Welcome to the Crypto Minute! In an SEC filing, Tesla said that Bitcoin's a good liquid alternative to cash. Trying to orange pill my friends. Oh no, Warren Buffett doesn't like Bitcoin. However, will we recover? When the market gives you limes, you make margaritas. Yuga Labs, the creators of the board API hot club, dropped their land deed, NFTSA, also known as Eather Deed. Now it will be known forever as the Great Gas Wars 2022. Solana went dark for seven hours. Solana, business hours have now resumed. Would you like to start using our blockchain again? Turns out all you needed to build generational wealth was to me and us domains. In Cuba, 100,000 people are using crypto in response to US sanctions. Crypto.com reduced their card rewards substantially. Argentina's largest private bank just launched a crypto trading feature. Twitter bots just keep growing. Coinbase used Bitcoin as collateral to borrow cash from Goldman Sachs. Red and green candles battling it out for Star Wars Day. Backends NFT marketplace opens its waitlist. Bitcoin will never let you guess its next move. Allgram scores a FIFA partnership. Keep stacking and I'll see you next week.


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Okay, if you Google for diversity, you get pictures like this. And it's a funny kind of diversity, because there are, actually, there's some skin color variation, but there's not that much actual diversity. And they look the same, they act the same. They don't have any different kind of, they don't have a fancy hairdo, they don't have anything special. So they are actually very, very similar. I do have a bit of a slight issue, one moment. Yes, let's start over. Yeah, long time ago, I was a young boy. And my parents had a philosophy that it's better to teach children to deal with dangerous tools than to tell them that I shouldn't touch dangerous tools, right? And I guess it worked, because I have still all my fingers. But this is the sprocket knife I got, and my sister also got one. And if you look carefully, you can see the problem with this sprocket knife. The problem with this sprocket knife is it has no corkscrew bottle opener. My sister's sprocket knife did have that, and I hated that. I was like six years old, so I didn't care about actually opening bottles. But I know a missing feature when I see it. So, and I knew it was silly, but still, but still. So, if it was up to me, I got this one. This one is hardcore. So that one is such a much better piece of equipment, because it can't do so much. Just look at it. It can do everything. I can't actually see the bottle opener, but I guess it's there. It must be there, right? So, in a way, the beauty of a Swiss Army knife is it can do anything. But if you try to do anything with it, it's horrible. It can do effectively nothing. If you open a can with it, it looked like the can was attacked by a shark. And if you try to chop a chopper, you can't even hold that thing. So, they are actually kind of silly. But at the same time, there's sort of a poster child. A poster child for feature bloat. For features to have because you want to have it. Not to actually do the thing. That's like boring. So, they seem to be very much into the feature bloat. And we, as industry, as software engineers, we make a lot of Swiss Army knives in our field. And it starts like you're really focused. You want to make it specific, too. You have really tight, tight specifications. You build it, and then the feature requests flow in, and you add it, and you add it. And while every feature is useful, it kind of dilutes all the other features. Because it will be a little bit less suitable for all the other stuff. So, at some point, it is nearly, it's like a Swiss Army knife, in the sense that it is actually good for nothing. So, a similar way of looking at this is this expression. We all know this expression. If all you have is a hammer, yeah, everything looks like a nail. And usually that is used to indicate that you should use the right tool for the right job. But it actually has a bit of a more profound meaning. It means that your thinking influences the choice of tools. But your choice of tools also influences your thinking. And if we move over to, if all you have is a Swiss Army knife, where do we end up with this? Because it does not really translate. But my theory is, well, it's not really a theory, because I've lived it. You end up with wanting a bigger Swiss Army knife. You always want more. And single-purpose tools like a hammer don't have that problem.  Well, you can, but not in an elegant way. So, you're this kind of inevitable that you have this feature blown. And at some point you end up with something that is really silly. So, I'm actually, of course, not really talking about Swiss Army knives. I'm talking about databases, our old friends. They are everywhere. And we grew up with them. And if I could start with some history, when we started building applications, it turned out that storing data reliably and quickly is hard. So, we didn't want to do it over and over again for every application. So, we have a generic database that can store data. And we just take it, we can take that database and we can build our app around it. So, what happened then is that over time, those database turned into really expensive flagship products, like IBM, Hedwan, Oracle, all those databases. And they tried to pack in the features because they want to be the best database. And they still do, they still add more and more features. And the average application does not use even a small fraction of them. So, feature bloat, even there. So, what I want to argue today is that it's time to let that feature bloat feeling go. And to really focus on what is really what I want to do. Because in many ways it's so much nicer to have a single purpose tool. So, with that I'll start to introduce myself. I am Frank Leroux. I'm a bit of a technology hipster in the sense that I like to use new and shiny technology just before it's actually practical to do so. And that's what my colleagues around me always say. And I kind of know, but it is a strong motivator. Because if I want to do something with something new and shiny, I work hard. If it's boring, I don't. So, it is a human weakness, I guess. So, if we look at the traditional stack of application. Something like this, a database code in some kind of language and some kind of client. It's there must be millions of those out there, right? So, and if we are talking about those applications and we talk about the code, we talk about the database. We store it in the database, we get it from the database. And I think that is wrong because that's Swiss Army knife thinking. We need to get rid of that notion that there is one place. We put everything. I mean, it's a very compelling to do that because then you have everything in one place. But there are also reasons not to do that. And I want to talk about it here. So, a stack like this kind of derailed in the sense over the years because it got more and more difficult to change code. And for that, we invented the holy microservice. I'm not going to dwell here long. We know what microservice are, right? I'm going to... Okay. So, microservice, the idea of a microservice is that we slice a bit of our application and we only access that application through a networked API. Within that application, we can choose whatever language and technology stack we like and we can use any database we like. And what's in there is just a technical detail and we slice up our application so that we have all kind of blobs of microservices and internally, we can swap out different databases. We are much freer in our technology choice because it's not one big thing anymore. So, the application would look like something like this, right? So, we have here one server that uses Scala and a GraphDB, one that uses Node and a DocumentDB, and one uses Java with a nice old SQL database. So, that's completely fine and that's like what microservices promise, right? You can always pick the right tool. So, but if you look at presentations about microservices, mostly they end here. They say, now everything is good. And I believe that for a brief while. Because after that while, I started thinking about my day-to-day problems and then I noticed that they kind of avoid a certain problem. And that's that different parts of the architecture need the same data. But in a different, they look at it differently, but it is the same data. We need to share that data between the microservices, but the microservices are only API calls. So, that's not actually that easy to solve. Because if we look at an example, so we have an application here, for example, has a SQL database, has some code in the language, but it also has a part of analytics that does some aggregation or something about that database. And we have a UI and we have an analytics UI. Fair enough. So, suppose we want to go into microservice land and we want to split this. So, for example, if we use a microservice, we can use a different database. Maybe SQL is not that practical for analytics. So, we need to use a different database. But, that's also an example, an advantage story, is that we now don't burden the SQL database with when we use analytics but it has its own database. But there is a problem here, and that's around here in the middle. Analytics API. What's an analytics API? I made it up, but it's what you need to get the data over there, right? Because analytics is not something that's a small piece of data. That's usually you need everything.  Technically works. In practice, I don't really see that happening because it's basically what batches. But it is very much of a burden to the source database. And it is, if you look at this, generally you could say that we should have left it in the original service. So, you can also go more granular and that you can just query specific pieces of data with a list data also fine, but you have the same problem. You are querying a lot of data and actually the performance might be even worse. So, can we do this? Is this fair? I mean, it's a nice attempt, but it's not really a microservice in the sense that we are not abstracting away the database that's inside the original service. We're exposing the guts of the database all the way out. So, this is cheating, even though it will work this fine. But then again, you could also have left it in the original service. So, what we end up usually doing is that we have the two services and we have some magic. We have some tool that replicates the data from one into the other. So, those tools you probably know, like DBVisit, like Golden Gate from Oracle, usually expensive. And what they do is they take the data from one database and they put them in the other. Which is a valid thing to do, but that leaves out the fact that we might not want to use a SQL database here. And that's pretty limited. There's some leeway you can tune it a little bit, but generally you need to put the data away in the same shape you found it. And that is not good for analytics, generally. So, yeah. Another problem is, I addressed the first two, the last one, this doesn't scale. If you have two services, it's fine. Pretty okay. Four. And the rest, no way. You'll just spend all your time pumping data back and forth. So, there is a model that works. Event-driven microservices. Who knows them? Good. Good, good, good. So, event-driven microservices have really gotten a lot more popularity. Like anything new in our industry, it's nothing new. It has been around for ages, but now all of a sudden people talk about it. So, what's it about? Instead of having services that ask each other things, we have services that tell things. They push events. They say, this happened. And they listen to topics. And they hear what other services said. They don't care who said it. And they also don't care who listens to what they say. So, it's a bit more decoupled. And it's generally backed by a public subscribed bus. So, how would it look in our example? So, in our main service, we will just listen to all the change in the database. We will push a message onto the bus. And it will just post it there. And the analytics will just listen to all the changes. And it can build up its own database in whatever form it wants. Yeah, so that's basically what we do here. And this scales very nicely because you can plug in something else that also listens and does something else. But neither of these two need to know about that. So, that scales pretty nicely. So, for that bus, we generally use Kafka. Who have you heard of Kafka? Everybody. That's good. Wow. Nearly everybody. So, really quick, Kafka. A persistent message bus. So, if you post a message, you'll probably not lose it. In that nutshell, you can put a lot of messages through it. Thousands, millions per second. An important point compared to some other buses is that it's okay. If there are some fast clients and some slow clients. So, some consume it quickly. And some might take a week. And that's okay.  