So the great thing about setting goals is that it's supposed to help you achieve them and I don't always do that. I hope you're all good today. I usually do a reading goals video at the start of the year every year but this year I don't actually have that many goals. My kind of goal this year is to not set as many goals. I kind of want my reading to be freer, a bit less constrained. I don't want to have as many goals on my brain. Do you know what I mean?  You get me? One piece of advice, don't read anything. If you don't read it, it can't harm you. So I'm not saying as many reading goals this year and the ones I am setting are pretty basic so I thought it'd be fun to combine this with chatting about whether I fulfilled my 2021 reading goals or not. So I've already watched the video because I wanted to get all my statistics up from what I have achieved, what I haven't. Otherwise I'd be just sitting here ages. There'd be like loads of dead screen time of me on my laptop like... So I have watched it already but I'm gonna put little clips in here of what I've said in that video last year.  So let's chat about what I did achieve and what I didn't and then we'll get into my goals, my reading goals and my channel goals for this year. So my first big goal for last year for reading was to read a hundred books. I am gonna be in my final year of uni this year and then after that do I find a job? But like what? So I don't think I even want to try and go that much about a hundred if we're being realistic even if I scrape a hundred by reading like four graphic novels on the 31st of December I'm happy with that.  I read 116 books in 2021. I keep saying this year but we're 10 days into 2022 so let's like get it together. Let's get it together. I didn't read much in the first couple months of the year. I read 131 books in 2020 then 116 in 2021. Whoa years, all the years emerging. So I think if it hadn't have been for like the first five months of the year where I read five, seven, eight books each month I would have read more than I read in 2020 but uni was a bitch but we're done now we're done. Also I think it's so funny that in that video I was like I'm gonna be a uni and then I'm gonna have to find a job.  This is your job which is kind of crazy. Yeah I achieved my goal of reading 100 books very happy with that. I knew that it was gonna be a bit more of a struggle but I definitely pulled through in the kind of latter half of the month to get it up a bit. Now my second goal was to read more new releases. I feel like as a booktuber I have a responsibility to tell you about new releases that are coming out and review them. I would say I'm going to try and get like two to three new releases a month and fit them into my reading somehow and I definitely did this. I listen I bought a lot of new releases I've bought a lot more than I've read. Me with 50 unread 2021 releases but no money in my bank account. But I went and had a look back I'm not sure on the exact number but in terms of how many 2021 releases I read last year it was at least 30 at least 30. It could have been a few more but it was at least 30 that I read last year which I think is really good because I generally read 116 books. It's like a quarter of what I read was new releases and in the video I say I want to read two to three a month and 30 is 2.5 a month so I was pretty spot on with knowing how much I would actually read. That's something I definitely want to continue into this year.  Books hasn't happened yet and we'll get into that in my goals but it's definitely something I want to continue and also continue reading like 2021 and 2020 releases especially 2021 because I have a lot of them that I still have to get through. Goal number three was to track my reading. I've said in the past that I want to do it and I'm gonna do it and then I never end up doing it and I absolutely did this. I had a reading spreadsheet for the first time ever which I really enjoyed. I really enjoyed seeing my statistics. I'm just gonna continue tracking my reading it's a big part of my reading now. I've also on like good reads I find their shelves quite useful. I've also organized that into like genres so I can see what genres I have and that is more up to date so I feel like my tracking has gone really really well this year. Goal number four was to read more romance and horror. I think I read maybe like three each but I want to read more of them. I want to be a romance horror horror. I think in the video I've said I've only ever read like three of each right? Like three romance three horror. Well in 2021 I read six romance and eight horror so I feel like I did this. Yay wee. In one year I read double to triple the amount of what I'd read previously of these genres so I'm kind of happy with that. Like I wasn't when I said read more romance and horror I was not expecting them to suddenly be my biggest genres because they're not what I like to read mice but I definitely wanted to explore them a bit more and I feel like I have especially horror. I feel like I'm starting to understand what I like but horror like I find horror books are so much more different to one another than romance like romance you pick one up you kind of know what you're getting. I mean a romance book for me this year was my third favorite book of the year which is kind of crazy. I never thought I'd be saying that so I guess this goal was achieved and I became a little bit of a romance ho. What can I say? What can I say? Like a little bit. Like a little bit. But yeah I feel like romance I've figured out more what I like because they're kind of similar you know what you're getting but horror can be all over the place in terms of story structure, in terms of idea so I haven't quite figured that genre out yet but I'm really happy with what I read in 2021. Goal number five was to read at least 50% non-white authors. I just want to be reading as diverse as possible and I think there's so many amazing stories out there that we need to speak about. So this one I got close to achieving but I didn't quite achieve. I read 43.1% non-white authors so I think I read 56.9% white authors and 43.1% non-white authors. So not perfect. I would have liked that number to be over 50% but I feel like I still achieve my goal of diversifying my reading more and being a lot more conscious of it, being a lot more conscious of who I was reading or what I was reading, what perspectives I was consuming. I feel like it's formed the habit which was the intention of it. I may not have hit 50% but it's very hard for me because so many of my series, so many of my blogs on my channel I have no control over what I'm reading. Booktube print test wrapped up. Booktube rewind. I have no control over what I'm going to read and it's kind of in the lap of the guards of the booktuber who's picking what I read or unwrapping a certain book or what booktuber's reading. Do you know what I mean? So I think it's hard to fulfill this when those factors are at play. You know it's not just me picking what I read. If it was just me picking whatever I wanted to read I could have bossed this. I could have done it but it's not. Like often when I'm reading I don't have a choice in the matter. For example this month in terms of little spoiler sneak peek for the vlogs that I've got coming up this month. One were books that were voted for so I had no control and one the books were chosen for me by someone else so I have no control. So I literally have no control over what I'm reading this month. Anyway so I'm in control but I'm out of control and I'm also um controlled so in a way I'm everything. So I think that shows it's often it's hard to fulfill this but I think I still did an alright job with it. I think it made me a lot more conscious and like I said it formed that habit so I was happy with that element of it. Goal 6 was to read more non-fiction. I used to read 50% non-fiction 50% fiction before I started my channel. I say this every year.  Like girl that was only a couple months of your life it's not that deep like you weren't reading long before you started your channel. So I had the goal of reading one a month I read eight. So listen we weren't completely far off I'm not actually sure how many I read in 2020 to compare that that's why I'm happy I'm tracking my reading now so going forward I will be able to track it but yeah I read eight it wasn't quite one a month we're gonna talk about non-fiction more later in this video. My next goal was to read 80 pages every day. I just want to start reading a fair amount every day this previously hasn't been the way I read I will often read like nothing for two days and then read like 300 pages in one day that's just the way I've read. I did not do this I did not change my ways I still sometimes read 300 pages a day and then don't read anything for three days after like that's just the way my reading works. I read an average I worked it out of 104 pages a day I think yeah 104 pages a day which I'm happy with I want on average over 100 pages a day on average but in terms of like actually reading every day over 80 pages did not fucking happen no hope no hope this one was abandoned straight away never thought about it again I ain't staying here sorry I love you but I ain't staying here and then just quickly I did have some YouTube goals last year the first one was to hit 15,000 subscribers we're at a point now where my head can't really wrap around the number my goal for this year for subscribers is 15k and like my head like past 10k like the fact that we're only a thousand from 10k like I can't I can't really compute it which we absolutely did we did reach 15,000 subscribers I reached 16k in 2021 like I said I'm still gonna set a goal for this at the end of this video for this year but I still find it kind of crazy like once we pass 10,000 like my brain can't wrap around like in terms of views in terms of subscribers like my brain is just like sure that's a number but in terms of like sheer amount of people if I think about it too long I'll break down and you'll never see me on this channel again like I can't really allow myself to think about how many people that is because I want to make a video again that's a lot of people seeing me and seeing me being stupid like so I set the number goals as more of like you know an analytical kind of thing but if I think about it seriously no my other goal was to kind of improve my channel branding and aesthetics you don't understand how to redo my header my channel header my video intro and my end screen has been on my to-do list for four months every week does it ever get done no because I'm terrified I need to just do it I hate them like I hate them so much I made them in like 20 minutes when I started my channel a year and a half ago and I did this now listen I say in the video last year that for four months it had been on my to-do list every week to redo my header intro outro all that stuff it took me until like July of this year when I'd finished uni and recovered for a bit to finally get around to doing it but I really like my branding now I think it's fun I think it's me I'll probably get sick of it in like a year and I'll change it again but for now I still like it I still like my kind of branding on my channel and I was very happy with what I made and I feel like I waited until the right moment to make it but I did hate that old shit that was so ugly the stuff I used to have on my channel that was so ugly and I feel like it would turn people off of watching the video so glad she got cut she got the cut okay so like I said I don't have many goals oh squeaky check I don't have many goals this year I want it to be chill out I want my reading to be a bit freer but I do have a few so my first one is a big one it's a big one my first two in fact are the big goals this is so big it's mind-boggling I want to read 150 books this year I've been thinking about it I've been thinking about it and I was like I think I can do it like I think I can give it a go now like I said that is not happening for me right now uh Goodreads is very excited to tell me I'm three books behind schedule but honestly we're just we're just not talking to her we're just not paying attention that girl I think she is disgusting stupid love rat I've never read 150 books in a year like I said in 2020 I read 131 then last year I read 116 we're hoping for 150 this year I feel like we can do it now that like I say I'm doing YouTube full time reading is my job so I feel like I can do it but it is gonna be hard like it's gonna be a push so I'm a little bit scared but I'm gonna hopefully in January February read a lot of like novellas short audiobooks like I'm gonna try in the next couple weeks to get that up a bit and yeah I don't know it's a big goal I am scared of it like I thought about it for a long time I thought listen setting 100 is safe I will read that I could set 125 but it's not as exciting so we're going for 150 everyone