Yeah, so this is death to slide decks why your next presentation needs to be a web page again My name is Rachel Winchester if you could just call me win And this website is at my own domain visual webmaster comm slash death to slide decks There's one thing I want to point out if you really like this presentation And you want to see it again, or if you want to recommend it to your friend I'm actually giving it this presentation again next week to the black press meetup. So Black press shout out to black press You can join the black press meetup at all the other meetups at meetup.com and this is next Wednesday at noon All right Great. Here's my presentation. It's gonna go full screen So I'll start off with just a question pose to a gift to all of you and you can answer out loud Just just say what whatever comes to mind when you think of the word page what comes to mind Okay, okay, okay, so the reason I'm asking is because Your answer could have a have to do with a lot of different factors I took I said before that I am a product designer, but my background is actually in art history I have a degree in art history And art history is a lot more than just you know studying Paintings in museums. It's looking at the entire visual culture. How do people see based on the technology that's around them? What are they looking at? What are they look using to see so a lot of Your answer for this question could depend on What you saw around you growing up if you had web pages around or just books If you grew up with the internet and computers or if you grew up with Using that technology in that way or or not so I'm trying to Get you to think about technology visual culture, but also history and and and generations All right, so this presentation is structured in three parts and I'm starting with the when so again I studied art history it was I love doing that and I Was a great way to look back at how things were to understand Why we are the way we are in the present and understand things moving forward? So I structured this this present state presentation to start with the when so that we can take a look at history And then we can figure out why we need to change and why web page presentations make so much sense right now So in the past Slide shows started around circa sixteen hundred these were images shown one by one in sequence an image would be Created in a in a material called acetate that was my transparent and Put in front of a projector with a light source and the image would be projected on to the screen So we get the slide deck from the slide show images one by one in a deck Shown in succession in a sequence sequence to kind of tell a story and give up a Presentation so that's where we get the slide deck. That was a long time ago Who remembers the 1600s? Right around 1987 is when Microsoft PowerPoint came out with With when Microsoft came out with PowerPoint So this actually predates the mass adoption of the internet So the first PowerPoint was used on computers that weren't necessarily connected to the internet Here's a picture of Apple Macintosh one that I think is from 1984 So the original PowerPoint was on devices like this Going into the future. We're still using PowerPoint on newer devices Laptops touchscreens smartphones and tablets, but it's still the same PowerPoint Maybe they've added some more bells and whistles and audio and and other things, but it's still the same idea Slides shown in succession one by one Google added some cool stuff by making it putting it in the cloud. You can collaborate in real time, but it's still PowerPoint So now how do we transition into the future? So slide decks slide shows and PowerPoints are tied to a time when content was static and contained in finite pages We navigated through content by moving from slide to slide page page to page. We had to turn the page or hit next So here's an old photo of some ladies in the library or someplace reading very large newspapers Having to turn those giant sheets over and over, but that that was the content that was the way news Was put out on in that kind of way on those giant sheets of paper But even fast forward to more modern times where we're using e-readers It's still the same format So they just kind of take the same format of a page or a book and they put it inside of a digital device So with this e-reader, excuse me, e-reader You probably have to swipe to turn the page or maybe click the button So it gives you that illusion of reading a paper of book so Noticing all these technological advances. I really look back to my own art historical Knowledge and remembered this huge point in history the horse in motion by Edward Moybridge this Is such a big point in history because he's solved a huge problem that a lot of us were having so Transitions have always been a problem with visual storytelling But now there are modern solutions. So Edward Moybridge in Philadelphia of all places in 1878 Figured out how to give the illusion of motion in a video by rapidly showing These 16 frames in in rapid succession So you see each frame is just a little bit different from the next and Each of these 16 frames are showed in succession in that in that gift So it's the illusion of motion and that's how video is created So he solved a huge huge problem by coming up with this By coming up with motion picture this way this also reminded me of Just myself growing up and seeing seeing other types of imagery not necessarily video, but other types of digital digital screens images on screens where the transitions Got better and better with time So on the left you see a gift of Gallagher the arcade game If you were growing up in maybe the 60s 70s 80s Going to the arcade and seeing all these Eight bit games get better and better in a higher higher resolution You know, maybe this This game was was this kind of made sense to you However me when I was introduced to this game maybe around 2005 or 2010 just thinking about my age This game was disorienting. It was it was weird I couldn't tell like if the missile actually hit me or if I got the missile to hit the alien or the Whatever those things are the frame rate was it was just too choppy Because the frame rate wasn't wasn't what I was used to seeing when I'd watch Nickelodeon and cartoon with video and And and you know high resolution video with fast frame rates where the where everything is very lifelike This was not lifelike for me and it was disorienting Another example from the past if you think of Older computers with a really old interface if you scroll down through content too fast The content will take a while to load So if you're used to scrolling up and down your page like we are today like this We know the content loads quickly But in this older interface if you scroll down all the way to the end of the page You have to wait a few seconds for the content to appear So as transitions get smoother content becomes seamless today we can present content in a way that matches how we naturally view and experience the world So in the present we have digital video circa 1986 As I've explained before digital video is made of images displayed in rapid frequency I decided to show this video because showing clapping Capturing clapping and wrap that rapid movement and then displaying it again to an audience That's hard. That was hard historically speaking. This is a very new kind of accomplishment Also in with video we've got graphics interchange format so Gifts gifts or gifs gifts gifts. I was saying it right, right? Okay So gifts are still a newer kind of media file, but not the same as a digital video It's much smaller media file because as you can probably tell there's Maybe a handful maybe a dozen images in this in this sequence that are in that media file So it's much smaller than a video file, which is why gifts are popular because they're much smaller files Hmm So not only has content evolved drastically, but so has the way that we interact with content So digital interfaces exist now we've started with Keyboards and and a mouse and now we've got touchscreen Now we've got even VR and other other types of actions and ways to control digital interfaces We've even got video games This guy's playing a video game a first-person shooter game inside a smartphone Using only his thumbs to control his character in that virtual world And also using his thumbs to control the interface and hit the buttons around the outside This is all very new. I mean we're maybe used to it now, especially people at a word camp very techy people But historically speaking, this is very new So some of this history that I'm referencing is web 3 web 3 is a multi-faceted words has a lot to do with you know blockchain and and AI and and just The idea that internet is everywhere So because the internet is everywhere and the internet is ubiquitous It's starting to we're starting to understand that visual language Everyone people young and old are Are using internet connected devices digital devices and we now all are starting to understand The internet as its own language So Times have changed and it's time to transition into the future On number one. I only have a couple in here because So my thesis is that web page web page Presentations fit in with today's visual culture. They match how we naturally view and experience the world So today in 2023 Everyone's got a smartphone who doesn't have a smartphone. Okay, who doesn't have a laptop? Who doesn't have a TV? Okay So this kind of imagery again thinking about art history and visual culture It's the things that we're using to see and it's the things that we're seeing around us that help us create this kind of visual Language that we can use to communicate So let's all get on the same page now Okay, I guess that wasn't funny So in the future aka now starting now We should think about content in terms of where it's like frame window feed viewing area and canvas And I think a lot of you are probably already aware of these words using within use being used with tech And we can think about interactions in terms of scroll zoom swipe click pinch, etc So again, this is this visual language that we are starting to all get on board with just because the internet is ubiquitous So this is really getting into the why why we should switch to web page Presentations and leave PowerPoints in the past So content today again in digital interfaces we have a canvas so canvas Before I mean without a before a computer a canvas was just a place where an artist can create similarly designers use Softwares like figma adobe's and sketch and those have canvases where we create so this darker square on the inside of this Figma screen is called Inside of the screen is called the canvas Frames everyone knows frames from their glasses a lot of