So, you just have, if that analytics part gets behind for a day, well, so be it. It's not that bad. So, we use this for our clients. It's a small company. We work for sports associations in the Netherlands. It's around for a while. It's not exactly a green field application. We have a lot of regrets in our code already. We've been around 10 years planning competitions and everything. And all the stuff around matches. We have about a million players in mostly like football and handball and some others, 4,000 clubs, 40,000 matches every week. It's a spiky load, but it's predictable. Our traditional stack was an Oracle database with Java-based application servers and a whole bunch of clients. Nothing special here. So, we wanted to move from a club centric that the clubs did all their changes on our database to a personal level. So, instead of the 4,000 clubs, the 1 million players were our clients. So, that kind of implies quite a big upgrade in the amount of traffic we're going to get. Oracle was not going to scale. Or maybe it is, but we don't have that kind of money. So, we need to do something like this. So, what we started with was this. Exactly what we talked about. We have our Oracle database. We have the changes. And we have the Kafka bus. And here we insert those messages into a MongoDB database. Because that one scales a bit better. This was really easy and worked really well until the client applications started using that database. And they informed me that that database really, really sucked. Because we are using a relational data model, we copied one-on-one to a database that can't do joins. And that hurts. Because that's assumption of a relational database, that you can join all the data you need. The advantage of a document database is that you can put a lot more in a document, but we didn't do that because we were copying one-on-one. So, that's also not really great. So, we had a bit of an issue there. So, if we take this as an example, you have a SQL. You have some persons. You have some phone numbers and communication things that are associated. In SQL, you would just query both in one query. No problem. In MongoDB, you would want something like this. That you put the phone numbers, et cetera, inside the document. But to do that, we need to join those change streams from two different tables, because we don't... We need to join them, but we don't know when those change are going to show up. So, there might be millions here and millions there, and we need to join the right one. And in the meantime, we need to remember everything that happens there before we can join them and then continue on. Yeah? So, we can use Kafka Streams for that. That's a streaming engine, basically. And that does that. But it's a whole lot harder and heavier than you might think, because basically, you need to store everything that happened, and Kafka Streams uses RoxyB for that. But that's a huge beast. There's a lot of data there. So, it's actually a lot harder than it looks. And this is just one join. And if you have more joins behind each other, the documents get bigger, the load gets heavier. So, it's actually not a trivial thing to do. Easy as it sounds. So, if you look at our specific case, we have, like, half a billion rows of SQL data, but that turns into some pretty serious amounts of data to move around. And if we want to build that entire data set to our replicate set, it takes hours or days. So, once it's warmed up and every pending change is gone, and then it will just process all the messages, then it's fine, then it will be quick. But before that, it's a lot of work. Also, when developing stream transformations, it's harder than you're used to when you're just doing stateless code. If you're doing something stateless, like a web page that gets something from a database, you can edit your code and you press F5, and then you see if it does what you expected to do. And you can repeat that. But for this, if I change the code that creates a new set, actually, I should really redo the whole batch, right? So, that's not a very trivial thing. So, you can cut some corners, you can have nice small data sets, but it's a whole lot more work than when you are used, when you're developing stateless code. So, we went in production the day before yesterday. So, it was quite an effort to go here and prepare to say how great it is before you actually know. It behaves well. I lost quite a bit of sleep, but it works. Kafka Stream was in a really rough state when we started, and now it's much better. A problem is, though, is that the average database nowadays is not really used to being a team player. Like every database expects, I am all you will ever need, and I will do everything for you. So, that translates in the fact that if you want to capture all the changes from a database, for example, a SQL database, how do you capture that? It's possible. Every database has a way in to capture the changes. You can use triggers, or you can tail the archive log in Oracle, but it's never really an API. You only have to fight for it. And to stream the data in is also not something they really help you with. But now we've done the hard part. Once we have this infrastructure in place, we can start using fund database, because we have all the streams of data. We can grab some random database of the street, plug it in, and see what happens, because that has gone pretty easy now. So, I'll go through a small parade of databases, which might make sense. For example, Elasticsearch. We feed a lot of our data now into Elasticsearch, and that gives you the ability to search in an unstructured way. Something like SQL doesn't really like searching in an unstructured way. It can do it if you really force it to, but it doesn't really work that smoothly.  And, often, users nowadays, they expect to be able to Google for anything. So, they want to Google-like interface within an application. You just write down some random words, and it will somehow find what you're talking about without saying what it is you're specifying. So, users kind of like that. Neo4j. Graph database. Graphs are amazing. It's really like a bit of a rabbit hole once you go into graph database, because most of the graph database people are a bit weird in the head, but they do really powerful things. And some analytics, for example, are really easy to express in graphs and not so much in relational database. For example, if we want to know how often you play against another person in another team, if you think about it as a graph traversal problem, it's not that hard. If you write down the SQL statement, it's two pages. So, even if it's not exactly something you couldn't do before, it's so much easier. Firebase. Who knows Firebase? Nice. It's sort of a back-end as a server. It looks like it's basically a JSON file in the sky. You have like the model of the database. This is a JSON file, and you can insert stuff in different paths inside a JSON document. And there are some really powerful client libraries that you can subscribe to certain parts of the file. And when you change it, your webpage will change or your mobile app will change because all the notifications will be done for you. None of these databases makes sense to use as your only database. Maybe for really simple cases, but generally you don't want to put your whole enterprise business in a JSON file in the sky. But for specific things, if you want to publish something, these things make it a whole lot easier. And now we have the time to do it. Another way of looking at it, sort of the inverse way of looking at it, if we take all the infrastructure we have now with all the database we've plugged in, you could just draw a box around it and give it the name, and then you have a database. And that's called a multi-model database. Microsoft is a nice one that just came out a few months ago. Cosmos DB. It's too early for me to tell if it will go anywhere, but it's a really interesting take on where we are going with databases. And maybe that makes a lot more sense than the stuff I said, but I'll believe it when I see it. So in closing, I have argued today that databases are better as a team than as an individual. And you need to realize that going from a database, a single database to a team of database is no joke. There is a lot of pain you will endure on the way, because you go to a more eventual consistency model that will hurt. There is a lot of data moving around, and you just need quite a bit more power, but it is worth it. And also, it is really nice to use the right tool for the job. I mean, if you really can write except Cypher query in Neo4j to exactly specify which graph traversal you will do, and it works, that is so much nicer than just trying to make that database do what you want to do, and we're struggling and struggling, and at some point you'll succeed, and you won't really enjoy the victory anymore. And I would also like for you all to check how much the use of the tools you use influences how you think, and you can only do that by trying different tools. If you always use only SQL, you will think that SQL is all you need. And if you start playing around, you'll get a lot more input in what makes sense and why. So that, and finally, the last thought I want to leave you with is that nobody uses a Swiss Army knife professionally, right?  A chef in the kitchen won't use a Swiss Army knife. Nobody uses a Swiss Army knife. I am pretty sure that even soldiers in Switzerland don't use a Swiss Army knife. So in that respect, why should we use a Swiss Army knife? And that's all I got. Thank you. Thank you, Frank. We have a whole lot of questions and discussions and all sorts of things happening on Slack. I'm going to try and choose a few good ones.  If you were sort of starting that from scratch and you had the choice, what would you use instead? And specifically, somebody asked, for a small scale web app, what would be your database of choice for that role that's not feature bloated and performs well? I think I would go with MongoDB now because it has done everything I wanted from it.  They all look great on paper. So you need to feel it a little bit. But in general, you will always have a situation where you have made choices and that might be right and might be wrong. But you always have to go on and deal with your choices. Okay. It was a question asking whether you looked at existing solutions before you sort of rolled out your own version with the Kafka screams and stuff. For example, Datomic. If so, why did you reject them? I don't know all of them. Okay, obviously. But we had that solution in place. But the fact remained that we were... that we really wanted some more freedom, actually. And also, yeah. So most of the database solutions are point-to-point. And we wanted... Once you get that change stream into a Kafka system, you can do anything you want. And I believe, and I still do, that that investment was worth it. Yes. So once the database is set up and you've got data streaming in, all of that makes sense, what happens then in terms of adding a new service, loading all of that data in? Is that a big ETL job? And also, if you change migrations on those databases and want to redo things, do you put things back into the streams? How does that work? Yeah, so I glossed over a whole lot of things. Of course. But generally, Kafka can hold a lot of data. You can hold terabytes just inside the streams. So there are some tricks to make the size of data not completely go berserk. But apart from that, you can just put everything in a queue and you just restart the processing of the queue. And depending on the database, it will refill. And it will put some load on Kafka, but not so much on the source database. That will be unaware of this happening. Okay. How do you, or can you, write integration and end tests across this whole stack to test everything's working? That's difficult. That's difficult because of eventual consistency. That makes pretty much everything difficult. But what we do is generally just periodically push changes in a certain table that don't really are semantically important. And we check how long that change takes to end at the other side. And with that, we can monitor if there is any lag. Okay. This is an interesting question. So I'll read the whole question. You've answered the first bit. Does Kafka store a log of all the events, which I think you've said? Yes, it does. Yeah. I mean, there are some tricks there, but yes. Yeah. Does that then mean that all of the actual databases are effectively a projection of the Kafka streams and that your Kafka streams now become your single point of truth? Yeah. You could say that. I don't see many people who are so brave to remove the original database because you technically could do that. But, yeah, that's basically the, this is called event sourcing that you just really think that the events are the thing that's important and you can create anything out of that. And that's like the other approach to take to this subject. But I started from the more practical thing. You want to replicate data. But yes, that's definitely true. Awesome. There's more questions on Slack, but I'm going to leave it there for now. But please do dig in and join in. It's been great. Thank you very much. Thank you.


