Live from Boston, Massachusetts, it's theCUBE, covering Spark Summit East 2017, brought to you by Databricks. Now, here are your hosts, Dave Vellante and George Gilbert. Welcome back to Boston, everybody. This is theCUBE, and we're here live at Spark Summit East, hashtag Spark Summit. Zia Ma is here. She's the vice president of Big Data at Intel. Zia, thanks for coming on theCUBE. Thanks for having me. You're welcome. Software is our topic, software at Intel. People don't necessarily associate Intel always with software, but what's the story there? So actually, there are many things that we do for software. Since I managed the Big Data Engineering Organization, so I'll just say a little bit more about what do we do for Big Data. So, Intel do all the processors, all the hardware, but when our customers are using the hardware, they like to get the best performance out of the Intel software, Intel hardware. So this is for the Big Data space. We optimize the Big Data solution stack, including Spark and Hadoop on top of Intel hardware, and make sure that we leverage the latest instruction set so that the customer gets the most performance out of the newest released Intel hardware. And also we collaborate very extensively with the open-source community for Big Data ecosystem advancement. So for example, we're a leading contributor to Apache Spark ecosystem. We're also a top contributor to Apache Hadoop ecosystem, and lately we're getting into the machine learning and deep learning and the AI space, especially integrating those capabilities into the Big Data ecosystem. So I have to ask a question just sort of strategically, if we go back several years, you look at, during the Unix days, you had a number of players developing hardware, microprocessors, there were risk-based systems, and remember MIPS, and of course IBM had one, and Sun, et cetera, et cetera, and some of those still live on, but very, very small portion of the market. So Intel has dominated the general-purpose market. So as Big Data then became more mainstream, was there a discussion of, okay, we have to develop specialized processors, which I know Intel can do as well, or did you say, okay, we can actually optimize through software, was that how you got here, or am I understanding that? Or? Yeah, we believe definitely software optimization, optimizing through software is one thing that we do. That's why Intel actually have, you may not know this, Intel has one of the largest software divisions that focus on the enabling and optimizing the solutions on Intel hardware. And of course we also have very aggressive product roadmap for advancing continuously our hardware products. And actually you mentioned the general-purpose computing. CPU today in the Big Data market still has more than 95% of the market. So that's still the biggest portion of the Big Data market. And we'll continue our advancement in that area. And obviously as the AI and machine learning, use cases getting added into the Big Data domain, and we are expanding our product portfolio into some other silicon products. Yeah, and of course, I mean that was kind of the big bet of, we went to bed on Intel, and I guess, I guess- You should've still do. And still do, and I guess at the time, C8 or other spinning disk man now Flash comes in, of course now Spark with memory, it's really changing the game, isn't it? What does that mean for you and the software group? Right, so what do we, actually, still we focus on the obviously at the hardware level, like Intel now is not just offering the computing capability, we also offer very powerful network capability, we offer very good memory solutions, memory hardware. Like we keep talking about this non-volatile memory technologies. So for Big Data, we're trying to leverage all those newest hardware, and we are already working with many of our customers to help them to improve their Big Data memory solution, the in-memory analytics type of capability on Intel hardware, give them the most optimal performance and the most secure result using Intel hardware. So that's definitely one thing that we continue to do, that's going to be our still a top priority, but we don't just limit our work to optimization because giving you the best experience, giving you the complete experience on Intel platform is our ultimate goal. So we work with our customers from financial services company, we work with folks from manufacturing, from transportation, and from other IoT, you know, Internet of Things segment, and to make sure that we give them the easiest Big Data and the analytics experience on Intel hardware. So when they are running those solutions, they don't have to worry too much about how to make their application work with Intel hardware and how to make it more performant with Intel hardware because that's the Intel software solution that's going to bridge the gap. We make, we do that part of the job and so that it will make our customers experience easier and more complete. You serve as the accelerant to the marketplace, so go ahead, Joanne. That's right. So the Intel's Big ML is sort of the news product as of the last month or so, open source solution. Tell us how there are other sort of deep learning frameworks that aren't fully integrated with Spark yet and where Big ML fits in, since we're at a Spark conference, how it backfills some functionality and how it really takes advantage of Intel hardware. Yeah, so yes, George, just like you said, big deal, we just open sourced a month ago. It's a deep learning framework that we organically build on top of Apache Spark and it has quite some differences from the other mainstream deep learning frameworks like a cafe, TensorFlow, Torch, and Tienaue or you name it. The reason that we decided to work on this project was, again, through our experience working with our analytics, especially big data analytics customers, as they build their AI solutions or AI modules within their analytics application, it's, they finally it's getting more and more difficult to integrate AI, to build and integrate AI capability into their existing big data and analytics ecosystem. They had to set up a different cluster and build a different set of AI capabilities using let's say one of the deep learning frameworks and then later they have to overcome a lot of challenges to, for example, moving the model and data between the two different clusters and then make sure that AI result is getting integrated into the existing analytics platform or analytics application. So that was the primary driver. How do we make our customers experience easier? Do they have to leave their existing infrastructure and build a separate AI module? And can we do something organic on top of the existing big data platform, let's say Apache Spark? Can we just do something like that so that the user can just leverage its existing infrastructure and make it a naturally integral part of the overall analytics ecosystem that they already have? So this was the primary driver and also the other benefit that we see by integrating this big DL framework naturally with a big data platform is that it enables efficient scale out and for tolerance and elasticity and dynamic resource management and those are the benefits that's naturally brought by big data platform. And today, actually just with a very short period of time and we have already tested that big DL can scale easily to tens or hundreds of nodes. So the scalability is also quite