people wearing glasses around here frames on around a painting or an image so a frame is something that that Encompasses the content similarly It's going back to figma a frame will encompass content So here I've designed an app screen and this purple square that I've selected is a frame So I've selected that piece of content I could even select the whole phone and that could that'll be a frame so frames are just The part of content that you're currently looking at More frames With digital video so depending on the frame rate of a video or gif it can look choppy or smooth So frames are very important with video because that's what makes it lifelike And it's also what makes gifts so funny Because gifts are choppy. I think they work so great for comedy Baseball fans out there raise your hand if you watch baseball are you a Braves fan Oh Sorry, sorry But yeah, that's the the Phillies fanatic That's the green monster and the Phillies flyers the hockey team mascot Gritty, yeah, Philly has the best mascots All right viewing areas similar to frames and screens viewing areas Aka viewports are the area of a world that you are currently looking at So this guy is using a VR headset every time he he's he moves his head side to side He's he's looking at a different viewing area so similarly with Video games when you are in it have a character in the world The world that you are currently seeing the part of the world that you're currently seeing is a viewing area Windows I think windows are easy Everyone's seen windows and we've got windows computers with windows in them. Sorry accidentally Oh, yeah, more more windows Thank you for that Interactions with content scroll the scroll is super popular We've had scrolls before Before digital interfaces and now we've got a lot more scrolling in digital interfaces scrolling is very popular it's practically addicting as We've learned from tick-tock and Twitter and Facebook and all those feeds with an endless scroll Tick-tock on the clock and the scrolling don't stop anyone on tick-tock Use your hand if you're on tick-tock How could you can spend all day on it? Can't you? Yeah, yeah It's the content, but also the way we interact with it Pan and zoom if you are a photographer you probably use these words pretty often When you pan is when you move through a viewing area and zoom is when you go in or out of a viewing area so if this photographer was had her focus on The cyclist the cyclist would be in focus as you see and the background is blurry because she's following the cyclist Sand zoom and pinch Can be used all together with digital interfaces digital interfaces, especially touch screen this guy is using a Tablet to I don't know discover the moon panning zooming pinching just to explore the viewing area and click press I Think we all know what that is. All right, so here's a screen. I put together to kind of as it like an analogy of Well, it's a bit of everything put together So first we're looking at the beautiful city of Philadelphia. Who's been to Philly? The city of brotherly love. Yes. Oh The video stopped. Hey, huh, okay. Well That is one of the biggest downsides to having a presentation in a web page But that has also happened on a inner on some of the online When I've given this talk online But what I'm trying to show you is you have The whole world a whole world is filled with content as a presenter as a public speaker You have an entire world of content at at your fingertips to include in your presentation You want to tell a story you want to have a narrative have a thesis or an argument? So you need to strategically take the content and sequence it and Display it to your audience to tell that story So if the world is filled with content and I'm showing you the whole world I have added this Monitor on top of the world to show you that you can Select and show the specific part of the world that you want to show to your viewers at that time So here's the mall that's going towards South Philly. I think that's City Hall But also this screen the section of this web page is probably something that you can really Most easily build in a web page is not something you can easily put in a PowerPoint I use elementor to build most of my sites, especially this especially my presentations and Elementor has a as a really cool feature where you can turn on mouse effects. So I've added The mouse follow I forgot what it's called. Sorry, but this image is is essentially just following my mouse as I move it around But it's also a web page and with the web page. We can pan and zoom we can pinch and and and put some content in more focus or we can zoom out and and and See the bigger picture So this section is pretty much a way to show all of what I've been talking about Coming together in one It still works as a as a static image, but it would work even better if the video was working So sorry about that But maybe it's a good time to talk about the pros and cons of web page Presentation so I think we've realized a big con is just Wi-Fi So pros and cons some advantages of web page Presentations and I'll show some cons potential issues to be aware of but also how to get past them And I'd love to if you guys think of more pros and cons I'd love for you to let me know in the Q&A afterwards So first of all, of course, it's a living document. It's a web page You can update as needed. You can fix errors if your brand changes You can update the brand in in the style guide, you know, it'll just transfer over to your web page You can treat it as a web page as we all know. We're all WordPress people here You can be a lot more creative so you can do everything you can do with a PowerPoint and so much more And then of course SEO and marketing. I think are the biggest reasons that I make web page presentations You it's it's on my web page. Oh, it's our it's on my website I share the link everywhere every time I give the same presentation. I post that same link So this this page on my website is probably the most visited page but it's it's really It's great for for all that SEO and marketing As far as cons go so you have to develop a website or a web page at least I guess that's not too hard for people in this room, but for other people They're they're easy ways to learn to build a website. You can use page builders and CMS I also have a tutorial for how to use Elementor and get started with Elementor If you if you want to use the same tools that I do But if you don't have the resources to make a site or the time you could try these other options So Google Docs has a new page list format. Has anyone tried the page list format yet? I Love it. It is revolutionary, isn't it? It's under the I think it's under the view tab, but when you scroll down instead of having a page break It's just seamless content So if you you can use that as a presentation put it in full-screen mode Make images big that make the text big and you can just turn a Google doc into a presentation same with tumblr if you have tumblr is kind of like a Social media site, but your page can be a little bit more customizable so you can add more aesthetic stuff And then a blog post so let's say you are part of an organization that has a website And maybe you contribute to the blog of a blog A blog post can easily be turned into a presentation So instead of creating your own website, you can just add to your organization's website instead Accessibility Is a con but it's also an interesting question So I mean you can do the normal tips like you know add alt text to photos follow the correct heading outline structure course scroll slowly and Other some some other tips like using mouse effects instead of animation. So using Making sure I can control movement with my mouse rather than having an animation just autoplay without my control so That's that's one reason to use mouth it mouse effects use REM for paragraph text this Exactly thank you so it can scale easier and Also add a disclaimer in the header, so I'm just going to scroll up real quickly to show you my accessibility disclaimer in the header This is above my title slide, so I started on this slide But it wasn't the first oh, and it's not a slide. It's a section of a web page Accessibility notice I I it's this is a presentation which means this web page is supposed to be I'm supposed to show it to you. It's not necessarily made for you to go through on your own So that's the accessibility question if someone does find an accessibility issue Going through this page on their own is that an issue I'm going to use the table of contents to There we go Yeah, so if I am supposed to be presenting this web page if someone finds an accessibility Issue when they discover the web page on their own is that necessarily an issue? So that's that's still a question. I'm trying to figure out And another con is performance I'm sure you've noticed this is a long web page with a lot of photos and other other assets So it could it could definitely be hard to keep the performance where it needs to be so you can add Features like lazy load. You can create a static website. My my website is static. So that means that After I create the page when I deploy the website It's all the pages are kind of compressed into a tiny html html file and it's and it's when a When a visitor visits my site that small file is sent to their computer their Client, so it's not a dynamic two-way connection the way like you know an e-commerce site might be it's just it's it's a It's just a it's just so it's simple web page And you can also consider your web host when creating web pages like this So I'll just go over a little bit more about how to build web page presentations and Some elements that I like to add and some tips and tricks That that I try to stick to Of course media is a big one to Photos videos and gifts are essential for visual storytelling so add a lot of photos and make them big Links are essential for marketing and SEO to keep your spot into your presentation have links open in a new tab rather in that same page And for accessibility add a disclaimer to your header that the links open in the new tab Table of contents as you see to the left in the sidebar. I have this table of contents here It's a little Formatted a little weird there, but it's great for Again going back and forth through the sections especially in Q&A when I want to quickly go to a specific section Social share I had my social share buttons are at the top and again at the bottom essential for marketing And make it easy for your audience by putting them in obvious places Accordion accordions are great. You can hide content until you want your audience to see it like I had in the pros and cons section Reading progress bar. So that's that red bar at the top that you see As I scroll down the page it gets longer and longer. So it shows you how how close to the end I am And sometimes your web the web pages scroll bar isn't visible in every web browser So some users turn this off intentionally. So it's good to have that reading progress bar Animations keep things interesting by adding movement, but don't overdo it They can be distracting Hover effects use it to draw attentions to a specific spot in your presentation I was using hover effects with the photos So all of the photos were in black and white and then when I referenced them I put the mouse I I hovered over the image and it would turn to full color a Header and footer announces the beginning and end of a presentation great branding opportunity and Also a great way to collect collect questions and comments. So I do have an actual form in the footer where you can Submit submit questions and comments the sidebar helps with navigation and is another branding opportunity and Then forms collect feedback comments and questions in targeted spots So if I wanted to I could have put a form Maybe in the third section right after I asked you that question What it will comes to mind when you think of the word page I could add a form there and have people actually submit their answers And it helps make the presentation more interactive And some more tips these are great tips I guess for all visual storytelling, but especially web page presentations Minimize clean up your browser window by hiding the book books mark Excuse me bookmarks bar and closing unnecessary tabs.  