I'm Cameron O'Neill, director for Redal in the Asia Pacific region. I'm here today with Patrick from Tech Sound, which is one of our customers over in China. Tech Sound has previously done a lot of work with corporate customers, with music events, and now is moving into the exciting world of eSports. So Patrick, coming from a corporate AV background, a musical AV background, what are the main differences for an audio company or a technology company moving or thinking about moving into eSports? Well, I would say the number one issue is player latency. And as well as how do we recreate an environment on a stage that is just as good, if not better than the environment that the pro players are used to training under? Yeah, because these games were all made as a solo experience, something to do in your home, not something that's going to be brought up onto a stage or onto a broadcast. And yeah, and I think that adds an additional challenge as we are required to throw the players into a state of isolation from say the audience or the commentators, which we call shoutcasters in the eSports world. And that would be even harder when you've got maybe a sport or an event, a game, where you have a fog of war, something where you can't see the whole field or you're in a first person view where you can't necessarily see everyone else. You don't want to have interference from audience students. Absolutely, and I think that's what makes eSports very interesting because you have so many facets in the player and the audience's reality that do not necessarily exist in our reality. And I think a lot of people are jumping on this as well. I think Google just announced their stadium project. And what Google is trying to do is very similar to what Netflix did for television by implementing interactive online streaming of games. We are essentially creating a path for players that would normally have to spend a lot on gaming rig to get into gaming and anything that increases the number of audiences that are, the number of audiences that's interested in eSports is definitely good for our industry.  So they're lowering the barrier to entry really. Absolutely. It's like the industry is already disrupting itself only a couple of years in. I mean, you've already got the technologies like AR, VR, XR, augmented virtual and extended reality. Are they impacting your world? Well, what we're seeing is AR is certainly being used in eSports in a very similar way that is being used in traditional sports. In traditional sports you would use augmented reality to draw lines on a field that only the online viewers could see. But taking that a step further, what you can do with AR and eSports is you can actually go as far as to recreate characters from the game in the audience reality. Yeah, so it's going to hit that holy grail of increasing the audience, not just the experience, but also their engagement with the sport and with the players. Yeah, some games are even going as far as allowing players to use their PCs to spectate on the game inside the game.  How do you further immerse the player into the sport? You become your own cameraman on the field of play. And with a point of view that you'd never get in a traditional sport. Cool. So, Redal and Tech Sound we've been partners for a couple of years now. We are definitely a more traditional broadcast manufacturer. How do you think those technologies work together and what's the kind of advice for the technology manufacturers out there? Well, I think all the technology manufacturers in Pro Audio really need to be ready to implement this consumer game technology into our pro world. With Redal in particular, although with the broadcast side of things not much has changed other than the ability to use a computer as an in-game camera. However, on the pro player side and on the audience immersion side while we are currently using the equipment, not how it may have intentionally been designed to be used we would certainly be excited to see more products that are specifically designed for this application. Yeah, I mean you said before, you know, we talked about that self-guided camera. I don't think it's flat screen and stereo sound that's going to work there. You're probably going to want something else to enhance that experience. Yeah, to take that even a step further, you know, the real question to ask is what kind of products are going to be able to take say the HRTF audio from a game in a first person shooter and translate that into spatial audio? Yeah, I mean if you have a thousand people watching a game that could be a thousand different points of audio and trying to make a thousand 5.1 mixes isn't going to work. You need to take a spatial audio thing and have that process for each individual user. Pretty exciting, isn't it? Yeah, it's good fun. Looking forward to it. You too. Cool. Patrick, thank you very much for joining us here and hope to see you in the future. Thank you camera.


In 1944, during the Second Battle of Guam, America regained the island of Guam from Japanese control. Today, thousands of locals and visitors took to the streets to celebrate their liberation and the continued colorful culture of the Chamorro people and those who call this beautiful island home. For the first time, Third Marine Division, based out of Okinawa, Japan, joined in on the festivities. I'm proud of my Marines. I'm proud of the Marines who currently serve. And all those who went performing, you know, all we can do is ensure that we are walking the same path and we are carrying the history and traditions from which they are set for us. For the people of Guam, Duncaloon and Sejuice Mossy, and we look forward to coming back and celebrating Liberation Day here in Guam for years to come. I'm Petty Officer Amanda Kitchener, Naval Base, Guam.


What's up guys so in this video I'm going to showcase how you can set some custom title presets using Apple Motion. Now what this means is if you've downloaded and installed in Final Cut Pro a whole bunch of custom title effects then you probably make some edits to them each in every video that you like to reuse every time and it can become quite a hassle if you have to make plenty of edits to the same title every time you do some edits. So I'm going to showcase how you can save them as presets so the next time you do your edits you can save all of your changes to the attributes you make to any of the titles using Apple Motion so that next time you do some editing you can just do a simple drag-and-drop and all of your saved preset edits will always be there. Now just a quick disclaimer you will need to download Apple Motion for this and that is not a free application so this one costs me £49 you can download this from the Apple Mac Store. So let's dive in I've got Final Cut Pro open here and I've just added a stock video as an example so let's say every video that you edit you want to put some information about your social media. Now I'm going to do this in real time for something that I would like to use as a preset going forward for my videos so I'm going to do this as a permanent thing whilst I showcase to you guys how to do it. So I've got this set of titles that gives you loads of different social media lower thirds. Now there's one that's not in here which is for TikTok. Now I'm just going to grab one that I don't use like this Twitch one for example and if I quickly just add that to the timeline to showcase to you guys how that looks as you can see it comes out you can change the text to your social media handle and then it disappears like that. So let me get rid of it from the timeline what you would need to do is from the title library you right click and do open in motion. This is where you'll be able to do one of two things you can either save and overwrite the existing title as a permanent change or you can do a save as and duplicate it and save it somewhere else in the title library in Final Cut Pro. So now that I've opened it into Apple Motion I can make all of my changes and edits to this in here and that includes the text the logos the colors and the animations. In this tutorial I want to be showcasing how to use Apple Motion that's something that you guys will have to learn offline and I want to be covering that in this video so you will need to have a minimum basic understanding of how Apple Motion works to do this but I will showcase to you guys anyway some simple changes. So the first thing I will do is change the username here. So I'm going to match this with some of the previous title effects that I've used so I'm going to be creating one for my TikTok. So I will put the handle like this. I'll just make some other changes as well. Next I'm just going to replace that logo with the TikTok logo so I'll just place that within the circle and I'll add a quick animation just to make it look a little bit nicer. So again this is something that you would need to learn separately and that won't be as part of this tutorial but just to showcase to you guys how that works. So let me go ahead and finish off making all of the different edits and changes that I like to for this specific title effect and I'll showcase how to save it. So there you go. That's all my changes. So as you can see this is the final version. It's very clean and it was pretty straightforward and simple to do. So now I'm ready to save this back into Final Cut Pro. So all I need to do is go into File, Save As and then I will give it a name TikTok Lower Third and then I can save it in one of the categories or the folder names in Final Cut Pro in the title library. So the one where this one existed as Twitch was based in Social Media Lower Third so I can save it in there or I have the option to save it in any other title library. Let me go ahead and save it back into the original one and then click Publish. Once that's done, you can now go back into Final Cut Pro and then underneath Social Media Lower Thirds, you should be able to see your latest publication. Let's look for TikTok and there you go. It's right there. So now all I can do from this moment going forward is just do a simple drag and drop and that will be set for me to use every single time like so. So that's how you can actually make customizations to any custom third party title animation effects and have those saved changes as kind of like a preset in the Final Cut Pro title library or using Apple Motion. So I would definitely recommend if you've not used Apple Motion before just go through some basic tutorials on how to change things like text and colors and logos images that kind of thing. You can dive in a little bit more deeper around animations which is a little bit more advanced but if you want to just make some basic edits then this is how you can do it. So that's it guys. Pretty straightforward. I'm going to now use a lot of my presets that I've saved using Apple Motion in all of my video edits. It's saving me a lot of time. Any questions around this? Do drop a comment down below. If you did find this useful make sure you give this video a huge thumbs up. Make sure to subscribe. I'll do Final Cut Pro tutorials from time to time. I also take recommendations so if there's anything you guys want to see in Final Cut Pro drop a comment down below and I'll see if I can help you guys out with a future video. Other than that thanks for watching guys. See you at the next one.


