I'm going to look at the camera briefly. She knows people. She knows she's aware of everything. Or two things. Well, it's the two things that matter. I'll say two. We're enough to make it. Yeah. So thank you guys for filling in for Tom. This is super exciting. We are just for the people watching. We are live on Alpha Greek video, but not radio, because for some reason that ain't working. And we are live on diamondclub.fm and diamondclub.tv and the YouTubes and all the things. So really just spectacular liveness. So I say we just go. And I will fade down the music at some point, Patrick, like you jump in when you wish. I'll fade it down, so it's definitely not over you. All right. So I let the music go, and then I start speaking, and we see what happens. Yes. And also, I'm not doing any other music cues in show than the open and the close, because it just creates garbably garbage, and then I could just put it in post after really simply. When you go from headlines, sorry, when you go into headlines, just a breath. Just leave a breath for me. And then when you finish news from you, just leave a breath for me. And then I'll play you out with the headline music and all the post stuff. Excellent. All right. Let's do this. Let's. I wish I had. I was born ready, but I just know that you are. You know, I could even do it live. It's a show. I was born ready. All right. Disappear. All right. I'm sticking around for a second, and then I'll disappear. Okay. Here we go. This is here to begin technical production, June 9, 2015, finding out that Beja is trying to fill in for Tom Merritt, who is out on assignment, and I hear his parents are proud. Ayaz Akhtar is here to help me fill the void left in our heart by Tom Merritt's absence. I'm not sure we're going to manage. Tom has a pretty sizable place in our hearts. I'm going to say, how are you doing, Ayaz? How do you think our chances of filling in for Tom successfully are? I'm doing pretty well. I'm getting more and more terrified and more I think of the concept of filling in for Tom. I'm just going to think of it this way. Tom's on the episode, but very, very quiet for some reason. Excellent. That will help us deal with his absence indeed. You know what? We did want to talk about Apple non-stop for the entirety of the episode, but the combined might of the producing team and all of our bosses decided that we shouldn't. So we're going to go ahead and discuss headlines as we usually would and have a different topic for the discussion topic, which is not Apple, unfortunately, but will be interesting nonetheless. Let's start with the headlines. Major Nelson has revealed that Microsoft has released a new Xbox One console. Very exciting. Except, well, not really. It's just a variant that includes a 1TB hard drive, new matte finish, a newly designed Xbox One wireless controller, and in select regions, Halo, the Master Chief Collection, which presumably finally works. The new controller will feature a 3.5mm stereo jack so you can plug in a headset directly into the controller with settings for volume, voice, and game balance and mic level in the console's menu setting. That's as opposed to the proprietary jack that the old controller had. The updated console will ship June 16th, so right after the start of E3, in the US and select markets for $400. You can also buy the controller separately for $60, or in special edition covered forces that look badass in silver and black for $65. The price of the current 500GB console will now drop to $350. So Patrick lied a little bit. We're going to talk a tiny bit about Apple because there are some clarifications and some nuggets about yesterday's WWDC. Recode clarified that Apple Music will support offline listening for albums, songs, and video. What about podcasts to ask everybody in the audience? Well, Technology Writer Jordan Merrick rounded up some more details that weren't in the WWDC presentation, including the fact that OS X El Capitan will support third-party photo editing tools in the Photos app, and the Maps for OS X app will gain support for transit directions. He has lots more interesting nuggets on his site, which will be in the show notes. And Sunbun submitted this Verge article about Apple's first Android app called Move to iOS. That helps transfer essentials like contacts, messages, calendars, mail accounts, and other media from an Android device to any iPhone or iPad running iOS 9 wirelessly. So very quickly, don't speak too loud. What was for you the most important portion of the conference last yesterday? It was the iPad getting awesome looking features that kind of looks like OS X and iOS 9 are going to merge. Yes, I agree. Finally, we can use the iPad for productivity stuff and working. I think that was tremendous. I really wish I was here yesterday to discuss it with everyone, but we weren't. So let's move on to Business Insider that has a write-up of a report from the information which says Facebook had previously unknown plans. Actually I had heard about those plans to build a satellite which would have provided cheap internet access in the developing world. As it turns out, it's not so cheap to build and launch a satellite. The venture would have cost around $500 million and so it was cancelled all before we ever knew about it. But I just said, I sort of heard about it before. Anyway, Facebook might still continue with its plans to deliver cheap internet via least satellites. Not so good. It's kept secret. To make Twitter conversations easier to follow, Twitter has tweaked how they're shown on the tweet page. Tweets that are part of a conversation are connected by a line. To see more of the replies to a specific tweet within a conversation, you can click View Other Replies. The feature will roll out to all users on Twitter.com today and roll out to mobile apps in the future. You know it's really interesting how Twitter has managed to add functionality to its service and to the tweets and embed stuff in the tweets without losing the nature and the shortness and the easiness of the tweets themselves and the service in general. Here it's I think it's very useful for logged out users, people who might not even have an account who can go to a tweet and follow different portions of the conversations. And that could be why they're doing it. I mean it's going to be useful for everyone. But specifically for maybe Q&As or these kinds of conversations, I think it might be a pretty important, not important, but interesting step. Yeah, it's sort of done a pretty decent job slowly introducing these things without changing what Twitter is.  It's like, what are you having for lunch? But they've really done this quite well. The Los Angeles Times reports that the US FBI wants Congress to expand their authority to tap into secure messaging apps. According to the FBI, Islamic militants and their followers are using instant messaging apps such as WhatsApp and Kik as well as data destroying apps such as Wicker and Shure Spot. The FBI estimates that about 200,000 people around the world see terrorist messaging each day from Islamic State zealots and that the group's recruiters then troll Twitter, Facebook and other sites to see who is reposting their messages and then invite them to text directly on encrypted or data destroying apps. On a lot of note, the bridge reports that starting today, Sony's PlayStation Now Game Streaming Service is available on select Samsung Smart TVs in the US and in Canada. Although you won't need a PlayStation console to play, you will need a DualShock 4 controller. The service will support standard features like trophies, online multiplayer and cloud based gamesaves. Now PlayStation Now started as an online streaming game rental service, but Sony has since offered a monthly $20 subscription fee option. It goes down to $15 if you take a 3 month bundle. It becomes kind of interesting. If you can play PlayStation games, mostly it's PlayStation 3 games, but without even needing a PlayStation, just plug it into your smart TV, you buy a controller, it could be an interesting deal for some people. The latest edition of the Ericsson Mobility Report shows mobile subscriptions in Q1 2015 was at 910 million for all of Africa, including 21 million new subscribers according to BizTech Africa. The report also indicates smartphone subscriptions will be more than double reaching 6.1 billion and that in Sub-Saharan Africa, GSM Edge subscriptions will be predominant until 2020. Just a quick comment on this, 2020 seems like science fiction time for those of us who were alive before the year 2000, but it's coming in 5 years, which means in 5 years Sub-Saharan Africa will be predominantly 3G, which means you can do some serious work and serious interneting, which I think is pretty significant and it will also have a bearing on what we're discussing a little bit later. I thought it was pretty interesting. Africa is getting equipped for general internet purposes through mobile very quickly. So there we go. I think some of us might be snobs to think 3G is so slow compared to 4G. 3G is plenty fast for lots of things, don't forget that. For older people like me, TechCrunch reports that messaging app Jot has become super popular in US middle schools. Jot allows users to send private messages on a closed network without a data plan or a Wi-Fi connection. The app uses a mesh network that operates via low energy Bluetooth or a router within 100 feet of each user. Jot began testing at select middle schools in March and it like blew up. So I'm not exactly clear on how it works because if it has a router within 100 feet, it means it uses Wi-Fi, but it's not a Bluetooth router. It wouldn't need to use that router to get addresses and things, so it's not using it like a hub. So it's a little bit more in the middle. Yeah, there have been a few mesh networking messaging apps in the past, I guess the combination of Bluetooth and Luck made this one blow up. Captain, yes, time for some news from you. I almost forgot to mention that you can help us decide the lineup and the discussions we're going to have on the show by contributing. To Reddit, it's very easy.  It helps us inform the important stories of the day. So the example of Captain Kipper, who sent us this story from Geekwire, covering Amazon's push to become an SSL certificate provider by applying to be a root certificate authority. By becoming a root CA, Amazon can sell SSL certificates that are automatically trusted by common web browsers and operating systems. It is unclear how big the revenue opportunity for Amazon is for digital certificates and how aggressively they will market them, but Geekwire seems to think that providing encryption certificates is a natural add-on service for Amazon web services, which does make sense. No word if you have an Amazon Prime subscription if you get free certificates. That was a terrible joke. Anyway, SP shared and sent us the Reuters report that the top US tech company sent a strongly word letter to President Obama yesterday saying, we are opposed to any policy actions or measures that would undermine encryption as an available and effective tool. The letter was sent through two industry associations, the Information Technology Industry Council and the Software and Information Industry Association. They represent tech giants you might have heard of, like Apple, Google, Facebook, IBM and Microsoft. The letter was also sent to other government officials, including FBI Director James Comey, who was like, come on people, I just told you we have a problem with WhatsApp. Yeah, the never-ending battle between surveillance and privacy. Alright, that is the look at the headlines. And we are going to move on to our discussion story, which since, did I mention, we couldn't discuss the announcements that Apple made? I think I might have. So we're not talking about Apple, mainly because there really wasn't anything new happening in that field. And also, there wasn't a huge amount of major stories that showed up in, you know, the follow-up to Apple's Big Day, which wasn't so big, I suppose. However, I have had a pretty interesting experience in Finland. Oh, did I mention I'm in Finland? I'm in Finland for a few weeks. And the infrastructure, the internet infrastructure in Finland is pretty outstanding. And it led me to rethink about how things have been evolving in the past few years. So let me paint you a picture of how the internet works in Finland in general. I'm not even going to talk about how things are in the main cities, or maybe the one main city of Helsinki. Connectivity is excellent. That is not too surprising. However, in the countryside where I'm currently residing, we're in a house that is pretty remote.  