just proud of the group. I think we all could agree that the way that we came out was one of our best starts all year. And just to sit back and watch them build momentum, give each other feedback, and generate offense. Pretty proud. Some questions? Callie had a great game today. Stayed proposed the whole game, really turned away some great opportunities, especially at the end of the third. What did you think of her play today? Yeah, I think you nailed it with Callie. Timely saves. I think she built confidence as the game went on. And when you need those saves the most, and it was really cool just to see her in our element and build that confidence today. Can you talk about the penalty kill, especially with, you know, the five-minute major, and then also the two back-to-back regular? Yeah, our PK just takes it as an opportunity every time. And I think it's that mentality that allows us to be consistent that, you know, we want to get short-handed goals, we want to go on offense, we want to play aggressive, and we hope to generate momentum from every PK. And the girls did a really good job there. And the green jacket. Just going back to Callie, getting her start in the B-POT second game back, just how does that speak to the confidence your group has in her coming back from injury? It definitely speaks to the confidence that we have in Callie. And, you know, I think not a lot of people can see the comeback to her, and how much effort she's put in behind the scenes. But I think that was a huge testament as well, knowing that she's left no stone on turn, she's done everything that she can within her ability to be ready for this group. And I think that there too also gives a lot of confidence. You guys have talked a lot about, you know, building a foundation the first half of the semester. Just how much does this mean to your group to have, you know, a tangible first time in four years going to the championship to kind of be a result of your hard work in first semester? Yeah, I think you nailed it. Just to prove that we're a good hockey team, and to be able to get to experience the benefits of that, being able to perform in high pressure moments and execute when we needed to execute, I think it just goes even further building that confidence that we're a good team. And I think we feel good that we have a great foundation to still go through adversity and get better and, you know, get the outcomes that we walked down the stretch. The Wokie Twins have been an offensive force the last couple of weeks, but how important has their defensive play been, especially in overtime? That's been a really cool part. It's, you know, these guys have played such a great 200 foot game all year, and now just to see, you know, amazing skill come through and generate offense creatively and transition and then on the power play on the PK in all different ways. And I think that's what makes them a threat. You know, I think, really, Luisa, make the people around them better, whoever they're playing with. It's been really cool to see, but 200 foot game impactful in every zone of the ice. And I think when you do that, the offense will come. Back to you, Lily. It almost seems like your and your sister always know where each other are on the ice. What's it like playing on the line with her? I think we just created, like, a good chemistry just from playing, like, so long with each other and making each other better at practice. So that's where our chemistry comes from. Do you agree? For Lily too, in a back and forth game like that, just how do you guys stay consistent and stay focused on the bench when in the first period, you know, it was really tightly contested? I would say we just focus on our habits and the smaller things and then hopefully we get a goal out of it. For Lily, you guys knew that you were going to play one game at TD Garden, but knowing it's the championship game, how much more special is it? Yeah, a lot more special, I would say. I mean, it's special in general just to play there, but obviously it's the finance, so that makes it even more special, especially as a woman. Being able to play at an NHL ring is pretty awesome. What was the new solid goal going in the shootout? What was that initial feeling that you had? Can you describe? Of course, I was in relief, so I was really happy that I could score that goal for this team, and I was just hoping that Catty would have the next shootout.  Yeah, it's pretty special, I would say. And I mean, since transferring, we have been looking forward to playing in the B-pot, so this is a really special moment for us. Let me have time for two more questions. I think more of what you saw today, you know, just knowing that our habits create transition offense for us, and we're a threat off the rush, we're a threat off of our forecheck, and then knowing how to wear teams down, how to maintain possession, how to create opportunities as a unit of five on the ice, and then when it's your time to set the next line up for success, then you set the next line up for success. And I think when we do that, we allow all of our skill and diversity of skill to come through when we start to wear teams down and create momentum. Just for you personally, Coach, you know, first year running the program, what does it mean to you, you played in B-pots, to bring the program back to the championship? Oh, it's an honor, and I think it's something that you reflect on more and more, you know, as it goes by, but I just am so excited for this group. I'm excited about how we're representing the future of our program and putting in the hard work and getting to be rewarded from all of that, and excited to see and make history and get to play at TD Garden and represent Boston University. All right, Coach and Lily, thank you so much. Congratulations. Thank you.


Good evening. Welcome to the 6 p.m. press conference for the CZU lightning complex My name is Jonathan Cox deputy chief for cow fire here in the San Mateo Santa Cruz unit As always just make sure we have take our conversations outside mute our telephones And for all the media to remain masked with COVID masks at all times Just a quick incident update on this fire. We currently have 50 we can confirm 57,000 acres have burned on this fire and we have 2% containment now 24,000 structures remain threatened out in front of the fire And we have we can confirm 97 structures have been destroyed Just so everyone's clear on what that means in the last two days We've had two damage inspection teams out in the field starting tomorrow We're going to have five damage inspection teams. So we anticipate those numbers to climb in the next few days We still have two firefighter injuries from the beginning of this incident Their status remains unchanged. A minor injury is not hospitalized And our final personnel count for today is we have 1157 personnel assigned to this incident With that, I'll hand it over to incident management team three operations section chief chief brunner Good evening. So throughout the day, we've seen a little bit of help from mother nature as far as Little bit cooling temperatures a little change in the wind We have not seen erratic fire behavior throughout the day. It's allowed us to make some progress albeit somewhat small due to our lack of resources But we have seen little progress as far as being able to start to construct some contingency lines Especially in the southern end of the fire that's in progress as we speak So it's giving us a good opportunity to get in there and take care of that We still are doing a lot of point protection on structures throughout the entire fire Which is allowed us because of the intensity has dropped slightly Further crews to get in there and get some good work done fire is still making progress and burning down throughout the San Lorenzo Valley into the highway nine corridor And on the western end of the fire and the north end of the fire The fire is Being mitigated slightly by the onshore marine flow the the fog layers and so forth So that's allowing us to get in there where we can do some work Especially behind the community of Davenport to provide a layer of protection for for that community We're looking to see what's going to happen tonight. The weather should be more favorable We should not have as heavy of the north winds that we've been experiencing so Our expectation is the fire behavior should Mitigate slightly only slightly But we will continue to make progress where we can take advantage of those opportunities where we can And and make some progress with the coming weather the next few days we can we can do some pretty good work Beyond that it's going to provide us some challenges and we're preparing to meet those challenges with the weather as the coming days approach Speaking next from the Santa Cruz county sheriff's office is deputy chief clark Well, good evening. So I thought I'd touch on a little bit about what we did today We completed the evacuations of the city of scott's valley about 12 000 residents We completed that about mid-afternoon The next thing I want to talk about is is is really reiterating and we we've said this At prior press conferences, please do not return back into the evacuated zone If you don't have to it provide it presents hazards for everybody that's trying to work in that area Like I can totally understand if you have medication if you if you feel like you have to go back But please try to seek alternative means to obtain that stuff contact your doctor If there's an alternate route you can go to obtaining critical things that you need that would be extremely helpful Just based on the dynamic and changing nature of the fire and really allowing fire personnel to to devote everything they can to helping put out this fire In terms of Burglary suppression. So today we had about 60 personnel Looking looking for potential looters and protecting property.  