Hey, this is Brett, and this 2020 Ford F-250 Crew Cab Short Box Lariat is stock number 13875Z. I am here at Summit Automotive in Fond du Lac, Wisconsin. You're new and used heavy-duty truck headquarters. This 2020 Ford F-250 has a 6.2-liter V8 gas engine. It's paired up with a six-speed automatic transmission. This truck has been fully safety and inspected by our service shop. Per the state of Wisconsin inspection process, it has a fresh oil and filter change. All the fluids have been checked and topped off, and it is 100% ready to go. That being said, I'm going to go all the way around in this video inside underneath, start it up, and take a look under the hood to give you the most accurate representation possible. Magnetic, gray metallic is the color. I shoot all my videos in 4k, and if you like the video, you can subscribe to my YouTube channel, which is youtube.com, slash Summit Auto. Click the bell notifications, you'll get updates in the videos I do each and every day, as well as having access to one of the largest catalogs of vehicle walk-arounds and truck walk-arounds on YouTube. This one comes with the painted and polished aluminum wheels, and it has brand new Kelly Edge AT tires. These are LT27565R18s, and they still have the little wear knobs on them. We put those on in our safety inspection. Frame and underbody is in really nice condition on this truck. Front fender is in nice shape, but in sceny major dents or dings in there. Headlight lenses are nice and clear, and the front bumper and lower valance are absolutely perfect. You get the chrome tow hooks, the chrome trimmed grille, and the hood is in fantastic condition as well. Didn't see any major dents or dings on there. Passengers side front fender is in nice condition, and the passenger side front wheel, no major scuffs or scrapes on there. As you go down this side of this 2020 Ford F-250, take note of how clean that body is, how reflective and mirror-like that paint is. I take these HD videos, so if you are far away or even if you're close by and you just cannot make the trip down, but you're still interested in purchasing the truck, you can see the truck, hear the truck, and have confidence in the vehicle that you're looking at before you even get here. So when you do get here, there's absolutely no surprises, and you can make a smart and informed buying decision from wherever you're at. If this video helps you make that buying decision later, salesmen know that you saw the video that was helpful and that Brett sent you. Back wheels in excellent shape, and the back tires have just as much tread as front tires. They are brand new as while it can still see the little wear knobs on there. Frame in underbody is exceptionally clean, and it's like that all the way underneath, has all the remaining factory exhaust. Little rockers and cab corners are in great shape, and it comes with some really nice factory chrome step bars. Passenger side, box, looks really good, no dents or dings on there. Coming around the back, rear bumpers in great shape, those have the backup parking sensors. Full towing package, which includes a receiver hitch four pin and seven pin wiring, and the tailgate is in very nice condition as well. No major dents or dings in there. Comes with a very nice Dura liner, drop in bed liner. You get LED bed lights, side box tie downs, and the bed liners in nice condition. Tailgate shuts nice and easily. As you go down the driver's side, just as clean as that passenger side, no major dents or dings on the box here. Do have a bug, get off there. Back wheel is in excellent shape as well. Magnetic, one of my favorite colors. See just how much metal flake is in that paint. Really a good looking color. And as you go down the rest of the side of the truck, the doors and cab all look really good. Didn't see any dents or dings in there, and it does have the telescopic toamir is built in blind spot monitoring, built in directional signals. They power fold in, power telescope out. I'll show you how that works. As the driver's side door code entry, we do have the code for this truck, and if you ever wanted to figure out what your code is for your 17 to 22 super duty and the upper right hand part screwing down via showing you how to find that.  No rips or tears on the seats. They're in fantastic condition. Power driver seat, weather tech, floor mats, power pedals, auto headlamps, side LED lights on the mirrors, tilt, telescopic steering wheel, power windows, locks and mirrors, and memory driver seat. Like I said, these mirrors do power fold in. I always like showing both sides so that you know that both sides are working properly. And then they do telescope out, and in, out, and in. Always want to make sure that those lights work when you're looking at these trucks or those mirrors work, I should say. Back seats, just as clean as front seats, no rips or tears back here. It does have the latch child safety system for any child car seats you may have. Power sliding rear window with the built-in rear defrost. Seats are in great shape. You get a collapsible floor back here for storage area. You can put that up like so. That goes back down like that. Weather tech floor mat, nice flat floor back here. You do get a 110 volt, 400 watt plug-in back here, as well as a USB, USB-C jack, and a 12-volt power point. These seats fold back down like so. And you do get child safety locks on the back doors, bottoms, and inside of the doors are all in fantastic condition. Take a look at the tire and loading information sticker, as well as the VIN sticker here. We'll hop inside, check out the miles, radio, and everything that this truck has to offer on the interior. This one does have the remote start and the keyless entry right there. That start just like so. You can see that this one has, let me dim it down here for you. You can see that better. Fifty, five thousand, eight hundred, and sixteen miles. You do get the digital spinometer on there and compass display. Instrument cluster is very nice and clean. It comes with the leather wrap steering wheel, Bluetooth, and audio controls on the right, and cruise controls and information center controls on the left. This one does have the six-speed automatic transmission with the optional tap shift. You get the sync eight-inch radio on here. You do have the factory navigation system. There is highway 41, so this is working nicely. You get AM, there we go, AM, FM, and Sirius XM and Bluetooth capabilities on here. You also have all your climate controls and your menu right there. And then you have all your different settings on here, including Ford Pass Connect, Android Auto, and Apple CarPlay. This is also where your backup camera shows up when the truck is running. So we'll check that out in just a second. You do get the B&O premium sound system, stability control, hazard lights, and lane departure warning. Turned out four-wheel drive, factory brake control. You do get dual climate controls right here. Heated and cooled seat buttons there as well.  We get two cup holders and a nice storage area, and now you have four cup holders. So that's the magic trick. 12-volt power point, 110-volt, 400-watt max plug-in, dual glove boxes, and the passenger side. Floor mat and seat are in fantastic condition. No rips or tears on there, smells very clean inside this truck, and the headliner is in really nice clean condition as well. You do get home link buttons for your garage doors. I'm sorry, not home link. Map lights and power side and rear window button there. And we'll start it up and take a look under the hood. Also check out those headlights that somebody put LED headlights in this truck. Starts right up no check engine lights or anything like that on there, and I did want to show you the backup camera before I forget. There we go. That's working nicely. I would personally like to thank you for checking out the video today, and hopefully from this HD video. You've been able to verify the quality condition options and of course cleanliness of this truck all the way around there. Those LED side lights in the mirrors, they are super bright, and yes they did do LED lights in the headlights and the fog lights. They are all extremely bright on this truck, look really good. And under the hood we have a 6.2 liter V8 gas engine, engine base very clean, runs very smooth. Once again I would like to thank you for checking out the video, and once again this truck has been fully safe to inspect by our service shop. It has a fresh oil and filter change, all the fluids have been checked and topped off. It has four brand new tires, and it is 100% ready to go. There is the emission sticker, and I would highly recommend this truck from a quality and condition standpoint. I think whoever is going to get it, either drive it here, drive here to get it, or get it shipped to them, is going to love it. And to see more pictures of this truck, or one of our other 550 new and used cars trucks, SCVs, minivans, Wranglers, halftones, three-quarter tons, one tons, you name it, we got to go to that website right there, summitauto.com. Full pictures and descriptions of every single vehicle, all at summitauto.com. And if you like to check out more HD videos you can go to youtube.com slash summit auto. Click the bell notifications, you'll get updates on the videos they do each and every day, as well as having access to one of the largest catalogs of vehicle walk-arounds on YouTube. 8,000 videos and counting. In fact, in a second you will see a link to subscribe to my YouTube channel, the upper left, a link to more F-250 videos that have done in the upper right link to this vehicle on our website, more locked in a link to one of our latest YouTube videos in the lower right. Click those check us out. We're super excited to help you with this Ultra Clean 2020 Ford F-250 Crew Cab Short Box Lariat in Magnetic Gray Metallic. Thanks again for checking out the video. I really appreciate it. Remember to like, subscribe, and share. And thanks again.