It's a few kilometers away from the closest village. And it still has a fiber internet connection. And I was very surprised to hear that that was even possible, because not only in the US, but also in Europe, you know, I mean, in Western Europe, in France, in my case, the fiber connections have been very slow to roll out. ADSL and VDSL have been coming more and more, but fiber isn't really there yet. And here, in the countryside, it is very, well, what's happening basically is that the government is subsidizing portions of the connection to those remote areas. Basically they have a deal where they will pay a portion of the connecting cables from a densely populated area to a non-densely populated area. They will pay about 30 to 60% of those cables. And then communities or cities or cooperative associations, however, you know, whatever, however it happens to work, will pay for the remainder of that and for the connection to their own house. When everything is said and done, my understanding is that it costs between $2,000 and $3,000 to connect one house. So that is certainly pricey for sure, but when you think of everything it enables, and I think we're going to discuss this in a little bit, I think it's kind of a interesting deal, especially if you take into account the fact that actually mobile connections are also very well spread in the country. For example, in these kinds of houses, it's not rare at all. So basically, 3G is everywhere, 4G is very common, including in those remote areas. And on top of that, what I was telling Tom last week is that the data plans, the mobile plans usually don't have data caps. So what you're paying for is the different speeds, and they go from 50 megabits to 300 megabits. We're still talking about 4G now, of course it's maximum theoretical speed, but that is the speed you're allowed to get with no data caps. So basically, what it reminded me of was that time when we thought the internet is going to be everywhere and it is going to reshape the geographical distribution of populations and work and have a huge influence on the economy. And that was sort of a technophile's wet dream even 5 or 10 years ago. Today though, the internet is so well integrated into our society that we've sort of lost that fantasy and we're sort of looking, oh well, who is connected, who isn't, what does the internet work for, what does it not work for. And we're not really paying attention to the fact that if we just buckled up and actually connected everything we could, we could change the way people think about even working. We've had an issue, at least in France and in Western Europe, with rural exodus. People, villages and small towns don't know how to keep people living in those places. That could be a way to bring back some of those people. Is that something that would speak to you, Ayaz? Would you consider moving to the country if you had a good internet connection? Well I'm born and raised a city boy but I grew up in the suburbs of Queens which is still city-like compared to a lot of other places. And I have lived in somewhat rural areas, I lived in Vermont, I've lived in upstate New York and one of the determinations in buying a house upstate was actually to have fiber to the curb and it did. So that was relatively helpful. There were other circumstances about the location that said, you know the fact that they have three restaurants and there's nothing to do at night, was it not exactly the best thing for me at the time and I'm back in the city in New York City now so I really do enjoy that. But this kind of connection would make so many things more possible because one of the things about having a good speed somewhere is it's fine for like, you know, watching video and you want to be able to download audio, that's great. But one of the big things that fiber allows is for things like actually having good upload speeds. So if, let's say Patrick and I want to have a conversation or something, I could see him and I could see his reactions a lot better because I'm sending video back and forth. That's something I think that's been underserved like crazy, particularly just in the United States. Upload speeds, nowhere near as good as download speeds for lots of services and that's supposed to be like, oh it's okay because you only consume but the thing is if you want to create a world or even the United States, you want to create an environment where you can have people anywhere, talk to anybody and work with them anywhere. It really does help to have a really solid network because wireless is very good but wired fundamentally can be much better because it's not subject to so much more interference, you're not worried about bumping into things like okay, there's other airwaves around. So putting this up all together, maybe there's a chance I would go to a rural place. That's my long-winded answer. Well you know, I think it's, when we look at it like this, it's cool that what we think about initially for those very high-speed broadband connections, we think about Netflix immediately but today I think a lot of people are thinking, you know, telecommuting is becoming part of our, the way we look at the workplace and I'm using air quotes here. More and more people, thanks to the magic of the internet, are just working from home, from anywhere. And you were talking about the fact that there weren't any restaurants in that area you were looking at. Obviously it's a vicious circle because since there aren't any people living there then the restaurants go away and the businesses go away and, but if you can reignite this, as I was saying earlier, a few years ago the real world and the business world was not used to the idea of doing so much through the internet. I think today a lot of people and not just, you know, San Francisco hipsters would consider working from somewhere, you know, maybe even it doesn't have to be the real middle of nowhere. Maybe it can just be a couple of hours away from the city where you can still go in for a day or two if you really need to, but most of the time you're going to be staying in a larger house and a more comfortable place. There's also another aspect to this which is spurring businesses through the sole fact that you will have a decent connection. And again I'm going to turn to the examples I have here in Finland. My wife was telling me about this, what was it, an elk farm or a moose farm or something like that which actually has something like 500, 5000 likes on Facebook. And that's the kind of thing that you think, you know, maybe raising elk somewhere in the middle of the country could be, you know, some business venture somehow, but when you think of it with the internet aspect, you can make it into so much more. You get your marketing done for you, you get your, you can turn it into an attraction, a tourist attraction, everything becomes so much easier that it doesn't necessarily have to be, if you work with the internet only, it can become, it can enable the kinds of innovation that we've seen in cities like, you know, Uber and Airbnb, these kinds of things are possible because of the internet, but they solve city problems. Maybe we can solve country problems if we connect them. You know, when you were talking about this whole elk farm or this elk area, I was thinking, it was kind of like, who would want to go visit that? And I'm thinking, you know, maybe people wouldn't necessarily want to actually physically travel, but with a good enough internet connection between two different areas, you could upload massive giant VR files, you can have a 360 experience or even a live experience as if you were there. That's something that, when you don't think of internet, like, oh yeah, internet's going to give me all of this content and it's going to bring me to other places, but in theory, if you have enough of a pipe, you can basically send every single 360 degree image from one space to the next. That takes a ton of bandwidth, so the more that these places are connected, maybe you sell these tours. Maybe you have, it does spur VR actual adoption. Why would you ever need this thing that is attached to your face? Because you want to go places without having to actually go places. So if you can virtually be there, that could be very interesting because I'm thinking, hey, maybe I wouldn't want to go to this elk attraction, but that's because I've never seen it. Maybe I've never experienced it and I want to get that extra bit. First, I check it out online because people do this with homes, right? They look it online, they try to look at the VR version of it. That's on a 2D screen, which is not close to reality. But then eventually you go there to check it out. So it could just be the next level of, or there's maybe an intermediate level between browsing and actually visiting. You go, oh, I saw it on the internet and it's got a really good idea of what it was like. And for this, you do need a good connection. There's no way around it. And I do think that we're still, let's say, 30 years into the internet. Within 20 or 30 years extra, it has to become like electricity. And the problem is there are those that are going to be ahead of it and those that are going to be behind it. And I do worry, it is a genuine non-political or biased worry, that putting other concerns, it's so essential that putting other concerns before it is misguided. But maybe that's my enthusiasm for all things tech talking. But what do you think? Should we make an effort? And how important is it that we actually enable those things? Is it me having wet dreams or is it actually important? I don't think you're having wet dreams on this, but I might be a nerd as well. But when it comes to internet, having access is basically having the ability to have knowledge. And that's something at least in the US. We're not actually against. We have libraries everywhere. And when I was living in Vermont, I actually got involved with the political campaign, very unusual for me because I'm not exactly an activist. But this guy that was running was trying to actually wire up all of Vermont with fiber. And Vermont's basically its own country if you compare it to the rest of the United States. They have health care for the young and the old. I know it's really weird. So if you're born there or you're in pain or you're old, they'll take care of you. The taxes are very high. But the people of Vermont are like, that's fine. We live here. We're going to take care of ourselves. So it takes a certain kind of mindset to get things done on a certain level. So this has to start on either a city level, a state level, federal. That's usually a lot slower moving. You can get a lot of things moving more within a state first. I think we've seen some progress, not a heck of a lot of progress. It's a little slow when it comes to adoption of this. Because the idea is like, well, the free market will set it up so that the best places either will draw people or they will figure out a solution in the free market. And we're talking about Finland taking the time to wire up these places even though the wireless access is substantially fast. I mean, it's pretty fast compared to other things. It's a long-term thinking of saying, listen, if we invest in our country and we actually bother to do this, this last mile fiber, this is going to enable a whole future that we might not even be able to imagine yet. So I think sometimes you've got to start real small. Because the federal level, at least in the US, it's very difficult to get that stuff started, let alone finished. All right, we could talk about this for a lot longer. And we actually jumped over a story about national IPv6, IPv6 day in Finland. Again, I think I'm going to get a check from the Finnish Tourist Bureau. But yeah, the usual story, IPv4 is saturated. IPv6 is coming. And it's coming not as fast as some might have hoped, but it is. But let's move on to the pick of the day. And I am going to pick my own pick, actually, which I couldn't discuss last week, because ironically, in wonderful Finland that I just spent half an hour complimenting, my internet connection went out in the middle of