I know I've made some videos about Eugene and Cootie lately, but there's a much bigger problem and it comes to YouTube ads and marketing as a whole. Everybody, this is Chris from the Rewired Soul where we talk about the problem, but focus on the solution. And if you're new to my channel, my channel is all about mental health, and what I like to do is pull different topics from the YouTube community to try to teach you how to improve your mental and emotional well-being. But part of what I do too is try to be an advocate for mental health and increase awareness. There's some messed up stuff going on when it comes to marketing to people who have different types of mental illnesses and just kind of advertising as a whole. But anyways, if you're into improving your mental health, make sure you subscribe and ring that notification bell. And before I jump into that topic, also I just got the urge to send out an email blast this morning, so if you are on my mental health motivation email list, go check it out. I made a video about, not a video, I made an email about embracing your struggle. Especially after I made that video about spill the other day, a lot of people can relate to what I was saying where we wish we had it as easy as other people. But in this email I talk about how your struggle is actually a really good thing and it pretty much makes you awesome and very resilient. So anyways, I will link that email down below if you want to check it out and if you want to sign up for the email list. I just send out emails, try to give your day a little jumpstart in the right direction. Alright, but anyways, so yeah, what's going on with this video? What am I doing? What am I talking about today? So I made a couple of videos about Eugenia Cooney and raising awareness and I was getting, I was getting some comments about the ads on my video and I'm like, okay, like nobody comments about ads on my video. Like what are you talking about? So I was asking and people were telling me that like some of these places that sell like some of the flat tummy teas or those suckers are advertising on my videos about Eugenia Cooney and that is messed up. Okay, so let me talk to you guys for a minute. Let me talk to you all for a minute. Okay, just about how like YouTube ad works and how like the YouTube algorithm works. So when I make a video, I've touched on this a little bit before and all of you creators out there, take notes. You put as much information in the title description as tags. So YouTube knows who to send your videos out to, right?  Well, from the advertiser angle, right? They can target who they want to market to.  So this means, this means that these companies that are promoting this like weight loss, flat tummy tea or the suckers or whatever other products, they are specifically telling YouTube, we want to target people who have eating disorders. We want to target people who like channels like Eugenia Cooney. Think about that for a second. That is messed up. This is something that I've wanted to talk about for a long time. So on Instagram, the way that algorithm works is that you use hashtags, right? So hashtags, that's so people can find what you're talking about, different categories. Well, I have different saved documents for different hashtag lists that I've made. Some are for depression, some are for anxiety, some are for relationships, some are motivational. But one of them that I have is for sobriety, okay? Sobriety is one of them. And this is, oh my God, oh my God. Okay, so some of them, some of them, like I post about like, hey, staying sober, like, you know, here, here's some motivation to stay sober. There are alcohol and drug companies out there that are looking for posts that use hashtags like sober and sobriety so they can market to drug addicts and alcoholics. Like, am I the only one seeing a problem with this? Like, what is happening?  I know you're struggling with your alcoholism, but let me tell you about this tequila that I got over here. Like, that's nuts. And this is something I've had an issue with for a long time. Even before I got into YouTube and Instagram and stuff. So as somebody in recovery, like, staying sober is difficult. More people are dying from addiction than pretty much any other preventable cause of death out there, right? And there are bars out there, for example, who will give you free drinks if you turn in one of your AA coins. Like, think about that. Like, I get it. I get that it's like kind of like quirky and a joke and stuff like that. But like, can you imagine, can you imagine a guy or a woman who has destroyed their family because of their alcoholism, right? Their spouse is about to leave them. Their kids won't talk to them. They're about to lose their job, whatever it is. They get sober. They get sober for maybe two months, maybe three months. Things are starting to get better for them.  And one day they're struggling, right? One day they get a little stressed out. And there's a bar right down the street that will take your coin and give you free drinks. So the bar, they think that's a funny little marketing campaign, like, ah, you don't need to stay sober. No big deal. This person relapses at that bar, turns in their AA chip, and now they lose their wife, they lose their kids, they lose their job, they lose everything that they've worked hard for these last 90 days. Like, is this making sense to you? Like, this is just one of those issues that we face as a society where a lot of these places just see us as numbers and not as people. That's one of the reasons I try to stay so connected to all of you in the comments section. Like, I never want to forget that each one of you is a human being.  Like, we can't just do stuff like this, and I wanted to make this video to increase awareness, but the other topic that I want to talk about is just marketing as a whole. So I know both men and women struggle with body image issues, but this is a big problem with women in general.  Like, why are we not talking about this? I put a poll up on my community page just asking how many people are in recovery from an eating disorder and how many people are currently in the midst of an eating disorder. There were thousands upon thousands of votes and every vote means that somebody is either currently in the midst of it or is recovering from it and that blew my mind. I'm like, oh my God, and so what is the reason for this? So I'm not going to do a whole video right now about the cause of eating disorders, but I do want to talk about the problem of marketing right now, okay? So something that I'm always trying to teach all of you is to check your value system, right? I talk about these YouTubers who need to check in with their values because a lot of them think like, oh, once I get this many subscribers, I'll be happy. Or you might be like, oh, once I get this job, then I'll be happy. Once I get this relationship, then I'll be happy. Once I get this amount of money, then I'll be happy, right? Well, a lot of this also has to do with body image and something that's been kind of in the news recently, I know Philip DeFranco talked about it, was that there are a bunch of celebrities endorsing these products like flat tummy teas and these suckers and it's all over Instagram. It's all over Instagram. So one of the best things that I ever heard that I'm going to tell all of you and I want you to remember this, like write it down, put it on your mirror, do something like, just remember this in your brain. The worst advertisement ever would be one that told you that you were perfect. The worst advertisement ever would be one that said you're fine just the way that you are. The worst advertisement ever would say, you don't need anything to make people like you more, right? Because then why would you buy it? Advertisements are designed to make you feel bad about yourself. That's what they do because if there's nothing for you to fix, why would you buy anything? Okay, like this is an issue and we need to be aware of that. So when you see all these like Instagram models or celebrities like the Kardashians or whoever it is, like telling you like, oh, use this product, use this product, you'll look like me and then you can be happy. Like you guys, do you know how many people with the quote unquote perfect body are fricking miserable? Like do you understand that? Like remember, like I made a video about body positivity like a while ago before my channel started getting bigger. Like I am, you know, always in and out of this weight loss journey and stuff like that. But something I realized is that only losing the weight and not working on this is not gonna help. It's not gonna solve my problems.  So this is an insane, this is an insane ad. So Tristan and I were doing some research before I recorded this video. I'm like, check this out real quick. Everyone, my name is Stephanie and today we are back with another mukbang. I'll eat well. I'll eat well. Skinny mint gummies. So this is the first one. It's supposed to make your body burn fat. This one is to help curb your appetite. You know when you want like the midnight munchies? Yes. I have obviously been loving and been using. So that is Stephanie Sue. She is a mukbang or mukbang or mukbong, whatever YouTuber or she stops her face on camera. That's a whole different thing that maybe I'll talk about at some other point. But anyways, she is like a twig and she's endorsing these like weight loss gummies. Like what? What you guys? Okay. All right. So like remember how earlier I was talking about how like in my email, how I embrace your struggle and stuff, like I always thought it wasn't fair. Like I've been a bigger dude my entire life but my best friend, my best friend in the world who I grew up with, he eats like five times more than I do and he has the body of a Greek God. Okay? Some people were just blessed with amazing genetics and amazing metabolism.  No, you cannot. Okay? You cannot. And we need, this is one of the reasons why I promote people like learning about science and stuff because you need to realize that that is a false expectation, okay? But the last thing I want to touch on is this. Please read that email that I sent out because it ties perfectly into this conversation that we're having. Okay? Embrace your struggle. Embrace your struggle, okay? Go to the gym, eat healthier, meal plan, do all these things that I'm trying to do myself because at the end of the day you will feel so much better about what you did. Taking shortcuts does not give you that same sense of accomplishment and often with any shortcut in life there are drawbacks to it. And when it comes to these, it's the fact that they're unhealthy for you, okay? When you're taking these weight loss teas and all these other things or appetite suppressants, whatever it is, okay? So please remember that. Please remember again, the worst advertisements on earth would be ones that are trying to make you feel good about yourself. Advertisements are designed to make you feel terrible. All right? But anyways, I would love to hear from all of you down in the comments below. Like, have you seen advertisements pop up like for things that are supposed to be against something, right? Like for example, I'll throw this out there, like earlier this year there was a big hubbub about LGBTQ YouTubers having anti-LGBTQ ads on their videos, like that's messed up. Anyways, let's have a conversation down below, okay? But anyways, if you liked this video please give it a thumbs up if you're new. Make sure you subscribe and ring that notification bell because I make a ton of videos. And a huge thank you to everybody supporting the channel over on Patreon. You are all amazing and I got all your questions for the next Patreon Q&A.  So if you want to become a patron and submit me your questions, click or tap right there. All right, thanks so much for watching. I'll see you next time.