We're going to continue With likely that strength of people For as long as as people are displaced, so we're going to have people in the area Looking for these folks, but if you if you see something, please call In terms of large animal rescue, uh, I wanted to provide some information as far as that was concerned If there are if you have the large animals that need to be evacuated I'm going to provide you a number and this is to our large animal rescue Folks that'll that'll help get those animals out that phone number is a 31 3 3 1 6 2 2 7 a 31 3 3 1 6 2 2 7 as for large animal evacuation Uh, I want to touch on missing persons So, uh, this is kind of come up and it's obviously something that we that we encourage if you can't find your if you haven't heard from your loved one Please call us And like I said yesterday, uh, you can go to the red cross, uh website to to look to see whether or not They've been reported there. Uh, if you can't find them there Call our dispatch and uh, we'll we'll uh, we'll look into it To provide you some numbers we we've taken about 19 missing persons cases since this fire started 19 Uh, we we we have detectives that have looked into it. We brought that number down to two So there are two missing persons cases at this point that we're actively trying to confirm And so we're going to continue that until there's a resolution to both of those Uh, and then lastly, I just want to touch on there was another, uh, Evacuation center that was opened at simpkins swim center. So that's that's open today. You can go there Uh, if you've been displaced, you need a place to go But also I'd encourage you to contact, uh, the, uh, the shelter hotline at eight six six two seven two Two two three seven to be connected with, uh, shelter services. Thank you Speaking next from the san mateo county sheriff's office is detective blanksway Good evening right now. We two are in safety and security mode We have teams of people including law enforcement and volunteers who are in the area providing those, um safety and security measures for the northern part of this fire the areas that reach into san mateo county We want to say thank you to everybody who has evacuated so far And those that are doing so right now We're still seeing a lot of people who are entering the area for whatever reason and we want to reiterate that These are hard closures We have law enforcement personnel who are staffed at these closures And we're not allowing anybody back in for safety and security reasons We stress this because we want to make sure that you're not putting any of our first responders in a situation Where they have to risk their lives to rescue you I know that everybody wants updates as far as what's going on at their house And what's going on at their neighbor street and and their friends property We can't give you those numbers right now right now We're trying to assess the needs of the community and it's a really great opportunity for us to show our strength as a community by Listening to our fire experts and cooperating with these orders and showing that you can be patient and wait for this information As many of the other fire experts have said this is going to be a long haul Or we we would like your patience in that So, thank you very much and um again your cooperation and patience is much appreciated Speaking next one of the unified incident commanders from cal fire incident management team three chief c Hey, good evening. This morning. I talked about trying to obtain some small wins. Obviously. We did today We've got two percent containment on on this fire. I know it's not much, but it's a small win Lots of small wins equal a big win at the end of the day Today was a good day for us We've had smoke conditions that have kept the fire in check with increased amenities and reduced winds We'll see what comes overnight Tonight we're anticipating that the upper elevations that the humidity will drop again On the high ridge tops, but down in the lower levels the humanities will stay increased So it'll moderate the fire behavior in those areas With that said the firefighters are have been working extremely hard for many many hours over the last several days And they're going to continue to do so This camp's increased in size by a hundred personnel today and a few resources have come in Over the course of the next week or so We're going to continue to see that small trickle effect coming in as we see out of state resources deployed here in california to assist us Right now we've got 13 incident management teams deployed on two dozen different large fires in the state of california This emergency system is strapped But please be aware everyone's doing the best they can for the citizens of the california. Thank you And our final speaker the unit chief for cal fire in the local San Mateo Santa Cruz unit chief larkin So we're entering into day six Of this incident and as we keep saying this is going to be a long haul Our firefighters are out there doing the absolute best they can with the limited resources that we have To protect the citizens of Santa Cruz county and san mateo county As we reiterate each time there are still a lot of citizens that have chosen to stay in the community And I had first hand Witness to a lot of those people driving around the fire area when I went out to do just a little bit of Wind shield surveying and myself of the the devastation that is occurring and It's very difficult to maneuver around this fire as it is with the narrow roads and to add that public back in there is very Very difficult and very unsafe for our firefighters. So if you're in the area And you don't need to be there. Please leave. Let us do our job So it's the safest environment we can provide for our firefighters This is going to be the long haul as we keep saying we're day six into this fire and we've only got it six percent contained So Bear with us. We're doing the best we can with the resources we have and as resources come in the team's going to allocate those resources Equally to the assignments that need to be completed. So thank you All right, as usual or happen to answer any questions. I just asked that we group them together tonight Just went off the shuffle too much. So, uh, whoever we want to start with Sure Yeah, speaking to tourism You know, the county's advocated that that that please if you don't live here, please don't come here to to to recreate We've displaced a lot of people from the San Lorenzo Valley Those people need places to stay and so there's only so much of that and so by increasing tourism into the county It only it only takes away rooms for folks that really need them. So again, you know, we would encourage you Please if you do not live here, uh, don't come here to recreate. We we'd love to see on a different day. Just not now The additional questions Okay Sure, so the question is really about the operations that are going on in the bonding doing So as you can imagine the bonding doing area has been hit very hard by the fire And as a first responders our heart's go out to those folks That is very devastating what they've experienced whether their house has been destroyed or their house They're out of their homes now. I can I can sympathize with that Unfortunately, as I've said before we have very few resources. So typically in an area that size We would have probably 10 20 times the resources to combat that fire. We simply don't have it It's just the resources are only finite as you heard chief see say already before the number of fires throughout the state We're really tapped out. We're bringing in stuff from out of state that that should indicate that how how far drawn down We are with resources So we do the best we can We have to change our strategy. We have to change our tactics To address a fire situation like this As we said before it's unprecedented This isn't just a typical fire that we face day in and day out. This is Unprecedented. This is historic. So take that into mind with the limited resources and doing the best we can We can only have like we keep saying small wins and we do point protection. So it's life number one And we've been Taking care of that by evacuating the people so that their life safety is the number one priority Then we move on to the property conservation and property protection We're doing But with the magnitude of this fire and the fuels and all those things we can only do so much with what we've got. So I know it's it seems easy to ask for patience in this We are doing the absolute best we can we've had firefighters that work double triple shifts So we're maximizing our personnel out But there's only so much that they can give before they become unsafe And we cannot let them be unsafe because then they are ineffective and it becomes a downward spiral at that point So All all we can ask is I know it's hard to ask for patience in this but we are doing absolutely everything we possibly can Oh, certainly, I mean this is an emotional event You know lives are on the line the property everything that they've worked for hard for We can appreciate that. We're all involved. You know been there in that situation one way or the other So we are just the message is we are doing everything we possibly can and we're trying to mitigate that situation And as soon as we can they'll be getting back to their homes and hopefully back to their normal lives Okay, so for right now We we don't feel that there's a direct threat To either the campus nor to that community. We are putting in at this point in time. It started last night and in throughout the day A good contingency line So we have a good plan that we're having success with the weather has mitigated to such that we can have success with that We have some resources we can put into play And so we are working extremely hard to put in a a contingency line that will protect those communities so At this point in time and the way the weather is mitigating It's it's a there are probably the more safer of the communities If they weren't we would have already done the evacuation been very aggressive with our evacuations for for the right purpose because Of the lack of resources and because of the fast-moving fire So the fact that they're not evacuated at this point or we have not required their evacuation Is is a good indicator as to their safety and the safety of their community Could you tell me please? I'm further that he can bear up for the murder use The Santa