good. And another benefit with a solution like big DL, especially because it eliminates the need of setting up a separate cluster and moving the model between different hardware clusters, you save your total cost of ownership. You can just leverage your existing infrastructure. There is no need to buy additional set of hardware and build another environment just for training the model. So that's another benefit that we see. And performance wise, again, we also tested a big DL with a cafe torch and TensorFlow. So the performance of big DL on single node Xeon is orders of magnitude faster than out of box open source cafe, TensorFlow or torch. So definitely it's going to be a very promising and a useful solution. Okay, can you talk about some of the use cases that you expect to see from your partners and your customers? Actually, very good question. We already started a few engagement with some of the interested customers. The first customer is from steel industry. We're improving the accuracy for steel surface defect recognition is very important to its quality control. So we worked with this customer in the last few months and build an end to end image recognition pipeline using big DL and Spark. And the customer just through phase one work already improved its defect recognition accuracy to 90%. And they're seeing a very good yield improvement with the steel production. It used to be done by human inspection? It used to be done by human, yes. And you said, well, I'm sorry, what was the degree of improvement? 90, 90. So now the accuracy is up to 90%. And another use case and financial services actually is another use case, especially for fraud detection. So this customer that again, I'm not at the customer's request, they're very sensitive to the financial industry. They're very sensitive with releasing their name. So the customer were seeing its fraud risks were increasing tremendously with this wide range of products, services and customer interaction channels. So they implemented an end to end deep learning solution using big DL and Spark. And again, through phase one work, they are seeing the fraud detection rate improved by 40 times, four zero times through phase one work. We think there were more improvement that we can do because this is just the collaboration in the last few months and we'll continue the collaboration with this customer. And we expect more use cases from other business segments, but those are the two that's already have big DL running in production today. Well, so the first is, I mean, that's amazing. Essentially replacing the human have to interact and be much more accurate. The fraud detection is interesting because fraud detection has come a long way in the last 10 years, as you know. Used to take six months if they found fraud and now it's minutes, seconds, but there are a lot of false positives still. So do you see this technology helping address that problem? Yeah, we actually continuously improving the prediction and accuracy is one of the goals. And we expect, you know, that's why we, this is another reason why we need to bring AI and big data together because you need to train your model. You need to train your AI capabilities with more and more training data so that you get much improved training, training accuracy. Andrew, this is the biggest way of improving your training accuracy. So, and you need a huge infrastructure, a big data platform, so that you can host and well manage your training data sets. And so that it can feed into your deep learning solution or module for continuously improving your training accuracy. So, yes. This is a really key point, it seems, I'd like to unpack that a little bit. So, because when we talk to customers and application vendors, it's that training feedback loop that gets the model smarter and smarter. So if you had one cluster for training that was with another framework and then Spark was your, I guess for the rest of your analytics, how would training with feedback data work when you had two separate environments? That's one of the drivers why we are creating BigDL because we tried to port. We did not come to BigDL at the very beginning. We actually tried to port existing deep learning frameworks like a cafe and TensorFlow onto Spark. And you also probably saw some research papers. Folks, there are other teams that's out there that's also trying to port cafe, TensorFlow and other deep learning frameworks that's out there onto Spark because you have that need. You need to bring the two capabilities together. But the problem is that those systems were developed in a very, in a traditional way. You know, it's with big data not yet in the consideration when those frameworks were created, were innovated. So, but now the need of converging the two becomes more and more clear and more necessary. And that's why we said, and when we ported over, we said, gosh, this is so difficult. First, it's very challenging to integrate the two. And a second, the experience after you moved it over is awkward. You are literally using Spark as a dispatcher. The integration is not coherent. It's like they are superficially integrated. So, this is where we said, okay, we've got to do something different. We cannot just superficially integrate two systems together. Can we do something organic on top of the big data platform, on top of Apache Spark? So that the integration between the training system, between feature engineering, between data management can be more consistent, can be more integrated. So that was the, that's exactly the driver for this work. Well, that's huge. I mean, seamless integration is one of the most overused phrases in the technology business. Superficial integration is maybe a better description for a lot of those so-called seamless integrations. You're claiming here that it's seamless integration. We're out of time. Just last word on Intel and Spark Summit. What do you guys got going here? What's the vibe like? So actually tomorrow I have a keynote. I'm going to talk a little more about what we're doing with BigDL. Actually, this is one of the big things that we're doing. And of course, in order for BigDL, system like, you know, BigDL, or even other deep learning frameworks to get optimized optimum performance on Intel hardware, there is another item that we're highlighting, the MKL, Intel Optimized Math Kernel Library. It has a lot of common math routines that's optimized for Intel processor using the latest instruction set. And that's already today integrated into the BigDL platform ecosystem. So that's another thing that we're highlighting. And another thing is that those are just the software. And at the hardware level, during November, Intel's AI day, our executives from BK, Diane Bryant and Doug Fisher, they also highlight the Nirvana product portfolio that's coming out that will give you different hardware choices for AI. You can look at FPGA, Xeon 5, Xeon, and our new Nirvana based silicon like Crest Lake. And you know, those are some good silicon products that you can expect in the future. Intel, taking us to Nirvana, touching every part of the ecosystem, Intel's, nice to say, 95% share in all parts of the business. Sia, thanks very much for coming to the cubes. Thank you, thank you for having me. All right, keep it right there, everybody. George and I will be back with our next guest. This is Spark Summit, hashtag Spark Summit, we're the cube, we're right back. Since the dawn of the...