It's official, NASA is sending a helicopter to Mars, a small one, and a robotic one, and it's going to be complimenting the Mars 2020 rover to collect more data while at Mars. This is your space pod for Tuesday, May 15th, 2018. Not only will this vehicle demonstrate the viability and potential of heavier than air vehicles on the red planet, it will collect valuable science as well as some awesome imagery of the Mars 2020 rover. The project was started in August of 2013 as a technology department project at NASA's Jet Propulsion Laboratory, or JPL. The results of the team's four-year design, testing, and redesign weighs in at a little under four pounds, or 1.8 kilograms. Its fuselage is about the size of a softball, and its twin counter-rotating blades will bite into the Martian thin atmosphere at almost 3,000 revolutions per minute, about 10 times the rate of a helicopter on Earth. The helicopter also contains built-in capabilities needed for operations at Mars, including solar cells to charge its lithium-ion batteries, and a heating mechanism to keep it warm through the cold Martian nights. But before the helicopter can fly at Mars, it has to get there. It'll do so attached to the belly pan of the Mars 2020 rover, which is identical to the Curiosity rover. Once the rover is on the planet's surface, a suitable location will be found to deploy the helicopter down from the vehicle and place it onto the ground, and then the rover will be driven away from the helicopter at a safe distance, and the commands will be relayed to the helicopter. After its batteries are charged and several tests are performed, controllers on Earth will command the Mars helicopter to take its first autonomous flight into history. They don't have a pilot, and the delay from Earth is several light minutes anyway, so there's no way that they will be able to have a joystick controller to do it in real time. At least according to Mimi Ong, who is a project manager at JPL, she said that instead, we have an autonomous capability that will be able to receive and interpret commands from the ground and then fly the mission on its own. The full 30-day test flight campaign will include up to five flights of incrementally further flight distances, up to a few hundred meters and longer durations as long as 90 seconds over a period. On its first flight, the helicopter will make a short vertical climb to three meters, where it will hover for about 30 seconds. As a technology demonstration mission, the Mars helicopter is considered a high-risk but high-reward project. If it does not work, the Mars 2020 mission will not be impacted. If it does work, helicopters may have a real future as low-flying scouts and aerial vehicles to access locations not reachable by ground travel on Mars. In any case, the Mars 2020 rover will be launched on the United Launch Alliance Atlas V rocket from Space Launch Complex 41 in Cape Canaveral and is expected to reach Mars in February of 2021. So be sure, if you haven't already, to check out our last live show, where we talked with Tim Ellis from Relativity Space, the CEO of Relativity Space. That was a very awesome interview. So be sure to check that out if you haven't already and every live show that we have every Saturday at 1800 Coordinated Universal Time. Until the next time, I see you guys, though. Keep on moving onwards and upwards, and don't forget, add Astra to the stars.


OK, I think that's the last few strikers coming in now. So maybe we'll get started. And my name is Ian Houston. And along with Alex Kagashima here, we're going to talk about data science on Cloud Foundry. And something Andrew Clay Schaeffer said in his talk this afternoon really resonated with me about we're trying to build a community of practice. And I think that's really what we're doing here as well. So we're going to talk a little bit about how we think about doing data science on CF. But we'd also really like to hear any input from you, what you've done, what you've tried, what worked, what hasn't worked. And we'll talk a little bit about how you can maybe get involved later. So first of all, who are we? We're both working as data scientists at Pivotal Labs, which is the agile software development arm of Pivotal. And we both actually work in Europe, Alex in Berlin, myself in London. And we've been using Cloud Foundry for the last few years to deliver data-driven applications for our customers. And what we really do with our customers is we try and work with them to get value out of their data. Maybe just have a quick show of hands. Who here would identify themselves as a data scientist? OK, we've got a few. So it's not maybe as rare as I thought. And who works with data scientists or provides services or operations for data scientists? OK, so a lot more hands are growing up. And who doesn't know or has heard the buzzword but doesn't really know what a data scientist is? And I wonder why I keep putting those words together. Anyone? So you all know what data science is, OK, that's great. Really brief recap, then, maybe, is to understand what is a data scientist and what is part of their job. So this Venn diagram is famously created by Drew Conway and kind of shows the mix of skills you need to have to be a data scientist. So you need programming skills, definitely. You're hacking and coding skills. But you also need quite a lot of maths and statistical knowledge. And then to actually apply that to a problem, you need domain knowledge in one area. And when you get the intersection of all these three, you get data science and a data scientist. Maybe a different way of saying it is this quote from Josh Wills. Says that a data scientist is a person who's better at statistics than a software engineer and better at software engineering than a statistician. And the point about this is that we're not really software engineers. We don't have computer science backgrounds in the main. Like I have a physics research background and some of the machine learning backgrounds. But we didn't really go through a traditional software engineering education. And I think what that means is that something like platform like Cloud Foundry is actually really ideal for us. Because we are the people who really don't want to get bogged down in setting up and configuring servers and maintaining and doing operations on them. Because really we're trying to get quickly to business value by understanding data and providing some insights. So where software developers in the past had to stand up servers themselves and provision and do those kind of things, as a data scientist, that is really not my core skill, my core competency. So I want to be actually doing a data science task. I don't really want to be doing that. So that's why Cloud Foundry is kind of interesting for us. Briefly though, what are the type of projects that we actually work on? And well, there are a wide variety of them. Here's three sort of straightforward examples. And for example, you could be an insurance company that wants to understand the risk. You have insurable risks, buildings in different places. And maybe you want to understand how natural disasters like earthquakes or flooding will affect those buildings. So how much money would you lose if a particular country or a particular region flooded? And so we have a client who's trying to do this, and they're trying to run large-scale, very computationally intensive tasks. And what we're trying to do is trying to help them to run that in a parallel way, maybe use in-database systems, and go from running, maybe being able to run one or 10 of these statistical procedures to being able to run 1,000 or 10,000 of them to get a better understanding and better insight and, in effect, reduce the risk that they have. We've heard a lot today about the internet of things or the industrial internet, and predictive maintenance would come under that sort of heading. This is where we have some mechanical thing, maybe hard drives, or maybe it's an oil drilling platform. And you're trying to predict when it'll fail, because the cost of having that system out of production is very high. So I've heard people have systems with a cost of hundreds of thousands of dollars if it's out for one hour or one day. And if we can predict when those outages might happen, we'll be able to either repair them in advance or send the right spare parts that need to be there, or maybe take them out of production and put something else in its place in time that we don't actually get that downtime. So we do that with a mixture of large-scale machine learning processes, understanding the live data feeds that are coming in from those industrial internet applications, and trying to predict and then take action because of that. And then the third one here is understanding your customer. So lots of enterprises and large companies have siloed data where they understand a little bit about their customer over here and another little bit over here, but these never talk to each other. So trying to bring those together, trying to understand your customer from a holistic point of view, and then being able to provide better services, better customer experience because of that. And that's quite a lot of what we do. But there's a lot of other things, for example, like trying to reduce fraud in banking or trying to predict the destination of your journey in a car, and we do a lot of these different things, and we want to be able to provide the services we do, the data science services, in a quick and easy way and get to those data-driven apps. So what does a data scientist really need out of a platform or what sort of infrastructure do they need to do their work? Really, I think it boils down to three things. We need somewhere to store data in some easy way to capture that data. So for example, in the Internet of Things, the wide variety of different types of data coming in from different devices, we need a way to be able to channel that data somewhere and be able to store it long-term and be able to access that easily as well, like not have it in long-term storage, which is very hard to get at. And for example, I'm working with a client at the moment, and we've tried to do a data extract, a relatively small size of data, like it fit in my free Dropbox account, but it took over 24 hours to get that extract out, and over 24 hours we couldn't work on the data. So we need somewhere easy to put data and access it. We need somewhere where we can do large-scale, intensive computations, so running at scale with distributed computation systems, like Apache Spark or on top of Hadoop, and MapReduce Paradigm, that kind of thing. But finally, and this is where we really get to value, we need to be able to deliver results, whether that's purely just as a list of results on a website or it's a data API where someone can go accesses and get predictions for different things, and Alex is gonna talk a little bit about that. Or it might be simply an interactive data visualization where you're able to explore the data and see what the consequences are. So we need all three of these things, and I'm gonna talk about the first one, then Alex is gonna talk about the next two. So I think the first of these is the data storage, the how do we get data in and how do we keep it somewhere. And in Cloud Foundry terms, platform terms, these are data services. So we want an easy way to get access to these services without me having to go and download Redis myself and install it and try and tune it. I want an easy way to get a key value store and just push things towards it. And I also want to be able to build an application that can actually feed that relatively easily as well. So instead of just getting someone delivering me a hard drive and I have to load it up somewhere with the Internet of Things and other online, real-time streaming data, we're gonna get these streams of data in and we're gonna need to be able to do something with it quickly. So it's kind of a natural way of