Cruz a lot of people there I think this is the place you never heard Is that I mean because you know there's been a lot of vegetation that's connected in various places It seems like it's not one of the communities in that way Is could it could it sound like Santa Cruz That fire from a fire like this you know I hate to say is this is california and anything's possible in particular with wildfires I mean we've seen time and again over the past number of years communities that We've said hey, we'll never burn or never historically have burned And we've seen unfortunately that they have or they've been affected directly by the fire So I would never let your guard down with that. I think it's it's a relatively safe community. But again Most communities in california are surrounded or nearby to wildland areas and with the extreme conditions we're seeing You know as prepared as we are as fire service professionals We do become surprised as to these conditions I mean we who would have ever expected a fire of this magnitude in this area It just it just hasn't happened and the conditions with the drought The fuels as dry as they are the conditions they are the build-up of the fuels all these factors come into play that create an unexpected situation so What I've learned in my career is to expect the unexpected and prepare for it the best we can and I would be prepared In any community. I am in the state of california We should and we always preach this Fire season non fire season that you prepare for wildfire And if the time is that your your community's threatened and you need to go that you're ready to go and you go when you're asked to go So yeah, the question keeps coming up about aircraft and and Really what's been hindering us is a few things one you keep hearing about the drawdown of resources So it's a finite resource very limited. We do have some That are assigned to this This incident and that we share with other incidents our error program our error operations directors Are working diligently with our partners and the other fires of sharing that resource so What little we have we are sharing and and we're utilizing one we can use it The biggest factor has been the weather in particular what the smoke has been doing laying into most of the areas So we can't fly the aircraft into these heavily smoked out areas. It's just not safe and they're quite frankly They're not effective so the areas that that the air is clear and it's safe to do so our Aerial supervision has Directed those aircraft into those areas We've utilized them to the fullest extent that we can utilize them And we've had some success Utilizing those but unfortunately a good portion of the fire has not been accessible for the aircraft to be utilized so Trust me as much as we can use it. We're using it and We're using it to the maximum effect, but right now just mother nature isn't cooperating with this We've we've used we've had a variety of our fixed wing air tankers that we've utilized And those again are shared aircraft So we've had anywhere from Two to four tanker air tankers that have been able to drop on the northern part of the fire in particular We've also have a number of helicopters that we've got assigned to the incident and that we've shared so Anywhere from about four to six helicopters that we've been able to fly throughout the varying times of the day And just using their maximum effect based on their the fuel that they have available And and the flight distances and all the little intricacies regarding aircraft But whatever we have we've been flying them as much as we possibly can when we can And those two to four drops were today Throughout the day. Yeah, and well those are the tankers, but they've done multiple drops Throughout the day Yes, absolutely, yes Okay, so a lot of a lot of parts of that question so the The area is the most concerned and in our priorities has been the the highway nine corridor because of the population base And also upon upon the ridge On empire grade road and community bondy dunes So those are the areas that the most population that have the direct effect of fire Not that the outlying communities are not but they're not currently immediately threatened So we take the limited resource we have we've divvied those out to those areas They're doing what we call point protection because that's all the the Strategically and tactically that we can do to be effective with the limited resources and that means that They're given a large area They're very mobile They do what's called bump and run tactics where they bounce from house to house knock down fire move fire around the house The best they can bump onto the next one Normally if we have everything that we possibly could have as far as resources We'd be able to be a little bit more static with that put in a lot more control lines and so forth due to the topography the steepness of the of the mountains and the The heavy fuels it's very difficult to do traditional Tactics where we can put in large control lines dozer lines hand lines and do that sort of operation So because of that we're really having to change the way we do business on our strategy and our tactics As far as the the gas lines and so forth Most of the homes here are propane and propane tanks and that sort of thing those are hazardous and As far as when the fire does impinge and impact those And and they can explode and and we have had that happen numerous times in areas that have been Directly exposed to fire. So they are a hazard. We identify those hazards We try to protect against those those tanks being impeded and infected by the by the fire and Taking all those precautions in regards to the amount of firefighters. Ideally if I could have 20,000 firefighters that love to have them, but that's just not the reality Typically on an event like this. We probably have like I said about 10 to 20 times the amount of the firefighters and the resources whether the area resources our hand crews or engines, whatever to To affect the the suppression of the fire Great everyone up here is going to be available for questions to conclusion I just want to end with a couple things a couple questions have come up about what types of resources we do have on this fire We have federal state and local government all all working together on this fire In addition to that, we have a very strong coordination with both san mateo county emergency Center as well as santa cruz county emergency operation center Obviously when we evacuate people, there's a big coordination that goes on behind the scenes about where do they go? How do they get there and how do their needs get met? So although we stand up here? There is a large team of people who actually make this emergency Response possible. So on that note, uh, we'll end it here. We'll see you back here again tomorrow morning at 6 a.m This concludes the app press conference. Thank you


अ ंज आ मुट्रिल् ten candidates बिस्म rumor khma какие मुट्रिल् आज हम मुट्रिल Kirin मुट्रिल S. canal Near दो किसम library भशाélior उस्ते लेगे उसकेंiter आप स्वी असी स्वी नमबर अव्स्ट्यंस श्विटुव स्विल्गट होग अविक शिटी श्विल्गट मेर्त पास कर सकतेः या मेशज देगा अविक शिटाईवाशा़ रश्ढिया पीभी ईच्ञीं के षेबोश्टी कचर सकतेः जिच्फी आप आप स्विलग other wise, if we use other input-output functions, then all these things have to be managed by ourselves. Come, let's go towards this program. In this program, first we see a function, print string. If we use this function further, then it is a utility function, it can be used further. In this, you can see ellipses, as you can see, three dots are in the parameter list. This means that this variable number of parameters will be taken. And again, that is what we will use, VA list, VA start, VA end and VA arc. We will use standard C library functions to read this entire variable list. Similarly, the variable list is being read in the loop. And you can see that this variable list is being read again and again in this loop. And whatever string we have here, its length is here. And it is doing the right console. Whatever is our string, it will take one string and it will do the right console. In case, here you see a knot is being applied. In case, the operation of this right console fails for some reason, then it will do the right file. Someone has specified their file here as input. So whatever file you have not been able to write on the console for some reason, if the console is occupied, then it will write your data on this file. And after the entire loop is finished, this function will be returned. The second function is our print message. Print message is basically calling the function of our print string. You have passed a handle, a file and passed a string. You have passed this handle and the string has passed the print string. And as a result, the string will be printed. The second function we have made here is the console prompt function. Console prompt means that it will prompt the user. It will give some message to the user and after giving the message, it will take some input from the user. Here you see these two handles are being created. This is the console input file and this is the console output file. Here it has created the console input file and its handle has come in HN. Here it has created the console output file and its handle has come in H out. It has opened both for generic read. After doing this, it has set the console mode. It has enabled it for the wrap and for the process output. After doing all this, it has made the print string. What it has done? It has printed the P prompt message. The P prompt message was passed here as parameter. It will display that message. After displaying it, after prompting it, it has read the console. It has read the console and whatever characters come, it will be stored in the P response. In case of success, this is a variable. If there is no error, then this variable will be true. In that case, it will place the P response at the end and it will place the null character at the end of the string to specify and it will return success. P response because it was your pointer. So, this value will return the calling program and the user's response will be read.


