Boom, what's up, everyone? Welcome to Simulation. I'm your host, Alan Sokian. We are still at COFES, the Congress on the Future of Engineering Software for our second annual partnership with them. We are now sitting down with Dr. Robert Graber. Hello. Hi. Thanks for coming on to the show. Really appreciate it. Thank you. Very excited. Are you coming here all the way from Germany? Yes. Yes. Well, headquarters in Germany, but, you know, I have a history in the Bay Area, so I love always coming back here. Yes. The Bay Area loves Germany. We love you very much. We love CAD very much, which is a lot of what you're focused on right now. Also focused on building serving, so we'll be talking about that, too. Robert's background is very interesting. He's now been 18 years as the CTO at Graber. Also, prior to that, did his PhD in Civil and Environmental Engineering at Stanford doing energy management in commercial real estate and impacts on building energy performance. So let's talk about this on a, on a, on your journey perspective. So, you know, how did this all, how did your interest even in this engineering and software get started? Sure. So I think even from a young age was always interesting computers, you know, gaming and but also trying to figure out how everything worked. My father actually was one of the very, so very first entrepreneurs in the sort of computing time. So that was always a role model for me. I basically did high school in Germany, but left to go to the UK when I was 16. I finished my high school there and then as a natural progression was to kind of go for an engineering or computing degree in London. And so that's what I did. I ended up doing a four-year degree and a master. As part of that, my interest got sparked in mobile devices. So this is pre-iPhone, pre-Android, the Windows Phone was just starting at that time. This is in 2000 or 1999. And I was interested in saying, is it possible to bring engineering software, which was, you know, what we'd had worked on in the company or my father's business, would it be possible to have that work in mobile, right? And so I took that one as a challenge and part of my sort of final year project was doing real-time graphics on mobile devices and then seeing can we take that even further and bring on a full CAD system onto mobile. And this is sort of my first real software project was CAD support product. And you mentioned building, serving. One of the questions we asked ourselves, why would you want to do CAD on mobile, right? Viewing, you could even use a PDF or something simple for that. Why would you want to have access to a full CAD system? And so I realized a lot of the jobs in which we actually consume CAD or create CAD have to do with the physical world, right? Whether that's in AAC when you actually, you know, you have physical spaces that we're designing here. So, or it's on the shop floor when you're actually making the product. And so we realized there's no real point in doing an architectural design package in the field, but doing a surveying package makes a lot of sense because now you're recreating the geometry from what's actually in the physical world. Okay, so when you're finding yourself getting first involved in engineering, your father is obviously this big role model for you getting involved.  And this is somewhere where I don't think I've really heard this quite yet. Yeah, CAD on a mobile device versus on your desktop or laptop. Yeah, I mean, I want to hear more about the reasons behind that, but I want to also have you explain post. Was this at London when this was happening? So this is sort of at the tail end of my degree. I was really interested in this question about what's possible with these mobile devices, right? It was the first devices that went beyond the flip phone. They had a screen that touch interface. And so now, for the first time, you can envision more professional applications, not just telephoning and text messaging. And so that's something I explore with my real-time graphics. So I realized, hey, there is something possible. You can actually really drive 3D graphics on these devices. You know, I bought at a small space, but you could. And so I realized that if going back to Germany and joining my father's business would be a great way to explore that further. And that's what we did. We created a small team to start then building a mobile CAD package. A mobile CAD package. Now, but then you went to Stanford after London. So, no, I actually went to work for eight years. Eight years before. Wow. So I left academia completely, went to work, explored some of these topics mobile. We then did a big project of rethinking how our desktop catch would look like, the next generation of that. That's something I worked on for quite a number of years, building out a team to support that. And then as part of COVA is actually this conference, we got in touch with one of the professors at Stanford who was looking at the intersection of civil engineering and computation. Like, how can you use computational methods to drive change in the civil engineering field? Martin Fisher at SIFE, which is a Center for Integrated Facility Engineering. And we basically start talking about different topics that could be interesting. And over two or three years, having those conversations, we realized maybe it's the best would be for me to kind of join the PhD program. And I did that in 2009. Okay, okay, okay. So then now, before we get to the PhD program, so what were some of these in 2000 when you're putting together this team at Graber? What are you guys doing with CAD on mobile?  So yes, so we first obviously looked at sort of visualization and markup. So one of the things we developed was the ability to embed voice messages into drawings.   It was actually embedded in the design. So not only does it have, you know, your notes but also has context and where exactly it was referring to, the same for pictures. And that was one use case. The other use case was really data collection. And so we have partners that would build, for example, GPS tracking software. We embedded the software and heavy machinery in the construction field where there were just terminals and we use a mobile CAD to really capture what was being done in the field. And the product that we built ourselves was really all around this idea of very efficient onsite plan production of drawings of existing buildings. Because owners have this problem that, you know, they design a building, maybe the designs are even lost, but the building changes over time. You need to capture what's actually there, either for rental purposes, for fire safety. We did lots of works in the UK with big retail brands. We did it with the prison authority because they just really need to get half up to date plans. And so that was sort of a part of our business that we've always kept working on because it was an ideal use case for mobile CAD. Okay, okay. So one of the things is, you know, when you're looking at CAD's capabilities back in 2000, you're thinking about how to augment it, make it more effective for both on a communication level between people that are working on the same project as well as make it more efficient for the environments that it's being put into like construction and leveraging already some of the newer technologies like sensor technologies, GPS. So what we're, you know, and then you kept teaching us that you were doing more of this work and trying to add onto the onto CAD. So then did it seem like there was something that was like sticking as you were adding things into CAD to make it a better tool? What were the things that you were seeing most people were wanting to see CAD to get better at? I think what we actually realized is that the CAD is an interesting underlying fundamental technology ready to create these designs, but we weren't necessarily working with CAD users.  The person that's in the field is not a CAD user, typically. It's a surveyor. It is a maybe something that's not as skilled and, you know, desktop computers. And so we had to really rethink how we actually interact, how the user interacts with the product, make a much more workflow driven than, you know, this open set of 500 features that you can use in any order. No, you have to go through step from one to 10. We've evolved that product line actually over the last years into even more sort of specific niches. And one of those that we have today is the kitchen survey market. So it's a market where in Germany the customer would go to kitchen studio, design his kitchen with the seller. But they won't produce it until they've actually gone out to verify that the measure that would actually fit as details about where the outlets, how's the windowsill position, what kind of materials are we looking at? And so we would build a specialized solution and somebody who is a trained kitchen sales person has no problem picking up the software, understanding what is required of him, what measurements does he have to take. So fundamentally it's still CAD, but it's so much more of a workflow driven tool. And this is what we've seen always from the beginning. CAD is really like a fundamental building block for a lot of engineering software where the ideas you're presenting geometry or you have, and through the specific applications you derive meaning what that geometry actually represents. Is it a screw? Is it a wall? Is it something very different? And that's why so much engineering software builds on top of CAD. Yeah, when you're walking us through this really interesting realization about how when someone's behind the computer that is making the CAD, then what ends up happening is the person in the field is typically not someone that's making CAD. So then you have to have this degree of relatabilities for the person in the field to be able to use CAD in a way that at least visually that makes sense. And then there's this whole, that becomes this communication process. And what then was your big, you talked about the Stanford professor that helped you get moving into your PhD program. What was that fire under you that sparked you to also go and start studying? Well, I was obviously interested in kind of exploring some questions a little bit further that in an economic business context it's a little bit harder to keep asking the why question. At some point you're going to deliver a product that you can sell to customers, right? And so it was interesting as a completely different environment to understand to drive a little bit deeper. And so I thought my first conception when I arrived at Stanford was like, okay, I'm going to take this building serving specific question and I really drive a little bit deeper on that. But through sort of successive why questions we ended up with a very different or ended up with a very different PhD because I said, okay, well, why do we even measuring this, right? So the first question is why do you care about the data? So then one of the use cases we thought it could be for energy efficiency or energy management. You want to understand how efficient is this space? So then we started looking at, okay, well, how precise does the data need to be for it to actually make a difference in the energy calculation, right? So what degrees of precision are we looking for? Is it centimeters or meters? And you find out very