doing this in CF with data services, so you can have your managed service. And there's lots of examples now and I think we've heard a lot about these today and we'll tomorrow. But even things like highly available MySQL or Redis or even Rabbit Message Queue, we want to be able to create them easily and we want to be able to bind our applications to them as well. But lots of people have dedicated standalone big data infrastructure. They might have their own Hadoop installation, something like an Apache Spark cluster or whatever else. And user provided services allow you to connect to those really quickly and easily and enable you to use your existing infrastructure without having to manage it through Cloud Foundry. Now you may want to get to the point where you manage it and provision it using something like Bosch but using user provided services for now gets you to meet that distributed data requirement today if your service isn't managed by CF at the moment. And one good way of thinking about this is the ease with which you can switch from a test data store to a real-life production data store. And a sort of traditional way of doing this in data science might have to actually go and edit your files and change the way the data flow happens. Here we can just bind to a different service. So I can have one app pushed to CF that it's bound to my test post-gray instance and then I push another app but I bind that to my production instance or I switch between the two. So that provides a really easy way of going from one to the other. So that was the data services part. Alex, you're going to talk a little bit about the computation and the delivery of results. Sure. Thanks, Ian. So I'm going to talk a little bit about the compute part. So on the one hand, I'm going to explain a little bit what are the typical challenges when we work on actual customer projects with this and show the concept of a little prototype we developed. But first of all, so as data scientists, what we usually do in our work is we, obviously we implement code. So some people, they have this image that we stand in front of a whiteboard with a lab code and then code stuff in C or something like that. That's not how it is. So what we use mainly is Python and R. So these are two fairly high level languages and the reason we use them is because they have really, really good library support for a lot of machine learning algorithms. So these are really our favorite tools. So when Ian and I started out working working on Cloud Foundry, the first thing we found is there's no R build pack and the Python build pack, which was there is kind of, let's say it doesn't really have a lot of the libraries out of the box that we usually need. So what Ian did is he used the Anaconda Python distribution by Continuum IO and built his own build pack out of it. And if we use that, there's a lot of stuff like Cycle Learn, for example, which is a machine learning library and we can use that out of the box. So that was very handy. I used a lot of R, especially in university, so I'm a big R guy. So what I did is I created the build pack for R, which was kind of challenging, but at some point I got it done. So these two things were really helpful and really essential before we actually could do anything on Cloud Foundry, right? So first thing first, we had the build packs, which was good. So let's take a look at our usual work. So Ian already mentioned briefly, what we do is we work as kind of consultants for our customers of the Pivotal Big Data Suite. And what we do there is we kind of try to get some meaningful, valuable information out of big, big data sets. So the way this happens in practice, so we work with a lot of enterprise customers, so you see these siloed data and siloed systems at the customer, and then what we do is we get a big data extract and put all of this in some kind of distributed big data platform, which is nowadays usually HDFS, and then we work on top of it with Spark or something else. It could also be Green Plum, some that's an MPP relational database. And once we have it there, we are happy data scientists. We can see all the data with great speed, so we don't need to go through long-running extract processes because these already took place. So we already pushed everything over there, and what we do over there is we develop the actual model. So we think about how can we, for example, let's say for a specific customer, predict his lifetime value, for example. We use different statistical models, machine learning models that we train there, so we show a lot of data to that particular algorithm, and then that algorithm somehow learns how valuable a customer is. So everything happens over there. But the big problem is actually how do we push this model back here? Because the business, they actually need the prediction here in their legacy system landscape, right? So that's actually kind of a big issue that we face in a lot of our customer engagements. Very often after we created a really fancy model, we created a really great algorithm, and then we show a PowerPoint, but then the model kind of dies in the PowerPoint, is what we say. So nothing, not a lot happens. So this is kind of the issue that we have, and we were looking at some ideas on how to solve that with Cloud Foundry, which leads to roughly two thoughts on how you can actually do data science on Cloud Foundry. So this is just a very rough idea on how we think about this. There are a lot of different variants to it, but essentially, so what you can do, let's start here on the right side. What you can do is keep using your big data platform, which is good because there's a lot of libraries there, you can use Spark, and you can do the computation on the data in place, which is very good, and you kind of use Cloud Foundry mainly as a visualization thing. So once you have some aggregated results, you are able to show it to your customer in a web app that you deploy in Cloud Foundry, which is good. The other approaches that you actually somehow try to leverage the compute power that's available in Cloud Foundry, and use the big data store just for storage. So you don't do any computation in there. Yeah, so these are the two different approaches. There's also some variants to it. Let's say you don't want to store the data for some reason, then you can just leave that out and just do some online learning computations up there. So there's different variants to it, but these are the two rough ideas, how you could do it. So what we did is we created this prototype of a prediction API. We call it, so what we want to do with it is basically have a better way of actually interfacing with other software. So this is actually deployed at dsoncf.cfapps.io. When you go on there, you just get the readme landing page, basically, which tells you how you can send JSON there to do stuff with the API. And if you're in a pivotal organization on GitHub, you can actually get the code here. So what does this do? So basically you have this REST API endpoint and you can send it a request that says, hey, create me a model, which then creates a model in the backend. That model then is able to ingest data. So you send the data as a JSON blob as well. And it's kicking off some periodic retraining. So in machine learning, there's this notion of training. You show the model a lot of data and then the model gets smarter and smarter about the data. So this framework is actually able to do some periodic retraining, saves everything in Redis for now, which you can bind really easily on Cloud Foundry. And then you can also kind of send scoring requests to this API. So you tell it, you let it know about the data point. For example, all the transactions of a customer and then the model gives you a prediction back on how valuable that customer is, for example. So that is kind of the API idea that we have and on how we can actually leverage Cloud Foundry for data science. We created this kind of interface, which means you basically, if you want to create a model in that kind of framework, you have to implement this class interface, which means you need to have a train function, a score function and a get parameters function. And this is all done in Python. Oh, and by the way, this is using Ian's Python build pack, I mentioned previously. So what are some data-driven applications that we did? What are some examples on our work? So one thing which is really cool, which Ian created is this transport for London demo. So what this does is it scrapes a live feed of all the disruptions on London streets. And then you can see the current disruptions that are happening, but what it also does is it gives you a prediction on how long these disruptions are going to last. And that is based on historical data. So we scrape this data feed, store it, show it, show the live status, put some predictions in there, and the model also gets periodically retrained on the historical data. And you can access it right there. I think it's fair to say this is like the simplest possible way of using Cloud Foundry. Yeah, yeah, so this is basically the right approach that you see here. Another thing that I created with my R build pack is we call it insurance demo. So it's basically an insurance data set. And this app basically allows you to explore the data a little bit. And the goal here is to find valuable new customers. And what you can do in this app is try to create some rules manually, but also it lets you just train a model that picks out these customers for you and then you can compare the performance of your manual rules and the model. And the model is usually a lot better. And that's an example of the second one where the computation is actually happening in the Cloud Foundry app itself. So it's not happening on the big data platforms. Yes, so it's possible in this case because the data set is really small. It's like a megabyte or something like that. Okay, so these are two examples on data-driven applications we did. With that, I'm going to hand it over to Ian again. Yeah, well, just, I think these are two public examples. We've done quite a lot of customer work as well where we've used these ideas and we've gone a bit further in those. But what we really want to hear is about the rest of the community and what they're doing. Already gone down to the GE booth and heard a little bit about Predix. And I'm sure there's a lot of other examples in the community of people where people are using Cloud Foundry to not only just display results but maybe provide data APIs and provide, you know, understand some of the sort of issues we're talking about. So we'd be really happy to hear anything that anyone has to say about that. And we set up this website as a place where you can just show examples of how to do these kind of things and you can send us something on that Twitter account. But also we'd be happy to hear right now if anyone's doing any of this or if you have any other questions as well, so. Questions? Okay. Well, I think it depends on how much, on the, if you're setting it up internally on your own CF instance, you just have to provision it as you will. Say on PWS, which is the Pivotal Web Services, and that's Cloud Provided, Redis. So you keep paying for higher and higher levels, higher and higher tiers of service. But, you know, I think it goes up for quite a far way at the moment. I mean, Redis isn't really made for the storage of really large amounts. It's in memory. So its feature is that it's quick. But I mean, for this prediction API architecture that I showed, this is mainly, we think of it as kind of a prototype, proof of concept type of thing that we eventually also want to hook up to something like Spark, for example. So we can also do batch training on really large data sets. So that's a good question. So in the example Alex showed, the ingest is just, again, a REST API that you can send data to. For example, you've helped build this connected car demo, which is streaming data live