Hello, thanks for coming to this talk. Hopefully, we can have a little bit of discussion, either at this talk or later on on the mailing list. But this talk is structured in four parts. We will talk a little bit more background what machine learning is, or at least what it is in the concept of this talk. Then a little bit about threads and a little bit about opportunities. And in a nutshell, this talk is not like the sky is falling over us or anything. Not doing anything is really an option. The threads are maybe five to 10 years from now, really. And some of the threads can be addressed outside of Debian. But I truly believe that the opportunities are more at the distro level. And that's also something that you are welcome to disagree. So what is machine learning? Machine learning has this issue that people take it a little bit religious, just like the computer learn stuff. In reality, it's just some statistical modeling with a focus on predictive uses of the statistical model. And the most common case is there is a phase that is called the training phase, or you can also call it estimation. And for the way we normally work, we can call it more like a compilation phase, where we take as input vectors of features, including some target feature. And that's what we could call the feature data. And the output is a trained model. In general, this thing is fairly large. And the trained model, you can think of it as a statistical summary of the input data. And then you have an execution or prediction or interpretation phase, where the input is a vector of features without the target feature and the trained model. And the output is a predicted target feature. So for an example that I'm familiar with, and it's something I may want to package in some moment as part of the Debian science linguistics track, is a Stanford syntactic parser. It's written in Java. It's a GPL license. It's a fairly mature code. It's a surprisingly well-written code for coming from natural language processing. It's a probabilistic concept free grammar parser. And the trained model takes two megabytes. The source data is the Penn Tree Bank that was assembled at the University of Pennsylvania. Several years of Wall Street Journal annotated with syntactic data. It takes a whole CD-ROM. So you go from 600 meg to two meg. And this one is only available under a closed license. And you cannot distribute it, et cetera. And this is how the output of the parser looks like. We take a completely random sentence, not coming from the Debian website at any point in time. Like an operating system is a set of basic programs and utilities that make your computer run. And here you have a sentence. And this is the subject and the object, et cetera. So clearly, in general, a syntactic parser is just a part of a larger system, like, for example, the summarization and other activities. But this is a type of software we may want to distribute in Debian. And the issue is that some of these models are actually easy to understand and modify by hand. For example, in an earlier work I had, we were doing word-sense disambiguation. And we were using an algorithm called C45 rules that produced this type of nice-looking rules. So for example, you say, if the word after the name includes encodes, and it doesn't include encodes before, then most likely it's a gene. Because gene encodes stuff, yes, while proteins are encoded and things like that. So these type of rules are rules that you can think this could be a preferred form of modification because you can understand them and change them. On the other hand, most models are un-understandable models, which people told me used to say, incomprehensible. That are not really intended to be understood or modified by hands. And you have things like neural networks, although are not that popular these days, poor vector machines, Markov models, conditional random fields. It's a large collection of floating point numbers. They are very, very opaque. There is no real intention behind them to be dealt with by hand. So you most probably are familiar with neural networks because they are still quite thought. You have some input features come here, and you have these different ways that in this diagram are represented by different thickness on the lines. All these numbers get multiplied by these weights, and a function gets applied in each node and feed forward until you get to the different output class.  This makes a huge, huge space of possible weights representations. So just to represent the space for one bit neural network, it looks like this.  And support vector machines are even worse because the concept is to find a hyperplane that divides positive from negative training data. But most SVMs use this kernel trick where they map features into a higher dimensional space with the idea that if the mapping is smart enough, then you can find an easy separation hyperplane. These are fairly deep technical stuff. But the main point is that you're not supposed really to look at the support vector machines and say, hey, I could modify this hyperplane in 10,000 dimensions a little bit and get a better model. What you do is you retrain from the source data. To make things even a little more moody, feature vectors, in general, are not unlike generating YAC or Bison C files. Yes, you have this difference between training data that is the transcribed speech, for example, and the feature data that are these little wave segments with associated transcriptions. Or, for example, if you have a trainable spell corrector, your training data is the Wikipedia history and your feature data are the edits that modify a word with less than two characters. This stuff is fairly detailed. Even if we just distribute the feature data, it will be way ahead of what is happening right now. OK, so what are the threats? I distinguish between threats to freedom and threats to practicality. The main threat to freedom is obsolescence. Yes, slowly the users are going to get accustomed to applications that rely on these large training sets. And this is not unlike the type of threats being addressed by the Freedombox Foundation for cloud services. Yes, so for applications such as optical character recognition, like book scanning, speech recognition that will be the detection, computer vision, automatically tag your friends on photos, and question answering, Siri and Watson. Yes, if people start expecting these type of things that they want to run on their devices, well, then we are going to lie ahead, like behind quite a bit. And overall, what we see here is that we have a diminishing value behind source code. Yes, and there is more value on data. Facebook, LinkedIn, Google+, Flickr, and that's something from BDL stock this morning that he was saying, well, when you are using these platforms, you are giving away your private information. Well, moreover, you are actually empowering these players to build better models. So it's not only that you are hurting yourself by disclosing your private information, you're also hurting the free software cost by enabling them to build better tools that we won't be able to compete without having access to all that data. And I don't know if you are aware, but there is this data vendors. You can go to this InfoChimp's marketplace and buy a nice chunk of 100,000 LinkedIn profiles and things like that. They are fairly expensive, but you are being sold right now. And again, even if we have the money to buy these, we can distribute the models as free software, but we won't be able to distribute the data. And unless you will, so to speak, the Pantry Bank data is being released by the Linguistic Data Consortium that is a nonprofit sharing the maintenance of this data. But still, most of the data distributed by the LDC is proprietary because the newspaper articles belong to the Wall Street Journal. So even if you want to make the notations for free, people need to get the newspaper articles. And to me, these issues of machine learning are yet another clever GPL circumvention trick. Yes, in the last decade, we have seen plenty of smart ways to navigate around the GPL. And in this case, then you have the vendor releasing the source code of what at the end of the day end up being just an interpreter, yes, and keeping the data behind train models completely closed. So the fundamental freedoms of being able to study, modify, and adapt to your own uses are being held back from the users. And to me, the exact metaphor and analogy here are binary firmware blobs, yes. Because in general, machine learning models have been treated so far in Debbie and more as video game assets. But in reality, you have the decision information that is used for coding. Now, if we were to go down the rabbit hole and consider the data and the building of the model as part of something we want to consider completely part of Debbie and us programs, then we have the issue that this is not similar to anything else we are doing in Debbie and at this time. Training machine learning models takes a whole different type of build machine. It's not unheard of to use 64 gig machines for three days. Yes, so redoing this work, well, we really will need to have a very, very good reason to do it. And most likely, we'll need to have different type of sponsors for that. And even worse, it becomes when you start talking about distributing this training data. Because as I was mentioning, well, you have some data, for example, derived from Wikipedia. And you may end up having to host that type of large data files. So this issue has been discussed already a few years back in 2009. There was a post from Mr. Matthew Blondel to the Debian legal mailing list. And what he asked was the first one was whether Debian can ship models in Maine without distributing the original data. And the answer there provided was yes, because the model is considered