Now we can work eight to five and then shut down and spend five to ten with our family and be present If we run an efficient business Where we're not To go and then I was gonna learn so much out of this I didn't know what I was gonna learn out of this But I knew I was gonna learn something because I the thing about me is I'll never quit I've been coaching now for almost a little over two years But I'll be here for 22 years because when I start something. I just don't stop and That's even after there's no results and that's real estate I didn't sell anything for eight months when I first got in the business But I wasn't gonna stop because I saw other people selling stuff and that's one thing I want you to realize is that there are people selling Millions and millions of dollars in property there are people there are thousands and thousands and Tens of thousands of people that are doing what you want to do So why shouldn't you be able to do it if you work hard? My thing has always been I want to outwork everyone if I feel like if I outwork them then I will beat them okay, so When the market crash I lost everything. I was so Curious to why I failed because I was the most honest hard-working Dependable I was everything that I'm supposed to be But I lost it all so I started reading. I read a hundred books during that three-year period. I watched the market. I Tried to figure out what the top producers that were still selling through the down market were doing and what the ones like myself that had to get out of the business what they were doing wrong and Long story short a Few things that I learned. I just want to I just want to hit a few key points that I realized during this time Something in one of those books. I don't remember what book it was or what it what happened But something clicked in my mind and I realized there it is Up to that point from when I got in real estate to that point everything was about the deal Everything was about the money the transaction the closing getting them to sign the contract getting the deal done That was the main focus And something clicked and I realized Wait a minute. It's about the people The people are still going to do more deals later Right, and they they did this deal not because of a deal. They did this deal because Their investors they're trying to do this or their mom died or their kids went to college or they got a new job Or they're downgrading. They just had a baby. They got a they need extra bedroom There's a bigger reason for what's going on in their life This calls on her to make this decision to buy or sell a property And we're so focused on the deal the deal and we we were blinded to What's going on actually in our client's life above the deal that's actually making the deal happen? And if we could tap into that Now we got something now. We have a real relationship with someone Right and every person you create a relationship with these these prospects these People in your market. They're buying us on properties as many of those people that you can create a lifelong relationship with lifelong real Relationship voice to voice those are worth 10 to 20 deals to you Over the life of your career the repeat business referrals and referrals of referrals And see everybody wants a prospect ready to do a deal right now today When I got back in the business, I built it on just people not deals I don't care if they want to do a deal today or not If they want to do a deal later, I'm fine with that Let's create a game plan around how we're going to make that happen in six months two years five years Let's create a game plan with me as your agent you as the buyer or seller of how we're going to make this happen And what we're going to do in the meantime to set herself up for the best situation I was looking on MLS went and when we're at the bottom of the market and I realized some of my clients that I represented at the top We're still buying and selling at the bottom But I wasn't representing them. I was out of the business That was a aha moment for me And that's when I realized wait a minute it that reassured that reaffirmed what what clicked in the book that it's about people And that's when I was like Okay, this is I'm on to something here If I would have maintained a relationship with those people through the crash Then I would have represented them at the bottom So that opened my mind up to the fact that since it's a people business And you can't talk to every single person ever in your life in your market Can't talk to every single person in Minneapolis ever it creates a situation where business is unlimited And what we're doing is we're putting ourselves in a box and saying there's only so many leads Or classifying a lead is a motivated person wants to buy herself something right this second When to me that's not a lead Everyone is a lead every human being in your market is a lead. They're going to buy ourselves something Right so To take it a step further Another reason why it's unlimited is because you can't handle all the deals that are available to just you There's more deals. There's more loyal clients in your market loyal that will only deal with you forever More of them in your market than you'll ever find It's unlimited And we're not thinking this way see I could sit up here and tell you what I do day to day I could give you the the exactly what you need to be doing what you need to say how you need to do it How you need to follow up conversion all that stuff But until you understand the mindset behind why you're doing it and and what this is all about We can't even go there. That's why I want to come here and talk for 20 minutes I can't tell you how to do it Because a lot of you haven't heard of me yet So I have to tell you how I'm think about it first right so From contract to close is a process from relationship from point of contact to contract is a process It's all a long process regardless of how you slice it up You can't do a hundred deals right now and get paid for all of them today Never happen. It's a process Because the process is the process it creates a situation where we can't handle it all Because it's a process. Okay, so now our now our success is predicated on How much you can handle? How many deals can you handle at once because every night? Everybody can handle a different amount. I can handle a lot. I always have 20 deals under contract 50 listings always I Refer to it as a cup. Everyone has a different size cup Your cup represents how much you can handle right the objective is to find out how big your cup is by overwhelming yourself with business right to the point of Being so overwhelmed you can't handle it all and now you know where your breaking point is because some people have Two pendings and three listings and they're done. They're spending 40 hours a week for like Three months and I don't understand. I'll never get that because I have 20 Pendings and 50 listings and I spend maybe one day out of the week total when you slice up all the hours Different hours I spend on it. I still have four free days a week to go after a new business Everybody's so into what they have going on right now. That's your cup See what I'm saying So realize that everybody has a different size cup figure out how what the size is your cup because you overwhelmed yourself with business one day You made a thousand calls you spend a bunch of money on ads or something You overwhelmed yourself with business to find out where you stand now. We're gonna scale back to just the size of our cup and we're gonna stay right there long term So reach our full potential and what I don't want you to do is compare yourself to me Compare yourself to the other top producer in Minneapolis compare yourself to anybody else Because they're not you and you shouldn't worry about what if they're producing more good for them Let's stay over here. Let's produce all that we can produce and get and get 1% better every day That's what I want If you don't get anything out of this but this one thing I want you to get 1% better every day with something What I really want you all to do is join my free coaching program and learn how to effectively Communicate who you are as a person that you care about people to your clients Not that you're just there for a deal because that's what I teach The next thing I realized Is that it's okay if the market crashes? Right fear is holding everyone back. It's the reason why you're here right now.  You're scared of rejection. You're scared of making phone calls. You're scared of I don't know what you're scared of But you're scared of something that's holding you back. You're scared to overwhelm yourself with business You got a family. You don't want to overwhelm yourself with business because you want to spend time with your family Right, but if you run your business and you build it on efficiency Now we can work 8 to 5 and then shut down and spend 5 to 10 with our family and be present If we run an efficient business Where we're not when the market crashes Closings continue to happen every single day I want to repeat this when the market crashes what's going on in your market right now What's going on in every market around the world and when the market crashes Closings continue to happen every single day So why do we get scared of it? Why do we get out of the business? I'll tell you why because it shifts From first time home buyers and all these people that are excited when the market's going up Those aren't the same clients as when the market is down The clientele changes and because you don't you don't realize this your clients went away because the market's shifted Now you you think I got to leave too No, we stay in there. We continue creating new business and find out who wants to do what the buyers at the bottom at the crash They want to buy right now because it's cheap before it goes up The sellers have to sell they're in trouble They got sell right now So the market crashing creates urgency in the market And all the agents leave and i'm just sitting here selling everything Right So I don't want you to be scared of anything because you know why We create what we fear If you If you you're scared to make phone calls because you think people are going to hang up on you That's what's going to happen If you're scared the market's going to crash you're going to have to get out of the business That's what's going to happen. It's going to crash and you'll get out of the business Whatever you're scared of whatever you fear. That's what it's going to create. That's what that's what it's going to happen So quit thinking about it and quit fearing it be fearless And go right This is a big one relationships over transactions This is a big one what I want you to do with your clients from now on Is I want you to connect with them first before You convert Connect first then convert What what we're doing as an industry is trying to convert convert convert Right and we're we're leaving so many deals on the table Because of our conversion process we're we're running so many prospects away that want to buy later That we don't get those deals later because we pressured them to convert now And we're not building our databases to the potential it could be Whereas in three years you could be the number one agent in many anapolis You got it.  