back from a car, like a widget stuck into your car, and it just hits a HTTP endpoint and that's provided by a CF app. And then the CF app knows, oh, you've sent some data to me, I'll go store it and I'll run my machine learning, I'll run my scoring on top of that. Yeah, yeah, exactly. So basically, there was a talk earlier about how CF right now uses HTTP as the transport, but there's moves to also use TCP because that allows you to, or sorry, moves to use other things like AIMQP and other ways of getting data in because the internet of things runs on a lot of different protocols, not just HTTP. I think there were some more questions. Yeah, they're different. So, I mean, one thing you could think about is, so I'm actually using Spark and some customer projects now, and I'm using PySpark, and since the prototype that we wrote, it's written in Python, you could actually easily integrate PySpark with some Spark backend that you have running somewhere. So I think right now, you're using a Spark instance that's separate, it's on your standalone big data hardware, big data infrastructure, where I'd like to get to, and I don't know what sort of timescale this is around, I'd like to be able to provision a Spark cluster the same way as I provision today, I can provision a Cassandra cluster in PCF. So I can do that all through Cloud Foundry, maybe Bosch is provisioning Spark, and I can just bind to that. So that would be the ideal thing for me. I don't want to, Yeah. I want the minimum amount of fuss for me to get access to something. So it's the difference, we heard Andrew Clay-Shaver talk a bit about the old days of provisioning a web application as you go and request a server and it takes three months and then someone has to set it up. That's kind of still the same for big data infrastructure today, in many ways, especially for on-premises. It's sometimes even longer than three months. So. But you couldn't imagine, people obviously spin up AWS and you have Spark living on there, but we also have large bits of kit being moved around. So if you make it easier for people to start, get to start their data science work as quickly as possible and then that's provisioning it through CF or Bosch, would be the way I'd see that going. Question there? So that's a good question. I think, I don't think I have a hard and fast rule. I think the way everything is going is to be more distributed rather than one single large VM set. Obviously there are some overheads, but you have people running large-scale machine learning systems purely on top of AWS with all the sort of overhead that that entails and someone like Netflix is able to run their machine learning pipeline purely on that infrastructure without having to go down to bare metal at any point. So it's definitely doable and you probably have to be a little bit clever about it because you're not getting 100% of the speed that you would on bare metal, but what you're gaining is the ease to provision and the ease of getting started. Whereas on bare metal, you have to then be responsible for maintaining all of that. Yeah, exactly. Yes. That I would say there's no general rule of thumb that you can apply here. I mean, it also highly depends on the use case that you have. So I would say in general, if you follow that traditional approach where you have one big data extract and you put that somewhere, then I would definitely do that on top of Spark. So I'm not inside of Cloud Foundry, but somewhere where I have the storage and the compute together. Yeah, yeah, yeah. If the data is streaming in though, maybe it's a different use case. If the data is streaming in, then it might make sense to deploy some online learning on Cloud Foundry directly. And I think the other thing to think about is Cloud Foundry installations maybe originally set up for web applications, which don't need large volumes of RAM compared to maybe some machine learning applications. So for example, on the hosted versions, it tends to be like a two gig RAM limit. But really, I'd want a lot more memory if I was to do this as a CF app inside the CF installation. So maybe what you get is resource pools that have much better, I think this is gonna be part of Diego if I'm not wrong. Someone correct me here. Assign resource pools that have a lot better infrastructure for a big data computation and let your app pick those when it needs to. So I think that's, it's about how much you give the application each time. Any more questions? Okay, thank you very much. Thank you very much.


Do you know where you can see all of your YouTube poll results in one place? Do you know what YouTube polls are? If not, click that I up there to learn all about how they can help you grow. The results are in YouTube analytics in your creator studio. Simply scroll down to analytics, click cards, and there you can see all of your poll results. For example, in this poll, I asked what videos do you prefer on Freedom Central and 59% of you said tips to help me grow. So I hope you'll consider this a tip to help you grow video. What do these polls look like? Well, here it is. It's that little I right there and this is the poll and the results of that poll are in YouTube analytics. Let's look at a few other poll results that we've done in the past. What is your favorite game? 54% of you said something else. 29% said Minecraft. 10% said Clash of Clans and only 4% said League of Legends. Will you work with freedom in the Philippines? 45% of you said yes, but you cannot move and 32% said yes and you can move to the Philippines. This is how it helps you grow. Poll your audience about questions that help determine what videos you should make next, that what giveaway you should run, what schedule you should upload your videos at. All of these questions will help you fine-tune your channel so that your audience will appreciate what you give them and more people will subscribe and you will grow faster. Now let's look at a few of your comments from previous videos where you asked good questions. One question was asked by Thang D. Does freedom pay every cent or Thang D. Yes, we pay you a penny if you earn a penny by PayPal. And did you know we also pay all the PayPal fees, which means everything that you earn you get. We never deduct any fees from PayPal, unlike other networks which make you pay those PayPal fees as a hidden fee. Freedom doesn't do that. Let's go to the next question. Template maker said George, I have tried to use a music from position music and he has some issues. So I replied just open a ticket template maker on our support center at www.support.tm. Let me go there to show you how it looks. It's a website that has two links. Open a ticket or ask the community. Open a ticket is where you go to create a ticket. When you have a specific private question, ask the community is where you go where you want to ask 120,000 members a question about your videos or to get reviews about your channel. So make a choice and get answers. Let's look at another question. Venus mods asked George, what plugin is that? Now the video that he's referring to is the why Germany blocks 61% of YouTube didn't see that video. Click that I up there and watch it. It's an interesting topic about Gemma. Now, what did I use to create that video? So Venus mods, I think you are asking, how did I record myself here? It's not a plugin. It's a tool, a Windows software called open broadcaster system or OBS for short. I've been using that instead of X split because so many of you have asked me for advice and tips on how to use OBS. I figured I should learn how to use it before I can give you advice. Would you like me to create a series of videos teaching you how to use OBS? Tell me in the comments below or take that poll right there and answer the question. Do you want me to create an OBS tip series? I would love to know and I will if enough of you say you want it. Let's go to the next question. So this wasn't a question. It was a YouTube story. Daily driver reviews shared his YouTube story in video with us and what do we do? We post those stories right on the Freedom Central channel in a playlist on the front page so you can get more views. So do you have your YouTube story in video? Send a link to it down there in the description or in the comments below this video. And if I see it, I will put it into that playlist on Freedom Central. Let me show you that playlist so you know where you will be promoted so you can get more views right on the channel. If you scroll down at youtube.com slash freedom. There is my YouTube story. And here is daily driver's YouTube story, the one that was just mentioned in that comment. Let's watch a little bit and enjoy. Nice to drive. I know this has nothing to do with the videos I make now. I was just a kid by then. Oh, well, it's there. All the videos are still up. Not make a reveal. I couldn't find the motivation to keep going and I didn't have that many views. So I gave up at the beginning. So if you're interested to learn more about this particular story, head on over to daily driver reviews and you can find his channel link right on the Freedom Central page. Just go to the my YouTube story playlist. And thank you for sharing your story daily driver reviews. So secret French fry, beautiful name, by the way, made a simple comment and he was saying he is very happy with what he's gotten. He couldn't be prouder of it. He has two channels. They are moving slow but growing ever so slightly. He never gives up. Well done secret French fry. You will be a success if you continue to never give up. He's currently at 136 subscribers and if you like his content, let's see how quickly he can grow with this short spotlight on Freedom Central. And I want to point out that he wrote something very wise. He said the point in learning and growing is understanding fails and success. Because without failure or constructive criticism, there is no success, which simply means you have to learn from your mistakes. So don't be afraid to make mistakes. It's very true secret French fry. So let's have a look at his channel. Shall we? I'd myself. Damn it. Hey everybody, I'm secret French fry. I'm a virtual. Hello secret French fry. I have halos. So secret French fries playing RuneScape, but I would encourage you to improve your introduction video. This video that people see before they subscribe to your channel to answer the question, why should I subscribe to you? Tell us what's special about your channel. Tell us what schedule you upload your videos at. Show us some highlights of your best content to encourage me to click that little subscribe button. Sadly, secret French fry, you have not encouraged me from the little bit we saw of your default video for me to subscribe. So if you improve that, send us a link down there in the comments below and I'll watch it again. And if it excites me, I will subscribe to your channel. So I hope you've enjoyed this episode of the George show, a little bit of tips, a little bit about answering your questions and a little bit about shout outs and featuring some of the freedom family members, all things that you've asked me to do on the George show. So what I will do in the future is have segments of the show where I will feature comments while I will where I will feature channels that are deserving as well as give you helpful tips to grow faster because that is the spirit of freedom. We help each other grow together as a family. So if you have any additional tips that you'd like to share with the freedom family, please tell us down there in the comments below. And who am I? I am Papa George as some of you call me. I am George CEO and founder of this beautiful family called freedom and you've been watching and we will grow together as a family because this is the freedom family. You are part of it. We are all part of it and we're all growing together. So grow with us and partner with freedom. That play now button right there on the video bar is all you need to click to get started.