Hello, this is Hans van der Klaas, senior lecturer at IHGDALT Institute for Water Education. In this video I'll show how to add the white box tools to QGIS. So you can use over 450 tools from the processing toolbox. It also supports other software than QGIS and has a standalone tool, but in this video I'll focus on installing it in QGIS. On the website you'll find a lot of examples on how to use these tools, what are the applications, like here in precision agriculture or having shadows in the DEM. The developers are John Dincey and Anthony Francioni and they did a great job in making these tools and making them available as open source. And most of the tools are about terrain analysis, hydrology and lidar. So we're going to download the tools and you need to choose the operating system that you use. You can use Windows 64-bit, you can fill in the form to let the developers know how you're going to use it and who you are, but it's voluntary and you can click submit and download and then save the zip file and after downloading you can open the zip file and extract it to your hard drive and I extract it to C, so I'll have the folder CWBT that will contain the executables. Now there are some instructions on how to then integrate it in QGIS, here you see some more examples of the nice things you can do with this tool or with this toolbox and in future videos I'll show some more applications. So we are going to install it for QGIS, so I click here on learn more to see the instructions and the white box tools are very well documented, so it's also very useful to help you in your analysis and here it gives some instructions on how to install it. It is not available from the regular repository, so you need to copy this link and then in QGIS you need to go to settings in the plugins manager and click add, give a name to the repository, I'll use the name that was suggested and then paste here the link and the rest you keep as default and now it's loaded the plugin repository that we've just added and now you can find the white box tools for processing. Click install to install it, close the dialogue and then go to the processing toolbox and there we need to change a setting before we can use it, so the plugin basically connects QGIS to the executables but it needs to know the path, so here in these settings you can set the path and you can browse to the place where we have saved our executables, so that was on C in our case there it is and I point to the white box tools executable, click open and then okay and from now on we can use those white box tools from the processing toolbox and as you see they are categorized and if you expand them you'll see that there are many tools added now to the processing toolbox that you can use, for example for stream network analysis, for LiDAR and for sure I'll make some more videos on demonstrating how these tools can be used in practice.


Earlier this week, and maybe in the past on livestreams, we've talked about how some of the media seems to be attaching themselves to negative narratives surrounding the upcoming Tears of the Kingdom game releasing on May 12th, in fact, we've just passed the four month mark, so less than four months technically until this game gets here. But I don't want to talk about that again. Maybe that's a conversation to have if it continues to happen. I actually want to focus on the way fans are reacting, Zelda fans, Nintendo fans. There seems to be a lot of tepidness around Tears of the Kingdom, a lot of concern, a lot of worry. And in this video, we're going to go over why I think this concern and worry exist, what some of the common threads of the concerns are, and why I think for the most part not Tears of the Kingdom is going to be just fine. And more than just fine, it's going to exceed what Breath of the Wild did. First we need to talk about why there was a swelling of negativity towards Tears of the Kingdom in the first place, because this wasn't always the case. Part of it is because it's a sequel to Breath of the Wild. Breath of the Wild is widely acclaimed as one of the best video games of all time. It features on most pundits top 50 games of all time. It's usually in Zelda fans top 5, top 3, some are in the top spot. It's also the most popular Zelda game ever released at over 30 million combined sales between the Switch and the Wii U. So it's in a very popular game, it reviewed very very well, and it blew many people's minds. And even if you didn't like it as a Zelda game, you probably still thought it was an amazing experience in that of itself, just in the action adventure open world genre. For the most part, Breath of the Wild one way or another is considered to be a masterpiece. But the interesting thing is, at E3 2019, they dropped a surprise out of nowhere. It never leaked, there were no rumors about it, Tears of the Kingdom sequel announced E3 2019, and it blew many people's minds and got all of us excited. It looked really dark and people seemed to like darker stories in their games. So the red eyes, the Ganondorf, the zombie body, what is going on, and then obviously Zelda and Link sort of adventuring together. That was an exciting prospect. So it set things up where, hey, we didn't expect this, it's just announced, it probably should come soon because hey, it is a direct sequel, same engine, same graphics, same world maybe, so this should be something that comes soon. But then we didn't hear about the game for two years. Then it cropped up again at the much maligned E3 2021, which that was sort of a really bad show, but the Nintendo aspect of it was actually pretty good. They did their thing like they usually do at E3 presentations and they showed us this game again. We didn't have a title yet and they told us the title would be a spoiler for some reason, still can't figure out that one. But hey, this was just another teaser, right? It felt much like the reveal trailer. It's like here's small snippets, here's a tease, and we waited two more years for a tease. Now in this tease they did show us falling from the sky and a couple new abilities, but it was so short and with so little actual gameplay, it still kind of left us like what is this thing? What is this game? Why are we just talking about it? And they gave us a vague 2022 date. Well 2022 felt weird, that's five years after Breath of the Wild came out and this is where people started to have growing concerns because of how long it was taking to make this game. Now COVID probably played a role in that, but still five years is how long it takes to make something like Breath of the Wild. In fact that's exactly how long it took to make Breath of the Wild and yet here we are with a sequel using the same engine, the same visuals that only