Hey friends today we are hanging out in Epcot for the International Flower and Garden Festival. My family is in town and my mom has never been to the Flower and Garden Festival and Ariel's never been to Epcot so we figured we'd come on out, ride some rides, eat some food and have a beautiful Epcot kind of day. Any hoes let's go do this. We have made our way into Epcot and I think we're gonna start our day over at the Journey of Water because my mom Bonnie and Ariel have never been in it and it's something new in the park. My mom is all about Flower and Garden Festival but Ariel she loves Frozen so we're gonna try to ride Frozen ever after and then meet Anna and Elsa and then my mom's favorite ride is Soaring just like me. Bonnie wants to ride Guardians of the Galaxy so we got a lightning lane for Guardians of the Galaxy and I'm excited. I love spending evenings at Epcot. You know what I mean? They're the best. I'm excited to see what my mom and Bonnie think of Moana. Oh and also maybe we can meet Moana too because Ariel I think her two favorite movies is no actually it would be Frozen and Incanto right? Yeah and then Moana. It's like Moana you control it. Put your hand out. Make it go higher. Make it go higher and lower. Look. Cheese. Moana is fun and I love that you get to like you know cool off a little bit in there and it's nicely shaded and you get to play with water and I think she loved it. Like you know what I mean? I think the kids are gonna love some of the changes that they're doing in the future and now I think we're gonna head into World Showcase. We have to make it over to Norway because you know Ariel she really wants to see Anna and Elsa and we have a return time for Frozen ever after. Wow Epcot is definitely one of my favorite parks but it's a big park and I don't know if we're gonna be able to do a lot of flower and garden festival that's what my mom wanted to do but she wants to make sure Bonnie and Ariel gets everything they want to get done and we had a late start because we want to stay for the fireworks tonight but luckily we are using the DAS pass for Ariel. My mom doesn't qualify for that so any ride that Ariel wants to ride we can ride and book it so we have a lightning lane for Frozen ever after and it's a return time it was a wait time of 60 minutes and we were able to book just to come back in 60 minutes. I hear Disney is changing it and I don't know much about the changes because I've never used it before but it's definitely very handy and it's a lot you know more accommodating for people like you know Ariel who really can't sit in the queue and stuff like that but let me know what you guys think about the changes but today I have to say I'm happy that we're able to use it and be able to get around with Ariel and get as much accomplished as possible. We're gonna make our way straight into World Showcase Bonnie my mom and Ariel want to stay for the fireworks so we decided to come later in the evening and the weather is so beautiful I love coming later in the evening it's actually so breezy and nice and I think it's less crowded. One of Ariel's favorite movies is Frozen so we're gonna ride Frozen ever after and see if we can get her in to go see Anna and Elsa and then me and Bonnie have a lightning lane for Guardians of the Galaxy and then dinner reservations at the Garden Grill so we got a pretty action-packed stay you know first stop is gonna be here in Norway to go see Anna and Elsa look it's only a 25-minute wait I know right are you excited it's so nicely themed in here this is Anna and Elsa's house look at Ariel hi nice to see ya I love the snow bowing to get a snow bow with you on it take your majesty of course I never thought you'd be so beautiful sure there you go take the flowers outside freeze ice and cold this is also on your bow it's everyone you have our whole family with you too look at that snow bow take it right here bye she's like I want Bonnie I can't believe that that was just a walk on we got to meet Anna and Elsa right in the start and now we're gonna go ride Frozen and honestly I love that Ariel was excited but Bonnie was excited too Frozen was one of her favorite oh you heard me I knew you were excited yeah I knew you were excited you're like oh Ariel you want one with bonbon in it Bonnie's one Bonnie did you ever ride the ride that used to be here Melstrom did you know right here this used to be exposed you would actually see them riding the ride in there isn't that cool I wish you got to ride it you would have loved it but this ride's amazing I mean I love Frozen can't lie you got a ma snowman Elsa was like he was hot sometimes she made everyone a snow snowman are you ready to see Elsa it's important she said happy snowman oh that's Bonnie you got it Ariel all right my mom loves flowers and she's always wanted to come to the flower and garden festival doesn't it smell nice I know what one we're making our way over to Guardians of the Galaxy and then we're gonna have dinner and then we're gonna come back and explore world showcase get it all in you know look at all the topi Aries we made our way down to Guardians of the Galaxy are you ready bond I'm ready what song you're looking for September yeah yeah I would say so you don't remember what song had last time we wrote about twice yeah yeah oh you have to look back yo the queue in Guardians is so nice I feel so cooled off already yo thanks for the skies I'm so excited to meet you guys and you too Bonnie I just watched your video the other day oh yeah my mom and Ariel outside I said where's mom and Ariel yeah I can't get them on this one oh I love this shirt and the fancy one enjoy the ride Guardians literally makes me just so happy and now we're gonna go get something near the garden grill soaring and then back to world showcase if we can do some more flowering garden but if not we'll have other days here this is just the first day being an Epcot and I'm excited my mom's favorite ride is soaring I love riding soaring and so far we rode we're gonna ride three of the most popular attractions at Epcot you know what I mean probably the greatest thing about Epcot being all finished is the fact that you can walk from one side to the other by going straight through instead of around my look at those flowers aren't they pretty do you see it mom to the right the fig metopiary what is it that's the name of the flower you're impatient I'm loving my new Roosevelt shirt it's fraggle rock but it just looks so beautiful especially who at Epcot like it looks I absolutely love it and if you guys go in the link I actually have if you in the description and I have a link that you could shop all the shirts that I wear including my shirts that they were wearing in the Guardians how cool was that it has been such a great start to the year going to Tokyo New Orleans California back home New York and now my mom is visiting and my sister in Ariel down here in Florida I'm happy and I'm happy I get to make videos to share with you guys you know what I mean I love being able to create memories and also a little entertainment at the same time Garden Grill is and all you care to enjoy character dining and Chip and Daler there Mickey and I think Pluto and the restaurant rotates I've been here many times I love it and my sister specifically asked to actually come to this one she was like I want to eat at the Garden Grill I had space 220 reservations but no she said she wants Garden Grill so I mean I do I love it I love some turkey here it is Garden Grill restaurant and they can see the whole entire restaurant is spinning oh and Mickey over there Garden Grill is $62 for adults and $40 for children comes with cornbread and honey butter harvest garden salad carrots and celery backyard grilled steak with chimichurri sauce rotisserie style turkey with gravy mashed potatoes green beans vegetables and a seasonal pie oh and macaroni and cheese we got here today just around 4 p.m. and it is 6 30 now 6 30 so two and a half hours you got to meet Anna Elsa go through Moana ride frozen ever after gardening to the galaxy and now Garden Grill and my mom still wants to do soaring Bonnie wants to see the fireworks I just want to make everyone happy I want them to have the best like time at Epcot and you know I mean I can I can navigate through the park pretty quick the table ready oh look at this we're starting right inside living with the land I love it this is the house this is the right easy the boats going by right there and the chickens and the dog on the porch and you can actually yeah you can see in the window you can see in the room look at that I'm gonna try to zoom in for you so you guys can see look at that and thank you you like your ball my mom's collecting all the autographs she's been collecting them I'm getting a thank you just draw it on my face this is a new menu actually the cornbreads new and I don't know if you guys know but the salads and some of the stuff is actually from living with the land the attraction all the cucumbers in the tomato so you're actually eating produce from the attraction but they also have an apple pie now instead of the triple berry like a shortcake I'm excited I love apple pie for appetizers they bring out the cornbread with the honey butter the salad and then celery and carrots I'm diving into that corn bread it's Mickey who else is in there noise what are you writing in there what did you write did you write something in that autograph book you wrote red noise instead of red nose you were trying to like try to decide that use that as like a cheat code who has a red nose Mickey y'all that was the greatest yes look at that hey mom how do you know that's chip no red noise red noise look at that zucchini squash steak turkey mashed potatoes mac and cheese with goldfish oh looking good where's the stuffing though no stuffing I missed the stuffing I mean I like that it's still turkey and the steak is delicious but I need some stuff in I got zucchini squash and green beans or corn corn would be good too amazing I feel like the turkey is definitely different it's I think it's better the steak is still the same in the mashed potatoes no no stuffing like I mentioned but overall it's really good I mean I love it and I can't wait for the dessert we've almost made a whole lap around I think they said it takes about a whole hour to make one full rotation now we're getting back into the mighty who's of the buffalo but you can actually see some of it over here look at that isn't that cool I love that Mickey's back thanks bow is good I'm full look at that and that's edible it's you know white chocolate look at this apple pie it's like I don't know what it is like a dumpling yeah I'm gonna just oh no way look at that it is good much too good we're coming back to the house and you can see the boats going by look at that doesn't look like anybody's on that boat so it's probably a zero minute wait for living with the land Garden Grill is always a win in my book I love it there and now we're gonna go downstairs and ride so and my mom's favorite ride my favorite ride my mom is just leaving us today she's like I'm out of here she's ready to get to so and it's the wrong right she's been asking about Ariel's gonna be a first time riding it too my mom's got her dogs out on soren look at her she's got her Rogers out she's got her dogs out put those dogs away they're parking we're almost clear to take up hope so we're in the town we are ready for take up put those dogs away ma come on soren was great and Ariel loved it my mom had her dogs out just breezing her toes but they honestly if you do have loose fitting shoes you should take them off my mom's got some flip-flops she got a five below so I don't trust them up in the air but now we're gonna try to catch the fireworks and I don't know about time I mean we're gonna try to get there we got 36 minutes to find a good spot you see what we could do my money's on mom Bonnie's calves are gonna give way here oh she's taking a demanding lead we're trying to get to the fireworks I feel bad because I was kind of kind of thinking this was gonna be a flower and garden kind of day but we got distracted doing a lot of other stuff but we'll come back mom is gonna get her flower and garden festival whole day one day Frushi butterfly tent oh look right behind you it's a butterfly tent where you just walk around and there's just tons of butterflies all over the place I know I know I didn't know if we were gonna make it in time but we've got like 20 minutes to spare I think we got a good spot right here you know right nice and easy nice and cozy I I honestly have to say I think I'm a fan of this show luminous I mean I like it better than her money is the last one but I don't know if I like it better than illumination but I feel like they just pick the music to make people sad you know what I mean like the music's very sappy there's getting excited for the fireworks time to start making our way out look how easy it is getting out here now no right oh I hope the walls never come back and with that I think we are done here today it was a good day at Epcot and I'm happy that we got to do everything we wanted to do the only thing we didn't get to do is my mommy gets to explore food not food of flower and garden but we're gonna come back and do a separate day for that even though that was what it was today was but you know I want to make sure everyone gets a little bit of what they want my mom Bonnie and Ariel me I'm just here to show them a good time because like I want to like I said I just want to hang out with my family and do the things that I've been doing for a while by myself but now I get to have my family here so I hope you enjoy the video I enjoyed making it and we'll see you next time bye