I don't want you to ask people if they want to buy or sell I want you to see I want you to tell them you're in real estate A house around the corner sold is there anything I can do for you? And if not is there an agent you would work with And if not, okay, cool. Look, I'm sure at some point you will do something I'd love the opportunity to work with you in the future. Can I stay in touch? They're not going to do anything right now. Anyway, we're talking to them. Anyway, we're we're there This is a precious moment. We're just going to give it up Okay, I'm running out of time. I got a lot more to say But I got to move on here If you guys want me to be your coach Someone who is selling 100 properties a year as a single agent travels around the world spreading this message I'm literally my mission is to reduce the failure rate in the real estate industry and I'm doing it There's 17 000 agents in the program and I get messages every day Of someone who said that they were about to quit three months ago when they found me they implemented my 90 day action plan My online course We do live training twice a month and direct access to me Take a picture And when you get in there and you realize how free it is and how real it is I want you to tell all your friends about it too because I'm literally trying to create a movement that educates agents how to effectively communicate who they are because Everybody here is a great agent that really cares about their clients The problem is you're not communicating that to your client because you're going by the old 1980s scripts And it's coming across as though you're just another agent trying to do a deal I want you to find out why people want to buy or sell and help them through those big life moments Okay So if I did my job If I helped you get just a little bit closer To becoming the top producer that I that you want to be I want you to stand up And give it up for me because i'm out of here


when we look and reach for the solutions of the past, they did not give us a resolution. They did not give us the outcome that we were looking for. I do believe, and that's why it works so hard in technology and entrepreneurship, we're not going to make the change unless we find economic engines in these communities, these local communities that are job creators. Everybody can't be an entrepreneur, but for those who can, if they stay in those communities and produce income streams and wealth streams in those communities, we can make a change. My name is Joanne Rohl. I'm the Dean of the School of Business at Meagher Eris College, City University of New York. I am also the immediate past president of the National HBCU Business Dean's Roundtable. I first fell in love with economics in high school, but honestly, I didn't know black people could be economists. I went to the University of Miami and saw my very first black economist professor, and that was it. I went to a couple of other schools, but when I got to Howard, it was like feeling at rest, feeling like I have found a place.  Why do we have to read so many books? Why do we have to do all of this? And one of my professors, he's long gone now, Dr. Joseph Howe, chance he was a renowned labor economist, and he said, you know, the books weren't written for us. You may have to rewrite the books, but first you have to know what's in them. And so that's shaped me. A lot of the things that we do, the problems we see, are not solved. And so we have to come up with innovations and creativities that are not necessarily there. And Howard taught me or showed me that we could be creative and we could solve problems with solutions that don't currently exist. So I quote a lot in listening because I find that my work is profound. And listening is a breath. It's like a life understanding where some different perspectives or someone, so where someone else comes from. So I'm a black female economist. And when I first started, there were not many people that looked like me. And so I had to listen a lot to really understand where they came from and what perspective they had. And then I could participate at a different kind of level. A lot of times what I find is people want to talk. But they're talking so much they can't hear. I'm the vice president of the Global Listening Center. It's an international organization that focuses on the importance of listening to each other. And I'm talking about it at a very high humanity-like level. We have a lot of crises that is inflamed by us not listening to each other and hearing each other. So listening is very important. Poverty and the poor have been with us forever. But also, it's in parallel, the abundance we are experiencing now is also profound. So, you know, it's a challenge to say, and I'm a student of equity. I have been since my doctoral work. It seems as if humanity has to have the will to address the struggle. And unfortunately, I mean, the number one goal for the United Nations, SDG, is the elimination of eradication of poverty. And we look like we're getting further away from it, especially after COVID instead of closer to it. It is my hope. And that's why I like the study of technology and entrepreneurship. If we push down because the price point of some of the technologies is coming down so that entrepreneurial opportunities could be available to the poor, maybe we could push up more of this opportunity so that I'm not looking for the poor to go away, just to have fewer poor and poverty across the globe. I'm not pointing my finger at any one place in the globe. We have poverty in every place of the globe. But if we pull together our technology and our entrepreneurship in areas where there is less abundance, I believe that we can make a change and we can change the world. My idea is that many times we're always looking at someone else to be the change maker. We look at ourselves and we say, what can I do? So where I would like to, and what I've been encouraging in the last couple of speeches, I did a couple of speech at the UN, UNGA 78 side events. And I encourage everyone, the uniqueness in you. Everyone has a purpose. Everyone can help make a difference. Don't wait for Joanne to figure out what it is that you should do, but find within yourself how can you help not necessarily the big differences, but the small things that you can do. For example, when we first started working with some of the global leaders around the world, there were a lot of voices talking about the future of work, but it wasn't from the bottom up, from the top down, a lot of folks. And I'm not disparaging that. But we put out our first book, The Future of Work and Entrepreneurship for the underserved to be voices that were not normally heard in this space. Because a lot of times people say, what the poor should do? But I'm like, well, have you ever been poor? Do you really know what it is? Have you ever missed a meal? And you're going to tell us? And so that body of work came from countries, at least 20 or more countries. The scholars were from all over. And then there was a demand to do more. So we did another book and we put out the sustainability and the future of work and entrepreneurship, talking about similar issues. And we put out a call for papers. It was peer reviewed. A tremendous number of scholars from around the world wanted to participate, to let you know that there is a demand for these voices to be heard. And so when I was looking at the work, it's like, okay, so we've done this, but how have we changed? How have we shifted? How have we started making the difference? And so where I've taken the work, actually, the book should be published later this year. It's talking more about really practical things that all stakeholders can do, the private sector, the public sector partnerships, the community leaders, even students can have a role in making a difference and making a change. And we don't have to wait for one person or one body. In fact, what I say often is the wealth in the world is not in the hands of the government. The wealth in the world now is in private hands. And so we need to stop looking at the solutions of the past and to go back to my Howard statement that I said earlier. We have some problems that are old problems, but need new solutions. And if we all work together, collaborate together and join in this movement of making a difference by action. You know, academicians love to talk and so they can talk all day long and produce papers that no one ever reads but themselves. I would like to take the work down to the people who can make a difference and who don't necessarily read the academic work, but are waiting to see a sea change and what we do with the underserved. Within the U.S. specifically, since the civil rights, you know, the gap, the income gap, we would think with all of the policies, all the public, blah, blah, blah, that's going on, that gap with his shrink. For a lot of structural reasons, it has not. It has gotten wider. The income gap, the wealth gap. And so when we look and reach for the solutions of the past, they did not give us a resolution. They did not give us the outcome that we were looking for. I do believe, and that's why it works so hard in technology and entrepreneurship. We're not going to make the change unless we find economic engines in these communities, these local communities that are job creators. For those, everybody can't be an entrepreneur, but for those who can, if they stay in those communities and produce income streams and wealth streams in those communities, we can make a change. We haven't yet, because I think we thought that someone else was going to do it. And no one else has. And so we have to do it ourselves. We have to become the change makers. We have to become the job creators. We have to become the ones that lift the people up. And they will work with you. And that's been my experience. We work with several economic development corporations in the community, and they work with us. And it's tedious work. It's not easy work, but I do see differences and changes. So my suggestion is to everyone, don't just look around to your neighbor. Say, what can I do today? Can I help an entrepreneur, a small business person? Can I give to those who can help others? What can I do? Make it personal. Don't make it institutional. When we make this personal, I mean, I guess you can see that this is very personal for me. I grew up in a segregated town. I'm old enough to have lived through segregation. I believe in the economic development that can come from entrepreneurship and business development in communities that are undeserved. A very personal experience. So it's not an accident that I became an economist. I love the tools that economists give us. They give us both quantitative and qualitative tools to attack our socioeconomic problems. And so, with that, I think that's me. That's what I do and what I love, and this is what I've become. I just did a blog post about a young man, and his goal was to take underserved folks in the community and teach them about investing and Wall Street. And I said that was the personal, because he had been an investor on Wall Street. He had been a trader on Wall Street, and he didn't see people who looked like them. So instead of just walking away and creating his own body of wealth, he said, I want to reach back and I want to help others in my community become traders, become investors, or whatever it is, but to develop financial wealth. And so that's what I'm talking about. He took it personal to the development of others so that the whole could be greater, and it's not just him.