You ever see someone in a presentation like Hover over a button and you don't know if they click it or not Yeah, yeah, you got to say when you're when you're gonna click something. It definitely helps Compose Composition and storytelling will always matter the most think how can a web page format tell my story better Create make something visually appealing to your audience express yourself in your brand and creative ways and have fun with your designs so I Think web page presentations are cool and new But I think there are all other types of other formats for presentations that I should try out and see how that works, too So next time you might see from me presentations on like a digital canvas, so maybe an entire presentation in figma Or presentations in VR and an AR. I know some people who are already doing that in this room All right, so thank you. Thank you all for coming to my to my presentation I We I would love to just discuss your questions and especially pros and cons if you've noticed anymore And I do have this form as well if you have longer More more feedback to give or anything like that So thank you so much for coming. Okay. Well, I start I start with the accessibility notice. That's some presentation dates I don't know why I forgot to add word camp Atlanta here It should be like right here, but the black press meetup is there, too and share buttons oh So you've probably missed my opening question And so you probably didn't get the pun because you didn't see the opening question Yes You know Yeah Actually Yeah Thanks, thank you Yes You I start everything with an outline. I I I love outlines some things up with the formatting of of the Of my table of contents right now, but yeah, I'm just trying to show you the outline But yeah, I started with an outline, you know You know, I have the three sections one why how Three is a good number It's you know kind of reminds me of those five paragraph essays We always had to write in school and got an intro three arguments and then an outro So yeah, I tend to start with an outline and then fill the content in the outline and then the thing just gets longer and longer So I had to kind of never really think in terms of turning a page Yeah Yes Edge Oh Yeah Yeah, I'm using the table of contents elements in Elementor so Maybe oh Am I no longer connected to the internet? Oh Well, I'll reconnect Anymore question. Oh, I'm connected again. Okay Well, I guess really the biggest con is that it is you do need the internet. Yes You mean the the form Well in this presentation, I just have a form at the very end to collect questions and comments About the presentation I Said, you know one another example is if I wanted to I can add another form here like Posing the question. What do you think of the word page? What comes to mind and then having a form where people can actually input answers into the site? You know, I could have you know directed you to say like oh scan this QR code to visit the The page on your own browser and then you know enter your feedback here I could have added interactions like that Oh, I'm actually using just Google Forms. Yeah, just a Google Form embed, but you can do that too Yeah, I tend to use a lot of Google products so And it's a static site so I think because it's a static site it it works better with a Google form than a native form I Can make it bigger You Is Yeah, I mean if this was an internal presentation I would probably you know, maybe put up a password page or you know not not posted anywhere or you know I There's definitely a difference between an internal presentation and a marketing presentation Or Well, if I were to build a web page presentation for a client I would probably approach that the same way I would build a website for a client You know, do they already have a site? Can I just add a web page to that site? Does it make sense for them to have their presentations at a different URL? Would a blog? Would it would a Google page list format make more sense because that would just be a Google URL But yeah, I would just treat that just like any other website client You see generation But I Why Yeah, I think the the most resistance comes from just Thinking about having to put a web page together So if you're already building websites and you're a WordPress person doing this daily That's not intimidating, but if all you do is you know, you're you're normally just in Word doc Then building a web page might be intimidating As far as like The format of like do do I see if older people prefer slides versus younger people prefer page lists? I Don't know yet. I don't know yet Yeah, I don't know I mean I My audience has for this presentation has been mostly WordPress meet-ups and mostly like this audience like this And I get kind of the same reactions like it They like scrolling down I Haven't had anyone say yeah Yeah, I haven't had anyone say to me like oh I miss I miss hitting next site. I miss the the page turn Yes Actually when I when I see people use a smartphone and I and I can see that their thumb is moving this way I kind of think they're on Tinder Yeah, I usually see this but yeah, I guess you know you can read a book on a phone and do that Oh The videos I Outline yeah Yeah, so I think Sometimes I'd manually build a table of contents using anchor links, you know like sometimes I don't want You know all of the headings to be in the in the table of contents of sometimes I'll build it manually, but um Yeah, okay, okay, I like that idea I Like that idea Well, this is my first time giving this presentation In person all other times I've been like webinars where I'm just like alone in my room So thank you for being such an amazing audience with so much feedback and other tips for the future I Would love to see you guys make some web page presentations If you do send them to me, so maybe I'll put some other examples at the bottom of this web page Question Just this page or the one with the skeleton Oh this And if they feel like Steve Jobs in this turtleneck, thanks


There's quite a lot to do, hopefully if you've got a laptop you could follow along. In particular there's, you should have two sets of files, one of which you'll be called presentation, which will have an index file on, so that will look something like this. If you click index you'll get to this and you'll also have an OVA file, which is a virtual appliance for Oracle VirtualBox. So there are a bunch of different stuff and there's the main presentation. Okay, so I'm going to be talking about, this is a deep learning talk. I'm going to kind of assume you've seen deep learning talks before, because this whole thing has been listed with them. So this, typically for these, for talks at FOSAsia, you might be learning, you're either sleeping or you're learning stuff. Okay, if it's a machine learning talk, you'll be learning to learn. Okay, this because we're going meta. So about me, so if you're a member of the TensorFlow group, which is Sam and I are running this thing out at Google, or just have been around, you'll have seen me. I've been in Singapore since September 2013. During 2014 I essentially converted myself from finance guy back to PhD guy by doing machine learning, deep learning, natural language processing, robots and drones, having a good year out. But since 2015 I've been kind of serious doing natural language processing, deep learning, produce some papers which have been accepted to conferences, and doing a dev course for deep learning. So there's, I'll talk about that later, maybe. I'm kind of, we do kind of deep learning consulting, prototyping. Does anyone else need one of these? Do you have a laptop? Okay. Okay. But in terms of products, which is kind of behind the scenes at the moment, we're interested in conversational computing, which is like, if you've got a Google Assistant, you kind of know how limited it is, it's kind of interesting, but it's very limited. We think there's going to be like a next, there's got to be a next generation of that. And for that we're going to need natural voice generation and also kind of knowledge-based interaction. So we're playing with these things. So the idea here is learning, you're going to be learning to learn to learn. So I'm going to talk about the basic ideas of learning, how you would learn from a lot of data, how you would learn from some data, and then how you would learn from just a little data. And if you've got VirtualBox, you'll be installing the OVA, whatever. So the first thing, though, doesn't require the VirtualBox to be installed. If you click on the TensorFlow link in the thing, which is here, you'll get up this very simple thing. How many people here have seen the Google Playground, this TensorFlow Playground? Virtually everyone. Do you have a laptop? Do you need one of these sticks? OK. So this is a quick neural network thing. Basically, what we have on this left side is some training data, which consists of these orange and these blue dots. And it's going to try and optimize it so that if you give it some test points with the same kind of distribution, it will classify them. And it starts off in a random state. And fortunately, in this state, it's actually classifying the blue ones with the blue