How can I manage file uploads on my Moodle site? Files added to your Moodle site come from repositories which we can view from site administration, plugins, repositories. Let's look at the main repositories here. Private files is a personal area for uploading and managing a set of files. Recent files gives quick access to your most recently accessed files if you want to use them again. Server files allows teachers to access uploaded files in courses where they teach. Uploader file allows your users to bring files into your site, from their own devices or from external file storage areas such as Google, OneDrive or NextCloud. Learners and teachers on your Moodle site will be able to upload files such as word process documents, images, videos, sound files and more. Content Bank is where H5P activities are made and stored. You can view and edit the types of files allowed on your site from site administration, server, file types. These all take up space on the server where your site is hosted. If you have a Moodle cloud site there are limitations on the maximum file storage allowed. See your package details to check yours. As an administrator you can set the maximum file upload size from site administration, general, security, site security settings if your hosting allows. This maximum can be changed to a lower amount in course and activity settings by teachers. And if you use private files you can also set the limit for them there. If we return to site administration, plugins, repositories and click manage repositories we see the list of available repositories on your site. Some repositories have settings pages where you can change the name of the repository to make it clearer for your users and to make other changes. Enabled and visible means teachers and learners can bring in files from these areas. It's fine to leave the default settings if you're not sure. If you want to prevent access to a particular repository such as private files you can disable it here. If we scroll down past the enabled repositories we see external repositories such as Dropbox, Google Drive, Nextcloud and Microsoft OneDrive. When you enable these you then have to configure them. Click the question mark icon bottom right to access any documentation. To summarise, files are added to Moodle from repositories. Administrators control the availability of repositories. Maximum file upload size can be changed from site administration, general, security, site security settings if allowed by your hosting.


Awesome. Hello, everyone. I'm Bhakti, and I work for Google Research. And I come from the responsible AI team. I hope all of you are enjoying your conference. And I'm here to talk about flagging and fixing bias in machine learning. So today, we're going to be talking about bias in ML. And then we're going to talk about some open source offerings from Google that you can use in your workflows to make your workflows fairer. And then we'll end with a conclusion. So let's begin with the riddle. Here's the riddle.  The son is rushed to the hospital. And as he's about to operate, the surgeon says, I can't operate. That boy is my son. What do you think is happening here? And how would you go about solving this riddle? Any answers? Go ahead. The surgeon is his mother, OK? But that's one way to look at it. And any other answers? That's correct, by the way. Grandpa, OK. But it's a son, right? So now the grandson. But yeah, close. We're getting there. But you are right. It could be that the doctor or the surgeon is the mother and the doctor is a she. And this was exactly what the study that was conducted in Boston University showed, that when you think of a surgeon, a lot of people who identify themselves as feminists also forget or overlooked at the fact that the surgeon could be a she or the boy's gay second father, and so on. So that's bias. And I'm going to play a very quick video for all of you to think about machine learning and bias. Let's play a game. Close your eyes and picture a shoe. OK, did anyone picture this? This? How about this? We may not even know why, but each of us is biased toward one shoe over the others. Now imagine that you're trying to teach a computer to recognize a shoe. You may end up exposing it to your own bias. That's how bias happens in machine learning. But first, what is machine learning? Well, it's used in a lot of technology we use today. Machine learning helps us get from place to place, gives us suggestions, translates stuff, even understands what you say to it. How does it work? With traditional programming, people hand code the solution to a problem step by step. With machine learning, computers learn the solution by finding patterns and data. So it's easy to think there's no human bias in that. But just because something is based on data doesn't automatically make it neutral. Even with good intentions, it's impossible to separate ourselves from our own human biases. So our human biases become part of the technology we create in many different ways. There's interaction bias. Like this recent game, where people were asked to draw shoes for the computer. Most people drew ones like this. So as more people interacted with the game, the computer didn't even recognize these. Latent bias. For example, if you were training a computer on what a physicist looks like, and you're using pictures of past physicists, your algorithm will end up with a latent bias skewing towards men. And selection bias. Say you're training a model to recognize faces. Whether you grab images from the internet or your own photo library, are you making sure to select photos that represent everyone? Since some of our most advanced products use machine learning, we've been working to prevent that technology from perpetuating negative human bias. From tackling offensive or clearly misleading information from appearing at the top of your search results page, to adding a feedback tool on the search bar so people can flag hateful or inappropriate autocomplete suggestions. It's a complex issue, and there's no magic bullet. But it starts with all of us being aware of it so we can all be part of the conversation. Because technology should work for everyone. So I hope that gave you an intro of what machine learning and how human bias can creep in into our ML systems as we deal with data and algorithms that are not hand coded. They run completely on the data we feed in. So this is your typical ML workflow. Most of you are aware about all these steps. It begins by defining a problem. You collect and prepare your data. You train a model, evaluate, and integrate and monitor. And obviously, I trade through the process to ensure that you are not drifting away from concepts and your models are not becoming stale and you're getting the right data at each time. But essentially, bias can creep in at each of these steps in your typical ML workflow. For example, when you're defining a problem, you can have automation bias or historical bias or interpretive bias. Historical bias is something where the world itself is biased and you're defining the problem in a wrong way. Or automation bias.  For example, here, this was an Eastern Airlines flight 401. And the pilots entirely relied on this one light, gear indicator light. And they thought that the machine would indicate whether they were at the right altitude. And they did not know that they were disconnected throughout and you had 101 depths because of that. So when you're defining a problem, you really need to think, is it making sense to rely completely on computers or should computers be augmenting the human decision instead of making it all together? Likewise, when you're collecting and prepared data, you can have sampling bias where you're just sampling from a particular population. Representation bias where you're not looking at a diverse set of users. Or measurement bias where you're only measuring things in a way that complies to your own inherent biases. So yeah, if most people, like this meme here, most people are taking surveys, then you know that people like taking surveys. So that's a bias that can creep in during your prepared data phase. And when you're training the model, a lot of times you can have algorithmic bias or experimenters bias. And what this means is you either overfit or underfit your model to suit your own judgment. And you often try to correlate things. And this can inherently show up as spurious correlations in your ML workflows. And evaluate or implicit bias. Imagine you're trying to design a gesture recognition system and this is me doing okay gesture, which is prominent in North America. But if I go and do the same thing in Brazil, they will boo me out. Why? Because it's a very offensive gesture right there. So you need to understand how you're evaluating it and how you're evaluating over different groups as well. And finally, deployment bias. Are you even deploying this model for the right audience? And that's where deployment bias comes in, like we saw for the gesture recognition model. So in your typical ML workflow, to make things fairer, you would need to add special considerations at each and every step to ensure that you have a responsible and fairer workflow throughout. So when you're defining the problem, think if automation should exist. Should you have a AI model that predicts criminality of people? Probably not. That's a bad idea to have automation for. Who's the intended user of the system? Are you designing it for the right user? Or is it just a subset? And what are the risks associated if the model gets it wrong? What's gonna happen? What's the fallback? And what would success look like? We think about business metrics like accuracy, latency, but have you thought about fairness as a metric when it comes to ML models? So that's on defining a problem. And when you're collecting and preparing the data, think about where the data is coming from, how it's sampled, how diverse it is, and are there correlations in the data that give you indications on what the biases are? And of course, the data, is it privacy-protecting? And finally, when you're training the model, think about who trained the model and how, because the experimenter himself or herself might be introducing bias into the system, and ask if you're making special fairness considerations while training your models. And finally, while evaluating, ensure your evaluation is done on a wide variety of users and you're using exhaustive data sets and if fairness goals are being met when you're evaluating the model. And finally, when you integrate and monitor, ensure that your model is not stale, you're still catering to the user group you are designed to serve.  