preferred for modification. The objective of this talk is to say, well, this answer is something we may want to revisit moving forward. Yes, because the reasoning there was that this is the same thing as having two-dimensional render images rendered from a 3D model in games that are not being distributed, the 3D model. To me, that's different because the two-dimensional images don't contain any logic about the program. And the second question was a little off topic for Debian. It was about fingerprinting of the data. And there were some answers related to that. One of the interesting things about this is that it's a massive framework. And that, in a sense, informed me about the dangers of giving this talk. So I hope we have a far extinguisher nearby. But there was a quote, for example, that I want to, as I said, free data is important for the very same reasons that free programs are. And going back to the fundamental freedoms of modifying and studying and adapting, I personally agree very much so with that quote. But on the other hand, it is a slippery slope. As another quote says, well, if you want ship models because you don't have access to the source data, then you shouldn't ship pictures because they are initially photographed of an object. And the preferred form of modification is the original object. Yes, if you want to see it. And of course, this was just flaming. But it is indeed the case, particularly when you go back to what I was saying of the training data and the feature vectors, et cetera. I did a little bit of a search within the Debian archive. And I didn't find anything that I would really put my finger on it. And this chemistry kit seems to have some data there. But I cannot really, I'm not really familiar. I know for sure OpenCV has a face that it can say, in this photo, there is this square where there is a face. And that's been trained from a library of photos. But I couldn't really find where in our distribution of OpenCV that data is. And I know that UIMA sandbox has some trained models. But I'm not familiar whether we are really distributing the sandbox at this time. So truly, I mean, this is not an issue we have right now. There is a very nice dictation software that I'm going to talk in a bit that is now being distributed with GPL data. And well, in that case, if we want to pack it, we have to do something about that source audio files. On the other hand, something, these make huge opportunities for Debian. In my perspective, the main challenge for Debian is to change users into contributors. Yes, how can we make people get more engaged with the project? And in a sense, we can try to get contributors that will push new training data. When they use a software and the software underperforms, they can help make the software better by sending data files. So I think that could mimic the success case of the many translation teams we have, for example. And data contributors can, in some sense, send data patches that will fix a bug by improving the model. This is very tricky because the statistical models tend to be asymptotic. So every time you retrain them, the model will improve. But in some cases, it will decrease in others. But still, this is a very good way to bring people into the project and feel they have more ownership on the software they use. And moreover, something that I would like to see to is more inter-distro collaboration. There is a lot of work going on between Debian and its derivatives, particularly Ubuntu. But this will be also interesting to see a collaboration with RPM-based distributions. And one of the things that is very nice about training data is that sharing data is usually much easier than sharing code. Because the format of the data seldom changes. And in a sense, that's what leads to object orientation. You encapsulate data because data is more stable. For example, all syntactic parsers in the last 15 years or so have all been trained on exactly the same 3-bank data set. So there are certain data sets that are fairly stable. And sharing annotation work should be easier, yes, than sharing source code patches. Now, here are some questions. How can we acquire this data? Do we want to build something like a free software volunteer-driven mechanical torque-like tool? I'll explain on the next slide what mechanical torque, if you've never heard of it, there are already existing initiatives like Librebox, where volunteers read books allowed from the project Gutenberg. And the second is, how can we assure the data and its derived model is kept free? Yes, we may need something similar to the Creative Commons soup of licenses. And it's, of course, the question whether just applying GPL to the data will be enough. These questions completely exceed me. I don't have the feeling that GPL is enough, but that's being used by other projects. OK, so the mechanical torque is part of the Amazon Web Services offering. It's a commercial proprietary platform where you write a task that is easy to do by humans, like there is a person in this picture, but it's difficult for computers. You have paid workers that are called in the slang of mechanical torqueers and do these tasks for really, really, really, really completely unacceptable, tiny wages. And of course, this poses plenty of ethical issues. First thing is whether this is just pure exploitation of the torqueers. And even more interesting is, well, maybe these people are being paid to help develop tools, develop systems that go against their own moral values, but they just don't know what they are doing, because the tasks are so precise and small. This website, Boxforge, it's found out about it very, very recently. And it contains a large number of GPL transcribed samples. And the objective is to put together a state of the art lactation systems using free software. And the intention is that the acoustic models that are derived from these sound samples have to remain free. And interestingly, the state of the art tool to train the acoustic model is called HTK. And it's proprietary. There is still no free software equivalent for it. But they have a, so basically this will be the compiler. There is no free compiler. But there is a speech recognition engine called Julius that is free software. And you can download a demo they have. But if we want to package Boxforge models, we also have to distribute all their source data, all these sound files. We'll have to build these models at build time. That will be difficult, because HTK, even though it's freely available, you have to sign a license to obtain it. These are not questions that haven't been answered in Debian before, but it might be the case for these type of cases. We don't need to distribute the source data ourselves. Just be sure that they are available on their site. OK, so the first thing is, don't take it personally with me. I'm not changing anything as things are right now. It should be OK for the time being. I still think it would be nice if we can find other people who are interested in this problem that we were willing to talk with maybe the software law center about any licensing or how to proceed about trying to make some inter-distro projects. And overall, I still think it would be good if we can revisit our current policy and transition away from considering trained models as the preferable form for modification. So in a sense, packages that contain these models that we don't have access to the data should be just put in contrib, because they are freely available, but the users cannot make use of them without access to these proprietary resource. And we may want to differentiate between the Debian archive hosting all the code and assets versus all the source data. That's a little more difficult. Debian really likes the fact of being completely self-contained at the archive level. Still, there are other organizations like archive.org that specialize themselves in hosting large amount of data. And finally, I don't know if you, the first C-lang 2.9 and 3.0 recompilation of the whole Debian archive that was undertaken recently was done on a European resource called Grid 5000. So there are large clouds available for research use through the world that we may be able to get time on to train models if we go for them as an organization. But what do you guys think? I had a kind of a comment and doubt about the GPL and these things. It seems to me that there's a problem that's common to metaprogramming. The model is not being coded by people. The model is output. So as I understand the GPL, the data set is kind of like it's a source of the model. So if you are distributing the code to a model that's been GPL, it seems to me you would be required to distribute the data set too. Yeah, that's the understanding of the Boxforge people, yes. And they seem to be very confident that the GPL is all they need to protect the data from that type of usage. I find that metaprogramming introduces very odd questions because there are questions such as who creates a piece of code. If you distribute software that does metaprogramming and somebody else runs it on their computer and generates code, but they're doing nothing but running it and you're getting novel code.  