Greetings and salutations. This is Monica Wahee, your library college lecturer bringing to you chapter 4.1 scatter diagrams and linear correlation. So here's what you're going to learn. At the end of this lecture, you should be able to explain what a scattergram is and how to make one state what strength and direction mean with respect to correlations and compute correlation coefficient are using the computational formula. And finally, you should be able to describe why correlation is not necessarily causation. So let's jump right into it. First, we're going to talk about making a scatter diagram. And the thing on the right side of the screen is not a scatter diagram, but it's kind of scattered. So I put it there, it's kind of pretty. And then next, we're going to talk about correlation coefficient are and how to make it. And then finally, we're going to do a shout out to causation and lurking variables, which remember we talked about before, but we're going to talk about them again, in relationship to are. So let's start with a scattergram. And I also call it a scatter plot, because it's like everything in statistics, there's got to be about eight names for everything. So scattergram and scatter plot mean the same thing. So let's just get with the setup here. So scattergrams or scatter plots are graphs of x y pairs. So what's an x y pair, x y pairs are measurements, two measurements made of the same individual or the same unit. So if you measure my height and my weight, that's an x y pair. If you measure my height and then my friend's weight, that's not an x y pair, because that's two different people, right? So these x y pairs, the x part is called the explanatory or independent variable. And it's always graphed on the x axis. So remember in algebra, you would do these graphs, where you had this vertical line, and that was the y axis, and this horizontal line, which was the x axis that I always had trouble remember, which was which, but that's how it is. And so whichever acts, whichever of the pairs is x, expect that to be graphed along the x axis. And it's also called the explanatory and or independent, remember, there's got to be million names for everything, explanatory or independent variable. So if I talked to you and said, here's an x y pair. And this one is the independent variable, or this one is the explanatory variable, you need to like just secretly know I'm talking about the x of the two. And then surprise, here's the y of the two. And the y is also called response variable. It's also called the dependent variable. And that is graphed on the y axis. So again, like I said, I used to have trouble remembering is the vertical one, the y axis or the horizontal one.  And that's how I remember that's the y axis, it doesn't hurt the y it's used to that. So if you can stretch the wise tail down and you get vertical, remember, that's the y axis, and then the other one is the x axis. Okay, and then also you have to find a way to remember which one means what like does x mean explanatory and independent or what or does it mean response independent. So how I do it is, you know how we sing the ABCs ABC, D E F G. Well, if you fast forward, the end is w x, y, z, right? So the x comes before the y, you know, in the alphabet. So I do x, and then an arrow to y. And then I imagine in my head that's saying x causes why even though and doesn't necessarily cause wise, you'll see at the end of this lecture. But I think about it that way. Because if that happens, then why is dependent on x and x is independent, it can do whatever it wants. But why is dependent? So that's my way of remembering x is the independent variable and y is a dependent variable. So anyway, that's a long way of saying the scatogram is a graph of these x y pairs. And that's what we're going to do is make that graph. So we needed some x y pairs, right? So I asked the question, do the number of diagnoses a patient has? Does that correlate with the number of medications she or he takes? So if you don't have that many diagnoses, you probably aren't on that many meds, right? But if you have a lot of diagnoses, you should be on a lot of meds. But we all know people in a real life can sort of violate that just depending. I mean, you could have one really bad diagnosis with a lot of meds, or you can have a bunch of diagnoses that are all taken care of with one meds. So it's not perfect. But this is kind of a reasonable thing to think. So what I did was I put up here just for x y pairs, as you can see, so I got four pretend patients. And you can see here's the first patient, that person has an x of one because they only one diagnosis but like I was saying, must be a bad diagnosis because that person has a y of three years on three meds for it, right? So that's how you read this table. So let's start making our scattergram out of these data. Okay, so here we go. So I labeled the x axis number of diagnoses, right, just to keep things straight, and the y axis number of medications. And then you'll see where I put the dot right, because x is one, I went over to one number of diagnosis, right, one diagnosis. And then, because why was three, I went up three to this three, right, and there goes the dot, that's where that first person gets a dot. Okay, you put it there. And that's what you're going to do with these other ones to is put dots. Okay, I just threw all the dots down so you can kind of see what was going on. But here's the second person, right? So that person had an x of three, so I went over three. And I just put those green arrows and just so you could see what was going on, they're really not part of the scatter plot, it's just more like, like, cheating, you know, to show you because we're just practicing, right? And then that person so had an x of three, and then a y of five, and you see where the dot goes, right? And then here, you can see where the fourth got dot, or I'm sorry, the third dot goes because there's a four and a four. And then here we have the fourth dot. So this is the scattergram of these four patients, of course, a lot of times you have like hundreds of patients in there. But I just showed you the simple example. Okay, now, because we did that, I can talk about linear correlation, and you'll kind of get it, right, linear correlation, that term means that when you make a scatter plot of XY pairs, it kind of looks like a line. Now over here on the right is not like biology, that's not like statistics, that's like algebra, right? Because back in algebra, you'd have these perfect lines where the dot was right on the line and see the x and y notice, there's no diagnosis, nothing that's algebra, right? So perfect linear correlation looks like graphing points in algebra. And if you actually make a scatterplot of like people XY pairs, and you see that, you should suspect there's something wrong. It actually happened to me once. One of our statisticians came to me and said, Monica, look at this, you won't believe this. I said, Whoa, I don't believe this. What are you graphing? And he said on the x axis, he had put the weight of every of the person's liver. And on the y axis, he put the weight of the whole person. And I'm like, I, how do you weigh people's livers? Like that sounds painful.  And what he learned was that you don't weigh people's livers, you use an equation to estimate the weight of their liver and guess what's in the equation is their actual weight. So I'm like, that's why I came out like on a line is because you were using the y to calculate the x and he was like, Oh, you're so smart for a secretary. So then I became an epidemiologist. But anyway, if you ever see this in biology, just suspect something fishy, because really, things just don't end up right on the line. But if they get really close, you can say it's close to perfect linear correlation. I just wanted to let you know that's what we're what's going on here with this linear correlation. Okay, so let's talk about facts about linear correlation. So things can be linearly correlated without being perfectly on the line. Obviously, our little thing was so if if when you make those dots, your scattergram, if you imagine a line going through it, if you imagine that the line is going up, like it kind of looks like it's going up, this is called a positive correlation. But you don't always have a line going up. So I want you to look at this and I made up these data to but on the x axis is the number of patient complaints. So as we go on, the patients are matter and matter, they're grouchier and grouchier, making more complaints. On the y axis, we have number of nurses staffed on the shift, right. And so as you go up, there's more nurses. Well, sure enough, when you got a lot of nurses, you don't have as many patient complaints, right, because they're being attended to. So this is what you would say is some people say inverse correlation. But in this presentation, I'm calling it a negative correlation, because as one goes up, the other goes down. And as one goes down, the other goes up, because and that's depicted visually with this line going down. So you see, you can imagine a line going down, that's a negative correlation, neither is better, you know, positive versus negative, it just explains how these things are behaving together, how x and y behave together. But then you can have situations where there's really no correlation, like x and y really don't have anything to do with each other. So as you've seen, you know, when you're, when you have patients in the hospital, some of them have really big families and those families come a lot. And some of them don't really have that many loved ones. So as you can see along x, here are total unique visitors, meaning you just count each person once. So you could have there, there's a patient who only has one unique visitor. But if you look at why day spent in the hospital, that person has been there seven days and that that visitor keeps coming, right? And then you have maybe a patient here, the second one, there's two unique visitors. And that person's only been in one day, but both those people have been there. Then you have people like a person with three unique visitors. And they've been in the hospital for days, right? And those are probably the same three people coming back. So it really doesn't matter how long a person's in the hospital, if they've got a lot of loved ones who keep coming, they'll keep coming or not, right, according to this correlation. So you end up imagining a straight line. And that's no correlation. That's fine to nothing is better or worse. It's just that you make the scatogram to try and understand how x and y are related. This is always fun. Like in books, they always make some sort of goofy picture. I don't know why they do this, I would never get a goofy picture, like they show in books about, you know, this, I made up the correlation, this is in the lobby, the number of the games in the lobby and the number of the books in the lobby, they should really have nothing to do with each other. But if you see something just way goofy like this, just say it's no correlation. I don't even know how I get this. Hi there. All right. So we've been talking about correlation, and it actually has two attributes. So far, we've only talked about one, and that is direction. We talked about positive, negative, and no correlation. So whenever you're talking about a correlation, you have to say what direction it is. But you also have to say the other thing, which is what strength it is. So now we're going to talk about how you figure out what the strength is. So strength refers to how close to the line, all of the dots, they fall really close to the line, it is considered strong. If they fall kind of close to the line, it's called moderate. And if they aren't very close to the