Aaron, as the playoffs begin, how driven are you to win a world championship? This is, you know, I'm excited, that's for sure. Definitely driven just like everybody else in this room. Had quite a few opportunities the past couple of years to go out there and win a World Series, and we've come up short. So I think every single one of those times that we've kind of fallen on our face has been a definitely a learning experience for me and a lot of guys in this room. And we're just excited to get back out there.  I'm also excited that we got quite a few guys that this is their first time getting into the postseason and getting a chance to play some meaningful games. So this is an exciting time and a lot of buzz in that clubhouse right now. So we're just ready to get rolling for tomorrow. Go to the second row. Aaron, while you were chasing history, it felt like the expectation was every at bat. People had their phones out and they wanted to see a home run. Is there a part of you that's relieved that is no longer the expectation?   I'm relieved that it's over with so that we can kind of go back to focusing on playing a game and winning a ball game. You know, I was a little stranger for a while. Every time you step into the plate, people are expecting you to hit a home run. And every time I step up there, I'm trying to do what I can to help the team win. So it was a kind of a weird situation there. But, you know, happy I could do it and kind of share that moment with my teammates and family.  And we're going to still have those same feelings, those same jitters with the crowd on their feet, especially Yankee Stadium, these next two games. It's going to be, you know, a packed house. So I think those past couple of weeks are kind of going through that and having this whole team kind of be there and witness that whole situation. I think it kind of prepared us for what's to come here in the postseason. A couple in the third row, I think.  Past errors, maybe a guy like Barry Bonds, he grew up watching, sees more intentional walks. Were you surprised with the home runs in the season that you had that you weren't intentionally walked as much as you were? And do you think maybe that changes with a shorter series where every a bat's so crucial? You know, I think a lot of teams understand how tough it is to hit, you know, especially with the majorly pitching that we have in, you know, this day and era of, I feel like every single starter's throwing 95 plus. Every guy in the bullpen's throwing 100. It's still tough to square up the baseball and, you know, go up there and try to do something productive.  And if he swings out of the zone, he swings out of the zone. If he doesn't, he'll take his walk. So, you know, I didn't expect to walk anymore, expect to walk any less. But in this, in the postseason, I expect, you know, teams just, they'll have scouting reports. They'll do what they need to do. There'll be certain situations where they come after me or other guys.  So, you know, if they're going to walk me and put me on base, you know, I'm happy for it. Because I know we got a stack lineup behind me of guys that are, you know, waiting to drop me in. Thanks. Just staying in the third row. And then, Eric, you guys have advanced far in the postseason during your time here. What makes you confident that this year could be different to get over the hump and win a title? Well, I feel like in years past, we've just kind of lacked, you know, having some of the, you know, top-end starting rotation pitching. And I feel like, you know, a couple of years ago, we went out and got Gary Cole, you know, watching what Nestor Cortez has developed into these past couple of years, you know, getting Severino back healthy. And, you know, we saw what he did his last start in Texas. It's impressive. I can go on and on about our starting pitching and then, you know, hand it off to our bullpen. It makes for a short game. So I think, you know, besides what our offense is capable of doing every single night, which we showcased all year long, I think when it comes down to it, it's defense and starting pitching is going to win you games in October and, you know, something that we definitely specialize in.  Eric, in the first row, and you're right. Aaron, for players who have not experienced it, how would you describe to them what Yankee Stadium is like for a playoff game? Man, it's heaven. That's where you want to be.  You can't hear the roll call, even when I'm in right field, right next to the bleacher creatures, I can barely hear what they're saying out there.  Still going to go out there and do the same thing we've done for 162 games.  You are going to be, have some butterflies in your stomach, but, you know, if you prepared the right way, did what you need to do, pre-game, and now it's time to go out there and embrace those moments and have fun with it and, and just play ball. Brian in the second row in your life. Along those lines, Aaron, the atmosphere here has been a little weird and, you know, everybody focused on your at bats only. What are you hoping it looks, feels, and sounds like tomorrow? Just like all the other post-season games we've had here, just loud, fans on their feet from the very first pitch, so the last out, constant noise, you know, and I think the fans, they're going to show out numbers, you know, just based on what we did all year, you know, staying in first place, winning our division, you know, not winning a World Series for quite a few years. I think, I think the fans are hungry just like we are for, for a championship. So, you know, they're a big part of this, this whole thing for us. They're out there battling with us every single pitch, and when they bring the energy, us as players, we feel that and we feed off of it. So, we're just all looking forward to, you know, putting on a good show for them in this, in this post-season. Stay over here with Ron. A lot of times we see before and at bat, especially when the other team's in the bullpen, you reviewing things on the iPad. What are you looking to pick up? The release point, the ball spins out in that like minute or two, what knowledge do you get? Well, you write down, we got different notes that we take that we can put on, put on those iPads, like personal notes of mine.  