and now there is the Los Angeles sero survey. And I think you could have maybe waited and collected a bunch of these studies and maybe done one thoughtful story in one go or at least gotten a lot more outside voices than you normally would before writing that one story because they aren't peer reviewed. So you do have to treat preprints differently. Right. And interestingly, of course, none of our articles are peer reviewed. So I'm curious what process you go through to make sure that your own interpretations and analysis are sound before publishing? I just run preprints by way, way more people than I normally would with, you know, if something's already published in a journal, I know that it's gone through that peer review process. If it hasn't been, I will run it by a lot more outside experts than I normally would and just go that extra mile and really ask myself, like, do I have to write this now? Can I wait for it to go through that peer review process? And you can ask the author. Sometimes they'll say, oh, yeah, this has already been accepted by JAMA or the Lancet or whatever, and that gives me an extra measure of confidence. If that's the case, that's helpful to know. And if not, and it's like, you know, this is such an important study that I need to write about it right now, then I get all those outside voices. I try to get many independent outside voices that are from a number of different institutions, get all their critiques. And if all of them are really, really negative, then again, I have to ask, like, why am I writing about this study in the first place? Like the bar just gets so much higher if it's not in a journal and hasn't gone through the peer review process. And I assume, Caroline, even when ProPublica or Post or others are doing their own analysis, we do the same thing. We'd go to outside experts and say, here are the numbers I'm calculating. Does my methodology make sense? Is there a good explanation for these conclusions rather than just posting something on Twitter or throwing it on our website? We first normally talk to experts first. Yeah, exactly. And you know, there's a bit of like self analysis in here too, like looking at what we call data smells, you know, it does what's in the data question, your basic assumptions of it, you know, does it show an opposite trend to what you're expecting? Are there massive gaps or negative values where there shouldn't be, you know, it's kind of like sanity checking the data as well. And similarly, I know there are questions about different models that organizations are using. A lot of people are looking at the University of Washington model. It has a website that's very easy to use, predicting when peaks are gonna be for hospitalizations and other issues. But there are lots of other models it seems and there are questions about what variables go into each model, how the numbers are calculated and they can produce conflicting results. So that has to be challenging to deal with. Yeah, so I did a whole column on forecasting and projections earlier on, where, which was partly for reporters and partly for, you know, the public. And I think again, the question really is like who is your audience, right? And who are you writing for? And one thing that I try to keep that at the back of my mind, because I think there's a difference here. Like if you are writing for really a lay public, again, you have to remind them, like is this an estimation, right?  And I said, it seemed awfully specific where they said that, you know, this means that in New York, this was back in early March, that, you know, last week there were, you know, it was something like 1,583 to 2,000, blah, blah, blah. You know, it was like down to the digit, number of people infected. And I was like, I read that sentence and I feel like it gives a lay audience this sense that you can be that precise and calculate down to a single digit, how many people are infected. And for me as a writer, I would never give that level of precision because it signals something to a reader. I would give, I would round and use the words around, you know. And I said, what does this say to you as an epidemiologist? And it was really interesting because she said, I like seeing that sort of precision because from one epidemiologist to another, I can then go and redo his model and make sure that our numbers match exactly. So it's very useful from one researcher to another. But I agree with you, for a lay audience, that's not the message we wanna send because I said, what is the takeaway you would want for a lay audience? She said, the takeaway I would want a lay audience to hear is it's not 400 and it's not a million. You're in the low thousands, you know. So really, like that's the kind of the question that I always, when I'm talking to someone who's doing modeling, I say, what is the takeaway you would want for a lay audience? Like, are we talking? And really, she said with models, you need to be thinking in orders of magnitude. And I think that our responsibility as reporters is to then say, okay, so I'm gonna give an orders of magnitude type of number to my readers. Got it. And I'm also curious, are there any mistakes that you see lots of people repeatedly making that bug you? One I see all the time is people say, oh, there've been four million people tested if there've been four million tests. But some of the tests require multiple samples. People could have been tested multiple times. So they're different numbers. I also see people say, oh, there are this many cases when it's number of confirmed cases and there are other studies showing there are probably many times more people who've been infected but haven't been tested.  It's like, well, no, not, I mean, yeah, but no. You know, that has just been what's reported and what's being conveyed, like being reported by the states. And again, that comes back to this, like what Caroline was just saying about this precision implying that we know, there are 526 cases in this county in Illinois or something. But maybe that's on us too. I know the instinct is to try and report the data that we, to the granularity we have available, but maybe there are better ways in that that we do report the data that implies more of this imprecision about the data. You know, that's something I think we can ask ourselves and address as we try to put together these pages that are like tracking the spread of the disease or whatever. Another one is like just people being exposed to types of scales and visuals that they're not used to seeing. So like, we're seeing a lot more logarithmic scales than we're used to and they don't chart things, that growth doesn't look the same way on a log scale than a linear scale. But if you're looking at it and think you're on a linear scale, then you might think things are like declining or flattening out when actually that is very much not the case, you know? Yeah, I