quickly that the geometry really can vary by quite a lot for it not to have a significant impact on the energy. So then the question is why do we even go out to measure this? And then the question was why do we even want to do an energy model, right? Why would you want to, what's interesting in that? Well, you can say, well, maybe I have a goal of reducing the efficiency. And then I kept doing this. And then we kind of realized there's not really a uniform approach to doing energy management in the corporate real estate specifically. I picked corporate real estate for one point because it was large enough that actual energy efficiency could make a big dollar difference, right? If you save 10% on a million dollar bill, you're saving money. But it's also close enough to the core of sort of the management of the company that's visible enough that they could say, well, maybe it's useful for us to do so. And so I started setting different companies across the U.S. And I realized there's really very high degrees of professionalism or different degrees of professionalism in that industry today. There's even a challenge of using the correct terminology or the same terminology. And so I ended up really thinking about what are the methods that exist today, what are the methods that potentially could make sense and do the tools that are available really serve the industry well. And so I started with saying I want to measure spaces to basically saying, well, we need to first understand, we have to first generate a common language and a common practices around how we're going to deal with all this data that we're producing. And so from a very sort of technical starting point, we ended up with something that fundamentally was really about talking to people and understanding how they work today. Yeah, the idea of taking a look at something that seems to be a very common theme like a commercial building and being able to see it through a lens of how can I make it better. And not only how can I make the tools better for the engineers and designers, but also how can I make it more energy efficient. These are very important things to pass along with the way of thinking to younger people. How can I make things better than when I look around me? How can I design them to be better? And then did you end up experiencing success with changing the design of buildings in order to decrease the amount of cost of energy? Well, I think the point I was trying to make is that my realization is just simply to quantify is the building efficient is a very complicated question. And the second question is certain tools in the market are supposed to help you understand is your building efficient? But also is the building more efficient than it was previously? So you've changed the windows potentially, you've changed the lights, right? Is your building actually more efficient and do you have a good tool for helping you understand that? And that's actually not a very well addressed problem at this point. Even like energy expenditure. So you can look at energy expenditure, but there's a variable factor that impact that, right? Weather occupancy, among other things, opening hours, etc. But year to year you can see that in like June of 2018 versus June of 2019 that that's usually similar weather. So yes, so we're getting a little bit into the weeds right now. These are also interesting weeds that we like unpacking, yeah. So you typically use normalization for that, right? You say this year we had this many heating degree days and this year we maybe might have had a different number so we know when to normalize for that because we don't want to just look at the bare consumption. We want to look at consumption normalizing for weather, right? But then we want to look at normalizing for weather and occupancy. Yeah, because you may have gained employment by 10 or 20. And then the question is, okay, now let's put an example. I added 10% staff. It means the HPC is running extra hours to just kind of cool us down. Is that the building manager's fault? Right? So these are real questions, right? Saying, what are you interested in? What are you interested in measuring? Are you interested in measuring? Oh, interesting. Because then... Do you measure per person their next energy? Yeah, these are all relevant. These are all sort of questions you need to ask yourself what your end goal is. If your end goal is to get a plaque on the wall, which is what some people want to do. They want to show that they care about the energy efficiency. They want to have an energy star or whatever. Or is your goal to figure out how an efficient building in your organization should look like? Or do you want to figure out the big picture designs of the future? Yeah, and so there's a lot of... We ended up realizing that why people do these things, why people actually invest in energy efficiency, they do it for very many different reasons. We had some companies that we studied that really cared about dollars. They wanted to save dollars. And if they could get a two-year return on a lighting retrofit, let's do it. That's a gateway to invest our money. But some companies that really only cared about the messaging to customers or their own employees. Say, look, we take care of this, we care about the environment just as you do, and we're going to take care of one example of the space you're in. And we have other companies that wanted to show excellence. It was a custodian bank, and it's really hard for them to show their customers that they are very deliberate and thoughtful in how they approach business. And one way they could show it is by being very considerate about how they manage processes around the buildings. And one of those was how do we keep ever being more efficient in there. So the simple question when you say, well, we're going to save us money is too simple. And so that was part of the research. Again, I came from a computer science background. My interest was how do I capture space efficiently, how do I capture energy efficiently without stepping back saying, why do we want to do this? And part of the joy of a PhD is that you keep getting these questions and you're like, well, I don't know, let me go find out. And you keep doing that until you get to a point where you say, well, I think I've reached a point where there's no consensus here. So maybe it's worthwhile to study this part first before we even go down all the way and saying, well, what are the right tools for measuring buildings? Yeah. It's cool that you bring up how the different people have different ways of perceiving the way that they are energy efficient.  Some people tell their employees. Some people tell their customers. Some people want to potentially build the next generation of gardens on the rooftops, solar panels on the rooftops, complete water recycling systems, all this kind of crazy things and composting the food in the garden, all this crazy stuff that is fascinating and that could potentially, but yeah, how do you measure it for all the variables? This is really good stuff. Okay. When you're doing that and then you're finding yourself moving, you're still doing work with Graeber while you're finishing up the PhD and coming back to Boston. Sure. Yeah? So, of course, the Graeber, the business continued. And one of the things we did during that time was quite asking ourselves again, how do people want to use our software, right? We talked about the desktop user really being sort of somebody who spends a lot of time with CAD, but realizing that it actually is CAD to some degrees is also a communication tool, right? Same as documents are a communication tool. And how do we make that, again, available to more people in an organization? And so, with the arrival of iOS and Android, we then brought on, we bought new mobile CAD systems onto those devices again. So somebody who's out in the construction site or somebody who's on the shop, shop floor can take the designs with them. And then we, actually, another COFIS conversation a couple of years ago led to us working on a full cloud-based CAD system, right? And again, the main driver for that is necessarily say, cool, we can do it. It's more about, well, how do we access that user who's not going to install that, you know, big install of CAD software, get licensing and all that setup? You know, how can I share a design with you so you can comment on it, that you can review and give me feedback on without me having to set up something very complicated, right? And sending out a PDF serves that to some degree, but I have no way of getting that back if I don't want you to give that to you anymore. I have no, I have very little control of the experience you're going to have while you're viewing that. And so those are the things we can, you know, I was part of, you know, moving that forward even during the PhD, but of course in a much reduced role as that was really my focus. And then what have you been seeing then in CAD and in building, surveying over the last, or has it been 10 years since the PhD? I finished 2015 or 2016, so recently, a couple of years. Three years, yeah, yeah, since PhD. So what has it been like in the last, you know, three years when you've been doing some of the, some more of the building surveying and CAD at the cutting edge, what have you been seeing? So CAD, you know, is quite diversified in what kind of tools we're using. So AAC, computer-aided design versus mechanical versus sort of what we traditionally do, which is sort of, you know, we call it 2D CAD but it's actually 2D and 3D, but they're all slightly different paradigms and we do see a lot of that growing together, right? And, you know, you see more and more design happening in tools like Revit, SolidWorks, sort of to name this two leading products, but there's still a need to communicate again. So all type of tools which are drawing, really drawing tools to have that as a documentation and legal contract and also communication tool can continuously be relevant but it becomes more and more relevant to be able to consume and work without other content. You were teaching me about this earlier just that all of these platforms have their own exporting, their own system. They all have their own formats. They all sort of traditionally have proprietary file formats but there's a bigger push by the users to say, hey, we've got to figure out how to collaborate here and get the different functions of a business to work together. And so I think it's quite an interesting time. We've had, you know, this cloud shift now happening for the last five years and this used to be a leading industry. It's sort of a little bit more laggard simply because of the large install base of the sort of traditional desktop applications. But we're trying to also enable some of our partners who currently are finding it a little bit hard to kind of move to the cloud and mobile with our tools to make that a little bit easier because we do believe in the potential for enhanced capabilities there. Yeah, yeah. So, okay, so assisting some of your clients to make the transition to cloud and then is there also a, is there a specific CAD tool that you find maximizes collaborative capability? Well, I don't want to, so there's obviously everybody's trying to solve that problem in some way or form, right? Traditionally, if you look at the