the show. But my pick was an app called Dewitt Display for iOS. It works on iPhones and iPads. Sorry, Android or Windows phone users. There's not a version of this for you guys yet. There are other apps that do almost the same thing, I'm sure. But what Dewitt Display does is that it serves as you connect your iDevice through USB and USB only, no Wi-Fi. But you can then use it as a secondary screen for your Mac. And the thing is there are many different apps that do this, but this one does it with almost no lag. And I tried it. It's a little bit pricey, but it does work very well. So I was skeptical that it could have that kind of no lag experience, which is the most important aspect of it. And it does work. So I have no problem recommending it. It's, I think, 15 bucks. But it's available for, as I was saying, iPad and iPhone. I guess iPad is the most useful. It also works on Windows, though. So if you're an iOS user and you have a Windows main computer, it works for you as well. It can be handy in some situations. Messages of the day. We have a message here from Rich from Lovely Cleveland, who has thoughts on Apple Music. See, we sprinkled it here and there very discreetly. Esteemed guest hosts, he says. Thinking about the Apple Music service has announced, and I started considering that Apple may not need this to initially be massively popular to be successful. This may be a move to deny or simply slow the scaling of other streaming services to profitability. Everyone considers Spotify to be the giant in the space, but to this point, they are not a profitable service. They need to continue their impressive growth to scale to the point where they can be. Apple could win or at least create a foothold in the space simply by disrupting Spotify's growth. At this point, their biggest advantage is their convenient and already built into the devices of millions of people are using. And when subscribing to a mobile that you have a big price advantage, sorry, I jumbled the phrase there, basically what Rich is saying that Apple, having the advantage they have, could simply be trying to put a wrench in the shoe of Spotify in an expression I just made up. That is one, I guess it's maybe Spotify as a mechanic in this weird metaphor. So that seems like it'd be totally fitting and not fitting in the shoe. But anyway, I don't know if Apple would be bothering to do something to disrupt a competitor at this point so much that they were trying to figure out because they had so many diverse things. Here's iTunes Match, here's iTunes Radio, here's that old regular radio thing.  Then they had Beats. They bought Beats. What are we going to do? So they have this ability to finally make this nice either streamlined version if you want to call that way or they just made this giant ball of services. Here you go. It's three different things at once. And yeah, Apple's got a huge advantage. And they will be pre-installed on tons of devices because I think once you go to iOS 8.4, that new music app will show up on the 30th of June. And boom, you have access to that. And they're offering three months free. So a lot of people were wondering about the pricing if Apple was bothering to subsidize the cost of this service to get adoption up there. But that's all speculation and nobody knows exactly how those deals went down. Yeah, I have to say, as much as I often agree with Rich from Lovely Cleveland, in this case, I don't think they would go through such lengths just to, how did I put it? Put a wrench in Spotify's shoe. Exactly. I think they're in it to win it. They are definitely, and more importantly, the business of selling music is slowly going away. And this is a big part of what makes Apple and Apple devices successful. So I think they want to stay relevant in that space and that's their main goal with all of this. Of course, making Spotify less successful is going to be a consequence of this if they succeed, but I don't think it's there. It's that way that they looked at it. All right, that is a show. Thanks, IAZ. How do you think we did? We were establishing a scale just before the show started of how many percentiles of a tom we did. Did we do good or not very good? How do you think we did? A quarter of a tom, a third of a tom? I'm gonna say we probably reached our maximum somewhere about 72% tomosity, but I think we averaged around 66%. So two thirds, pretty good. I'm gonna pat you on the back virtually, good job. And thanks, Jenny and Roger, and everybody putting the show together and having me on today. Thank you. Is there any way people can find you on the internet? Yeah, this is a really easy way. Twitter, twitter.com slash IAZ, IYAZ. I usually post all kinds of things there, like when I'm on DTNS or when I release a top five or do an episode of Netflix or if I do episode of podcast with our pretense on GFQ network. All that stuff is always on Twitter. If you don't want to hear about any of that stuff, you've made a huge error and follow me on Twitter unless you want to learn about beer or other fitness things too, because that's also what I write on Twitter. All right, twitter.com slash IAZ. And for me, it's twitter.com slash not Patrick. You can also find my shows about video games, for example. We're gonna have a show next week after E3, which is the big super bowl of gaming. I'm gonna team up with Scott Johnson to do a show after our live streaming of the conferences. And that's at Frenchspin.com. And a special thanks to the more than 5,000 patrons, 5,065 patrons who support the show and it was written and contributors like me because I do contribute to the show. You can support the show through Patreon or other means and you can find all of the information at dailytechnewshow.com slash support. Our email address is feedback at dailytechnewshow.com. You can call us at 51259daily. That's<PHONE_NUMBER>. That's complicated to say and you can listen to the show live at tunein.alphagateradio.com and join us tomorrow for a new guest. So we'll be back next time. Scott Johnson is right here to be the host of The Tech News Show. This show is part of the Frogpants Network. Get more at frogpants.com. Time and Club, hope you have enjoyed this program. Great show everybody. What should we call it? We should call it the one where Jenny was like, I don't know what's playing right now.  Yeah, it's like, okay. It's almost two. It's so incredibly awkward when the, just because of the ducking, when the sound and the person saying the words and the music are in two different Google Hangouts, there's no fixing it.  Like so whoever speaks the magical words that open and close the show, inevitably they have to be the ones to play the music or else all just heck breaks loose. But there are some good titles. We do have some good titles. Roger, do you want to read these titles while I start the process of the things with the things? I will put in my vote for 10x1, which I loved. So at the top at nine votes is slow moving fiber. Following that is a blank in the blank of Spotify. A lot of these are, so who's WHC, T-E-H-B-D? That's Ian. I don't want to speak French. Fiber for your internet diet. Twitter Tweaks Tweets. It's a bit of a tongue twister. Tom is listening, jot down that note, a show without merit, but with a scarf. Tom's not here, we're finished. Ha! Oh, that's nice. But that's so easy. It can't be WWDC day two without Apple clarifications. Tweaking Twitter. I like 72% Tomosity. See, I'm just trying to find Tim Vex. I do not see him. Oh, there it is. How can you have any data if you don't finish your fiber? Wow. Make your pick. I trust anything you say, but I really love the Pink Floyd reference, I have to say. Number 11 is slow moving fiber, so that's top so far. Gentlemen, decisions, decisions. It's your show, you make the call. I'm torn between, was Tom listening? Yes. He submitted, oh God. Tom was listening. Now, it's good I didn't know, I would have been mortified. I like, Tom's not here, we're finished, even though you think it's crappy. I don't think it's crappy. I didn't say it was crappy, I just said it was easy. And 72% Tomosity is cool as well, but maybe Tom wouldn't want us to make the title about him. No, he would not. I can come here. I would bow to the numbers here, slow moving fiber, it's got the most votes. All right, so slow moving fiber it is. At number 11, vote, 11 votes, slow moving fiber. And as your reward for picking that title, you guys now have unlimited, and by unlimited, I mean like 10 minutes, time to talk about whatever you want about Apple. Go. Dude, this morning I was reading Apple's music page to figure out what happens to iTunes match. And Apple says it's two different services that are independent. Same. And it's, but the thing is in the very same, like in the same page, the part of the page, on the left side it says that if you don't have songs, if you're an Apple Music subscriber, and they don't have songs that you have, you can, you'll upload your files to them. So I don't know how that's not match, but it's not match apparently. So I, I can't quite, it's like splitting hairs and I'm trying to figure that out still. Go. It's probably because they don't, they're still working on a business model and they're still kind of making sure, well maybe we don't want to get rid of this too soon, or fold it in, because you want to see how the, the, the winds blow, so to speak. Yeah, well it's, it's two dollars, effectively a month to have iTunes match, it's 25 bucks a year. So. Yeah, I don't really understand how it wouldn't go away and be, especially since yeah, it doesn't make sense, but you know, I'm, I'm with Roger on this one, I think they're still figuring everything out. They have no idea what they're doing there. You know, it's, you know what, it's, Apple's always treated as this kind of monolithic Borg like, you know, we have, we have a, we have this huge, elaborate game plan that we're going to go by, by the numbers and I think it's actually not, you know, like that. I think they have a general direction they want to go and they need to kind of test and push to see where, you know, where things will work and where things probably won't work.  And if you're Apple Music, you kind of get match built in, because you're paying them, you know, 10 bucks a month. That's probably why they're separate, so they still have it without it being tremendous. It just seems lame, but whatever. All I know is I'm waiting for Skylake MacBooks to roll out in the fall. And that's the only thing I care about from Apple. I know it's sad, it's sad. What's code name is that? Skylake is the new chip set after, or a CPU after Broadwell and it's just, it's the talk portion of Intel's TikTok map where talk, is a complete architectural change, where TIC is just a refinement of an existing process. For example, Broadwell is a TIC for the Haswell talk, which basically saw a die shrink. I think it's down to 14 now, I mean. Is this 14 already? What is Skylake burning that makes it a talk? So, I guess it is, hold on, make sure. Make sure this is. Well, you were, so Roger wants it. It's newer, but finally, I think it's gonna be, this is where DDR4 actually cements itself as a widespread memory technology instead of DDR3, which you've been using for the past six years, seven years. It just seems like over a very long time. Basically, the biggest advantage, of course, is you get roughly the same performance for a lot less power draw, which means you can make a lot more thinner, amazingly light notebooks, or you can make super powerful laptops that have considerably more oomph behind it. So you could, like me, right now I have a desktop PC, which I edit on, and then I have my MacBook, which I do all my other work on. Be great to have just one machine.  I think I'm gonna go. It's past midnight here. What? Wait, are there werewolves and stuff that rampage across the, wait, what are the Finnish, like, scary monster mythologies?  Yeah, I don't think they're quite Finnish. I don't, honestly, I don't know. Are you think you're all finished? You were born ready, and now you're all finished. Oh, okay, when you put it like that, it does work. You do need a t-shirt that says that, but you have born ready on the front, and then I'm finished on the back. So you could just end the show by turning around. Well, on that note, what could be a better way than to end the video broadcast? Thank you. Almost anything. Almost anything. Ha, ha, ha.