line is weak. Now remember, that's totally different from what direction it is, it could be positive strong, or negative strong, right, could be positive, moderate or negative moderate. So this is just a statement, the strength is a statement of how close the dots you make in your scattergram fall close to the line that you end up drawing. So I thought I'd just give you a few examples. So look at this, I just made this up. This is what a strong negative one would look like. Notice how those pink dots are almost on the line. And this is a strong positive. Again, even one of the dots is on the line, right, not all of them, you know, or it'd be perfect, but it's never perfect. So this is really close, but it's strong positive. So strong just refers to the fact that the dots are almost on the line. Now this is almost the same correlation, but the dots are not really almost on the line. The line has to be fair and kind of go in between them, but they're kind of far away. And so just eyeballing it, you would say this is moderate. And here it gets weak. And mainly it's because the dots are more all over the place. But you'll notice there's one that's like right on the x axis. And then hey, look up there, like in the title, there's one up there, like way up there. And that's like an outlier. And sometimes when you get outliers, they can really whack things out. So even though this is a weak correlation, that line looks like so powerful, because it's almost basically connecting these two outliers. So you just got to be careful. And that's part of why you make a scattergram first is outliers can have a really powerful effect on the correlation, especially if it's in any of the four corners of the plot, like if you get a weird outlier kind of in the middle, it's not going to do as much as if it's in the upper right upper left, lower right or lower left, it can really affect the direction like like, you know, it's like a seesaw, or a teeter totter, you know, an outlier can get on and really change the direction of it. And it can also mess with how strong or weak the correlation is. So that's why you really want to start with the scatterplot. And that's why the way this chapter is organized starts with the scatterplot is you just want to look for outliers and also just see how x and y look when you plot them. Now we're going to get on to correlation coefficient are we're going to get on to computation and actually making a number. So you can not just use watery terms like direction, you know, positive negative, or moderate, strong, weak, to explain it, but you can actually put a number on how correlated x and y are. So remember the word coefficient, we did it with coefficient of variation, which is different. So the CV, you know, is one kind of coefficient. But what we're going to talk about is a different kind this time, our coefficient this time is called r. And just coefficient means a number, we just like to use the statistics. Now it seems kind of weird, because like, I'm talking about correlation, and people are like, Well, why is it our why isn't it like C for correlation? And I'm like, I don't know, I didn't invent it. But this is how you can remember, you can go correlation, correlation. So correlation coefficient are so just remember our means correlation. And technically, our means sample correlation, population correlation coefficient, right, like his, you know, imagine you're correlating like height and weight in the population, like all everybody in a particular state, you actually need a Greek letter for that. And I showed on the screen, I don't know, it's this fancy P, I don't know the right name of it. But we don't actually cover it in this class. So I just wanted to show it to you, we're only going to focus on our which is the sample correlation coefficient. So what is our well, it's like I said, it's the numerical quantification of how correlated a set of x, y pairs are. And it's actually calculated by plugging all of the x, y pairs into the equation, I'll show you how to do it. I'll, and you can see that if you do it by hand, if you have a lot of x, y pairs, it'll take forever. So I tried to limit that. And like, remember, standard deviation and variance, there was like a defining formula and a computational formula. This time, I'm only going to show you the computational formula, it's in my opinion, way easier to do. But it gets you the same number. Alright, so that's what we're going to do is we're going to take a set of x, y pairs, and we're going to calculate are. But then how do you interpret?  The R calculation produces a number and the lowest number possible is negative 1.0. So that's perfect negative correlation. So if we were like in Algebra, and we had a line going down, and all the dots were on it, then the R would be negative 1.0. But that never happens, right? So if you want to think about it is like if you have a negative correlation, and you get an R that's like negative 0.95 or something really close to negative 1.0, that it's close to negative 1.0. So it's close to perfect negative correlation. That's how you want to think about it. And then the opposite is the highest possible number you can get for R is 1.0. But most people never do that except for that one mistake I was telling you about. And that would be perfect positive correlation. So if you see that you calculate an R, and it gets really close like 0.95, like I said, or 98 or whatever, then you're thinking, Whoa, this is really close to perfect positive correlation, right? And then everything else is in between. So like, you know, 0.5 or negative 0.3 or 0.02 or negative 0.09, like all of those are between negative 1.0 and 1.0. And that's where our should be. So let's say you calculate R and you get eight. Okay, you did it wrong, right? Or you calculate R and you get negative 2.3, like that's not right, it's got to be between negative 1.0 and 1.0. And if you make a scattergram, you should know whether it should be on the negative side or the positive side, or it should give you a hint. So this is just more to calibrate what to expect from R because it's kind of a big calculation. So I'm just going to give you some pictorial examples because remember, every single time we make R, right? We also have a scatterplot behind it. And I just thought, you know, it would be helpful to see some real life examples of R. These are real life examples. Okay, real life. You don't get this from just anything, right? I'm just teasing. But anyway, so I started with some negative hours because I'm feeling negative today. I went into the literature and I found this article about Oh, it's on MIT in Harvard. It's about the evolutionary principles of modular gene regulation in yeast. And all I know is I'm supposed to cut down on eating bread. So that's all I know about this. But they had these really nice scatter plots. So and they calculated R for them. So and they had a little line on them. So I thought I showed them to you. So if you look at the one that's labeled D, see where the dots are, right, and see where the line is. And this looks kind of like a moderate to strong negative correlation, right? Because the dots are kind of close to the line. And then when the group calculated R, they got negative point seven. And so that kind of makes sense because and then I put my opinion in the lower right, these aren't official cut points or anything. But I usually use these as a guy. See how I said negative point four to negative point seven is moderate. So I would have called that D one moderate. Now let's look at E. So see how the dots don't cluster so close to the line, as they do with the D one, that's going to make it a weaker correlation, it's still it's still negative, right? So it's negative point four And when you look at my little opinion, I still call that moderate, but it's on the low end. See that. And then if you look at F, see how many of them are like way far away from that line, and they're dragging it down. So now it's in the even weaker correlation, negative point two, five, right? And so then that's weak. And so this is just some examples to give you a pictorial. And now I'll be I promise to be more positive. Here's some positive ours. They didn't draw a line on this one. This is a different article, right? So obesity is associated with macrophage accumulation and adipose tissue. So again, try to cut down on bread. But anyway, if you look on the left side, you'll see all of these x y pairs plotted on the scattergram. And even though we don't have a line there, we can imagine it's going up. So we would expect this to be positive. But we also would imagine they're not really clustering around the line very tightly. So when we see that the R is point six, we're not surprised. I mean, it's on the high side of moderate in my world, which makes sense. But go look on the right one, you know, under the B one, look at how those you could almost connect the dots and get a line out of that. So that's really tightly hugging the line. And then we're not surprised to see that the R is point nine two. So that's pretty strong. So I just wanted to give you these pictorials before we actually went forth and calculated R because that's one thing you can do is do the scatterplot have an expectation what R should look like. And then if you calculate R and it's totally wacky, you know that you did something wrong. Okay, let's calculate R and let's use the computational formula. Okay, I threw the formula up in the upper left, and don't feel overwhelmed by it, we're going to take that apart very carefully, right? But before we even do that, I just want you to have a flashback to chapter 3.2. See all those sums of those capital signals in the equation. So we're going to handle calculating are a lot like we handled calculation, calculating variance and standard deviation. We're going to make like a table with columns, and then we're going to fill in those columns with calculations. And then we're going to add up the columns to get all those numbers. So already, you were good at that in 3.2, you'll be good at this too. And then I made up a story because it's a lot easier to check your work if there's some story behind it and statistics. So pretend we have seven patients that have been going to your clinic for a year, they're good patients, they keep coming. So they came to the clinic over the year. And at the last visit of the year, you measured the diastolic blood pressure. And what you predicted was or what you thought would make sense is those with a higher diastolic blood pressure would have had more appointments over the year, because probably they're trying to stabilize the blood pressure, or maybe they have other problems that are driving it up. This makes perfect sense, right? So what you wanted to do is see if you were right. So you're going to take the diastolic blood pressure at the last appointment as your acts, you know, because you think that that's maybe the explanatory variable, or, you know, that would be the independent variable that would make it. So have something to do with whether or not they had a lot of appointments. And then you take why as the number of appointments over the last year, because you'd say, Okay, if high DBP problem means they have more appointments, that's just your idea. Maybe you're wrong, but we're going to do that. Okay. So I put in the title, just a reminder, access DBP, and why is number of appointments? So you don't forget.  