And for the third time, welcome to those in the room to this session on AI and machine learning. I'm Brenno Horos. They're working at the HIST Center at the University of Oslo. I have the pleasure of simply hosting this session with Elmarie Klassen from HIST South Africa, Bahid Rustami from Macarons, who will be presenting use cases for using the HS2 data and integrating with these new methods. So I will just give the floor directly to Bahid. Please welcome. Hey, hello from my side as well. Can you hear me well? Okay. Hey, so thanks Brenno. My name is Bahid Rustami. I'm a machine learning scientist at Macarons. So I'm a very technical person, but I will try to keep the talk as non-technical as possible.  And then I will try to explain a bit what is AI and machine learning and hopefully demystify this buzzword that you hear all over and hopefully keep you until the end of the talk. Because also a perfect time probably for a short nap after lunch. And then I will go to one of our use cases in Sierra Leone when we work on forecasting essential medicine and vaccine. So Macarons is an AI company and our vision is to create more from less by making pervasive system intelligence. And we are working towards a work where nothing is wasted and where each resources generates the greatest impact. And it's only possible by using AI and you will get to know more how this is possible later. And also our mission is to extend what is possible with finite resources, solving the global problem of ethically matching supply to demand. And also this bring us this kind of big challenges of adopting whatever AI and machine learning methodology exists out there to be adopted to the low resource setting. So I will talk mainly about our work in global health, but we are working in transforming supply chain in basically every type of supply chain in agriculture, environment in education and in industry. So Astriata is our core product and is a platform that I will go to in the next slide showing you what are the functionality of our product. But here is a map of the deployment that we have with different government in the across, so in Africa, Asia and also United States. And in dark blue, you can see our staff that is actually also distributed all over the world, and we are a fully remote companies. So, so Astriata as I mentioned is a is a platform and is our core product and it has three pillars. So, Astriata infrastructure Astriata forecasting and Astriata behavior. So Astriata infrastructure tries to answer simply the question of what is there. So if you think of, for example, a map at the national level. So what what Astriata provides you is that you can zoom in in this map go to the health facility level and and see what is there what is in the shelf there like both in terms of staff and also in terms of material which are there. And also it gives you a on top of that it gives you a kind of optimization strategy to how to redistribute the resources which are there to to actually use them efficiently. And Astriata forecast tries to answer question like what's needed where and how how much so basically it gives you a number that what is the need of a specific products that you're looking for in next month or next quarter or next year. And and Astriata behavior tries to answer question like who will show up for care and who won't. And this actually gives you a good understanding of that who for example is a patient who will drop out from education and you can or will be a loss to follow up. So basically combining all these three together we can have this comprehensive solution that at the end helps the decision maker to to to decide how to allocate resources. So now just talking a bit about the so the name of the session is AI and machine learning with DHS to so you all know about DHS to I'll try to explain the what is artificial intelligence versus machine learning and what is the so deep learning maybe some of you heard about. So artificial intelligence is the I think terminology existed forever even. I think pre-socratic philosopher talk about or even Greek mythology and the idea is to create intelligence which is artificial so mimicking human intelligence but making artificial kind of version of that, which can be outsourced and kind of come up with some limitation that I might have. And there are different approaches people followed in artificial intelligence and machine learning is one of them which been quite successful in last decades because of the power of computation which grow and also because of different approaches that developed both in academia and industry and and the idea there is to learn from data without explicit instruction. And then within the machine learning approaches the one of the most successful one in the recent decades is a so-called deep learning where you when you use a neural network which is inspired by human nervous system and is only inspired because still far away from how the real brain works. And but it's been quite successful in solving real world problem. Now the example I want to talk about is that so what are the potential of AI solution compared to traditional solution I think most of you are familiar with many different types of solution you developed for your problems in more classical settings. And I want to hear now compare what what AI gives you compared to that so if you think of traditional solution usually you have a problem. So you try to find solution for that so you define some rules and then you can combine the data you have with some rules. Then you get some outcome and based on that outcome you try to make decisions so you can think of like Excel sheet you have and some like operation you do between your columns and rows and get one number and then make decision with that. The characteristic of this type of solution is that is a static so as far as the rules are fixed outcome is always the same. Eventually over time the error increases because outside world is changing but the rules you put in are fixed so so the error that you make with your solution goes up. It needs perfect data so even if you have a perfect rules for your problem and the data is noisy you will get a bad outcome. And this kind of solution become absolute very fast because as I said the outside world is changing unless you're changing your rules constantly this kind of solution doesn't work. On the other hand with AI solution what we do usually is that first of all when there is a problem for example in global health we talked to the expert in the in the domain. We just gather some prior knowledge of like what is the problem limitation and we don't need the exact solution but we just need some prior knowledge. We combine this prior knowledge with the data we have at hand and then first we just let the methodology that we develop to infer the rules and then based on those rules we try to do prediction and as the new data comes we constantly improve our prediction. So characteristic of this type of approach is that so it's dynamic as the new data comes the prediction you make constantly is improving. The error eventually decreases because the algorithm you have a place actually like learns from the new data which is coming and also it doesn't need perfect data and this is also one big. Direction also at Macro is we have to really like come up with solution how to impute data how to deal with missing values and grow like intelligence solution to deal with that. And also the important part of this is adaptable and to just summarize this slide I think if you think of pandemic as a good example when the pandemic. What happens with the traditional solution is that the so the outside world, the pattern that demands everything's changes like hospitals are occupied for different reasons, but the rules you have for your solution are still fix really big error on the outcome you have but what happens with the solution is that at the onset of pandemic you make a big error of how you observe the word but as the time passes in the course of days or weeks or months. The algorithm learns from the now new rules which actually exist in the outside world, so it will correct the error it makes. Okay, so now I want to go to the. So one of the use cases of our product that we grow and also deployed in Sierra Leone working with the government of Sierra Leone and MoHS. And so I will focus on the forecasting we had there but also just want to mention that we also have a HR optimization tools where basically we. We kind of like developed solution how to basically like re-staffing the different health facility to make sure that all the medical cares are supported. So, so for forecasting what we did was that they're talking to the government we we got a we got access to DHS to data so so our engineers developed like API to to connect and pull data from DHS to. And then we we also agreed with the government to to focus on eight essential medicine that they had and so they told us to work on and three types of vaccine. And what we did first was to looking at DHS to data and come up with the actually like a measure of what is the error in place like right now the system that works. What kind of error the system makes and we use the three months rolling average for forecasting which means that we look at the last three months consumption I use that as a forecast for future. And that was the baseline error that we defined and then we developed our solution and here I just show you the result and then I will go to the details of the challenge and solution we had. And we could show that depends on the type of products we were looking at we could manage to improve the forecasting error by 34 to 59% for essential medicine and 56 to 89% for for the for vaccine. So now like a starting with the data so DHS to data that we had we did some quality check off the data and here I want to show you the I think this this plot is here shows you that the all the data we had so we had data from January. 2019 to January 2022. And on the y axis you can see the percentage of missing values in this data. So, so in 2019 we had like more than 80% missing value. It got better, like in 2020 but it always stayed around 40%. And this is I think for for whoever is doing forecasting is a quite big challenge to to manage to improve anything with these type of data. And also underneath I just want to show you two example of the data we had for for those who've seen time series before to have a better understanding of what type of time series I'm talking about so here you see on the left and the right in the bottom you see the two different types of medicine at one CHP. And on the x axis you see the time and also the y axis shows the the consumption over over months. You see that there are a lot of missing values also the other interesting thing in this data is that there's a lot of zeroes there are like 20% of these data which are not the real consumption so that's what we call false zeros, which could be a human error or sometimes there was a so the the missing values and they just replace it by zero. And we had to also come up with some strategy to detect those zeros because they all influence the whatever prediction you want to make. So, yeah so so that's solution without going to the technical part of that whoever's interested would be happy to talk about it afterwards. But what we do is that for forecasting usually if you think of like when you want to forecast for one variable you look and think of like for example for weather or stock market. You look at the historical data you look at your variable how it was behaving in the past, and you try to find some patterns like some trends or seasonality or some auto correlation in the in the in the this variable you see. And based on that you try to predict the future and this is what people call like univariate forecast. If you have multiple variable but we can do is that not just looking at one variable itself but you can look at the correlation across them and see that for example if the future of one variable can be detected or forecasted by by looking at the others. And these are like there are methodology already out there but all of them needs long historical data to be able to have a good forecast. So here in our case here I'm just showing you four variables we had 9000 variables so we had eight for essential medicine we had eight products and we had around 1200 health facility that we wanted to forecast for. So we had more than 9000 of these variables that I would call time series. And also we didn't have a he's like a long historical data so pair time series or pair product at the facility, we had on average 10 to 15 samples and this is I think for those people who work in the