Welcome to What's Next, a seminar series presented by IBM Research, in which we spend time with some of our scientists and researchers learning about the exciting work they do. I'm Shaheen Parks. I'm a content strategist here at IBM. And I'm so pleased today to be joined with not one, but two guests. Next to me I have Dr. Thomas Ash from Ivonic Industries. Thanks for having me. Glad you could join us. And we have Dr. Ji Chen, who is part of the MIT IBM Watson AI lab based here in Cambridge. Today, Dr. Chen will be talking with us about using graphs and grammars to automate drug and material discovery. And one of the key benefits of the methods that he'll talk about today is that it requires a very small amount of data to be effective. When we compare it with other methods that you might use to achieve similar results, those methods would need orders of magnitude more training examples to get to even similar levels of effectiveness. Dr. Chen has degrees in computer science and mathematics, and his research interests include statistics, machine learning and scientific computing. Dr. Ash is a background in chemistry and material simulation. And at Ivonic, he is responsible for data science in the research and development organization, and he's part of the effort to move towards a digitization of R&D. Today, Dr. Ash will be able to provide a very complementary industry perspective on the research work that Dr. Chen will talk about. With that, I'll hand it over to Ji. Thank you, Sharin. Hello. I'm here very excited to talk about a joint work with MRT recently that generates molecules by using a very small amount of data. So this is an exciting innovation by using graph and grammar. I'm going to talk about the details of that. Drug design and material discovery amounts to generating a good candidate-self molecules so that we can screen them and find the ones that we need. So generating molecules is an important step in the past decades. Deep learning methods and AI methods have been flourishing in this regard. This method called generate models, they are able to learn from asset molecules and generate new ones. A general limitation of these methods is that they require a large amount of training data. So the method that I'm talking about today would be using a very small amount of data and it is very data efficient. For example, it is able to use only a dozen chain-net examples that can already generate molecules whose quality are as good as deep learning methods, which actually requires maybe 100,000 chain-net examples. So in these methods, we will be achieving a molecule as a graph and then learn graph grammar to do the job. If you think about getting molecules that are of good use to us, this is like finding a needle in a haystack. Because all the possible amount of molecules are actually a lot very vast. So this such place, according to certain estimation, would campaign 10 to 60 molecules or jobs. And if we talk about other applications like materials and formulas, then such space would be even larger. So the approach we are taking here is like a circle. So we will start with cheating data like molecule here as a graph. Then we will be learning a graph that can generate these molecules. So the graph grammar here serves two purposes. One purpose is that it is a generate model and generate new molecules. But the other use of it is that it can also give us a very useful tool to get representations of a graph, so that we can use them to get better computer predictions of the molecule properties. Now predicting these properties are actually also an important step here, because otherwise they have to be done by human being who goes to the lab and do tests or do simulations. The computer predictions would actually speed up this process. Furthermore, with the computer predictions of the properties, we can now apply numerical simulations, sorry, and numerical optimization to find the regions of molecules that are good candidates. So this would reduce the amount of candidates that we need to screen out when we find the ones that we need. So our work here focus on the first step here, which is to learn the graph grammar by using a small set of molecules.  It's like 10 to the 60s, something like that. But actually, if you look into the chemical industry, we are very limited in what we actually would like to do on large scale assets that we have. So we need to already focus at the beginning to generate those molecules that are of interest to us, and we definitely see that these methods can be helpful there. Exactly. So the grammar we are doing here is actually a good innovation in deep learning, because this is a tool for us to narrow down the search space and try to find the good things that we need. So I think grammar is actually of no strange to us. When we learn a new language, we learn a grammar, and when we learn to write a program, we also learn grammar that can specify how a program should be. Effectively, a grammar is a set of rules that tells us how to compose a complicated object, either a sentence or a computer program. It turns out that graphs, which are actually the things that we use to model what we use, also have grammars, and that is the gist of our working. So when you say that the grammar is essentially a set of rules, is that similar to how we might describe an algorithm? Yeah, exactly. So a program contains a lot of statements, and these statements are in certain order, which actually forms the algorithm that tells us if we do the first step and then the next step, then eventually we complete the job. So the grammar here also provides us all these elements, and we just need to figure out what are the steps in order to compose a graph that we need. And actually, if you look into the education of chemists, it's basically the same. So you learn a new language, but that language is not meant for communicating with people, but it's meant for communicating with scientists. So you learn chemistry basically as another language, which is foreign in the beginning, but once you get, let's say, a better feeling of it, then you are somehow fluent in chemistry and you can actually speak that language basically the same as you speak English. So the chemist is learning the grammar. Exactly. Yeah. I love that metaphor, right? So chemistry is also a language, so let's learn it. All right, so how do we learn that? First of all, we look at a graph, we look at a molecule as a graph. So the adepts here are the graph nodes, and the bonds are the graph edges. Now, of course, for molecules, it is a little bit special because there are certain structures here that we definitely won't contain. So for example, there's a ring here. So to generalize the edges that contains only bonds, we actually also use so-called hydra edges to contain things like rain. So these hydra edges are actually a generalization of edges that contain only two nodes. But for simplicity, I'll keep using the terminology of edges when I'm describing this work. So now that we know that the molecule we'll be looking at, we'll be looking at this by using the graph representation. So the next step is what exactly is the grammar? Here I show an example of the grammar, which contains whole production rules. So the first rule, which is here, starts from an empty state X, which says that nothing. And that will be replacing this empty state by using a ring. Now, inside this ring, there are some nodes that we know what they are. For example, these blue nodes, they are the specific atoms. But we also have the open nodes, which are the R star, the little black square here. These are open nodes, and we don't know what they are, but they can be replaced if there is a match by using some other production rules. And as you can see here for all of the production rules, the left-hand side is something that contains the open node R star, and then the right-hand side says that we'll be replacing that by adding edges and bonds. So this is exactly the way that we compose a graph, which is to edit the free adds, nodes, and edges, and then eventually the entire object we have. So let me give an example. Now, here we start from the empty state X, and it will simply be applying certain rules to expand the graph, and then eventually there is no only open nodes. For example, in the first step, as you saw in the past slide, we'll be using the first rule to replace the empty state by a ring. And afterwards, we see that there are two open nodes in a ring, so we'll be applying another rule that replaced these two open nodes by another ring. So now we have two rings, but still inside the rings there are some open nodes. So we'll be further applying applicable rules and expand the graph and do these multiple times, and eventually there's no any open nodes in the graph. That is the graph that we want. So, G, I imagine this is a very simplified example with four production rules. How many production rules would you expect in general, in like a real example? Now, so that actually entirely depends on the training data set. Okay. For example, for this particular example here, we're actually using a training data set of Isosynics, and there are only a dozen molecules. From there we can actually learn a grammar that contains the production rules to generate all these Isosynics, and the number of rules there roughly five or ten. So it really depends on where you learn from. And maybe just jumping in on that. So in the first step, you basically come from an empty set, and then you have like some scaffold of molecules. So I expect that there are like more of these rules, right? So it's not just one that can generate something from an empty set, but more of those, right? Right. So the initial state where the rule that starts from an initial state, so this is actually a very special rule, and depends on the training set and depends on what eventually we learn about. It is possible that there's only one rule that says that that starts from specifically the star state. So in that case, it basically says that every time I'm going to start with this range here, but it is actually also possible that the grammar we learn eventually would contain multiple rules that contains the star states. And then in that case, we actually have to choose which rule to use when we generate molecules. So there is actually a very natural question, right? So how do we get these rules? How do we get a grammar? Now, the way to get this grammar is actually the reverse thinking. So rather than starting from the empty state and get the final graph, then we'll be actually starting from the complete graph and then trying to strip off the graph component by component until eventually nothing remains. So for example, what you see here is that we start with a molecule and then in the first step, we'll be removing these four nodes here and replace it by an open single R star. So because of this reverse thinking, the production rule actually says that we'll be replacing the open star that the open nodes are start by these four nodes connected by the address. So every time we strip off a component of the algebra house, we make a production rule and then eventually we make enough production rules so that nothing remains. So this is the way that we can get a grammar which if we apply reversely, we get a graph. Yeah, it's actually amazing because what you can see here, and I mean this is also an example that pits to the set that we showed, it actually has rules that generate real chemistry like chemical functions or chemical entities that have a function. Exactly. So later on I'm actually going to show you some specific experiment results and from there we can see that certain functional groups for a certain class of molecules are actually appearing in these production rules that we learned. Right, so now that we talk about how do we learn production rules, but now we actually also have an open question because the way that we define what components to strip off and the order of stripping these components are actually arbitrary. So what would be the best production rules or what would be the best grammar? So we inject the machine learning here in order that we can get an optimal grammar. Now the way we do this is that we'll be imposing probabilities for the edges because these probabilities will be turning us with what components are formed and what components are removed. So then we'll be doing an optimization rule. We start with an existing grammar that can use the production rules and then use the grammar to generate a large set of molecules and then we measure how good these molecules are. There are multiple indicators, for example, we can measure how many of them can actually be synthesized or we can measure whether or not they are diverse enough to cover the entire space. So whatever they are, we'll be using these indicators and go back and specify an update in better edge weights. So these edges will give us another set of grammar rules and then we iterate and do this until the optimization