And to see all of this in action, we have a lot of offerings that can help you solve these problems and some of them are open source so you can use them right away into your ML workflows. And today we'll be covering some of them. So let's start with defining a problem. So when you're defining a problem, when you're defining a problem, we have the people plus AI guidebook and Google's AI principles. So think about using AI for socially beneficial causes, ensure that it's not reinforcing bias and it's tested for safety. So these AI principles and a lot of work essentially when you're designing fairness goes into just doing this step right and hence this is a very socio-technical concept. It's not about tech users diving into the core and doing it all by themselves. It involves putting a lot of stakeholders in the same room and getting that problem definition right. And this can be a good way to start. And this also includes things you shouldn't pursue. So if your AI application is likely to cause harms, drones and military and that getting wrong can have harmful consequences, creating it for weapons, surveillance, violating internationally accepted or legal norms, you should not do that. That's our principle and this is one way to get started with defining your problem. There's also the people plus AI guidebook that you can use and like I said, defining success is a major part of getting fairness right and people plus AI guidebook can help you get there. So it can tell you how do you define what success looks like for the user? How should you put feedback and control and how should you fail gracefully? Because a lot of times these models will go wrong. There's no two ways about it. What happens then? How do you incorporate a feedback loop in the process and make them better? Because it's an iterated process, remember? So people plus AI guidebook can help you with that. And again, the most important part is understanding what fairness looks like in academic research, there are 21 definitions of fairness. What works for Google Maps may not work for YouTube and it may not work for the model you are trying to design. So you need to think about what works well, what fairness definition works well for your problem use case and think of fairness as a metric. So I'm gonna take you through some examples here. So this is one definition of fairness where you could essentially be group unaware.  And let's just say 55 is the threshold and 55 is the credit score of the population. And you can see that group unaware makes the bank this much profit. And let's look at another definition of fairness which is demographic parity. You can say that we are not biased because this time we are not unaware about the identity of the group, but this time we are gonna ensure that the positive rates of people getting the loan is comparable in both the cases. So in this case, 37% and as you can see that the loan thresholds are different for both these groups. And what that means is the profit is also going to be different. So what I'm trying to get here is the moment you change your fairness definition, it takes a different turn when it comes to business metrics and you need to maintain that trade-off. And you can explore these playables on our website as well to understand how this impacts the models under the hood. There's another one called equality of opportunity. And again, this one is about getting the true positive rate, right? So people who pay back are essentially same for both these groups. And again, this has a different impact on the business metric, which is total profit for the bank as well. So as you can see, getting that user metric right is the fairness metric right is critical and doing a right trade-off with your business metrics is crucial to getting this part right.  When you're defining a problem, like for example, when you're designing this feature for Pixel that essentially does the portrait mode on Pixel phones, you need to think about who are the users, how are they segregated, what's their age group, gender, race, and you should be thorough in defining this so that eventually you test it exhaustively on these user groups as well. So now that you have defined the problem, this is the piece that's hard and takes most of our time at Google, the next piece is collecting and preparing data. So you can use this tool called Know Your Data. So this is a tool, again, it's available to use and you can play around with it. So what this does is it basically gives you a bird's eye view of what your data looks like and it also has some inbuilt signals that can tell you if there are human faces in your data or if your picture has been blurry or if it has been taken in the night or in the day and so on. And the most important piece here is it tells you about correlations. So now, people who are smiling are oftentimes, are they oftentimes the people who are wearing a hat? And this can tell you that and quantify it as a metric and it can tell you this. This is particularly important because you may think, oh, my model is not even having gender as an input, but oftentimes if your model has some other input like has makeup, you can easily tell what the gender is because correlations exist. So with KYD, you can look for skewed samples. So do you have more examples of women than men in your data? And that's a skewed representation, you can find that out. For example, here you can see that the word, it's a text-based data set and you can see the word attractive. Oftentimes correlates very much with females and a beautiful, again, correlates very much with female and so on. And you can see these correlations exist and you might think your model is group unaware, but it might be picking up on these proxies and causing bias downstream. Then you can also look for other signals that exist with KYD. So you can essentially look for features that are not a part of your data set, but you can build upon those. Like I said, if this is a self-driving data set, are there people in this data set and you can use KYD to figure that out? And you can also look at tainted samples and proxies. So like I said, even if a sensitive attribute is not used for training an ML system, other features can often turn up as proxies and you can essentially map that out using KYD. For example, this is another text data set and you can see that when you correlate words like, active words like riding, playing, jumping, you can see that it's skewed towards the younger population and not so much towards the elderly population. That doesn't mean that the elderly population can't do any of these, but you can clearly see this skew in the data. So this is something that you can use KYD for. And finally, you can also see sample size disparity with KYD. If you have a majority group and a minority group, how many examples are coming from each group? This is something you can also look with KYD. So for this example, there are fewer women who are in the data set than men and so on and you can easily probe that using KYD. And yes, getting a good, fair representation in your data is very important. I mean, it's the hard part, but you can get a lot of bang for the buck if you get this process right. Now imagine that you have done your best to collect and prepare data, but there's only so much you can do and now whatever considerations you have to make are at the training step. There's not much you can do. Then there are some fairness considerations you can do at the training step to make your model fairer. So essentially, you can remediate your model if you have identified bias. So essentially, if you have a data set and it's skewed, you can collect more data, but if you don't have the budget, you can generate synthetic data or you can sample the data in certain ways so that the distribution is same. So these are data pre-processing techniques. You could also do training time modeling techniques to remediate bias. So essentially, you can change the model's objectives in itself. You can add constraints to the model while training it or you could do some post-training methods to remediate for bias. And this is again at the model level. So you tune the model's outputs in a certain way so that the interpretation of the outputs can improve the performance across the fairness metrics that you have set. So this is one other technique and this is also open source. This is from our model remediation toolkit. This is Mendeff. Imagine that you are trying to train a model and you want the performance of these two distributions to be the same. You don't have the data resources, but you are trying to do something at the model level. So you can use this Mendeff law so that you can add to your model and then you can see that the majority and the minority group have equalized distributions in this case. This is particularly helpful if you are say training a toxicity classifier and it's trying to call a certain group more toxic than the other. So you can use this Mendeff laws in your training and you can observe that it improves the performance there. There's also the TensorFlow constraint optimization. So now a lot of times these fairness goals that we talked about, the 21 fairness definitions, they can be written as constraints. So you can say that the true positive rate of majority needs to be equal to the true positive rate of the minority and that's demographic parity essentially and likewise for equality of opportunity. Now you can take all these constraints using the TensorFlow constraint optimization toolkit and add them to your training objective and this is again open source and you can see that this helps improve the model's performance over the various groups. On your right you can see that you're trying to train a classifier which essentially predicts whether a person is smiling or not and without constraints you can see that it oftentimes thinks that all people are not smiling or their false positive rates are higher and then with the constraints added you can see that the gap reduces and the overall false positive rate for both the groups also reduces. So you can use this toolkit or you could use counterfactual logic pairing. So again, you're training this toxicity classifier and you put these sentences in a toxicity classifier and you can see that all these sentences, none of them appear to be toxic but the model is learning these correlations, right? So it's seen as toxic and the toxicity scores are very different for all of these groups. What do you do then? So then essentially you can use this counterfactual logic pairing loss which is again open source. You can use this and you penalize the model for predicting any of these sentences differently and when you train it over and over you will see that essentially it not only brings the toxicity score of the model down for all these terms but also closes the gap between the various groups. So you've trained your model, you have tried your best to prepare the data in a diverse way. Now it's time to evaluate the model and again, this should be in an iterative loop, like I said. There are several tools to iterate or flag bias in ML models. So one is fairness indicators. Again, this is open source. So essentially you can look at sliced metrics across the various groups and you can see how you're performing over the female group or the male group or the LGBTQ group and so on and you can slice it across the user groups, whatever user groups you have that you thought about in defining the problem stage and see how you're performing. And oftentimes when you deal with fairness your data is going to be very sparse. You're not gonna have enough information about each of these groups. So then fairness indicators toolkits comes with confidence intervals. So it tells you how confident it is about each of these metrics. So you can even work with sparse data when it comes to these evaluations. You can also use the what if tool. Again, this is open source. So essentially you have a model, you have its predictions. What if I just change the age group and is that