mechanical CAD space, you know, you use something like the PDM systems of the world where, you know, all the data would be managed in one place, but only one person could check out that data at one point and work with that, right? That's inherently limiting because if you and I want to make changes to the same part, it's not possible, right? And there was a lot of pain points there and we're seeing more and more systems now move towards sort of a more data-based type approach where everybody can make changes and they all flow together into one sort of master repository. And it's more in the mechanical space today, excuse me, and less in the AC spaces, but I see it there also coming now. So those sort of common data environments where people all publish their data together. And I think that fundamentally changes the collaboration game because now I'm no longer limited by, you know, single access. We can all access together. Our solutions, for example, today already include abilities to share view-only links that, you know, not only give you access to design, but they also are live. So whatever, if I make a change, even after I've shared the design with you, you'll see the live version right away. And I think that's super powerful just to ease, never having to worry about, am I looking at the latest, and I think it's just, it gets enabled by cloud mostly, just the idea that you no longer passing on files, you basically have a central repository, and we're all looking at the same truth. Yeah, yeah. Interesting, the central repository seems to be one of the main ways to make sure that the collaboration is always at the final, that you're always collaborating on the final product, on the edge. And then what about with building serving on that side of things? So building serving for us, I think the main changes now, it used to be that, you know, we would go out, or our clients would go out, we really focused on selling that software. We'd go out and create a DWG, so a classic drawing file. What we're seeing difference now, in the kitchen serving market, they don't care about that, they want the kitchen design to come back to the software they're using. So the DWG is sort of a nice intermediate, but they really care about, okay, what does it mean, what kind of cabinet do you need, what size, right? And we're finding that now with the sort of prevalence of BIM and 3D tools in the AAC space, more and more clients want to say, we need the BIM model so that we can provide our potential tenants or potential buyers a 3D virtual walkthrough environment. And so the needs have changed and this is a major part of potential like virtual and augmented reality experiences, these walkthroughs and the building environments. So that seems to be a reoccurring theme from some of the clients that you work with is that they want to be able to enter into their building surveying with the tools. It really depends on what your goals are, right? I think for a professional to look at a drawing file, it's very easy to interpret, okay, this is a window, this is a door, right? Even if you're not too familiar, you kind of understand what the symbology means, but it gets very technical very quickly. Like this is like a socket or this is a lighting fixture, for example. A 3D environment is richer in that it actually makes it extremely obvious how to navigate the space and what the symbols mean because it's just a much closer representation of our real world, right? And so depending on who your audience is, like if you say I want to use sort of giving a potential tenant just a floor plan, I'm going to give them a 3D model that they can spin, they can slice, they can look at. It gives them much better sense, okay, what is the scale of these properties. If you do a mashup with one of the furniture vendors and say, well, how would that look if I put a bat in here, right? And so they are just sort of without that it's really hard to do virtual reality and augmented reality, but if you have that you can then give people a way to experience the space in a much more complete way that with a 2D design it's just so much interpretive there that it's hard to do that. What are you most looking forward to engineering software converging and moving towards? I think on the engineering side I would say that we've seen some first developments with Onshape and Fusion C60 and other cloud-based mechanical CAD systems that they're starting to unlock some of those collaborative and other features through the cloud and I think it's just such a key turning thing you can do to change what kind of software you can architect that I think we're going to see in the next 5 to 10 years many more solutions just jumping on that and then finally unlocking some of the capabilities some of the very obvious things is rendering and simulation because that's a very easy thing to parallelize and send out but a lot of the core design solutions are still desktop single user single threading even working and those solutions will hopefully get modernized and I think that will lead to another level of productivity we talked at this conference a lot about generative design that's sort of a more advanced design mechanism that's only really possible through working with the cloud and having sort of a very highly scalable computational resource at your disposal yeah and hopefully that gets democratized to more people around the world it's definitely looking like it's going in that direction yes or no right yes as in it's now accessible like you don't no longer have to build your own data center but also in a way no because now the quality of the design really depends to some degree on how much you're willing to spend on the compute for it oh yeah sure sure there's that side of it too so yes I think overall the trend is definitely positive and obviously compute gets ever cheaper and ever more affordable but it's no longer you know what can I come up with but how much am I willing to spend on it that has been a trend of the rise of civilization that the more money that you can sometimes spend on something the higher of a quality that that product can be that then can go out into the market and make more to make you more money and then last question is what would you say is an important skill for kids and their parents to develop in the 21st century what is an important skill I found what has been sort of my driver at least I'm not sure about that translate but it's really been a curiosity for technology how does it work and you know I was always trying to play around with these things and exposing myself continue to expose myself to new software and also machinery just to really be sort of flexible and being able to you know be comfortable in all of those things and I found this is also a way even now as you say you're starting points for 18 years now in this business it's a way to keep fresh and even beyond being a child like how do you how does it still be interesting by trying to do new things all the time trying to keep it fresh and I think it comes natural to I think nearly all kids is to wonder about the world and you know just let them let them try things out you heard it here first keep it fresh I love it, this has been a lot of fun thanks for coming on the show Robert really appreciate it, it's been really enlightening there's a lot to still learn and unpack check out the links below for Robert's work also check out the links below to Kofes also give us your thoughts in the comments below we'd love to hear from you about what we talked about in this conversation go share it with other people as well support the artists and entrepreneurs that you believe in you can find all of Simulations links below and go and build the future everyone manifest your dreams into the world thank you so much for tuning in and we will see you soon peace


Hey coach today. We're going to talk about why one-on-one basketball training lessons are not scalable My name is coach Nick Aldeiro and I'm a success coach with make money coaching sports If you're looking for extra help after this video You can text me at<PHONE_NUMBER> or you can schedule an application Call on our calendar link. It'll be in the description. Okay, so the first one about why One-to-one basketball training lessons. They're just not scalable. It's time There's only so much time that you have in the day and then you say well, yeah, no, I could do it It's easy yet a lot of coaches say that at first, but then when they've been doing it and doing and doing it What does that lead to it leads to burnout now as much as you might love your sport? It happens to people who do too much one-on-one coaching If you if you have if you're doing 10 hours a day of one-on-one coaching or Eight or whatever seven even five you are eventually going to get burnt out Okay, and there's only so much time in the day You start as your own business or you want it thinking about starting your own business to be in control of your time So one-on-one is not time-effective. Okay, so number two the Limitations that you have other than time are you can only work with that one child or that one player So what does that mean? There's no extra income coming in to that time slot So your time is limited the money is limited also. Okay, number three If he can do it, I can do it you lose that now. What does that mean? That means there's no other kids to watch to raise the level of play so if Johnny's if Johnny's training and there's no Jack Right, it's very hard for Johnny to step his game up when there's no other kid Inspiring him motivating him. There's no competition between the two and that's not that you have to have anybody play against each other, right? Because that's not really what Most skills training businesses are about it's about that your individual aspect But like it'll what one kid sees another kid be able to do it It inspires them like all right well if he can do it then then I could probably do it And they learn from each other. Yes, of course, you could demonstrate But you are not a child that you're gonna they're just gonna wave that off and be like, oh, you're you're the coach you're older Of course you can do it, but like when another kid can do it You lose that if he can do it or she can do it. I can do it type attitude. Okay number four, there's a lack of energy and Most kids and I one-on-one session maybe not to start but as time goes on 95% of kids won't and I don't have any science to back this up, but from my experience 95% of kids That it's probably even lower than that to be honest or higher than that 99% of kids Won't consistently bring that same effort energy and intensity when it's just you and them Of course, there's gonna be exceptions to this but for the most part most kids They're not gonna be super jacked up to do the training session every single time Especially when you have a bunch of one-on-ones with them. Okay, and number five, it takes away from your group and It especially takes away from the group when those investments if you do have a group Those investments are too close together. Now some coaches just starting out. They don't even have a group option but what you want to do ideally is you want to move those one-on-ones into a group and And add players and save time and and generate more income and still give kids the best experience Okay, so here are some exceptions to that one-on-one rule, right? If it's in a premium investment, and then everybody else who's like, you know a coach, you know, that's not in our budget That's cool. We have a group option too. So But when most coaches starting out their one-on-one Investment is way way too low and they're not valuing their time and expertise Enough, okay. I hope this video was helpful. If you're looking for extra help You can text<PHONE_NUMBER> or you can just schedule a call on the calendar And that's an application call to see if you're a good fit to work with Make money coaching sports so we can help you further. All right, I'll see you next video