I've been doing all my homework the night before and that day, it's, it never hurts to get another, another set of eyes on, you know, what's his fastball doing? What's his breaking ball doing?  So I don't try to look at too much, just a quick refresher on what this guy has and then go out there to make a plan and go to work. Do a couple more, Barry and then John, on your right hand. So just a note on the intentional walk. Barry was walked 120 times in 2004 alone and 688 times in his career and he still had 762 home runs. So we're pretty minor with you, don't you think? Yeah, yeah, I never said it, I never said it was a lot. You know, it's, I'm still getting over 600 plate appearances. So that's one, one joke my dad always said. He's like, man, you're getting up to the plate about 600 or 500 times. You tell me you can't hit 80 out. But he has, we have fun with that. But Barry, Barry was a different animal, man. He's, he would get maybe one pitch a series and drive it out, one pitch a game. It was impressive and definitely fun to watch as a kid. So I'm glad, I'm glad I didn't get a walk to 100 and something times, that would have been a little while. So if Barry said you guys have never met. No. Okay, what would, as a, when you meet him and I'm sure you will, would you approach it as a kid like to his idol or to a peer? Well, definitely as an idol. You know, I got respect for the guys that came before me and played this game before me and everything they've done.  It could be even, you know, defensive stuff is amount of gold gloves. He's one, he's one out there and in left field. So, you know, whenever I get a chance to meet him, I'll definitely, we'll have a lengthy conversation. That's for sure. Kristian, the third row on your left. You've talked about before, not having success in the postseason and how that's kind of driven you a little bit. Is there one thing that you take away from that, a feeling or a memory? And when do you think about it? How do you use that?     It's, you know, I was all kind of sitting there in silence looking at each other and nobody knows what to say.   So I think it's definitely that silence in the clubhouse. Did three last ones, John, on your right? Aaron, obviously, even after the team was able to clinch the postseason spot and everything like that, Aaron was able to kind of move guys in and out of the lineup a little bit, but not so much with you. You were carrying a lot there. Were you able in the last couple of days to refresh yourself and like reset in a sense or to get out of that mode where you were in last Tuesday? Yeah, I think when I was kind of fighting Skip about the last game, I kind of wanted to get in there, but he said, nah, take the day and I really just needed that one day and I was ready to go right for that, but we've had five days off now to kind of, you know, for me, I can come in here and, you know, knock out my cage routine, do what I need to do on defense, you know, different recovery stuff I need to do and, you know, go back home, recover, watch some film and just get ready for the next day.  We'll stay on that same road to the right. First of all, congratulations. 62 don't happen a lot of time. When you look back five years ago right now and then we with Cleveland, mostly all the time when the junks faced the Cleveland Palsies that they dominated, what you're spating to happening in these cities? Well, first off, thank you. And it's gonna be a tough series.   They cause havoc on the base fast, that's for sure.   They're hungry. They got a lot of fight, you know, just from the past couple of games of watching them play the race. They're, you know, they were never out of that game even when they got down early in game one, you know, Ramirez responded to run home or to give him the lead and then they hold on to it by handing it off to their good bullpen. It's pretty impressive. So I'm looking forward to this, to this matchup.  Finish up with Bob against the wall and you're right. Aaron, you were just talking about this, that awful silence in the clubhouse and that sick feeling walking in after elimination loss. What does it feel like after the sixth or seventh year of that? And because you are the centerpiece of this offense, do you feel a greater responsibility to not let that happen again? Well, I feel a responsibility every single year, even when I was a rookie, you know, coming in here and going in on my first postseason run in 2017, you know, I felt responsibility, you know, not only for this team, but the city to go out there and bring something home and, you know, as the years have gone on and we've, you know, fallen short and came close and fallen short and, you know, it doesn't get any better every year, you know, and, you know, that's why you got to learn from all those experiences, you got to learn from those downfalls, you got to learn from the, you know, sleepless nights and you got to take what you can from it, turn it into a positive and just get ready for the next year. And that's what I've tried to do every single season. And this year we put ourselves in a great position. We got a great team to go out there and kind of close this thing out.