background or orange ones with the orange background. But if I start it on some other random state, it doesn't know anything. But if I press this Play button, it will then adjust the parameters so that it forms a nice boundary between these two things. So this is the first part of the talk in that you have now learned how to make machine learn, OK? So this is one level of learning. And it's a fairly simple one. I just wanted to show that you have a task which has some input data and some test data. And we're going to try and do our best to figure this out. So let me just, I'm going to have to keep going. So with this, and if you're interested in the basics of neural networks and you haven't seen this before, this is kind of an interesting thing because you can see that with these features which is kind of lefty righty and upy downy, you can actually synthesize a diagonal line, OK? Now, that's fine if I know that a diagonal line will work. But if I go to a distribution like this, I know that there's, you can kind of tell there's no diagonal line which will cut this into oranges and blues, right? It will just fail, OK? And the only way to make this work is by adding additional hidden layers, what we call hidden layers. And so what we can do with these intermediate things is that they will each produce diagonal lines. And if I can train this sufficiently, it will work out what diagonal lines it needs to make an isolated thing. So here we're doing another training. And it's now figured out that if it picks the right internal features, it can actually draw a triangle around the blue dots, and so it's now learnt, OK? And if you play around with this, you'll find that there are limits as to how much the simple model can learn, but you can expand it by adding layers or by adding neurons or whatever. There's a whole little playground beautifully done by the group with us. OK, so this is training a neural network to learn. And let me just, OK, so there's that. And so what we've understood, OK, is what the goal here was to learn to predict these regions. You've got the input features. The single neurons can only make a single straight line. The way in which it's learned is by assigning blame, but we won't get into the whole back propagation thing here, OK? But also these deep networks can also create the features that they need. So I didn't have to tell it it needs to make a triangle. That's what it did. It's figured out that a triangle will solve this problem. And if I added more different features, it might solve something else, right? But in particular, if I have a problem where I don't understand the domain, I might not be able to understand what features it should be creating. The whole point of the deep learning is it can do this for itself, OK? If you're lucky. OK, so neural network's next. So now we're on to a thing called ImageNet. So this was a huge competition. And how many people here have heard of ImageNet? Few or some? OK, some motion? OK, so ImageNet was a huge competition organized, I think, since the 90s even, but certainly to 2000s, where computer vision people would essentially battle with each other to try and recognize images of, let's say, hot dog, OK? And they would be in the whole image in 22,000 categories. But the basic game of ImageNet was a thousand different categories. And what happened is that over time, up until about 2010, people would engineer special features. They would make a bread detector and a meat detector. And they'd say, well, if the bread's going around the meat, then this is a hot dog kind of thing, right? Or if it's got ears, it's probably not a hot dog. So feature engineering was all the rage, OK? And then came the neural net people, who suddenly these deep networks started to work on this image stuff, partly because they discovered the GPUs could be applied to doing this. So suddenly everything changed. And this is where we started off with, this is 2014. There's a Google network. This is now quite a big network compared to the TensorFlow playground. But it got more and more complicated. This is before the neural networks came along, with 25% error rates. When the networks came along, and there was also a grinding to a halt back in there. So the networks came along, it fell to 16, then 11, then 7, it's very rapidly converged to this, which is now almost three years old. This is now better than humans. So this is one thing where one of the limitations in learning this task is that you need more and more humans to label this, because the human error rates individually will be higher than the network. So you have to have all of these samples labeled by committees now. And the same would go for say radiology examples, where these radiology programs are better than a radiologist. But you can't be better than, there's no metric to say whether you're better than all radiologists, because all of them will be making error rates at kind of the same rate as you do. It's like a battle of the radiologists at that point. Anyway, so you also, this is the point on which you say, okay, I wish I had installed the OVA, because there is a virtual machine in there, which will hopefully you can do import appliance on your Oracle VirtualBox, you will then boot into Linux, and that will allow you to then go for your host machine that is on local host, there'll be on 8080, there is a Jupyter notebook thing. So basically you'll be able to just open Jupyter on this tab. Okay, the image will also do other things. So you can SSH into it. So all of these presentations on your, in your presentations folder anyway. So you can see exactly what's going on. You can get a console, the, you know, user password, in fact user and password. There's a proper, ah, yes, good idea. Okay, it's got a whole bunch of stuff in here. It's not as, I'd say this one is not as fully featured as it has been in previous years, in that I'm really interested in just a few of these examples. I'll be updating this as time goes on, because what has happened is that the frameworks have moved on significantly since last year, and so are the data sets, and for reasons I'll explain. So, or data sets and models, in that the model sizes have got much smaller, and I'll explain how this has happened, right. And, but also new framework has come along, PyTorch, which is kind of very interesting framework. So I, of course, need to do that. One of the older frameworks, the Arno's been retired. Okay, so there's quite a lot of movement in this whole space. Um, so hands on image net. So I've got, I've got one of these here. So this will be under CNN, Transfer Learning, GRAS. So there'll be this. I'll just go back to refresh. So this, so under this notebooks folder, there should be a two for CNNs. Within the CNNs, there's five for Transfer Learning, and then there's this one with Image Classified Keras. Hopefully I can find it again. Okay. I will do this in two stages. Um, and what we want to do is, okay. First stage, I'm going to do ImageNet classification. So this is basically a network which is pre-trained to do ImageNet. Okay. Um, these have been, these pre-trained networks have been created by say Google or lots of other people are interested in doing this. In video, mainly in video, because they want to sell chips, right. They will use vast numbers of images, huge computational resources, and this will learn exactly the classes which it was told to, okay, from all this data. And so if I go through this, basically I'm going to import from TensorFlow, I'll import Keras, which is now a first, kind of first order thing. Um, and we're going to be using one of the things in the Keras model zoo. I'm not sure whether, hopefully you can see this. There's a whole bunch of different architectures for doing this ImageNet problem as, and the very earliest one is this VGG16. Okay. What you can see here is this VGG16, which is more like a 2012 kind of network, has got 140 million parameters and it's scoring 70 odd percent. Okay. But what happened as time went on, is quickly we moved over here to this inception of V1, which has scored not quite the same, but a huge, usually fewer number of parameters. And then people started to do trade-offs with bigger models, much better performance. And so there's this kind of frontier as different generations go past of better and better performance. Okay. And so one of the nice things about Keras, which is a framework which runs on top of TensorFlow predominantly now, is it's all of these things in a kind of a model zoo. And so if we, let's just skip over this for a minute.  This NASNET mobile model has got, it's got 24 million parameters and it takes a little while to load because it's kind of complicated. It's structurally complicated. So the VGG had many, many more parameters, but it's a very simple network. Just do this stuff, then this stuff, then this stuff, then this stuff. This NASNET is many layers of this kind of stuff. This is very, very interconnected, intricate stuff. Okay. Okay. From here, having loaded this model image net, I have some helper functions to convert an image on disk into something which I can feed into the network. I have a thing which will then get a single prediction. So it will take the image input. It will then pass it through the model to get the predictions. It will then decode the predictions and give it back to me. So if I have a picture of a cat, it will do this and it will say that this is 67% confident that it's a tabby cat or a tiger cat. Okay. So this is, there's been very, very few lines of code, but I've been using Google's pre-made TensorFlow, you know, pre-made Keras model. And it works. So the VM's got all of these images in there.  So it's a Siamese cat. It doesn't know about white owls. So it thinks this is an Arctic fox or it doesn't really have much idea what this is. It thinks this is a dingo. I don't think it has a sheba thingy. There's a tabby again. Okay. So this is, this pre-trained model has these categories. And these categories, from the thousand we originally trained it from. And so this is a restriction.  It has a few cats. It's got many, many dogs, but it doesn't know much about faces because it's only got, you know, a few varieties of face, hardly any. I think, I think even Google realized there might be a problem getting us to recognize faces. On the other hand, they also have an equal number of gorillas which has been a problem for them. Okay. So what have we just done? We've shown that this image net classification we've trained it from zero using a vast number of images. Huge computational resources. There's instructions thing on there. If anyone else has got a spare one and needs one, please switch. It does exactly what we've told it to because we're going to get huge amounts of data. Maybe each of these categories we've had a thousand images or something. And this is kind of typical and why NVIDIA and the cloud providers are, you know, all egomode or Alice. Now let's do something a bit more.  