converges. So when it converges, the grammar we get will be the one that generate molecules that are synthesized and there are synthesizable maximally and there are also maximally diverse. That's actually great and I want to jump in on that because generating molecules is easy, right? I mean you can just draw it on a piece of paper but generating molecules that are actually synthesizable, that is the challenging task because you do not want to spend like hours in the lab until you eventually find out that you can probably not make that molecule but you want to have molecules in a set that are predicted somehow that you can actually make in the end because it doesn't matter if a molecule or a hypothetical molecule has brilliant properties but you cannot make it so it's probably just something that you can just throw away right away, right? Exactly and I think this is actually also one of the great things about these methods because if you look at existing methods in literature many of them, when they learn the generative model they only consider how well you can reconstruct the original training examples but now we are actually not considering how well we can generate existing training examples because the grammar itself actually guarantees that so in addition what we do is to look at other indicators and one very important thing is the synthesized bit so we'll be using that as an indicator and try to optimize our model so that eventually we can generate as many molecules as possible which are synthesized So maybe let's look at some experiment results I hope that I can actually convince you further that we're actually doing a great job So what I'm showing here is that we have certain methods to compare with and certain metrics that we also compare with So the bottom one here, the DEG is our methods which stands for Data Deficient Grammar On the top, some state-of-the-art AI methods used for generating molecules So actually many of them are also in the style of encoding a molecule into an equilibrium-laden space and encoding them back to the original model So when we compare them then we look at various different metrics For example, how many of them can be synthesized which is definitely one thing that we focus on but additionally we also look at other metrics For example, when we look at existing generative methods they are actually cheating the training set as a distribution So when they build a model they want to generate data that forms a distribution that is aligned with the original distribution So we have all these four metrics that measure how well such alignment of the distributions are And then, unsurprisingly, we also have a metric that just tells us what is the proportion of the molecules that can actually be synthesized Yet, one more interesting thing is that we have another metric which is called membership So this membership is particularly talking about if our training set consists of a class of molecules So there is a specific class here So when we generate, then do the generating molecules also pour under this class I think this is actually one of the very rare things that we also compare when we are evaluating our methods And for all these metrics obviously the higher the better So you'll see that for many of the metrics we are actually achieving the best And even for the 1th year which is possibly the 2nd but it is actually still close to the best So this is for the ISO setting class And what is not shown here are actually also some other training sets and some other classes and the results can include similar findings It's good to see that actually in action because I mean, you briefly touched like there are other methods that can generate valid molecules Like you get a molecule that actually is a molecule and does not have like a carbon atom with 5 bonds attached to it or something like that And in that region we see that there are methods that are probably doing equally well like we do, right? 100% valid molecules Brilliant thing, good to go But the interesting thing that you touched at the end like the synthesizability and the membership we can see that this method is actually outstanding there because getting to I mean this RS value of 27% basically means operational score So that is actually one of the tools that we use to measure the synthesizability of the molecule Exactly, good that you have that I mean it's on the bottom, right? synthesizability and that's actually something that we want to have as I already mentioned like it should be synthesizable but also many of the challenges that we face are in the last column that you have here like they should be of a similar chemistry because usually if you have like a certain chemistry in your company you do not want to generate arbitrary molecules that somebody could eventually make you want to make molecules that you can make and that fits to your portfolio, right? And that's actually very nice to see that we can actually have membership scores that are amazing Yeah, it's just like I mean give me some isocyanides and I'll generate isocyanides for you but not something else Exactly Do you have a question for you? What do you think it is about this method that is leading to these strong results that's different from some of the alternatives that are listed here? I would say so one important thing is about the data efficiency as I mentioned at the very beginning, right? So a lot of methods here like the graph MVP or GTV all these methods are deep learning methods that require a large amount of training data so as a matter of fact when we are doing these very particular data sets isocyanides I mean so there are only a dozen a properly less than a dozen training examples here and it's just because human being actually don't know more than that so then when we are applying when we are trying to do with these data sets it is very natural that existing methods which can only work with large units would likely fail That makes a lot of sense Perfect So then maybe let me also show you some other examples just more than numbers, right? Yeah What I'm showing here is that for different data sets we can generate new examples so all these are newly generated ones and I don't know, so Thomas you are actually experts Do you actually is it possible that some of them you actually haven't seen them before which really says that we are generating something that you are curious about? Well, you can always draw them, right? But the interesting thing is is mainly that you put in like that little amount of data only 12 examples of a particular molecular class and you actually are able to learn what you have, like the language of that particular data set and so what I love to see here is that you actually generate isocyanates if you throw in isocyanates and that doesn't have to be the case if you just use some other method, right? I mean, you can basically draw each and every molecule so that's what I meant with it's easy to just draw them on a piece of paper but those seem, I would say, at least reasonable, right? So you have like the functional group that you are expecting and you have something carbon-based in the middle, right? Speaking of isocyanates, so let's look at what exactly these production rules are because we're actually all curious, so why do we exactly generate isocyanates? Now, we played with three data sets a game here so now we show some of the examples of production rules that we learned from the training sets and if you look at, for example, the top rule so this is for isocyanates and if you look at the first rule and the third rule we explicitly see the functional group here which contains the nitrogen, the carbon and oxygen connected by double bonds so these are the exact things that we expect to see for isocyanates and what actually is the production rules? Actually, we learned these production rules and we learned these production rules by not hearing someone else telling us that, hey, you really need to help and see an all-way connected by double bonds but we really just look at the training data sets and they're all good and can't figure it out. Yeah, and if I look into that rules that you generated just for the isocyanates as an example it's also nice that you don't get any other functionalities because if I want to have an Easter on Accrelate or something like that, I would put it in my data set and then I can generate those but I only put in isocyanates so I'm basically generating only isocyanates and that's actually pretty amazing. I'm glad that you like it. Yeah, I think there is actually pretty much the work that we've been doing recently I mean, so one thing that excites me about this is that the methods that we're using or the method that are actually developed is making a very innovative uses of graphs and grammar and this allows us to train a model that uses only a very limited amount of data so it is super data efficient. So we hope that this method will actually significantly speed up the process of identifying more useful graphs and materials and because in real life we just don't have that many molecules to stop it. Absolutely and so Thomas I want to ask you that question in real life do these methods support the work that you're doing? Yeah, hopefully. So traditionally if you generate molecules you always rely somehow on the creativity of a chemist and we definitely want to embrace that creativity that a chemist has but we want to support it with some kind of I would say artificial creativity so that's what we do, yeah. We learn a language, we learn the language of a particular dataset and we can give our chemists just pictures of new molecules that they probably never thought about and if we add then the property prediction part of it that you also mentioned in the beginning that becomes a very powerful tool to quickly identify which routes can I actually navigate in my chemical space which would give me superior properties of what I actually already know. So it sounds like both are really valuable pieces of the process, the generation but also the property prediction? Yeah, definitely, definitely. And also it becomes in handy that this method is actually pretty data efficient so we can also in principle describe the molecules in a very efficient way which gives us probably a little bit more breathing space to do the property prediction on very limited training data because I mean if you have data for 12 molecules that's not much but if you look into the chemical industry it often happens that you do not have billions of molecules but more like in the probably tens to hundreds where you can actually effectively generate data. Right, and is that a question of being able to generate data or actually even just having knowledge of the molecules existing? Well, what we always have to keep in mind is that generating data in chemistry usually is not like pushing a button and then you get a result for a molecule you have to think about how do I make that molecule then somebody has to go out with it and probably perform a multi-step reaction to get a molecule and then perform some other reaction on it to get for example a polymeric data and that can be actually quite broad so it is actually also something which has to take the cost in mind because it's very costly for us to generate knowledge on molecules. I think this real world perspective is so helpful because I know that a lot of the work we do here is how we use publicly available data sets or images or words and things that unlike molecules maybe are a little bit easier to come by so while we have not infinite data we certainly have often more than 12 examples so I think having that perspective of knowing this type of use case there's a real natural bound to the amount of data we'll ever have really highlights the importance of having methods that are able to address that. Yeah, that's definitely true I mean it's easy to take a picture and if you train something on cat pictures you can just take more cat pictures but it's probably a little bit harder to generate data on molecules so that's definitely true. Yeah, absolutely. As I mentioned at the beginning our next step would be looking out how can we protect the properties by using a better method and just getting back to this data efficient paradigm so their efficiency is actually not only a benefit of our methods when we generate molecules it is actually also something important when we are trying to develop a prediction model for the properties that is something in our agenda next and we actually will be so excited to see how we can use this grammar and particularly the production rules and the ordering of using the production rules and also a good representation for the molecules when we are building these predictors. Yeah, I'd love to see that. Well on that note I want to thank both of you for taking the time to talk with us today and I also want to thank our audience for listening in. It's been great and we will be back again soon so stay tuned and we'll see you all soon.