think Todd, you picked up on my biggest pet peeve is people not paying attention to units, right? Like, and I've kind of been, this has been like my soapbox rant for the longest time is like, please try to get your units and people because I think again, like that is what people, readers care about, right? They see a million tests and they think that that is a million people. When you say like, we're rolling out a million tests, they will automatically think that is a million people who can get tested. And depending on the type of test, and this is like absolutely confusing like the CDC test you had to divide by two, the Abbott test, the rapid test, it is one test per person. So depending on which test you're doing, it is a different equation and it really is a reporter's responsibility to figure out what the heck is being said. And so it is a way for frankly, for officials to inflate numbers. And so it is, and it's the only way to really get an apples to apples comparison is if we get a testing capacity in people. And so I think that's a journalist's responsibility to always get the units and people that way we can compare state by state, country by country. So I do think that that is a mistake, well, a mistake or I think a confusion that annoys me when I see that. And yeah, I think just not explaining that everything should be like, this is a reported number of deaths or reported number of cases at this point in time as Arman said, I think those are really common. I think also just, this is more philosophical. It's just presuming we know things. I mostly see this frankly on Twitter and on TV, but just this air of like, we know what to do. Like, if this state just did this, then like we would solve the crisis. Like, no, like nobody knows what to do. We have only known this, like humanity has only known this virus since January. Well, I mean, in China it was a little bit earlier than that but in the US we haven't really known it that long. And every time I dig into this, whether it is on really understanding how it is transmitted or I recently was doing a lot of reporting on, you know, doctors struggling to understand how best to use ventilators, how to best treat critically ill patients. Everybody is struggling to do their best by patients and to really understand what to do. And so I think there are no easy answers in this crisis and I think you can give, I think this is a failure of communication both by our officials and also actually by journalists, when we make it sound like there is an obvious or easy answer. And failing to acknowledge that like, to some degree we are all still learning. And so that that irks me whenever it comes across like well, obviously. That sounds good. Why don't we go to questions from the audience are starting to pile up. One that's been upvoted the most is from a Berkman fellow, Baobao Zhang, who wondered how do you feel about non-experts weighing in with their own analysis on Medium or Twitter elsewhere and non-journalists and not all of those people do what journalists do is going to experts first to vet their conclusions. I definitely feel like it's a free society and that's what platforms like Medium exist for. So you're specifically citing Thomas Pleo's Hammer and the Dance. I think it's fine if people want to publish and I think that they definitely find their own audiences. I do think that things like that sometimes are, like I think they find their own audiences basically. Like I had a lot of people actually send me that specific post and be like, I cannot understand this. Can you like write a version that is like, that like hand holds a little bit more? Because I think the part where oftentimes like experts who are experts in their field, whether they're like data scientists or like I see this a lot where like a clinician or somebody will be writing, they use a lot of, they tend to use a lot of jargon and don't break it down to the degree that I tend to try to do. And some people do, some people are fantastic communicators naturally, but I think that's a tendency that I tend to see a lot of jargon. And so I think there's a place for them and then I think sometimes there's the shortcoming is that I think they're not trained to be able to use the language that like helps them reach as many people as they could and to give as much context as I think like a journalist would know how to do. That's my off the top of my head answer. Okay, that's good. There's also a question about, how do we deal with issues where, we publish an article based on data and then the data changes or the information changes or this probably comes up all the time with healthcare studies Caroline where a new study comes that contradicts the past study or a study's been retracted. So how do we deal with this when people are still passing around the old article or chart based on old outdated data and information? Yeah, I don't know what a nightmare it is right now with the situation. So one thing I am doing now even more so than I normally do is I am aggressively dating my information like all like it's in my stories on my sentence I'll say like, you know, this fact as of Wednesday afternoon, you know, according to the association of public health labs, you know, there were like the US could had a testing capacity of a million tests as of Wednesday afternoon because literally by Thursday morning the number is going to change. So I try to like tag as much of my information as possible like I'm linking a lot more aggressively than I normally do and also adding the date and timestamps so whenever whoever comes along to my articles sees that information, they will know as of when that information was true. So unfortunately, like some people are not gonna read that carefully but at least the timestamp will be next to that. So I cannot go back and update my articles constantly but at least the information that somebody reads will have a timestamp next to that. So I think that's probably the best thing you can do and then yes, like update as you go. And I think again, this is where the language that you use at the time you write also helps you write because I also say use language like at this time scientists understand this to be X. So like when I was working on a column about asymptomatic transmission, there was a lot of language I had in there which was like as of now, scientists understand that whatever. So again, like there's a date at the top of my article I'm using a lot of language that indicates like I'm giving you the best of understanding at this time and then I'm also linking to studies and putting language in that's like as of this interview that I did on this date, this is what I was told. So I think sort of all of that in combination hopefully even if a reader comes along later we'll know that that was information that was current at the time that I wrote that article and I think that's the best you can