You probably don't have a thousand images, but maybe we can do it with twenty images or something. Maybe my product is good enough that if I have twenty different samples, if only there was some way of understanding images. So this is kind of a nice little trick called transfer learning. And basically, so I'll load this other network again. So basically, hopefully, there you can see it. So this is the image network. Basically, we have this input image coming in. We have a black box, which is whatever Keras model has been loaded with its own weight. And it comes out with, here's kind of a bunch of probabilities or log probes, whatever, that this is the right class and this is a thousand long. And then we then take like a maximum over this and the maximum is going to be the answer. So this will be Tavikant or whatever it is. So this is the way the image has been trained. It's been trained. If it gets an image of Tavikant, it should say Tavikant. And it gets an image of, if it says, no, Eskimo or whatever, then it's wrong. And you should penalize the network. It should be saying Tavikant. So you train this and it gradually converges to something. What we do in this case for this transfer learning is we use the same exact trained model. We're not going to touch that trained model, but we're going to take off this top layer and just use this answer here. And we're going to pass these answers into an SVM, which is a kind of a standard, like a 1990s kind of machine learning model. And this will then, we will then train this on our classes. So in some ways, if we pass in something which this network has never seen, so I produce, we've seen a parrot, we've seen a baboon, okay? Baboons and orangutans. It may not have that much knowledge of those. But when it sees a baboon, it will have a certain kind of error pattern. It will misclassify it, because it doesn't know what baboons are. But it will like different things about the baboons than it would about the orangutans. So the kind of the pattern of errors will be different based upon, is it baboon or orangutan. But you can actually take this pattern of error and then use that as input to our own classifier. So that's what we're going to do. So basically a little cropping function, getting logits. So this is the thing which essentially takes off, that we've got the top layer and I'm now going to produce a thing which is about a thousand big, but it's more like of these error terms. So it's not just a one answer, it's all of the answers. And then we're now going to try and see. So I've got two sets of cars in here called classic and modern cars, in directories. And what this will do is it goes through everything in the classes and everything in the directories and just predicts out what the network says. Then it stores out that prediction for when I then run an SVM recognizer between the two sets. So hopefully it took 5.8 seconds each. So basically this is my training set. I've got a classic car, another classic car, another classic car, and then I've got, so I've got about 10 of these. Then I've got modern car, modern car. These are sports cars. So what I want to do is can it recognize the difference between classic cars and modern sports cars. And these are things it hasn't seen as an image and it hasn't been trained on this difference. But there are things about these images that it might like. In particular, it knows about dinner plates and flowers. And so it might like these, that the classic cars tend to have these nice circular kind of wheels and these kind of different smooth lines. And so it will confuse the classic cars with particular types of flowers. It knows quite a lot about flowers or whatever. Whereas the modern cars, these are very much more like lotus things or whatever. This is a different kind of flower which the errors it would make similar they've got different kind of lines and grills and kind of very different feel to it. So you can think that even though it doesn't know about these cars, there will be stuff which it knows about in images which it will make the same mistakes again and again. So that's my training set, the 20 off. I can then train instead of taking a week or something to train my image net or it's now the time's going down but instead of taking a very large number of GPUs I can train my SVM like that. So that took less than a second. I've got very few examples I'm only trying to tell the difference between 10 different things 10 on each side. But then I can then go through a test set saying well let's load in each one find what the image net thing says but then use the classifier I just made to classify that is this a modern or classic one? So it can then go through this and say well this is an image it's never seen before and it says that this is a modern sports car and this is a classic sports car modern sports car and well it's a Prius it thinks it's a modern sports car so it doesn't know any difference because I haven't given it the lame hybrid category so it's got these mostly right but this one it thinks is a modern car which I don't think it is but it's also kind of impenetrable how this is working but it works pretty well and I would say if you've got a product categorization thing this is what you should use and it's not just me saying Google has got this whole auto ML product which they're now selling which is you give it a tons of images and they're going to mulch it with their existing networks and try and classify the stuff as best they can so a very very similar principle is what they're using and if you want to play around with this it's quite easy to like create another folder change the class thing describe how to do it so some guy so I did this I think maybe it was Fossasia last year or the year before some guy actually used this to classify members of his family which is amazing that it works because the image in it doesn't know much about faces so this is where also the Google data set they may have a much broader set than image net they may have chosen stuff which is relevant to lots of different things they don't tell you what they're doing but they may have chosen their examples much more evenly than image net which the actual test concentrates on dogs for some reason and various things anyway maybe it was recognized in members of his family based on what they typically wore or the backgrounds they were in or something which is it's a bit of a trickery thing but it does work yes before the support vector machine or before the software yes so there's a carousel and there's two you'll see that there's a couple of ways of loading the model one of which is with top which will actually give you the classification out and there you've got just basically the dangling ends of whatever it would need but then you can then repurpose that for yourself so the transfer learning basically we've used the pre-trained image net model we're leveraging it to classify these new classes but we need hardly as much training data because the network itself knows about vision sufficiently that it's got good lead into what we're going to do so now let's go a bit better we're about halfway through so the previous methods learn fundamentally from kind of large amounts of data but humans learn from very little data we don't need that much like input what we would like to do is make models which would also learn from very little data like very very little data ideally what we want to do we don't want to have to construct these models what we'd want to do is make models which learn how to learn that it would be much more sense if they figured out how to learn that learning rather than me trying to construct something which learns because I don't necessarily understand how humans can do it what I want to do is have a model learn how to do that learning so this is called meta-learning and there are two main streams of meta-learning which are now coming to the fore one is to learn how to build the best model and this is called the structure meta-learning so there's lots of different ways of building these models but the architecture is really tough to do and people used to say that this is done by graduate student descent in that you'd have graduate students and you'd apply them to the problem and they'd fiddle around until it worked and that was expensive but it did work and that's been the flow of graduate students into industry we call this structure meta-learning a good graduate student will get to the right structure much quicker than a bad graduate student so there's a meta-learning process to train that graduate student ideally we want to train a network to do the same thing another type of meta-learning and the one I'm going to focus on is what I want to do is build a model which learns how to solve things quickly so the model itself will be kind of preconditioned to kind of like want to learn stuff so it's not just set up to understand the problem to be fitted to the problem it will understand problems as quickly as possible so what I'll do is it's in sequence and the first one I'm not going to talk much about and I'll show you why the structure meta-learning basically what I want to do is I want to enable computers to build models they could do that by searching all the different architectures efficiently and then hopefully they could guide their own search by predicting what architectures might work well because they've got a history of they've got a whole trail of architectures and how well they did work they should be able to say well every time I added a layer it worked better so I'll add a few more layers until it doesn't really work so well but the thing is you've already seen the results of this in that is it still open? it's not still open don't there you go sorry this thing again so I said that here's this is kind of the original one built by some guy actually in his bedroom this is a whole bunch of google engineers and microsoft engineers with these resnets but then these nasnets is now like the efficient frontier is these nasnets and the nasnet looks like this and this was not designed by humans basically google has had this process which would rearrange these blocks again and again they've given it essentially the ability to look at the layer before and the layer before that and just let it go to town basically and so they have these normal cells and these reduction cells and they let it go to town the criteria will be I want this to have a few parameters as possible or I want to do as few mobile ops ops on my mobile phone