Hi everyone, welcome back. So next we have a video from Alicia Foggerving and it's all about our content partnerships hubs. Hi everyone, my name is Alicia Foggerving and I work as a developer at Wikimedia Sferia, the Swedish chapter of the Wikimedia Foundation. I'm here today, subbing for my colleague Eric Lutz, who unfortunately couldn't make it to Singapore. I'm recording my presentation because as much as I'd love to talk to you live, time zones are a thing and they do not work in my favor right now. I'm here to talk about an initiative that I have myself been very deeply involved in from its very beginning. So I'm more than happy to be able to give my perspective on it. That is the helpdesk, part of Wikimedia Sferia's content partnership hub. Wikimedia Sferia is working towards becoming a content partnerships hub that is a resource for Wikimedians all over the world to support them in their partnerships with GLAMs and other actors. There's a lot of work being done in the community. GLAMs and other content owners have always been extremely important to the Wikimedia movement. They share images, texts and data that we together put on Wikimedia Commons, Wikipedia, Wikidata, Wikisource and so on, so that the whole world can enjoy and learn from them. And we at Wikimedia Sferia are eager to help Wikimedians become even better. We do have a lot of experience and tools needed, for example for large scale Commons uploads, so why not share our resources with those who need them. Now this content partnership hub project, it has several strategic parts. We work together with IGOs, such as UNESCO, to encourage and empower them to share free knowledge on our platforms. We do capacity building, that is, we work together with the community to help them grow in their roles as Wikimedians. And we also do large strategic uploads of files and data to the Wikimedia platforms. For example, data about cultural and natural heritage in a number of countries. Still, we are just one of many Wikimedia chapters and user groups around the world. When talking to others, we have noticed that there is a real skill bottleneck in the movement. There's a lot of interesting content, lots of potential partners, such as museums out there. And there are, of course, lots of Wikimedians who'd like to work with that content, but who do not feel that they have the technical know-how to do that efficiently. For example, how to upload and categorize lots of files on Commons. How to use Sparkle to navigate around Wikidata or how to work with specialized data software, such as OpenRefine. And the helpdesk answers to this need. In a nutshell, the helpdesk is a service that we at Wikimedia Spheria provide, a support center for the global movement. Say, for example, you have a collaboration with a local museum who would like to share thousands of openly licensed images on the Wikimedia platforms. But you've never done this sort of work. An upload project like this consists of several parts. You need to talk to the museum to agree on the best way for them to share the data about the images. You need to upload them to Wikimedia Commons, organize them in meaningful categories, maybe working with structured data so that other people can find, understand and reuse them. You then need to work with your local community or maybe even with a community in a different country to bring their attention to the new upload so that they can start illustrating Wikipedia articles with the new material. And if you've never done it before, then it can seem like a daunting task. So what can the helpdesk do for you in this sort of situation? We can use our experience to judge and advise whether it is a worthwhile project to begin with. We can put you in touch with the right people in the movement to spread the word about your project. And we can of course advise you on what software or which workflows are most suitable. We can provide training on using it efficiently. And of course this might make you think is just one Wikimedia chapter in Sweden really best equipped to make this sort of judgment? And of course not. We're just a small group of people. It's not easy for us to accurately judge what sort of upload projects will make the most impact across the world in communities or languages that we are not familiar with. And that's why the work of the helpdesk is guided by an expert committee. We're a group of very experienced and very knowledgeable Wikimedians with diverse experiences who help us decide where to invest our limited resources. So how does it work in practice? Imagine, like I described previously, you have this great idea of a partnership with a local museum. You can get advice and guidance from someone who has done it before. When you email the helpdesk, you can see the address here. Your request is processed by the expert committee. They give an initial feedback and prioritize the ticket, taking into account factors such as how time sensitive it is and does it fill a real knowledge gap on the Wikimedia platforms. For example, we know that the content on the platforms has a bias towards rich, well-developed countries, because that's where most of the contributors are. On Wikidata, which is my main area of work, you can find items for pretty much every nature reserve, public library or culturally significant building in Sweden or Norway, but the situation in Africa is very different. On this map, you can see a selection of countries where we have made an attempt to improve the situation. We have worked with Wikimedia Ukraine, coordinate the Global Wikilovs Earth competitions, and we worked on improving the coverage of natural heritage, such as nature reserves and other protected areas in a number of places where they were missing on Wikidata. Many items didn't exist, maybe the information maybe only existed on Wikipedia. In some cases, a number of items already existed, but were empty or almost empty and wouldn't show up in a search. In order to run a good successful Wikilovs Earth competition, you need to know what's out there to photograph. And since the Wikilovs competitions in general, both Wikilovs Earth and Wikilovs monuments, are a great way to encourage the local communities to get involved and to contribute more to the platforms, that's where we chose to support them. The expert committee is, without doubt, the most important asset of the help desk. I know some of them are around in Singapore right now, so do say hi to them. I wish I could do the same. Working with them and discussing the needs of the movement has been very inspiring. The concept of the help desk has been growing thanks to their input. For example, we have realized how important it is to focus our work on long term support and capacity building, rather than on just providing hands on technical help uploads when it's needed. The thing is, we want people to develop their skills to learn so that they are able to teach others in their own communities and develop their own ideas. As I said, the help desk has still not reached its final form. Here are some of the things we are working on right now. In September, in the middle of September, I believe, we're going to share our experience from working with Wikilovs monuments data on Wikidata in a series of learning clinics under the Let's Connect umbrella. We hope to inspire and empower more community members who maybe are casual Wikidata users to get to know the platform better and develop into power users. We are working on establishing working groups to focus on specific subject areas. And of course, we hope to be able to support more requests and share what we have learned with the rest of the movement. Thank you so much for listening. My own role in the help desk is that I do a lot of the technical work involved in the upload projects, as I have worked with Wikidata and Wikimedia Commons for a number of years. I wish I could be in person with you and hear about your own projects and ideas. But at least you got to see the face of the guy who will read and work on your help desk requests, should you ever make one. Thank you and I hope you have a great time. Thank you very much. Everyone will have a break now and we'll be back here at 11.15 for the next session.


Welcome folks, we are the Dow industry straight down 167, Nasdaq's up 3, S&P's down 9.5, gold, gold contract flat 18.20 an ounce, we have silver down 5 cents, $20.14 an ounce, light sweet crude, down a buck 12, $76.46 a barrel, notes and bonds, 10-year note, down one tick, 10-29 and a 30-year up 9 at $125.11 and $Kingdoll, $Kingdoll's up 47 ticks, trading $105.661, Euro's 105, yen is out here at $137, British pound is at $118 to $1 at U.S.Dollar. We get over and take a look at the spy folks and this is going to be really dangerous, why? Because now you're building cause, cause for downtown. You're going to sideways move out here today, you're coming into Thursday and Friday and a week that you gave it up on basically yesterday and I suspect what you're going to see here is that, you know, we're just going to wander around here. The spy's down 76 right now, you have, you have light volume, there's no doubt about that but after you get a day like yesterday, you're going to expect that folks, so the energy was taken out of the market, he's at 108 million shares yesterday after making a high out there with 72 million, you're only at 54 right now and you're going to sideways move. We go into the NDX, we take a look at the NDX, the three Q's, what do you have with the three Q's, same type of setup, you know, can't get anywhere, you're down, well you're up 36 cents, you're doing 39 million shares, bottom line, I suspect your building cause is going to go after this B point down there, we'll see if we get any juice at the B point. You take a look at the gold contract, now this one's going to get really interesting because the gold contract, it's 1810, that's the B point on the gold contract and we hit 1813 today and you see that expansion of volume, that's a big problem man, okay, we did over 200,000 yesterday, let's see what we did yesterday, so yesterday, yeah yesterday we did 248, now you're only coming into 185, we're only down 180, it's not a good situation coming into that because of not only the days of the week or where we are, but then you take a look at the good old US dollar, same type of setup, dollar explodes higher yesterday, today, what are you doing, you're going sideways, you're still at a higher price, markets bottom line, all of those indices folks, what you're seeing here, the market is all about energy, energy on the way up, energy on the way down, when you build cars and we've already come down, danger us, come right back folks, stay right there.