do. Yeah, and from a database standpoint we can either build our pages and apps to like plug into live data that updates so that you are seeing updated data as of like the timestamp at the top of the page or right on the chart or whatever. And again, we try to be transparent about when that data is updating or like Caroline says, we can build it statically with like an illustrator or just save it as a static SVG and have to make very clear that this is data as of X. Otherwise, we've been in situations when we're trying to publish a story and like we just have to keep updating the charts like five times because the data keeps changing as we're writing the story. Yeah, and other more subtle things like so ProPublica normally does really long sort of deep dive investigations and actually our social folks are used to just like retweeting our stories like forever because we often are doing such long retrospective investigations that you could like retweet our stories like two years from now and like there's no reason why somebody couldn't read them again later and we've completely reconfigured that. So like they no longer will retweet a story because they know the information could be totally old. So even thinking about that like your social strategy, they will check in and be like, can I still tweet this story from our main account? Is that information still new? Like thinking about that kind of thing. And then obviously if there's some really major new information like for example, if I had written a whole column on asymptomatic transmission and there's some really major information that's really relevant to know, I will put an update at the top of that story. So being selective. Okay, that's both good points. Next question's from Eva Wolf-Angle who's a night science journalism fellow who asks about the fact that researchers often try to communicate uncertainty and I guess there are two challenges journalists face. One is how to communicate that same uncertainty and then there's also the question, do we undercut our own stories and reporting and data when we communicate that uncertainty are people just going to say, oh it's an estimate, it has such a wide range, it has a margin of error, you can't really rely on it. So how do you deal with those challenges? I mean I try to convey that in describing the process of science, right? So just to give a very specific example, I was in the column I was working on on asymptomatic transmission, there was a part where I talked about how new studies have shown that viral load is actually higher at the start of the disease, course of disease for COVID-19, which means that you could be more contagious even before symptoms started, but I went out of my way to explain how this is unexpected because for COVID-19's close cousins like coronavirus cousins, SARS and MERS, you were most contagious, you had the highest viral load in the middle of the course of disease, when your symptoms were highest and I think just like explaining that which would be like why your natural presumption would be the original presumption was that COVID-19 would behave the same way is something that like any reader can understand that like naturally you'd look at historic models and be like you'd expect it to behave the same way. And just I think like trying to explain the process of science helps and I feel like I just over explain and I think like showing that uncertainty or even just saying things like I just was working on a story about like ventilator use and I had a clinician give me a number and then he called me back and he was like you know but like that number I gave you like I just like I know you hate this, journalists hate this but like it might change and I was like no, no, no, that's fine. Like that's fine and I appreciate that you wanted to clarify that. So then I just added a line, a very short line saying like he added that it's early days and more information will be gathered and I think that's fine and a good indicator to readers that like more studies are gonna happen. So I definitely feel like there are ways for readers to indicate that or for writers to indicate that for their readers. Yeah and from a visual standpoint, I mean in terms of like how to communicate uncertainty look to the annual discussion every hurricane season about how to like chart the likely path of a hurricane. You know, it's like visuals you want to give somebody something to look at that tries to convey the data as best as possible. And I think with these in the case of this outbreak, the best we can do is like, you know, work that into the, you know, to the kind of the chatter and the headline around the chart and the annotations, you know, say that it's reported cases or confirmed cases or reported deaths. Try and convey the uncertainty in like what's around the chart rather than the numbers which are what's actually being reported and what we actually have to chart. Got it. And when I take on a question by Saul Tanamban who asked about a questions raised by COVID skeptics who will often point out when we report deaths argue that they're over counted or completely where there are no COVID deaths in extreme cases and say, well, they're really dying from a heart attack or they're really dying from pneumonia or they're dying from some other cause. And yes, they tested positive for COVID but that wasn't necessarily what caused their death. How do you deal with those types of questions? That's interesting. I think that that I don't know that that's like a useful debate right now, right? Like I think like all you can do because I think you can have that debate at like either and right, because then you get into the debates on, you know, the people who are dying at home like and like, did they die of COVID? Did they die of not COVID? Like how do you then count like the people who, you know, are the like excess impact from COVID because they died at home because they didn't want to go in for help or like, you know, like I think there's so many swirling questions around death related to COVID that are going to be so hard to untangle. And I think like as journalists, the only thing you can really do is just be really straight and really flat and be like, here are the number of people who died with a positive COVID test. And just like leave it at that. And then here are the number of people who died at home. And here is how it compares to the number of people who died at home like last year at the same time and like show that gap if you are able to get that number from your state. Like I just don't know that you can, that those debates are really helpful or like getting into those weeds and trying to parse that is gonna get anywhere at this point because you can have that same debate about the flu. Like while so and so had a positive flu test but did they die of their underlying condition or did they die like their pneumonia came from the