forecasting. They know is a kind of like quite impossible to do any type of forecasting in this. So what we did was, first of all, we developed quite different strategy to create more from the data which is in at hand so created different features from this data. And also try to not only focus on the DHS to data but looking at the whatever publicly available data out there like satellite imagery web scraping whatever that could be correlated with the consumption that we have at hand. And also looking at all these nine times series at the same time try to find the whatever like different order of correlation that we can find in this pattern across different facility or product to help our prediction and these are all done with the so called supervised learning or machine learning approaches that that we developed at my Christ. So going to to now to the to the to the result. I want to show you so what we did for validation is that is that we kept the last three months of the data from DHS to as a test set so we put it like a side. And then we trained our model and the rest of the data and then we asked. So first we look at the baseline so that baseline as I explained was these three months rolling average. And we asked like what is the error if you use these three months rolling average and this is what you see in red for each product so for example for amongst the ceiling here you see that if you use three months rolling average we get around 69% error. In green you can see our error so if you use macro is solution we get 37% so we had like around 46% improvement for this product and and of course depends on what product you have we managed to improve the forecasting error between 34% to 59%. So for vaccine we use actually very similar approach but here we had another prior knowledge let's say that's what I meant that talking to expert in the field that we knew that consumption of these three vaccine are actually very similar so when when they're at the facility they use one type of vaccine very very likely they use also others so we use this knowledge to first impute the data and then actually following very similar approach we had. And also I just have to mention I don't have a figure for that but in vaccine the missing value percentage that I showed at the beginning was way lower. And that also helped us to actually the improvement that we managed to have here was was drastically better so we here we had like between 56% to 89% improvement for the for the vaccine. So, so now I want to actually like tell you like what are these percentage means so so I'm talking about 56% error what does it mean in quantity so for example if you think of vaccine. So here for example I'm showing BCG vaccine. If the for example MOHS wants to actually like has 100,000 vaccine wants to distribute. If they make decision based on three months rolling average what would happen is that they would have plus minus 64,000 error to the kind of like the quantity that they distribute. So if they would use the macro is forecasting the deal actually goes down to 7000 so so you can see that depends on the total supply that you have, you can have actually like quite drastic improvement of the wastage that that that you might actually have. So actually that's what I wanted to share to sum up I want to just mention that. So all our effort is actually bringing AI in the and actually adopt all this methodology to be applicable in one of the actually most difficult and poor data environment. And this needs a lot of actual research and also practical work so we work with the also different university we have like research direction which tries to readapt all the machine learning techniques which are out there and normally actually are very much optimized for big data or for organization that they have a lot of data. But we really try to get this methodology try to adopt it to be applicable for low resource setting. And I just want to finish by by this sentence saying that I heard a lot from many people, especially in global health sector that we don't have data to do machine learning but but I just want to say that this is exactly what we are doing we really try to revisit all the methodology out there to be applicable even in this example I showed you we managed to to improve the the forecasting quite a lot. Thank you very much. Thank you very much. I think once you display you can switch. Swap displays. Yeah, top top left third icon from the right one across. Yeah. Right. Okay, should we get started. Right, I'll start down here. Hi everyone. So I've had the privilege of working with Omri for for many years. She tends to take my job over time and does better than me and leaves me with quite little to do. So I'm going to kick it off and then hand it over to her. So they're two sort of fundamental misconceptions around data science that we picked up over the years and by data science I mean AI and predominantly machine learning is the space that we're in at the moment and the first misconception is that it's mainly wealthy countries wealthy settings that should be doing AI and machine learning and they're going to they're going to benefit the most and they should do it. And what we're finding is that's fundamentally wrong because AI and I think you started seeing that in the previous presentation is is most useful for resource constrained settings, which is actually all of our settings in reality I don't think anyone here would feel there in a setting where there's too many resources, but particularly in some of the countries like African countries where those resource constraints are so so severe. The second misconception that we've picked up over the years is that other people are doing this really well. They're brilliant people out there that are doing data science in health so well, don't even bother getting into it. And that we've also picked up is fundamentally wrong and those are the few that are following some of the research that's come out of after COVID and there's some of the meta analysis on the machine learning work that was done in the previous presentation is found that actually this is a very messy space that's not done particularly well. And that the strong use cases that are being done well are very very few. And so it's a space that we decided actually probably is worth in getting into. So those are general misconceptions I'll tell you about some sort of more personal mistakes that I've made along my career that I think also helped to make the point here. So in early 2000s and one of my biggest frustrations working in South Africa, particularly as I got out into the more rural settings was, wow, health sector is a bit of a mess. We don't resource very well we don't plan very well we don't manage anything very well. Maybe I should get more into public health. And I started moving into public health and public health informatics and started looking at the sort of data that we had available to make decisions and I realized wow the data is not very good. Maybe what we should be doing is working more in digital health health informatics and starting this generate better data because if we've got better historical data, we can make much better decisions. And I probably spent a decade doing that kind of work and what we've learned is that our historical data is not usually that useful and that valuable because it doesn't tell us that much about what we currently are experiencing. And so coming full circle it's starting to get us really excited about the world of data science and machine learning because if we can have improved our systems and have better quality data coming through. But then we can work with it in a new way in a more predictive way. We, we may start getting to a point where I'll start feeling okay well this this is this is looking better. Anyway so that's about me. Let me push some buttons here. So we were going to spend some time talking about terminology but our colleagues dealt with a lot of the terminology so we won't worry too much about that. But for those that are new in this space initially and certainly we found that as well as there's a lot of language being thrown around. But fundamentally what we're talking about when we talk about data science is AI and machine learning and much more towards towards machine learning. So this is what we're going to cover cover today. So this is a picture that some of you probably know very well. And it talks a little bit about that cycle that that I was referring to but it's about how we, as we can start doing more things with data we can generate information as we can start being able to share that information and internalize it and then push it back and share it back to other people so we're just moving towards this knowledge space. What we're really trying to get is to get beyond that and get back down into into the inside and what some people talk about wisdom and get more predictive around their understanding what it's really saying about our environment and then actually use that to take decisions that then have an impact on our environment. And if you actually start generating those benefits that are pushing back to health system changes health output changes, low maternal mortality better infant mortality how can we make those sorts of decisions that's what we're actually actually going for. And in the DHIS world as you progress along your DHIS maturity you move from there and can start feeling the improvements and get really excited but while you're still in that top sector. We probably haven't generated any health system output improvements or outcome improvements. And that's, and that's one of the big problems is we, we, if we get stuck there we spending more and more money more and more resources are being sucked into this information space around trying to make better decisions, but we're not improving the health of our populations. We may not even be improving the health system much, but we need to get down to this space where we start seeing those those decisions becoming more productive and actually driving the, the outcome changes around impact. So a little about a bit about his and my his but the top we meet his South Africa's journey. In 2016, we started getting exciting excited about this because we realized okay. We're not so far behind they're not a lot of smart people doing this brilliantly so we shouldn't bother. And, and maybe it's worth it for for our setting. So we got past some of those early misconceptions, and we created a data science unit within his. It had no staff, it had no projects it had actually no know how at all, but we decided that let's make a start we'll create the unit. And we were faced with our first use case challenge, which was South African government wants to allocate 14,000 health professionals a year. So that's medical interns, medical doctors doing community service training nurses pharmacists a whole lot of people where government allocates them specifically to facilities. And the political mandate or reason for that is to get more equitable distribution of these junior professionals in a way that should help transform the health system. If you can get those junior professionals to start going up to more rural settings and the less attractive facilities in a more equitable way. You should start balancing some, some system system issues. So that was the challenge that we are faced with and we said well goodness. How do we do that. And we discovered this was actually a data science challenge and how we put together algorithms and how we start understanding those individuals, collecting information from them to say well okay you 14,000 people, where would you like to go. Then could we introduce some incentives to say well, if you go towards those less attractive facilities will introduce some incentives. So if you choose a less attractive facility in your top five choices will guarantee you get it. If you can only choose the really attractive facilities in your top five choices well you know you take your chances you may end up somewhere else. So those are the kinds of discussions we started happening with the Ministry of Health and how do we put those into some kind of a tool. And that's when we, that was the first challenge that was put to put to our team. Anyway just a little bit of a history we're going to get into some different use cases now. And it was as we got into that where we started realizing we need, we need to start appointing some people that really understand the space and we appointed our first data scientist in in 2019. And then some more use cases started coming along and these therefore that we're going to get into into a moment. And I'll be handing over to to Elmerie because then