So it seems like the corruption has taken over Roblox and this learning with Pibi escaped the glitch hobby. And apparently we're trapped in the learning with Pibi Daycare as we are trying to escape from all these traps and whatnot. And it does say new updates and new cartoon characters so that should be pretty sick. And also it says run from the glitch with Pibi, discover hidden secrets, and unlock new locations. So that's what we're gonna be doing in this video and before we do make sure to smash the like button and subscribe with the notification bell on if you're new around here. And let's begin. Alright and here we go. Oh my god. Yo what's up dude? He's back in my merch. Oh here's a very handsome fella. Oh god this dude got corrupted already. Okay wait is that us? Is that a bus from Spongebob?  That is totally from Spongebob right guys? Okay what? We just got teleported. Okay cool. Okay we're in uh Green Hill Zone. It appears. Hey is that Tails? Oh my god. Oh my god what happened to Tails? It seems like he got corrupted by Pibi's glitch. Alright so let's go see what's up with Sonic over here.   Okay well I guess we gotta go fast and race Sonic then. Can we go down here?  This thing is just tossing us. Oh my god dude it's like just dragging us all down here. And here goes nothing. Okay what? Seems like we have a pier somewhere. Where the heck are we at? We're like in some sort of car or something what? Okay that is weird. Alright let's keep on moving the crusty crab. Look at these hands reaching out of the corruption. It's like they're trying to escape but it's too late they're already trapped in there. Oh good my boy. Dude he just got smacked by those hands. Alright now let's keep going. I think he could also go down in the sewer but we'll skip the sewer. And we'll keep going down the road. Dude what's up with everybody falling over? Alright what is that? You won't survive the glitch. What? Well watch me survive it. I will survive it. Wait is that cur- Ow. Dude something just hit us. What the heck? Okay is that the house from Courage? It's gotta be Courage's house. Oh it is. Look at this. Oh that is pretty sick. I used to love watching that cartoon. Can we go inside? Yeah we can't go inside of the house. Come on man. Return the slab dude. Return the slab. Ba da ba ba. Da ba da ba da ba da. I can totally relate to that my mans. Alright let's go. Let's just keep walking. Alright that is pretty cool. They have Courage's dog and dude he just fell in there. Okay what is this over here? It's the sewer. I guess the sewer takes you another way. Okay we hear the corruption. Okay let's just keep on moving man. We did not want to get caught up in the glitch. Oh cow shake? Hey did I get my checkpoint? I don't know if I did or not but let's keep on going. Be careful with the cow shake. You know what it does to people. It makes them sick. Okay there's one of the traps. Not following the spikes my mans. My leg. Wait is that the chocolate guy? It is the chocolate guy.  Okay let's keep going. And I died. Hey that's not funny man. He's laughing at me because I died. That is totally messed up. Okay let's go go go. No stopping. Keep it moving fellas. Alright come on we got it. Okay oh what? Oh look the mystery machine is over there. Let's go check out. You can't walk up to it.  They have cool little Easter eggs here but you can't go up to them. Okay whatever let's keep on going. And we have made it to Sunday's to learning daycare. Okay what is this? Some sort of bus? It's like an ice cream rocket ship dude. Okay whatever. But is this over here? Okay watch out the giant Crayolas. Bruh I told you to watch with crayons bro. People don't listen around here. Okay oh be careful. Okay we haven't found any other secrets. Come inside. Play. Come play inside. Alright dude whatever. Thanks for the invitation man. Okay now what? Let's keep going here. Pencils are deadly. Yes they are. Okay uh oh man. Okay I don't know how exactly this has to do with the corruption but what happened to my chainsaw?   Okay I guess we just keep going. Follow the moving wall because it knows the way apparently.  I don't know but he didn't. Yeah what the heck is going on with this place dude? There's like bikes sticking out the wall and everything. Okay oh not this one. I'm gonna go with the red. Ay we got it. Come on boys. Just follow me. Okay now oh what? Okay it's teleported. Okay we're out of that place. Wait oh look at Pibby. Dave bun bun. Okay Pibby I will try my best to save bun bun. Alright let's keep on moving. Did that spell hug? Haha he died. Alright let's keep it moving bro. Oh look find double D dude. My man's is corrupted.  Okay let's go um. Got the name of this cartoon. Can we go in here? Oh wait there's an open door. Okay let's go in here. Check it out. And it doesn't take you anywhere. I thought it was the hidden door. Okay no. Oh watch out the lasers. Hey oh. Let's just die. Dude there has to be something here. Like a hidden passage. Okay this has an arrow. Well that's like FNF. I guess none of these take you anywhere. Okay whatever man. Let's keep going. Jump on these blocks. Get nice. Find the secrets. You see she's telling us. Like find the secrets bro. Oh bro how do people keep dying out here man. The bodies drop. Hello Mr. Teddy Bear.  Dude has his heart sticking out. Uh what. Jump on the table. There you go. Everybody's jumping up and dying. You guys gotta chill bro. Right nice. What the heck. That seems like a trap. I knew it. I guess we gotta go down. Hey girl I am drying my bath to escape. Or you could do that. Big brain. Okay uh come on. We haven't found a secret hidden passage yet.  Oh it's to the bob mi hoi mi noi. Mi hoi mi noi. Okay there has to be a passage somewhere here. My god it's so dark. Why is it so dark. There's no secret hidden passage this way. Okay let's get him in. Oh my god they're not falling to the lava. Okay dude. Come on this is driving me crazy. I know there's a secret hidden passage somewhere. Okay be careful. Maybe the lockers. Oh wait what about this one. What. I knew it. Okay we're actually inside of a locker from what it seems like. Okay uh jump. Jump. Okay nice. Oh. What. What was that on about. Okay let's dry over time. Let's go on the side. And. Hey guess that was it. Bro what the heck was that man. Whatever let's keep going. Oh what.  Okay I'll try my best not to die. I will try my best. But I'm trying to figure out the rest. And we are corrupted.  I have no idea. Okay let's keep going. Okay bro. Get my chainsaws back at least. Oh the fairly odd parents. What did it say. That's something. Why did the letters disappear. Why did I say die there bro. Like bro it's a kid game bro. Please help. Are in trouble. Okay her name is Wanda from what I remember. Oh. Woo you boy. Oh dang bro. Timmy and Cosmo. Are corrupted. What the heck. Oh my god. How did I know that. I did it. I just walked through the freaking door man. Come on give me a break. Dude this is pretty cool. Yo this looks twisted yo. Oh I guess that's pretty much it. We got a Timmy Tirmier Nightmare award. That's pretty sick man. Oh someone got slashed. Okay hurry the corruption is coming. Yeah you can't even touch the crayon on pibby. I feel like this whole obby is a troll. Okay nice. We almost got stabbed there indeed. Okay keep going keep going. Oh my god I can't see anything because of the giant chainsaw. It might be my fault though. Okay let's go first person. Uh oh. Okay cool so we found one. We found like two secret hidden passages so far. Bro it's the milk. You cannot touch the milk. It's forbidden okay. What the crayon is melting. Oh that is one giant cupcake. Okay cool. Where the heck do we go. I'm following this IDK guy. Pretty okay. Watch out with the knives dude. Why is there knives sticking out of the walls dude. What the heck. Oh my god. Alright cool. Okay now what. Let's try to find another secret passage. What's going on. Like the corruption is trying to break through. Oh what. I'm not sure if this is a secret hidden passage but oh my god you can see planet earth. Definitely something. Let's go back. I think the game is broken. Or that part is broken. Okay now we go up this way.  They are everywhere. Like want you to die man. Alright oh okay we gotta watch out here. Uh jump. That did not work out. Oh my god we're corrupted again. Where the power of Christ compel you. What the. Okay I'm starting to believe that you can't get past this. Okay you can't get past it. Oh that was close. Okay another secret hidden passage. No where do we go in here. Okay nice. And I assume we jump inside. Trying to find another secret passage. No I guess no more. Cartoon Network Studios. Okay these are the credits I guess. Well I suppose that is the end of this learning with Pippi Pippi unless there's a secret hidden passage here. No come on man. What's up my boy. Alright you guys well there we have it. Learning with Pippi escaped the glitch hobby. It was pretty cool. Two thumbs up for it. I liked it because I used to love watching Cartoon Network as a child growing up. The good old days man. I'll add this kid. Alright. Added you my boy. Alright well thank you all for watching and I will see you guys in the next one. Peace.