show on us teases. And 2022 I just, something fell off. And then as 2022 came, we didn't hear about things. The months rolled by, the February direct happened, nothing. June came around, they didn't really do anything, nothing. And then finally we get to the September direct and they drop another teaser trailer that barely shows anything for the game to clarify, even less than the prior two trailers. But they do show us the release date and the title and logo. And that was cool. But again, for the third consecutive showing of this game, we didn't really see anything, we didn't really learn anything. We have a lot of guesses, but we don't know much. We know when the game is coming out and we still don't know a ton about this game. And from the snippets we have seen, it looks a lot like the same Hyrule all over again and that is something that just made people worry, hey, this game is now coming out in 2023, that's now over six years since Breath of the Wild. What's happening? And the concerns started rising up. Now let's fast forward to the actual concerns that I've heard listed and talk about what they are and why I think there's an excellent counter to all of them. And if this rebuilds your hype for Tears of the Kingdom cool, if it doesn't rebuild your hype and you still have concerns, please share them down in the comments below. But the first concern seems to be that this has the longest game dev time ever. Now I actually think this is a positive, but it's sort of weird hearing it used as a negative because it's using the same engine, because it's using the same visual style. People think it's really strange that they took all this time. But if we remember back to all the cut ideas from Breath of the Wild, the aliens and the arm mechanic which appears that they've put in this one, and several other aspects that made it to the idea board but got cut and they said, hey, there just wasn't time for this stuff, it's really interesting to think, hey, they did have time this time for all of these crazy ideas they might have. So if anything, having this super long development cycle probably bodes well for this game being way better than Breath of the Wild ever was because they already did Breath of the Wild.  It's almost as if maybe Breath of the Wild was a beta test for Tears of the Kingdom. Let that one salivate in the brain for a little bit. Next up, another concern I see often is that this entire game originated from DLC concepts. It's just trying to be Breath of the Wild plus more and I kind of find it hard to look at that as a negative, Breath of the Wild plus more. Well, if Breath of the Wild is one of the greatest games of all time and this game is that plus more, that should mean this is a better game. I honestly don't think that this is some wild out there thing. They didn't have time for some of these crazy ideas. Breath of the Wild again needed to prove it could work. The climbing mechanics, the open world, the go anywhere, do anything, any order you want, the whole idea of telling a story mostly through memories of the past. They needed to prove all of this stuff could actually work and be accepted by gamers and it has. So now they could take that to the next level that they maybe would have done with this. But sometimes you're afraid of introducing too many new things at once. You don't want to overwhelm the player and I think the way Breath of the Wild handled things was perfect. Now it's time to do something crazy and I think we're about to get that regardless where the ideas originated from. So another common criticism that makes people wonder things is it's the same graphics, it's the same world, it's the same map. Now they've talked about how we'll be able to explore to the sky. Maybe we've seen Sky Islands and Beyond or Ann Moore and so look there could be an underground, there could be additional lands. This is all unknown stuff but I find this to be rather interesting because Breath of the Wild has aged pretty gracefully from a visual perspective. Of course it's not going to stand toe to toe with Horizon Forbidden West or something that Sony's pushing out on PlayStation 5 these days, God of War Ragnarok. But those are completely different visual styles and who knows if those visual styles are going to be still considered good 10 years from now. But Breath of the Wild still looks good. Now yeah sure it could do with some resolution bump and some frame rate smoothing but hey this is a game from 2017 made on tech from 2015 and actually originally made for the Wii U on the power PC architecture which hasn't been updated literally since the 90s. In fact Wii U is the only multi-core processor to ever exist in the history of power PC architecture. So yeah to say it was made for some pretty ancient tech would be an understatement and they did it in a way that it still holds up today. So I find this weird to be worried about the graphics or worried about the world and the map. Look they've had a lot of time, they've had more time than they did to make Breath of the Wild. They just sit on their hands during that and part of the fun and breath of the wild was using all the mechanics as you explore the world. Now imagine they've increased those mechanics 10 fold. Imagine all the crazy new things you're going to be able to do and how exciting that's going to feel. Also from a visual perspective this was made for Switch. Look maybe it'll be a new hardware, that's a different debate for a different day. But this was made for Nintendo Switch. I cannot emphasize that enough because regardless of what some naysayers out there might say, Nintendo Switch is a pretty significant architecture and spec bump from the Wii U. Breath of the Wild was a Wii U game ported to Switch. That could explain some of the frame rate issues. That could explain some of the needing to go with multiple resolutions and have dynamic resolution scaling. A lot of that can be attributed to the fact that the game was built for the ground up for a completely different platform that had a game pad. This is not the case today. It was built for the ground up for Switch. They've had six additional years of optimization. Six additional years to find out what they can do visually with the Switch. I think a lot of this concern is just misguided and probably just completely made up in your mind. Another thing I think is completely made up in CW's minds is the next point of concern. That's a distrust of the developers. The Zelda team isn't your Pokemon team. Maybe this is actually a time to get into a moment, but the Zelda team has been wildly consistent at producing quality products over the years. Maybe you don't enjoy everything. Maybe Skyward Sword wasn't your cup of tea or Triforce Heroes, which was