flu but they also had diabetes. What does that mean? I just don't know where you'll get with that. And it reminds me of like after Hurricane Maria when they went and did studies of like, what did the access mortality rate look at like in Puerto Rico after Hurricane Maria. It also like I worked on the homicide report at the LA Times for several years and the LA County Coroner if somebody was shot and then died like say 10 years later of complications from that gunshot wound like eventual health impacts, it's still ruled a homicide that, because they died because of complications from that gunshot wound. So you know, this is not just solely restricted to COVID-19 data, it's just mortality statistics in general. Right, another question that came up is what is the most reliable source for COVID-19 data? And I think there are at least a half a dozen sources that have aggregated national data and a couple sources with global data. Armand, go ahead. Yeah, most reliable is the key. I mean, you know, Johns Hopkins has really been putting in tons of work into aggregating as much data as possible. You can take a scroll through their issues list on GitHub just to kind of deal with the, or to get an idea of the volume of requests this has generated. And of course the World Health Organizations and then everybody, I think a number of media organizations including us are also trying to aggregate at the US level like state data and county data. I can't tell you which is the most accurate. I would say just for US case counts, we mainly use Johns Hopkins data. We long ago gave up on the CDC, which is very unfortunate to have to say that, but they don't update on weekends and they are like 24 hours behind on their weekday updates. So we use Johns Hopkins for just our daily case counts. In terms of like testing capacity, we mostly point to the COVID tracking project, depending on international sometimes, depending on what it is, WHO or JHU. But again, it really depends on exactly what you're trying to get at. Got it. And of course, sometimes for very local stories, there may be only one possible source of data coming from a county or coming from a hospital or somewhere. Yeah, and actually going to your local health department might directly is probably gonna be the most up-to-date information, which will be even faster and more up-to-date than going to a site like JHU, frankly. There was also a question from Magna Cheney, who's a Neiman affiliate, asking about the best practice for archiving stories. So Caroline, you mentioned having a time date stamp can be one way. Yeah, The Guardian does this thing where they have a warning up really high, what they say, like warning the story is like more than a year old, or they have some sort of very visible warning up high, which I always appreciate whenever I see that. So that could be one way to do it. Okay. And Michelle O'Neill asked, well, what can we report that's meaningful without having the basic data that we want? And I guess that comes up with, when we want to say what states or where are the hotspots, or how is the U.S. doing versus other countries when there are all these questions that we've brought up about how many people are actually infected given the differences in testing and how many people have died given differences in counting. And because of all the uncertainties with numbers, it must be really challenging to figure out what we can really say with confidence. Yeah, I agree. I mean, I think we need to make like basic assumptions or adjustments when we can, for denominators that don't exist or other things that we don't consider reliable. Like on our pages that show the data that we know about cases and deaths throughout the country, like instead of normalizing, we're looking at cases per population or known cases per population of the state, per population of the area. But again, we have to be clear that we don't, this is all just based on what's being reported. Okay, there was also a question with what do you do when you don't have data or information or you have conflicting data so you can't be sure? Do you just avoid writing about it or do you write about it as best you can? Certainly difficult to make charts when you don't have data. Well, I just think that there is a, there is value to writing about the lack of information, especially when you feel like your, say like your local health department is withholding information that should be public, right? So I think that there was early on, a lot of good journalism being done about the need for demographic information about who is being infected, right? And now we're getting a lot more of that information, which is pointing out like big problems in who is being infected. So I would not dismiss that as a possibility for where you start reporting on just the lack of good information, which can actually spur change and get you the information that you then want. Great. So I think we're just have time for one last question. Gina Pavone notes that there's talk of doing, using an app for contact tracing. There's a project at MIT for that. And there's also been stories based on cell phone data that's been released. And Gina wonders how journalists deal with aspects of privacy or reporting on the sort of the challenges of releasing that data and using that type of sensitive data and making the topic accessible to a mass audience. Yeah. I mean, I think that this is a hot topic across a lot of different countries, a lot of different localities. And I think there like one, really understanding the nitty gritty of how it's gonna be used is important. I think there are a lot of think pieces about these apps right now that I'm seeing, which ask a lot of like philosophical and hypothetical questions. But I see way fewer stories that actually get into the innards of how they're gonna be used, which would actually help answer some of these think pieces. So I think that would be useful journalism to be done. It's much easier to be like, well, what about privacy? But then you don't actually know what's gonna happen. I think the other question though, which was raised with me with some public health experts that I asked was like, how is this gonna actually intersect with the existing public health infrastructure, right? Because if there are a bunch of people who have downloaded an app and it's not talking to public health officials and not helping them do their actual work, that's also useless, right? So this needs to fit into the existing public health ecosystem. So I think there's a lot of good reporting that can be done around that. And then again, like this needs to fit into existing testing capacity. There's no point in having like a great contact tracing app if you then can't test people and find out who is sick in the first place. So there's a lot of questions about like, do we have like a very shiny looking object that doesn't mesh with the actual realities of needs? And I think helping people understand, your readers actually understand how this app needs to fit into the actual workflow of containing the virus. Like all of those things can be helpful to your readers. And then ultimately also just like the mathematics, right? Of how many people would actually need to download the app for this to be useful in achieving what it needs to do. Because there needs to be, there is a minimum number of people who need to have the app for a contact tracing app to work. Got it. Well, thank you so much. Thanks for the panelists and for everyone who tuned in. There will be a recording available in a couple of days on the Berkman Client Center event page. And there's also going to be a quick poll added survey at the very end. So thanks again for Armand and Caroline and thanks for everybody who was watching.


