Live from San Francisco, it's theCUBE, covering Google Cloud Next 19. Brought to you by Google Cloud and its ecosystem partners.  I'm John Horace Dave Vellante. We're here for three days of wall-to-wall coverage, breaking down all the content from Google Cloud's big conference here, Google Next 2019. Our next guest, Joe Kava, Vice President of Google Data Centers, spans all the data centers that Google and Google Cloud deploy. He's the man in charge of thousands of full-time employees, thousands of contractors, tens of thousands of construction workers. He's building out the infrastructure and footprint to make the cloud work for us. Joe, welcome to theCUBE. Thank you both very much. So, Sundar Pakai, the CEO of Google, kicked off the keynote. The new CEO of Google Cloud, Thomas Kurian, came on only 10 weeks into the job. Clearly, the investment in Google Cloud, new building separate from campus. So, Google and Google Cloud are two separate groups that's been reported clearly by us and others. But at the end of the day, you got to run all this stuff on somewhere.  I know personally in following Google and covering Google, the excellence in engineering, the excellence in building out data centers. What is the status of, just quickly take a minute to explain how it's organized. You got Google proper, which is where everyone knows Google, Google search, et cetera, Gmail, and Google Cloud. How does that operate? What's some of the data points? Okay. So, as the head of the teams that do everything from procuring land and writing energy contracts and buying renewable energy to designing, building and operating all the data centers, Cloud is one of my largest customers. But my other customers are search and ads and Gmail and G Suite. So, really our data centers at Google are built for the entire Google enterprise. And Cloud happens to be one of our largest internal customers in that enterprise. Talk about some of the stats, countries, regions, data centers. What's the nuance? Because you have regions, you have availability zones. Talk about some of the stats inside the numbers. So, at the, starting at the Google level, we have data centers in four continents. So, we're in North America, South America, Asia, and Europe, of course. We have a probably one of the world's largest global private networks with 13 undersea cables that are our own and hundreds of thousands of miles of dark fiber and lit fiber that we operate, like I said, probably one of the world's largest networks. We have in Europe, we're in five countries in Europe, we're in two countries in Asia, we're in one country in South America. And that's at the Google, in North America, of course, we have many, many, many sites across all of North America. That's at the Google level. Now, Cloud has 19 regions that they operate in and 58 zones. So, each region, of course, has multiple zones in it. You know, we cover, Google has presence in over 200 countries worldwide. So, really, it is truly global operations. So, the 200 countries is Google-wide. The 19 Cloud regions and 58 availability zones, that's Google Cloud, is that right? Okay, and so, do you not sort of mix infrastructure for Cloud and things like Gmail and Maps and Search? Is that correct, they're separate infrastructures? It's not so separate infrastructure. So, when my team builds a data center, any one of our internal customers could be in that data center. In addition to the Google-owned and operated data centers, we also have some sites that are leased in certain regions and Cloud may be occupying those. But regardless of whether it's owned or leased, it's the same hardware in there, it's the same operation staff that are in there, the same expertise, the same deep knowledge about operating Cloud environments. And so, regardless of whether we built it or we leased it, it's the same experience. From a CIO's perspective, it's the same SLA, no matter what availability zone it is. I mean, that's what really matters, yeah, okay. Talk about the scale, because one of the things I liked in the keynote, Sundar is awesome, he creates great keynote. He used scale multiple times.  What are some of the key learnings that you guys have, because you have to be very efficient, Google likes to solve hard problems. You guys have done some things with sustainability, specifically. Talk about some of the learnings as you guys have been building out these data centers for years, with Cloud on a massive expansion. You got to watch the environment, you got to do some things, what are some of the learnings, what are some of the notable accomplishments you guys are forging on, and what are some of the goals? So at Google, we've been at this for two decades. For more than 20 years, we've been building and innovating on hyper-efficiency, hyper-scale, basically trying to build infrastructure that was more sustainable than had ever been thought possible. And then as our cloud business started to expand and boom, frankly, we set apart to build the world's most sustainable cloud. And really what that means is that, you know, we were the first company to announce that we were buying 100% renewable energy, new renewable PPAs to match 100% of our consumption. And in 2017, we achieved that. That was after being carbon-neutral for 10 years before that. So going all the way back to 2007, we were a carbon-neutral company by mostly buying high-quality carbon offsets. Then we decided that, no, we want to advance the transition to renewable and sustainable energy. So we started buying direct power purchase agreements for wind and solar. And then in 2017, we announced that we had matched 100%. What that means is that we've acquired over three gigawatts of new solar and wind power purchase agreements. And now we're taking it a step further. We have a very ambitious kind of moonshot arguably to not only match our consumption, but match it 24 hours a day, seven days a week, 365. So you can imagine the complexity with this because the wind doesn't always blow, the sun doesn't always shine. And so that's going to take moonshot thinking in order for us to get there. But we feel so strongly about it. We're so committed to this cause that we've got dedicated team working on this right now. So it's not just squeezing PUE out of the data center. I'm sure you're doing that, but like you say, it's a new shot. Absolutely, we've been doing that since the earliest days. I've been at Google for over 11 years. From the very first day I got there, I was completely blown away with the numbers that I was seeing about the PUE. And for maybe your audience, PUE is a measure of efficiency in a data center. And at the time, like back 2008, Google was achieving numbers that the EPA thought wouldn't be achieved until like 2020. And so I started to dig in and look how, and it was astounding to me, the lengths that the company had gone to to optimize every single step of the way from the high voltage transformers in our own dedicated substations, excuse me, that are much more efficient than typical utility transformers, all the way through minimizing the number of transformations going from grid level, like 345,000 volts down to server voltage level, minimizing the number of transformations, reinventing the way people think about cooling. When we, when I got to Google, I was also amazed. Our data centers are running at like roughly about 80 degrees Fahrenheit. Most data centers run at like 65 degrees Fahrenheit. Our data centers consume about half of the energy of a traditional enterprise data center at the same size. And in addition to that, we're producing about seven times the compute capacity for the same amount of watts that enterprise data centers. This comes from a practice of engineering, really purpose-built engineering from the day one into the overall holistic plan of the build-up. It's frankly, it's a relentless focus on efficiency and innovation right from day one. When I got there, it had already been well in motion, but it's optimizing across the entire stack. It's optimizing software to be efficient, optimizing the server architecture to be more efficient, optimizing the power supplies in the servers, optimizing the racks, designing the racks to be working with the cooling equipment specifically. Our cooling systems are unique to Google. They're not traditional air conditioning units that you would buy for traditional data centers. Sometimes we'll site data centers where we can use natural environment in Finland, our data center is right on the Gulf of Finland, and we use cold seawater from the Gulf of Finland to cool the data center. So to be clear, you're doing quite a bit of vertical integration, whether it's your own transformers or power supplies and other equipment, right? Library optic across the Atlantic as Sundar pointed out.  And the efficiencies you pass on in savings to the customers and society with the sustainability piece. That's right. Two angles on that. Really, it's good business, of course, because it's bottom line, but more importantly, it's also the right thing for us to do. We feel very strongly that we need to be responsible for our impact on the environment and to minimize that impact and to be accountable for it. And we realize that the only way we can truly be accountable for our impact on the environment and for our energy consumption is to have it matched with renewable energy 24 hours a day, seven days away. Not to take a sidetrack here, but we've been covering the tech business for many, many decades and certainly recently tech kind of got a bad name because of some headlines. But I always look for tech stories that, you know, everyone's like, oh, tech's bad for people. There's always good story. I think this is an example of tech for good. You guys have taken real engineering, building large scale systems and facilities that have software running on it. It's really a tech for good story. So congratulations on that. That's awesome work. Now I want to kind of ask you to put you on the spot here because I think one conversation we're hearing a lot and I want to get your expert opinion on this. It could be Google and also as a person in the industry. Security in the supply chain has come up a lot in terms of whether chips have been hacked. We've heard things like that in the story. Some of them have proven to be misinformation and fake news, but you got to watch security. Google's really hardcore on security is you know, you live that. How do you look at the supply chain? Because if you're not just throwing contractors at this, you could be taking a very holistic ground zero engineering approach to a holistic picture. How do you guys manage the security challenge in the supply chain throughout the facilities from chips to access, things of that nature? Sure, so there's two aspects. There's always the logical and the physical security aspect. From the physical security aspect in our warehouses that we manage, of course we apply the same rigorous standards for physical security that we do at their data centers. That's multi-layer and various different types of security technologies that we apply. But on the logical side, I think you're probably familiar with our Titan chip set that we developed and those Titan chips are put in all of our servers and from the time that they're built to the time that they're in the facility, those chips sets are securing the servers. From the logical side though, my colleagues on our information security team are truly the experts that could address that. And that's where the software shines. That's right. Again, this is not just one, it's not a silo. You got to build physical build. It's kind of a bigger, it's a holistic integrated model. It is, and this is from the data center industry perspective, for as long as there's been IT, there's always been the debate between facilities and IT, right? When I got to Google, I was also so relieved to say that was all technical infrastructure. And the IT systems, the software that runs on those data centers are all under the same technical infrastructure group.  