causing my model to flip its decision? You can find out all these what if scenarios using the what if tool and understand if there's bias, if it's over indexing on age to give a particular response and if it's over indexing on some other sensitive attribute to predicted decision. And there's also the language interpretability tool. So if you're working with languages and then here you're looking at a sentiment analysis model, just changing the word waiter to waitress is causing the model to behave differently. Sentiment is the same. But it's just that you're changing the gender so you can see how much a term is causing the model to flip its decision or to wait in one way or the other. You can use the language interpretability tool which is also open source. And once you're done evaluation you can essentially use model cards to basically communicate what your model is doing to the broader audience. Not just to model developers, of course, as coders we know what the model does and we can read into code but for the lawmakers, for the model users it might be hard and transparency and communicating what the model is good at and what the model is bad at is just as important as building the model itself. So we have the model card toolkit. So what this does is it basically tells you what is the model good at? What are the intended use cases? For example, phase detection. What should you be using this for? What is it good at? What data sets has it been trained on? And what are its limitations and ethics and considerations? And you can do this for your model as well. And this again is an open source tool that you can use. So with that, there are several offerings that we have to make your workflows fairer. And a lot of these offerings are open source. So be sure to use them. And with that, thanks a lot. And yeah. Oh, you saw it too. Oh, gosh. Yeah, I think I just made it in time. But any questions? Yeah, go ahead. I'm curious how many of you have considered fairness in your AI or ML workflows? Maybe a show of hands. Oh, wow. That's great. Where any of these findings or toolkits new to you? If yes, then show of hands, perhaps. Okay, great. So this talk was helpful. Are you saying, do we have tools that can automatically annotate? What kind of pictures are? Oh, right. Yeah, I think the question is around, do we have tools on identifying where data sets or how well represented a data set is in terms of geographical locations? We don't have any annotators that can do this out of the box. But if you have those location annotations as to where the image was taken, then you can obviously explore them using no data and see how well represented that is. We also had this competition called open, not a competition, but a crowdsource challenge called the open images challenge. And we saw that a lot of times when you source data sets, oftentimes it's from North America or it's not as diverse as it should be. So we did this challenge and we got people to crowdsource images, including a lot of Googlers and then we created this data set.  It could mean a Christian wedding or a Hindu wedding and so on. So it was a more deliberate process. But yes, we did go through some data auditing to come to that point and realize that there was a skewer, there was misrepresentation. Any other questions? Well, thank you very much for your time and I hope you use some of these in your workflows. Thanks.


Bienvenue and welcome to day two of the OpenStack Summit. Please welcome Chief Operating Officer, OpenStack Foundation, Mark Collier. We could make it here for day two. So I want to talk about a few things. And I want to first start with a quote that a lot of you are probably familiar with. It's from William Gibson. He's a futurist. Over 20 years ago, he said that the future is here. It's just not very evenly distributed. And that's a pretty powerful idea. I think that's why so many people get excited about this. But when you think about it, it's actually kind of sad, right? I mean, I would like to see the future be more evenly distributed. And I guess I'm a bit of an optimist in that I believe that 20 years from now, the future will be more evenly distributed. And there are a lot of headlines that will tell you all the doom and gloom in the world. But I think that there's a lot of reason for optimism. And I think we'll look back 20 years from now at this quote and say, he was right at the time, but a lot's changed. And in fact, the world will be more evenly distributed. And the future will be more evenly distributed. And so I'm going to be talking all about distributed, the concept of distributed as it applies to technology, of course, but also society and just all the ways in which this trend is fundamentally changing everything about our world, everything about our planet. It's the biggest trend in our lifetime, in my opinion. And I think if we pay attention to it, we might see some reasons why OpenStack does have some relevance in this trend. So when you think about evenly distributed opportunity, it's economic opportunity, it's technology access. Certainly access to the internet is a fundamental part of the future. And not everybody's there yet. So William Gibson is right today. Not everybody has access to the internet, believe it or not. We are all very lucky. Everybody in this room is probably tweeting right now. Well, that's not the case everywhere in the world. But over the next six years, another billion people will come online. And the thing that is really important to note is that they're not just getting online. They're all going to have smartphones. So this is not very long from now, six years from now. Another billion people on the planet will have internet access on a smartphone. And that is distributed power. And when we talk about evenly distributing the future and economic opportunity, I think we have to not just limit our field of vision to the West or the first world or the rich countries that some of us may have flown here from or live in. We need to really think about the whole world and everybody's opportunity. And if you look at the projections in sub-Saharan Africa, you'll see that in just five years, two-thirds of people in sub-Saharan Africa will have access to 3G coverage. That's incredible. I mean, for me, this just is something I personally am passionate about. I think that everybody should have access to the technologies and the economic opportunities of our times. It shouldn't be limited to a handful of countries or people. And so I think this is very good reason for optimism that the future will be more evenly distributed, that we're on the right path. And if you think about those phones, those smartphones, that the next billion people will have, that all of you have in your pockets or perhaps that you're typing on right now, they're really supercomputers.  So as we think about the next billion people getting that access, everybody gets a supercomputer, right? That's pretty exciting. I think this is a stat I recently read in a Andreessen Horowitz presentation that kind of blew me away. So on iPhone launch weekend, Apple sold 25 times more transistors than all PCs on the planet Earth in 1995. That's on that one weekend. So that's just a massive transformation in terms of power in people's hands. And it's not just about these $1,000 phones that some of us may be fortunate enough to afford, especially if you're an open-stack engineer. You're probably doing OK right now. But there are a lot of people out there that are now having access to smartphones because of these types of initiatives. Google has a handset now that runs Android primarily in India. It's about $100. And the Firefox phone is targeted at $35. So that's a pretty big gulf in terms of $1,000 for a new iPhone versus $35. But the fundamental power in your hand is not that much different. If it's connecting you to people, if it's allowing you to organize as citizens in your country and affect change, it's a lot of power. So when you combine that ubiquitous connectivity, which many of us enjoy, but the rest of the world is quickly going to have access to, with a supercomputer in your pocket, that is distributed power. And I think we really have to let this sink in, just how fundamental of a change this is going to be for our planet and for governments and for all of us. And it's exciting because when I talk about power, I'm not just talking about MIPS and CPU, computation and transistors. I mean, we're talking about the ability to make the world the world we want. And hopefully, in my view, it's one in which the future is more evenly distributed and those opportunities can reach out to every country in Africa and beyond. And to share a few more data points here about this trend, this particular graph shows the interest peaking in an application called FireChat. Now, many of you may not have heard of FireChat, but this is an application on a mobile phone. It can run on a $35 handset as well as a $1,000 handset. And this exploded in popularity in Hong Kong during the recent protests when people found that they had limited connectivity to the internet. And in fact, they needed to stay organized. And so they used a peer-to-peer application that was actually able to keep people connected through a mesh network without even having access to the internet. So that's distributed power. It's affecting the whole world. And another example that I think was really quite interesting that happened just in the past couple of weeks. Many of you may be familiar with this, but in Hungary, there was a proposal to tax the internet. So remember that distributed power, one of the two key elements is ubiquitous connectivity.  They were just going to tax it. And people were not happy about that. In fact, they took to the streets with mass protests. Do not take away my internet, they said. And then in fact, they had to cancel this internet tax. It was going to be a tax on bandwidth. And so the question you have to ask yourself is, in 2014, this happened in the last two weeks, where is the power right now? Is it the guy in the suit? Or is it all those people in the street holding up their cell phone? I think it's pretty clear that the power is shifting. It's becoming more distributed. And this is something that's affecting democracy in a positive way, in my opinion. And I think that's pretty exciting. So once upon a time, all you had to do was be the guy in the suit. And that's no longer the case. I don't have a suit on, thankfully. But anyway, I think these are all just signs of this trend of distributed power. But it's not just about citizens and democracy and organization and making the world we want. I think there's a lot of signs that economic opportunity is starting to become more distributed. This is a really cool excerpt from an amazing report that Bill Gates put out.  