So look in the first column, it's just the patient number, it's nothing, you know, exciting, we just want to keep track of which patient is one, right? And then notice under acts, we just have all of their DBP. So this patient one at the last appointment had a 70 mmHg, and patient two at 115 mmHg, that's kind of alarming. But these are fake data. So don't get worried about these patients. But anyway, we just fill in x and then also, when you add their chart out, you can look up how many appointments they had over the last year and patient one only had three, whereas patient two had like 45, which you can believe because sometimes they're coming in all the time to get stuff adjusted. But then, you know, patient three only had 21 and patient four at seven. So you can see these are the XY pairs for each of these patients, right? And it's pretty simple to go to the bottom and sum up each of the columns, we have some of x is 678 and some of y is 166. And also, I'm reminding you of the R calculation, I put that in the upper right, just so we can see what we're doing. I just want to call your attention to one of the terms in there, which is sum of x, which I put in the parentheses here. And that we already know now, just from making the first part of this table and adding it up. So we already have that thing in there. Now, I just wanted to point out, if you saw the sum of x over here, it's not exactly the sum of it's a sum of x y. So the y is mushed right next to it. That's not some of x at some of x y. And that's later in the game, we're going to put the sum of x y at the bottom of the last column. So it's so that first term there, that's not some of x, that's some of x y. Okay, now downstairs, we see the sum of x to the second, right? And that looks an awful lot like the one next to it on the left, which says some of x to the second right. And so how do you tell the difference between the kind without the parentheses and the kind with the parentheses. So this is how I do it. The rule is always regardless of what's going on, do what's in the parentheses first. So that's easy to do if you have parentheses, if you've got the parentheses version, you know that the sum of x to the second with the parentheses in it is you just do the sum of x, and you do the sum of x and you times and by each other, right? But what if you don't have any? Well, what I do is I say, Well, if I did have some, I do it this way. But if I don't have any, then I know I have to do the sum of the x squared column, right? So that's where you take x times x, x times x, x times x on each line, put it there and sum that up. So that's how I go through it, no matter where I am in statistics or algebra. If I see that some symbol, and then the x squared, I first look for the parentheses. If they're there, I know what to do. If they're not there, then I know you don't do the thing where you just take the sum of x squared, you have to go and look at the bottom of the column of the x to the second column and take the sum of that. I hope this is helpful. Alright, so as you can see, there's I've shown you on the top of the equation is where you just take the sum of x and the sum of y. And on the bottom, I'm showing you where you take those and you take the square of them. And then in the other term is the one where you just take the sum of the call. Alright. And so there you go. So what happened here? Well, we filled in x to the second. So if you go to patient one, 70 times 70 is 4900. That's where we're getting that number. So you go through and then patient two 115 times 115 is 13,225.  And that's what goes in that first term over there. And then I'll bet you can guess what the next slide is. Surprise. Now we do the y one. So don't get confused because you kind of have to skip a column there. So three times three is nine. And so that's why in the y squared on 45 times 45 is 2025. That's how we're doing those. You sum all that up and then go look up at the equation. That's where you put that sum of y squared. Now we have x y and this reminds me of a student I had before. She was really confused. She's like, Monica, I don't know what to do with x y the x y quality. And I go, What do you mean?  She goes, x times y, where's the times? Like, how do you know it's supposed to be times? Like, I don't see any times. I'm right. I don't see any times either. Like, there's no, like, like, how do you know to do that? Well, anyway, I'll just tell you, I guess, imagine like a little multiplication symbol between x and y. That's what's supposed to be there. That's what you're supposed to imagine. I guess I was so used to looking at it was like, You're right. I don't know. I guess you're just supposed to assume that. So take x times y. So for patient two, we just took 115 times 45. And that's how we got 5175. So you go through each of those. This is a lot of processing. And then you sum it up at the bottom. That's a big number. And then you see I circled it in the our equation. So I think we figured out where to put everything. Obviously, n is seven, right, because we have seven patients, you see a bunch of n's in there. So I think we have all our our ingredients. So let's move forward. So all I did here was rewrite the exact same equation with all the ingredients in it. Right. So like I said, the n is seven. And so wherever you see and you'll see a seven. See that sum of x, y on the top, you see where that goes. See some of x and some of y and then downstairs, you'll see I filled in all those numbers too. Now let me just talk to you a little bit about both levels, the numerator and the denominator. In the numerator, because we have order of operation, you need to do out the end times the sum of x y that seven times 18458, you need to do that out first. And then you need to do the other one, you know, the 678 times 166 first. And then after you're done with those two things, you have to subtract the second one from the first one. That's the order you have to do that and to get the numerator right. Now for the denominator, it's a little bit the same, but a little more complicated. You see on the left side, you have that seven times 67,892, you have to do that out. And then you have 678 squared, you have to do that out. Then you have to take that subtracted from the first one. And after that, after you have that, you take a square root of all of that. And that's your first turn. And then you still have to go over to the other one, you have to take seven times 6768, keep that, then take 166 times 166, keep that, then that term you subtract from the first one. And after you're done with all that you take the square root of that. And then those two things, you have to multiply together. So that's a lot of work and you have to do in the right order. So here, I just wanted you to see how you probably want to just work out this term separately, first, and then work out this term separately. And just like that thing I was telling you about x, y, those two terms, once you work them out, you take the square root of the left one and the square root of the right one, you have to multiply them together to get the denominator. So this slide is to help you see, I threw the numerator on that was relatively easy. But these are the two different numbers you should get from the left side of the denominator, and the right side of the denominator just to check your work. And then of course, once you multiply them by each other, you get this number 17,561.3. So ultimately, with the calculation for r comes down to is you're trying to calculate the numerator, and you're trying to calculate the denominator. And at the end, you divide the numerator by the denominator, and you get the answer, which is r. So we're going to do that now. And here's what we got. It's we got 0.949. And because we see that it's positive, then we know it's a positive correlation. And then remember my opinion, and also probably everyone's opinion, because if you run that up, you go 0.95 Well, that's getting really close to 1.0. So most people would agree that that's pretty strong. So how you would diagnose this correlation is you would say it's positive and it's strong. Okay, I just want to wrap this up by giving you a few facts about our that I may not have covered yet. First, our requires data with a bivariate normal distribution, which is something we didn't check before doing our R in this class, because I just don't cover that. But please know if you take another statistics class and they bring up our they might talk about checking for the bivariate normal distribution. So just know about that. Next, please know that R also does not have any units. So other things that don't have units remember the coefficient of variation didn't have any units, some things just don't have units and R is one of them. Also, we did talk about how perfect linear correlation is where R equals negative 1.0. That's if it's a negative correlation, or R equals 1.0, which is a positive correlation. But I might not have mentioned that no linear correlation is R equals zero. Now you probably won't see that in real life. But sometimes I'll make an R and the R is either positive or negative, but it's 0.000000 something, right? Regardless of whether it's positive or negative, if it's 0.000000 something, it's really close to zero. So that means there's probably like no linear correlation. And then we learned about positive R and negative R. But I just wanted to remind you of the behavior of x and y when you get those circumstances. Okay, so if you have a positive R, it means as x goes up, y goes up. But it also means as x goes down, y goes down. So they travel together. When you get a negative R, it means as x goes up, y goes down. But also it means opposite as x goes down, y goes up so they travel in the opposite directions. Now, here's another fact about our little factoid. If you choose to switch the axes, like let's say I designate, you give me x, y pairs, and I designate a certain variable as x and a certain one as y, and you actually designate them the opposite. It really doesn't matter, even in the equation, because you'll end up with the same R value. So it doesn't matter if you call the x my x y, and I call your, you know, y x, like we can switch them, but you'll still end up with the same R with the calculation. Then finally, even if you converted x and y to different units, you get the same R. So let's say that you were in England, and you were doing the correlation between height and weight, and you were using the metric system on the same patients that I was using the US system, even though we'd have different numbers, because obviously you have to convert them, we'd still get the same R when we were done. So finally, we get to the last subject of this lecture, which is lurking variables, which you've heard about before. But the main point I want to make is correlation is not causation. So you don't want to be misled by correlations. So beware of lurking variables. So remember, lurking variables are things lurking behind the scenes that cause things, right? And so you may have realized that selecting x and y, like if you have x, y pairs, designating which one is x and which one is y is kind of political, because you're implying that x could cause y. So let's say that you're correlating height and weight, taller people are heavier. So you would cause x to be height and y to be weight, you know, people don't go, Oh, I'm too short, I should gain weight so I can grow taller. You know, that's just not the way things work. So you have to put x as the height. And why is the weight? But there are in reality other causes of weight besides height. In fact, there are things that cause both height and weight like genetics, right? So a genetic profile that leads to tallness and also obesity could be a lurking variable in the relationship between height and weight. So there could be some tall people that are always obese, and it's not really just because they're tall, it could be because they have the genetics that program them to be tall and also obese, right? And so here's an example where you got to be real careful with correlation. So there's been this claim that eating ice cream causes murders, because they notice when in areas where ice cream sales go up, murder rates rise. And I don't know about you. But when I have some really good ice cream, it just makes me so mad. I'm just kidding. I mean, why would this happen? Right? Well, the reality is summer and warm weather are lurking variables because you sell more ice cream in the summer. You know, the ice cream consumption goes up. But also people are outside more and more murders occur. And you know, I'm from Minnesota, where it gets really cold for periods of the winter. And oh my gosh, there are totally no murders, then, like people just don't commit murders when it's really frigid out, it's just really inconvenient. So that's a situation where there's a lurking variable.  