we're gonna go for it I'm gonna read the most books I've ever read in a year and that is kind of my big goal but my even bigger goal is my second one and that is to do with series okay are we ready it's crazy it's crazy I think this is crazy the whole ongoing conversation about how many series I'm reading I've had a lot of people have shamed me a lot of people have shamed me for how many series I'm in the middle of I went and I updated my spreadsheet today and I counted how many series I'm in the middle of and I'm in the middle of 41 in the middle of 41 series yeah it's a problem it's a problem it's a problem um I don't know how I've let it get to this point but really this year is gonna be the year of finishing series I went and I counted through my spreadsheet and about 16 of the series I don't think are gonna be completed this year a few of them may be but like the vast majority of them I know are ongoing will not end this year so I can't finish them so that amount I had to be like okay I'm gonna be ending the year with like at least 16 series ongoing even if I read every other single one and I thought what is realistic what's realistic I decided my goal by the end of the year is to be in the middle of 26 series so I want to have those 16 obviously I can't finish this year and 10 more I can be currently reading 10 more series other than those 16 but other than that I want to get it down to 26 which I feel like is realistic I feel like I can do that it might be a flop I'm confident no the truth is that's the only series that's like 15 15 series I need to finish this year which I feel like I can do I genuinely feel like I can do I might do even better but I want to give myself a little bit of leeway to maybe start a few series this year but kind of the the impetus is there to like if I start a series to finish it oh I'm just so scared you may be like Megan it's not that hard to finish 15 series in a year that's not really a big goal why are you making it such a big deal well let me tell you something in 2021 do you want to know how many series I completed five I completed five series in the whole of 2021 five do you want to know how many I started 20 so I feel like getting it down to 26 with that 16 being ones I can't complete I feel like I can do it but that's a big focus me this year it's going to be a big focus on my channel it's going to be a big focus in my reading is finishing those series I have got a video series about series coming can you believe it and then I just have two kind of mini goals around genres so nonfiction right the past two years I have set the goal of reading one a month one a month minimum um so 12 a year and I have failed it so this year I've decided to up it and I want to read 15 nonfiction books this year 15 15 I really want to do this I miss reading nonfiction the only thing I don't want to do is because I want to vlog a lot of it I'm scared I'm going to try and fill this number up with like random ones I find on script or as audiobooks and not the nonfiction I'm most excited to read and that's what I want to do so a real focus for me has to be fitting them into reading vlogs somehow I love learning about new things I love learning about people other than myself so I really do want to achieve this goal I really want to read 15 nonfiction books I don't feel like it's that hard I failed it at previous months but this year is really going to be a year of reading for me and kind of focusing on what I want to focus on so I I feel like we could do it sure Jan and then my last goal is to read six classics so in 2021 I did not read a single classic I didn't read one I didn't read a single classic and I would really like to read some more classics in 2022 and then let's just quickly talk about youtube goals I know no one really cares about this I've put it at the end in terms of subscriber count I've always found I've reached and got a bit over whatever goal I've set so I feel like it's just the point of putting that number into the universe like I said I don't care if I don't reach this like it's okay like for me it's I just love having my channel so it's not that deep like I'm not gonna cry but I feel like it does something for me psychologically towards motivating me to push harder in what I'm doing so my goal for my subscriber count this year is 25k which like fuck me getting into the 20s is gonna be momentous to me like I can't even imagine that I feel like when you get into the 20s like you're at that bitch a little bit and I'm not ready to be that bitch so yeah 25k like I said I can't really imagine that number but it's what we're working towards it's what we're hoping for I also okay let's just say these next two goals together we'll chat about them in conjunction I want one video that gets over 25k views and I want to push myself to take more risks in the videos I'm making so I think often I just make the videos that I want to watch I'm not very good or I don't really care often about well I don't care but I think in terms of videos I come up with and I'm really excited about they're what I really want to watch not necessarily what is going to do well within booktube not necessarily what is going to do well in the booktube algorithm or even the wider youtube algorithm I don't think I'm great at making videos for that I'm not very great at making videos the general viewer is gonna like want to watch you know what I mean and I want to do that a little bit I want to take risks where I make some videos that could either flop or could do really well do you know what I mean there are a bit different to what I'm doing right now I don't want to change what I'm doing like I don't want to start making complete different kind of types of content because I love what I make do you know what I mean like I make the videos I want to make because they're what I want to watch they're what I love I think that I enjoy that my channel is very catered to me and like what I enjoy and I think that's what makes me enjoy it so much so I don't want to change up too much but I want to do some videos here and there that could pop off do you know what I mean that could like hit it off on youtube and I would like one video that gets over 25k views because for me that's like oh that's like hit it that's like gone somewhere and I've never had a video I think my most viewed video is 21k which is my really old London bookshop video which is shit now when I look back at it it was a great idea then it was very early days of my channel and I'm like Jesus like a lot of people have seen that oh my goodness I'm so embarrassed so embarrassed they love you so there we have it that is me reacting I guess to last year's goals and setting this year's ones like I said I didn't have a ton for this year I kind of want to keep it chill keep doing what I'm doing focus on other like personal aspects of my life as well like my health because I've definitely let my health slip um in the latter half of 2021 because I you know I'm working from home and I was stressed for a lot of it so I definitely want to focus on myself a bit more in my well-being so I've kind of left the reading a bit more chill this year but thank you for watching to the end if you're still here and you care that much thank you for watching to the end if you've gone till the end comment the warthog emoji I love the warthog emoji me and Tom you know like the mimo is it mimoji's I don't know the ones where it's like bigger ones where they're like making like facial expressions me and Tom always send them to one another the warthog it's our favourite so comment the warthog emoji good vibes through yeah the warthog is good vibes um comment that down below if you've gone to the end of this video thank you so much for watching and I'll see you very soon in another one bye


In cartography the manmade features are very important which are in the form of buildings or built up areas. So these are actually the areas where you have the human inputs over the surface. But those are actually also truly represented by certain symbols which are actually we can see in the form of heights here. In highlighting the heights of the area the benchmarks are very important. These are marks places on building or on other places indicating the height above sea level determined by actual service. So all these heights are determined by the service ground service which are conducted by the by the certain techniques recent techniques like total station at Pheudo light and automatic level or even GPS. So on map these are indicated by the letters benchmark followed by the heights in feet or meter above sea level. Here we have the height of the place which is written on the building side which is 348 feet above sea level. Form lines resembles contour but represents no actual elevation. Stretch from visual observation or from unreliable map source to show collectively the configuration of the terrain. These are approximately contours they are interpolated by eye sketching in between the instrumentally fixed contour. These are shown by broken lines they are easily distinguished from contour. Form lines help especially to bring out the minor details of the topography which are not easily shown by contour in an area of low relief and with large contour intervals. The next important method for representation of the relief feature when we are dealing with elevation is trigonometrical stations. These are points on the surface of the earth which were used as stations for triangulation surveys. The triangulation surveys are done by certain techniques. The older techniques are chain survey, plane table survey, Pheudo light survey, prismatic surveys and the recent tools are used in triangulation surveys which are the total station with the help of automatic level we can check the elevation of the area. Today most of the GPS are used which not only dealing with the height of the area but also it gives us the lengths and longitudes of the area. In trigonometrical stations the map is actually done by or map is made by the process of certain surveys and these surveys are actually links with the height of the region.


Welcome to our red meat and wool growth program livestock tech expo. I'd like to begin by acknowledging the traditional custodians of the land on which we gather today and pay my respects to their elders past and present. I extend that respect to any Aboriginal and Torres Strait Islander peoples here today. For those who you who don't know me, my name is Emily Mellor and I'm the program manager for red meat and wool growth program. This is a South Australian government initiative being delivered by the Department of Primary Industries and Regions to accelerate the growth in the red meat and wool sector. The major focus of the program is to rebuild the SA flock and herd through enhancing business skills and farm management best practice, increasing the adoption of ag tech and database decision making, demonstrating and promoting industry credentials to our markets and increasing biosecurity knowledge and best practice across our industry. The red meat and wool growth program is supported by Meat and Livestock Australia, sheep and cattle industry funds, Australian wool innovation through Sheep Connect SA and Animal Health Australia. The livestock tech expos are a really important element of our ag tech extension efforts, along with other activities including the livestock enterprise planning, livestock ag tech adoption rebate, the producer tech groups and our focus farms. For more information on any of these initiatives or an opportunity for you to be involved in our program, please come and see us at the purza display down in the marquee later this afternoon. You can also pick up a resources folder containing further information on the program, producer case studies and fact sheets on a range of important ag tech and further production information. Today's expo is all about enhancing the understanding and adoption of ag tech in the livestock production community. We've got a fantastic line up of speakers talking about the application of different technology in livestock production. So this morning session will take us through to lunch. After lunch we'll shift our focus to the livestock tech exhibitors and some networking opportunities. We have a range of exhibitors here today and they're really keen to engage with you, promote their particular ag tech and what it can do for your business, but also to give feedback on where ag tech can assist you in the future. Each of these tech exhibitors will have an opportunity to give a three minute speech on what they have to offer, so you know who you would like to go and talk to and then you'll be free to interact with the exhibitors, explore what ag tech best fits your business and then for you to also network and share your experiences with other producers, which I think during COVID times has been a bit lacking for all of us, so it's a good opportunity.