Well, for years, there was a discussion in general IT about those groups coming together. And I think the way they come together is the cloud, frankly. Because you haven't seen a lot of change within organizations of IT and facilities really working together, that's right. That's right, yeah. Well, Joe, thanks for coming on theCUBE. Thanks for sharing your insight. Final word, what's the thoughts, folks watching out there, who are trying to understand how to bring IP technology into facilities in general? I mean, a lot of people still have data centers, they still have on-premise activity from light bulbs to whatever. Any learnings in parting wisdom, folks watching there, in the facilities and or physical building space on how to build out these, whether it's smart cities, whether it's in construction and experiences you could share with folks out there, looking to build a holistic long-term plan. Yeah, there's a few things. First of all, we've published all of our energy efficiency best practices, and so I encourage everyone to take a look at those best practices because the best energy savings is the energy not consumed in the first place. So do all the right things to reduce the overall energy consumption in the first place. Two, we want to help further the transition to renewable energy, and so we've published a lot about our power purchase agreements, and a lot of the policy work that enables us to do those is also set in place for other large energy consumers that want to do the same thing. So our policy work can help to allow others to do the same thing. The third part of our sustainability aspect is really a circular economy. We want to have zero waste to landfill. We've currently achieved 91% diversion of all of our data center operations, so 91% is diverted to landfill, but we have an objective of 100%, no waste to landfill. And then that means you have to do smart things like better reuse, better recycling, better reselling of products that are still good but may be out of date for your use. And then just to end it off, we've really invested in our machine learning and AI intelligence. Both on the data center operations, we have now ML running some of our cooling systems in fully autonomous mode and doing a much better job of matching the cooling needs to the workloads at the time. And we took that same learning with our deep mind group partnered with them and we've applied that to a wind farm now as well so that they can better predict what the output of the wind farm is going to be 36 hours in advance that allows the operators of the grid to better bring on more energy and get higher value out of that wind energy. Great engineering story, at scale, congratulations. I love the societal impact, tech for good, congratulations. Love to have you back, talk about the impact of IoT. Joe, thanks for coming on. It's convergence at the edge, yeah. It's all coming together, wind farms to data center. Data center's not going away, obviously the cloud needs to run on servers and has to be done in an engineered fashion, Google's leading the charge there. It's a cube, live coverage. Day one of three days of coverage. We'll be right back after this short break.


The American love affair with trees brought about the planting of millions of trees across the Great Plains for protection, food, lumber, and beauty. A view of the countryside shows thousands of miles of windbreaks which were planted all across the plains. Although many of these trees were initially planted to control the severe erosion of the dirty thirties, windbreaks have also provided increased snow harvest, helped keep snow off roads, provided habitat for wildlife, and protected homes, and millions of acres of crops from extreme temperatures and winds. Decades have gone by and windbreaks have aged. Trees have died and benefits from these old windbreaks have declined. Many of the field windbreaks in northern plains states are in need of renovation, about half of which have declined to the point where they are currently ineffective. What are the reasons for this marked decline in windbreak quality? Grasses and other weeds compete with trees for precious moisture. Some trees were planted too close together and others were poorly maintained. Some have been damaged by herbicides, insects, equipment, and livestock. Many of the trees planted 30 to 40 years ago were Siberian Elm with the windbreak lifespan of about 40 years. Others were planted to American Elm, many of which have been lost to Dutch Elm disease over the last 30 years. These two examples demonstrate the need for planting a diversity of species which match the particular site conditions. That leaves us at the present time with fewer trees going in and more trees coming out. Many windbreaks have reached the end of their expected life. Our renovation needs of today are as great as our planting needs of yesterday and if done properly can be as successful. From here forward, a concerted, creative, and effective renovation effort is needed. What is renovation and how is it done? Windbreak renovation is done to maintain the effectiveness and health of an entire windbreak. Oftentimes, this means that we have to cut live, healthy trees. We do this in order to maintain the function and sustainability of that windbreak over time. Windbreak renovation begins with the creation of a plan. Some renovation techniques have been used in many parts of the Great Plains. Some renovation techniques such as sod release, coppicing, thinning, pruning, supplemental planting, and root pruning can be easily done with existing skills and available equipment. However, complete removal of a tree row is most often done with large, specialized equipment. For years, traditional windbreak renovation methods have used construction equipment to remove tree tops, stumps, and roots instead of forest and tree management equipment. After the wood has been removed, the sites have usually been leveled, and new tree rows have been established using traditional site preparation and tree planting equipment. End loaders, back hose, and farm tractors have also been used to remove old tree rows. With patience and a skilled operator, these machines can do a good job. End loaders are perhaps better equipped to stack a tall, reasonably clean brush pile than our dozers. Equipment modifications, such as construction rock buckets, provide a means of sifting out soil during removal. Depending upon the size of the machine and the size of the tree, loaders are effective at taking out trees and stumps. Within the forest industry, there are many machines, such as feller bunchers, that are capable of renovating prairie windbreaks quickly and efficiently. Initial cost, transportation limits, and distances between renovation sites have limited the use of these machines. Feller bunchers are expensive and inaccessible in some areas. Less expensive alternatives to feller bunchers are hydraulic saws. Row removal with these saws has been demonstrated in North Dakota with good results. As with feller bunchers, these saws can be used to remove the top growth while leaving the stumps and roots in place. This method has worked well where new trees are planted in and around the old tree row. In the hands of a skilled operator, a half-mile-long single-row windbreak can be clipped off in a day. If producers wish to farm the area of the old tree row, a hydraulic saw should not be used. Using chainsaws to remove trees is a method that has been around for many years. It is effective, affordable, available, and a lot of hard work. For certain sites, it is an appropriate method for removing trees. For safety and efficiency, only skilled chainsaw users should undertake the removal of large, dead trees. Personal protective equipment such as a helmet, hearing protection, eye and face protection, safety gloves, safety chaps or pants, and protective footwear must be used whenever operating a chainsaw. If re-sprouting from tree and shrub stumps is not desirable, herbicides may be applied to the inner bark. Now the inner bark is located right between the outer bark and the cambium layer, which is the actual growing part of the tree. I've added a chemical dye so you can see it better where I'm showing you, but also it'll let you know which trees you've treated and which ones you haven't if you're doing a lot of them. By putting the chemical in the inner bark or flow them, it'll transport downward, and it'll help prevent the sprouting and kill the roots in the tree. Since the roots of similar adjacent trees may grow together, any stump treatments are made after considering the effects of the chemical on the nearby trees that remain. Windbreaks of larger diameter trees of ash, basswood, cottonwood, or elm may have marketable quantities of saw timber, which could be logged and sold to help defray some of the removal costs. In North Dakota, many of the windbreaks in the eastern part of the state were cut to salvage usable American elm logs as Dutch elm disease swept through the area during the late 70s and early 1980s. Trees which have no salvageable lumber may be burned. Burning is difficult when soil is pushed into the pile. Normally when you're trying to burn up a green trees or freshly removed windbreak, the difficulty is getting the fine fuels and a proper fuel mix so that you get a good burn. On this one you can see where we've kind of pushed some of the windbreak together. We have a lot of large diameter stock that's, in this particular case, it's a live one yet, so it's still green.  Now if we were to come in here and try to get some fuel underneath it, even if we have fine fuels, paper, and some diesel fuel, we still don't have much that's going to dry this out and get it burning. That's why you'll see a lot of these windbreaks around the state that have big round bales that are trying to put in there to get them burning. So what we'll demonstrate then is how to start the pile as you start pushing the material on it and then that gives you a cleaner burn and it should, in theory, be burned up by the end of the evening. Proper placement of trees on the burned pile while the pile is burning allows for a complete burn. Repositioning of material as needed is possible as long as a machine is in the area. The machine does not have to be a feller buncher. A farm tractor, loader, or hydraulic saw work just fine. Before starting a fire, consider any local and state laws that require permits or other approval prior to burning. In most cases, final cleanup can be completed in the evening or the next morning with hand tools if proper attention has been paid to the stacking of the pile and monitoring of the burn. Yet after stumps are removed and burned, there is still a root ball of baked dirt and unburned wood that is often buried. Row removal and replacement is not the only type of renovation that could help the wind breaks of the Great Plains. Sod release, compassing, thinning, pruning, supplemental planting, and root pruning are all effective renovation methods depending on the need of a particular windbreak. Many of these methods are labor intensive, maybe hard work, and often require training to be done effectively. A practice with much potential to improve windbreak conditions and effectiveness is sod release. Introduced grasses, such as bro, have caused many otherwise healthy windbreaks to essentially stop growing. In drier areas, dense sod has added stress and help to kill windbreak trees. A directed application of glyphosate can effectively release some windbreaks from the negative effects of sod competition. However, some species of trees do not respond to sod release if they have been suppressed for too many years. Copacing is the removal of top growth on woody plants and allowing them to regrow from the base. While copacing is very effective for some woody species, certain trees and shrubs do not respond well to the practice. It is especially effective on shrubs that have become tall with thin foliage or have broken down. Care must be taken to not damage the root or root collar. Better regeneration is often seen when copacing is done during the dormant season rather than the growing season. Pole pruning chainsaws can improve efficiency when copacing shrub rows by providing quicker access to the base of plants than standard chainsaws. When completed, a properly-copiced shrub is invigorated and regains the ability to effectively slow the wind and trap snow. Thinning reduces competition between trees. This may be done to manage natural regeneration of trees within windbreaks or to remove trees in windbreaks which were originally planted with the trees too close together. Over-mature trees or those subject to extreme competition may not respond to thinning. We often do not think of selection as