There's a lurking variable behind it, that's having something to do with both. Here's another one. And this was my professor in my biostatistics class use this, he put up a really like a time series chart over a long time, like since the 1900s. And he pointed out, as people purchase more onions, over time as onion consumption goes up and down, the stock market rises, right? So when the stock markets, little people aren't eating as many onions. And this is just true over generations in the US. So, um, yeah, we've had some problems with our economy in the US, do you think we should all start eating a bunch of onions? Right. So the healthy economy is a lurking variable. And a healthy economy, people buy more food, they including onions, and also a healthy economy boosts the stock market. So you got to be careful about this correlation is not causation, you know, and so if you want to make the stock market go up, don't make everybody eat onions. And definitely don't make a stop eating ice cream, that would make me very upset. So at the end of the day, you're not going to be able to affect the murder rate by bringing down the ice cream consumption rate, and you're not going to be able to fix the stock market by making people eat onions. And so that's the whole concept behind lurking variables and correlation is not necessarily causation. So in conclusion, when you're doing your correlations first make a scattergram, because you want to get an idea of visual idea of the strength in their direction. And you also want to look for outliers. Then go on and calculate R by hand, but be really careful because it's a big hairy calculation. And you don't want to make any mistakes. And then finally, when you go to interpret R, be careful of lurking variables. And remember that correlation is not necessarily causation. And now time for some ice cream.


A generation of young people has never known a world without knowledge, entertainment and communication at the touch of a button.  And almost all industries are now using big data, artificial intelligence, automation and the Internet of Things to improve their processes and products. And that has meant boom time for the companies providing those services, companies that didn't exist just a few years ago, but now are among the biggest in the world. But this has come with a unique set of challenges that our society is grappling to address. Created in an ethos of the free market, does their size and power now mean that they are making it hard or impossible for competitors to thrive and innovate? Is big tech just too big? Hello and welcome back to another episode of Stakeholder Capitalism, a show from the World Economic Forum exploring how economies can be made to work for progress, people and the planet. I'm Natalie Pierce. And I'm Peter Vanham. And in today's episode, we'll be exploring the topic of technology, and particularly the market power of big tech firms. Peter, can you lay out the problem that we'll explore in today's episode? Of course. Let's look at some graphs. Let's talk about big tech and their economic impact. I want to show you first a graph that shows the largest companies in the world by market capitalization. In 2004, their average market capitalization was close to $250 billion. By 2019, it had quadrupled to, in some cases, over $1 trillion. And what are those biggest companies in the world? Well, in 2004, they were all kinds of companies, industrial companies, you name it. By 2019, they were mostly tech companies, companies like Amazon, Apple, Microsoft, tech companies, big tech. And then let's look at what happened in the economy at the same time. I want to show you two things. First is what we call labor productivity growth. That is, how much faster are we getting at making things in a given unit of time? And that productivity growth fell from over 2% in 2004 to less than 1% in 2019. In other words, we didn't get as much better at producing things as we did in the beginning of the century. And the second thing that happened is that the income share of the bottom 50% of workers in that same period declined. In 2004, in the US, the share of the total of income of the poorest or the lowest 50% of income earners was 14.5%. By 2019, it was edging towards 13%. So the workers got a lower share of income and productivity growth in that time went down. So if I understand correctly, the value of companies is going up drastically. And yet we still see a decrease in the income of workers. So how linked are these two trends? Well, we don't know. I think it's something that we have to find out. Our first expert guest is Zia Koreshi. He is a fellow at the Brookings Institute where he explores economic development and the relationship between technology and productivity. Hello, Zia. Thank you for joining us on today's episode. Hello. Pleasure to join both of you. Zia, Peter just showed us a very interesting graph. And on that graph, he showed as big tech firms have rose, productivity has actually fallen. I was wondering if you could tell me more about the correlation between those two things, but first define what productivity actually is and why it matters for our economy. Productivity, it's the level of efficiency of an economy. If you can produce more with the same level of inputs, you have higher productivity. Better human skills, improvements in organizational capital, different way of organizing and managing things. So in order to achieve the same level of economic growth or to increase it, you need higher labor productivity. Productivity is not everything, but in the long run, it is almost everything. So Zia, we've seen now that there is this correlation between the rise of big tech companies and a decline in productivity growth. What explains this contradiction? The successful firms, the big tech now, the tech giants, I mean, they are doing well. The problem is that the benefits of technological change, they have not diffused widely throughout the economy. There are several reasons why we see this phenomenon. There has been a decline in competition in markets. When you have the rise of monopoly power and a weakening of competition, that is not good for a wider diffusion of technologies and for future innovation because competition is a major force that promotes these kinds of outcomes. So the way big companies exercise market power matters too to keep their potential competitors at bay. There are a number of other complementary things that need to happen in terms of skill, in terms of adapting your organization, making investment in new technologies. For small to medium firms, the slow response on the supply side in education and training system to respond to the change in demand for skills, these are all sort of factors that have played a role. You said that there is increased monopoly and oligopoly. What explains that? What does that look like in the market? For instance, in the U.S., if you look at the share of top three, four firms in the total sales of that industry in which they function, they have captured an increasing share of profits. That is profits in excess of competitive market conditions. That share is estimated to have increased to about one-fifth compared to negligible levels in the 80s. So this is a whopping increase. So building on what you just said, in Peter's graph, we saw that the value of big tech firms is going up, but in turn decreasing is middle class incomes. Is big tech, do you think, to blame? These elements are connected with the emergence of tech giants. But there is also a larger overarching phenomenon, which is technology and the nature of technological change itself, which has shifted demand for labor away from low to middle level skills to higher level skills. So that has, in a very significant way, increased inequality of labor income. And there has been a polarization in markets, especially with the share of jobs which use routine middle level skills declining because these types of work are more easily automated. And some of this is just the larger macroeconomic impact of technological change and how it has transformed labor market. Ziya, as we close part one of this episode, what do you think are the specific policy responses that are needed? What has been happening is that technology has causing major transformations in markets. But policies have not adapted to those transformations in a way that could produce better outcomes. So in terms of the role of policies, there needs to be a strengthening revamping of competition policy. One is, of course, a toughening antitrust. More importantly, responding to the new regulatory challenges of digital economy, the regulation of data. Second is to improve the innovation ecosystem so that it promotes innovation, but at the same time, it promotes a wider diffusion of knowledge embodied in new technologies through reform of pattern system. Third is the strengthening of infrastructure that supports digital transformation so that we reduce digital divide in its various manifestations. I mean, today, access to broadband is as important as access to electricity was in the last series. Fourth is that we need to revamp education and training systems to emphasize the acquisition of skills that complement new technologies. So there needs to be much more investment and the right kind of investment in upskilling, reskilling workers, lifelong learning, et cetera. And fourth, the social protection systems to align them with the change in markets and the natural work so that they better support workers in retraining and then transitioning to new jobs. If we have more responsive policies in these areas, policies that adapt better and quicker to the changes and transformation that technology is unleashing, that would make the growth process itself more inclusive. So before we pose the question how we redistribute gains from growth, we will have growth itself more inclusive. So to get our economy more productive again and to have less inequality, there's a whole host of things we'll need to do. Thank you so much, Zia. Thank you, Peter. I enjoyed this conversation very much. Our second guest is Maritje Schaake. Maritje is a former member of the European Parliament for the Netherlands and she's now studying tech policy at Stanford University. Hello, Maritje. Hi. Nice to see you. Maritje, we are looking today at the power of big tech. I want to ask you to start us off. We've seen, of course, that the biggest companies in the world today are indeed technology companies. They're just very big. What you've also looked at is, let's say, their market power and the structure of the markets in which they operate. Could you tell us a little bit more how those markets look like? There's a handful of very big players and there is a growing concern about what abuse of their market power these big tech companies might wage. So there are economic harms, a question of whether newcomers will ever be able to catch up. But there's also growing discussion in the United States, in the European Union, about what societal harms stem