Now, the Roma Wine Company of Fresno, California, presents... Suspense! Tonight, Roma Wines bring you short order. A suspense play produced, edited and directed for Roma Wines by William Spear. Suspense, Radio's outstanding Theatre of Thrills, is presented for your enjoyment by Roma Wines. That's R-O-M-A. Roma Wines, those excellent California wines that can add so much pleasantness to the way you live, to your happiness in entertaining guests, to your enjoyment of everyday meals. Yes, right now a glassful would be very pleasant, as Roma Wines bring you short order, a remarkable tale of suspense. Thank you very much. Come back. Bailey's Diner. Well, this is Mr. Bailey speaking.  Well, I hired a fry cook day before yesterday. I'm sorry I forgot to tell the newspaper to stop running that ad until this morning. I got a good man. No, no, one man's all I need. Just got a small place here. That's all right. Goodbye. You see that, Johnson? You better keep on your toes. Hey, your people after your job, you're not careful, you know, something like... Well, what's the matter? Don't you want to take my money? Oh, sure. Yes. Yes, of course, sir. 75 out of one. Five, one. Thank you. Okay. Johnson, good lord. Did you see that man's face? Yeah, you're telling me. It's enough to haunt your dreams. Kind of made you nervous, didn't you, Mr. Bailey? Well, after all, it's kind of a shock to look up and see you. Notice you hung kind of close to that gun. You keep under the cash register. Oh, did I? Automatic reflex, I guess. Oh, poor guy. I ought to be ashamed. Probably got that way in an explosion accident or something, you know. Yeah, looks like a plastic surgery job. Only some doctor like Frankenstein must have done the surgery. Oh, thank you. Come back. Yes, sir. He liked your cooking too, Johnson. Two deluxe sandwiches, two coffees. You know, that's not bad. Seems to me, business has been picking up ever since you started working here. Just thought you'd like to know. Thanks a lot. You like this work, Johnson? Yeah, it'll do. The hour's kind of getting me sometimes, and when the rush hour starts in half an hour, I can't pretend I'll be liking it. But it's all right.  Never get anywhere working for someone else, you know. Well, I'm doing okay now, Mr. Bailey. Yeah. You'll never go hungry for lack of a job. You're too good a cook, but you're own business. Now, you take me. I'm doing well, even if I do say so. People come here to eat. All right, I see that they get them. Makes you feel pretty good having your own place. Makes the saving and the scraping seem sort of worthwhile. You seem to get the business. Of course, you got a terrific location. Well, this place has a name that means something. At least I think it has. As a matter of fact, there was a man in here trying to buy it just last week. That's all. That's right. Real estate agent. His name is Sloan. He had a customer. Who's his customer? Oh, I don't know. I told him I didn't want to sell. Oh, here, how about opening that refrigerator door for me, will you? Okay. Thanks. No, I'm not going to sell. Couldn't afford to. I'm not in a position to retire. The way things are, it'd be too hard to start up somewhere else. Uh-oh. Well, here we go again. Good evening. Good evening. Oh, yes, sir. What'll it be? Uh, special, I reckon. Oh, good evening, sir. Is it still chilly out? Oh, yeah, a little. Thought some of your chili would warm me up. Get it? I get it, yeah. Chili coming up. All right. Maley's Place. Oh, Virginia, what's the... What? What? All the windows? Well, who could possibly... Well, where were you? Well, now, why would anyone want... Oh, no, no, none of those kids would do a thing like that. They're nice kids. Yeah, hoodlums, I guess. Well, I don't know what you can do. Got no witnesses or anything? You sure it was rocks, huh? Well, I guess there's nothing you can do. Well, I wish I could, too, but I got to stay here. All right, dear. Yes, all right. Goodbye. Bad news, Mr. Bailey? Darn this thing of hoodlums. It just broke every window in my house. I don't know what to think. Hey, hey, Bailey. There's no kind of bread you got here. Better than usual. Oh, you like it? Yeah. Well, it costs a little more. Oh, good evening. Hello? Yes. Yes, sir. What'll it be? Hamburger and coffee. Right. How do you have the hamburger? Well done. Cream in the coffee? No. Black. Yeah, right. Hey, Bailey, come here a minute. Oh, yes, sir. Pardon me, will you, please? Did you see the face on that fella that came in a minute ago? Yes, I did. It's pretty bad, isn't it? Bad? I'll say. Boy, I can stand a lot of things, but that gets me. Well, I've left half my meal on my plate. I was enjoying myself until I came in and sat over there, and I didn't want anything more. Well, that's too bad. Oh, look, don't pay. No, no, no, it's not your fault. Maybe mine. Gee, how do you suppose he got that way? Oh, a burn, perhaps, or maybe some other kind of accident. I wouldn't know. Boy, that's the worst I ever saw. It's too bad, whatever happened. Sure. Well, yes, too bad. Yes, it is. Ketchup. Okay. Here you are. What? This little paper cup? Where's the bottle? Uh, sorry, but ketchup's hard to get. That's all we can serve anybody. Oh, profiteers. Will there be anything more? No. Okay. You check and pay at the desk. Thank you. Hey, Mr. Bailey. Yes, Johnson? How's your luck? Well, sometimes good, sometimes bad. Why? The way I figure somebody around here sure gonna need plenty of luck. Why? I don't know. I just got a feeling. If that isn't bad luck for somebody sitting back there at the counter, I'll eat this grill here. And I never saw a recipe for making a steel grill tender. We better order some more pork tomorrow, Mr. Bailey. Well, running low, are we, Johnson? Yeah, a little. If they keep hitting our barbecues the way they have so far this evening, I'm sure we'll be needing it. All right, I'll make a note of it. Lucky we got any unspoiled meat left after that guy was in here twice yesterday. I thought the milk and sour, bingo. Just like that when he looked at it. Yeah, but it didn't. Ah, Johnson, you shouldn't talk like that. He can't help it. You know he can't. We should feel sorry for him, not joke about it like that. Yeah, I'm sorry. Yeah, sure. You gotta have sympathy for a guy like that. Just the same. I hate to look at it. Oh, I guess we'll have to look at it some more. I think he's coming up to the door now. Uh... Uh, good evening. Hello? Yes, sir. What'll it be? Hamburger and coffee. Make the coffee black. Right. Make that hamburger well done. Okay. Oh, good evening. Good evening. Yes, sir. What for you? Why, uh, I'll have, uh... Holy. How's that? Huh? Nothing. Nothing at all. In fact, I... I don't think I want anything. I just remembered appointment. Just forget it. Oh, what do you know? Uh, your hamburger, mister. And your coffee. Catch it, please. Okay. Still no bottle? No bottle. Sorry. Here. You go buy an extra bottle. Put it back on the shelf just for me.  Yeah. I like this place. Go on, take that and see that you get some good ketchup, too. Oh, it ain't that, mister. It ain't the money. You can't buy this stuff when they don't stock it. Well, you better ask Mr. Bailey. Uh, uh, Mr. Bailey. Yes, Johnson? Oh, you tell him. I just gave your man some money to buy a bottle of ketchup. But he doesn't want to take it. Well, you see, sir, it's not that we can't afford to buy ketchup. No, indeed, we want to please the customer. Nothing a lot of people seem to have forgotten how to do nowadays, but ketchup's very hard to get just now, and we have to ask our customers to bear with us. You keep your money. I like plenty of ketchup. Nothing like ketchup, I always say. Well, there ought to be enough in that paper cup. Won't that do you? Well, not quite. Any chance of a refill? I'm afraid that's all we can allow. Gentlemen says he's gonna eat here regular. What? I said that... Oh, just a moment. Good evening. Could I do something for you? Well, I certainly thought I... Oh, no. No thanks. No, no. Well, we hadn't finished our discussion. Yeah. As I was saying, Mr. Bailey, it looks like we got ourselves a regular customer. Three evenings now that he's been eating here, Johnson, and I wish you'd take a look at the figures. Take last night. Ordinarily, there'd be $10 to $20 worth of business just between $6 to $6.30 alone. From $6 to $10, how much? $1.35. Yeah, I know. Some of them won't even order. Some of them take a few bites and quit. At least it's not the food we can be thankful for that. Hey, tell me, Johnson, how can you stand it over there in front of him all the time? Oh, mostly I keep looking someplace else. That's why I took down the mirror. For a while, I thought I'd just work along and not look at him. But I couldn't help looking in the mirror every now and then. So I think maybe the customers could stand it better without the glass, too. If they get to the sitting-down stage. Yeah, if they do. Well, anyway, I took it down. Might help if he didn't get up every now and then and walk over to the door to look out. People can't help seeing him, then. Takes him a long time to eat, doesn't it? Yes. Hey, say, I've got an idea. What's that? Look, when he comes in... Uh, uh, uh... Good evening. Hello. Uh, yes, sir? The usual. Right. Oh, uh, by the way, Mr, uh... What's your name? Well, as I was saying, neighbor, we make a practice here for our special customers. Not just anybody, mind you, but for our special customers of sending meals out. Now, I was thinking since you've become one of our regular customers that perhaps you'd appreciate it if I'd send your evening meal over to you every day at your-at your room. How does that strike you? No. Thanks. Rather eat here. But, uh, we don't have any comfortable chairs. There's no jukebox, no radio. That's okay. Don't miss them anyway. That's not very comfortable. A lot of food odor in the air.  