So, now we will calculate what the optimal policy and the optimal and the value function or the cost to go is for the particular inventory control problem that we defined in the in the previous lecture. So, once again what we will be doing is use the dynamic programming algorithm. So, we will so apply the DP algorithm for the above problem. So, that is going to be our our task for now.  Now, since the terminal if you recall the terminal cost that we had assumed was equal to equal to 0 here. So, g n of x n was was taken as 0. So, and since we are talking of time n equal to 3 as the time period. So, now what we will write this for k equal to 3. So, for k equal to 3, we have that j 3 of x 3 is equal to 0 and we have this for all x 3. So, for all values of x 3 j 3 of x 3 is equal to 0. Now, for for an intermediate k, so that means for k less than 3 and k greater than equal to 0. In other words, for me write this differently for k equal to 0, 0, 1 or 2, then we have we can write j k of x k is equal to the minimum over u k. What is u k required to be well u k is is constrained to be between 2 minus x k and 0. Now, is the expectation is of this particular cost, the cost is some of the stage wise cost which is remember the stage wise cost comprised of these 2 terms here the 1 times u k that is just u k plus this particular term the squared term here, this was our stage wise cost.  Now, the cost to go is for the next time set. So, that is j k plus 1 of x k plus 1 and x k plus 1 can be written from these state dynamics that we have here. So, x k plus 1 is given by this particular expression. So, I will now write that here. So, that is just simply the max of 0 and x k plus u k minus w k. So, this is therefore, j k of x k. Now, we have we now we will actually do this for every value of k. So, let us write this for k equals 2. So, for k equals 2, notice that the since the demand that the since the amount of stock that we order and the amount of demand that we have can take only non-negative integer values. The inventory can also only take non-negative in the stock that you have at any time period can also take only non-negative integer values. And since you cannot have so the only possible values that the inventory can take the amount of stock that you have at the beginning of time at the beginning of any time period can only take integer values in this particular problem in particular non-negative integer values. The reason for that is simply that we can only order we can only order an inventory we can only order integer quantities and the demand also is realized in terms of integers. So, you can after a little bit of reflection it will become clear that the amount of inventory at any time step can only take 3 possible values in fact, which is 0, 1 or 2. So, xk can take 3 possible values 0, 1 or 2. Well, in that case let us write out now j2 of x2 for each of these values. So, j2 of x2 for each of these values. So, j2 of let us write this first for x2 equal to 0. So, for x2 equal to 0 we are now writing j2 of 0, well j2 of 0 is equal to the minimum over the minimum over u2. Now x2 is 0 which means if x2 is 0 then u2 can take values up from either 0, 1 or 2. So, notice this particular constraint that we have u2 if x2 is 0 then the right hand side here is 2 and left hand side is 0. So, u2 can take values either 0, 1 or 2 so u2 in can be 0, 1 or 2 and the expectation the cost is the expectation of u2 plus x2 plus u2 minus w2 squared. Now, what was x2, well we said x2 was 0, so what I will just remove this term all together and simply write this as u2 minus w2 the whole squared plus the cost, the cost to go at the last step. Well the cost to go at the last step here is already defined here that is j3 of x3 is equal to 0 for any stages, any state x3, so consequently this is also 0. So, in other words the final cost after I will erase all of these things and write this as my final cost. So, j2 of 0 is equal to the minimum over u2 where u2 can be 0, 1 or 2 of u2 plus u2 minus w2 the whole squared, now the expectation here is over w2 because w2 is the only thing that is random out here, now w2 is random and can take remember 3 possible values according to this probability distribution that we have written here. So this expression here can be expanded out I can write this as minimum over u2 in 0, 1 or 0, 1 or 2 expectation of u2 plus now this what I will do now is write out this expectation actually in detail, so I will write this as u2 plus now the where w2 equal to 0 happens with probability 0.1, so I would get 0.1 times u2 squared plus now w2 equal to 1 happens with probability 0.7, so this is going to be 0.7 times u2 minus 1 the whole squared plus w2 equal to 2 happens with probability 0.2, this is 0.2 times u2 minus 2 the whole squared, so this is now the expression that we get and we want to minimize this over all possible choices of over the possible choices of u2 the choices that are listed here which is 0, 1 or 2, so now let us do this computation it turns out that if u2 is 0, so if u2 is equal to 0 then this expression evaluates to let us write this out, so if u2 is 0 then I get 0, 0 and 0.7 times 1 squared which is just 1, so if u2 is equal to 0 this is going to be 0.7 plus 0.2 into 2 square which is 4 and that becomes equal to 1.5, similarly if u2 is equal to 1 I can again do this calculation and it turns out that this is this expression after doing the appropriate calculations turns out to be 1.3, you can check this yourself and finally u2 equal to 2 I can calculate compute this again and that gives me 3.1, so if I do these compare these 3 numbers here u2 equal to 0 is giving me 1.5, u2 equal to 1 is giving me 1.3 and u2 equal to 2 is giving me 3.1, what this is telling me is that if I was at the beginning of time period 2 and if I did not and I started off with 0 inventory at time, if I had 0 inventory at time period 2 then ordering nothing would give me a cost of 1.5, ordering 1 unit would give me a cost of 1.3 and ordering 2 units would give me a cost of 3.1, what this is effectively saying is doing nothing would give us a cost of 1.5 and ordering too much is also costly it gives you a cost of 3.1. The right thing to do is order the in between number which is 1, this gives us the minimum cost for starting at time period 2 with 0 inventory, so if you are starting off with 0 inventory at time period 2 the optimal thing for you to do would be to order 1 unit of inventory. So the optimal thing here the optimal 1 is this is optimal. In other words u2 star of u2 star equal to mu2 star of 0 at 0 is equal to 1. So the expectation has been computed here. So as a result we find that the optimal thing to do is to order 1 unit, let us see what the value function amounts to which is what is this particular expression we found that this expression is actually 1.3. So we have got therefore mu2 star of 0 is equal to 1 and j2 of 0 is equal to 1.3. Now we are not done with stage 2 yet because the same has to be done now for another value of x2. Now x2 can suppose x2 takes value 1. Now if x2 takes value 1 then again I need to do this, I need to write j2 of 1 is equal to the minimum. Now remember we have that uk can be only is between 0 and 2 minus xk now my x2 is equal to 1. So uk is between 0 and 1 which means uk can take only 2 possible values which is either 0 or 1. So u2 now is either 0 or 1 expectation is let us write out this expectation again. Again you have u2 plus x2 is 1 so it is 1 plus u2 minus w2 whole square that is the expectation. So this is now the minimum over u2 in the 0 or 1 and this particular expression let us evaluate this expectation it is u2 plus w2 equal to again 0 happens with probability 0.1. So it is 0.1 times 1 plus u2 square plus 0.7 0.7 times u2 square plus 0.2 times u2 minus 1 I got 0.7 times u2 square this is by taking w2 equal to 1 that happens with probability 0.7 and the last one here is by taking w2 equal to 2. So it gives me u2 minus 1 the whole square with probability 0.2 so that is the expectation. Once we can now write this out what is going to be what and compute this for each value of u2. So let us write this out for u2 equal to 0 so u2 equal to 0 gives us this expression becomes again 0.1 now 0.1 into 1 plus 0.7 into 0 plus 0.2 into 1 so that is actually equal to just 0.3 and u2 equal to 1 is gives me 1 plus 0.1 into 2 square which is let me write this as 4 plus 0.2 plus 0.7 sorry into u2 equal to 1 so that is into 1 plus 0.2 u2 equal to 1 that is again that becomes 0. So if I evaluate this this is 0.7 plus 0.4 plus 1 that evaluates to 2.1. So again we can compare the two so if you are starting with an inventory of one unit ordering nothing costs you 0.3 and ordering an additional unit costs you 2.1. So the optimal thing to do in that case is to order nothing at all u2 star of 1 is equal to 0 and j2 of 1 is equal to so what is j2 of 1 well it is the cost that we just computed here so it is 0.3. So now let us do this for x2 equal to 2 which was the only remaining value so when x2 is equal to 2 notice that because u2 is going to be between 0 and 2 minus x2 this basically tells us u2 can only take one value which is u2 equal to 0. In other words there is nothing to optimize over right out here the j2 of 2 is simply equal to the expectation of the same cost but with u2 equal to 0 which is now amounts to 2 minus. So the cost is this with u2 equal to 0 let me write out the cost u2 plus x2 plus u2 minus w2 x2 plus u2 minus w2 the whole squared and with u2 equal to 0. So then in that case this is simply the expectation of where remember x2 is 2 so 2 plus this minus so it is going to be 2 minus w2 squared the expectation of that the expectation of that one can quickly evaluate is actually turns out to be 1.1. So consequently we find that we find that mu2 star of 2 is also 0 and j2 of 2 j2 of 2 is 1.1 this now completes the calculation for time period 2 one can now do this for do the same for time period 1. So time period 1 let us let us go to now write for k equal to 1 so k equal to 1 again we have 3 possible 3 possible states once again we have possibility