out there, Prasad. Welcome to theCUBE. Dave Vellante. Thank you. Thank you for having me. Good to see you, this is my co-host John Furrier. Hi, John. Okay, I think Dave, I want to start it off by just saying that the big news here is the sizzle, the sizzle of VMC World's cloud and big data, but the stake has been this home services solution message. There are people making money out there doing solutions, whether they're consultancies or startups, and it's growing, there's more needs out there. There's a lot of demand. So tell us what your opinion is on that and your angle on the solutions market in general. I think the solutions market is the ultimate optimization of value on our core platforms. So if you do our jobs right on our platforms, the solution demands should be insatiable. And my job is to really make sure that there is indeed the case and drive solutions that are easy to consume and easy to scale. Versailles, you know, it's 27 years at Intel. We were thinking we were going to try to break Pat Gelsinger's record there, but that's quite a career. We had Pat on last year. Pat's been great to us. He's been a guest on theCUBE many, many times. One of the things he talked about was the Intel culture, you know, like the Army, marching to the cadence of Moore's law. And he said, you know, EMC's culture, not so much, but maybe that's changing a little bit. So first of all, what brought you to EMC and talk a little bit about the cultural differences. Maybe the culture shock. No, that's a great question because after 27 years in one company, moving to another company was not the top of mind for me. But as I was looking at where the industry was going from a solutions perspective, it was clear, as Joe said today, that the industry is going through one of those big transitions that happens once in 20 years. And the confluence of cloud computing along with big data was not just a theoretical premise when I interviewed for this job. It was clear that this company, EMC, was on a path to acquiring assets that were going to make that reality. And cloud has been my passion from my Intel days. And the ability to move up the stack from silicon to actually build solutions was a huge opportunity I couldn't pass. So that's why I'm here.  EMC is just NVM where I bet the farm on Intel.  Let's talk about that a little bit. I mean, basically you take these industry solutions and it could be third-party products, EMC products, put them together and then demonstrate how they work in a real-world customer environment. What does that do for a customer? I mean, obviously it reduces some risk and maybe speeds up deployment. But what do customers tell you about that? Customers care about two key values, right? One is, hey, is this going to make a big shift to the value that I serve my business with? And second is, is it going to make a substantial shift in my bottom-line cost of running the business? And so the goal of what we are trying to implement from a solution standpoint is make it dead simple to implement, not give them a bag of parts and make the cost of integration so hard that we don't go anywhere. And each of these projects becomes a man of the program. Secondly, it's got to be changing the game. It's got to be a game changer for the business that the solution's intended for. So if you take FSI or you take oil and gas or healthcare, can we take the collection of our products, be it a green plum, be it a VNX storage environment, or be it our security capability, or be it our content management with Documentum, and can we stitch them together to create a total solution that makes a huge difference? That's what we're trying to accomplish. Yeah, so you do obviously do a lot of stuff around SAP, Oracle, Microsoft Solutions, a lot of the traditional enterprise apps. Have you started? I mean, I'm sure you started, but when will we start to see, or can you give us some examples of these so-called big data applications emerging in terms of proven solutions that you can actually point customers to? Yeah, we are in the early days, to be honest, but a lot of what I'm seeing, and this goes back to even my own days as a guy who used to run Intel IT, is the refactoring of the core ETL environment from the OLTP environments, the refactoring of costly, you know, multi-million dollar notes that run the analytics environment into commodity hardware, and enabling massive scale-out parallelism in the compute model for analytics. And so we are absolutely seeing the implementation of use cases for analytics that leverage that technology. The big enterprise applications are not moving there overnight because of the inherent nature of the mission criticality of these environments, and the inertia of moving from legacy to the new environment. It's complicated. It's complex. But where we see opportunities is emerging usages that have been hard to crack from an analytics standpoint. So if you take the FSI community, a key focus is can we enable real-time risk management as part of portfolio and risk on the trading floor? So here you have 30 to 40 million messages a second of market data streaming in, and can you leverage that market data into meaningful analytics that say, hey, based on this, our risk portfolio has got a shift based on the nature of the bits of data coming in. That is massive ingestion of data, real-time information, translating from that data to making decisions on portfolios. The question I have for you is, obviously the Intel background is stellar environment for dealing with massive change, Moore's law, for example, Dave mentioned. We're living in a time now where the real-time web or real-time internet mobile and web is really, really relevant. Cloud is scaling. It's a system. It's an operating system. So we've always admired Pat Gelsinger for that systems view. And so you have this infrastructure operating environment going on. That sounds a lot like Intel to me, except it's not a chip. So can you give us insight into how you build those solutions, how you look at that? I mean, can you? Well, absolutely. I think the notion of the data center itself having a lot of analogy to a computer with the core platform, the BIOS, the operating system, has a means to establishing simplicity and abstraction on which you run third-party applications is not a far-fetched idea. In fact, that's what we are collectively doing with VMware and EMC and Cisco in many ways. At the compute platform layer with VC and Vblock, we are converging the compute network and storage to a much more simplified model. And above that, we are driving the abstraction of a set of orchestration for management of these resources and implementing applications with ease directly by end customers through this notion of self-provisioning, right? So taking that concept into a broader implementation through third-party applications, enterprise applications is very much the focus. I live in Palo Alto, California, where the sandbox of Silicon Valley is in my backyard. I live there and I love living there. It's one of the best places to live and it's innovation. So that is enabling a lot of entrepreneurship and intrapreneurship at big companies to build on that abstraction layer mentioned. You guys are enabling that. How are you guys looking at that? I'll see big data announcement today, something that we've been talking about. But that's an explosive market, the software, this new mobile apps, Facebook apps, enterprise apps, the consumerization of IT, it's happening. Absolutely. You had a censor on earlier, I mean. Tell us what's happening. Well, the way we are looking at it, and I'll give you my solutions perspective, is I think innovation for solutions is gonna happen with a very deep partnership with the key industry players in the enterprise. So our goal is to establish basically an R&D lab in Santa Clara in Silicon Valley and look at innovative new usage models. And let's just take the hybrid cloud as an example. The hybrid cloud has got two or three key challenges. One is how do you enable this notion of cloud bursting? When you have a peak demand, can you essentially elastically increase the compute of resources on the public cloud and run your application without a blip in the SLA? These kinds of usage models have known issues on network latency and bandwidth, known issues on identity management, and the implementation of a policy-based orchestration that has full visibility both on the private side as well as the public slide of all the resources. And there's only one way I know how to solve these problems which is to go deep with a partnership with the Cisco's or the SAP's or the Microsoft's and really have a joint lab innovation environment and knock them out one by one. So that's what we're going to go do. So we're here with Prasad Rampali who's the Senior Vice President of Solutions Engineering at EMC. We're talking about what we call proven solutions. What EMC refers to is proven solutions. Prasad, what's your vision for this group, this initiative which started a number of years ago and I think really started off as okay, we EMC have to be in the business of proving out these solutions and you had to earn your way into the minds and hearts of customers. It's a lot of hard work as you well know. And so I feel like you've sort of gone through that phase successfully. What's your vision for the future of this group? Well, thank you for asking. That's a great question. So I've been with EMC now for six months and we're launching what we affectionately call as Gen Two of the solution evolution. And Gen Two has got three key parts. The first is you can create solutions all day long but you have to have the ability to scale it across the thousands of customers very quickly. So how do you establish a scale model from the time you create a solution through the direct sales force, through the indirect channel in such a way that this penetration model is moving at six six times the speed is a key focus for us. The second is shift left and the idea here is for us to create game-changing solutions, we can have solutions as a wrapper after the fact when we have already launched our products. We have to be in situ integrating. What was that one again? The second one? The second one is shift left design in solutions with our platforms. And the idea here is we want to integrate our platform capabilities with third-party solution frameworks very deeply at the time of launch, not six months or two years after the launch. And that would require a completely new development model with our products, very deep partnerships with key ISVs and technical partners. Like SAP and so forth. Like SAP and really change the game on some differentiated feature. Let's take SAP and as an example. Do you have a joint lab in Santa Clara with SAP, right? That's the... Yeah, today we do, yes. But we want to take that to a whole different level but today it's mostly a showcase kind of environment. We want to take it to an R&D joint development environment both here as well as perhaps in Waldorf. And we want to be able to demonstrate integration points between SAP's landscape management and the application level all the way with visibility to the intelligent storage system and how we enable that level of policy-based management that could be happening at an application layer that SAP cares about but the infrastructure just adapts to it through that level of integration. So that's the third, so scale, shift left and... And the third one is really solution R&D. When Todd started this organization about five years ago clearly the focus was to enable sales. And I think he did a fantastic job. I'm building on that and saying, hey, let's go shift left. Integrate solutions with our core products and differentiate features. And even go two years out where really look at the next big technology inflection, right? Flash is viewed as the technology inflection for storage today, what's the next big one that's coming? And really preempt that if you will with appropriate solutions that could be tied with that R&D process going forward. Yeah, I think that's a great vision Prasad because I would imagine it's very difficult to take a quote, unquote, proven solution and then scale that as you say. So customer says, well, that's not me. Okay, can you do that for me? And can you then now become one offs? How do you scale that? And so if you can crack that problem, that's game changing. And then the other thing is, I mean, I've talked to a number of CIOs, application heads, infrastructure heads about this capability. Generically, whether it's EMCs or IBMs or whomevers. And the infrastructure guys love it, right? Because of course it's their job. It's going to make my job easier. It's going to lower my risk. I'm going to get stuff in fat, that's all, I get that. CIOs, they tell me, well, it's good. My guys like it. It doesn't really make a difference to me. And then the application heads say, well, it's somebody else's problem. So this notion of scale and shift left and the solutions R&D, that starts to appeal to a much wider audience. And I think it's a great vision. You know, I'm terribly excited by it because I think with this recipe, if it takes off in the next three, four years, we can produce game changing solutions that perhaps will be as important as our own platforms and will drive differentiation both in terms of top line revenue as well as bottom line gross margins. Hi, Prasad. This is very interesting. Prasad Rampali, Senior Vice President of EMCs, Solutions Engineering Group. Somebody that we've really focused on a lot. We've advised our clients in the Wikibon community to take advantage of these offerings. They're some of the best freebies in the business, right? A lot of them, most of them, maybe all of them are free, which is fantastic. And of course, the idea is it leads to other sales. Why not? But they really reduce the risk. They speed the time to market. They help you share best practices. And it's great to see the investment and Prasad, I love your vision. So thanks very much for coming on theCUBE and sharing. Thank you for having me.