Hi Hi, my hair's a mess. What do I do? It's me, it's the it's LGL gaming. Oh wait, there was my old old old old old old old old I used to do and if so swanked Old intro for my videos, actually I'm not even sure if there's any more intro for one of my videos Do you know why we bring this up? How? No, we don't know unless if you do know which is You check up on my social medias today is my five-year five-year anniversary of doing YouTube Wait for it wait for it Whoo, so thank you all for being part of my journey for this long and because of that we're gonna end YouTube right now. Goodbye You got prank but really thank you everyone for being part of this journey It really does mean a lot to me.  You've changed my life. You've made me think of new ways of everything and legit. Thank you guys so much I couldn't Be doing YouTube if it weren't for you guys, so I love you so anyways today We are going to go to the community tab And we're gonna see what happens with the results of the poll that I made last night before I went to sleep So there was a video that was supposed to come out yesterday. It's taken way longer to edit So it will probably come out from the next couple of days But during the next couple days there will also be reactions coming out because you know we like reactions and all that stuff So you guys decide what for tomorrow's reaction out of these options Pentagon Chalala I was one teasers BTS intro straight kids elevator one year anniversary that one by a mile So we're gonna be right into that today or 20 plus times K-pop idols got smashed in the nuts Why did I go pirate for that I don't know I'm loofy It's what I'm gonna do is what I'm gonna do to you if you disagree of me being not loofy I mean if you disagree of me being loofy, I will do this I will make a cobra come out of my hand and I'll go You know, I was thinking about actually having this all the way zoomed out But then it's just so far zoomed out I feel like I'm just not talking to you and it's so much empty space, you know So we're not gonna be zoomed in So, yeah, since that one we're gonna be ready into the straight kids special gift elevator dance practice video Which is um Yeah, like first hit a music video, and I'm not gonna lie. I I Hands down think I was the first person to ever react to straight kids hands down I was the first person that reacted to elevator and then after my reaction video to elevator came out so many people started reading to it because they couldn't find it it was on the live and Haha, I reacted to it first. Haha. So like this is awesome for me I've been with straight kids ages seriously Ages ages like since elevator came out. I've been a fan of them And I'm I think I'm always gonna be a fan of these guys. So congratulations for your one-year anniversary And congratulations for my five-year anniversary So let's just watch this. It's not coming from my headphones. It's coming through the speakers So much oh Oh, I completely forgot about that the memories Coming back Chungbin made me lose breath.  Stand straight kids.  Oh, it's just so cool. It's so cool It's just so cool. I know it. You know it. We all know it. It's just wow. Oh Dude, and look at the sink Stray sink. Oh We're sink kids. Yeah, they're sink kids from now on who needs straight kids when you got sink kids. Oh It's so weird So cool, they're all cool. They're cool kids now They used to be straight kids then they were sink kids, but now they're cool kids. I saw that I Saw that don't think I didn't see that Dude, look at his freaking arms man.  Oh Oh the thing the thing about straight kids is just Everything they do is just awesome Am I right? Oh, am I right on right? I wish I could dance man. All I can do is this Move my arms randomly Okay, but seriously Oh This will bet so many memories. Oh, I can't even speak Speak speak so yeah, this brought back so many memories for me. Just like me and like Like for the entire video in my head It was like a film reel and all my memories were straight kids was just like, you know Like going to give a flashing by and like it was it was awesome. It was seriously awesome I seriously love how they had us so much. It is such a good pre-day view song I seriously love it to pieces and then they came with district nine and district nine was like So good and now we got like Mirror which is like so epic that has to be done in slow motion like just like this Anything done in slow motion is just way way way better. So that's why And that's why we stand them because these guys are so friggin awesome We love them to pieces. Hashtag happy first anniversary for straight kids Even though it's like being a few days since that happened and I am sorry about being late. It's just I am swag Not after that. Nope Should we try to be phoenix for this? Australian Exe So many of you have been asking Asking okay Now we're gonna do it in a deep voice yo Thank you all for watching this video. I can't do the let's just do a deep voice because that's Felix.  And yeah, I hope you guys are cool Because my voice is so deep right now. It's just like so deep as I can a different pitch for you guys And it's so high so I get over there. I hope you guys have a great day. Thank you. See you this morning Adios my doofy. I love you


So thank you, Christine, for organizing this conference and for inviting me. And thank you, C4, for hosting this. So the title of my talk now is Migration and Forest Transitions at Multiple Scales, Evidence from a Globalizing Latin America. And what I've done here is basically tried to cobble together examples from my own research over the last 15 or so years in Latin America, where there's some evidence directly or indirectly linking migration with forest transitions. My own research has to do largely with this topic, but also with other population and health transitions as they relate to environmental change. But so again, what I'm attempting to do here is really to focus in on the topic of this workshop, how are migration forest transitions linked? Giving due respect to what we don't know, some cases, to some degree, even hand-waving about potential linkages, given some of the things that we do know. There's no easy way to organize these rather disparate empirical studies that I've conducted around this theme. So I've kind of gone on a geographer. So I'm starting with the macro scale, more or less, and then going down to small scale to look at some of these links. There's three framing concepts that I'd like to introduce, integrating migration forest transitions. The first is that, despite all of the world's net population growth projected to occur in the world's poorest cities from here until 2100, the pace, magnitude, and geography of rural demographic transitions will have a disproportionate effect on global population size and distribution. So Cecilia showed the other day in her talk how she mentioned also that U.N. projections indicate that most population growth or all population growth will happen in urban areas. It's more than just that. It's that all of the net growth will occur in the world's poorest cities. But this is going to happen despite, all right, we have contact. This is going to unfold despite rapid urbanization. This will be largely, as Cecilia pointed out, to the majority of this change, of the source of this population growth will remain rural areas. So that is why rural demographic transitions are going to have such an outsized impact on future global population size and distribution. And then the second point I think that's important integrating migration forest transitions is this. Just to put it simply sort of in one phrase, how many people, eating what, produced where, will describe the vast majority of future forest change on the face of the earth? And point three is that point one will have a huge influence on point two. So what do I mean by this? This is, I think, a very illustrative histogram showing population growth by decade, starting with the, more or less, the beginning of the industrial revolution, and then projected out to 2100. And what we observe here is that where it's population growth in the world's wealthier nations, Europe, North America, peaked by the middle of last century, now it's declining. And that the bulk of world's total population and its growth is happening in developing regions. And one thing that I can't emphasize enough is that although population projections have basically been revised downward over the last several years as developing regions have progressed through the demographic transition more rapidly than demographers anticipated, the huge error bar around how many people we will be is precisely in the remote regions of developing countries, precisely where there's, guess what, forests. We don't really have data on these people. So one thing to take home from this is that yes, the projections have been revised downwards. Should they be? Should they be? Probably they should, but as much as they are, I don't know. And then the other thing is that if you understand how population growth works, kind of like the way stocks grow, geometrically rather than arithmetically, that an anomalous area in the demographic transition in terms of lagging in the demographic transition can have a huge outside impact over time in terms of the world's total population. And this is not just something that's just trivial and that certainly remote rural places, many of them do have remaining high fertility, and in some cases, whole countries still do, such as Niger. So why research tropical deforestation? I don't need to preach to the choir here, but approximately one-fifth of the world's large intact or old growth forests remain from approximately several thousand years ago. Why do I start with approximately 8,000 years ago with this graph, which would probably make many geographers blench? But it's just a rough, it's just to give you an idea. Why 8,000 years ago? What's happening then? It's a thing called the agricultural revolution. And if you're concerned about forests, then it's important to understand that with due respect to many different kinds of forest systems and human uses of them, by far, the hugest imprint of human occupation on the Earth's surface has been the conversion of forests to agriculture. So if we, the estimates here are very rough, but if we're just going to throw out some general numbers in terms of the Earth's surface, which is very much the domain of geographers, what's going on the Earth's surface. Maybe more or less 30% is in some sort of forest. Maybe 30% or so is producing food. And maybe somewhere between 35% to 40% of the remaining land is frozen, or desert, or mountainous, or wetlands, or somehow not easily or preferably used for agricultural production. And well under 1% is in human infrastructure and urban areas. So let's take a step back here for a second and say, well, then around a third more or less is in production for food stuffs. Well, over three quarters of that is in livestock or in crops to feed livestock. And so that is really the big human imprint on Earth's surface and certainly on forests. So why do we care about demographic transition? How does this relate? One thing that I can't emphasize enough is that both in the popular media and knowledge, but also among demographers, what's the big deal in demography right now? It's urbanization. This is what you hear about. It's all about urbanization. And in fact, and we are, as I mentioned earlier, revising downwards our population estimates because the pace of urbanization has exceeded what we anticipated. That's the developing world. And the developed world was the big story, aging. So we're shrinking our population. So that's the big deal. Yeah, except these transitions are happening and unfolding really at different rates in different places. This is some work I did with WWF, looking at population change in and around their core conservation areas. And I made this index of relative progression along demographic