they get to the tricky stuff that I have to give to her. Thank you for the time here. Good. Yeah, yeah, so I'm going to take you through three very high level use cases that we have and then one into a bit more detail, just to show you the kind of things that we are working with. So we have a project project with Africa CDC looking at event based surveillance event based surveillance is sort of outside of integrated disease surveillance where event based surveillance looks at events that is not specifically reported or diseases that are not specifically reported so you're looking at events that comes from social media or from the media kind of thing and I'm going to go through four steps and my three use cases to how we identify, improve, innovate and impact on in these use cases. What we're looking at here is to use a P Twitter is a WHO product that sort of uses natural language processing to identify so you can add in tags like say covert Ebola or whatever, and then you get all the tweets that has happened about that. Now, for me to say I'm sick and tired of reading about covered. That doesn't mean it's an event of covered it's just my sort of frustrated tweet on social media. But if you were to say that you know monkeypox happened in a specific country. That might actually be something that is not yet picked up. And the challenge that the epidemiologist that Africa CDC have is that if you were to look at say, even 1000 tweets a week for them to go through all of those. It's a blood of human capital that is spent on just figuring out which is really events that they need to respond to, never mind then going to respond to those events so what we can do is to pull all these events we can get the 1000 events from later. But what we then need to do is to we are refining the output through a machine learning process to identify, which is exactly those tweets that are likely to be events. So we perhaps give them 100 to look through and say, are these real events. And then if they are, then we post them into the DSI system through the events that we have pulled. And then hopefully over time, we're going to learn from those 100 that maybe 60 of them were not real events and we learn how to better present them with a lesser amount of possible what they call signals that could be become events. So that is just a simple use case we're working on. And then the second one may be a little bit more interesting for the DHS to community. There is something like predictors in DHS to and I know it's very much used. The challenge that we have was predictors in DHS to is that you must select a certain algorithm, either exponential logarithmic and sometimes facility profiles and the data in facilities are very different. You might have a small facility that we looked at this specifically for sort of HIV patients in care and looking at how do we increase the patients in care for 95, 95, 95. Now, certain facilities are increasing the number of patients other facilities have a different process where where you might have started treating those patients in hospitals. Now you're looking at them treated more in in primary healthcare facilities and hospitals are going down. So using one algorithm is not always giving you a correct solutions there. So what we have done is that we developed a best fit algorithm for running this at every facility level and determining the algorithm that needs to run that that you need to use to predict that in for each facility. And then what we're doing is to link that to machine learning process. It is quite processing heavy so we're running that in a machine learning application. But pulling then also developing a DHS to front end application which can select which elements or indicators you want to run it on. It runs it in the machine learning application and then would post it back into DHS to prediction into data elements and then Essentially, we see a lot of use case for this in terms of predicting targets predicting in ideas are perhaps thresholds that you would want and essentially hopefully we can very soon contribute those to back to the community as a solution that you can use with the app. And we will see how do we implement the ML solution most likely would be something like a Python server that we can give you the the whole process if you can install it if not, we can write it as a service or something like that. And then health workforce modeling is the other big case that we have. So, you know, many of you might also be aware of the wise and model to where you look at how many doctors should you have based on population, 10 doctors but then 100,000 population so many nurses and so on. But what we found in South Africa is that those norms are not always practical for the Ministry of Health to implement and how do we develop a scientific model for health workforce modeling in terms of how many stuff do we need. So we have developed this looking at a specific WHO and South African norms for predicting And we use one district as a proof of concept. And now we are basically refining this ML model and build to build a planning model and then also what we're doing is to build a front end on a module that gives would give the Ministry, Ministry options to do scenario based planning. So if I tweak the the norms to from 10 to 9 doctors then what does what would that output be. If it is epidemic then how many ICU beds would I need and how many ICU trying stuff to a need versus maybe a non epidemic scenario and things like that. So, And then, you know, it's really to be able to add that value at yes we have HR data we know how many data there is, we have indicators but we don't have this type of intelligence in terms of helping them to say if this then what, you know, and what is the how can we help you make better decisions. What is that question that you really need answered on on that level. And so this is just some of our workforce models modeling results we looking at it over head count as well as population based. And I'm not going to go too much into this because I do want to, but you can see basically here for and it's also in South Africa, it's very much about equitable distribution. There are areas that historically got enough stuff and more than enough stuff and other places that have got a lot less stuff and patients are really struggling. So you can see here that we actually coming out with a surplus and shortfall or a gap in terms of the number of stuff that that is needed. I'm nearly done. Just to then say, in terms of the workforce planning context, there is a bit more of a in South Africa we have a public sector that serves 80% of the post relays. So population with around 500,000 health workers and then 20% of the population is served with around 400,000 health workers. So you can see that there is a lot of sort of misalignment there as well. And really here in workforce planning is to help the ministry to determine optimal staffing levels to mitigate attrition and to recruit what recruiting interventions that you have, what training capacity is required in the government worthy staff. So on all of these we basically look at attrition and just developed one simple use case. And these are the sort of steps that we are following. And just firstly to say what what is the question that we really need to answer. And that here we're saying it's determining the staffing needs by analysis of exits and considering who are those imminent retirees and or people who may leave the department and what what would their reasons be? Are they unhappy? Are they going to resign or retire or what is the reason for the for the exit? And then we needed to see what data do we have access to and where can we source new data, additional data that would help us to answer these questions. Do we even have enough data? Do we have access to this data? And then to get that access was another process. We then did an initial analysis on this data. And sort of these are some of the sort of looking at ex-employees and current employees. And this is a graph shows the reason for the resignations. The bar shows the mean and the upper and lower courts for each reason. We want to understand how variables are the reasons, how variable are the reasons for leaving for each age group. If you look at the second bar resignations, it shows that the mean is 38 years of age and the range is between 30 and 48 years of age with some outliers above 80, which could be a data quality issue. And this graph explores the years that people have been an employee as a service. And we wanted to know if we are with money as a factor to make people stay. The education level in this is the size of the bubbles and the lowest salaries, a lot of them are leaving earlier. So, and below 10 years of service, while the longer you stay and the higher salary you earn, the longer you are actually showing. So, this is really around knowledge discovery and understanding what data that we have. And the next steps then is to look at the finding the use case and decide whether we actually have a viable case to go further into this. Because it is expensive, you need to run, you need to get a lot of data, you need to spend time doing all these things. And honestly, not all data science projects are successful and actually give you a result that actually is worthwhile the investment that you make in it. So we decide whether in our early explorations, are we actually having something worthwhile to go further into or should we just stop here and rethink or not. And then what we're doing, the data engineers, which usually the repeated data, those are tasks such as take a position processing and governance. And we then go to the data scientist role of training the algorithm. And as described before we separate the test set, more or less 20% of the data, and we use the race of the data to train the algorithm. So what we did is, if we could predict from data that we had, which employees would actually leave in the next three months or not. And through this process, you essentially iterate this training. You know, repeat that until you're happy with your, your algorithms and your model. And then once you trust your model, we deploy it on a subset of employees of the December 2020 data, comparing the predictors predicted results against what who actually then the resigned. So we predicted who would resign in December 2020 and then compare it against who actually resigned in 20 or who left in 2020 December 2020. So you can see on the left, the actual employees who who left the department and the prediction of employees who are likely to leave the department. So if it is a one, then they are likely to leave and if not, if it is a zero, then they were not likely to leave. And then you can see for those who had actually left and that, you know, versus what we had predicted to leave. And we got quite an accurate result there. So then essentially what we need to do is to display this data for management to make decisions. And this is just some of the graphics around that. And on next steps is to scale up the data sets to refine the accuracy, identify and access additional data sets to adapt the model to predict further into the future. And essentially what is the value for this for the Department of Health, they basically can understand why do people leave, who is likely to leave and where will the gaps be in the future. So if we have only a few big iterations in the country and, and 30% of them is going to leave then how are they going to replace those, those iterations because then they can take action. Maybe they could prevent that attrition, through a person said to approach plan for training or recruitment of new staff, limit the cost of the training, and limit the loss, limit the loss of the main expertise and over their overall ability to plan better. So this is the strategy 2030 of the National Department of Health, in terms of attaining universal health coverage. And really what they need to do is to plan for effect efficient workforce to attain universal health coverage and this work is essentially touching on all of these green parts of their strategy. And, yeah, and for us as his essentially our endeavors to get into data science are growing we are learning we have got data science to data scientists that we will have on staff shortly, and it creates new opportunities for us, and adding value to our clients because we're moving away from just looking at data is data is data and information but looking for that intelligence, worse than man impact that we could reach with that data. Thank you. And just the acknowledgments and for the projects that we are working that this on is a CDC project. And this is some information about this. Thanks.