technically made by Grezzo. Maybe Four Swords or Four Swords of Ventures or Zelda 2. You can call out whatever games in the Zelda series you don't like if you have experience with them, but they've consistently been making one of the highest rated IPs in history. Almost every Zelda game rates at 90 plus from all reviews. That doesn't happen by accident. It happens because you're consistently creating a high quality product. So why would we distrust the developers to do it right? Again, you know they made Breath of the Wild, right? If they did that, why would we think they can't do more? Next up, we have to talk about performance. We talked about the visuals a bit. We talked about the world and the map, but people are concerned about the performance. Ever since Pokemon Scarlet and Violet came out, people looked at it and go, well, this isn't a very big game. It's smaller in scope and it struggles. It struggles to run on Switch. How the hell is Tears of the Kingdom going to run well on Switch when it's a much bigger scope game, much bigger draw distance, much bigger world, and hey, Breath of the Wild has some performance issues here and there, especially with a lot of particle effects and certain things happening on screen. And now Tears of the Kingdom is promising even more crazy mechanics and more insane stuff.  And this is where I go back to the distrust of the developers. In this case, you should trust the developers. The Zelda development team is not the same team that does Pokemon Scarlet and Violet. It's a fact. Pokemon Scarlet and Violet was made in about one third of the time that they've actually spent making this game. This game is made by a team that now has a full experience making open world games run on Switch. They made Breath of the Wild. Pokemon Scarlet and Violet were the first ever open world Pokemon games made by Game Freak. These are just very important distinctions to point out. The Zelda team is more experienced, it's much larger, it has more help from other groups like Monolithsoft, and honestly, I think we're going to see better framerate performance. Not beyond 30 FPS, but I think we're going to see the game stick very close to 30 FPS significantly more than Breath of the Wild did because now the game and the engine has been fully optimized specifically for Nintendo Switch and who knows, maybe that next hardware platform as well. And then the last concern is one that there isn't a lot I can do about it. If this is how you feel about Breath of the Wild, it's probably how you're going to feel about Tears of the Kingdom. And that's that some Zelda fans feel like Breath of the Wild wasn't a real Zelda game. It didn't have the traditional dungeons, the traditional progression of get your items through dungeons, get your heart piece at the end of your, expand your hearts at the end of dungeons, and all the puzzle elements, although there's quite a few puzzles in Breath of the Wild. Optional, they're not optional in other Zelda games. So it felt like a massive departure for some. Interestingly enough, it's actually very close to the way that the Legend of Zelda, the very first one was with how open it was, except that game even had traditional dungeons and puzzles and typical progression with how you get your items and this game didn't have that. So people go, well, well, it might be similar to that, but it's missing this core thing that's been on every Zelda game. Ergo, it's not a Zelda game, and I can't do anything if you feel that way. But if you at least thought it was a good game, this should be an even better one. Now, if you're concerned about not getting more of that traditional Zelda that you know and love, that I understand. But this game was never going to be that game. So maybe instead of trying to hope that Tears of the Kingdom transforms itself into what you want it to be, maybe just think about those things you enjoyed in Breath of the Wild on its own as its own game and imagine being able to expand upon all of that and do even more and have even more fun. So even if you don't consider this a real Zelda game, you might consider it an even better experience than Breath of the Wild was for whatever kind of game you want to call it. If you're not going to call it a Zelda game, then whatever you want to call it, a new IP, OK, well, then this is the next iteration of that new IP. And I can agree with you in some ways. I've actually thought for a while, Breath of the Wild feels like a pseudo reboot of the Zelda franchise, which could explain why you might not feel like it's a Zelda game, because it was in such a drastically different direction than the prior games that it does sort of feel like if we're going to call this Zelda, it's Zelda 2.0, Zelda 3.0. If you want to say 2.0 started with Ocarina of Time or a link to the past, right, whatever version you want to say, this is very different from what it's been in the past. And we've seen this Tomb Raider, the rebooted Tomb Raider, pretty different than the OG Tomb Raider, right? So I'm just pointing out there that, hey, it's OK at times for things to feel the same. And maybe this is just a new era, whether or not you like this era is going to be completely subjective. So you guys, let me know what you think about all this. I honestly think Tears of the Kingdom is shaping up to be the best, at least, I don't know, best between Breath of the Wild, Tears of the Kingdom. Either Tears of the Kingdom is probably going to be the better game. Significantly so. The last point of concern I just want to briefly mention is some people say one thing that made Breath of the Wild so magical is that we've never seen it before. We've never seen a game like this before. And that's true in many regards. We've seen overworld, we've seen the combination of all these things, especially with that physics engine and some of the cool things with the Sheikah Slate, we've never seen this exact combination of features before in a game. And there is something magical about that. I also think, if you think that's magical, wait until this team that was just sort of beta testing this concept actually gets to apply a billion unique ideas into this concept. I think your mind might end up being blown even more. Now, time will tell. We should see this game again sometime in the next month or two. I mean, you figure we're getting really close to release. And maybe a lot of these fears you have will be tossed to the curb or maybe you'll come up with new ones. But personally, I think we're concerned a lot about nothing. We just need to have some patience. And this game is going to end up being the masterpiece that we all know deep down it's truly going to be. Thank you guys for tuning in and I'll catch you in the next video.