I like it. Not too many people around. Nice place suits me. Then, uh, not interest. That's the idea. Hamburger and coffee. How about... Yeah, the ketchup. Here it is. Good. Nothing like ketchup, I always say. By the way... Yeah? Look for me about noon tomorrow. I think I'll be taking lunch with you from now on every day. A short order by John F. Souter. Roma Wines' presentation tonight in radio's outstanding theater of thrills. Suspense. Between the acts of suspense, this is Ted Meyers for Roma Wines. Elsa Maxwell is an acknowledged expert on the niceties of dining and entertaining. Recently, she said, gracious little touches can do so much to make meals more enjoyable. Dine by subdued light, if possible, adjust radio or phonograph for soft, mellow music. And as the crowning touch, serve well-chilled Roma California Sautern. A most excellent idea from Miss Maxwell. Good Roma Sautern is pale gold, delightful in bouquet, and even more important, exquisite in taste. Created in the Roma tradition, Roma Sautern is always unvaryingly good. The goodness of luscious grapes selected at peak of flavor richness in sunny California's choicest vineyards carefully pressed, then unhurriedly guided to perfection by the ancient wine skill of Roma's famed wineries. Good Roma wines are always delicious, yet cost only pennies a glass. Remember, because of uniformly fine quality at reasonable cost, more Americans enjoy Roma than any other wine. R-O-M-A. Roma wines. And now, Roma wines bring back to our Hollywood soundstage, Joseph Curran's as Bailey, Conrad Vinyan as his assistant Johnson, and Gerald Moore as the stranger. In short order, a play well calculated to keep you in suspense. Johnson, I'm at my wit's end. What are we gonna do? I don't know. I got no more ideas. Two weeks now, and we're losing money every day. I could cook it so I wouldn't want to eat it. Well, you've tried that, haven't you? Yeah, twice. And it didn't work. Yeah, that's right. And I don't know what we're gonna... I'll just a minute, Johnson. Okay. I'll check on the bunch of things. All right. Hello, Bailey's place. Oh, yeah. Yeah, dear. Huh? Oh, no. Well, you must have misplaced it, honey. Every place? Well, how much was in it? Oh, no. Well, what are we going to do for the rest of the week? But I can't, honey. I really can't. Why, about three bucks or something? I don't know. Well, I don't... You know, it's just dropped off during the last week. Oh, no, no, he's fine. I don't say that, honey. There's nothing to matter with Johnson. And I'm not going to get a new cook. What? Well, I haven't told you, but... Well, why don't you cut down on a few things once in a while? Oh, Johnson, is there any aspirin back there? Yeah, you want the bottle? Yeah, my head's splitting. Here it is. Oh, thanks. Here he is. Hey, now look, I got an idea.  What is it? Well, I'll try it. And if you don't like it, don't say nothing. Hello? Like I say, Mr. Bailey, this kid was a pretty game fighter. He didn't have a thing, but a hard left. Mind if I put in? I'd like to eat. Uh, you bring your lunch with you? What's that? If you brought your lunch, okay. Lay it on the counter and eat it. It'll be funny, Johnson. Bring me the usual. I got other things to do. What other things? I don't see any other customers. Want me to call the boss?  I'm tired of seeing you around. You go someplace else and eat. We'll see about that. Hey, Bailey. Yes, sir? What can I do for you?  Yes? Well, do something about it. What do you want me to do? Tell him to serve me. Or else I'll have him fired. Johnson's a good cook. Good cooks are scarce nowadays. What is this?  I just told you good cooks are hard to get. What about customers? Well, it's too bad, but it's... I see. Look, both of you. I came in here to get something to eat. And we're gonna get it if I have to sit here all night. Suit yourself. Yes. Oh, I'll get it. Bailey's place? Yes, yes, dear. I am, but... What? Wrecked. Were you in it? Were you hurt? Oh, in front of the house. Oh, I don't know what's happening, Virginia. It just seems every time... Well, of course, I'm glad you were in the house one. Well, how bad was it? Almost a complete wreck. Well, could they find out anything from the driver of the other car? Did he have any insurance? Well, they never do, do they? Well, no, I'm all right, dear. It's almost out of my mind, is all. It's getting so I'm afraid to answer the phone. Well, we'll just have to do without a car. That's all. Oh, I'm sorry, dear. Yeah, yeah. Well, I'll talk to you later. Bye. Mr. Bailey. Yes? What's the matter, Mr. Bailey? Bad news?  Had no insurance, of course. No money to pay. Oh, that's tough. Sir, that's tough. Him. Him. Look at him. Still sitting there, waiting. I don't have to think of something. So now I can't seem to think at all. Yeah, I'm stopped too. Boy, you sure get the luck, don't you? What's the matter? I never used to have luck like this. Just lately. Just... Just since he started coming in here. Yeah, could be. Look like bad news right from the start to me. There he sits. We could get him out of here once and for all. We'll go to the door and see if Ryan's inside. If he is, call him in, will you? All right. I wouldn't. Oh, okay. Right outside. Oh, Ryan. Would you come here a minute? Okay. Well, Mr. Bailey wants to see you. What can I do for you, Mr. Bailey? You see that man sitting at the counter, Ryan? I want him either arrested or thrown out of here. I don't care which. That's so. Given you trouble, is he? Hey, you. You talking to me? Nobody else. Come here. Hi, Mr. Bailey. What's the charge? Well, he... I'm making a nuisance of himself. What's this? All I do is come in here to eat. I'm making a nuisance of myself. I don't get it. Look at him, Ryan. I am. Not very pretty, is he? Officer. The law doesn't give you the right to criticize a man's face. I'm sorry, Mr. Hello, Mr. Bailey. Every day he comes in here two or three times. I can't get anybody else to come near the place while he's here. He stays and stays. He drives on most of my business away. I have to eat? Same as anybody else? You doing anything bad? Get tough, insult people, disturb people? No, all I do is come in and eat. Look, we reserve the right to refuse service to any customer. No, no, no, no, Mr. Bailey. That's all very well, but technically speaking... What do you mean? He means that even if you don't like it, you can't run me out if I run mine, my own business. He means you can't run me out if I ask you to serve me. How about that? Well... If I ask for something to eat and offer you money for it, you've got to sell it to me. Oh, no, I don't. No, you'd better. Or I'll have you in court before you know it. Brady's right about that, Mr. Bailey. Well, all right. Sorry, I can't help you, Mr. Bailey. Is there anything else? Oh, no. We'll be getting on then. All right. Well, how about it? All right, all right, all right.  Johnson, get him whatever he wants. Okay. I'm not going to answer it. I'm not going to answer it. Mr. Bailey, the phone, you bust it. I don't care. Mr. Bailey, put my gun down. What are you going to do? You see. Now, look here, you. I can be pushed just so far.  Go away. I'm hungry. Did you hear what I said? I hear you now. Go away. Now, look, I'm going to count three. One. Go away. Two. Three. I shot you point blank. Good Lord. Don't forget to catch up, you. Oh, no. You got the lay of the place now, Mr. Tanner. You figure on making any changes? No, no. Bailey had a good thing here. That'll leave it just the way it was. We'll hold the trade easier if we do. How did he seem when the deal was closed? I can't say. I let the lawyers handle everything. He took a beating on the deal, or I don't know you. Not too much. I figure he recovered about 70% of his investment. He was lucky. I felt sorry for him. You didn't talk to him at all, huh? No, no, no. I didn't even see him. You think he'd know you even without the makeup? Maybe. Now, are you taking any chances, huh? Lucky I changed the bullets in that gun for blanks, or you'd be a dead pigeon. Yeah. Yeah, I'm glad I foresaw that possibility.  You might. Don't worry, Johnson. You'll be taken care of. I'm not worrying. I never had reason to yet, have I? No. But just for your information, Johnson, we haven't committed any crime. We didn't take this place away from Bailey by force. We didn't swindle him. I paid money right on the line for it. Just remember that. Oh, I will. Customer. What? Why, it's Mr. Bailey. Oh, come right in. Hello, Johnson. Come on. Have a seat. Oh, by the way, you know Mr. Tanner, don't you? He bought the place. I never met him. Bad night. A pleasure, Mr. Bailey. You know, there's something familiar about you. Maybe I did meet you someplace. I was in once or twice. Looked the place over before I had Sloane talk to you. Oh, that's it. How are you making out? Just getting started. I'm sort of breaking Mr. Tanner in, you might say. Hope you have better luck than I did. I was doing fine until this man started coming in. Johnson knows the man, I mean. Bad luck in person. If he ever comes back, you just better close up and go home. That's right. I wonder if I have any mind left to tell the truth. I'm not even sure I do. Mr. Bailey, would you let me fix you something while you're in here? No, thanks. I'm not hungry. We've got some good steak. No, thanks, Johnson. Not even steak. Okay, you're the boss. Not anymore. But I would like to step behind the counter one last time. Just to sort of look around. Do you mind, Mr. Tanner? Oh, come ahead. Well, you haven't changed anything, I see. Not a thing. We intend to operate the same way you did. I think it'll pay. Thanks for the compliment. But I hope you don't draw my luck. How about some coffee, Mr. Bailey? You look tired. Coffee? That sounds like a good idea. I don't mind if I do. Yours is cream and sugar, right? No, no, thanks. Black this time. Hey, this coffee is hot. Yeah, I forgot to cut the burner back, and the whole tank full is plenty hot. Well, let it cool. It's too hot for me. Just one last look. Things I won't be seeing for a while, I guess. Buns, butterpats, coffee cream.  Mustard, ketchup. Ketchup. Where did you get all this ketchup, Johnson? I ordered those. Ordered them, or so to die, but I never even got a look at a bottle of ketchup. You're lucky. All in knowing how, I guess. Yeah, I guess you're right. I'd rather like it myself, you know. Nothing like ketchup, I always say. What was that? I said I'm rather fond of ketchup. Fond of ketchup? Ketchup? I think I know who you are now, Tanner. I think I know who you are, that face. Sure, that face. Makeup wasn't that face. And Johnson had to be in on it with you, too, didn't he? Johnson helped you, didn't he, Tanner? He fixed the gun, didn't he? Now, Bailey, wait a minute, I can explain. Oh, you admit it, I'm telling the truth, isn't that so, Tanner? Isn't that so? Hey, Bailey, stop! Pull him off, Tanner, or I'll get a clock. Coffee. You always take it black. It's the face. And then what is this? Oh, it's all right, Ryan. There's nothing wrong, Ryan. Nothing really wrong. That's not his real face, Ryan. He likes it that way.  What else do you want? Oh, yes, ketchup. Plenty of ketchup. Nothing like ketchup, I always say. Nothing like ketchup, I always say. I'll tell some acts well recently remarked. Serving Roma wine and soda is smart 1945 style hospitality. You'll find this delightful ice drink as refreshing as it is delicious. Yes, and Roma wine and soda is so easy to prepare. Half-filled tall glasses with Roma, California burgundy or sautern. Add ice cubes and a bit of sugar. And for a decorative touch, garnish with cherries or fruit. And for a delightful aperitif, sip delicious Roma sweet vermouth, well chilled. Zestful, full-flavored Roma vermouth, both sweet and dry, is blended and developed with all the traditional winemaking skill of Roma wineries. It's made and bottled in the heart of California's famous vineyards, yet surprisingly low priced. Try Roma vermouth soon, won't you? Next Thursday you will hear Dane Clark as star of Suspense Radio's Outstanding Theatre Thrill. Presented by Roma Wines, R-O-M-A. Made in California for enjoyment throughout the world. This is CBS, the Columbia Broadcasting System.