But he's gone on to do some pretty amazing things with his billions of dollars. And this report I found incredibly inspiring. A lot of very good reason for optimism. And the picture on the left is Nairobi in 1969. And on the right is the same city in 2009. So I think that there is a lot of evidence that opportunity is spreading. And it's not limited to just a handful of countries. And in my view, that's a very positive thing. And I think some of the technologies, like ubiquitous connectivity, mobile computing, and of course, we know that all those experiences are backed by cloud. So eventually, I have to talk about cloud. I promised Jonathan that I wouldn't just use this time to talk about my economic views. So we actually are going to move a little bit down into from humanity to IT. So I guess we're going to have to talk about IT eventually. And if there's any time left, I'll talk about OpenStack. But no promises. So IT, where does this trend towards distributed power play out in IT? So my thesis is it's playing out everywhere all around us. So IT is certainly not immune to that trend. And so in the battle days, as an employee, IT said, I built your Windows machine, Dave. And you took your Windows machine and applied the 300 patches to it. And off you went, right? You didn't have a lot of choice. And many of you probably didn't want a Windows machine. But that's what you got. And times are certainly changing in that regard. And Jonathan mentioned this yesterday in his keynote, that power is distributing to the business units, right? They are making more decisions about technology than ever before because it affects the bottom line, because technology is strategic, because software is strategic. And central planning committees are not what they used to be. And that's OK with me. I think you'll see that the power actually is distributed even further than departments and into individuals, right? I'm sure many of you, if you didn't like the phone that your company gave you or they didn't give you a phone, you went and bought the one you wanted or a laptop, et cetera. So that's certainly a trend in the IT world. And to really kind of boil this down to my core belief here, which is that over the long run, distributed beats monolithic. Now, this does have some applicability in cloud computing. I promised I would eventually talk about cloud. So I think if we look at the cloud computing market and ask ourselves, how is this trend towards distributed power playing out in the cloud computing market? And I think let's be honest, there's a monolith in the room that we can't ignore. And they're a very powerful company. And look, they have some amazing technology that a lot of people have found valuable. But the great irony, I think, of cloud computing is that while cloud architectures are distributed by nature, in fact, we used to call it distributed computing before some marketing guy came along and said, cloud is a better name for it. So the architectures have been distributed, but not the power. It's been fairly dominated by one player. And the headlines will certainly bear that out. Amazon dominates, dominates, dominates. I mean, this is what we've been hearing in the headlines for a while, right? And they have a very impressive track record. And I think, however, if we take a little bit closer look, it's fair to point out that these headlines are all from 2013. And things move fast in a distributed world, right? I mean, a month ago, Hungary was going to tax the internet, and now there's protests and they're not. So things move quickly. And headlines in 2013 are not necessarily the best indicator of what's really happening on the ground. And so for me, I would say that if one monolithic provider were enough, then what are all of you doing here? Probably have some other opportunities, some other things you're interested in besides Amazon. And in fact, if we look forward past the headlines and look more at the trend lines, I think you'll see that in fact OpenStack, which is a distributed effort by design built by all of you, used by all of you from hundreds of companies, we're on a pretty good trend here. And I think that this may not have shown up in the headlines yet. But in the trend lines, I would say that the distributed power of OpenStack that's built by many different people and used by in many different use cases is a pretty powerful thing. And Amazon is certainly impressive, but I think one provider is simply not going to be enough. And just yesterday, we heard from some very impressive users, BMW, Time Warner Cable, BBVA, who decided that they wanted cloud, but they needed it in their own data center. This is not unusual. This is a very popular model for cloud computing, and OpenStack's one of the ways to do that. And so because software is eating the world, as everybody has been saying for a while, it's no surprise that everyone's going to have a little bit different cloud strategy because it affects every business. So there's not going to be one cloud strategy that's going to work for everybody. There's not going to be one cloud provider that's going to work for everybody. And in my view, what we really need is not one vendor. We need one stack, and that's actually what we can collaborate on across the industry with input from users. And in this world, we believe that the distributed trend is going to play itself out with, of course, distributed computing, but certainly with a number of different vendors. We have over 400 companies supporting OpenStack. This is one of the major reasons that we here, users are interested in adopting OpenStack because they have a lot of choice, as Jonathan talked about yesterday. We certainly have a lot of different models, whether it's a distribution, an appliance, a public cloud, a private cloud, a hybrid cloud. And I'm excited for you all to hear from some of our users that are going to be speaking today because many of them are pressing the envelope on the many different models in which you can use OpenStack to meet your needs. And it's certainly distributed by vertical. We have banks, we have film and television, many different industries are adopting OpenStack in their own way. And ultimately, distributed R&D is what builds such a great platform on such a fast pace. We've had over 2,000 developers contribute to OpenStack from 100 companies from all over the world. And so at the end of the day, I think that my thesis and my claim to you is that power will be distributed in every level, but including cloud computing. I do not think cloud will be immune to this trend. And I think power is actually already distributed when we think about cloud computing. So just share a little bit more data with you. I love data and graphs and maps for that matter. So hopefully you'll enjoy a quick tour through the wide world of cloud computing. So this map, does anybody know what these dots represent? Anybody? AWS, yeah. So these are data centers or regions where Amazon has public clouds. And you might look at this and say, well, that's very impressive. They have a lot of capacity. They're in every region of the world. But if that capacity is not in the country that you need, then it's not really capacity, right? Like if you need a cloud in France and someone comes to you and says, don't worry, I've got one in Germany, you know, that claim is a little bit suspect. And I would say that it's not really capacity, it's audacity to tell you that that's good enough for you. Don't worry, we figured it out, we're in Germany. Well, that's great if you need the technology they have and you're in Germany, but if you need a cloud in France, might not be enough. And so if we look at all of the public clouds we have today, powered by OpenStack on the same map, you know, we have multiple clouds in Germany, multiple clouds in France, multiple clouds in the UK, and many other regions of the world, Mexico City. And I think that's pretty exciting. In fact, you know, just to highlight one example, a recent OpenStack Power public cloud that you should really learn more about because they're doing some really innovative things, it's called cloud and heat. Now this is a company that is actually distributed in terms of how they deploy their cloud into multiple buildings. And the reason why they have these buildings distributed throughout Germany is that they are using the heat, the excess heat from the servers to heat the buildings, hence cloud and heat. So there are many different ways, many different models, many ways to innovate, and that is certainly what we're seeing with the OpenStack public clouds. And I believe that we will see many, many more throughout the world when we reconvene at our next summit in just six months. And last but not least, if you add in all of the private clouds running OpenStack, just the ones we know about, the ones, many of you who've been nice enough to fill out our user survey and tell us about your clouds, I think you'll see that in fact, OpenStack is everywhere today. And that's just evidence that one provider is not enough. And I think that that's pretty self-evident at this point or we wouldn't be seeing clouds all over the planet. And at the end of the day, one thing I wanna make clear is that I didn't get up here today to bash Amazon, believe it or not. I actually think that they're a very impressive technology company. And in fact, two out of three of the people we're gonna be hearing from today of our users are using Amazon with OpenStack. And that's great. And contrary to what you may see in the headlines, OpenStack actually has no natural enemies, believe it or not. Open Source is really not about enemies. It's about helping everybody use the technology in the way that they want. And in fact, much like human beings have no natural predators, OpenStack has no natural enemies. Amazon's not an enemy, there really are no enemies. OpenStack is available for everyone for free. Anyone can come to the summit, provided you buy your ticket in time. So remember that next time. But we want everybody here. It's a big tent, and that's the nature of Open Source. And sometimes that gets a little bit lost in all the headlines, but at the end of the day, it's exciting to hear from users today who are using OpenStack with Amazon. And that's absolutely the right thing to do for them. And if we look at OpenStack itself, the technology, I promised I would maybe get to OpenStack if we had enough time. So all right, a couple of minutes on OpenStack. So the technology itself is distributed. Distributed computing, cloud, they're really kind of one and the same. And I believe that if we look forward a couple of years and we think about where OpenStack is going to evolve to, it itself as a technology will become more distributed, I believe. There are a lot of discussions going on about this in the design summits this week. And with the technical committee and a lot of the leaders in the community about how we can make it easier for our users to mix and match and compose different services made up of different OpenStack projects. Cause you may not need every single project to solve your need. And so I don't think OpenStack itself as a technology will be immune to this trend. And I think we'll see stronger API contracts, more emphasis on how individual components can be mixed and matched to meet the needs of different people. Because at the end of the day, OpenStack is not trying to be a monolith any more than anyone else if we want to be on the right side of history. We should be thinking of ourselves as distributed all the way down. So the last thing I want to do is talk a little bit about the summits. Now we are certainly a very distributed community. So we have people here in this room from 59 countries. And if your country is not on this map please come see me afterwards and we'll color it in. We have orange grounds in the back but I think I got them all. So this is how diverse and how distributed our community is. And it's very important that we brought our very first summit in Europe to Paris because we have a huge community here but not just to serve the needs of the community in Paris but we all came from across the globe to be here. And that's the way we manage our summits. They're all global by nature but we do distribute them around the world. And a year and a half ago we went to Portland and then we moved on to Hong Kong, our first summit outside of the United States, a huge milestone. And of course, Atlanta six months ago and today we're in Paris. So those of you who are astute observers may have noticed on the back of your hoodies that in fact the road does go on from Paris and there are a couple more stops planned for next year. So a few of you have probably deduced where we're going next year but I'm very excited to tell you that we're going to Vancouver in May of 2015 which is a gorgeous city and I hope all of you can make it and take that train. There's not an actual train but it's a metaphor. Stay with me here and then on to Tokyo. And you know, what's so exciting to me about this trend, I'm into trends if you haven't figured that out by now is that this will be our third, by the time we get to Tokyo, this will be our third consecutive summit outside the United States which is just absolutely fitting for a distributed community working on a distributed cloud platform and I hope that that trend continues and I hope to see you all in Vancouver and Tokyo and around the rest of the conference. Thank you very much.