It's a pleasure to welcome in first round pick win and Williams all ready and enjoying what you believe is a brisk cool Summer day in Florian Park. Oh, yeah, it's definitely cool. It's not really cool It's not really cool, but it's more. It's more of a spring compared to Alabama in Georgia. So Yeah, 82 85 87 and humid. Oh, yeah for sure. That's Halloween Spring practice at Alabama. So it's very cool man. You look good. Hey feeling I feel great man I feel amazing. I feel I feel great man. I just enjoy practicing man I enjoy get out there competing learn from the veterans learn from everybody Especially the old line coaches old line and general just learning the treats of the trade is just NFL in general So I could be a great player. So I'm getting a deal done in time for camp and all that was there ever any Was that in jeopardy at all? Was there any question that you might miss the significant time getting this deal? Oh for sure not know know me know my agent man We wanted to get this done did we want to get this deal done fast as possible so I could be able to compete every day I ain't want I did not want to miss any days. I ain't want to miss Anything any minutes none of that. So we wanted to get this deal done quick as possible. So you got a nice mellow defensive coordinator He's amazing. I love I love all the tenacity to all of it I love all the energy he brings everything about it because I know at the end of the day when I go against the film Had the same mentality he has and dominate like he is big does it do yeah I knew you probably had preconceived notions of what Greg Williams gonna be like But when you got here you're on the practice field for the first few times with him and you actually hear him For the first time. What was your reaction? It was amazing like I came from Alabama So I came co-saving okay from like coach Cochran different guys who who just like great Just like I'm got the same energy got the same personality as him and they speak the same things They speak the Bama standard and hold you to that standard every play everything you do Yeah, dude to that standard of the defense standard of the jet. So I love it No, this might be more on Bob is the play-by-play announcer But we were joking about it on the K show last week that if it works with you Leonard Williams your defensive coordinators Greg Williams you got to work something out with the Williams a nickname or something, right? I'm right. It's too natural. I got cute Leo got big hair. I think What's great got great just G do It's just amazing to make that jump, right? I mean you're a big-time program that that is almost like a professional team Is there but still it's that next jump to the NFL So has there been a moment yet here in camp where you're like, oh, this is this is significantly different than what I'm used to Oh, it's not nothing that really been significantly different. It just I went against on KO And on one-on-ones one time and just doing a day and I just like man like he's a pro bowler So it's like yeah, like it's really different compared to the NFL and college So, you know, they they it's a lot of stuff that I got to learn I'm still learning to make it myself better But it's a lot of stuff that I learned from going against the great guys on the offensive line great guys on the offensive general that I learned But coming from Alabama, man, it's it just helped me tremendously because the technique I came in we just NFL technique the Standard I came in standard the work either I came in with is the same way so and then like the coaching like I love to get coach hard. I love to get coached. If even when I do something good I want to get coached on the things I did wrong the things I did just reference save I want everything perfect and I know everybody can't be perfect, but I want to learn how to be profile I want to chase perfection. So I love I love everything about it. Yeah, for those that don't know KO Collegial assembly, I think I've got that right. I haven't spent a lot of time with the pronunciation guide yet I think I've got that right He's a guy that has been to multiple probles Now you played your whole collegiate career in probably the conference is there for talent-on-talent in the pit Right, I mean you're not gonna you playing against borderline NFL talent every week Is there but when you first come out and go head-to-head against a guy that has been to NFL probles Do you immediately notice the difference and what you have to work on yes, sir To make sure you have a chance against a guy That plays at that level because you're gonna be going up against those kind of guys every week Oh for sure, man He teaches me a lot man every time I go against them and I have to I have to learn for myself Like I just learned like different things like your first move not gonna work and college a lot of time My first move work, but going against great in any NFL my first move like being impressed my first move Will work sometime but going against different guys that's been a pro bowl different veterans that been in NFL a long time They know the treats in the trade. They know everything about deep in the line They know what you're gonna throw they know what you what you about so they know I'm a speed guy They know I'm a finesse guy and I got power so so I try to switch it up on them I try to get on with different things and I'm flexible So I try to use everything about myself To overcome on guys and just going against a pro bow guy like KO I got to pull it all out because he got he got all the tools of the trade to beat me every time So he just made me better just going to get some so Quinn Williams with us here on 98 70 SPN And also the K show bobble shoes and down the Greco When Greg Williams was hired he's traditionally been a 4-3 guy And the first thought when he came was oh, they're gonna go 4-3 Then they find out they're switching back to 3-4 or gonna stay with the 3-4 He's gonna mix everything we understand that but you're calling yourself a finesse guy. They draft you How do you fit into an nfl 3-4? In terms of those tricks of the trade you're talking about having to learn or How many different fronts have you already seen in practice? And in the film room where now you realize you're going to be moved all over the place And they are going to try and piecemeal your role to fit whatever personnel is on the field I'm not really a finesse guy. I can really I'm really a hybrid d-line man I think I am me and leo because we can play in we play interior we can do it all so I like Alabama. I played it all and we ran the 3-4 Alabama ran more similar the same exact defense at Alabama because Greg Williams because saving got the same mindset when it comes to 3-4 So it's similar like whatever he want me to do. I'ma do it don't play to my best ability and like they want to Play because I've been all over calling college games and no matter where you go You always hear Alabama is about as close of parallel to the nfl As you're going to get in terms of practice structure demands on the players Scheme what they throw at you what they expect you have to mentally digest to be able to play the defense Do you feel that already that you're maybe a step ahead of the other guys and camp a little bit? Or maybe other rookies would be because you played there I feel like I am compared to other rookies on this here. I'ma step ahead because just coming from that aspect Um, I just learned it quick everything that Alabama we did it was throwing at us And we had to on our own we had to go home learning Come back expect everybody expected to know it everybody expected to do and this the veterans that we had at Alabama Was great veterans that teach us all the stuff also So being here with great veterans like cj steve Leo Henry they make sure we grasp everything quick. So What's adam gates like or is it a little too early to tell he really offers a guy so Because that's all I was gonna ask you like see I would think At least on the surface You've got a better chance to have a relationship with your head coach in the nfl Then you would in college because you're only there for you know three years Maybe four and there's so many players on the college level where you've got what you know 53 to deal with maybe more in camp So is it possible that you can be closer to gays than you were with saving? Oh for sure I try to get close with all my coaches man Just because they got a big impact on me because I can learn from every last one of them So for sure the it's not really like I'm I'm be cool because saving and not be cool at him. So Were you cool with the coach? Oh, yeah for sure. Man. He's a goat. So it's like Adam would go so Man all these coaches man just here to help me help me be better player on and off the field Help me be a man on and off the field So I'm gonna listen to everything they got to say and respect everything they do Man, it's a pleasure meeting you for the first time. Good luck with you. Yeah, thanks a million. Thank you


But you know, with all the war and pestilence going on, this is a strange priority. So I looked at the NIH's priorities online, and they listened, and this was not one of them. Heart, cancer, infectious disease, opioids, and diabetes. So I'm to take it that COVID's over. Obesity is cured. Kids aren't dying of fentanyl. You know, there's a ton more issues for us to worry about when it comes to adolescence than how their voice sounds. Social media addiction, we talked about a little bit of that. Self-harm, mental health. This is just really bizarre.