Given an expression involving a radical, we can add, subtract, multiply, or divide. And nothing important changes if we allow variables to be part of the expression. For example, suppose we want to expand the product. We'll use our area model, and we'll let one side of our rectangle be 5 plus square root of x, and the other side will be x minus square root of 3. So we'll compute the areas of our four individual rectangles, and our area, our product, will be the sum of all four areas. And the only simplification we can really make is going to come from this product of square roots. Remember that provided that our radicands are both positive, the product of a square root is the square root of the product. And we could try to rationalize the numerator. So remember that we can rationalize a radical expression by multiplying by a conjugate. And remember if you're dealing with a set of square roots, the conjugate is going to be the same terms, but you're going to change additions to subtractions and vice versa. So multiplying numerator and denominator by the conjugate. And remember factored form is best. Don't expand unless you need to. Well in this case we are trying to rationalize the numerator, and so if you're trying to rationalize a numerator or denominator, expand to show you've actually eliminated the square roots. Now since we don't care what happens to the denominator, we'll leave that in factored form. But we do want to expand the numerator, and we'll get. And remember the numerator should be considered to be one term. And this means we can remove a common factor from numerator and denominator, and so our expression simplifies. It's sometimes convenient to incorporate a variable inside a radical. Our ability to do this relies on two ideas. Provided that a and b are greater than or equal to zero, then the product of the square roots is the square root of the product. And also provided that a is greater than or equal to zero, the square root of a squared is equal to a. And remember equals means replaceable, and so these can work in both directions. For example if we want to rewrite this as the square root of a single expression, so five we can bring under a radical as square root of five squared. And because we have a product of square roots, it's also the square root of the product. Or we can take something like this. So the first thing is to get the things that are outside the radical into a square root. One should also be inside a square root. One of the other properties of working with square roots is the square root of a quotient is the quotient of the square roots. And now we have a product of square roots. So we can rewrite that as the square root of a product, expand and simplify.


The excitement and expanding use of chat GBT has ignited a race among AI labs to develop and deploy even more powerful tools. As this is happening, many stakeholders such as government regulators, business leaders are now scrambling to understand the impact the rapid emergence of new AI tools will have on consumers, businesses, society, and global economies. Regulators are considering new rules and guidelines for AI, while even some leaders in the tech community are calling for a timeout to better understand their amplifications of generative AI. In this podcast, we will explore the implications of these new AI tools and the challenges and opportunities they present for business and others in society.


You're welcome back to the breakfast on Plus TV Africa. Just on the back of the conversation we just had with the spokesman of the Irrigue Development Association, we've invited Mr Mohamed Abdelahi, he's the chairman of the Mieti Alla Cattle Breeders Association in Plateau State. Good morning Mr Abdelahi. Good morning ma'am. I first want to hear your side of the story representing you know the Fulani Cattle Breeders Association in Plateau regarding the attack that occurred in Nyango in Plateau State on Saturday and Sunday. Now the people in the community say that Fulani herdsmen allegedly attacked them twice, first early in the day and later at night burning over 250 houses. Can you tell me what really are the facts according to you? Let them tell you the gospel truth of what is happening in Nyango and its areas. On Wednesday it was not on Saturday but it started on Wednesday where they attacked Rafim Pawna and killed nine cows and then a one herder which on Friday mocks are not able to attend their Friday prayers because of the afraid of attack by Irrigue. Where they were moving themselves in Kwa Chudu and and and the book Mayango which on Saturday they went to pack to to hold in sophisticated firearms with their masquerades. Four of their masquerades were there that they will fight and finish Rafim Pawna and Fulani that are residing that area which immediately they informed me I informed the security agencies which they were prompt and they went there and they helped any other further killings in the area. Though we lost so many people and we so many of the injured now we have in different hospitals about 16 persons in with different degree of injuries and then and then we have recorded five dead persons and some other missing until now we have not ever to see that but they continue complaining that it they have been attacked they have been attacked who attacked them where and how. So what where is the reports of dozens killed and 250 houses burnt are you saying that that report is false? Let them say the gospel truth let them discuss to you what exactly happened because these Irrigue people they have compounded their own problem where they may nobody control their own problem because they went as far as the killing people in the same basal local government they went to south and killed people they went to Riyam and killed people went to Barikil Ladi they went to just east they went to just north and so on and so forth they crossed the plateau that they went to Pauchi state and killed another people killed their cows just recently about a week ago they lost they killed six herders in Kaduna state and roasted their 362 cows moved them into the area and killed all the cows there. So exactly what is happening in Baisa in Myanmar is somebody cannot say it exactly but they know what they have done and who are they are fighting with all right but it is not our own people because our people are there come in the villages and they are they are looking at this happening there. All right well we're not we're going to focus on plateau state now and not talk with regards Kaduna because you just mentioned Kaduna the news reports say that you know dozens of people were killed in the last 48 hours in that local government area and also about 250 houses were burnt so are you saying that that never happened and instead it is these Irrigue community persons that are attacking full and ease in different local government areas. Let them tell you what they did before they they have been attacked if really they have been attacked.  Wait I cannot I cannot testify to that exactly because I am not there I cannot see things by myself I is only information I've been given but I was told some since on Saturday security agencies were beefed of police and the operation self-heaven are all there in the in the area and they are trying to halt any other for that killing so if they tell me that the houses was 250 were burnt people were killed and I will be stopped let the security agencies tell us what they are doing there to halt the killings and the structures of property which I don't think it is true if they are just saying it to to to to just give the house keep the house sympathy for other communities and government and so on and so forth. Alright so can you confirm if there were can you confirm if there have been any reprisal attacks because from your statement now it seems like just the Irrigue community were present from the Irrigue community attacking full and ease so would you confirm if there have been any you know counter attacks of the full and ease you know how to fight back or you know attack these communities back in the last 72 hours. All the leaders I am talking to in Bazaar local government that is my community leaders they are telling me there is nothing like that it has not happened and they are very common in the villages especially Maraban, Tere, Tuzdenkura, Katume, Rafim Bauna and so on and so forth. They are very common there before yesterday the commissioner of what is called me around 11 o'clock o'clock in the night I find out some of the leaders you are even on their sleeve like the art work of mango was on your sleeve and he told me let him confirm and later on he called me he told me there's nothing going on in the area so I am this talk if they said somebody has attacked them with the heavy security places they are just by the backyard of the the third armored division and they are telling me there have been attacks and nothing has been done it is baffling me it is disturbing me these people are not telling you the girls the truth because they are not got fearing people they will just tell you something and information that we we give them sympathy and they will not get that sympathy they are looking for. So Mr Abdelahi what do you know about the fight for ancestral land in miango plateau state because the spokesman of a river community mentioned that that's what the bone of contention is that full annee headers so to speak want to take over their ancestral land and that's why they are attacking them and trying to displace them from their community forget about this is that that's what they are saying since 2005 they displaced all the full annees around there within their mix live behind their houses their their landed properties all other household properties and fly away and they are up to today not in miango district and called district no full annee existed in these areas though they have some properties we are the irrigate testify that yes they know about their own properties but they are not there some of them fly to kaduna state some of them fly to bautism state and so on and so forth so if they tell you that story who are those flighties within their own area that are attacking them. Mr Abdelahi just for the bigger picture do you see a likely end so this clashes that we have in between farmers headers especially those of full annee extraction and what do you think that one more Mr Abdelahi and also what do you think that religious leaders traditional rulers like yourself can do to douse the tension. Before I answer you I want to ask a question please ask irrigate people all other communities surrounding irrigate about five or six communities including the attend people the of ganawari the the kamarunchawai the rukuba the aphids are they are more and so on and so forth that surround irrigate land as today often today in peace with everybody there is no skirmishes in the area why is it continually pitting itself in the irrigative though why all other communities are in peace that their own community is always on fire what is happening we let them ask we let us be held our discussion number two how to stop these killings and destructions number one is justice must be done if injustice is there then we may not get the peace we are looking for so we need fear play to everybody and treat everybody equal so that everybody means we have the sense of belonging number two we need a strong collaboration between government security agencies we the community leaders traditional leaders religion leaders and other opinion leaders to come out and sit on table and talk the issues and profile solutions to the issues if not these killings will continue and then finally on the security end they should be free and fair to everybody all right uh nuru abdullahi the chairman meet here like how to breeders uh association in platterstake thank you very much for your time this morning thank you thanks for sharing your perspective with us good morning all right and that's where we will be wrapping up this morning on the breakfast really really tense conversations here and there but thanks for joining us if you missed out on these things and you would like to catch up remember to catch us up on our social media platforms it is at plus tv africa on facebook and instagram same with our youtube channel at plus tv africa and of course at plus tv africa lifestyle i am also gay and i am a netta philx asking you to have a beautiful Tuesday