I've always loved baseball video games ever since I was a kid, but I have to admit, once Ken Griffey Jr. presents Major League Baseball came out and I was able to buy it brand new, I kinda stopped paying attention to other baseball games. I mean, what can beat Griffey Baseball? Well, the answer is nothing, but as I got older, I figured out that was the wrong way to look at things. It's not about finding something better than Griffey Baseball, it's about finding something different. Finding an alternative. That's how I look at games like Super Baseball 2020. This was originally released in Japan by Neo Geo as an arcade game before it was ported to Sega Genesis and later the Super Nintendo, and just as the title implies, this is futuristic baseball. Okay, it's clearly not a realistic depiction of baseball in the year 2020, considering that's fricking next year, and we've got robots on tank dreads blasting baseballs into the stratosphere on a field with redrawn foul lines and new rules of play, yeah, maybe they should have picked a more distant year. Still, this is a fun reimagining of a traditional baseball game with all sorts of weird new stuff going on. There's two primary game modes, two player versus, and a single player season which has a password save, and there's 12 teams to choose from. You can pick between teams made up of men, women or robots, all with unique strengths and weaknesses. Now, of course, robot players are going to be stronger and faster, but they have much lower stamina than the human players, and they can break down or even fricking explode during a game. Human players have their own obvious limits, but hey, at least they don't explode, but they can come equipped with stuff like jet packs, armor, and sensors to improve hitting and fielding. That brings me to the monetary system in this game.   Hit a grand slam, and you win 10 grand. You even earn money on defense, simply making outs in the field. You use this money to upgrade players during the game, and I want to stress during the game because this money does not roll over into the next game. Human players can upgrade hitting, fielding, and pitching, and upgrade to your R self-explanatory, whereas robots can only be upgraded in a very general sense, like just more power in general or more endurance so they don't go boom on you. Humans can also be upgraded into robots, which is kind of a weird touch. I feel like I'm turning the baseball equal of Captain Picard into Locutus of Borg. I should point out that some of these upgrades can be temporary, a human can exhaust themselves if they're overwork, and they can end up going back to what their original stats were. I really like all this stuff. It allows for more management and strategy in a game that, uh, inherently requires management and strategy. And by that, I mean, it's not just a throw hard and swing hard arcade-style baseball game. You gotta implement the right upgrades at the right time, and manage your money wisely. Another fun element of Super Baseball 2020 is the field. Every game takes place at what's called Cyber Egg Field, and the foul lines have been completely redrawn, so now the only foul territory is behind home plate, extending just directly behind first base and third base. In addition, the only place you can hit a home run is directly over the center field wall. Also, the stands are covered in a glass barrier, so if you hit it someplace in the stands, it'll just bounce off the glass and come rolling back onto the field. This really speeds up the pace of each game, because with less foul territory, there's more balls in play, so thanks to the field structure, it's easy to get a complete game finished in something like 12 to 15 minutes, compared to the 20 or so minutes for Griffey Baseball or longer with other baseball games. There's other quirks on the field as well, like stop zones, where the ball will come to a dead stop if it crosses over it, and jump zones, where an outfielder can jump like 75 feet into the air to try and catch a potential home run. Just bear in mind that if you do attempt to catch here, or attempt to dive anywhere else on the field, that counts as wear and tear on your player. And while we're talking fielding here, I think that's this game's biggest flaw. You can't see your fielder's position relative to the ball on the map in the lower right, so sometimes fielding can turn into a crapshoot, and that kind of sucks. I should also point out that the instruction manual for this game is super helpful because it breaks down each player's stats and ratings, so if you play this one, you'll find you're having trouble hitting, play as the American Dreams or the Battle Heroes. If pitching is giving you problems, try the Taiwan Mega Powers or the Tropical Girls. If your fielding sucks, try playing as the Mechanical Brains or the Ninja Black Sox. I really like when sports games put this much thought into a manual. It seems like it's pretty rare. Anyway, yeah, Super Baseball 2020 is a fun playthrough, especially if you like stuff like Base Wars for NES. This is kind of like the next step up from that game. Yeah, it's a little disappointing that money doesn't carry over from game to game, and the fielding can be a bit problematic, but it's a great twist on the old Baseball Formula, and the reduced file territory really helps speed up the pace of play, which is always a good thing in a Baseball game. So yeah, Griffey Baseball may be great, but Super Baseball 2020 is a fun alternative. And I want to thank you for watching, and I hope you have a great rest of your day.