No, no, no, that type of thing I think is a concept of the compiler, the target, the source. I mean, GCC is GPL, for example, and you can use it to compile any code you want. That's not so much the issue, but it's more about in this case whether the source code and object code that are the terms used there really apply to this where you have these speech transcripts and then you have an acoustic model as output. My bet for that would be GPL v3, I think. A lot of the changes in language make it a lot easier to apply to things like this. So I wanted to thank you for doing this talk, because I think it's a really interesting challenge for us going forward. I don't have any clear answers, to point out another analog to this kind of problem, which I think we're going to see more and more, which is fonts. And so we already are struggling with this a little bit in the fonts packaging team of if someone has a font that they develop using proprietary font software and they distribute it as a TTF. And we can modify TTFs directly. We have all the information of the font. It's entirely modifiable by us, but it's not their modifications. So that's one issue. But the part that's closer to what you're describing is that there are now a handful of auto-curning services that are being run on the net where you take a font and that you haven't actually curned. That is, you haven't specified the spacing between letters. And these auto-curning services can tune up your font and make it look better automatically through some algorithmic process. And so you just got this big change in your font in whatever formats you had. And now you've got a new font. Is that a free font or not? Because you had to pay this proprietary service to do it. And it's not clear what we can redistribute if we don't have the auto-curning functionality in Debian as well. So there's a bunch of places where these kind of questions come up. And is that being discussed in Debian legal or just discussed within the fonts packaging? We've discussed, on the fonts packaging team, we've discussed more the, okay, does the author modify this themselves with a free tool or do they use some other preferred form of modification? We haven't really gotten into the issue of the auto-curning services. I think that's relatively new. I don't know, other people on the fonts team might have more history than that. Hi, I'm afraid I'm gonna be bad. I don't really have a question, but I've got quite a sequence of points to make as a different, just, I mean, I agree with a lot of what you've said, but as another machine learning person, I'd like to give a slightly different perspective on a few things. First of all, you kind of seem to be throwing together all machine learning models here as being black boxes. Yeah, I was really trying to, I didn't wanna spend the whole talk on that. But I mean, well, I would agree with that, say for neural networks and support vector machines, a lot of models, for example, generative models, you can go in and interpret them and they do mean something. So I think there's a kind of, there are different sides here. There could be some grayness in the middle, but there are some models where it definitely does mean something once you've learned it. A different, and the second thing about the data sets, you seem to, I would, to be provocative, you seem to be a bit stuck in the past in thinking that we have these big, perfect data sets that are of great worth and that we really need to care about and that they are fixed forever. I mean, yes, you mentioned syntactic parses still using the same data sets that have been around for decades, but that could be because syntactic parsing is kind of irrelevant and gone by now. No, no, actually the progress of syntactic parses is stuck because they are with that data. Yeah, but I mean, all the current translation software, say, doesn't try to use syntactic parsing. It just uses lots of data. And in the world where actually what you just want is a lot of data, clearly in some way the issue, I mean, yes, I agree on, it depends on the kind of task. So say for something like voice recognition, there is a genuine issue because collecting, well, maybe Librevoxay could be a solution, but at the moment collecting good voice data to use costs money and there hasn't been a community initiative to do the same. But a lot of the problems we're actually worried about at the moment, I don't think the way you're painting it is quite, I don't think it's as black as that, as bleak, because a lot of things we care about at the moment are actually working with internet data, with web data. And in many cases, the people working on these wouldn't ever think even of building a fixed dataset that they redistribute. So what they tend to have is a script that fetches, say, some collection of web data than they use. And obviously, yes, there is, in principle, it would be nice to have be able to say the dataset is free and you transfer it around, but it's not just a question about, well, for one point it's not just a question about free nurse or proprietary, because a lot of the time no one has permission to redistribute that data. If you fetch a million images from Flickr, or if you fetch all the text pages you can find on the web, no one possibly has permission to do that. And yes, you can't distribute the data. But in general, after they do that fetching, they will go for some sessions of mechanical torque or stuff like that to annotate stuff. No, not necessarily at all. A lot of methods use completely unannotated data. Yeah, well, in natural language processing, there's people still annotate stuff if you want to get labels and stuff. Yeah, but again, the most promising techniques recently have not been done. Oh, but that's just a research bias. I'm talking about what the papers I saw three weeks ago in the NLP conference. People still use plenty of supervised learning. Yeah, yeah. But again, I'm saying it is a problem for some categories, safer voice recognition. Say if you really care about parsing syntax trees so you can draw a nice structure of your sentence diagrams. But I think there's a lot of, you seem to be suggesting that for all machine learning methods here we would have a problem for this in Debian. Yeah, I mean the thing is you have to see also that engineering tracks research maybe 10, 15 years or even more. I mean, most people when you say machine learning, they think of neural networks when nobody's using neural network for the last 10, 15 years. But I would put the neural networks because I wanted to communicate with the people in the audience.  On Daniel's point about the font packages, I don't believe that's a precise parallel to transferring around a model that you join because what you're talking about for the kerning question is a magic service that does something and that you don't really understand. However, there is a machine learning parallel for this which is that Google have recently, well in the last few years had some service where you can throw data at them with labels and they will give you back a model that predicts the labels. And in this situation that is pretty scary. You have no idea what method they're doing. I mean, even if you didn't care about freeness, in fact, next week they might completely change the method which might improve your results, might make it much worse and so on. So that is a thing we would need to be alert to to watch that I think that kind of, if you have the kind of model that came from that kind of service then in my opinion that probably just effectively probably is a non-free model for the reasons that you're picking up. Although I think there could be other types of model where it's okay and it is more similar to the kind of distributing a JPEG okay question. So I wanna push back a little bit on one of the things that you said. You said you were saying it to be provocative. So I'm provoked. So it seems like you're saying it's not that bleak. There's a lot of machine learning techniques that don't rely on these closed proprietary datasets. They just pull random stuff off the web or whatever. But our build these don't have network access and even if they did have network access we want repeatable builds. So either we don't ship any models at all in that context and then it's the user's job to build the model from their own network connection later which is particularly un-useful if someone wants just something that will do voice recognition or something that will do analysis of internet text or we have these weird non-repeatable build situations which is and you can't actually get that same model the next time you build the package and that's not acceptable from the Debian archive at the moment. And we also want legal builds. These companies can do that because they keep it secret. Yes, if we publicly come and say we are distributing this file that is a derivative from this blog that says copyright blah, blah, blah and there is this log on the Debian build that say well we fetched the log of this person we may get just you. Yeah, I have a few points on this ping-pong thingy. One of them is that with the font and it's models generated by data and so on there's two trends that I think are really important and one of them is the blurring between the data and code line. And the other one is machine generation possibly distributed. I think the IP model is very based on humans generating stuff and specifically humans generating code and code being very well separated from data. So as these lines blur I expect the IP model will be increasingly strained and obviously not work in very weird ways. And the intellectual property. And about the builds. I think obviously yes, if you're making a model you can't expect the build machines to make the model for the build and what I expect is if we start distributing models like this and they're being automatically generated for builds, what I would expect is that you would sort of have a subsidiary system that is in charge just of generating models which are evolving and when you make a build you just ask that subsystem for the model that you want to include. Yeah, just on this question again. While I'm saying I don't think it's bleak it's also partly because I think doing anything other than just saying models is okay is utterly unrealistic for some of this because basically if I go off and collect some web data to train a semi-supervised model say then the data in a modern system the kind of data size that you would be looking at is minimum