So everyone our topic of the paper is MDCT in Evaluation of Mid-Architecture Traumas and Dr. Vivek Kosh has a resident in Department of Diagnosis at Telgaria Basin Institute of Medicine and Dr. Harman Hospital. Under the guidance of Dr. Kavitha, one of his students represented department of re-diagnosis. Our germ cell tumors include teratomus, they may arise, you know, or from pollutants and health of thymus. But teratomus, germ cell tumors, which contains elevators of all germ layers. They are classified as mature, cystic, mature naline ones. Mid-Architecture Traumas are usually detected in sedentary, chest or teratomus or the patient who is symptomatic. The tumor last size compresses the resistance structures and invades adjacent structures. Very city findings say they have a deficiency with any of the movement lesions. Like benign lesions are usually very defined. They are cystic and major to the lesion symptoms. The meninal lesions are nodular, poorly defined, ill-defined. They are more likely to have a soil enhancing mass, lesion mass within the teratomus or with a very small portion of the tumor containing fat. Meninal teratomus have a greater tendency to compress or evade adjacent structures. Mid-Architecture Traumas is an uncommon type of teratoma. It's rare to find teratoma in question mid-Architecture Traumas. So we have the papers to identify mid-Architecture teratomus and characterize them based on location, amazing features and innovation. This study was carried out in Department of Diagnosis and Dr. Arnold Hospital in a total of 30 patients. We started with a test series of screening variety for all those patients who were having a suspected of mid-Architecture Traumas pathology. Subsequently, CCD chest was run. And the CCD was run in 128-slice CMOS tomato CT scan. The patients who were clearly suspected of mid-Architecture Traumas or incident detected on chest x-ray, they were included in the study by those patients who were having severe allergic to contrast or pain in female savers from the study. Starting the cases, this is the first case of 50-year-old male with a complaint of chest pain for the last three months, came to the hospital, patient underwent x-rays. This is a chest x-ray POV. We can see there is a large mass which is obliterated out of the outline, which is surrounded by a lemon on one side. We can see that there is a hyaluronic acid clearly seen passing through the mass. Along with the cardiac cellulose are clear, not obscured with a mass. So, on the basis of x-ray, this gives an impression of post-traumidastinal mass. This is a CCT, X-ray. And essentially, we can see there is a large mass which is involved in both sides of the post-traumidastinal along with the other ports, calcification and patternation it has within the mass. It is based on the cardiomastinal structures anteriorly. The mass will also fit in both the main bronchosis, along with the aorta. We can see the mass is displacing the cardiomastinal structures anteriorly. The mass is going to be playing off carina along with lots of factors in both the bronchosis, along with the displacement of heart and gastrointestinal structures anteriorly. We can also see a lytic area in the T7 vertebral body anteriorly, which is a metastatic agent. So, this is a case of an image of a teratoma with malignant changes. Coming to the second case, this is a 30-year-old male with printable templates of heaviness in chest for the last three months. Our chest structure showed a softest density legion in midasinum. So, it's only CCT chest-wise.  However, fat from the aorta are maintained. The mass is small in size, well-defined internal cystic area. And the small size is not having mass effect on adjacent structures, not involving invading adjacent structures. So, this is a case of a match-out teratoma. This is a case 325-year-old male with printable templates of chest for the last one-and-a-half month. Chest structure shows a specious mass in the telsic open midasinum region. So, this is only CCT chest-wise. And we can see that we are correct with the axial and column and sagittal images of the CCT chest. You can see the mass is minimally enhancing. This is a large mass, which is involving internal middle midasinum, the obelisk and outline of the middle midasinum. And it also has calcified focal and high-potency areas within, which is encasing the subclavian arteries having lots of fat from the aorta, along with the midasinum and loss of fat from the midasinum, and having mass effect on adjacent cardinal and rastinal structures displacing at a great vessels and heart. So, this is a case of a match-out teratoma. This is a case 432-year-old male with printable chest in disney for the last five months. Chest structure shows a large mass in midasinum, which is displacing adjacent rastinal structures. These are CCT axial and column images, and we can see that there is a large mass in the anterior middle midasinum, which is displacing the heart inferiorly, along with the displacing adjacent midasinum structures and in wading, and having loss of fat came with the aorta, encasing the subclavian acava, along with a large effusion on the right side, which is a small effusion on the left side, but there is a large effusion on the right side, along with thickening of the pura, and the mass is having loss of fat came with the pulmonary artery with the aorta, displacing the midasinum structure posteriorly. We can see both the loss of fat came with the bronchus along with displacement rastinal structures posteriorly. So this is a case of immature teratoma with malignant disgeneration, which has gradually progressed over time. We can see that the mass is having, large mass is having, there is a pleural thickening along with the maliant, likely chance of maliant effusion. So this is a case of maliant, immature teratoma with maliant disgeneration, along with suspicious pleural metastasis. So in total of 30 cases, when the study 15 were having between midasinum masses, 12 were between midasinum masses, and 3 were in posterior midasinum masses. All the interim midasinum masses generally having mature mass while 5 were having mature teratomas. Out of middle midasinum masses, 7 were having mature teratoma, 4 cases of mature teratoma, and 1 teratoma with malignant disgeneration. Posterior midasinum masses in 2 cases of mature teratoma and 1 case of mature teratoma. Few of the reasons were seen involving more than one compartment, however, they were classified as the basis of the main compartment discussion. Mature teratomas are most common in the brain, the most of all the tissue is the actotermal element in the skin. Here, they often add muscles, tautness, this most often seen in children and young adults. In general, most of the mature teratomas can contain mainly elements of teratoma, layers, jumpscares, like skin, it's a bandage, however, it's a kind of stone to wear, it's a kind of stone, there's elements of endermal and there's a lot of cells within. Mature teratoma are poorly defined, contains the cells and tissues of fetal development. They are mainly seen in the potential and when they follow the brain course, however, if they are seen in adults, they follow the aggressive and malignant course, malignant teratomas, contains phlegm and tissue, in addition to mature and mature tissues, they are very proprosperous, they are seen exclusively in the main and usually in the customer machine, mature teratomas, angiosarcomas, aranomas, mature teratomas. Teratomas are common to find in mid-astom, they are seen in mid-astom, they are mainly seen in mid-astom, it's really different in mid-astom and teratomas, but one case of it is that they actually have to compartmentalize the mask in 20-metre post-environment which is lateral and peer-views, and on the basis of high-low mobile sensors, and they have also the classification of fetal changes within the mask.  So, we concluded there is a MDCT modality of choice to confirm a diagnosis between benign and malignant masks and distinct initiators in vision for better pre-op and more outcome, Cheshirgraf helps to narrow our definitions. Thank you.


Yeah The the UW purple I think was and I think she would sometimes call it purple G13 up here, but the interest like Having been around here for a long time. I Never saw the the UW purple or purple that she had Yeah, it is the same as the purple indica to coma perp Family of plants that's well known up here in Washington Which is like me and Caleb ended up becoming friends 20 years ago is because he had the oracle I had the PI from up here. We knew the those were families of plants that were localized to our areas and We were trying to figure out. Hey, are these things related? Are they similar and me personally having now worked with the purple indica a lot and Knowing its history up here. I mean it wasn't even called purple indica when I got it up here It was just an unknown clone that I received and I was just able to identify