My name is Michael Dos Santos and I'm a commercial content producer. We produce photography and video for advertising. Running for me is definitely a stress reliever when it comes to my work life. It definitely, you know, whether I'm working in Texas and Japan, London, anywhere in the world, I can run. And, you know, I've definitely run in some interesting places. I was in Marfa, Texas back in September. Went on a run for pretty much like a five-mile run just to start my day and ended up running alongside a cargo train that was like probably a good 300 to 400 feet long. Ran alongside her for like 20 minutes right at sunrise, you know, in the middle of the desert. It was, you know, pretty amazing and it's, you know, it's moments like that that like you're like wow. I mean I grew up always loving sports and then played, played a lot of sports in high school, played soccer for a year, played football for a couple years, ran track for two years. More as a sprinter but I didn't really get into long-distance running until my later years in college. Mostly to just stay in shape and lose weight. And then once I moved to New York, I kept with kind of some long-distance running and just training in general. I did some boxing for a few years and then I ran the Boston, I ran the New York Marathon twice. I'm blanking on the years but I know one of them was seven years ago because it was right before my daughter was born. That was the second time and then the time before that was probably like 12 years ago at this point. So New York Marathon is great, you know, it's New York. You run from Staten Island to Manhattan. You can literally see Manhattan and Central Park from the start line so that's pretty intimidating. But it's a great marathon, a lot of big turnout. So that was definitely a big accomplishment to run that marathon. But just being from Boston, running the Boston Marathon has always been a lifelong dream. Just growing up, watching it on Patriots Day whether it's on TV or when I was going to college in Boston, you know, going down there, kind of enjoying some of the party and the festivities around it. And just it's a pretty cherished marathon. It's a tough marathon so, you know, I'm excited to do it just to fulfill my dream and, you know, run the Boston Marathon because I'm a hometown guy. So I mean running for me, it's, you know, there's a lot to it for me. Part of it and the best part of it is it's almost a form of meditation for me, especially a long distance run. You know, if I'm out there running for an hour or two, like you know, you definitely have some time to think about some stuff and process things and just kind of get in the zone.  Sometimes, you know, I ran 13 miles the other day. I ran at my sister's house. I've never done that. She lives in Haverill. I live in Ipswich. Like it was pretty intense. I've been driving that, you know, path of that route for a while and to actually run it, it was pretty cool to just feel like, yeah, I ran it. I left my car there the night before because she had a party. I took an Uber home and, you know, ran there the next day to get my car. So, you know, I like a good challenge. I've always challenged myself through life. So I feel like fitness for me is just another way to challenge myself that's, you know, keeps me healthy. Obviously, I challenge myself at work. Sometimes it's a little too much stress and that's not the healthiest thing. So for me, you know, fitness, challenging myself through running. I do a lot of crossfit. I play hockey, so I stay pretty active. You know, running is just another form of fitness for me. But, you know, unlike the other ones, it's kind of, it also serves as a good, like, mental break. And representing the Pedro Martinez Foundation, you know, I'm honored to represent the Foundation. For me, Pedro, you know, he's a Boston hero, man. I love the Red Sox. I love Pedro. I watched Pedro. I was at the game. You know, he struck out 19 people like I'd follow Pedro forever. So I really love what him and his wife are doing with their charity. And, you know, I thought it was a perfect fit for me to run the marathon for him.


Welcome back learners. In the sections that we have discussed so far, we have learned about the meaning and nature of the state. Now in this particular section, we shall try to examine the evolution of the state. The modern state has evolved over a period spanning over thousands of years. The interplay of several factors such as skinship, religion, property, war, technological development, and political consciousness have all contributed to the emergence of the state. Sociologists have generally identified the following forms of state in the course of its historical evolution, which are the tribal state, the oriental empire, the Greek city state, the Roman world empire, the feudal state, and finally the modern nation state. Now, let us first try to discuss the tribal state. The tribal state was the earliest stage of state organization. The tribal states were small in size. The head of the tribe, often assisted by an advisory council, wielded authority and governed the state. The head of the tribe dictated and is dictates for obeyed by all members of the tribe. Anyone who disobeyed was punished by the head and punishment was meted out as per the customs and traditions of the tribe. The main purpose of the existence of these tribal states was the preservation of internal order and the waging of war with other tribal states. These states retained strong traces of common birth, common religion, and common trade interests. Now, we come to the next stage of state evolution, which is the oriental empire. In due course of time, the tribes settled in places where the basic needs and necessities were fulfilled. They settled in places where they could get food to eat, water to drink, and pastures for the cattle. Flourishing civilizations arose in the fertile valleys of the Nile in Egypt, the Euphrates, and Tigris in Mesopotamia, the Ganges in India, and the Yellow River and Yangtzeckiang in China. The increased prosperity led to the development of the art of war and conquests of territories. As the stronger groups won over the weaker ones, the inhabitants of these valleys were bound together into the empires of Egypt, Babylon, Assyria, India, and China. The oriental empires were neither strongly centralized nor closely knit together. They were made up of subordinate units, which though were practically autonomous in local affairs, were under central supervision. The units were under the obligation to furnish soldiers and to pay tributes. However, the large size of these empires eroded central authority, led to local reforms and external invasions. As a result, the empires disintegrated in due course of time. The Greek city-states. The next stage was the Greek city-states. The peculiar location of Greece helped in the evolution of a new form of political organization called the city-state in the ancient days. The mountains and the sea divided this area into small parts in the form of numerous valleys and islands. The different communities inhabiting these small areas developed features peculiar to the Rome. Accordingly, several city-states emerged in ancient Greece with a variety of political institutions. The size of the Greek city-states was confined to the city, which was the center of all activities. The city-states were the outcome of local patriotism. Athens and Sparta were two such city-states, which attained a higher level of political development in individual liberty. Gradually, the Greek city-states disintegrated due to mutual rivalry and frequent wars between themselves and external invasion. The Roman Empire. After the downfall of the Greek city-states, the main political development was the emergence of the Roman Empire. The Roman state passed through several periods. The first period was that of the monarchical state. During that period, the king was not only the head of the state, but also the chief priest of the community. The king was required to consult the council of elders and follow their advice. During the initial period of the Roman Empire, the nobles known as the patricians shared political power with the monarch. But the plebians, who included average working citizens of Rome, like farmers, bakers, builders, or craftsmen, enjoyed no political rights. Gradually, monarchy was succeeded by a republic ruled by elected representatives, and both patricians and plebians got equal political rights. During this period, Rome started annexing the neighboring territories. Consequently, the republic gave way to the Roman Empire, which extended over Austria, Germany, France, Spain, England, the Balkans, Greece, Asia Minor, and the whole of the Mediterranean coast and its hinterlands. In due course of time, the Roman Empire began to decay as institutions of democracy and local self-government disappeared. The feudal state, after the fall of the Roman Empire, central authority was eroded, and its vast territories fell into the hands of powerful feudal chiefs, that is the landlords, holding big estates. Each of these nobles created a community of his own, based on ownership of big estates. These feudal chiefs began to exercise powers in 5th century AD. Each feudal lord gave his land to tenants and chiefs, who in turn gave the same to tenants. This led to a hierarchical political organization upon the basis of land holding, with the king as the supreme lord at the top, and serves on the landless peasants at the bottom. In fact, the king exercised only superficial control over the feudal vassals, who enjoyed the real power within the domain. The erosion of the authority of kings led to the emergence of the Christian church as another symbol of authority. By the beginning of the 14th century, when popes were using their authority arbitrarily, the authority of the church was challenged and power of monarchy restored. The modern nation state, the modern state is largely identified as the nation state. Feudalism was succeeded by nation states, each one of which was based on the bonds of nationality and language, strengthened by national boundaries. The process of consolidation of the bonds of nationality and language led to the emergence of France, Spain, England, Switzerland, the Netherlands, Russia, and later Germany and Italy as nation states by the 16th century. Initially the nation states were mostly monarchies. However, since the middle of the 18th century, there has been a slow transition from the absolute monarchy to constitutional monarchy and democracy in large parts of Europe. With the growth and expansion of democracy, the principles of liberty, equality, popular sovereignty came to be established in a large part of Europe. Beginning in the 17th century, many European countries started extending the domination over Asia, Africa, and Latin America in search of new sources of raw material, cheap labor, and new markets. During the 19th century, there came into existence many colonial empires with Great Britain having colonies all over the world. Political awakening and national movements for independence in most of these colonies ultimately led to popular uprisings against the foreign powers, especially after the First World War. Subsequently, many of these countries gained independence, one after the other, from the colonial rulers, especially after the Second World War. In this context, mention may be made of countries like India, Pakistan, Burma, Sri Lanka, Egypt, Nigeria, etc. Thus, we find that in the history of human civilizations, various types of state systems have evolved in different ages, leading up to the modern nation states of today. Thank you.