a process of windbreak renovation. Before accepting natural regeneration management as a windbreak renovation practice, one must understand that a windbreak can be effective even if the trees are not growing in perfectly straight rows. And one must also understand that the entire renovation does not need to be done in one grueling renovation effort. If done in stages, these windbreaks are fun to renovate. The protection of the windbreak is never lost and the work can be spread over a period of years as time and energy permit. In some cases, a windbreak row may be removed in a multi-row windbreak and no trees are planted, allowing the remaining rows to flourish. Sometimes only certain branches need to be removed from trees. Pruning has been done in field windbreaks to allow better snow distribution on crop fields and to remove broken limbs after storms. In multiple-row windbreaks, it is done to allow better light penetration on the edges or to allow better access so that sods and other weeds can be controlled. Although pruning can be extremely beneficial for a tree, it can also be very damaging if done improperly. Before making a pruning cut, it's important to identify the branch bark ridge and the branch collar. The branch bark ridge is an area of raised bark right between the branch and the stem at which it is attached. And also the branch collar, which is an area which is swollen right at the base of the branch. It's important to do the cut right on the outside of the branch bark ridge down to the outside, just the outside of this branch collar. So we'll start a cut. Pole pruners should be used to remove dead limbs high up in trees. It is labor-intensive, but for certain situations, it is the most appropriate method. Planting can be done without removing old stumps. Here, a modified weed badger drills the holes and trees are planted by hand. Planting only takes 10 to 15 seconds per tree, and not many difficulties are experienced while drilling the holes, even though most holes are within three feet of the old stumps. One of the easiest and most commonly used methods is supplemental planting. It is nothing more than planting a portion of a new windbreak next to an existing windbreak. Root pruning has been around for years. Even though a windbreak will generally increase crop yield over an entire field, crop plants adjacent to the windbreak may be smaller than plants farther away from the windbreak. Tree roots near the surface are cut to reduce competition with adjacent crops for moisture or nutrients. Some woody species, such as poplars, may produce new trees where the roots were cut. Root pruning should be avoided with these types of species. Some areas of the Great Plains use this method to establish additional tree rows next to species that are aggressive root sprouters. Perhaps root pruning could be an effective way to reduce stress on new tree rows planted adjacent to existing tree rows. Root pruning will stress and possibly kill mature trees if they were not root pruned during establishment. But this practice may be an option if the mature trees will be removed after the new trees are established. There are many different types of windbreaks with at least as many different options for renovating them. Here, a portion of a single row field windbreak is removed for replanting. Windbreaks are definitely important, but I don't know if we'd ever put any single rows back in again.   I think at least seven to 15 rows is what we probably put back in. This multi-row windbreak is being renovated to improve wildlife habitat. For the most part, it's dying out, but it still provides good wildlife habitat for the deer, for deer and some waterfall nesting. If we did the traditional method, just come in and bulldoze the whole thing out and replant it, you'd lose all your habitats. So we thought that something with underplanting where you could just keep the existing trees, plant some new trees underneath, and maybe rehabilitate the shelter about that way with several purposes. Tree shelters are used to improve height growth of some tree species, protect trees from deer, and protect trees when herbicides are applied to reduce competition from sod and other weeds. When you put them on, all we do is we come down, we space up, put the stake in the ground next to the tree, on the northwest side, the failing wind side. Usually loosen the ties up. It's got two ties on it, just to kind of gently feed the tree through. And it's easier to put them on right away when the tree doesn't leaf out yet. Just be careful if you don't get leaves caught in there. Kind of dig the tube down in the ground, because if you don't have it down in the ground, you get a chimney effect and it dries out the tree. So you want to make sure the tube's tightened to the ground, almost tighten your ties up, and put your net on top, and you're ready to go. We do find that you have to check them a couple of times a year because the deer do come rub on them and you get winds and things that lock them over. So usually once in the spring and once in the fall, we come through and check the tube straight and some up, and have to replace some stakes, things like that. So it's a little bit of maintenance, but not too bad. The Great Plains has a rich legacy of developing and nurturing an agroforestry resource. That resource is hurting. Right down this row right here, a lot of these elm trees have been dying from what really does appear to be Dutch Elm disease. The time is ripe for those charged with resource management to develop and promote effective windbreak renovation techniques for landowners and conservation partners to meet the challenges of restoring our prairie forests. Our forefathers used hard work and perseverance to establish a rich resource of windbreaks across the plains, with our current resources of capital, equipment, communications, scientific windbreak knowledge, and generations of accumulated experience. Can we sit back and let this legacy wither away?


Backroads is made possible by the Minnesota Arts and Cultural Heritage Fund, with money by the vote of the people November 4th, 2008. Well, I'm D. Fafaro. I'm from Pinewood, Minnesota. My name is Lenore Sims, and I play fiddle and mandolin and viola for the group. And I'm April Larson. I'm from just south of Bagley, and I work in Bemidji. And we met many years ago at a music convention when we were all music teachers. And our band, I would say, it's about trying different styles and different kinds of music. And we've been playing together for 20, well, if I, yeah, 22 years. Well, we started out because we were all interested in Celtic music. And we met at the Music Educators Association, meeting down in the cities. And we just all kind of started talking about, oh, well, we like this kind of, you know, we really like Celtic music. Oh, I do, too, you know. And April said, oh, I've got lots of instruments. And we just started, we just started meeting weekly. Then from there, we've kind of expanded into other genres, too. So we do some Cajun, some bluegrass. We write some of our own stuff. We have done classical. We don't do a lot of it, but we like it. We do world folk all time, and just some contemporary things from other cultures. This next song, it's a love song that I wrote for my husband. He loves, no he hates country music. So I wrote him a country song. It actually did start out as a joke, and it just kept going. Rain drops and sun make a ring together. They help life to grow. We are influenced by other music. I just went on a trip to the Baltic Sea, and we were influenced by some Norwegian fiddler. Anytime we hear something, it's like, okay, I'm going to bring this to Kaylee. We just really listen to a lot of music, go to a lot of performances, hang out with some people who also really like music. And whenever we travel, we get to hear even more music like Tango in Argentina. And YouTube is a really big bonus because it gives us access to musicians in other countries who have been doing this for a while, and it's very exciting to find things that we like. Origins of different words. And he would often quote Shakespeare, quote in Shakespeare that he loved. And so I wrote this little waltz called Travel. We play more just to get together because we just like making music. And so we get together about once a week and just play music. We do travel, but it's not like the top thing on our agenda. We kind of like to just make music together, and if we see some place that looks like it would be a fun place to play, we go and play there. This next song is another song that I wrote for all the other little loves in my life. And all the little children that people make absolute fools of themselves when they try to make them smile. So this song is called When You Smile. When my world is falling down You keep me from losing ground A better world is what I am You can chase the grey away Make everything on earth okay It's with you I long to stay Smile, cause when you smile The world seems brighter when you Smile, my heart feels lighter with each smile Our bond grows tighter when you smile I would be a fool Break every little rule for you I just want to let you know That there's no place that I Will keep searching high and low for you Cause when you smile The world seems brighter when you Smile, my heart feels lighter with each smile Our bond grows tighter when you smile Cause when you smile The world seems brighter when you Smile, my heart feels lighter with each smile Our bond grows tighter when you smile When I see your smiling face No one else could take your place It's you smile I long to stay We've all grown a lot musically.  We're just more comfortable with it. And I think what we originally got together, I think we all thought we were just doing Irish music, Celtic music, and we started out in the Celtic genre very strongly. And that has changed dramatically, I think, in the last number of years. So we do more world music. April speaks Spanish beautifully and has done some Spanish songs and French songs. So we've been able to maybe advance and try out different genres as well. And when we first got together, we didn't even know D. Sing. For a number of months, she just did percussion and then started singing. And it was like, oh wow! So we have changed over the number of years and just exploring different genres. Very fun. And April keeps getting more instruments, so that's really fun. And more songs that I want us to do. Like it's just unending and I haven't written a rap song yet and I think that would be really fun. Oh dear. You ourselves doing music in our lives, well of course music is like as important as air to us. But whether we actually go on a big stage or not, I mean for me anyway personally it's not that relevant. But if I can keep performing music and keep growing my ability to become a better musician and give better music, for me that's what it's about. We have met for 20 years now on a weekly basis. And I think this has just been an amazing journey for the three of us. Through music we have been in each other's lives in different capacities. We've been together on this journey and music has tied our three lives together in so many ways. And again I think you're right April, it's whether we're on stage or whether we're just playing in the basement of my house it's great to be together, it's great to express ourselves through music and hopefully we can share it with others as well. That's our love too. But I think it's different than going out on a stage and doing this as a job I think it takes on a different feel. It's I think more relaxing at least for me it is. Yeah it's more of a love, just a love of music and it's a good way to communicate with other people. I tend to be kind of shy and when I play music I don't have to worry about the shyness. It just you know the music says it all. And just to spread the muse around we just enjoy doing that and it can be a small venue or a large venue it doesn't really matter. And I think at the base of it too is this big curiosity like what is going to happen when we learn this song what's going to happen when we do this or when we go to a concert somewhere it's a great curiosity. This is made possible by the Minnesota Arts and Cultural Heritage Fund with money by the vote of the people November 4th 2008. If you enjoy watching Backroads Online please consider making a tax-deductible donation at lptv.org.