from having a handful of these excessively powerful companies, particularly because a number of companies are essentially data companies and advertising companies. They're commercially driven. That's not a surprise, but they feel a lot of spaces where the public debate takes place, where political debates take place. And so there's a lot of questions about what it means when advertising giants are actually designing the information architecture of our democratic society. So it's a lively debate, I would say, about what is a proper role in the market for big tech and where has it become excessive and in need of regulation? Maritja, you talk about this market power as if it is self-evident that this market power exists. What is the reason, though, for big companies to have market power? Is it because they operate in a competitive market? That wouldn't be the case. Is it because they are operating in a monopolistic or an oligopolistic market? What's your take on that? Well, my take is there's not enough competition and also that the rules that we have for preventing monopolies from existing, preventing cartels, preventing price discrimination and consumer harms really stem from an era before these big tech companies were so prevalent. Now, imagine a service where the consumer essentially doesn't pay, right? They are quote unquote free services, but at the same time, the consumer contributes with all the data trails that they leave behind through the friends that they make, through the hobbies that they have, through the purchases that they do. And so for the data giants, this is the most valuable contribution. But the rules that we have don't exactly match with assessing whether that is a fair proposition. Is the market being assessed for whether there's dominant players or rather, does all this data end up in one big pile? And should we say that the market really is that of data? Another way in which these big tech companies have become so, so significant and so powerful is through mergers and acquisitions. And their scale and size plays another role, because if you have billions in the bank, it's easy to buy your competitor before they even have a chance, assemble more data, be less competitive. And this is kind of the vicious circle that we have to break. Maurice, is more regulation simply the answer? Well, I don't think they're simple answers, but regulation is certainly part of the answer. I think in the US for a long time, the message from Silicon Valley has been regulation stifles innovation. But in fact, the inverse is also true. If there's not a fair market, innovation is stifled. If there's only a handful of very comfortable, very wealthy players, then small competitors, innovative startups don't get the chance that they deserve. I think of regulation as enabling condition for freedom as a framework for an open society. I think it's time to get to the next step and ask regulation of what for what, because regulation is a process. It can end in a gazillion different destinations. So we need to also look at the substance. You know, what is the regulatory process leading to? And is it an improvement? That's what public policy is all about. And I think that public policy decisions should be made with the public mandates, not in boardrooms, the way that they are now. We've really seen a privatization of public policy in an unprecedented way, which, you know, I do think also requires regulation in the interest of preserving democracy and, you know, the kind of transparent rule of law based process that we believe in in democracies. Another way that you said you can restore the balance in the market is by introducing something you called middleware companies. Could you tell us a little bit more about that idea? So the idea of middleware is that it sort of sits between the internet user and the big tech companies and could help them curate the information that they find on the internet to be more to their own choices, not the advertisers choices or the big tech firm choices. So imagine you have middleware that is made by the public libraries and that will give you more search results with literature, for example, or middleware that could be made by the EFF, a civil rights organization focused on the internet and digital rights. They could, you know, prefer privacy protecting conditions when you surf the internet and so on and so forth. So the idea would lead to more agency for the individual internet user, more interoperability, and also therefore more competition on these massively powerful platforms. And hopefully they would give more of an ability to self select instead of sort of being fed all this information that people don't really have a way of knowing why and how they got the results that they did. One potential solution could be for consumers to pool their data to collectively bargain against big tech firms. Of course, this was a solution advocated by prominent businessman Andrew Yang. And I'm curious, what's your take on this strategy? Well, there's a lot of talk about how data governance can be more in service of the public, public values, but also individuals. And I think data trusts, which is probably the direction of thinking that he was articulating, are an interesting thought, but it really depends on who then governs these trusts. And I think one pitfall of data trusts is that they could sort of entrench the model of commodifying data, whereas at least in Europe, there's also a big conviction that actually, for example, the right to privacy and therefore your personal data are fundamental rights. And so you don't really want to go into a model where, for example, people who can afford it can pay more for better privacy protection and others are incentivized to trade their data. So I think there's a real risk that data governance could could end up being sort of a class battle. And I think that would be the wrong direction. I do think that we need democratization of data governance vis-a-vis the big tech companies. And so I see a lot of potential to empower public values and both individual and collective rights. Is bigness in itself a problem in terms of corporate companies? The fact of the matter is that some markets are dominated by very few companies and it can very easily lead to abuse as we have seen. I think it's important that not only through antitrust, but also through transparency, through better protection of anti-discrimination principles, the whole idea of having independent oversight over products and services should also really apply to tech companies. They've somehow carved out a bit of an exceptional position for themselves. And I don't think it's sustainable. We see a huge erosion of trust in tech companies. And it is also, therefore, in their interest to restore that trust. And I think the only way that can be done is by having clear and fair rules of the road that can be independently verified. Maurici, what is your call to action? What are the specific policies or regulations that you would recommend? Well, I really wish there was a silver bullet, but there's not. So it will have to be a series of initiatives that, on the one hand, for example, would force more transparency when it comes to political advertisements or the handling of data, like micro-targeting. Why are people seeing the ads that they're seeing? Another aspect is this access to information. There is a real problem, particularly when we look at the growing use of AI, to really be able as an individual, as a regulator, as a journalist, as a civil society actor to understand, to have insight into how these systems work. And the more powerful they become, the more important it is to have a sense of how they work and an ability to learn, not only on the part of companies, but on the part of the public and society. Well, we talked a lot about antitrust. I think antitrust competition rules need to be beefed up. But I'm also quite enthusiastic about what we see coming out of the EU when it comes to data governance provisions, when it comes to a democracy action plan that really seeks to protect democracy for its own sakes. So a lot of this is interrelated. A lot of this is, you know, twisting one knob here and making sure it doesn't spill over on the other hand. But it's just to say that there's such a vast impact of the digital revolution and of the fact that there is a handful of dominant players, that it is important to look into all the directions of where harms are felt, whether it's the mistreatment of minority populations or people in sensitive categories, or whether it's the lack of opportunity for new startups that just don't get a chance in the current environment. So lots of work to do, and I hope we can continue this conversation about, you know, what the market looks like, why it matters to basically everyone and what can be done to keep it fair and safe and open. Thank you so much, Marie-Jaye, for being with us and sharing your tremendous insight. Up next is our post-match analysis. In today's episode, we explored the market power of big tech and we asked the question, is big tech too big? This leads us to our post-match analysis. Peter, I found the paradox that you and Zia presented to be particularly striking. We're living in the modern era of tremendous technological innovation. And yet we see productivity declining, which has been a major driver of economic growth. I found this so surprising. Yeah, it's surprising and indeed a paradox. And so that leads us to the question, well, what went wrong? And it seems that a possible explanation, a strong possible explanation, is the fact that we've seen an increase in market concentration. Those companies that have created this huge technological progress are in fact holding on to them a little bit too strongly for it to translate into broad societal and economic progress. Thankfully, Zia, he obviously presented the challenges, but he also presented some solutions and potential ways forward. Yeah, that's right, because of course, when you have established that the problem is the market structure, the fact that there is a lack of competition, then of course the solution is to restore competition into the market. You need to make sure that there's enough new entrants that can come and create that competition, that they can grow into the market, become challengers to the established large corporations. And of course, there's the question of tax policies. You have to make sure that that very, very large company of course pays its fair share of taxes and you have to reform those tax policies if that's needed. Those two solutions stood out to me. Yeah, the policy reform element also came up in Marucci's conclusions. So she talked about particularly the need for policy reform to protect digital privacy, also to secure and safeguard democracy. So she talked about some solutions not only to combat the economic consequences, but also some of the social consequences of big tech. Yeah, because of course we've been looking mostly through the lens of the economy to the impact of technology. But there is of course also the question of the societal consequences that it has. You need to make sure that there is better digital privacy, that data are better protected. We have control over our data. And in turn, that may also lead again to a better competition into the market. So it all comes back to that question of market concentration and restoring competition. Thank you, Peter. What an insightful conversation. It feels like we've just scratched the surface, but I hope we'll continue to talk more. That brings us to the end of today's episode. Thank you for joining us. And we hope to see you in the final episode of our series where we will explore the tangible steps needed in order to implement stakeholder capitalism. See you next time.