transition. Red is basically scarcely begun the demographic transition. They very few of the variables related to demographic development have occurred yet. And then so you have places such as the vast Congo Basin, second largest tropical forest in the world, where demographic transition has scarcely begun, if at all. And then nearby in South Africa, if you've been to Joburg or Cape Town, the demographic transition is similar to what it is in North America or Europe. So this is quite interesting, I think, that we're right now living in a time that is unique, demographically. Never happened before. And very likely, it will never happen again. And we're in it right now. And that is that we have these two worlds. We have places where the demographic transition, as we define it, has largely been completed, other places where it's scarcely begun. We have that huge transition across space happening right now. And so what took a couple hundred years for North America and Europe to go through in this transition of high mortality and high fertility to eventually low mortality and low fertility is happening in developing countries in some cases in just decades. Where it's happening, where it's not, is going to be a huge predictor of future forest transitions, but increasingly in indirect ways. And I'll get to that in a second. So Latin America, where I've done most of my work, and which is going to be the topic of some of the empirical research here, is a place that I think also is surprising to some people that know Latin America, that it's largely urban. It's 75%, 80% urban, like North America and Europe. You talk about the Southern Cone, Chile, Uruguay, Argentina. They're even, in some ways, a bit further ahead, in some cases, the demographic transition in the United States. But there's still remain pockets where it's very early in the transition, high fertility, high, particularly infant mortality. And guess what, these are places, not surprisingly, it's not coincidental, near high conservation priority for us. So what I did, excuse me, with this talk, is to basically ask questions for probing questions. These are not wealth that I did this last night, but sort of linking to some of the research I've done to these questions of migration and forest transitions. What do we know, what don't we know? Just a first attempt to link, again, some of these questions with some of my own research. So my question one is, how and where do dynamic migration landscapes, both rural destination and origin areas, relate to demographic transitions? And so in a sense, we're talking about, because these ultimately will affect also forest change. So here I'm talking about this link of demographic change to another kind of demographic change to ultimately forest change. And so I'm going to report just briefly on some work I've been doing with colleagues at the Carolina Population Center since I was a graduate school there in the 90s on forest change and land use change in the Ecuadorian Amazon. And just for the purpose of this question, I'm focusing on fertility change. And during the 1990s, we found that the total fertility rate, which is the average number of children per woman during her reproductive years, her life cycle, fell from around 7 to 4 during the 1990s. Now again, think about in the US, how long did it take for this decline to happen? Well, the fertility rate was around 6. Then it went up to 7, particularly with Western expansion. Land availability increased fertility in the US, just like we see in these areas now. And then over about 150 years, 150 years it got to 4, and now it's around 2. So this is a change that happened again in North America, 100 and 150 years here, 10 years. In some ways, this is sort of a classic story of the oil discovery and then infrastructure development, transportation, infrastructure, and then spontaneous settlement, which we've seen in other places throughout Latin America. And we've published quite a bit of research on this topic. But again, here I'm focusing on fertility transition. So this transition, which took so long to happen in North America and Europe, and was more rather homogenous across space in the developing world, as I was trying to show you with some of the maps earlier from Africa and North America, can happen very quickly in a very close proximity. So here we have La Guardia, which is one of these rainforest cities, which has sprouted up over the last few decades. In the Amazon, where the total fertility rate now is well below 4. If you literally cross the river, the fertility rate is over 7. So this never happened before in North America and in Europe, where you had places right next door to each other where the fertility rate was so different. And what's the connection with forest transitions? Well, the causal arrow in some ways goes both ways. And I just want you to be aware of this. There's not a lot of research on this, but I think it's fairly compelling what is out there that availability of land for households that for whom land is the primary resource does have an impact on how many children you have. So amount of land with land increase, it was very positively associated with increased number of children born, and whether a child was born or not, which is a regression. So moving on to a similar topic of origin and destination regions and how these processes can affect fertility. This is some work I did with Jason Davis, who's now at the Carolina Population Center, on remittances and the effect of remittances in Guatemala and some Highland communities where we researched this on land use and fertility and consumption. And so we asked, is there a relation between a rise in remittances among households and a reduction in fertility? We also wanted to know if there was a change in land use. We saw no notable change in land use, just a couple of case studies around a sample of 85, randomly sampled households in two communities. And we didn't see a notable reduction in fertility either yet. But interestingly, something we did find that emerged from this research that surprised us was that one of the first things that households invested in was education for their children. And a lot of the research we had seen in South America showed that, for example, Brad Jokers' work in Ecuador, that people were investing, kind of putting their capital almost in their household construction. They don't, at least until recently, don't have bank accounts. So they're kind of investing in their household and in their land. We didn't see that. Here we saw people investing in their children. So here, of course, education is the strongest predictor, at least underlying predictor, fertility decline. So over time, we do think that if this is true in other cases, investing in children's education will, over time, reduce fertility, which can or might not. But in a direct way, all other things being equal might reduce pressures on the forest. Second question, how do coupled urban and nutrition transitions in one place affect forest transitions elsewhere? And why there? So we have these two worlds that I talked about demographically, the urban world and then the world that's more rural. And remember, I think Cecilia also mentioned that since 2008 or 2009, the world became 30% urban. And but that belies, again, this real dichotomy where we have places like North America and Europe that's 80% urban and places like rural Africa and still many parts of Latin America also that are well below 50% urban. And these two worlds are associated with two kinds of pathways when we're trying to link migration to forest change. Pathway one, remaining high population growth, more likely to be rural, rural moves, low technology, low yields, poverty, subsistence, and people dying of infectious disease. And pathway two, declining population growth or already stable urbanization or already urban. With that incremental increase in disposal income, increased meat consumption and animal protein consumption and produced not necessarily where they are anymore, usually not, but through high yield corporate agriculture with high technology. And with this increased diet with sugar fat, animal protein and people dying of degenerative diseases. So this is work that Mitch Aidled on our CNH project that he presented some work on. So you basically saw another version of this map before, significant areas of deforestation and significant areas of reforestation in Latin America during the first decade of 2000. And Mitch already talked a little bit of this trend, so I'll discuss how this may or may not be related to migration. As Mitch and others have shown, we have rural migration or rather population growth in rural areas in Latin America by the early 1990s, late 1980s, actually start to decline, whereas urban population has sort of continued in its linear ascent. And some places even lost rural, lost population growth during the 1990s. Much of Latin America now is a declining rural population. And this is something that many of you working certainly in Brazil understand. So we, this is a conference paper that we did in Arizona, I think, for the land change meeting. We looked at a relationship between population change and then we saw a level of 16,000 of them. For Latin America, a woody vegetation change, we found another, R squared of 0.000. This is about as low a correlation as you can find. So then we, do we conclude then that there's no relationship between population change and forest transitions? Well, if we just look at that data, that scale, we would. But things are more complex. We have in Southern, or Central, South America, Southern Brazil and Northern Argentina, Paraguay, the growth of soybeans and export agriculture. We have in Northern Mexico and also here in Northeast Brazil increased precipitation and some forestry growth based on that. And then we still have some, oops, excuse me, some remaining areas such as in Mesoamerica and also along the slopes of the Andes and the Amazon where there's still this sort of pathway one largely poverty related and still high population growth where rural migration subsistence based forest change. And so one of the things that we found was that pig and poultry production globally and soybean production are both soaring. And we wondered if these things are related to forest change in South America. So we did a regression where we looked at various variables related to forest change in these 16,000 municipalities. And we also looked at the second level effects potentially of state and biome. And we found that beef production was the largest predictor beef production at the national level of deforestation and mitigating reforestation. The biggest predictor in land use was soy production. So if we break this down by the, where these products are being shipped, we see that Russia is purchasing a lot of this US and then followed by these countries and the production is very large in Brazil followed by Argentina, Mexico, Colombia, and Uruguay. In terms of beef, the majority of the beef is consumed within Latin America. So the rising affluence largely in urban areas in Latin America within that region is related to, apparently, seem to be related to increasing meat and animal protein consumption. In terms of soy, much of it is going to China and the Netherlands, which is probably a proxy for larger EU distribution in other European nations, produced largely in Argentina, Brazil, followed by Paraguay, Bolivia, and Uruguay. We've had a lot of soy here while I've been visiting Indonesia. What percent of soy do you think is eaten directly by humans? That's produced? One percent, one percent. Now, and this is not scientific research. This is the industry reporting this, actually, one percent. And so the rest of it is largely for animal field, also for oils. Then the question is, is the land change within Latin America, that we see internally, is unrelated, at least at the municipal level, with population change, is it really unrelated