The Jack Benny program presented by Lucky Strike! Sixterbondi-mongli-mongli-mongli-mongli-mongli-mongli-mongli-mongli-mongli-mongli-mongli-mongli-mongli-mongli-mongli-mongli-mongli-mongli-mongli-mongli-mongli-mongli-mongli-mongli-mongli-monglili! Old Deniligan! Let that famous chant remind you, Luckies pay more! Yes, at the Tobacco auctions, Lucky Strike pays millions of dollars more than official parody prices for fine tobacco. LSMFT! LSMFT! Lucky Strike means fine tobacco! Tobacco that's smooth, mild, and mellow! But take the word of an expert, a man like Mr. C.B. Smith of Danville, Virginia, for 30 years an independent tobacco buyer. Recently, he said, I've spent my entire life in the tobacco business, and I can tell good tobacco at a glance. And year after year, I've seen the makers of Lucky Strike by fine real quality leave. I've smoked Lucky's myself for 29 years. And Mr. Smith is only one of many experts who smoke Lucky's. For a recent survey, reveals that more independent tobacco experts, auctioneers, buyers, and warehousemen, smoke Lucky Strike regularly than the next two leading brands combined. So take a tip from the experts for your own real deep down smoking enjoyment. Light up a truly finer cigarette, Lucky Strike. Remember, Lucky's pay more, millions of dollars more than official parody prices for fine tobacco. Good reason to make your next carton, Lucky Strike. The Lucky Strike program starring Jack Benny with Mary Livingston, Phil Harris, Rochester, Dennis Day, and yours truly, Don Wilson. Ladies and gentlemen, last Sunday, Jack Benny did his first broadcast of the season.  This week, he's going to try for eight. And here he is, Jack Benny. Thank you, thank you, thank you. Hello again, this is Jack Benny talking. And Don, I don't want to sound like a ham, but I think it was awful that on our opening program last week, I was only on for four minutes. Well, Jack, you may be interested to know that letters have been pouring in, commenting on your brief appearance. Really, Don? Now, what do they say? Life can be beautiful. Don, Don, I suppose you thought that was pretty funny. Oh, yes, I did, Jack. As a matter of fact, I made that up myself.  Yeah. Don, excuse me a minute. Hello. Is this the unemployment insurance office? It is. Well, you can start making out a thin check for a fat boy. Goodbye. And now, folks, for the remainder of this program, we will struggle along without the services of Mr. America, North, South and Central. So getting on with the show. Wait a minute, Jackson. Hold on a minute. You can't do that to Don Z. Do I hear a voice from NBC? Phil, did you say something? Certainly I did. What are you mad at Don for? You don't have to get sore just because somebody else gets a laugh. We've all got to make a buck, you know. Well, Phil, well, we got to look out for our future. I ain't worried about myself or Mary Dallas, but what about the rest of the game? Phil.   Phil, as long as the rail is pure brass and they don't run out of olives, you're happy. Now, Don. Yes, Jack. Believe me, Don.  It's just that I think it's time that the star is shown a little more respect by his subordinates. Subordinates? Yes, that's what you are.  Now let's get out. Hello, Jack. Hello, Phil. Hiya, sub. Sub? Yeah, Livy. Ain't you heard?  Subordinate? To whom? To the little man who we only needed for four minutes last week. That's to whom. Now, kids, I'm sorry I started the whole thing, so let's forget it. Oh, Don. Oh, excuse me a minute, Jack. I'd like to say something to Mary. To me, Don? Yes. I want to congratulate you on being selected as one of the 10 best dressed women in America. Oh, yes. Congratulations. Boys.  Well, I certainly was, Don. I thought it was quite an honor. Hey, Liv, let me get this straight. You were picked as one of the best dressed women in America. Uh-huh. On what Jackson pays you? How can you afford to dress like that? My mother used to be a cigarette girl at Cyril's. She was not. She was, too. She was known as No-Change Livingston. Oh, well, I apologize, Mary. Hey, Livy, I just thought of something. You know why Gypsy Rose Lee didn't enter the best dressed women contest? Now, why? Because she couldn't bear to lose. Oh, Harris, you may only be a subordinate, but that joke alone will run for four minutes. It will if they don't open the window. Now, look, kids. Kids, tonight we have a very important sketch to do, so let's get on with it. Tonight we're going to present our version of that famous Metro-Gowan mayor picture, Edward, my son. Now, in this sketch... Pardon me. Come in. Hello, Mr. Benny. Oh, Mel Blank. Hello, Mel. What can I do for you? I just dropped in to see if you had a part for me on your program today. Oh, I'm sorry, Mel. I don't need any extra actors today. Oh, but I can make the listeners think you got a lot of stars on a program. I can imitate Humphrey Bogart, Clark Gable, Edward G. Robinson, and Al Jolson. Mel, believe me. Believe me, I don't need an extra actor today. I'm sorry. Okay, but gee, I don't know what to tell my wife and five daughters. You have five daughters? I also imitate Eddie Cantor. Oh, get out of here. Now, let's see. Where was I? Oh, yes. In our sketch, I'll do the part that was played by Spencer Tracy. And Deborah Carr's part will be played by you, Mary. Now, wait a minute, Jack. Why don't you give Mel Blank a job? He won't charge you much. Mary, look.  Look, I filled that already. Now, Dennis, Dennis will play the part of Edward, my son. And Don... Jack, before we go any further, don't you think we ought to do the commercial? Oh, yes, you're right, Don. L.S.M.F.T. Now, let's get on with the show. Wait a minute. You can't say L.S.M.F.T. and call that a commercial. I've got the sportsmen quartet here, and they're all prepared. They've prepared a wonderful song, April Showers. April Showers in September? Don, that's not appropriate. That doesn't fit our program at all. Ah, Jack, don't let that bother you. The boys can give you anything you want on the spur of the moment. They can ad-lib. What would you like? Well, we should have something to fit in with our show. Like tonight, for instance, we're going to do Edward, my son. Edward, my son? Well, that's simple. Come on, fellas. Give him something on Edward, my son. Charlie, my boy. Oh, Charlie, my boy. He'll thrill you. Charlie, my boy. The shivers of joy. Boy, that song is too old. He is the kind of sort of Charlie, my boy. Look, I don't want that. I want Edward, my son. Edward, my son. Edward, my son. Oh, Edward, my son. We'll change it, arrange it, and here's how it's done. Instead of Charlie, Charlie, Charlie, my boy. We'll make it Edward, Edward, Edward, my son. We want to please you. It's all in fun. Boy, what about the commercial? Edward, my son. Oh, the commercial. Lucky's pay more. Yes, Lucky's pay more. So try them. Just buy them at your favorite store. You'll find that Lucky Strike's a great cigarette. It's made of fine tobacco best you can get. You'll know that Lucky's pay more. Yes, Lucky's pay more. So round and so firm and so free on the drawer. Drawer.  You said it. Just try a Lucky Strike. You'll say that's the one. Charlie, my boy, or Edward, my son. Who? Edward, Edward, my son. Well, that was simply wonderful. Amazing. I mean, what other quartet could have taken a song like Charlie, my boy and switched it completely to Edward, my son? Yeah. Now get them out of here, will you? Okay, you better sit down, boys. Hmm. And they can cut that out too.  