The Nigerian Extractive Industries Transparency Initiative has said that the nation has lost 619.7 million barrels of good oil valued at 46.1 billion Naira or 16.25 trillion Naira in 12 years, from 2009 to 2020. It also welcomed the decision of the federal government to set up a special investigative panel on oil theft and losses in Nigeria, describing it as a bold, courageous, and timely. Given the havoc the menace had wreaked in oil production and the country's revenue generation, we will be looking at that on the show today. Plus, the International Monetary Phone IMF and its most recent edition of the World Economic Outlook lowered its global growth forecast for this year and next. Well according to its baseline growth forecast, the global economy expected to grow by 2.8 percent in 2023 from 3.4 percent in 2020 before recovering to 3.0 percent in 2024. We're looking at that on the show today. Welcome to Business Insight and Plus TV Africa. I am Justin Acadone. The mining Niger's ability to derive value from its status as an oil producing country. An executive secretary of NETI, Dr. Wunaya Oji said the findings and recommendations on tackling crude oil theft have been submitted to the presidential committee on crude oil theft, in which NETI also served as a member. Now energy analyst Omono Okonko joins me right now to look at these developing issues. Many thanks for joining me, Omono. Good morning. All right, it is indeed my pleasure. Now let's start by asking this now. NETI policies brief on crude oil theft and the data shows that between 2009 and 2020, Nigeria lost 619.7 million barrels of crude oil valued at $46.16 billion or $16.25 trillion. Nair, what is the economic opportunity cost of this loss? Okay, so first of all, in 2022, President Buhari made a presentation to the National Assembly for the budget for 2022-2022. And that amount was $16 trillion. So you can imagine for 2022 budget, that's the exact same amount that was missing to crude oil theft within that period. Now aside from that, what could this money have been used for? Let's start with oil. We could have used part of that money to at least repair our refineries. I mean the worry, cardina, and protocols refineries. We could also use part of that money to develop more refineries. Right? And also on oil, on the oil sector, we could also use part of that money to clean up organized oil communities that have been, you know, that have been facing a loss of environmental issues because of crude oil theft. Now these people have been facing, they've been, you know, talking to the media and solving all these things. And they tell you, okay, we cannot farm, we cannot fish because our waters, our rivers are corrupted. Right? Part of that money could have been used to clean up these communities. Now let's go to natural gas. We have lots of natural gas in Nigeria. But guess what? We are not able to process this natural gas because we do not have the processing facilities. We could have used part of that money to create or rather to build infrastructure so we can make use of our natural gas. We can convert raw gas to liquefied petroleum gas, which is cooking gas. We could have used this also to transform raw gas also to liquefied natural gas and compress natural gas. Now let's look at other critical factors of the economy. Education. How many out of two children do we have in Nigeria right now? Million, over 20 million out of two children in Nigeria. Now we could have this part of that money to form education. Let's talk about healthcare. We are trying to say, oh, doctors should relocate to other, to develop countries. Right? But we are not addressing the issue that are making these doctors to travel out of Nigeria. Right? These doctors are smart. They are skilled. They don't be impossible with the little resources available in hospitals. Right? Look at the primary healthcare centers we have across the country. They are all in a solid state. Now we could have used part of that money to get equipment for doctors to work with. Right? And if we talk about insecurity, we are having a lot of problems with all kinds of terrible goods arising from all parts of the country. Right? Security agencies are lacking arms. Right? We could have used part of that money to buy arms. All right. I know it's really, really limitless. But for the sake of time, let's just look at some critical recommendations that NETI actually brought. It is asking for tracking of Nigeria's crude swap, the crude swap deals. How far do you think this can actually go in, actually stemming the tide? Well, it can go a long way if these recommendations are taken from just paper to action steps. Right? I remember for a number of years now, NETI has recommended a lot of action steps when it comes to food access, when it comes to field subsidies, but at the end of the day, none of this is implemented. Nigeria has an implementation problem that is established. Right? So it's recommending that we need to track crude swap deals. Right? It's a fantastic recommendation, but we need to be implemented at the end of the day. We need all stakeholders to work together to make sure that there is transparency in the oil and gas sector, as well as the quality of the mineral sector. So it's not just about writing it on paper. It is also about taking those recommendations and putting it into action steps. And another thing, NETI has always advocated for transparency in Nigeria's oil and gas sector. That is a given. Right? Now, if really the oil and gas sector is operating or rather operators in the oil and gas sector are working and making sure that Nigerians are aware of what they are doing, guess what? We do trust. And that engender goes in the sector. And another reason that we are having crude swap deals is because our refineries are not working. Now, in 2024, Dan will say refinery is supposed to come on stream. Right? If that comes on stream and our Wari, Kaduna, and Kotaku refineries are working, and private companies are able to set up modular refineries and buy the oil in myra, not in dollars, because that's a problem for them.  As we round off now, for sake of time, I'm on now, let's talk about this proposal of the federal government concerning the removal of a fuel subsidy. Specifically, it is actually budgeting a whopping some of 800 million US dollars as palliative. Do you think that it is, that is enough in itself to cushion the aftermath of the removal? It's not enough, obviously. Even with a minister of finance, Dr. Zainab Ahmed, she has admitted to that that it's not enough. They are looking for other ways to make sure that they have more funds to invest. Now, I would be more comfortable if that same money, or other monies that are going to be realized from this plan, are put into the repair and rehabilitation of our refinery, of our local refineries, number one. And I will also be more comfortable if this money is put into transportation subsidy, not from Abuja, but from, from, from state. You know, if your target is 774 local government areas in the country, you need to start from the state, not Abuja. So if you want people to be able to say, oh, well, the government has provided a transportation subsidy for me and my family, you do not start from Abuja. You start from the state. The states are willing to know all their local government areas. They are willing to understand the, you know, every state is different. They are willing to understand the realities of their state. So if you, if you want to do transportation subsidy, it's better to start from the state. So every local government area and people living in those areas are able to benefit from subsidies, or rather from transportation subsidies. So I would be more comfortable if it was, if the money was put into that, instead of just saying, well, you take this money, you take this money, it's not going to go anywhere. That's the truth. All right. Thank you, Amona. I wish we had more time to look at them, all of the other issues, but then I will bring you some of the time as I'm, you know, the information and of course this development continue. We have been speaking with energy analyst Amona Okonkur with NARA metrics. Thanks for being a part of the program. Would you appreciate your insights? Thank you. All right. It is still business insight and plus TV Africa. We still have the economic outlook for the world coming up next before that would just give you a bit of an insight on how to write an informal business proposal. Stay with us.


Hello everybody, Jonathan from AJ & Smart here. I'm sitting in one of our product design spaces. Rob just being creepy in the background there. And I'm going to be cutting to a clip in a second which is a Q&A that myself and Jake Knapp, Jake Knapp, the author of Sprint. It's taken straight from our online masterclass and the questions we're answering are questions that we got sent from our masterclass students on our Facebook group. And it's a super, super, super common question. It's a super common problem that people have when they're running sprints. What happens when someone gets protective of their ideas and starts to cause trouble about that? So let's have a look at the question. Let's have a look at the answer and I'll catch you at the end. Hey, welcome back to the Q&A section of the course. Hey, welcome. Whoa. All right. So I've got a really great question here from Oshin Boles. Oshin? Oshin. It's actually an Irish name I should be able to say it properly. Really, really great. Too much time in Berlin. Yeah, too much time in Berlin. This is a great question though. Jake, how do you deal with people who are very protective of their ideas and they feel like their idea hasn't been given the attention it deserves? For example, when they don't agree with the voting and feels that others aren't taking their ideas seriously. Okay. Yeah, that's a tough situation.  So, yeah, this someone is like, yeah. So I mean, there's I guess a couple approaches. One, and you kind of got to gauge the person and what is going to be helpful to them. So for some people, like you could think of kind of the tough love approach, which is like, look, like your solution didn't get attention. It may be because it just wasn't clear. Sometimes an idea that in the abstract sounds really good. When you put it down on paper is just not so compelling. And if that's what you think happened, if you think that the idea that people aren't paying attention to it because it just isn't worth the attention, and that person is really visibly upset or talks to you, you may have to just be blunt with them and say, look, the idea may have just not come through because it's just not as strong as the other people aren't feeling it. There's other situations, there's other sort of versions of this.  And maybe an idea that seems really strong and they're right, like doesn't get the attention that it ought to have. The other kind of like way to be blunt to a person like that, maybe to take them aside and say, or even in front of the group, if they're raising it as an issue and within the whole team, is that part of what happens in the design sprint is that decision making becomes transparent. You can see what your decider is choosing to go for and not. And that I think is sometimes hard. It's hard to see those things happen up front, but they're happening anyway. Right, it's not likely that that solution would have made it through if you weren't doing the sprint, but now you just saw it happen really fast. And sometimes that is hard to see. But if it's reality, like I don't think we're going to suddenly like sort of overwhelm the process. Okay, all that aside, though, I would say that if you're facilitating and you see that there's a solution that I've seen this happen where like a solution, I can see that there's merit to it, but the person's writing wasn't clear or like, you know, there's some kind of biasing that's going on that that actually is going to negatively affect their solution. Before the final voting goes in, if I like can do a job in the speed critique of making sure that I make it clear to people what's what's there, you know, drawing out my own solutions, as a facilitator, you have the ability to, you know, in the heat map and in the speed critique, and even maybe giving a quick presentation of the solutions before people do the straw pool, you can make sure that you give a solution every chance of being heard and recognized. So that's one other possibility is as a facilitator, if you see that since that coming on, can you help in some way? Can you steer a decider towards choosing two solutions that are very different from one another? I mean, you can do you can easily do that in the moment. You can say, I think we ought to go for two competing solutions here because you know they're dead set. So that's there's some little tweaks you can make. But for the most part, those kinds of frustrations come through because people maybe are experiencing their idea, which wasn't as good when it was made detailed as it was when they thought of it in their head and happens, happens to me for sure. Yeah, there's a few things that we do. So number one, and you'll see this over and over in the course, we try to set expectations as much as possible. So we try to remind people before anything happens that, you know, your idea may not get through. And it doesn't matter if it doesn't get through because we're going to be testing on Thursday. And it could be that some parts of your idea may be come back in the iteration or something like that just to calm them down. But one thing that we've started to do that's been super successful, you'll also see this later in the course, is that we have them give the facilitator their concept before they stick it up on the wall. So the so on the Monday, so that the facilitator can have a look and say, Oh, your handwriting, if you changed your handwriting, you'd have a much better chance of this idea getting through. So we try to remove the those barriers already before the voting happens. And that really, really helps. Yeah. In that case, you're not doing the filter live and trying to course correct live. You have a chance to help out. Yeah. That also might make someone feel like they had a better chance of having your idea heard. Exactly. Because you've given them every chance in advance to do it. Yeah. And I think that that framing, that idea of saying, look, when we get to the end of this sprint, what we're trying to do is learn more about our product market fit. We're trying to figure out if our customers understand the value proposition of our product. That's a bigger goal, I think, almost always than the specific solution that we're going for. What you get at the end of the sprint, if you fast forward to the end, it's going to be this understanding of your customer and your product and kind of the whole of it. And hopefully, you can convey some of that to the people in the room so that they don't get too fixated on the details of the solution. Because the reality is, most solutions, whichever one they are, will have a lot go wrong with them in that test, for sure. I think that's already a good enough answer to the question. Okay, cool. Should we do a really bad high five and leave the frame? Yeah. Thanks everybody. Goodbye. See you later. You're still in the frame. All right, welcome back. So I hope you enjoyed that video. If you're interested in learning more about how to run and sell your own design sprints, there's a one hour 20 minute live recording. So it's a replay of a live recording down below. You can click that, you can have a look at it, and it's pretty chunky detail, something that we didn't upload to YouTube yet. So yeah, have a look at it if you're interested in the design sprint. If you like this video, give it a like, subscribe, leave a comment, have some fun. Why did I say have some fun?