to population change? Well, this data is showing us that it might be related to another kind of population change, something that's going on in another place, another time, not just sheer numbers, but what that population's doing. So, virtually all of the soy that's shipped to China is fed to pigs in poultry. And we know that China has added over 100 million people to urban areas over the last couple of decades. And with that has been a sharp uptick in the consumption of meat products, particularly pigs and chicken, not so much beef, although increasingly, yes, beef is becoming important as well. So, it's a migration process, rural to urban, which facilitates an increase in disposable income vis-a-vis urban-based employment, which changes diets and demand for foodstuffs. And in a sense, what we see is that an exporting of the agricultural frontier to Latin America from China. So, population, including migration trends, are very much related to forest transitions, but increasingly, with greater distance in space and time between those population and related drivers and land change outcomes. So, scale is really important, as an analytical lens, clearly. This is a classic example of the ecological fallacy, where we ask, is population related to change? Seems like we have a pretty good study to examine that question. Came out with nothing, but if we look at the larger scale, both smaller scale and larger scale, we see how population does relate to the land change. So, another way to look at this is through the individual or the household, as a consumer, producer, and as a migrant. So, this leads into a third question. How do household decisions simultaneously and or sequentially, approximately, and distally in space and time, how might they affect forest transitions? Why, and when, and why then? So, one way to look at these issues is that a household or an individual in any given place is living within a context of macroscale demographic, political, economic, social, and ecological dynamics, and they make choices based on these pressures in these contexts, in these opportunities. And there'll be variation in this. A response for a rural household can be to seek off farm labor, can be to decide to have more or fewer children, it could be to manage land differently, or it could be to migrate. And these different options are related to different outcomes, and different, particularly different outcomes related to forest. So, if a choice is to migrate, one can migrate to different areas, internationally, urban destination, when a household decides to migrate to a rural destination, or turn to the top of the chart, there's a greater likelihood that you're gonna have this sort of pathway one deforestation process. This leads me to question four. So, how do different migration patterns and stages affect forest frontiers, both internal and external? So, I think it's important to distinguish between these two different kinds of frontiers. This pattern of the poverty, high population, subsistence agriculture, is often, the early stages of this is often at the external frontier. As these frontiers develop, then the remaining forest is actually on people's farms. Or, if it's a corporation, similarly the remaining forest within those farms. And so, these are really different contexts and very different questions you have to ask to understand forest change. If we're talking about the internal frontier, where people already are farming, where people already are impacting forests, then as you've seen many, many, many studies, for example, the Amazon researching what predicts deforestation, where people, researchers will go in and survey hundreds of households and they'll do a regression analysis of the household characteristics related to the farming, right? And how much forest they have left. And that's fine, and you can get a high predictive value for that for what predicts how much forest is left remaining on farms, the internal frontier, but you still don't get at the vast unoccupied forest still. To get at that question, you have to understand who's not there. Why are they not who could be there in the future, whether it's people or corporations? And that's a very different research question and research method to adequately address that. So a fifth question, how where and under what conditions do remittances affect origin forest scapes? This is work that Jason Davis and I did using the LAMP data, Latin American Migration Project by, started by Doug Massey, a demographer at Princeton, where we have several hundred households in most countries for Latin America, Central America rather, and there's very detailed information including on land use. And so we wanted to ask using this data how might remittances be related to land use and other outcomes. And we found that the international migration and the sending of remittances correlated with an increase in land and pasture purchases, but did not relate to changes in agricultural intensification methods. This is a small data set, but we're unaware of many other studies doing this. And if this sort of result is replicated, I think it has potentially profound implications for forest transitions. Of course, pasture has a much greater impact on forest than other kinds of land uses given the return in yields. Sixth question, where and why does scale, space and place matter? So this is an image from several years ago of Guatemala at the municipal level. And this is land in forest. And some of you may be familiar with the Mayarain forest. There's lots of forest there, very little forest along these rich volcanic slopes. And southern Guatemala. And so prima facie, you look at the situation of Guatemala and you say, well, this is a situation of land for people, for people without land, there's a map of population density. So you see a lot of migration from high population denseries to low population denseries, low forest cover to high forest cover. And we looked at this topic both using multi-level regression with the second level being the departamento, first level being the municipality. We also looked, we did globally weighted regressions, spatial regressions. And we basically found that we had both the pathway one and the pathway two process where we'd expect it happening. But do local dynamics then a forest change reflect these national continental patterns. So this is work I've been doing for the last 15-ish years in the Maya Biosphere Reserve, particularly in this core conservation area known as the Sierra de la Candon National Park. And the La Candon, both the richest biodiversity in the Maya Biosphere Reserve, receives quite a bit of rainfall and also has quite a bit of relief. But one thing that I wanna point out here, this relate to this seventh research question is, where are roads built? You know, it's something that we sort of, there is some research, there's particularly economists have done some research on this. But where roads are built are not random. So it's an important question to ask because without roads, it's very difficult that there's penetration in the forest in a meaningful way for real agricultural driven forest conversion. So this is the road, the Narajo Road in 1987. This is an aerial photograph. You can see in an 87 a French oil company built this road in actually the mid to early 1980s. You can see some farms starting to spread up along the road. And if you fix your gaze on this bend in the road down here, that's here. And there's a composite image of deforestation during the up through the 90s and then after 1990 up until 2000. And you can see that a lot of the forest was cleared during this time. About 10% of the park was deforested. And you can see there's sort of a standard pattern of the maize based corn, switten farming and sort of each one of these blotches kind of represents a maize plot farmed by a household. There's anomalous blotch here, which is also one household, but it's a different land use. What's going on here? This is cattle.  We wondered what would happen. So we have this pattern of deforestation. This is supposedly a corn conservation area in the Maya Bias Reserve. And then you have this, but much of it is now deforested or compromised. And so we really have this area here that remains in large intact tracts of forest. We wondered, well, you know, are people going to still keep moving further and further into the forest where now it's quite a long walk to the roads? Is it worth it to people? What's going to happen? So this is 2005 to 2007. And sure enough, we did see people moving even further into the forest. So roads, in a sense, collapse space. I'm at UCSB Geographer and we have the famous geographer Waldo Tobler, who has his first love geography that places closer to others are more similar than those that are farther apart. It seems kind of obvious. You know, in a real sort of obvious way, of course, this makes sense. But any of us who study these sort of dynamics understand that place is important too. And roads, the impact of a road really changes our dimensionality of what space means. I mean, all of a sudden it opens up these frontiers. So to understand, basically, the pace and magnitude location of deforestation in this park for my dissertation, I started field work here in the mid-90s, interviewed several hundred households and household heads and wives. And then a doctoral student of mine, Laurel Suter, followed up in 2009 with her dissertation and followed up in the same households. And we're doing a third survey now next summer. And we did the surveys in Spanish and about a quarter of the households speak Maya, almost all of them Kekchi Maya and we saw we did those in Kekchi. And this is the average farm in 1998. And you can see we're tackling internal frontier, lots of forest in the internal frontier. So there's still sort of, from a forestry perspective, these plots, these farm plots are still of interest. By 2009, it's only 10 years, you see that most of the forest has been eliminated and you have a very extensive swidden. Which again, is indicative of what sort of land use will have very potentially different impacts on the forest. Eighth question, how, when and where do demographic, political, economic, and ecological factors help contextualize or explain some of these forest transitions? So yes, demographic factors are important on the frontier. The fertility rate actually up through the nineties was about eight, which is as high as any place you can find on the planet, even historically. And larger households here were associated with more forest clearing. And this has been also replicated in other similar places in the Amazon. The transition here as an aside in when the fertility rate is so high and you wanna look at fertility transitions, you look at an eight of a TFR and you say, gosh, well, it hasn't begun, right? This is another way of getting at this. That if there's high desired family size, if the desired family size remains also, for example, eight, that's really at the earliest stage of the demographic transition, which we have found in places not here, but like Madagascar, Niger, rural places. Yes, the desired fertility rate is still seven or eight. But in the nineties, in the Lakando National Park in Guatemala, the desired fertility rate for a number of children was three among women and about four to five among men. So this has real implications for what, A, more equitable decision-making among men and women in a household, the impact that can have on family size. And also the potential impact of basically providing education and health access to people in remote areas. Political economic factors were also important, proximity to markets and capital matter and has different impacts on the forest, as you might expect. One perverse example of political economic factors was the issue of land titling, which from the Ag Econ literature, many, one might expect that it would lead to more conservative land use. Instead, it led to agricultural