Hey everybody, welcome back to Magic Orthodoxy. My name is David and this is the deck review. Hey, today we're going to look at the Casino Royale cards from Bomb Magic and the Taiwan playing card company. Alright, so what is Casino Royale? Casino Royale is a deck of cards that was made to demonstrate a certain paper-pulping technology for casino quality playing cards. Bomb Magic is trying to get on the scenes, just like other card companies, as being a reputable card producer. And they're using the Taiwan playing card company, the same card company that Legends and Expert has been using. And so they're using the word Royale as a fancy word because it's always been associated with some of the best casinos and obviously it's associated with the movie. And so this is supposed to be a stylish deck of casino cards. So it's not standard, okay? It's supposed to look like a stylish deck of casino cards.  That's indicative of casino decks. They always have that little cutaway there. And so this is kind of more like a prototype. It's like a this is what we can do kind of deck. And so it's really made to show off the paper and just the quality of cards that they can produce. This first edition of Casino Royale, it's the summer spring and it's called the Bright Edition. And so what they're showing off here is the royal finish that's on the service layer. They want to show you how well these cards span and how they don't feel greasy or you know, they don't feel cheaply made. Later on their hope is to release an autumn winter edition that'll be called Mystic. And they'll hope to do that at the end of the year. And so stay tuned for that. So the tuck ace all around is made to look exactly like a casino deck. It says Casino Royale has a little cutaway. And it says casino quality down here at the bottom. This side says Stymond back standard edition. This side says bomber playing card company. The bottom says royal finish. And the back has a scannable UPC and it says proud to be royal. The tongue flap has a little bit of ad copy. And that is pretty much it for the tuck ace. Let's take a look at these cards. Like I said, these cards come from the Taiwan playing card company and they are a nice medium grade stock. When you measure these with a caliper, they come out to about 2.97, which is just a little under what you might consider a casino card. Casino cards usually measure at 3.0 and above. So 2.97 would be a thin casino thickness. These are about the same thickness as the blood kings if you're familiar with that. Cards really do feel different. I could tell that right away when I picked them up. I knew immediately that this wasn't a United States playing card company deck. They smell different, feel different. They have a different snap to them. They even have a different surface texture. So it's really interesting. So I do like the corners. I do like the edges. This is a fun deck to pick up. So the back design is a diamond pattern that you would see on other casino cards. However, it looks like a circus.   It is a two-way image, but it's just missing some diamonds, it's got some holes. And then of course it has the white bordered back design, which is also not seen on a lot of casino decks. Most casino decks don't have a border, but some do. And so this is their take. This is their take on what a casino back would look like. With this deck, we're going to get two original jokers.  So I mean, that would go along with the name, Bomb Magic, or the Bomb Playing Card Company. And so yeah, it's these clowns that are wearing sport uniforms. And instead of balls, they're kicking their old school like circus looking cartoon clown bombs. So your four aces are all the same. They're all exploded pips made to look like they're made out of crystals or diamonds in the two different colors.  All the pips and indices cards are completely normal and will be instantly recognizable to anyone. The court cards do have a standard look to them, but they've been recolored in this turquoise color and this red color. And if you look a little closer, you'll actually notice that each court is holding a diamond or some sort of crystallized object. All right. So that is my review for the Casino Royale Deck from Bomb Magic and the Taiwan Playing Card Company. As always, I want to thank RarePlayingCards.com for providing these decks for me so that I could do these reviews for you. And if you would like to find these decks or any others, please visit RarePlayingCards.com. Thanks. Bye.


I would like to start with some broad trends that will anchor the basis of my remarks around the role of information and communications technologies in enabling and helping create this utility of the future. And I'll begin with a couple of broad trends because innovating in the utility industry, I mean I work in a global company and I get the privilege of going around the world and engaging with different problems in different parts of the world is characterized by its diversity. Right? It's very hard to generalize what kinds of problems, what does it mean to create the utility of the future if you not anchor it to a particular geopolitical situation. But there's a couple of trends that are very interesting. First in the developing world there's the fact that well over a billion people don't have access to electricity at all. And in the same way the chairman was alluding to the foundational characteristics that a grid, a modern grid has in the nation and in the economy. This aspect of providing electricity to that over a billion people is a key priority, right? And the choices that get made of how those grid gets constructed, the choices of generation, how consumption happens will impact not only those economies but will impact also the rest of us, right, in developed economies. But I'll highlight the very interesting trend on the left-hand side, the electricity connection with growth, with GDP growth. In this case you're seeing it for the United States. And there's two things that come into view when you look at that graph. The first