as possible but I want it to be as good as possible whereas other ones would be just the highest performance you can give me this is a big network and so this is where given that so there's a nasnet large which kerat has which has got 340 million parameters but I'm just using the mobile version which has got 24 mega parameters right so this is something which you can run there also even smaller models but there's trade-offs clearly google likes this so it's probably probably quite a good set of trade-offs at the moment okay so we've already run one of these models which has been produced by architecture search so the nasnet structure is created by searching over these architectures and you can actually do these trade-offs which are better than humans can because well it's now at the efficient frontier I'm not really going to talk about this this isn't really the focus here but we have talked about it at the TensorFlow Meetup whatever so Sam okay let's talk instead about something which is this is from a paper essentially this strain there was a paper published on the 8th of March so this as I said in the kind of write-up this is fairly modern content so one-shot learning humans can learn from very few examples so if I explain to you about a friend of mine devised a kitchen implement called the hammer whisk and on one end there's hammer to pulverize things and on the other end there's a whisk to make delightful meringue okay now everyone understands this implement and you could probably make one right and I haven't had to describe very I haven't had to give you a thousand example images or even ten example images of it okay everyone has in their mind a pretty good example of what a hammer whisk is about right so that's kind of your preconditions to accept these new ideas and that's kind of a powerful thing so what we really want is a model that can also learn tasks quickly and what do we how do we make something which learns tasks quickly well the thing is that each task well I could do lots of different tasks it's not that I have a lot of different classes it's each task might only have a few classes may only have a small amount of data but I want it to be flexible to learn new tasks again and again and again okay so let's backtrack a little so regular learning is I have a training set which has a bunch of different classes and each class has sample images that it needs to learn okay and the test set was can the model classify previously unseen images so this is just image net or MNIST or standard classification regular learning so now we're going to meta learning the training set will be a bunch of different tasks each task will be a different problem each of those problems will have a small amount of data and what I'll say is it would be successful meta learner if it could learn each task being a separate training example right training example is a set of different classes to learn there will be a whole bunch of little images it should learn as much as possible and then a test set is not a a set of those individual tasks is here's another task can you learn it quickly if it can learn that task quickly then the whole thing the meta learning has been a success so this would encourage you to both learn how to do a single task as well as possible but also be generic enough that it can learn many many tasks quickly okay so hands on meta learning so this is in here and this is called so there's a directory in there called 8 meta learning so it's on the same level as 2 cnn there's 8 meta learning there's a thing called reptile signs and what I'll do is hopefully I can pull this up notebooks meta learning reptile signs so what we've got here is I'll run this twice what we've got here is a torch program so this is so the previous one was using TensorFlow and Keras this is now torch torch is kind of an up and coming kind of it's like a numpy it's very kind of generic toolbox Google is also interested in doing this kind of dynamic teaching of these things but they feel like they're kind of coming from behind right now so this has been going for just over a year and it's now kind of dominating research people implementing papers and so if a paper comes out within about two days if it's an interesting paper in two days there'll be a torch version available not from the original researchers but some will just be validating using torch it's also a good way to learn so what this does is basically I'll there are two layers to this because this is meta learning so there's an inner optimization so this is if I have a single task how many steps, how many epochs how many batches, whatever, this internal task for the outer thing is how many tasks are now going to learn so each task in this case will be let me just have a task this gen task so basically this is kind of this is the key thing this is a random task I can generate as many of these random little tasks and the task I'm going to be trying to solve is a sine wave of random amplitude and random phase I'm going to give you 10 points on that sine wave and I want you to be able to predict other points where they would be within that same sine wave so if I learn this task well I will be able to say well this is a big sine wave and it's shifted off by this and here's good answers for the rest of it and if I can do that I will then score on that task I'm going to do many, many of these tasks so this is where I have a little model so this is some linear things, some tans, whatever this is my sine wave predictor thing I have a thing which trains the current model on this current problem and a little plotting thing and then this is an outer loop which basically takes a copy of the model runs a training on a random task and then uses that random training to like build the entire model so it's just more and more sensitive to learning other tasks so this is a process which they call this is an open eye AI paper and they call it reptile it's kind of building on this thing called mammal before anyway so basically what we have here is this is basically first step and you see that this is a task from the test set and I'm only going to use one task from the test set yes the training set is a whole bunch of tasks, this is a task I'm meta learning so it hasn't seen this task before and it's been given these 10 points on this sine curve the network starts out with hardly knowing anything but it gets trained for like 8 steps if I train it to 8 steps it gets slightly better slightly closer to the sine wave but this model doesn't actually produce very good answers it doesn't learn this very quickly and this will be typical for model training but what I then do is I say well I run this kind of reptile process again let's say so now after a few more iterations on lots more tasks it now is you can see it's got much more sensitive to learning to fit this thing it hasn't ever seen this sine wave this particular sine wave before it doesn't know how to learn sine waves similarly for this one now it's getting quite quick to get up to this and as the training progresses this thing learns to fit the sine wave as quickly as possible so this is a meta learning and it's learnt the task of learning sine waves as quickly as possible so this is kind of something where the sine waves aren't that beautiful an example but this is well you can see now from a network which starts off hardly knowing anything when it gets presented this new test example in a sense it's probably starting off more neutral than before because it's preparing to jump the quickest it can and as soon as it does that it then jumps to learning as much as it can about this sine wave so it can give you answers on this new task so that was meta learning on this kind of reptile thing so what we've seen is what we're trying to do is we're trying to learn tasks quickly each task in the meta training creates this sine wave with a random amplitude and phase the training data is just a few points within that and we're testing to see whether it learns the sine wave within that task but the meta test sees whether the single test gets to learnt quickly ok and so there's a paper there's a blog post this is kind of nice but it's you know this you're now learning to learn to learn so now there's another one called and this is where it would work even if you don't have the open the virtual machine thing going because it's just a javascript presentation a javascript thing there's a thing called three boxes now what this so here we have three boxes and this is my input and so let's just let's just clear it all out now I'm going to this is a basically has been trained to learn to learn it's all in javascript so there's basically they've built this model the openai group built this model in tensorflow exported it to a javascript front end so this can now run on any browser anywhere and it learns to learn things quickly so so I don't know what it was so I just for argument sake I'll say a, b and c so we get this is a task I've just given it one example of three different classes and I will now ask it to classify if I write c off of a c it thinks it might be an a but if I complete the c it thinks it's 99% c so this model has learnt from one shot learning for these three examples now if we if we start slightly more it likes a doesn't it so this is learnt quite well or we can do like here is a fish here is a cat and we've got a mouse so there's another set of three this is my new task what I could do is I could do this oh it thinks fish wow what if I do this for a mouse mouse so it's learnt the pre-trained network is ready to learn these things super quick so this is a one shot learning which it learns in your browser and you can play with this this is quite a nice little thing and so let's see how they do that so they're using something called the Omniglock data set so the question is how do they learn this task of learning tasks how do they learn the meta thing of learning tasks what they've trained on is a thing called the Omniglock data set which has got oh I've got there's a nice image of this thing basically it's lots of little symbols but it was quite difficult to find basically in lots of different character sets they've got people to draw little symbols so it could be the English data set it could be no, Hiragana it could be no, Tamil all these different things Greek, Cyrillic they've got lots of different people to write out these things but there's a thousand sixteen hundred of these different symbols now each of these characters was drawn by twenty people so it's a very very broad set of characters drawn by rather few people so this is the opposite of MNIST where MNIST is you've got ten characters zero, one, two, three, five, nine drawn five thousand times so MNIST is kind of