Now come on, kids, let's get out. Excuse me. Come in. Yes? Telegram for Jack Belly. Right here, boy. Boy? Jack, that's Rochester. Why is he wearing a Western Union outfit? Never mind, Mary. I'll take the Telegram, boy. Here you are, sir. You can go now. Wait a minute, Jack. So that's why you wouldn't hire a male blank for the messenger, boy. You made Rochester do it. Rochester, you can go. If that ain't the silliest thing I ever heard making Rochester an actor. I wouldn't laugh so much if I were you, Mr. Harris. What do you mean? As soon as I learn all the lyrics, so that's what I like about the South, you're a one-show man. Rochester, go already. Goodbye. Oh, say bye. What is it? After I drive you home from the studio, can I borrow your car and have the rest of the evening off with my new girlfriend, Susie? Well, I guess so, Rochester, but be careful this time. The last time you drove up to Mulholland Drive with Susie, the car broke down. You were stuck up there for six hours. Yeah! And now, kids, getting back to our sketch, Dennis will play the part of my son. Where is that kid? Dennis! Oh, here I am. Come on out here. I can't. Why not? Before the show, I was playing poker with the musicians and I lost my pants.  Dennis, why do you make up things like playing poker with musicians and losing your pants? You're wearing pants. I know, but they're not mine. Sammy the drummer lent them to me. So?  How come his pants are so tight on you? The piano player lost two. He's in here with me. I can't understand what makes you act so silly. Well, that's not my fault. My mother dropped me on my head when she was burping me. Oh, when you were a baby? No, last night. Now, look, Dennis, don't say another word, will you? Do it for me. Just sing your song. That's all... Hold it a minute, kid. Come in. Yes? Special delivery letter for Jack Benny. I'll take it, mailman. Thanks. Now, Dennis, I'll read this letter and you can sing your... Dennis, why do you look so puzzled? That mailman was dressed in a Western Union outfit. I know, never mind. And he looks exactly like Rochester. I know, Dennis. He talked like Rochester, too. I know, I know. Gee, that Mel Blank can imitate anybody. Go ahead and sing your song, will you? Very good, Dennis. Yeah, that was swell. Mel, haven't you gone home yet? No, I'm waiting around to see if you've got a part for me in your play. I told you I have nothing. All the parts have been cast. Now, go away and leave me alone. Gee, I don't know what to tell my wife and four daughters. I thought you had five daughters. I played poker with the musicians. Well, all right, Mel. Look, Mel Blank, look, there's a small bit in my sketch. You can play the part of Harry Simpkins, my business partner. Now, go ahead, Don. Introduce the play. Okay. Ladies and gentlemen, our version of Metro Golden Mare's sensational film success based on that great English play, Edward My Son. He's only a year old and he has a full set of teeth. So that's where they are. I've been looking all over for him. How does Daddy's baby feel on his first birthday? The gloop gloop? Yes, Deborah, but I wish his head didn't come to such a sharp point. Well, at least it saves us a price of a babysitter. When we go out at night, we just stick him in the wall. Quite ingenious, rather. The gloop gloop gloop. Deborah, why is the baby's diaper so tight? There's a piano player in there with him. Well, that must be Harry Simpkins, my new partner I spoke to you about. Isn't he the man who was sentenced to hang for murder and was pardoned three minutes after the trap was sprung? Yes, but don't say anything about his long neck. He's sensitive. Oh, I shunt. You shunt? And don't mention the 11 prison terms he served for robbery. Come in, don't stand there in the fog. Hello, Spencer. Hello. This is my wife, Deborah. Hello, Harry. So nice of you to come over for Edward's birthday. I wanted to get a bottle of champagne to celebrate, but the liquor stall was open. That's Harley Cricket. Here's, Harry and I made lots of money and we were able to pass most of it. Then one day I tipped off Scotland Yard about Harry and they came and took him to jail. I know I was... That was made Sir Spencer Bold. There, Deborah, five candles. Isn't that a beautiful birthday cake? Yeah, but it's five years old. We're rich now. Can't we afford a new cake? No, we can't. Why can't we? Because we can't. That's why we can't. Why spawn money foolishly? I'm saving every penny I make for Edward. By the way, Deborah, where is the little blighter? There he is in his crib. He shouldn't be in his crib. Today is his fifth birthday. Come to daddy, Edward. Goochie, goochie, gooo. Goochie, goochie, gooo. Say daddy, Edward. Say daddy. Uh, gloop. He said daddy. He said daddy. Now say daddy again. Uh, gloop. There. There, he said it again. He said it again. By the time he's ten, he'll be talking. Not unless you buy him Edgar Bergen. Candles on your cake. Put on your fingers, Edward. It'll be easier. Eleven, twelve.  Keep counting. I can't. That's all the fingers I've got. Well, take off your shoes. You're loaded down there. This is Edward's fourteenth birthday. Don't you think it's time we sent him to school? Well, I'm not sure. Perhaps we should consult his nurse. I'll call her and see what she thinks. Oh, Constance. Constance. Here I am, your lordship. Uh, Spencer. What is it, Deborah? We're so rich now. Don't you think we get the nurse a white dress instead of the Western Union outfit? Later. Now, Constance, I wanted to ask you if you think Edward is ready to start school. Well, I don't know, your lordship. You see, he can't take care of himself.  Which reminds me, Constance, is it true that last night when you were giving Edward a bath, you held his head under the water ten times? Yeah. You may... I know, Jack, I made a mistake. Yeah. You may go, Constance. Better luck next time. Now, Edward, we're thinking of putting you in school. Well, if I go to school, can I take my frog along? Yes, yes, you can take your frog. The next day, we went to school. We took the entrance examinations. Edward didn't pass, but the frog did.  With Edward in school, my incentive to work was greater than ever. I made millions in Piccadilly at nine. That isn't quite the same. I just said that. I thought I heard it. You'll hear it again at 9.30. But, Deborah, you can't imagine how much I've missed my boy since he's been away. Sometimes I... Oh, there's someone at the door. I'll get it. Wonder who it is. It's me, Edward, my son. Edward, it's you.  The apple of my mind. The pride of my life. The one I've dedicated my whole existence to. I've worked, struggled, and fought my way to the top so that you can have the better things of life. And now, now you've come back to me. And you've come back... Make yourself comfortable.  Lord Boll? Yes? Are you the father of Edward, my son? Yes, I am. It's my unpleasant duty to inform you that you're... Never mind that. We're not quite clear on the details, sir. All we know is that Edward bailed out. Bailed out? What happened to his plane? Oh, he wasn't in a plane. He was in a submarine. I understand. You do? Quite. So my son Edward was drowned. Yes, but we saved the piano player. Huckies pay more. Yes, at the tobacco auctions, luckies pay millions of dollars more than official parity prices for fine tobacco. Let's imagine we're at a tobacco auction. Look, there's aisle upon aisle, lined with baskets of tobacco ready for sale. And now at this moment, the bidding begins. The chant of the auctioneer rings to the rafters. And as the basket of particularly fine, light mellow leaf comes up for sale, the price climbs higher and higher. And now at the peak bid... It's all American. And another basket of fine tobacco goes to the makers of Lucky Strike. Yes, luckies pay more. Millions of dollars more than official parity prices for fine tobacco. LS, MFT. LS, MFT. Lucky Strike means fine tobacco. Fine tobacco that's light and naturally mild, that gives you more, far more, real, deep down smoking enjoyment. So light up a lucky. Prove to yourself how much finer, milder, more enjoyable luckies really are. You'll agree in all the world there's no finer cigarette than Lucky Strike. Ladies and gentlemen, I hope you'll tune in again next Sunday as we're going to have a special program with special guests, including Amos and Andy, Red Skelton and Edgar Birkin. And let me see what else. Oh, Gloop, Gloop. Huh? Tell him, tell him. Oh, yes. Don't forget to listen to Edward my son in a day in the life of Dennis Day. Good night. This is CVS, the Columbia Blockcasting Company.