that x2 is equal to 0 so let us begin with that so for x2 equal to 0 we are now calculating j1 of 0 j1 of 0 is the minimum of now dub since x2 not x2 it is x1 equal to 0 we are now calculating j1 of 0 which is the minimum of u1 when in in the constraint that satisfies the constraints that we have we have since x1 is equal to 0 u1 can take any of these 3 possible values 0 1 or 2. So now the expectation now is over w1 of this of the same expression as we had earlier we now have the stage wise cost again stage wise cost with when the state is equal to 1 is given by u1 plus u1 minus w1 squared plus then the now the cost to go the cost to go at time period 2 so that is j2 of let us do max of the j2 has to be evaluated at the next stage at the next state right so this will be j2 of the max of 0 comma the state at the next time step was max of 0 comma u1 minus w1 so it is actually x1 plus u1 minus w1 but x1 is equal to 0 which is what we are assuming here so therefore it becomes just this this is the expectation that we need to calculate we want to evaluate this for every for each of these for each of these value so now we need what we need to do here is substitute the values of j2 that we found from from from out here so j2 was computed here in these three for these in these three pieces here so that needs to be substituted out here okay so let us let us just do that that is that can be that can be done so we suppose if if u1 is if you it turns out that if u1 is equal to 0 then I will just do this expect this calculation once so with u1 equal to 0 the first term disappears and we are left with w1 square here so so this will again the the cost becomes so with u1 equal to 0 we can we can compute this we can compute this expectation here so since u1 is equal to 0 this term the first term u1 here is 0 the u1 here is also 0 and u1 here is also 0 okay so then therefore we are we have now 0.1 times the so 0.1 is the probability that w1 is 0 so if w1 is also 0 the first term and the second term are 0 only thing left is the is the second term is the third term so it is this times j2 now of 0 which is max of 0 0 0 right so j2 of 0 plus 0.7 times okay now what is this well it is we are we now need to take w equal to 1 so if u1 is 0 this is 0 and we are left we have the only the second and the third term with w equal to 1 so 0.7 into 1 plus now if w is equal to 1 and u1 is and we are talking of u1 equal to 0 then it then the max of this also evaluates to 0 so you get just j2 of 0 plus 0.2 times the 0.2 times the again this expectation again this term so that that evaluates to now with we are talking of w equal to 2 so this is will be 4 plus j2 of whatever remains so again here when w2 is equal to 2 once again what is inside that the argument of j2 is all again going to be 0 and we are left with again j2 of 0 okay now we can substitute from here the j2 of 0 that we had which is 1.3 substitute that back there and that gives you that gives you the final this particular expression as 2.8 one can similarly do this for u1 one can do this for u1 equal to 1 for u1 equal to 1 you would see that this cost evaluates to 2.5 and u1 equal to 2 this is u1 equal to 2 this evaluates to 3.68 so consequently we find that we find that the optimal action then in state at time 1 when the state is 0 the optimal action is actually u1 equal to 1 right so we find that mu1 now star of 0 is in fact equal to 1 and j1 of 0 is 2.5 okay so that gives us this particular this the optimal that gives us the optimal thing to do when you are in state 0 at time 1 one can repeat the same same sort of calculation once again and get that get I will write this out for you here you get j1 of now state 1 turns out to be 1.5 and mu1 star of 1 turns out to be 0 and j1 of 2 turns out to be 1.68 and mu1 star of 2 turns out to be 0 okay this is k equal to 1 and then finally we have we we can we need to this now completes the stage k equal to 1 one can now need needs one needs to now do this for k equal to 2 as well sorry k equal to 1 now needs to do this for k equal to 0 as well so at you again have at the similar sort of expression at k equal to 0 and again for each of the possible state values of the of the state at 0. Now since we since we are given that the initial state is 0 we only need to calculate j 0 of 0 so j 0 of 0 is all I am going to write out and this now is can be you can check is given by u 0 which is minimum over u 0 which is from 0 1 and 2 the expectation of u 0 plus 0 minus w 0 square plus j 1 now of the max of 0 comma u 0 minus w 0 once we again we can compute this compute what we are looking for it turns out so if you take action u u 0 equal to 0 the expectation evaluates to 4 if you take action u u 0 equal to 1 the expectation evaluates to 3.7 and you take action u 0 equal to 2 it evaluates to 4.818 all of this put together now tells us that the optimal action for us to choose in state 0 at time 0 is 3 is is is to order one unit one unit in one unit of inventory so it tells us that mu 0 star of 0 is equal to 1 and j 0 of 0 is equal to 3.7 so this therefore is the optimal cost of our problem j 0 of 0 is which is 3.7 is actually the optimal the optimal cost that we incur starting from 0 inventory and the policies that we need to adopt are we is that at initially you order one and then thereafter you order based on based on the these policies based on what the state gets realized as so at time 1 you order based on mu 1 star at time 2 you order based on mu 2 star where the these intermediate j's that we have computed are only as are serve as intermediate calculations the the final calculation the final total cost that we that that we would incur starting at stage at stage 0 bit with 0 inventory is 3.7 and that is given here. So with this we we have completed a complete analysis of the inventory control problem.


Photographic technology has changed a lot in the last century. Digital photography became cheap and convenient because of the CCD, the charge couple device inside this camera. In the past, we just placed a piece of film here behind the lens, but now we have a purely electronic image here to do all the work. Let me show you. Here it is, just a shiny slab. Yet its details are fascinating. This CCD captures the image and then transfers it to the camera's memory system to record it as electronic data. When exposed to light, different sections of the CCD build up charge, proportional to the light's intensity. We can then measure that charge and know precisely how right that section of the image should be. If you enlarge a picture on your computer, you can easily see the tiny picture elements called pixels. Each one corresponds to a single section of the CCD. This is the essence of the digital camera. There are several million of these sections of photosensitive silicon in a grid capturing an image. Now, the key to understanding how digital photography became cheap and ubiquitous lies in the ingenious way that the CCD transfers the image into the camera's memory. The easiest and most straightforward way would be to use wires to connect each pixel in an XY grid. But that presents a problem. Here's what goes wrong. After exposing the grid to light, we can use these wires to read the information pixel by pixel, measuring the charge on each section of the grid. This makes sense in principle, except all of the pixels and their electronic components leak a small bit of charge that distorts the charge coming from each pixel and leads to striations and patterns in the image. This distortion, called capacitive coupling, increases as the number of pixels increases. Now, CCD solves this problem in a very simple way. The pixels have no wires attached to them. A CCD is made from a slab of silicon. To make each pixel within the slab, engineers create insulating sections called channel stops. These divide the slab into rows. The surface is covered with a thin layer of insulating silicon dioxide. And then perpendicular to the channel stops, engineers deposit thin strips of metal, typically aluminum. Each pixel then is one section bounded by channel stops and aluminum. So now we have our grid of pixels. Recall that when light strikes the whole array, the silicon pixels build up charge proportional to the intensity of the light striking them. We then have a captured image stored as charge within each pixel. The great innovation of the CCD was how it moved the image from the silicon array to the camera's memory system without using external wires that would distort the image. The CCD shifts the charges from row to row without wires until they reach the bottom when a readout register transfers the charge to the camera's memory with very little distortion. The camera then counts the charges and constructs the image using that data. Now, one last important question. How do we get color?  But for a color photograph, we need to separate the entering light into red, green and blue. The most obvious solution is to filter the light into those three colors using three separate CCDs to capture red, blue and green information and combine them into a full color image. However, engineers have created a cheaper solution. Instead of using three separate CCDs, they use a little math so they only need one. In this consumer camera, they cover that single CCD with a filter with red, green and blue pixel size sections. This creates an image coming out of the CCD that's a mosaic of these three colors. The camera applies an algorithm to estimate the correct colors for each pixel. For example, if a green filter covered a pixel, we would need to estimate the red and blue components of that pixel. To do this, they'd use the adjacent pixels and average the color intensity for the pixels in question. It works because the images' significant details are much larger than each pixel. This sounds implausible, but you've seen the results yourself. I'm Bill Hammack, the Engineer Guy. This video is based on a chapter in the book Eight Amazing Engineering Stories. The chapter features more information about this subject. Learn more about the book at the address below.