Item No. SCP-1230 Object Class Safe Special Containment Procedures SCP-1230 is to be kept in a secure storage locker at Site-12. Access required mental and clearance too, with authorization and supervision by Clarence III Research and Security Staff respectively. Accessing personnel are not the view SCP-1230's contents. Personnel accessing SCP-1230 are required to submit written accounts of dreams experienced within 48 hours of access. See Addendum 1230A SCP-1230 has been relocated to a secure storage locker behind the desk of Site-12's main library. Access is available to Clarence II personnel deemed to be in satisfactory psychological condition by Site Psychiatric staff. Personnel accessing SCP-1230 must submit written accounts of their dreams within 48 hours of access and submit the follow-up psychological examination. Description SCP-1230 is an unlabeled, green hardcover book with no apparent exceptional qualities. When SCP-1230 is opened, it displays the phrase, a hero is born, on the first page viewed, while all other pages will be blank, resetting once the book is closed. This has no obvious effects at first, but upon falling asleep, the reader will dream of a fantasy world where they are the protagonists of a troubled land. Dreamers are completely aware and all senses work just as well as when awake. Dreams vary, depending on the imagination of the reader, and are mostly attuned to fantasies of adventure that the reader would enjoy. In the mind of the reader, these dreams have been documented to last anywhere from 45 seconds, see Experiment 1230-3 to 200 years, see Experiment 1230-5, but in reality, the reader will usually never be asleep longer than they would normally. Upon awaking, the reader is able to remember every aspect of their dream in detail. In SCP-1230 Induced Dreams, there is always a character called the Bookkeeper, SCP-1230-1, appearing as a bearded man in a green cloak who claims to be the personification of SCP-1230 himself. SCP-1230-1 has been reported to be very amicable and helpful towards dreamers. It has stated that it enjoys creating these fantasy scapes and always tries to shape them in such a way that the dreamer gathers the most entertainment out of it. It has expressed sorrow when the dream comes to an end and asked the dreamer to please visit again soon. Discovery In a small bookstore located ████, the shopkeeper had no recollection of owning the unlabeled book, but attempted to sell a story to local newspapers about a magical dream book. The Foundation was able to dispel the story of the hoax, and SCP-1230 was confiscated. Experiment 1230-01 Dr. ████ in an attempt to test his effective range opened SCP-1230 and boarded a flight to his hometown of ████, where he spent the night at a hotel. Upon his return, Dr. ████ reported that SCP-1230-1 appeared in his dreams and explained that once you read, a hero is born, the dream is immediately implanted into your subconscious, after which SCP-1230-1 is able to manipulate it remotely. Experiment 1230-02 A camera was set up above SCP-1230, and, using a mechanical arm, the book was opened. All pages were revealed to be blank. It seems SCP-1230 is only effective when opened by beings that are able to have dreams. SCP-1230-1 explained to a subsequent dreamer that it is actually only able to affect beings with an imagination, and that most creatures such as animals would not be affected. Experiment 1230-03 One D-Class was instructed to open the book, and, after much reassurance at his experiences would only be dreams, ordered to immediately find a way to kill himself in the dream. The subject was asleep for merely 45 seconds before he awoke with a start and a nervous sweat. He reported being at the summit of a volcano called the Ashen Spire, on a quest for Caladius, the Blessed Blade. When asked how the subject knew the names, he stated, it's like I knew them all along. He apparently leapt into the volcano and felt an intense heat before awakening. D-Class requested permission to give it another go. Request was denied. Experiment 1230-04 One D-Class was instructed to open the book, and attempt to non-fatally injure himself with his dreams. After six hours, the D-Class awoke and reported that he was able to feel a numb sort of pain where it was never so intense as to be unbearable. He also reported meeting an elderly cloaked man who asked him why he was harming himself, but thanked him for not immediately killing himself like that other rude fellow. Experiment 1230-05 Professor ████ filed a request for access to SCP-1230 and was quickly permitted, given his level 4 clearance. Staff members recalled that Professor ████ was almost visibly shaking with excitement, and some reported that Professor ████ was an avid fan of tabletop and role-playing games. Surveillance shows that Professor ████ opened the book, read the phrase, sat down beside the desk, and promptly fell asleep. Staff members were alarmed when Professor ████ did not awake after fifteen hours and alerted security. The on-site medical staff were able to confirm that Professor ████ was still alive and in good health. After approximately twenty-four hours since falling asleep, Professor ████ began to move, reported to have solely raised his head and looked around the room, appearing deeply confused. Security entered a room to ensure he was alright, to which he replied, Where am I? He was sent to medical, where staff explained where and who he was. Several minutes later, Professor ████ appeared to have regained his memory and excused himself to the restroom. When fifteen minutes passed and Professor ████ had not exited, a nurse entered to find he had hanged himself with his belt. A scribbled message on the wall revealed his last words, I can't go back to this. Dr ████ went to ask SCP-1230-1 what had happened, but upon opening SCP-1230, all of its pages were soaking wet with the same message on every page. I'm so sorry, I never intended for this to happen. I just wanted to make people happy, repeated, over and over. SCP-1230 remained in this state for three weeks, and its desk had to be wiped dry biweekly. In an attempt to communicate, Dr ████ placed a sticky note inside SCP-1230 with the statement, I'd like to talk to you, if that's alright. The next morning, Dr ████ filed a report about a dream he had concerning SCP-1230-1. SCP-1230-14 Upon falling asleep last night, I dreamt I was in a dark void, there was a street lamp, and underneath it was SCP-1230-1 sitting in a puddle. His cloak was visibly soaked, and he was sobbing profusely. I remember our conversation. Dr ████, is that you? My god man, where are we? Why? Couldn't think of anything to make for a landscape. Bookkeeper, what happened that day? Why did Professor ████ kill himself? We have to know your side of the story. He had such an active imagination. I was able to create a vast and beautiful universe for him, and it was obvious that he had wanted a life like that for so long. He conquered foul beasts and rescued princesses. He built kingdoms and even raised a family, but he never wanted to leave.  I reminded him that this was all merely an illusion, but he wouldn't listen to me. He stated that if he was ever forced to leave, he would immediately end his life. I tried to keep him happy for as long as I could. Bookkeeper, how long was the dream from his point of view? Two hundred years, doctor. I did my best, but I could only hold on to him for two hundred years. As sweet as dreams may be, eventually we all have to wake up. I awoke almost immediately after.  I'm astounded by his foolishness, but it's such a shame to have lost a brilliant mind to his own delusions. Shortly after the report was filed, surveillance showed Dr. Blank slipping another small paper in the SCP-1230. A few days later, SCP-1230 began showing as usual a hero was born greeting once again. When asked what the note said, Dr. Blank declined to give detailed comment, simply stating that he just gave it some friendly advice. Addendum 1230-A During initial testing, SCP-1230-1 asked dreaming personnel if it could be relocated to an area with many books, preferably fiction, so that it could think of even better ways to construct its fantasy scapes. After numerous experiments were performed to ensure the SCP-1230 posed no threat, the request was accepted, and SCP-1230 had been relocated to Site-12's library.