Which one of these coffees gives you the biggest caffeine buzz? Well, it's not this one. As the cheapest on the list, you might get what you pay for at only about 9 mg of caffeine per ounce. America might run on it, but at only 15 mg of caffeine, we could run a little faster. This is the best bang for your buck at 17.5 mg, meaning you could get a 280 mg caffeine boost in their regular cup for less than $2. Starbucks will have to settle for number two, at least caffeine-wise, at about 19 mg per ounce. And the number one most caffeinated coffee on our list is? It has almost 21 mg of caffeine per ounce, or 416 in their largest cup.


Good morning, everybody. As Krone has explained, I'm Susan McKeever. And just to explain a little bit more about what my role is, I work in CEDR for a part of my job. And CEDR is a data analytics group. And we work with companies. I have member companies. And I'm a principal investigator with them. So I do projects with companies, whereby we help them with data analytics-based projects. And I'm also, in my day job, a lecturer in computer science. And my research area is, as Krone explained, in data analytics. OK, so what I wanted to do first was to talk about some questions that companies ask me, when we're going to go through the area of data analytics with them and what they might do. Now, given that I'm on the technical side in a researcher, they have that focus, so allow for that. But these are the sorts of common questions that I get. Now, this one is a fairly basic one, but it's no harm to go over the terminology. Because one of the things I get asked by companies getting into it, are data analytics and machine learning the same thing. And just to put a picture on it, machine learning is basically an area within data analytics. So data analytics is the wider circle. And there are other areas within data analytics, such as data cleaning. You need to have good data. Visualization is absolutely key, whereby you have good visualization to see what you're actually telling your users. Domain expertise, the ability of the domain experts to be involved, who know about the data, and various other areas. But machine learning itself, which is a major area within it, they boil down to, machine learning basically boils down to being clever algorithms that spot patterns in data. That's what machine learning is. Clever algorithms that spot patterns in data. And just to break that down just a little bit, the two big areas, and I won't say they are the only two areas because there's never only, but the two big areas in machine learning, the first one is clustering. And clustering is about finding natural groupings in your data. And if you translate that into plain English, when you go over on the left-hand side and you feed information such as your set of all your customers, your clustering algorithm will be able to find out spot patterns that naturally group them. So you might have customers that are purchasing certain types of products at certain times of the day. You might have customers with other aspects. It will find those for you. And it does that in what's called an unsupervised way. So hence the blindfold. You're not labeling data, you're simply feeding in information. And your clustering algorithms will do that for you. So good examples for that would be customer data, as I mentioned, sales data, insurance claims data. So for example, who's claiming? What are the natural groups of people who are making claims in your insurance sector? And are there ones that stick out in a particular group which may relate, for example, to fraud? The other side, that was clustering. The other side, major big area, is around the area that falls into one which is prediction classification ranking. I've put those together. And what that is about is just how we as humans learn. So if you look at the picture on the left with the baby and the baby's reaching for the star and somebody is saying to the baby, look at the star. And over the next year or two, the baby will see different forms of stars and it'll know what a star is, that it has five points on it and so on. It'll know what the features are of the star. And it's learning through examples which is exactly what we're doing with machine learning. When we learn through, instead of examples, we just call it data. But that's what it is, it's examples. And all it's doing for supervised machine learning is spotting patterns from known labeled examples and using these then to predict for the next set that aren't labels, oh yeah, I recognize that, that's a star, that's a customer about to leave our organization. Okay, now to continue on with that then, some examples, when it comes to supervised machine learning and in the area of prediction classification ranking, some practical examples are in the insurance claims sector we're about to start a project whereby we work with the company to help them do more on the side of identifying fraudulent claims. And to do that, we'll be going back through the data to see which ones were fraudulent, which weren't and building algorithms to try to decipher what are the rules and patterns that are leading to one versus the other. So when another claim comes in, we can then, or the algorithm, we'll be able to say yeah, that's this percentage likely that it's fraudulent or not. And likewise, machine learning is used heavily in the medical domain. So for example, with the x-ray and reading x-rays, if you feed in examples of problematic x-rays versus x-rays that are healthy, you can build your model. The same with customer churn and in each of your companies, you will have examples of things that you want to be able to classify or predict or rank in some sort of order. Okay, the second question is, where do we start and what data should we be analyzing? Is our own company data enough? And there's a couple of angles that I would emphasize in that. So the first one is, know thyself. In other words, know what data and what quality of data you have when you're starting. This is on the technical side. How clean is your data? How much is it missing things? Got noisy data and inaccurate and so on and so on. If you have data that is not clean, then you're not going to get clean answers out or correct answers out. No more than with the baby with the star. If you gave it a jaggedy looking star that didn't really look like a star, then it wouldn't understand what it was trying to learn. Okay, the second thing I would emphasize is domain experts. They tend to get devalued out of the machine learning conversation. Domain experts are absolutely key. The people in your company who understand what the information is about. And label data. If you're going to do supervised machine learning, then you need to have the ability to know which were the good examples, the bad examples, or whatever labels you're going to need. Okay, now the second angle on data I would emphasize is it's not just your tabular database-based data. It's also your unstructured data. And I'll explain a little bit more on that in a second, but the unstructured data is stuff that isn't naturally sitting in your data stores. And what I sometimes say to companies is imagine you had this sort of hypothetical army of ultra-smart people and they could read every customer email, listen to every customer's conversation, read every document you produced. What would you suggest they might be able to find? And that's what your algorithms are doing for you. The third thing I would mention is the data outside of your company in the outside world, and that's probably going to be big data. Big data is the term around all the sort of endless supplies when it comes out to social media and sensor-based IoT data. But in your business, external data, so for example, if you're an insurance company and you have telematics going into cars where they'll have sensors and you can monitor the driving styles, then you can save their risky driver and you can maybe monitor what happened on an accident and so on and so on. Every company is going to be hitting the angle on external data, being able to give them more information. So whether it's demographics, weather, financial markets, patient-related stuff for health and so on. Okay, the third question that comes up more frequently in the last year is do I need to be thinking about unstructured data in my business? Is it the next big thing? And what I would emphasize is for structured data, and that's the stuff that's sitting in your databases in your nice tables with nice columns and easy to work with once you've cleaned it, that's the sort of traditional data. And the features, if you like, features being the five points on the star and so on, the features of it are predefined, they're your columns. And they're the sort of traditional machine learning techniques that you're using with those. Tradition algorithms you'll have heard of, the SVMs, the naive bays, the decision trees and so on. Unstructured data, on the other hand, is the data that doesn't sort of naturally lend itself to that. It's your images, your text, your audio. There are no tidy tables. And you need to know what yours are in your business. Okay, so for example, you take the Irish Times to leap over to that. Perhaps the common data that's been put in against our articles online are unstructured data they will want to explore for some reason. But you need to first of all know what yours is. Is it customer emails, free text fields we can write in, comments, call center conversations, social media posts and so forth. And there's buried treasure in there if you can get at it. What is it that has been hidden in there amongst the text and amongst the information that's much harder to exploit? Although we have the techniques now. Now the reason I'm showing this happy man is Eigen technologies, this is just an example of many hundreds of companies that are getting money. He's turning, as he calls it, documents into data with his company. And they've just got very good funding for that. So he's discovered in a different way. He is actually opening the treasure chest. And he's just sort of discovered that they can create a company that appeals right across the sectors with unstructured data. Okay, the other last question that I have is everyone seems to talk about deep learning. Should my company be using it? And so it's deep learning everywhere. And just to put it in context, deep learning is a sub-sector within machine learning. Because it's a sub-section within deep learning. And it's sort of a category of machine learning based on neural networks, which have been around for a long time. The thing with deep learning and why deep learning is becoming so much that the thing that is making deep inroads in machine learning is it's great for unstructured data. It's great for analyzing video, text, images because it doesn't need to know what the features are. It figures that out. So it figures out the kind of columns that need to be done. It does that work for you. And there's an increasing number of frameworks out there, whether it's Google TensorFlow or cognitive toolkit from Microsoft and so on, that allow you to do kind of off-the-shelf deep learning if you're a company and you want to try that out. So it's getting much more accessible in terms of the actual application of deep learning within companies. Okay, the caveat, of course, is you need a lot of data to do deep learning. You need a lot of data. Now, there are exceptions to that for those working in deep learning if you're using vector models for text and so on. You can maybe get some of those models in, but that's a whole other conversation, which I won't go into now. Okay, so with those questions in mind, what Cedar do is we develop software demonstrators. They're basically prototypes where we address common data analytics problems. And we sort of use state-of-the-art techniques to crack the nut on particular problems that our companies tell us about, our member companies. We've got about 35 to 40 demonstrators built up over the last five years. So I'm going to very briefly bring you through three of them. Okay, so the first one, sample demonstrators that we've built and built it last year, which is particularly popular, is every company is to be able to generate artificial data, whether it's because you want to do a sales demo or you want to test systems or you want to train, but you need to be able to generate artificial synthetic data, and you usually need to do it quite quickly under a deadline. So with this in mind, we built a demonstrator called Datagen, whereby if you have existing company data, such as a customer table or something like this, you can put it into Datagen. It will then quickly figure out a very clever way to figure out what all the features are, what the relationships are, what the correlations are, and to generate a synthetic copy, except that it's not the real data, which is great, because then you don't have to worry about your privacy or anonymization issues. Okay, you can of course also use it in manual mode if you want to actually handcraft the features, which is very useful too. One of our companies, the one who worked with the most on Datagen, Analytics Store, now offers synthetic data generation service via the Analytics Store, and that's one of several companies who are involved in it. Okay, now the second demonstrator I want to mention is one called Docopool, and that is a tool that focuses on analyzing text. So imagine you're a company that has many text documents. So for example, it might be emails coming in from your customers or it could be the description of a claim when a customer puts it in, but you don't really know what's in them. You would like a way to be able to explore them in some easy way. So what Docopool allows you to do is load in all the text documents and then it will automatically sort of slice up all your documents and figure out what the topics are. So if you look down on the bottom left, the topics then are the various circles and it tells you, well, this one was about asbestos claims, this group of documents were about something else, and it does that just by itself in a sense through algorithms we apply. Okay, the third demonstrator, three of three, just so you notice the last one, is one that we've just released and it's called deep learning for opaque data. So what we wanted to do was to enable deep learning to be accessible for companies because it's quite a sort of big deal to get into it. So to be able to do that and allow easy creation of deep learning models where you don't have to be an expert user to use it. And so we've made this very accessible demonstrator that does this and it's sort of lowering the barriers to entry of being able to try out deep learning for your data, typically your unstructured data, your opaque data, like for example, an image file or a binary file from a log is not easy to break down into features for machine learning. So that's why we're calling it opaque data. And we have all of those demonstrators and other sample seeder demonstrators are all available in the poster area later if any of you want to discuss any of them. Okay, so that was a whirlwind tour through various questions and demonstrators and I don't think you can ask me questions right now so that's just a token slide. And thank you very much for listening. Okay. Thank you, Susan. That was great.