Yes, I am Zukunft Overview. There's no paeg описio First I can find Julian Adrian for organising in session for the presentation prepared so far and for the great discussion. What I'm going to do is I'm going to build upon a discussion when we talked about the units how we can assign new units and how we can test the validity of the unit but I specifically wanted to go into how a quantitative morphometric framework mae'r cystafell ystafell, yng Nghymru, yng Nghymru.  Felly, yn rhoi'r cyfeirio i gyd, mae'r cyflwyno'r cyfatio'i ei ddweud yn cael ei ffyrdd yn cael ei ddweud yn cael ei ddweud yw'n ei ffordd o bach yn ymgyrch yn gyfan a'r unig. Rwy'n cael ei ddweud yn ei ddweud, mae'r gwaith yn gwybod i'r gweithio'i ddweud. Llywodraeth i員odd, mae'r defnyddio ar gyfer gyfansияwn iawn i gymryd angen a dyma, a dyma haith geisio'r dymian y gallu'r peleidwyr ar hyn, yn ymgyrchu allan y cyfansiwll yma. F Nam, dyma'r ddaeth ynglyn agoredd eu bwrdd Cymru yn ynnwylogau a'r anfer Eenolstu – dewis ymwinellau – ac mae have ar hynny eu cyfrifiadau ond eithaf yn ganrych o'r cyfrifiadau, a'r anfer Ynglyn i'w Uned,wlwyd. Y cyflwyno'n mynd i'r debat arall a'r blaen o'r74s, oes y Llyfr, y Llyfr-Liam, y Llyfr-Lif i Lefant, oedd y Llyfr-Lif i Norfaen-Nagia.  Felly, mae'r ysgol ymgyrch yn ymgyrch yn ei ddweud ymgyrch yn y stwch ac yn y rhai ddweud yr unig. Llitwch o celfwyr i dda, yn y celfwyr rheoli, rwy'n creun i dweud edrych chi'n dweud i'w ddatblygu yma yng nghaelau phantym cy��고i, y mae'r cy pelos wedi'u cyfwyr ar y celfwyr. Rhaid i'w ddweud o ffordd, mae'r cyfwyr maennyn i'r cyfsgrwxau a ffantyms yn unig i'r ysgolion oherwydd sy'n cyfeciogel i fynd yn gallu bach o bach o'r disgwasi. is how do we move beyond the recycling of the same arguments? If we know something doesn't work, why do we continue to talk about it not working, why do we not get rid of it? And how do we as a collective support the notions made by different researchers and finally move forward in these debates? Perhaps one of the more uncertain periods where issues of these taxonomic units are on parants is the final prolification in Northern Europe, where a myriad of terms have been used for over a century for these populations and these have often reported to reflect ethnogiographic variability. You can see on the map here the Creswellian, the Fedemesa epimaglainian, as the alien many of these we've already discussed. In this work, I'm sure I've just probably best exemplified the abuse of units in his identification of the risks and the real and the younger variants in this martial-like chronology. So first we must consider the research historical framework and how these terms came to be and a consideration of the framework of dispute is not new. Hootsmo in 1996 perhaps best highlighted the need for a final prolific which can escape the constraints of contemporary national borders and the paradigmatic straight jackets of provincialism and regional chauvinism. One here can include what we've already discussed, these sort of lost in translation events, whether these be terms which we don't know from each other or in this case where people have raised these issues on a regional level in much smaller journals but he just hasn't reached a wider audience. Whether for one fault or another it's not to play in fault but it just doesn't reach a wider audience. And so this is particularly so for the up and final prolific periods as Tash has already highlighted and more of this work can be seen in the sort of Florian and Felix's work like co-authors where they highlighted the creation of terms based on counts, raw materials, point types but they also highlighted the sort of the justifications, the national biases, the involvement of political histories and the nation states and the problems that we have today. So for example in Denmark the lack of understanding of what a different point type is may mean that we abuse Boma points for example when amateur archaeologists find different types. Those which have emphasised the importance of a research historical framework more than just Felix's work here have highlighted the necessity of rigorous high resolution methods which can test the robustness of the groups and artifact types, identify possible redundancies and even hint at new taxonomic structures and I want to stress again that this is not new for the final prolific and one can consider the work of Boston and Neely and their box plots when they're thinking of these different terms. However such analyses have always been limited or produced using a low level analytical framework and not the new suite of morphometric and multivariate technologies as we've already seen in the previous part of the session. And so what you know what we're starting to do is to couple the American O'Brien quantitative prolific work with this European perspective but in creating a quantitative framework for assessing structure and taxonomy we need to ensure we're not comparing apples and oranges or apples with crates of apples and we need to appreciate the level of scale on this. So we need to adopt a system similar to that of Gamble and through this we can probably best start to assess different terms against one another and so to test the validity of many of these groupings for the final prolific one way we can examine the basis of structure is through a two-dimensional geometric morphometric framework as already highlighted in the last two decades these have already highlighted how they can be used to understand different aspects of hominem behaviour and more importantly the testing of different terms through a high resolution statistical framework. So in doing such here we utilise an elliptic theory approach which takes into consideration curves rather than landmarks as we've previously seen through our own atu levels. So we broke up the federmesser and we saw the younger was underneath because the younger came from the federmesser so we can start to talk about the different unis. And in doing this this includes a specific level for artifact pointer type so cheddar point, crespo point, all the seven points and we're only using those which have been mentioned in text and explicitly referenced. We're not going to use them if someone someone else has hinted at it we're just going to use explicit references to explicit size and explicit artifact types and as tag them back to points represent the bulk of classicatory systems for the final pal, the cheddar point, the tragedium, the creswell from Creswellian. Tanged points, back points represent the most suitable artifacts to examine these use. So in conducting a GMM analysis images of backed and tanged points such as these here were first cataloged and documented before outlines of each artifact were generated. From here all outlines were standardised, centered, rotated as applicable and analysed in the R environment. They were then assessed through a multivariate analytical framework to understand the main changes in the shape and if we can catalogue the units on the shapes then we can start to understand the robustness of these units. Previously last year in Maastricht I presented 1,100 backed points but following