thing is that obviously there was a very close and continues to be a very close correlation between electricity usage and GDP, right? So we see that the trends of what goes up and goes down is very interconnected. So whether it's a leading or a follower indicator, but they're clearly connected. But what's fascinating is the other trend that gets overlaid on top. And that is the fact that even in the United States when there were GDP growths of four or five percent, electricity consumption was growing by double digits. But there is this very clear trend as time goes by, and something very important happened, right, in the last 10 years or so, which is they crossed. This is a measure of efficiency, of utilization of electricity, it's a good thing. But it presents very, very fundamental implications to the utilities and their business environment and their ability to aggregate capital and invest to do the transformation that is being demanded. So even for the foreseeable future, even in growth rates of two or three percent, the electricity consumption in the United States is only expected to grow moderately, if at all. There is a second trend that I want to allude to you on the traditional value chain of generation and transmission and distribution and customers. The flow of electrons is what determine the value chain and the information flow, basically also one in that direction, in the end resulting in a bill that a customer would have to pay. But compounding to these trends is two big things that are happening, and Arlen is a wonderful example of both the opportunity and the challenges are presented when you incorporate, for example, a very significant amount of renewable energy at the generation size. But it's clearly a source of uncertainty that gets injected into the system. But there's a second source of uncertainty that in the decade ahead we're going to see growing in spades. And that is the fact that the demand, which used to be very well understood and predictable, is going to be much more uncertain. And the uncertainty is going to be driven by the fact that increasing amount of local generation may happen at the home, more complex loads, whether it's electric vehicles, or demand response programs of which one has to carefully understand the implications of how people will respond to those incentives will happen. But the net result is now we have this uncertainty on both ends. The third trend is that there is a growing amount of instrumentation and interconnectedness of these devices. And it is in this context that I would like to discuss how information, information technology can contribute and deliver value to address some of these issues and also create some opportunities. And bringing these two worlds together is a very interesting challenge. Actually, not that many decades ago, information technology not only wasn't at level of maturity it is today, but it wasn't as prevalent in the utility world as it is today, and I say it's going to be in the decades ahead. It was a focus much more on machinery and equipment and reliability of that equipment and constructing the physical aspect of the grid. But the achievement that has been done with electrification has been nothing short of extraordinary. I always highlight this fact that despite the amazing achievements of technology in the 20th century, when the US National Academy of Engineering was asked to decide what was the greatest achievement of the century and engineering-wise, they said it was electrification. And I say this with the greatest respect as a result when we talk about creating the utility future on the basis of something extraordinary has been achieved already. Now, we understand that there's a transformation that is going to happen, that we're going to get a chance to discuss in the utility industry. But I also want to highlight that if I was in agreement about electrification, I would say information technology would have come a close second and it may be that in the 21st century, the contributions of information communications technologies that we'll have in the world may be the greatest impact of the century. And this graph, which was created by Ray Carswell, is a fascinating thing. So let me just describe what it says there. It says $1,000 of computing at constant currency. How much computation does it buy you in 100 years? And if you look at this, it says about 10 to the 12, 10 with 12 zeros of value at constant dollars. Nothing in the history of mankind from a technology point of view has produced that level of progress. And the result of that is that now we have this confluence of pervasive computing, mobile technology that everybody has, virtually free computing, so to speak, right? Cloud computing, infinite capacity in being able to process information. These internet of things, increasing instrumentation, pervasive networks as well, and social. The desire people to connect with one another and share information with one another. All of those things combined together are produced in more data than ever before. And the result of that is that we are the very beginning of this journey, right? Now we have to put in adjectives like zettabytes and so on that people don't know what they are and I have to look them up too. So they are like, you know, 10 to the 21s and 10 to the 23rds. Numbers that we were only using for like, you know, numbers of stars in the galaxies and so on, now are things that we actually have to think through influencing the business and technology choices that we make. So I'd like to give you two concrete examples of areas in which we are applying some of these ideas and the role of information technology in addressing how to optimize. And I choose that work carefully. Energy systems and create the optimized utility of the future. Why optimization? Because it's a business imperative. Those trends that I was alluding to at the beginning. To be able to invest, right, in creating this utility future and the future grids in a context of the product being created, not growing significantly. In the context of a high degree of uncertainty, there is no other avenue that to in a way sometimes do more with less, to optimize. And to be able to use some of that optimization extraction to invest back. So I'll give you two concrete examples. So the first one I'm going to start is what is some of the most interesting ideas at least in the United States around how do you optimize energy as a system? Many utilities across broad areas. And there's a fascinating project that we're involved in called the Pacific Northwest Smart Grid Regional Demonstration. It's part of the DOE projects that were created during the American Recovery and Reinvestment Act, you know, the time of the crisis. There was $5 billion allocated for smart grid projects. This is a set of demonstrations. This is the largest of that demonstration project. It's a $178 million