This is the animation minute first up. We have this awesome animation by Dominic. I Am not familiar with it at all, but this is a fantastic looking tutorial by Ian's would go and check it out the full tutorial on YouTube Oculus and island X lab have announced hours tales from the galaxy's edge Andrew is posting this really cool animation piece here Here's the final version of this cartoony piece from animation mentor. I love the modification of the squirrel rig Here's a 50% off sales on all the courses here. You can get those on gumroads. Check out the coupon code Jonathan is posting that the physics-based animation to the cascader is now open in beta All my spangela blog I posted a trailer for Batman death and the family always a fan of Batman movies. It's a very cool trailer Job posting again Richard is posting that they're looking for a powerhouse keyframe animator. I Had to include Cameron's RPS here. It's so cool TJ is posting a job posting for Maya Very cool clip here by Michael. You can watch the whole thing on demio Your fan of Billy Eilish and your fan of animation her new music video is fully animated The original ghost in the shell is getting a very cool 4k ultra HD re-release Fantastic news from Frank. He is directing at Netflix If you're watching the animation minute, you know, I love pixel art and calm on how can you not include this? David is posting this indeed fantastic looking racing game Another job posting from Richard looking for a 3d keyframe female animator and Now comes a big run of and post the rig was released on August 1st. Here's the very first animation done with it Then they redid the opening of avatar also very cool already at 3.3 million views And if you want to know who did this Rick station is posting here everybody's profiles and Good timing because item challenge is posting that the August challenge is all about element vendor And for my frame is posting a very cool breakdown of this facial animation test again all about a Speaking of which there is a picker does another picker and a facial picker Intended a low-poly glider Maya rig for a as well And Richard is posting that I animate is looking for instructors And finishing up with Nabil he posted two very cool acting pieces This isn't piece number one, and this is piece number two And as always all of these are linked in the description


Yeah, my name is Kuhn Krescher and I'm a biophysicist at the Max Planck Institute for Terrestrial Microbiology in Marburg. And at the FEMS meeting next year I'll talk about how we can combine single cell level imaging of biofilm development with relatively sophisticated data analysis techniques to really learn about cell-cell interactions in biofilms and microbial communities. So basically the main question that I will be answering is what are the cell-cell interactions inside bacterial biofilms.


So many people, I'm sure, listening are familiar with South Sudan and to a little village in northern Bargazal right on the border between Sudan and South Sudan. We were working with a group of women in what we call WPTs, or Women's Protection Teams, which are local South Sudanese women. And they had set up within their community a series of safe houses. So in an effort to address situations of sexual and gender-based violence without any service providers very close by, they realized that one of the things they needed to do was to be able to move to safer locations. And then they also needed a process on how they were going to deal with perpetrators and survivors and so on and so forth. Well, I was listening to Mary and the Women's Protection Team tell their story. I noticed around the edge of the circle there was a gentleman and his name is John. He's a local police officer. And when we'd first started working there, he'd been very skeptical. What can women do? What can women do for women protection, protecting themselves? And he said, I need to tell you something. And I said, what? He said, you know, I didn't believe that this was possible. And he said, you know what's happening? And he said, normally, all these disputes would go to the local chief's court. And the chief's court would organize and they would arbitrate what was happening. And they would come up with some sort of decision. Somebody would make a payment and that would be considered justice. And he says, now what happens is when people bring their problems to the local chief's court, the chiefs now say, we can deal with your problem here. And we'll do what you know what we're going to do.  This was really moving. And when we think about communities doing self-protection and what can we learn from that? We have to park our own ideas of what success is, where from different parts of the world, what does justice mean in those situations? It's really important that that is what we're imposing on local communities. They were making those choices for themselves. And that we make space for it and we have the patience for it and the encouragement for it. And that we use our presence and our solidarity and provide accompaniment for when things get difficult and stay in a secondary role for those local protection strategies.


This is Dave Vellante and this is SiliconANGLE.tv's continuous coverage of VMworld 2012. This is the official broadcast center of VMworld 2012 and we're here with theCUBE where we bring you the smartest people that we can find in the industry. We extract the signal from the noise and we have a spotlight now on something called software defined storage. We've been hearing a lot about software defined data center. The software defined network is a big term that's being bandied about in the industry today but we're going to talk about software defined storage. What is that? What does it mean to VMware customers and how are specifically cloud service providers taking advantage of it for their customers? And I'm here with Tim Brown, who's the CEO of a company called CoreRaid and one of CoreRaid's customers, Tim Dufour, who's the CEO of Rackforce. Rackforce, according to IDC, is the number one cloud service provider in Canada. Gentlemen, welcome to theCUBE. Thank you. Good to see you guys. So here we are, VMworld 2012. We were just talking off camera. It's back in San Francisco this year so we get to sleep a little bit more but we're here, there's a lot of energy and you're starting to see Tim this notion of the software defined data center come together. So I'm going to start with the customer perspective. So talk about your data center as a cloud service provider. What does that all mean to you? Is that buzzword or can you actually put that into practice? That's actually very important to us. So software defined data center to us means driving the complexity out of very complex systems. So we have a number of technicians that manage very complex storage, very complex servers. But at the end of the day, if you can have a software defined management system, basically you can take all those pieces, all those complex pieces and be able to rapidly provision and provide services out to the client base. So Kevin, CoreRaid talks about redefining storage. So how are you redefining storage? So there's a couple of important ways. If you look at the transition from old enterprise systems to the cloud, you move from small data running on big boxes to big data running across lots and lots of small boxes. That's a very different computer science problem. And so what we've done is we've taken that same scale out architecture that the big Google and Amazon networks have used and wrapped it in an enterprise class set of features for storage. And so what we just now launched is our software defined storage offering which really takes that scale out architecture and is able to automate that with one click provisioning with the whole scriptable programmable set of things that can really tie from the infrastructure all the way up the stack to the application. So for service providers, this lets them roll out services that can very quickly deliver capabilities that look like the consumer web that really have the performance and the resilience of the enterprise class infrastructure. So when you think about service providers and the way in which they're adopting these technologies, they're sort of ahead of the game. I mean, you saw Amazon has sort of invented the concept of the cloud and service providers are a bit ahead of the traditional IT enterprise even though we're seeing IT start to get more serious about some of these simplifying provisioning but they've got a much more challenging problem because they've got hundreds or if not thousands of applications. So Tim, talk a little bit about what Pat Gelsinger this morning talked about the museum of legacy infrastructure. What does your museum look like and how are you able to shed some of those legacy processes? What did it look like before and what does it look like now? Take us through that. Sure, it all starts with the data center itself, the data center infrastructure. So when we first started in 2001, we had a small kind of server room, I guess you could say and it was actually, it became obsolete in about four or five years just because it couldn't support the high density of the modern servers that were coming out. And so we morphed into a number of different data centers over the years. Our latest data center was built in 2009 and we opened the doors in July and that data center supports up to a thousand watts per square foot. So we can actually put in a lot of blade centers and a lot of high density storage platforms. That's basically the underlying infrastructure but moving up the stack to the servers. Now we're finding the servers of course getting smaller, more powerful, Moore's Law type of idea. And we also see that the workloads that are coming to us are extremely varied. We would have customers that really want just month-to-month test dev environments to customers that sign five year contracts that have very high reliability, high security requirements. So we have a very varied workload and that's basically, again, why the software divine aspects help us to template and profile customers. So you build an infrastructure as a service based on product, core aid, storage technology? Exactly, like our cloud environments are very much enterprise based. We didn't go to large scale public cloud infrastructure style, we're much more enterprise focused. So when a customer comes to us, we listen first, we try and understand what the requirements are and then we fit our solution around the customer rather than the customer fitting into the solution that's predefined. So somebody once said to me that companies don't buy from startups because they want to, they buy from startups because they have to. Why did you have to go to this type of solution from a company like Core Aid versus go into one of the big whales and combine this? Actually really appreciated the fact that the underlying architecture that they've built, Ethernet based. As soon as I heard Ethernet based scale out infrastructure, then that really struck a chord for me. I think again what we're trying to do is we're trying to simplify complexity and what we see with Core Aid is that it has a very simple architecture to it. But at the same time, very scalable, very robust and it just drives a lot of the complexity of the storage solution. So Kevin, let's talk about