the standard task that people will do what you have with this Omniglock thing is you've got lots of symbols drawn by only a few people and so if you can learn to do Omniglock well you're probably able to learn the difference between other characters drawn by people and that's exactly what I'm doing right, so so what we're doing is this one sort of classification each of these tasks trains on just one example for the three classes the model is pre-meta trained on Omniglock the actual model is running in JavaScript you provide a meta-test task and you give it a task it has never seen before and you have to play with this and it works pretty well so that kind of explains what this whole game has been the wrap up is this field is advancing extremely rapidly I mean models which I had last year or the year before are now just missing from this whole it's hardly worth including them because it's just bulking up the virtual thing for no gain the NASnet is extremely small designed by computer it's what you'll all have in your phones with the next Android update or whatever on the other hand this stuff is still within the grasp of individuals the open AI thing you saw it learning the signs thing learning the Omniglock thing is probably overnight on a single GPU it's not like you don't need Google scale to learn this stuff so it's still entirely within the grasp of individuals to do and so while Sam and I have been playing with voices still entirely possible to at least get your feet wet with this stuff so it's very exciting so the other thing is that open source really applies to the research field here as well in all of these papers even though Google really has commercial interests in this stuff they're publishing all the time now maybe I mean the one of the criticisms is that their papers don't exactly explain what they're doing or the deep mind papers are kind of notorious for claiming quite a lot and not really giving you what on the other hand if you're persistent you can probably and you corner someone at one of these conferences you can probably find out what they're really doing but at least it has the impression of it is all out there and you can replicate these things or fail to replicate these things and then criticize as the researchers will say oh you're kind of doing it wrong why don't you try this or this involves a hyperparameter using 800 GPUs you may not want to do that right so there's some kind of adverts here so we have a deep learning meetup group which is a TensorFlow group next meeting I think is like the 19th there's a twiddle there it's a hell of a Google which is kind of one of the dependencies we typically have a talk for people who are starting out someone from the bleeding something from the bleeding edge this would be pretty bleeding edge and lightning talks so we had a meetup I guess on Friday night we had some Googlers because they're in town someone who's interested in implementing stuff on a Raspberry Pi and someone who's interested in implementing stuff on the cloud like the Horavods and you know Infinity Bands and stuff so there's a whole wide range of interesting stuff going on we've got 2400 people in that group which is now Singapore has the largest TensorFlow group in the world which is kind of excellent we've also got a deep learning jumpstart workshop which is we're doing with SG Innovate if you go on the SG Innovate site you'll eventually find this thing the cost of this is like 600 bucks but there's deep discounts for Singapore's PRs and this will involve a full day at the weekend with download and information possibly starting out from zero you get to play with real models kind of like what we've been doing here but you will be forced to do it rather than just sitting and listening and then at the end of the day pick a project that you can do yourself and the whole point is we will then have two more follow-up sessions where kind of you should have done your homework and if you haven't been able to do it we'll kind of fix it so it's going to work because it's one thing just hearing about it and clicking through a notebook and it's another thing actually making it do something and so there's a lot more learning the frustration in programming is the real learning experience okay it's just the MOOC but it's kind of handed to you but the banging your head against the wall is a okay we've also previously held like an eight week deep learning developer course which was all done and good we hope to do another one of these again but it's kind of pending the funding situation because Singapore is extremely generous with funding for Singaporeans but it's a double A sword you also have to deal with the government which has its pluses and minuses okay I'm open for questions and just about on time thank you fire away or not yes right so this is where I believe they've also done it with stuff bigger than Omniglot but they're not going to be downloading that the an image net style network will be naturally much bigger to a JavaScript thing so yes I believe that in their reptile repo they have image network as well so this will learn to tell the difference given three image net images or whatever you'll learn to do that classification as quickly as it can so it will learn to do classification of arbitrary images as quickly as possible I think there may even be pre-trained networks for that but you're getting into this work is less than three weeks old okay so it's fairly fairly new and so if you're playing with it then you're one of very few people playing with it yeah so I'll probably hang around outside for a bit very happy to ask questions if you've got one of the USB keys can I have it back please we've had excellent return rates previous don't let Singapore down so there we go hopefully you've learned to learn to learn


Ready and postured, B-2 Spirit integrates with NATO ally at Lagesfield. 509th Bomb Wing, Sunday, March 21, 2021. Three B-2 Spirit stealth bombers, assigned to the 509th Bomb Wing, Whiteman Air Force Base, Missouri, along with more than 100 airmen, civilians and contractors, arrived at Lagesfield, Azores, Portugal, to participate in a series of bomber task force missions in the high north European region. Bomber task force missions are critical to ensuring Air Force global strike airmen are capable of executing operations at the direction of the National Command Authority. By training in Europe, air crew and airmen are familiarizing themselves with the European Theater and Airspace, to enhance enduring skills and relationships with allies and partners. �We�re ready to execute our missions and achieve all of European command�s objectives,� said U.S. Air Force Lieutenant Colonel Christopher Conant, 393rd Expeditionary Bomb Squadron Commander. The B-2�s deployment to Lagesfield provides a staging point allowing commanders to confront a broad range of global challenges in support of the National Defense Strategy, through the employment of a multi-role bomber. Lagesfield has been in continuous use with Portuguese and U.S. military presence since 1943,� said U.S. Air Force Colonel Tami S. Hingston, 65th Air Base Group Commander. It has been called upon for decades to enable the movement of expeditionary forces and has proved its geo-strategic importance time and time again. The men and women at Lagesfield have a long tradition of being trained, ready and able to support global operations when called upon. While the U.S. Air Force routinely deploys a variety of aircraft and units throughout Europe for training and to support geographic combatant command objectives, the B-2�s deployment to Lagesfield demonstrates the importance of working with U.S. allies and partners. We are strongly committed to our long-standing relationship with our Portuguese allies, a relationship based on trust, respect and the common goals of protecting our mutual security and strategic interests,� added Hingston. Lagesfield provides strategic communication and power projection platforms to support global operations, enable combined forces across three combatant commands and supports our international partnerships. The theater integration and training with allies, partners and other U.S. military units enhances interoperability capability across the entire theater. This co-operation increases Air Force Global Strikes Airmen readiness, and the B-2�s ability to respond to and support global operations. �If there�s one thing history has proven, it�s that warfighting is a team sport,� said Colinant. This won�t change, which is why it�s so valuable for us to get into theater and start integrating with our allies and partners. We are doing some new types of integration on this trip and it is really exciting to see the coalition get stronger. The employment of bombers contributes to stability throughout Europe, as they are intended to dethrone conflict. However, the B-2 can offer a rapid response capability to meet any need, anytime, anywhere. The B-2 bomber is the only asset in the world capable of combining stealth, a massive payload of weapons, and significant strike range, both unrefueled and refueled,� added Colinant. The B-2 can hold targets at risk for European command authorities, from home station or a forward operating location. Putting B-2s in theater at a location like Glagesfield, allows the 393D Expeditionary Bomb Squadron to provide a faster response and increase sorties for the combatant commander. It also provides the opportunity to integrate with our allies and partners, making everyone more lethal. As the B-2 conducts missions to enhance the readiness and training necessary for airmen to respond to potential disasters or challenges across the globe, Glagesfield provides a strategic stepping stone in the European Theater that assures mission success. Glagesfield is an important military organization vital to U.S., NATO and international security efforts,� added Hingston. Also, Glagesfield operates a key communication node that provides weather, telephone, email and internet coverage to aircraft, closing a coverage gap across the Atlantic Ocean, as well as the USAF�s second largest fuel reserve capability. The B-2�s presence at Glagesfield is a symbol of assurance and commitment to our allies and partners. By conducting missions from Glagesfield, Wyman AFB airmen demonstrate their ability to operate across the globe and are flexible and agile to respond to the changes in the operational environment. U.S. Air Forces in Europe routinely hosts and supports a variety of U.S. Air Force aircraft and units for training and to support geographic combat and command objectives. Operating with a variety of aircraft and units in Europe maintains a ready and postured force prepared to respond to and support global operations.