project involving 11 utilities across five states in the Northwest of the United States. And the big idea here is something called transactive energy management. And here's the idea. The idea is we need to introduce two new signals into the grid. These two signals are going to allow us to create a very distributed overlay of information that is going to utilize a cost-based economic signal for distributed control. So it takes advantage of the following trend. The trend is that there is embedded intelligence on almost everything. Of course, from a fancy substation to an electric vehicle to even your fancy toaster. There is some level of microcontrollers or microprocessors embedded in there. And there's enough computing power distributed to the entire network that they could receive signals that do two things. One is an incentive signal. The incentive signal reflects the true cost of electricity at any given point in the network. So when you're in the generation unit, there is a cost of electricity that can be imputed at that cost, at that node. As it flows and goes through it, that cost signal can be modified. It's a time series. It spans two days or three days. It can be in eight-minute intervals or 10-minute intervals. But basically, it just says what's the cost at any given point in the network. The feedback signal, it's another time series that flows in the opposite direction. And this is being generated by any given node in the network. And it basically says how much am I expected to consume over the next two days or three days? So if I'm that electric vehicle, I may say I'm going to consume a lot right now, but not a lot in the next eight hours and so on. And those updates get generated so long as you're connected. The idea is with those two signals, you can do very sophisticated things. So let me give you just a concrete example on how this may work and then generalize it for a second. So imagine that you have this overhead line. There's a transformer. There is three houses, three electric vehicles. The red line that you're seeing over there at the top left of the graph indicates the capacity load factor of that transformer. That's the maximum amount of power there. So what we're going to do is on those charge stations and in the transformer, we're going to make those transactive nodes, meaning those nodes now receive this incentive and feedback signal. So the first EV comes in and he's a flexible load person, meaning I like to charge now, but if I have to charge later, I can do that too, as the preferences have set up. So at the beginning, he connects and below the red line, no problem. I'm connected. I'm charging my car. The next one is a Tesla driver. He says, I only want to charge immediately, no matter what it costs. Under any circumstances, I want to charge. So what happens is that in this case, the signals are being sent, he's charging and you notice that we've reduced the amount of charging going into the first one, the one that was a little more flexible. We keep going and a third one, which is a very frugal electric vehicle owner, comes in, charges and because he's extremely cost sensitive, no amount of charging can take place right now in this context. But as the day goes on, at some point, his needs are being met and so on. Now, there's many different ways to solve this problem and I'm sure a variation of this is being done with electric vehicle programs here. But what is interesting is that this is a method that it's addressable for electric vehicles, but it also is going to be used across entire networks of systems. So this is the range of the project in which these five states are involved and it's all sorts of experiments from renewable integration to electric vehicle to demand response to at home heaters and these signals are actually expanding many utilities within utility going through sub branches and connected across broad regions. And I'm not going to go into details of what is here, but I just wanted to be able to go into the message that it's a framework that goes from something as simple as doing demand response to something as complex as balancing supply and demand at the level of a system operator. And the key thing is as you have increasing uncertainty on the generation and the consumption, these new signals are going to become central to enable the use cases and to deliver optimization. So let me give you now switch gears and give you a second example and now address it in the context of a single utility. What does optimization may look like in there? And this brought up an interesting point around how do you actually do the innovation itself? How do you bring up these ideas? How do you test out these ideas? And we became convinced that we needed to connect or create a new vehicle for collaboration a number of years ago and that's what led us to launch the Smarter Energy Research Institute. I'm going to show you a very short introductory video of this and then I'll talk about some of the ideas we're pursuing. So let me share a bit the agenda and the mode that we work together. The idea was to bring power engineers and mathematicians and computer scientists working together side by side to solve some problems that were long-standing and some new problems that were novel and approach them in different ways, right? So we engage a lot of dialogue with different utilities around the world and the first thing we had to do was to construct an agenda. Where were the area where we thought, right, advancing this idea of the utility of the future that we're more ripe to create benefit and value? And we settle on five areas. We settle around minimizing increasing reliability, minimizing outages. Now, as far as I know, you have an excellent, you know, reliability metric here, but I can tell you in the area where I live, I don't have an excellent reliability metric. I've been out of power to say the last two years, once eight days. The year before was six or seven days. And, you know, that happens in many areas now also in the world that have overhead lines and, you know, is one of also the impacts that you have increasingly violent storms. They knock down power lines and that's a big problem, right? A second area was, okay, it's a very asset-intensive industry. How could we manage assets much better? Again, to generate some surplus capital that can be used to do drive investment back into things we care about, that society cares about. Integration of renewables and distributed energy resources. Wide area situational awareness, right? How do we have a real estate, a real-time assessment of the grid using technologies like synchrophasers and so on? And then the last one, the participatory network. How are we going to transform their relationship with the consumers, right? And what active role are they going to be able to take? So, our approach on this was to say, let's take data that we already have, not