OK. I think it's about time to start. Hi, I'm Robert Foss. I work for Collabra, as you may have guessed. And I was not named after free and open source software. It's actually my mother's last name, too. She does not write software, not at all. Either way, working for Collabra means that I work on open source for a living. I mostly do Linux graphics work, and lately that has led me to do a fair amount of Android work. And this talk is going to be about where Android meets the mainline graphic stack. We're gonna cover a bit of ground here, but before we go into the details, let's have a look at what we're actually going to talk about. We're going to talk about the Android graphic stack and what it looks like and how it compares to the mainline graphic stack and where we are now with this work and why all of this stuff actually matters. So what does the Android graphic stack look like? This is sort of roughly it. It's a slight simplification, but these are the basic layers. All the pieces that we care about are here. On top, of course, we have the apps. This is the stuff we really care about. It's the whole point. That's what our users want. And below, we have Surface Flinger, which is the glue that lets different applications draw pixels to the same screen. It organizes the layers that different applications submit to it. And this is an example of what it does. It's an ordinary Android desktop and it contains a handful of different elements. Each of the different elements is provided by a different application. So if we look on top, there's the status bar. And to the right, we have the navigation bar. And if you notice in that corner, they sort of overlap a little bit. These layers are often transparent. And the majority of what you're seeing is the background layer, on top of which all of the other layers are rendered. So these layers have a depth order and they're just stacked on top of each other. The process of rendering this is called composing layers. And it means just stacking layers on top of each other. These layers and the composition of them is communicated through the HWC2 API. Below Surface Flinger, we have the non-kernel parts of the graphics driver. For various reasons like security and convenience, the graphics drivers don't entirely live in kernel space. And at the bottom... Oh, sorry. This is also where OpenGL and Vulkan and memory allocators and stuff like that is implemented. And in the case of Android, it's also where the HWC2 API is implemented. And at the bottom of the stack, we find the kernel, of course. So what's the HWC2 API then? It's an API that is used to communicate between Surface Flinger and the hardware drivers. It allows applications to submit layers to be composed onto the screen. Each of these layers is a buffer with some properties attached to it. It also provides some abstractions for graphical objects It's maybe not all that interesting, but it's very useful. And the whole idea of having the HWC2 product or API to begin with is to allow offloading work from the GPU onto specialized hardware. Display hardware has support for composing a handful of layers, maybe four. Four is a typical number. More is certainly possible, but it's a matter of diminishing returns. So this display hardware can compose layers faster and more energy efficiently than a GPU can. And this, of course, frees the GPU up to doing actual GPU stuff like OpenGL or playing games or whatever you like. So doing this work more efficiently means that you couldn't run your GPU faster or for longer, or your CPU faster or for longer for that matter, which really matters in the mobile space. So if that's the Android stack, what does the mainline stack look like? It sort of looks like this. This is the proprietary part that you would expect to see on an Android device. These are the drivers from Qualcomm, ARM, or whomever your GPU vendor is. And for the Android platform, these drivers implement the HWC2 API. So what's the open source equivalent of that blob then? Apart from just being a GPU driver, it also has to implement the HWC2 API, and the HWC2 API is not used for most Linux graphics stack. It's basically only used for the Chromium OS one and the Android one. So it's not what you'd see on Yorubuntu or Yoruf or whatever. The part that implements this API on the open source side is called DRM Hardware Composer, and it's a shim between drivers and surface flinger. All it really does is to implement the HWC2 API on top of the mainline graphic stack. And it was developed by Google, mainly by Jean-Paul over there, and Zach Reisner. And it was initially intended to be used only for Chromium OS, which is the OS that runs on your Chromebooks. If we have a deeper look into the driver, we can see that it is implemented in a few parts. DRM being the kernel subsystem that handles graphics and display hardware. LiveDRM is the user space library that simplifies talking to the DRM subsystem. And lastly, there's Mesa, which implements OpenGL, OpenCL, and so on. But there's one more thing. There is a groundlock. We're getting into the details at this point, but it's an important part. This module handles buffer allocations and associating properties to buffers. Properties like color format, buffer sizes, for example. There are a few implementations of groundlock, and they're slightly different. There's GBM groundlock, which is fairly commonly used, which was written by Rob Herring. There's DRM groundlock and Mini-GBM, Mini-GBM being used in Chromium OS, I believe. So, hopefully, this gives you an idea of where DRM hardware composer fits in, but what does it actually do? It receives layers through the HWC2 API, and each layer contains a buffer and some properties like opposition, cropping information, damage information, damage being parts of a layer that has changed since the last frame, which is useful to have if you want to optimize your display stack, which just lets you do less work. Basically, if there is no damage, you can just use the old stuff. That's good. So, when you have all these layers, you'll have to decide how to send them to the hardware, and display hardware is only able to support like four layers, basically, some more, some less, and adding more as a matter of diminishing returns. So, what happens when we have more layers than the hardware supports? This is fairly common, and what we basically have to do is squash some layers together until we reach the number of layers that is supported by the hardware. This is done through OpenGL or Vulkan, hopefully Vulkan soon. And choosing which layers and how to squash them leaves you some room for optimizations. Like, maybe you'll want to squash the layers that are very small, or, yeah, so that you'll be able to do the least amount of work possible on the less energy-efficient implementation of compositing. So, lastly, when we've organized all this stuff, we have to map each layer to a DRM plane. So, DRM planes are an abstraction for the same thing as layers, but they're just called different things. They're called planes instead of layers for whatever reason. They do the exact same thing. So, it's a buffer attached to some properties, but they're what's fed to the graphics hardware for output. For example, you likely want to put a hardware composer layer or cursor layer on the DRM cursor plane just so that it'll be handled in the most optimal way possible. Hardware oftentimes has fast pads for certain cases, and having a cursor plane is pretty common. So, having covered what this stuff is, let's have a look at what the current status is. Why did this happen now? Well, there are a few reasons. DRM hardware composer has been around for a while, and about a year ago, no, almost two years ago, the revision was bumped from one to two, and revision two requires synchronization support, which means that you can sort of wait for operations to be carried out on a buffer, and when they're done, you can just be notified that the buffer is ready for you to use now, which simplifies stuff overall. And in order to do this, implement hardware composer two, we needed buffer synchronization support, which was originally implemented in Android kernels, and then later last year, moved to Mainline Linux, and that work was carried out by Gustavo Paduan, a colleague of mine, and this is now supported by some, not all, GPU drivers. And the second part as to why now is the ADF, the atomic display framework. Hardware composer two requires synchronization, and synchronization support is only provided by one of the Linux kernel display frameworks, so KMS does not support it, but ADF does, and this is basically what all of the modern GPU drivers use, so it's not very uncommon. So does this stuff even work? And the answer is yes, on a few platforms. For example, the IMX6, which is a very common platform in the embedded space. It uses a Vivante GPU. There are a few flavors of these GPUs, but the GC3000 has full Android support, and it's been developed, or the open source driver has been developed by Christian Jeminer, Lukas Stack, and Vladimir van der Laan. So now there exists a high quality driver for Vante GPUs, and it's called Etnaviv, and if you're curious about it, you should try running it. It works really well, and if it doesn't, Christian, Lukas, or Vladimir are pretty great to talk to, so just file your complaints with them. There's also the DB410C. This is a platform supplied by Qualcomm. It's a cell phone platform that has been repurposed. It has the Android 306 GPU, which runs the Fridrino drivers, which were written by the driver wizard Rob Clark. There's also the