Welcome to WISSES Forum, 2018 marks a milestone. It's been 15 years since the launch of the first phase of WISSES and the adoption of the Geneva Plan of Action, giving stakeholders, organisers, sponsors and participants the opportunity to reflect on the work achieved to date. When WISSES was launched, its mission was to bridge a digital divide separating the rich countries from the poor by spreading access to the internet in the developing world. At the time, the Declaration of Principles was signed with the Plan of Action, setting out a goal of bringing half the world's population online by 2015. The mission is nearly accomplished. However, despite the considerable work achieved, some areas of the world remain unconnected. Connecting the unconnected is crucial to achieving the 2030 Agenda for Sustainable Development. I thank the World Summit on the Information Society Forum for promoting an inclusive process to meet these goals. Countries are using ICTs to leapfrog traditional development models in their efforts to achieve the sustainable development goals and provide humanitarian assistance. But as fast as these advances are occurring, they are not taking place fast enough in many, many places. The digital divide, including the gender digital divide, remains a significant challenge that we need to acknowledge and bridge. Throughout the years, WISSES has retained its spirit and managed to sustain the dedication of key stakeholders. In line with the original mission of WISSES, the prizes celebrate the leading ICT initiatives by organisations and entities across the world that help make a very big difference. Today 72 champions and 18 winners were awarded the prestigious accolade out of the record 685 projects submitted. We are from Mexico and this is very, very nice to be here. I'm very happy and we are so proud to winner this very prize. Well, I feel on top of the world because this is an exciting moment for us. We're using this opportunity to leverage that city to create employment. I feel very excited because technology and women is the most important thing we want to stress in Bolivia. We do it with teachers and students and we want to say muchas gracias a todos los profesores y estudiantes que nos ayudaron. So that's it for the prizes this year, but stay with us as there will be many more exciting events to follow in the next few days here at WISSES Forum 2018.


So next up is what's new in ML.NET with Cesar. Thank you. Cool. So let's get started. Welcome to this session about Machine Learning.NET. My name is Cesar Delatore. I work as Program Manager in the.NET team and ML.NET team. The goal for today is to cover what's new about ML.NET since we launched it in last May. So if you want to get a comprehensive introduction about ML.NET, many things that we don't have time today to talk about, you can listen to the session that we had in build in May, and there's a link. But today we're going to be focusing on what's new, and very short intro about ML.NET, but then focusing on things like the new database loader, deep learning, what new things we have with image classification, and training on TensorFlow, and finally a sneak peek about Jupyter Notebooks as well with ML.NET. So let's start with a quick intro on ML.NET. First of all, ML.NET is most of all a framework, and it is cross-platform and open source, and it is built for .NET developers. We aim to make it simple, and so you can create your own machine learning models without having a lot of knowledge on machine learning. Of course, then you use just .NET natively. Then another important point is that with ML.NET, you can create your own models. So it's custom AI, a supposed or pre-built AI where you just consume services.  Then we are also extending ML.NET to other frameworks or other libraries like deep learning, TensorFlow and many tools that I will be showing later. Finally, it is trusted or proven on a scale. Because even when we launch it publicly in May this year, there's been around more than eight years that ML.NET has been used internally by Microsoft in large products like Bing or Windows or Office or Azure as well. A few products in Azure are used internally for many years, ML.NET. So you can go and search more about in there. Another point that I want to highlight about ML.NET is that ML.NET because it is a framework, it runs anywhere. You can run it on-premises, on your servers, on your laptop, or in Azure, or any Cloud anywhere you want, and because it is cross-platform, you can run it on Windows, Linux, or Mac OS. So the frameworks that we support are .NET Core. That's why it's cross-platform. But also for legacy applications, you can also infuse machine learning into those applications with .NET Framework, and we even have Python bindings with Nimbus ML. So you can generate also ML.NET models with Python, and we support 64 bits and 32 bits. So what can you do with ML.NET? There are many scenarios that you can do. You can create models for having text about customers feedback and create a model for sentiment analysis, or for recommendations about products, or predictive price of something like a house, or predictive price about a taxi first, like the demo I'm going to do later, or anomaly detection, etc. You can go to that URL and see many samples that we have in there and do any of those scenarios. Then the other important thing about machine learning is that it's not just like building an application. You need to focus first on the model, on the data, which is what you see on the left. You need to prepare your data and build and train your model, and that's the back-storage process that you do as a model creator, as a developer, and you use tools on the CLI, maybe or console applications to build a model. Once you have the model, which is a zip file when you service that, then you can deploy that into your end-user applications in production. So that would be the model consumption. So there are two very different cycles when working with machine learning. Then what are the ways you can use ML.NET? Basically, we have three ways. One is just use the plain API in C-sharp or F-sharp. You can research the docs, so you can go to the samples and do a similar case than the sample and just write your code and train your model and then consume it. But then we also have a tooling to make it a lot easier especially when getting the start-up. You can use model builder within Visual Studio or the CLI, which is doing a very similar thing that model builder, like training your model and generating that for you. So I want to highlight that the easiest way to create an ML model is precisely using a model builder in Visual Studio. So I'm going to do a demo about that. Basically, when you model builder is right now in preview, so you need to download the V6 extension and install it on Visual Studio, and then you can run it in Visual Studio and create a model. So I'm going to do a demo about that very quickly. Basically, you can see here, I have just Visual Studio and the console app is completely empty. From there, I'm going to right-click and add the machine learning model. So this is like a wizard where you have different scenarios or custom and you can get into machine learning tasks. But for this demo, I'm going to do the price prediction scenario, which is under the covers of regression. I want to predict what's going to be the prices or the taxi first of taxi trips, depending on input variables like distance or time and so on. So basically, you can load the data and train the model with that data from a file or from a database. In this case, I'm going to use a file and the file is in here. It's basically a CSV file with a bunch of data about many taxi trips, how long were those taxi trips or how long in time were there, or and then of course, what's the fair amount in the past. So with that data and we have here like 20,000 records or more, we can train the model and I need to select what's going to be the column that I want to predict on, which is the fair amount. So with that, I can train a model, let's put 20 seconds and start training. So something interesting here is that it's not just training with a single algorithm. Under the covers we are using AutoML, which is looking for different algorithms and hyperparameters combinations, and then selecting the best model for that dataset. So in this case, just in 20 seconds, it's been looking for a few models and you'll see that the best algorithm in here has been for so far, like GBM regression. But you don't really need to know all these algorithms when you use a model builder because it's doing that for you all those tries and finding what's the best model for you. So now that the training is complete and this is just a demo, if you have a huge data and then you might be doing this training for minutes or even hours, but in this case it's quick because it's just small data. In here, when you go to the evaluation, then you can see what were the models, the best five models and you can see the light GBM regression was the best one. Finally, we can generate the code and the model. So if I click here and add projects, we get these projects added into your solution. Here you can see the model.save is precisely the serialized model that was for the best algorithm. So you could just use it in code, like in the code we have here generated for you. Just in a couple of lines and I'm going to put a breakpoint, you can see that you can create simple data, sample data, and then predict with a sample data. So let's debug it. Okay. So we just got sample data from a record, from a file, but you could also create your own data or hardcoded or could be coming from your application. In this case, you can see the sample data as input data, and then we do a prediction with that data calling the method predict from the prediction engine. Here you can see the prediction that in this case is like $16. So if I just move forward, you can see that for that test data, the actual value was $17.5, and it was pre-closed the predicted value. But this is not all. It's not just about generating the model and how to run it. We are also providing you the code that we actually use for training the model. So if I create a right model builder and then create model, this is a custom function that is going to create the model.  You can see how it is loading the data. So let's see the code. Loading the data from the file, then we are building the pipeline, doing data transformations like converting all the text into numbers because machine learning is all about math and we need everything in numbers, finally concatenating all the values as input features, and finally adding the algorithm it's going to be using and then training model. When you call fit, then you train a model and then you can save it as the zip file that we were seeing. So you can see here in the actual training, we have the metrics, how good was the model, and then the prediction that we saw previously. So that's how you train a model with model builder. Now let's move to the new things we have in ML.net. Well, before that, just to mention also that with the CLI, the common line interface, you can do the same thing that I just did in Visual Studio. You can also do it with the CLI. If you're just in a Mac or Linux and just an editor and you're not using Visual Studio, you can also use the CLI with a similar line or command below, ML net, auto train, the task, where the data and what you want to predict, and then you will get the code generated on the model in the same way. So let's see what's new in ML.net. First thing is the database loader, and basically what you can do with the database loader is just three, four lines of code. You can access a database and use it for training, and it's going to be loading streaming data, can be huge amount of data, and it's going to be used for training your model as needed, right? You can use it with a SQL server or any relational database supported by system.data, so it could be also Oracle or Postgres or MySQL, etc. This is in preview, but it's been used for a few weeks by customers, and let's see a demo about that, and how it is to use a database instead of a file like we were using in the other demo, right? So in this case, I have a project that I generated from model builder. It's pretty similar to the previous one, and I just made a change in the class for loading the data because I don't need the attributes, but then the only thing that I also did is I commented the code that was using for loading from the text file from the file, and instead of doing