data that we could have, but the data we already have, and try to reimagine some of those problems by building actual algorithms and applications that we can test and validate and prove. So, the institute is constructed around not doing studies, but creating working applications that we can test in the context of the utility. So, as an example on the first one, on the outages one, so we're doing something that takes very high resolution weather forecasting that we have developed over a number of decades that, you know, do weather forecasting at the level of one kilometer resolution with very fine temporal resolution. And we couple that with a damage model, a statistical damage model of the impact that previous storm has caused on the utility. And from there, we link it to an optimization problem to determine how do you position crews to optimally respond to the damage that this storm is going to cause. And we do all of that three days in advance. And when you do that three days in advance, you can prepare far better to the damage that is going to occur. And that's what this application does. It basically tells you what is the optimal prepositioning plan that you can do with a lot of lead time. This is important when, you know, in the United States, for example, when you require mutual assistance, there may be crews that are not in your utility that you have to bring in and it's first come, first serve. So, it actually leaves a very significant value to them there. A second example, this is what hydrocobac we're doing. And this is, they are deploying these technological synchrofacers that allows them to measure the phase angles and the voltage across their transmission network. And this data comes at 100 millisecond latency. So, the data comes in and historically, they could only use it for when an event had occurred, some problem had occurred, they could then use it after the fact to say, what cost it? What analysis could we do? But the desire here is, could we use it in my control room? Could I use it in real time? Could I make decisions as the data comes in? So, this presents an interesting information problem because you don't have time, as all this data comes in, to put it into a database, analyze it. The data as it comes in, you need to analyze and create the analytics and the visualization in 100 milliseconds. So, again, an interesting problem from an electrical point of view combined with information technology to solve a very real problem, because in the end, these impacts their ability to export. And that's a huge business proposition for them because they're exporting electricity in the United States. I heard today, they might be interested here in Ireland to be able to export clean energy to the UK. So, the ability to be able to operate within safe margins and not, worst case, every scenario has enormous implications for the business value that one can do after the fact. In these cases, a project we're doing with Alie Anders, the largest utility in the Netherlands, and this is very interesting because they were saying, okay, we're going to instrument all our distribution network. But they do the analysis of instrumented distribution network, the secondary substations to have very visible in the system and it's very expensive. It's like 6,000 to 12,000 euros per secondary substation. So, the idea was, boy, I wonder if we could use analytics to instrument just a subset and have the equivalent of virtual sensors to have a similar level of visibility but with much lower level of instrumentation. And that's exactly the project we're doing there where all of a sudden we're providing visibility of the network with a fraction of the instrumentation cost in the absence of that. Huge savings from investment that they can then allocate to new things. This is a very interesting project. It's called WISE that we're doing again with HydroKabac and this is about hydropower that are incorporating increasing amount of wind. They're 98% hydro in Canada, in Quebec. And the problem we have is as they ramp up wind production and they want to increase their export capacity, they were worth casing their safety limits of how they operate their hydro plants. So here we introduce techniques of stochastic optimization to redo the unit commitment problem. And as a result of that, we don't have the worst case and we've demonstrated that under any scenario that we have analyzed, we're able to operate within margin and increase again the export capacity of their network. It has enormous financial implications for them. To be able to do optimization at the level of 3% gains and 5% gains. And lastly on the project on the participatory network, here we're asking in the Netherlands, we're trying to understand, and I'm sure you've done similar experiments, but we're trying to understand if you're going to do a marketing campaign or reach out to customers to do energy savings, how do you know who are the best targets? And so we've done very sophisticated analytics to first be able to understand who is sustainability conscious in the company, I mean in the country. From there, to formulate the problem as an optimization problem, you say, look, if I have a budget of 100,000 euros and I want to be able to achieve a set of expected savings and energy-wise, who is the optimal target that I should do, right? And to be able to dynamically adjust this as we go. So I'll conclude with a dialogue that I'd like to have as well, which in addition to the innovation agenda itself, of the projects we're doing, I think it's very interesting to think about the model of innovation as well. And we've lived this, I work in an organization that's been 60 years doing research, and through its history, we've had to reinvent ourselves many times. Sadly, in the United States as an example, many world leading research organizations have disappeared, Bell Labs or Xerox Parks, a shot of what it was, many of these interesting places. And that has been, we've had to evolve in our business model, and we have this model right now of the world is our lab. We're going to reimagine how to build smart grids or smart healthcare systems. We cannot do that within the conference or laboratories. That requires partnering with others. This institute that we created is an interesting example of doing that. And what I would like to be able to also have a discussion with you, these are all examples of active research projects we have around the world in collaboration. But I'd like to be able to discuss aid innovation climate also in Ireland in the field of energy. And I have some points of view, but I'd love to hear from you around what makes some places so much better at innovating in this space than others. And what would you envision a good model would be for innovating in the energy space, in the ecosystems that you have developed here. So with that, thank you very much for your attention.