DB820C, which is another more modern platform supplied by Qualcomm. It is under active development, and support is being mainline for it, but it's maybe not there fully yet. It has an Adreno 560 GPU, and that is also supported by the Fridrino drivers. And lastly, there's the high-key 960. It's based on the Kirin 960 SoC, which has a Mali G71 GPU. The Mali G71 GPU does not have any functional open source drivers for it yet. There's an effort to write these drivers, but it's still the early days. The G71 hardware is internally called Bifrost by ARM, and there's a project called Bi-Openly that is intending to open source these drivers for that specific architecture. However, there not being any open source drivers does not prevent us from using DRM hardware composer, so there's an effort towards bringing a platform up and running based on that. So this is the new section of the talk. Previously, the DRM hardware composer project came about from the Chromium OS project needing it, and because of this, DRM hardware composer was hosted on the Chromium OS Garret, which was good, but maybe not ideal, but with the help of Sean Paul, the project has now been moved to free-desktop.org, and development is now done on the DRI Devil mailing list, which is how other Linux graphics projects are developed. So thanks to Google and Sean Paul, but also free-desktop.org for hosting us. So this is the last point of the talk. Why does any of this matter? We use open source software not because it feels good, but because it is better, and a very specific point as to it being better is the long-term support. If you deliver a product, some products like embedded devices, for example, they require serious long-term support. These devices aren't replaced every year or every fifth year, so hardware support has to be available for a very long time. 20 years of support is not unusual, unreasonable for some applications. It really does depend on your field. And with open source drivers, support can be provided by anyone. So when you develop one of these serious embedded product, you yourself will be obligated to support the products for a long time, which means that you have to make sure that you have the ability to fix issues for a long time. And if you're using a proprietary driver, your only option when it comes to support is going to the vendor and asking for it. And maybe they will say yes, but they will surely charge you for it. Additionally, long-term support runs the risk of a vendor disappearing, or ending their support for a product for any number of reasons, like promoting a newer product or going out of business. Many things can happen, especially in such a long time span. We also want to push the industry forward towards open source. I say we, and I think it's true for all of us in the open source community, both for personal reasons, like it being neat and giving us warm, fuzzy feelings, but also for professional reasons. It gives us more options and enables us to do more.  You won't have to do as much testing. Your Linux driver will be your Android driver. And additionally, you'll be able to leverage the common code that is already maintained and high quality. Like Mason and Kernel DRM give you a lot of these things for free. The atomic display framework from DRM gives you a really good base to build your driver upon. And Mason gives you much of what you'll need for Vulkan or OpenGL support. Mason and DRM are also very well tested. Many drivers are already based on both and have shipped for many years. So lots of even the rarest edge cases have been found and fixed. Optimizations that are applied to other drivers may also apply to yours.  It might be in the common code. Or in some cases, you'll be able to do very similar things to what the other drivers are doing and reap the benefits without having to do all that much work. Just a smaller amount of work. Yeah, thank you. That's it. Does anyone have any questions? So the evaluation boards you presented, do all of them now run Android on a mainline kernel? Is that? The DB410C does. The IMX6 does. The 820 does not run mainline at all yet, as far as I understand it. It's getting there. And the high key 960, I'm a lot less sure about its status, honestly. I know that it's being actively worked on, so that's certainly the goal. Okay, so definitely yes for the two mature ones. Yes. And the other ones are up and coming. Yes. Great, thank you. You cannot buy the 820C yet, but that's coming. Hi, is there an overhead in going essentially through two graphic stacks, both in time and in memory usage? So none of these layers are copied, like only references are moved. So I'm sure there's some amount of overhead, but I would call it negligible. It's not like copying entire buffers around. That's all handled by Growlock. That provides us that for free. And a follow-up question. Imagination technologies, GPUs, any progress on that that you know of? Not that I know of. As far as I know, no one dares to try. Yeah, I thought that would be the answer. Any other questions? With the recent release of Android 8, the driver model has changed through the stuff that's arranged and announced in project trouble. Does that affect this picture at all? It does affect this solution a little bit. Most of this project trouble stuff is over what we're doing. So the layer split that it sort of introduces is not in the middle of what we're doing. It's all like on top of us. So it's not that bad. It's no worse than moving between any other Android versions. And also, I might add that I think Android 8 is up and running. Rob Herring, I think, has it up and running. Would it work with closed-source OpenGL drivers? Yes. There's nothing preventing you from using it with closed-source drivers. Then I think it might work with Renaissance hardware. Okay. Yeah, I'd certainly be open to discussing that further. It's very interesting. I have a question. Why did the Android community run away from X and Wayland? I can't answer that. I think they had a quicker pace. Basically, they wanted to ship stuff immediately. So this hardware compositing stuff is a hugely power-saving feature, and it's something that you really want on Android. And I don't think anyone was prepared to wait for Wayland to be prepared and ready and have good hardware composition support. All right. No more questions? Oh, one there. What are the chances of us ever seeing a mobile phone with an open stack? I think the Purism phone that just sort of cleared crowdfunding will use something. I'm entirely sure that they'll use DRM Hardware Composer, but I would assume so. John? So we did ship DRM Hardware Composer on the Pixel C tablet, but it's using NVIDIA's vendor driver for GL. So half. Yeah, I think that's it for me. Thanks, everyone.


Getting more views. Want more views? Want more subscribers? Then brainwash yourself to success. How come my video thumbnails are captivating? Why does everyone wanna click them? How come my video thumbnails are captivating? Why does everyone wanna click them?  Why does everyone find them helpful?  Why does everyone find them helpful? How come my videos are unique and interesting? Why does it stand out from the millions out there? How come my videos are unique and interesting? Why does it stand out from the millions out there? How come I tag my videos effectively? Why are people easily able to search for them? How come I tag my videos effectively? Why are people easily able to search for them? How come my channel trailer is very catchy? Why do people subscribe after seeing it? How come my channel trailer is very catchy? Why do people subscribe after seeing it? How come my outros at the end are effective? Why do people interact with them? my videos? Why do they feel a sense of welcome and community? How come people comment on my videos? Why do they feel a sense of welcome and community? How come I upload great videos in a consistent basis? Why do people look forward towards each of my uploads? How come I upload great videos in a consistent basis? Why do people look forward towards each of my uploads? How come my channel header is attractive and displays my message? Why do viewers get a good idea of my channel based on it? How come my channel header is attractive and displays my message? Why do viewers get a good idea of my channel based on it? How come my playlists are well organized with great titles? Why can people sort through my videos easily? How come my playlists are well organized with great titles? Why can people sort through my videos easily? How come I understand and deliver the needs of my target audience? Why are my audience loyal to me? How come I understand and deliver the needs of my target audience? Why are my audience loyal to me? How come my intros peak the viewers? Why do they become eager to see more? How come my intros peak the viewers? Why do they become eager to see more? How come I can keep my audience's attention? Why do they stay until the end of my videos? How come I can keep my audience's attention? Why do they stay until the end of my videos? How come I am able to promote my channel effectively and legally? Why am I so great at promoting? How come I am able to promote my channel effectively and legally? Why am I so great at promoting? How come I am constantly trying to find new ways to improve my channel? Why am I so determined to get to the top? How come I am constantly trying to find new ways to improve my channel? Why am I so determined to get to the top? How come I enjoy being brainwashed? Why do I love this channel? How come I enjoy being brainwashed? Why do I love this channel?