hey guys welcome back to another book video today I'm going to be giving you guys a review for witch born by Nicholas Bowling Bowling I know that I always butcher these names and I'm sorry if you can see a reflection this book cover is very very glossy I noticed that when I picked it up I'm like holy crap it's really glossy so if you can see that white glare it's from the windows because I really need the light it is a very very dark and gloomy day so I'm sorry if the lighting is not the best but we are going to get through it so I wasn't sure about this book at first whether or not I wanted to review it just because I have a feeling that I'm probably not going to make reviews anymore for books that I am not that excited and thrilled to share with you guys but since this is the last book that I have read in the year of 2018 I thought I would give it a shot for you it's not that I did not like it or enjoy it it's just that it was not my cup of tea I did start a new book but I'm not going to be finishing that for sure for this year so yeah basically it's about a girl who lives with a mom who well like her mom is a witch basically and at the beginning of the story it starts by all the witch hunters coming to her house and attacking her and killing her and then leaving her child behind when because they didn't know that she had a child behind and then once they killed her they were like wait a minute didn't she have a child so they go into the house and they find her and then they take her away and they bring her to this place where she's kept prisoner and then people come and get her to steal her away to bring her to the queen for different reasons and then on the way she kind of escapes and goes to this tavern or in place where she meets like this cool innkeeper lady she's kind of a little bit of a I don't know did see a little bit and then she meets a boy that saves her and he keeps her secret away from the innkeeper finding out because she does have this it's called like a mom it or monet or something it's a doll that only like apparently witch people have kind of like a voodoo doll but not really at the same time so I don't really know because I have actually never heard of that before so I was kind of interested in that but he took everything away that they could find that would make her come out as a witch so they go on this journey together to figure out why her mother died and why her mother gave her this letter to go to somebody and find this person and at the same time the Queen Elizabeth and Queen Mary of Scots I think it is not Mary Scots but that's like I've literally just watched the trailer for that movie coming out but they're having like a fight in between each other and it's just this battle going on and at the same time they just really want to find her and figure out what to do with her and there's just this whole secret behind her birth and behind her whole life story so that's the most interesting part that I found throughout this book was the witch the witch background and all these different things that they mention my camera is about to die so if it does go out I will change over the battery um but yeah that's basically all that I really found interesting was the witch background and the life story behind her and the whole twist and turn if you hear my dog she's chewing on her food but yeah I mean it was okay it wasn't as interesting as I thought I knew that this book was more for like fall and the fall season but I kind of picked it up just because because I wanted cut it is not really a short read but it is at the same time it's kind of thick but I got through it very fast but then towards the like middle I kind of just forced myself to keep reading it because I already read a book that I just stopped and didn't want to finish it and this would have been another book that I just stopped and didn't want to finish it so I was like you know what it's the last book I'm probably going to finish before the end of the year mine as well so I mean it was okay I gave it a three out of five stars I think that's what the reading is on Goodreads for it it's like three and a half stars for the book but I so my camera just died unfortunately but like I was saying it is somewhat of a good book I really also did enjoy the whole London and how it took place well it's supposed to take place in London that part was cool because it really honestly makes me want to go to London especially right now when everyone's going during Christmas time but overall I kind of had high expectations for this because I know that it was one of my most anticipated releases and I've been saying that a lot that a lot of these books are my anticipated releases and I never really end up enjoying them so right now I currently am on chapter 2 on Lifelike by Jay Kristoff which I am very excited for he has a ton of books coming out next year so if you guys have not heard of him you should read his books he is great but that is the book that I'm going to be going into the new year with if you guys have any other book recommendations let me know I'm sorry that I could not give this book a great review but I thought I would share it with you guys anyway if you guys have read this book let me know your thoughts if you guys have not read this book you guys should read this book and let me know what you guys think otherwise but yeah that is all for my book review and I hope to see you guys in another book review video


Hey guys, Dylan Schumacher, 7L Defense. And today I wanted to add on to this dry fire series here by talking about a dry fire workout routine for my pistol and what I'm doing. This year, 2019, I'm gonna be doing a lot more shooting on the range to continue to grow as a shooter. And I realized very quickly, of course, that if I want to make my range time worth it, that means I need to do a lot more dry fire because dry fire, of course, is where we can do most of our improvement with a handgun. So I wanted to come up with a routine that I can do consistently and disciplined in order to increase my skills with a pistol. So I copied it pretty much verbatim from Reed Hendricks's Pistolcraft book, which again, excellent book. I have a video on that. I'll try to remember to link it below and you should definitely read that book. But I think it's just important to have a consistent routine. So this routine that I do takes about 45 to 60 minutes. It is a time commitment. You can, of course, cut down and change things out as you see fit, but this is what I've been doing recently in order to increase my skills. The first thing I do is about 30 draws. I just draw the gun and shoot the gun, right? Dry fire, okay? Go back and watch the other parts of dry fire to make sure your gun's cleared out, okay? But just draw the gun and pull the trigger. And I'll usually start at about a second and a half and then I'll slowly kind of work my way down. And then my last five, I always make sure to really try to be pushing it past where I can get. And for me right now, that's about a second, 1.1 seconds or one second. It's kind of where I'm missing. It's where I'm not quite fast enough yet. After that, I will work on 25 reloads. And for me, that's just having an empty mag in the gun, slide locked back. I will eject that magazine, grab a new magazine, shove it in the gun, hit the slide release and present the gun and fire the pistol. My part time on the reloads is around two seconds right now and I'm trying to continue to push that lower. I will also insert that new magazine over the hand rack and then present the pistol. I just try to work both in order to kind of stay proficient at both. My default, of course, is using the thumb. But I want to just have the other way in case, just I would need it or if I start running on 1911s or something like that. From there, I will work on a failure to fire. So for me, the way I'm dry firing these is I believe in diagnostic gun handling. I know that's a hot take. Some people think I'm nuts for saying that, but I really do. So I will present the pistol, I will pull the trigger, I will do a 10 degree tilt, I will look at the chamber, the ejection port there to see what's going on there and then I'll look at the back of the gun on the slide to see if that's out of battery at all. If neither one of those things gives away any clues, I will tap rack bang and of course continue to fire. So that's what I'll do here. You'll see me in the dry fire, I will fire, I will tilt and then I will tap rack bang. I will do 10 of these failure to fires. Part time on the failure to fire is about one and a half seconds. After that, I work in a stove pipe. So for me, I will just take a snap cap, shove it in the gun there. That's pretty obvious what's going on, right? You get a dead trigger, you do that 10 degree tilt, boom. It's a very clear stove pipe. So from there, I will just rack the gun and continue. There's no need to tap the magazine on a stove pipe, in my opinion. So I'll just rack the gun and keep moving. I'll do 10 also of the stove pipes. Part time for the stove pipe is about one and a half seconds. After that, I do everyone's favorite drill, which is double feeds, right? So I will set up the double feed and then I'm going to strip the magazine out, triple rack, new magazine in the gun, rack and go. I do about 10 of the double feeds and my part time is five seconds. From there, I will do a single hand draws only. So a single hand means other hands out of the way. Weapon hand gets the shirt up, accesses the gun and comes out. I do 20 of these and my part time is two seconds. After that, I will do support hand only. So I mix this one up a little bit. I will pick the pistol up off the bed and support hand and fire. And I'll pick the gun up off the ground and support hand and fire. Really, again, I'm just trying to practice picking that gun up, transferring that gun over, getting it into my support hand and firing. Part time is around two seconds. If I have to go pick the gun up, I'll be a little slower. Lastly, I will do five weapon hand only reloads. Typically for weapon hand only reload, I will drop the mag, I will go to my holster, put the gun on the holster that is, get the new mag, put it in the gun, come out and go. And then five support hand only reloads. For support hand only, I found it a lot easier to just put that sucker between my knees rather than fighting it to get in the holster and fighting it to get it back out. And then get the new mag in and go. So that's it. Like I said, it takes me about 45 to 60 minutes in order to do all of that and set all those up in between consecutively. And again, you can do as much or as little of that as you want, but I have found it to be very helpful for me in increasing my proficiency and my weapons manipulation and really getting that side picture, that side line. Couple of real good things to note here is when you do do dry fire, don't just come out, bang and then pull the gun right back right away. Get out, bang, follow through, call your shot, be like, okay, I know where that bullet would have went if it was a real gun, right? If it was a live round. And then come back in, set it back up and go again.  With your dedication and discipline, you will improve. Come see me for a class and then you can show those skills off. Do brave deeds and endure.