the software defined data center as we've been hearing from VMware, the sort of top down approach, abstract, pool, automate. How do you fit into that with your notion of software defined storage? So we're really coming at the same problem set from the bottoms up and that's where we can kind of meet and collaborate. So if you look at that abstract pool and automate, that's been done very, very well at the workloads with the applications, with virtual machines. It's starting to make its way down into the network but in storage it just doesn't exist. You can't do it today with the older architectures think like fiber channel, big boxes. It's very difficult to make that into a very programmable, flexible elastic architecture and that's been one of the big problems for the enterprise. So we're coming at this with an architecture that looks a lot more like those building blocks and then from that bare metal being able to then take that pool of resources, abstract them and then in a restful API automated way, this is the way the cloud guys do it really to then make that available as a service all the way up the stack. So you take someone like Rackforce, they're the number one cloud in Canada, number one cloud service provider. They just want the VMware service partner of the year. I mean, we're very proud to be working with them. The problem set that they have is how do we take enterprise class services, deliver them to our big enterprise customers but do it with the agility and with the simplicity and be able to innovate in terms of the way that we deliver that as a service. So enabling that is a huge upside for us if we can go help our customers do that and we think that it really moves the infrastructure forward. So there was a big move certainly over the last two or three years VMware announced the VStorage APIs for array integration and all the traditional storage companies worked really hard to integrate. Why didn't that solve this problem? Well, it's a great question though. We've done some of that same integration ourselves where you can go, for example, pause a workload before you take a snapshot. There's a number of those kinds of integrations that are useful, there's utility to them but what they don't do is they don't solve the fundamental provisioning and automation problem with most of those clouded infrastructures you have to assume someone has already provisioned storage for you. And in a fast data growing world in those type of environments, that's very difficult to assume and much less having the flexibility to go fit different workloads where I need flash drives over here, I need RAID 10 over there, I need very different configurations to achieve the enterprise application requirements. So the flexibility to go not only do the top level integration with the virtual machine but to drive that all the way down and match the infrastructure to those requirements. That's something that doesn't exist today. So Tim, I wonder if you could talk about, Kevin was mentioning simplifying, accommodating faster growth. Can you talk about, give us some kind of sense as to how fast you're growing and what does that all mean to you? Well, we became a cloud service provider in 2007 and I think year over year a cloud's been growing in the range of 50 to 70% per year. So very rapid growth and I just wanted to make another comment with regards to the automation piece that we have a number of skill levels in our staff and to be able to automate and have this one button provisioning, we see that as reducing a lot of operations overhead and I think that we can rely on more junior techs to do a lot of the provisioning for us. I think that that's why the software defined storage is so important for us. So how do you scale it? I mean, you're talking, Kevin was earlier talking about taking a page out of Google and scaling out. Talk about how you scale this solution. Right. Because you hear a software defined and you go, does it scale? So we have like the actual chassis themselves will have very similar hard drives inside those chassis. We'll have a chassis for SSD and we'll have a chassis for SATA drives and we'll actually build it like a building block like a brick wall if you will. We use, we're going into a production with RAIN. It's a redundant array of independent nodes. So that gives us the ultimate in reliability and in performance as well. So do you, are there any particular applications which you service better than others?  Talk about the use cases here. I think that the probably the most challenging use applications for us are probably the very advanced large e-commerce type of applications. They can't afford to have any downtime even during maintenance periods. So we have to build our infrastructure such that even during maintenance there is absolutely no downtime. So that's among the most challenging applications that we service. But at the same time, we do have customers that are in the test dev environments and things like that. And basically everything in between. How do you handling multi-tenancy? Multi-tenancy, we handle it in a number of different ways. There's certainly the, at the server level we can provide what we call a hosted private cloud. And so essentially it's when we take servers and we dedicate the actual hardware, the actual servers are dedicated to the customers. We can actually do that with the storage now with Core 8 as well. So we can dedicate a disk array inside of a chassis, inside of the storage chassis. So now the customer has dedicated servers, they have dedicated disk and we build kind of a security wrapper around that as well, security and performance. So basically it becomes a separate infrastructure for the customer, but it is hosted in our data center. How about disaster recovery? Where does that fit in? That's a topic that is really challenging for users. It's always top of mind, data protection generally, but disaster recovery specifically. How do you guys handle it? And I want to follow up with Kevin. Yeah, certainly a number of clients and that require disaster recovery. What we actually see is that the environment, the geographic region that we're in is very stable and we see for example Vancouver where it's less stable that we can see the primary workload actually moving to our data center and the backup becomes in the Vancouver data center for example. So we do have a number of disaster recovery clients and that's where the big data comes in, tends to come in as well so that's why we needed to have this very large scale out storage infrastructure as well. So you're hearing that from other clients? I mean is that a key value proposition? Backup is one of the killer apps for cloud because people can start small there and then what we see exactly the same trend where people will start out with something in the cloud for backup and they'll actually flip it or they'll start to really move things to some of these types of environments if the service provider can really deliver the resilience, the performance and really do a good job of outsourcing that. So you take something like multi-tenancy, that's a hard problem. If you look at solving that whether it's in a private or a public environment, this isn't a good example where it's a whole stack problem. It's not a storage problem or a server or a network problem. You have to make all of those pieces work together. So the software-defined data center and software-defined storage is part of that. This is all about how do you programmatically link those together and then be able to use the control plane to pull out data plane elements whether it's how do you segment off some storage, how do you create a VLAN, or how do you tie that all the way up into a virtual machine workload. So this is all of the underlying plumbing that is necessary to really make that very agile. Kevin, how do you compare the cloud service provider like Tim, for example, with the traditional IT customer? Where are they on that spectrum of cloudness, if you will? It's interesting because what we're finding is in large companies you'll have a lot of traditional slow-moving pockets and then you'll have someone who's got pain. And that pain these days is often caused by rapid data growth. So a lot of our customers we're seeing doubled year on year on year growth. So one, two, four, eight, 16, 32, right? So what worked at one or two stretches at four or eight and when you get to 16, 32, 64 it just won't work. And so what we're finding is that even in conservative Fortune 50 customers we're finding pools of people that really have a real new problem and they realize that they need to look more like Amazon or Google and they need to do it quickly. And so that's an opportunity either to work with someone like Tim to go and really build an architecture that's suited for that or where we'll come into their inside infrastructure if they've already got petabytes of data they may not want to move it. We can build that kind of infrastructure in-house. So we're seeing it in pockets where people are moving but it's growing then very quickly. Complexity is the pain. Coret is the aspirin I'm hearing. Morphine. But it has a little more powerful there. But I have to ask you when you really peel back the onion on complexity it seems like it's been this perpetual barrier to growth. Have we solved that problem do you feel as an industry? So I think that if you look at the stack there's a lot of folks that have focused on just their piece and some of the big vendors are buying up some companies and trying to put it into a stack but it's relatively fixed. We see those as sort of a kind of a modern mainframe but not a lot of flexibility and certainly not a commodity hardware economics. So what we believe is that that's the right idea to get things working together but in the end it's going to have to be much more open about how do you do with open standards with being able to then program that in a much more flexible way because if you're going to differentiate on services you need to be able to customize them. You need to be able to really tailor that to the business need and that's very hard to do from within a very fixed stack and a fixed frame. So that's where some of the innovation around the software defined API sets and so forth that they're going to drive people.  Right. Have you essentially solved that problem at the infrastructure level or is it still a challenge? I think it's still a challenge definitely. I think it's all about the process as well. It's the way we govern our change management and things like that. I think that's the ongoing key for us is that there's always going to be new technologies. There's always going to be new types of infrastructure but we have to be very careful in the way we do our change management and processes but again if you get the automation involved to drive out the complexity that really makes our job a lot easier. Abstract the complexity, pull the resources, automate seems to be the direction that the industry's headed in. Clearly that's what you guys are espousing so congratulations on your successes. Really appreciate you guys coming by theCUBE and good luck, enjoy the rest of the event. Thanks so much. Alright, thanks for watching everybody. We'll be right back with our next guest, keep it right there. This is SiliconANGLE.TV's continuous coverage live from VMworld 2012.