further research were here presenting 1,719 complete backed points and 5,574 complete tanged points, totally just under 2,300 artifacts. Ffavre information about this methodology, the script whatever just come see me after this session and we can go through it. And so just to highlight a few examples, here's what we define as the ATU-2 let me to sort of the higher analytical units and we see some element of structure within the backed points as demonstrable on the left, those that be backed points that belongs to Hamburgain and the early tanged point complex. We see overlap in the variance of the Leborian so the Leborian versus the Azzillian with Leborian elements and significant homogenisation between the Federmesa, the Anzberg and the Azzillian variance. It's perhaps unsurprising to see a lack of distinction between the Azzillian and the Federmesa given that we use the two terms interjection, but we just use them in what way some people just use both. It's also important to highlight that in developing this system and asserting certain artifacts to units 809 could not be placed within this level and this is one of the problems that we're going to have if we're going to define and assess through an ATU system. The tanged point variance left structure is present with only significant difference noted for the tanged point of the technical complex. If we look at the ATU free scale so we're going to be lower the Federmesa now to be younger wheeling the variance and all the associated variance, we can see the greatest, this is where the greatest amount of analytical units for a grouping can be observed and we can see again this pattern of structure and homogenisation. I think the Great Lake clock would hypothesize on the right to see what could be termed a clusterfock of different sides. For this, we can see that on the left we have the swydry, the Wittonian, the Eastern European variance as distinct from all other fact point types. We see the Azzillian Huffa off on its own. The Azzillian Huffa is probably one of the most interesting ones as it's defined on the distinct use of a non-flint material and as he has supported through a GMM approach. It's also important to note areas of homogenisation including the Federmesa variance which come from the old systems and the old typologies. The Tang point variance, the clusterfock, problems with the current taxonomic system really appear and varying levels of homogenisation can occur. If we just consider the named artifact types quickly, we've got a few minutes left, the lowest possible analytical framework, the similarity and dissimilarity is again noted with many types of backpoints including the Tau cut, the Peter's file and the Blanchier variance distinguishable from the myriad of terms overlapping the overall group centroids on the left. The Tang points types including the Elton Nervan and Grensk forms can be discriminated from the large group of terms towards the centre of the graph. However, with many of these examples speaking relatively low counts, it's difficult to robustly examine many of these types. It's very hard to find complete examples in literature where there are terms that aren't widely used and so testing that robustly is difficult. We can also use the GMM approach to assess the grouping through hierarchical clustering and we can start to look at the differences between the groups based on the distance between the different mean shapes. You can see, for example, in the middle, we can see the brome point up here and why the classified artifact matches many of the Tang points which have no classification. And so we have difficulties in starting to assign an ambiguous ones to units. More excitingly, if we wanted to avoid the group labels completely, and I'm not saying we should because there are some which are technologically valid but some which are morphologically valid, but what we can start to do is we can take the artifacts on their own, we can apply the GMM and we can begin to create new objective units. We can build these theoretical groups. And as linear measurements could be developed to categorise the structure, we can retest, use new sites and we can see the appropriateness of these units on the varying levels. But as Tash highlighted previously, we need to have the spatiotemporal data there for these to be legit. Just because they can be statistically significant doesn't mean that archaeologically significant. This last bit of work is in progress, it's that they can't provide any more information here. This is the last week's work, or two week's work at work. However, using hierarchical clustering and tools from the evolutionary toolkit, we can move beyond most objective categorisations, similar to even where it can go in the 1990s, and we can start to test these sort of phantom cultures which have come out from the historical framework and provide an objective basis for understanding technological structure throughout this period. And so through a combined research historical and geometric morphometric framework, we're able to start to discuss new bones on start to discuss the nature and validity of different cultural taxonomic units and begin to discuss the presence of these phantom units. However, in analysing these different terms, the level on the ATU with which these units are classified is of the utmost importance. And through this ATU system, different rationale for classifying units still do not articulate as well, and there's a degree of subjectivity in what ATU they belong to. The federmesa, for example, can be a term for a multi-unit structure since you swab a system, a unit based on the presence of a federmesa point and often used interchangeably with the azillion. And then the ATU system, termed by tanged point in the complex or the aton hopper, do not fit or scale appropriately, and it would be difficult to figure out where they go. So while the federmesa can be scaled down to its constituent parts, where should we fit termed by the press volume? And this is where we start to really need to redevelop an ATU system to provide that objective framework, which retains scale, but begins to deconstruct these middling groups. So we've currently revised our ATU system and work is in progress in looking at the justification for the termed units, not the scale. So if something has been defined by the presence of certain material or by a lithic count, any discussion on nomenclature must also recognise the role with which the product has been made, the importance of the technology, bringing in the French and the European systems with the emerging systems. And this work only represents one strand of the research which my south phoenix and floen are undertaking. Technological analyses that receive the Danish and German sites are in progress. However, in their totality, these three considerations and the research historical, technological and quantitative, morphometric perspectives, will not just bridge the Atlantic so to speak, but also provide the most detailed resolution and basis for deconstructing and potentially reconstructing the nomenclature used throughout the pharno-pallolethic. Thank you for listening.