expansion because with land title farmers, this is on the edge of the park where they're given land title in some instances, they often use that land title as leverage for loans and with the loans they purchase cattle. Ecological factors are important. Low, high population growth, but low population density leads to expensive farming. There's cultural differences as well and here temporal scale is important as well because if you had just gone here in the 90s and you would ask farmers about their land use, you'd see that the Maya farmers were clearing more forest because they had a more expensive swidden. But if you go back into the 2000s, you see that now the Ladino, the non-indigenous farmers, have cleared more forest because they're more likely to adopt cattle. This leads me to a ninth question. Are frontier migrants more likely to move to another frontier? You know, are these already sort of, in a sense, pre-selected for? So we asked people in the follow-up in 2009, in the household, how many people have moved out and where have they gone? And amazingly, just in that 10-year period, only about 63% remain in the same town. This is a very dynamic frontier. And this gets back to my household diagram, you know, in which, sort of borrowing from Kingsley Davis and the multi-phasic responses, these are very dynamic people, very dynamic households. They might be forest once and then several years later, they might be clearing forest in a new frontier. And in fact, nearly 10%, we're doing precisely that. You think, well, at 10%, that's not very many and maybe not but it's actually many times more people percentage-wise that are doing a rural-rural move than we would see globally or elsewhere in Guatemala. So how important are rural migrants and rural originaries to forest transitions? And who are these migrants? Where do they come from? Why and why do they leave? So this is sort of a follow-up to this research. You know what? I could describe deforestation in the park based on several hundred household surveys. Come up with an R-square in terms of predicting forest cover, in terms of predicting different land use outcomes, explain anywhere between two-thirds to three-quarters of the variation among households in land use, okay, and thus in deforestation, forest cover. But of course, there's a hundred percent correlation between people being there at all in the first place and forest clearing. So roads are a, in a sense, in this case, a necessary but insufficient condition, right? You know, why do people leave originaries to go to this remote frontier that's malaria-infested?  There's no healthcare, you know, why would you do that? And so this is an important point, most don't. So, you know, this is sort of a needle in haystack effect. You have a tiny, tiny fraction of rural migrants that have a really, really outsized impact per capita on the forest. And so, you know, from a political economic institutional standpoint, you can say, well, it's obvious why people are leaving, right?  For example, you have about 2% of the population controlling about three-quarters of the land in a country about the size of Ohio. But unlike Ohio, which has Cincinnati and Cleveland, right, and all these cities, Guatemala remains about 50% rural. Almost all of those rural inhabitants are indigenous, and certainly almost all the rural inhabitants are farmers. Well, you know, that's, in a sense, a real population-slash-land pressure that's building up. And on top of that, you had an extended civil war that displaced over a million people during the, it just ended officially in the mid-1990s. I mean, so you have these sort of institutional macro-structural conditions for our migration. But nonetheless, most people don't migrate. They don't, despite this, in a tiny, I mean, we're talking just several thousand households that moved to the Maya Bazaar Reserve and have that impact. So who are these people? And where do they come from? And so, using the same sort of household model, we're asking this question based on all these different factors, why, and under what conditions do people decide to migrate, and when they do so, to another rural destination, a remote rural destination. So to ask this question, triangulating three-duty services, including my own, went to the 30-ish, I think it was 28, actually, municipalities of highest outmigration to the Maya Bazaar Reserve and found that about 10% of men and women during the 1990s had outmigrated at all. And now, again, I'm selecting for areas of highest outmigration to the Maya Bazaar Reserve, and even in those places, again, a highest outmigration, only about 10% outmigrated during that decade, and of those 10%, only about a third outmigrated in search of other land. So we can surmise from this that, again, in the 28 municipalities of highest outmigration to the Maya Bazaar Reserve, less than 5% moved to the Maya Bazaar Reserve. I mean, this is very, very few people. So I think that in terms of the importance of, for forest policy, to the extent that they're, yes, the world is shifting toward the sort of pathway two, and we'll get to that in a minute, but to the extent that there's still a remaining pathway one, who these people are, where they come from, their individual characteristics, and their sort of macro communities, structural conditions are, I think, quite important. You can have a lot of potential bang for your buck in investing, in targeting those locations. So why did some people migrate to the Maya Bazaar Reserve? There's some common denominators, as you might imagine. These are in many ways rural subsistence farmers that are eking out a living in under very difficult conditions, ecological conditions. They are more likely to be located on steep slopes, areas prone to erosion or flooding. They tend to be in areas where there's skewed land distribution. They tend to be larger families, more likely indigenous. Quite a few came from places where there has been expansion in corporate landholdings, and this is an interesting slide that says, any of you speak Spanish, have another one, and you have this sign that says, but don't park here. These manicured lawns, and as far as I can see in the Pacific Slope of Guatemala, these really rich, dark, basaltic volcanic soils, it's just in sugarcane, pretty much to the horizon. So there's some structural reasons for this, but are origin places of these migrants different? And why, and what are policy implications of that? So we've talked about some characteristics of the people and the places, but can these still unfold differently in different places? Now I'm putting on my place geography hat. So lots of different case studies. I'll just give you two, because I have no idea how much time I have. Yeah, okay. So here are two municipalities of high out migration to the Maya Biosphere Reserve during the 80s and 90s, no longer. We have Morales, and we have Nueva Concepción. And Morales, this municipality grew more quickly than any other during the 1950s in Guatemala. The population doubled. Why? Because United Fruit had expanded land holdings there. We're talking about post World War II consumption, baby boom and consumption boom in Europe and North America. People want to eat bananas, and you have now technology that enables that with refrigerated big shipping containers. And so United Fruit gobbled up a lot of great land in Morales, and then you have a doubling of the population because there's a great demand for labor. People flood in. Quite a few, by the way, from this area in and near Nueva Concepción, the Pacific Coast. But within a decade, all of a sudden the migration pattern reversed. You had this area of highest immigration in the whole country to very rapid out migration, many to pretend, initially to Southern pretend, and then to the Maya Biosphere Reserve. What happened? Why is that? Well, as many of you know who work in rural areas, these kinds of jobs on plantations are often seasonal and low-paying. And so, farmers who relied on these jobs still needed to be subsistence farmers to maintain their families. And so with United Fruit gobbling up the best land, there just simply wasn't enough land for survival. The jobs were insufficient. So when a policy implication of this are jobs the answer, you provide apparently sufficient jobs. But there's a quality to this too. It's not just quantity, what kind of jobs? And so even if we double the number of jobs, you'd still have out migration here because you wouldn't have those kinds of jobs. People wouldn't have land to compensate to at least grow some subsistence corn. So another popular policy prescription for the developing world to alleviate inequalities and particularly among rural farmers is to redistribute land. And Nuevo Concepción was one of two municipalities in Guatemala that during the 1950s, it did exactly that, carrying out the dream of Jacob Waterbentz, the president who was assassinated. And so Nuevo Concepción in 1952, some large land holdings were redistributed among several thousand households. Each household was given 25 hectares. And in the intervening period from 1952 through the 1990s, there was basically this land redistribution and the rules governing it were quite strictly adhered to and there was no further land consolidation as is very common in other places. And yet from 1952 to the mid-90s, the average farm size splintered from around mid-20s, 25 hectares or so to only around three hectares. So how did that be? What happened? It's no land consolidation. It's a really rare case where you can isolate that. It's because the fertility rate remained high and people are married. Remember also in this demographic transition, one of the transitions that people don't think about often which is really critical is that marriage age gets older as this transition occurs.  So you literally have even with the same fertility rate, double the number of people living at any given time when the age of marriage is 15, 16, 17. And so within just a few decades, you have basically a splintering of land from around 25 hectares per household to three, you know, really isolating, high fertility as the cause. So was land distribution the answer? It was insufficient. Land distribution without taking into account these demographic processes was insufficient. And these are just two reasons why place matters. So is this a sunrise or sunset over Latin America's forests? As I came to this stop sign Southern Guatemala, which says here, stop, here says two ways this way and then one way this way, I'm unsure. But I'm sure of one thing and that is that migration transitions will continue to be intimately related to forest transitions, that there's still a pathway one that we need to be aware of. It'll be increasingly, it'll be very important in terms of understanding future transitions both directly how many people are still living this pathway one, still in that world and having a direct impact in space and time. And what will be the timing, pace, magnitude of that population becoming urban? Is it inexorable? Demographers seem to think so. Is it really, is there some spatial variation in that and what is the implication of that for forests? You know, how soon these or if at all populations live in urban areas and start having impacts through the pathway two. That's it. I look forward to going to the garden so anyone who wants to go.


The acting in this, of course, is superb. You have Oliver Reed, which is just a master. He can do anything. He's a wild one. Drunk or not, he can do it. Gives a lot of clout to this movie, too. You know, it's probably Cronenberg's first movie where he's got a well-known actors in it, and it helps, right? Yeah. It's kind of like the Loomis to the Halloween sort of deal, right? Exactly. That's exactly what it is. Samantha Egger was also a really known British actress, and she's really good. Art Hindle is really good, too. So you have three kind of main leads, which really hold it all together.