Hello. I'm Alison Hales. I'm one of the senior lecturers on the primary QTS programme. I'm also head of placements and placement partnerships. I was a primary school teacher for a number of years before I joined the university. I've been with the university for about 12 years now, and my area of expertise is primary history. So I've published and I've written on primary history, so if you come to join us, you'll see me for your history sessions. So, let me tell you about some of our placements that you might experience depending on the programme that you choose. If you're on the QTS programme, you will go into schools. Again, it may range from two weeks up to eight or nine weeks. One of our education programmes, again, they will vary in length depending on the route that you're choosing. So, let me give you an example. If you join our QTS Year 2 programme, you will have some days in the setting for professional development, so some professional development days, and then you would go in for a seven-week block in the autumn term. Alongside that, in Year 2, you would go into an enrichment placement, which is two weeks, and that will be in an alternative setting. So you're getting the idea of education in the wider context, and that might be in settings such as museums, hospital schools. We've had students in the past go to special schools, work with London Ambulance, and we've also had some students who have worked in schools abroad. So when you go into the placement, you're going to be working alongside trained mentors and other expert colleagues to ultimately develop your professional practice depending on the route that you've chosen. So you're going to be working with children or young learners, and you will be perfecting your teaching skills, you will be thinking about how the theory leads into practice, so the theory that you learn in the university and what that looks like in actual practice. The benefits of going into a placement is that you're getting a range of experiences, so it may be that you are in a small, rural primary school or early year setting, and then you will have the opportunity to go into perhaps a larger, urban, very diverse setting. So we're able here at Greenwich to offer you a range of experiences in that way. You're also going to be working alongside those trained mentors and expert colleagues in the setting. You are developing your personal and your professional attributes, what we would expect somebody who graduates from Greenwich to have. So the types of placements will depend on the route that you have chosen when you come to study here at Greenwich. So it could be that if you're on a QTS programme that you may go, if you're choosing the early years route, you're going to an early year setting. If it's primary, it's a primary school secondary or a further education setting if you're choosing the further education route. The length of the programmes will differ, so it depends on the programme itself and the stage of development within that programme will depend on the length. But let me give you an example. So if you were to choose our primary QTS programme in year two, you would have some professional development days. You would then go into a block placement in the autumn term, which lasts for seven weeks. After that, later on in the academic year, towards the summer, you'll do a two-week enrichment placement. That's working in an alternative setting, so you get to see education in the wider context. You might be in settings such as museums, libraries. We've had students who have worked in school hospitals, in special schools. We've also had students who have worked abroad for those two weeks. So we work really closely with our partners. They are instrumental in our work at university in terms of helping us develop our curriculum so that we can ensure that you, as a learner, are getting the very best of the best education in terms of recent research and practice that is out there. So we share expertise. We may have colleagues who come into the university who will take lectures and workshops. Likewise, we will go into schools and settings as well and work with colleagues within the school. We couldn't work without our partners. So we pride ourselves on that really close, collaborative, working relationship with them.


So a common student complaint is the exam was nothing like the homework, and a common instructor response, but it asked the same type of questions. And so to reconcile these two statements, here's a little insight on how instructors create exams from homework problems. So during the course of a unit, you might be asked questions like the following, write the equation of the line shown, solve this equation, let h of x be the altitude of a plane and interpret something in function notation, but on the exam you see this question. And it's nothing like these other questions that you've been asked, so how is this a reasonable exam question? And in fact this question is based on the questions you've answered, but there are three important changes. The ending point has moved, the starting point has moved, and the venue has changed. Let's talk about that. So this question should have some interpretation like the following. At one o'clock the altitude is zero kilometers above sea level. So that's the ending point of this question, but let's go a little bit further. Let's change that ending point and recognize that this also means that the plane is at sea level. And when we come to the exam question, since the exam question identifies that the plane took off from sea level, this suggests a time when the plane took off. Of course, we weren't actually given h of 1 equals zero, and that's because the starting point was also changed. Now we can find this by solving the equation, but we don't actually have that equation, but we can get it by answering this question, but we weren't given the graph. However the first thing we do to find the equation is to find two points on the graph, and we're given two points on the graph. And so let's put that all together. The given information tells us that h of x is a line through two points, and we can find the equation of the line. We then solve h of x equals zero, and that tells us the plane took off at one o'clock. And so let's talk about ways you can prepare for an exam. In any problem you can always ask if there's an earlier starting point. Instead of starting with an equation, you might need to find the equation, and maybe instead of being given a graph, you might need to produce the graph. Another possibility is to ask, what can I do with the answer? Once you've solved for a variable, what does it represent? Or once you've graphed an equation, what else can you find? And finally change the venue. In any problem, always ask how else could this information be presented? What information did the graph give you, and how else could it have been presented? Or what information did you get from the equation, and how else might it have been given to you?


This week we're checking out the Secretary of the Navy's new innovation portal, The Hatch, and a couple tips for your tuition assistance applications. First, a big congratulations to all our new Petty officers just advanced this cycle. Now let's talk about The Hatch. The Hatch is part of the second half's goal to empower sailors of all pay grades, specialties, and backgrounds to submit their own innovative ideas to improve our Navy. And they'll be reviewed by peers and considered by leadership. So let's look inside The Hatch. First, any sailor or department of the Navy's civilian can make an account by clicking here. No CAC required, just a dot mail email. So remember The Hatch is an unclassified domain. By logging in to my account in the landing page, some of the more popular ideas are featured here. In this feed, you can comment or vote the ideas of other sailors up or down. Here's one, to use the drones to paint the side of the ship. And there's several that recommend ways to consolidate the Navy's web portals. You can submit your ideas simply by clicking here. This is a real avenue for you to make the Navy better. If you see an issue, don't be the one complaining about it. Be the one with the solution. The Navy wants your ideas. Next, a quick reminder that if you're applying for tuition assistance, the earlier the better. Late applications are the number one reason for disapproval. Requests must be approved before the start of class. So that means you should submit your application well in advance. Shoot for 30 days in advance if possible. Lastly, registration for the Joint Women's Leadership Symposium is now open. This year it's in San Diego. If you're interested, more information can be found here. Now that's it for this week. If you follow the rundown, you'll see I'm a new host. MC1 Fabrizia will be back next week. But I'll also continue to host more episodes in the future. So if you have any feedback about this program or a question for the Chief of Personnel, feel free to drop us a comment or email. For the CNP, I'm Lieutenant Ryan Benitez. Thanks for watching.