that, we're going to put here the code for loading data from a SQL database, right? So this code is very simple, should be very familiar for any .NET developer. You can see that I have a connection string here, in this case it's a SQL localDB, but it could be any other database. I have the SQL statement to my table where I have the same data that I had in the file in this case, and then create a database loader and finally provide those parameters. In this case, it's SQL Client, but it could be Oracle or it could be any other database, and finally it will load that data into the iDataView, and the iDataView is able to stream data as needed when training, right? And then the training is exactly the same code doing data transformations and then calling fit, so that's nothing new. So if I just run it, you will see that it's training the same way, but now it's reading from this SQL database that I have in here. Just going to show you. So now it's cross-validating and training and getting the metrics, and it's doing the same thing, but it gains the SQL data, right? And let's move on to the next thing. Okay, so another important area where we are focusing a lot lately is about deep learning in ML.NET. So deep learning usually you use it for scenarios like computer vision, scenarios with media. Like you want to predict things about photos, images, or video, or sound, and then deep learning is based on neural networks. And so you have also some entry data, and then, for instance, you could predict if a picture is a dog or not a dog based on images instead of tabular data like we do with classical algorithms in ML.NET. So the leading libraries in deep learning are, for instance, TensorFlow or PyTorch and also about interoperability Onyx and their Onyx runtime. And our strategy is to integrate ML.NET with TensorFlow and Onyx and probably in the future with Torch. And so, but then creating a higher level API and that you can run also locally and create your own models for image classification, for instance. So before getting into the scenario, I want to highlight kind of a little bit of insights. So in the market, or in the deep learning world, many organizations like Google or Microsoft or universities have created complex DNN or deep neural networks, architectures and pre-trained models like Inception V3 or ResNet or many others focusing on specific scenarios like image classification or object detection. And they train those models with a huge amount of material or of images. And then you can use those models in ML.NET. The difference is that later on we are going to be able to train and do transfer relating with those, right? But let's start with a simple scenario, image classifier, right? So you could use one of those pre-trained models and then you get a picture and it says it is a dog or it is an elephant or it is a flower, right? So you would be using any of those pre-trained models like Inception V3 or ResNet and you can just use that model from TensorFlow and just score it or use it in ML.NET. You can see the samples that we had previously. And same thing for object detection, which is not the same. Object detection, you provide a photo, for instance, and then you detect different objects, like in that case, a dog or a person with the same object or the same photo. And same thing about this case, a cat or a dog within the same picture, right? But in this case, it's just about using those pre-trained models. But the important thing, this is the key question, is what if you want to classify images based on your custom domain, based on your own images and your own labels, right? So for that, you need to train your own model. And for instance, what if I don't want to just say, hey, this picture is a flower? I just want to say, I want to be more precise and say this is a rose or this is a tulip, right? So for that, you can train a model in ML.NET. And what we're doing is using our approach called transfer learning and kind of deriving from those pre-trained models and then train on your own images, right? So let's, I want to show you very quickly that you can do that with model builder. And here I have another sample where I just have an empty project. I can add machine learning again. And in this case, I will click image classification. I select the folder where I have the images. So this folder with images basically has a one subfolder per class or per different type of images, like they see it's one subfolder or roses or some flowers on tulips, right? So we basically are gonna say that we train this model based on those classes, right? So this would take a few minutes. I wanna move on and show it how we also are working on the API and doing the native transfer learning that I mentioned, right? So basically what we are doing is we are using the internal or low level TensorFlow Bidings for C-Sharp in TensorFlow.NET. And with that, we are retraining your model using transfer learning. So those models like inception of the three maybe was trained with many images, many photos that are comparable similar to the ones you want to use. So we are taking that knowledge from that pre-trained model and then retraining with your own images in the last layer of the TensorFlow model. But that means that we are really creating a TensorFlow model under the cover. So it's a deep learning training as well, right? And there's a big benefit that we are doing with this API for image classifier, which is we are doing that transfer learning for image classification in a single line or maybe three lines with all the parameters that you can see in here for all that transfer learning, right? So just with this code, we are able to do all the transfer learning and then finally generating the TensorFlow model, which is a PB file and the ML.NET model that you can use, right? So I'm gonna run the sample and here you can see the code using the API and the new API, which is image classification where we are providing, hey, I wanna use the, in this case, this pre-trained model ResNet or it could be inception with free and a few parameters on if you wanna use the callback to get feedback and so on. So I'm gonna debug and start running the training. It'll take a few seconds while I'm showing you the images. So in this case, it's gonna be training with around 400 images, right? Like four images per class and if you compare this code with the code that you'd need to use by just using TensorFlow and low-level API would be like 400 lines and in this case, we are able to do it by just adding this single call to this estimator image classification, loading the images from the folder, specifying the architecture, saying how many tries you wanna do on those images with epoch and that's it. And here should be the training starting. So it's gonna be, now I did the first epoch, it has like a 80% accuracy and in a few seconds we'll get many more epochs or training with the images. And finally, we would get the predictions that we have. So I'm gonna keep going because we have more content that I wanna cover, okay? And another thing is about data exploration and machine learning experimentation and formats that are good for learning like cursors and hands-on lab, right? So if I talk about that machine learning, what would you think? I probably would think about Jupyter notebooks, right? So this is what we are working now as well. We are providing support for .NET in general, C-sharp and F-sharp on Jupyter notebooks. Right now it's, we are working on it. If you wanna try ping me through Twitter and I will provide access on how to use it, but the public preview will be coming pretty soon in a few weeks, right? So basically with this support, we have a Jupyter kernel for .NET which is based on .NET Try and we can run ML.NET code. So I wanna show you a demo where I have here Jupyter installed in my machine. You can install it very easily with Anaconda and then launch it. And after that, I installed the kernel, right? So once you have it installed, you can see that now I can create a notebook for C-sharp or F-sharp. And here I have one of the notebooks that I created using ML.NET. So first of all, you can see here that I'm installing the NuGet packages that I'm gonna need for ML.NET or Xplot for plotting data and then that would take a few seconds getting those NuGet packages. Finally, I'm declaring here the input data classes and here I can have the code that is loading from the file like the previous code that you just saw but I can just run this piece of code and then show the schema and it's running for real and then showing data about how this data set is like I have these fields and or I could also show a few records. For instance, in this case, I see that what kind of data it is. I could do data transformations and show it later and even further, you can also use Xplot in plot charts like in this case, the distribution of the taxi trips per cost. Look, I can see that for instance, most of the taxi trips were in between five and $10 or I can also plot other interesting information about the time versus distance and then different color depending on the first. So I can see that the fur is pretty much related in this case to the distance and the time, right? And or if I just wanna see the relation between the taxi fur depending on the time or depending on the distance and but then depending on the passengers, for instance, I can see that there's no real relationship here, right? So you can get a lot of insights when plotting data in Jupyter and then of course you can also run and build your model here. So here we have the ML.NET pipeline for this model and I could just run it here and see the data transformations or I could append then the trainer and call fit and see how much time with this instruction, percentage, percentage time and then I see that it took three seconds to train this model, right? Or I can do predictions and then display the metrics and you see it in a very neat way in this document. So basically you can document your exploration or your model creation in a very neat way with Jupyter notebooks or finally I could see kind of how are the predictions versus the actual values or even in this case, it was a regression so I could be plotting a regression line like in this case, what's the regression line and where are the predictions versus the actual values, right? So, or even saving the model. So just highlight, stay tuned for this. We're gonna be working on it and pretty soon it'll be available in preview. And with that, just finishing, just wanna highlight that there are many other important features in ML.NET like for instance, how to take into account your model lifecycle in your CI CD pipelines in DevOps, not just for your application but also for your model or model explainability and future importance so what are the most important input columns that you have for your model, cross-validation or how to deploy your model into ASP.NET core applications or Azure functions. And by the way, also wanna highlight that we have a ML.NET YouTube playlist so you can take a look to many short videos about ML.NET in YouTube like one single video just about cross-validation or one single video and I'm pretty short to talk about how to deploy into a web API or things like that, right? And about the roadmap, I also wanna highlight a few topics. We're gonna be evolving and investing on deep learning not just for image classification like I was showing but also for object detection and doing this natively in deep learning in terms of flow. We will be moving the database loader as GA, improvements for Jupyter and moving that into a preview. And then we are also working a lot with the Azure ML team because that's all another important scenario on how to integrate ML.NET with Azure ML. And finally just a few links where you can get ML.NET resources like how to get started or where are the samples or the docs and the videos. And with that, I really thank you and that's all, thank you. Questions, if you wanna stick around so you thought that was really cool, I always like learning more about ML.NET. What's happening? I benefit from all of these machine learning tools but I don't actually get involved with the models, I just rate the rewards. Okay, so let's see. Well, ML.NET, whoa, whoa, whoa. I hit the wrong thing. The question was, will ML.NET support CNTK or the cognitive toolkit I think? Is that what CNTK stands for? Yeah, CNTK, yeah. So initially we don't have CNTK in the roadmap. These efforts are right now on TensorFlow, Onyx and could be also torch in the future. Okay, very cool.