terabytes. And I really do not believe that Debian is going to decide as a project we're going to insist on distributing around terabytes of data for particular packages. Oh, I forgot a comment about meaningfulness. I think any model is inherently understandable. The question is, is it easy to understand and is it made to be easily understood? I think we have, do you have a question? Okay, so from my experience the worst part about... Talk closer. Yes, the worst part about machine learning is always to find the correct dataset. Now we are talking about not only finding a dataset that will do the job for us we're talking about the dataset is that also has a suitable license for us. So for me that is first a much more difficult problem than maybe is understood from your presentation. Second, if you get to do that I'm sure many most researchers around the world are going to thank you eternally for that. To me that sounds like a really huge problem. But the issues of caring about the license is corner store to Debian and many other users and distributions thank us for that. So sure it makes our task more difficult but as a task we have set ourselves to do in the realm of software. Of course I'm not arguing to that. It would be a very, very good thing. But it's, I don't think it's feasible within a reasonable amount of time. That's what I mean. Why? It could take us a long while to get there but once we get there we will keep improving. Yes, and you will keep improving. You will have many more people than just the Linux world. Okay. Just coming back on the understanding, understandability of models.  Yeah, people show you nice pictures like this and in two dimensions it's kind of understandable what it's doing but in fact when you've got your kind of 10,000 dimensional data or whatever it is, you end up, the way an SVM works is it ends up choosing an effectively arbitrary set of data points that define this separation line and there is no fundamental meaning to those ones. It's just, it's found something that happened to work well with your training data but if you move one point by a fraction you get a completely different answer and so on. Whereas some other methods are much more understandable in that they're actually giving you statistical properties for example if your data that you can actually update it. And you have a updatable model. In some other types of models, generative models for example, every individual number that your model has would actually have a meaning and maybe thinking about all those numbers together is difficult but you can still, it's still impossible, it is possible in principle to dig into it and work out what's going on unlike with an SVM. But do you think you can actually modify it in a meaningful way? I mean unless there are models that you can update like an updatable database or stuff like that. Well it obviously depends what your task is, how complex that is but yes I mean if you have got something like a hidden markup model which is heavily used in speech recognition then yes I mean I don't, I think knowing what to modify and having a good idea about it would be pretty tricky but it would be straightforward if you somehow knew what was a sensible thing to do it would be very straightforward to modify it. Yeah that's the speech recognition final user adaptation that's in a sense so yeah. I mean again just to, on this point as well when I'm making the distinction between the model I think this is a genuine thing we should think about and maybe when we choose models in Debian for free software projects we should try to choose ones which are interpretable because for example often people love SVMs because they're a black box that there's lots of C libraries you can throw things in and you get an answer out with good results but there's another model I mean other models say logistic regression often gives almost as good results and basically the same result but gives a model that you can interpret so if you're trying to publish a research paper that gets 1% better than last year's paper and gets you into the conference then obviously you care about that tiny detail but if what we really care about is free software and that kind of thing then maybe we should also be preferring the models which making an active decision to prefer models which are interpretable and therefore avoid this problem. You have something. Very quick. You'll have machine learning about machine learning soon, ah recursion. Why would I need to understand the model itself? Yeah I mean it's like the source code of program okay you understand it but the binary you don't need to understand it. For me the model is quite often yes you won't need to understand the model because you just know it works you see the results. I don't see what the need to understand it is connected to the point of being free. And in that note we can continue the discussion on the mailing list offline. Thank you so much and thank you for not flaming.


Live from Mountain View, California, it's The Cube at OpenStack Silicon Valley brought to you by headline sponsor, Mirantis. Here are your hosts, John Furrier and Jeff Frick. Jeff Frick, we're here at OpenStack Silicon Valley 2014 and we're joined by our next guest, the many time Cube alum, one of our women in tech, Diana Muller. Welcome. Thank you for having me here again, John and Jeff. I think we just had you at, yeah, we had our cheat sheet, they took it away, but we just, you were on in Atlanta and we had you on in Portland a year ago. And I'll be back on in Paris, hopefully. Unfortunately, I don't think we're going to be in Paris, we're working on the Paris angle. Anyone would like to sponsor The Cube at OpenStack in Paris? Let us know. We've got to fly a lot of gear and people over there. Well, we have this event, which is a nice, nice impromptu flash mob of community, which, by the way, is packed house. It's amazing. This, you have to give kudos to Mirantis for pulling this off and the OpenStack Foundation. This event is great. It's very timely in Silicon Valley. I did come down from Vancouver, BC, but it's a direct flight. So that's awesome. It's not Singapore. It's not Paris. But again, at the same time. You know, there is a center of gravity of developers working on OpenStack here in Silicon Valley. It is incredibly timely and the event is incredibly well orchestrated. So it's a pleasure being here. So obviously OpenStack is the center all of the action. Red had the top contributor really in terms of the lines of code. Some will debate that, but you know, it's the numbers of the numbers. It depends on the day of the week. A bug fix does not count as a code, but if you count that in there, HP is also doing a decent job. So you have the big guys really contributing value. And you guys recently put a blog post out just on September 9th, you passed the new milestone. Two million served applications and growing. Talk about what that means. Can you detail that out for us? That's been OpenShift, which is the platform as a service that runs on bare metal, OpenStack, AWS. We have an online offering, OpenShift.com. And there's been a little bit of debate recently about scaling and who scales better than what on whatever. But one of the things that we're really clear about is that our OpenShift online service is rock solid today. And we just passed a milestone. We've had over two million applications created on OpenShift online. And what the wonderful thing, my role is, I'm the community manager for OpenShift. And so all the feedback from those two million applications and all those end users comes back into the OpenSource project and ends up being utilized in OpenShift origin. The OpenSource project and OpenShift enterprise, the productized version of it. And what we've seen since 2011 is this huge growth in people wanting to adopt application life-cycle management services, like platform as a service. And now what we're seeing is some really interesting new innovations coming and the next release of Origin V3 is out there now being beta tested by some of our customers. And we've got two million served, but I'm sure there's lots more being served on enterprises right now. And there'll be a lot more new innovation coming soon too. So here's the update on Red Hat and OpenStack. Obviously dockerization was a big talk at OpenStack in Atlanta. They just got $40 million in funding. The hype is there. Obviously it's got traction. No one can yet see the business model with the developers there. Maybe it's going to be something like a Red Hat. What's your take on that? How do you guys talk around and all the trend around Docker? So Docker's a wonderful thing. Containerization is really what makes OpenShift Rock, what makes the developers life so much easier being able to containerize and create now Docker images. It's a wonderful lightweight way of creating images and redistributing them. But the next generation of things is not just having a single container, but it's being able to manage those containers, version them, scale them up and scale them down, network them together, and use them as different services. And that sort of orchestration piece is not quite there yet as an offering from Docker or anything. But what we're working on at OpenShift is the next generation, which is using Docker images as our container model and Google Kubernetes as the orchestration for those Docker images, plus another side project of ours called GearD that helps us wire up those Docker images so that they know about each other and can be scaled in a cluster or a pod or what have you so that they can be scaled up as interacting Docker images. And it's a new, the next generation is here now for Docker images and orchestration. And you'll see lots of different methodologies for wiring up Docker images and deploying applications. So I think they've got the