There's two primary reasons people post their photos online. To get them seen and to get them sold. In this video I'm going to show you my strategy to make those two things happen. Let's get started. Hi, my name is James Wheeler. I've been posting my travel landscape photos online for about 10 years now. And I've had a lot of successes and probably even more failures. But through that I've come up with a strategy that really works for me for posting my photos online. Now before I get into it, if this is your first time on the channel make sure to go in and subscribe as well as if you like this video make sure to give it a thumbs up. It helps share the video with more people. Now before I get into my strategy I really need to define what my goals are. Now my primary goal is really to share my travel experiences and the beautiful places that I see. As well as I really like to inspire other people to travel to those places. However, as I got better at photography I added another goal in there where I would like to make enough money from my photos to pay for all my camera equipment as well as pay for my travels. Now if you want to get more information about how much money I make selling my photos online I've got another video for that and I'll put a link right around here somewhere where you can go in and take a look at that video if you want to now. Now one of the important things to note here is that for me those goals are in order. Meaning that it is really more important for me to create beautiful photos and share them with people online than it is to make money. So my strategy is to regularly upload single photos to many popular websites with full data so they can be found. Now what I'm going to do is break down each of those items there. So regularly uploading a single photo at a time, many different sites as well as including full metadata. Now the reason that I say to upload single photos regularly can really be summed up in two words. Social media. If you think about a social media platform like Instagram or Facebook most people are going to initially discover your content by going through and looking at a feed. Now when they go through and look at a feed they're primarily going to be shown one image so it doesn't make sense to go and put them in a carousel in Instagram or add them to a gallery in Facebook because as they're scrolling through they'll only see the first image. Here's an example. Most people won't click in to look at the gallery so even if you have four photos that are similar it's often best to upload those four photos on four separate days as single photos as opposed to uploading them as a gallery because you'll get more exposure that way. The second point in the strategy is to upload to many different sites. Now some people may disagree on this depending on their goals. If your goal is to build a community around your photos and around your photography then it may be a better idea to focus on one site and put all of your energy into uploading and building the community on one site before moving to multiple sites. However if you want your photos to just get seen by a lot of people and to be found by a lot of people I find it's much better to upload them to a lot of different sites. Different types of people are going to use different sites. Some people like Facebook, some people like Twitter, some people like Instagram. Stock buyers are often going to be on the microstock sites looking for photos there. So if you really want to get the most out of people seeing your photos you need to upload them a lot of different places. That brings us to the last point in the strategy and that's to make sure you include metadata in your photos. Now if you don't know what metadata is it's just data that describes other data. So in the case of photography what you want to make sure you do is that you have captions and descriptions and keywords associated to all of your images. Also if you're posting them on the social media sites you want to make sure you've also got them tagged with different hashtags. Now the reason this is important is that when people go looking for your photos what the search engines do is they use this metadata to decide which photos to show them. Let me show you an example. If I type in Galeano Island stock photography and then I go over to the images section in Google search you can see that a number of these photos here are my photos and I know from experience that people do search on this, find my photos and then we'll go in and purchase licenses. So it's really important to include this metadata in all of your photos. Also one thing to note if you're doing outside photography or travel landscape photography another piece of metadata that's really good to include is the geotagging information. And that's it that's my strategy for posting photos online. If you want more information about how I actually implement this strategy and what my workflow looks like I do have a YouTube video on that and I'll post a link to that YouTube video maybe around here somewhere. Also if you'd like to better understand how much success I've had with my strategy and how much I make selling my photos online I also have another video on that and I'll put a link down here somewhere. Lastly if you found this useful at all make sure you go ahead and hit the like button below and subscribe if you want to see more videos. Best of luck posting your photos online.


Loving, disciplined, generous were the words of family and friends to the late Maureenike Bodhi Ogumbar, knee Ogumbangu at a funeral service at the William Christian Church of God, the Fountain of Living Waters, and Thony Village in Lagos. The two-day burial ceremony attracted dignitaries, well-wishes from far and wide, who converged at Gurudu for the final burial rites. Our correspondent, Paul George, was there and now in reports. Women and women, young and old, at this congregation wore mournful looks. It was a moment of sadness and celebration of a life well-lived by Mrs. Maureenike Bodhi Ogumbar. Tributes poured in one after the other from our friends and those who had some experiences with her when she was alive. Her son, Aditha Ogumbar, and one of her daughters-in-law, Laio Shola Daniels, spoke about a good deed, wishing her eternal rest.  She practically gave her life for us. She was very tall, she was a disciplinarian, but after 50 years of life, we look back and realize that that discipline was worth it and we love her so much. She was so wonderful and we will miss her, but we know she has gone to a better place to be with the Lord. She was brutally honest. She knew what she wanted and more often than not, we may not agree with her, but we always found out that whatever she said was actually the right thing to do. She had a way with people. She was a people lover. Even though she would say she has favorites, she would tell me that the other one is her favorite. Then she would go to the other one and tell her that I'm her favorite. We can see the children. She raised four boys, single and deadly, and all of them turned out well. I know that she's already with Jesus. Her bosom friend and confidant, Adoni Kasim, shared tears as she recounted great memories of her. I miss her so much. Meanwhile, the senior pastor of the redeemed Christian Church of God, the fountain of living waters, Conrad May May, gave exhortations from the book of James chapter 4 verse 13 and 14. She was a frank someone. She would tell you the truth. It doesn't matter whether you agree or you don't agree with her. In my marriage, she helped me. I'm going to Jerusalem. She was the one that facilitated my journey. She told me that when she comes back, she will make sure that that's what I sleep on. She did my own. I miss her. It will be what you took on you, see? If I'm right, I'll call. If I'm wrong, I'll tell you the truth. The spiritual judgment in Genesis chapter 4 says you who have made plans for tomorrow, The next day, well-wishers converge on a kudu for the burial proper. The Paul Barra, in celebration of the late Nomi Ogumba Assum called her, displayed their dancing steps, adding color to the ceremony. The senior pastor of the Fountain of Living Waters, Pastor Meme, prayed. Eight years followed as her remains were finally committed to Mother Earth. The celebration of life continued with a reception after a short service held for the late MBO. The second son of the deceased, Aden Kashumotun, said if reincarnation was possible, he would want her mother back. Other members of the family, Victoria Omobo Ali Ogumba and Awolabi Ogumba also paid tributes to their late sister. Apart from her biological children, there are a lot of other people who call her mom that she basically adopted and sold into their lives and was a blessing to them. So I know that's a lot but you know my mother was many things and there are many things that we remember her for. If I could choose all over again, 100% I would choose her to be my mom. We just want her to continue to rest in the post-op of our Lord and Savior. She does not control nonsense. If you say anything to her, she will scold you immediately. Then this guy, she's a good woman. Very good. When you look at her, you have seen our mother. There's no good thing to say about her than it is well with her. And even in her death, she will still continue to love her. Morenike Bode Ogumba. Me Ogumba Wu was born on March 8, 1941 in Shagamo to Mr. Emmanuel Adebayo Ogumba and Madam Ramatsu Ogumba Wu. She died on December 26, 2023. Poor George plus TV News.