momentum now. We've got the Docker, I think it's called Docker Hub. Now they changed it from index. But there's thousands of images now being shared and being done. The other thing that Red Hat is doing is certifying the Docker images, so having rail-based ones so that enterprises feel safe and can trust using those Docker images, we've got a program in place now for certifying that. And I think that's going to be a key thing to the success of Docker because knowing that you can trust what's in the image is going to be core to a lot of people's. So what's the big change in Red Hat? Has there been any change of sentiment, mindset since Atlanta? I mean, Atlanta was an interesting show because we saw that real sea-chain shift where OpenStack clearly was going to be the lead horse and amongst the other approaches. Eucalyptus is now sold to HP. All the woods behind one arrow. The marketing war was won. Now we've got to produce value. Certainly the two million serve is a good value there. But what's going on internally at Red Hat? Are people guns-blaring? Has there been any shifting? Has the wind shifted at all? Well, other than Brian Steven shifting over to Google, I think, and I, which I, congratulations on that appointment, I think that's a wonderful opportunity for him. We are still full steam ahead using deploying and helping build OpenStack as a project. But what we're really trying to do is create a cohesive, complete cloud strategy. And we walk the walk and talk the talk about hybrid cloud. We have definitely trying to make sure that people have everything from cloud forms and what we call X-Pads, the middleware that they need to create those enterprise workloads on cloud and make sure that the OpenStack distribution is rock solid, that the storage offerings with SEF and Buster are rock solid and create a complete cloud offering. I think we're one of the few folks out there, there are others because there's lots of people here at this event, that are really concentrating on getting from the bottom to the top the entire cloud stack, working and running from application development to compute resources to storage and all the networking pieces. And so that's, we're still full steam ahead on OpenStack. So we have a question from Tim Crawford, our virtual co-host, Tim. Thanks for chiming in, really appreciate it.  We love to have the crowd chat in here, but Tim's question is specific to you. There's quite a bit of momentum behind Cloud Foundry today. How does OpenShift address that? What differentiators? So I think the real differentiator is, is that it's a, we actually have a larger scale deployment now in our public offering than anyone other than probably Heroku for platform as a service. We may not be the best marketers in the world. I'll say that about an engineering driven company, but we definitely have the customer base out there. We have the Docker capabilities built into this current release of Origin v3 that's now in beta testing. We have been really pushing very hard to make sure that our enterprise customers and our rail customers have everything they need to deploy both OpenShift and OpenStack. So we're really kind of doing a different approach. I think Cloud Foundry has really gone down the Hadoop and other add-on services and sort of, I would call it the Bosch approach. They're really more focusing on making sure that Bosch and Cloud Foundry and all the other pieces of their acquisitions work together as a seamless piece, which is great for them. But for us, we're really focusing on having a complete cloud. Do you think Cloud Foundry's trying to boil over the ocean a little bit too much? A term used in Techland for taking on too much? They're probably not trying to boil any ocean. I think they've got some pieces that they're duct taping together with Bosch and making into an offering to sell on top of VMware or wherever they can sell those services. And I think they're making a very good go of it, but it's not a complete cloud offering. So I think they have a very different target market. And I also think that we're much more in the side of being a more of an open source, community-driven effort as opposed to being else-wise being collected. You really held your tongue on that last sentence. You've got to give them a good college try. They're working hard. Certainly, you know, they're putting it out there. And also the enterprise of unique requirements. Martin Mikos today took the stage as the CEO of Elite Polyptus as a technicality because he technically hasn't closed the sale yet with HB Martin Fink, CTO and GM of the Clouds up there. But Martin Mikos mentioned that the word lamp. Yes. He kind of threw it out there in a very kind of nuanced way. He didn't really drill down on it, he kind of threw it out there because his history of MySQL, well-known, and everyone loves him for that, he's just a great guy. But it's interesting. Linux drove the lamp stack development. If you look at the open source community, since lamp, the population of developers has significantly increased. The number of deployments in open source has significantly increased. Linux was a driver for that. The question to you is that, is Red Hat looking at this next wave, not post-Linux to be the same or less? What is different about lamp versus all the openness within like open stack? So I think that for me, the real difference between where we are from back in the day with Linux and where we are now is that businesses are really driving the cloud technology. And it's not just the core of the business, it's from around the edges of the businesses. They're actually pulling in, someone will want to spin up a cloud for the sales. Someone will use sales force. Someone will use something. So businesses are really driving where we're going with cloud technology and driving the adoption of cloud technologies. And that's different than back in the day when the lamp stack or Linux was first getting off the ground. That was more of a developer, core admin group of people that were really putting their heads together, trying to solve an OS problem. And here, what we have is another, a couple generations later almost, of developers driving a larger ecosystem of services and networking devices and all kinds of storage to become a cohesive cloud offering. And I think the core difference between the lamp stack evolution and the cloud evolution is how much more all of the edges of businesses are driving the adoption of cloud, which is quite different than what we were in the past. So Tim had to chime back to your answer, which I typed in. If CF Cloud Foundry is boiling the cloud ocean, could the same be said for Red Hat? Well, I don't think Cloud Foundry is boiling any oceans. So I'm not sure if I was clear about that, but I just don't think they're boiling any oceans. They have some specific tools and offerings that they're tying together with Bosch and making a product offering. But what Red Hat is doing that's clearly different from what Pivotal is doing is that we are offering a complete cloud with everything from cloud forms to manage and manage IQ, the open source project for that to the core offering of OpenStack and RDO to the platform as a service to the storage pieces. We have a complete cloud offering that we're bringing to the table for our customers. And I think that's, that along with all of the middleware from the Jboss team and WildFloat and even the Jenkins continuous deployment pieces that work with OpenShift. And we're taking this to the enterprises as a complete cloud offering from which they can just... It used to be the battleground was passed, but now passing to just an app on top of the infrastructure. So there really is just go for it. People can go for their own approach. So, you know, I always call myself the platform as a server, the Paz Queen, right? I love Paz for what it brings to the developer life cycle for the ability to build and deploy applications. But I also think that it is in an OpenStack world, it is a module on top of OpenStack that infrastructure and there are going to be lots of different solutions to this problem. Awesome. So what other updates can you share with us for Red Hat? I think I'm going to save them all for the platform as a service panel that's coming up in just a few minutes at 4.50 here. Yeah, but they're not watching it. They're not streaming it live. So come on, share some of that, two of those jewels with us. I'm going to leave it at that. Anything else I would say could be used against me by the gentleman in the video. Okay, so are you, are you, the Cloud Foundry guy just came over. We'll take him to the woodshed too. So when he comes up. Please do. We know, James, we love you guys. So we have, competition's a good thing and at the end of the day, customers want bulletproof. They want reliability. I think at the end of the day, that's what we've learned at Red Hat Summit. Yeah, and I think that the key that for us is really in the public cloud, we have put our best foot forward with online. We've tested and tried and trued and scaled up to over two million app being deployed on OpenShift. It is an app. That same technology that we use on online, we eat our own dog food and we make it and productize it so that you can actually use it in your enterprise and trust that it's going to scale. Well Diane, you know at theCUBE, we love to watch, commentate, opine, get the facts, share the data. We'll be watching. We've been tracking you guys for a long time. Love Red Hat. Congratulations. Thank you. I want to find out how your panel went and make sure you send us a note. This is theCUBE. We'll be right back with our next guest, live in Silicon Valley for the OpenStack SV event, kind of sandwich between Paris and Atlanta here in Silicon Valley with the innovations happening. We'll be right back after the short break. This is theCUBE.