Hi everyone and welcome to another vlog. So today's vlog I'm going to talk all about leadership. I think it's a really important topic to talk about because as nurses we have to be leaders. I know a lot of people fear it and don't want to particularly be a leader but maybe that's just because you've misinterpreted what a leader is or some people get confused between leadership and management and leadership is completely different to management so I just wanted to cover this in a little vlog and hopefully inspire you to become an amazing leader that you already are and feel maybe a little bit more comfortable about it. So first things first I get really uncomfortable when people call me a leader and say positive things about me. I get really uncomfortable really awkward really shy about it and the massive imposter syndrome that we hear about all the time definitely kicks in. However what I've come to realise is student nurses can be leaders and actually you can have leadership across all different levels because before I started nursing I didn't think that I would ever be called a leader never in a million years and I would never in my own opinion this is disclaimer. I would never have thought a student could be a leader and now I'm seeing all of these amazing student nurses doing different things across social media and they are they're amazing and it just shows that you can be a good leader as a student nurse and I think that's really fantastic and I just wanted to get that message out there. Leadership as well goes hand in hand with being a good role model so I would say all of my mentors have been really good leaders because I've looked up to them all they've taught every single one of them good bad the ugly they've all taught me something really really important and I've taken a lot away from that and to me that's good leadership and we have to be good leaders for our patients we have to get our patients on board with the treatment the management plan that we're giving them the care plans we're putting in place they have to follow those things and if we're not that person that they can trust that they can follow that they can listen to and they're not going to follow your advice and that's where leadership sort of breaks down a little bit so we have to be good nurses we have to follow evidence-based practice but to follow all of our knowledge all of our training implement that into what we're doing with patient care patient safety so that patients will listen to us understand and follow our lead and that is just that's you being a good leader and you whoever is watching this video right now if you're a nurse if you're a student nurse qualified not qualified if you've done that for a patient if you've put something in place for them some sort of management plan they have followed that you're a leader because you've got them to do that and that is encouraging them to be their best selves because you want them to get better and you're encouraging them to get better and they're following your advice to me that's amazing leadership and that's exactly what the nmc wants us to be the nmc wants us to be this leader and not just for the benefit of our patients but for the benefit of our colleagues as well for the benefit of student nurses so we are going to eventually if you're a student nurse we are going to have students come under our wing when we're newly qualified nurses when we're qualified nurses for a year two year plus because nmc standards now everybody has to become this mentor or practice supervisor or practice assessor and you're not getting out of it you're going to have a student under your wing so you have to be a good role model a good leader for them and show those leadership skills hopefully it's going to have a knock on effect and they're going to be inspired by you to do more be better be their best selves and just encourage them to be fantastic nurses because it all starts with those students doesn't it so if we can aim and get those students on board being fantastic the future of nursing is just going to be amazing i would also say that leadership comes in all sorts of ways so like i said you have to be a leader for your patients for your colleagues and for your students but also if you're a mother if you're a father you know you have to be that good leader that good role model as well for your children so that they become their best selves you're encouraging them to become better to be the best version of themselves so that when they're older they can have an amazing future ahead of them you're a leader guys you're leaders you don't even know it you're criticizing it and you're amazing fantastic leaders so please take all of this information on board because to me leadership isn't about management it's not about telling people what to do and hoping that they do it no it's about just doing the best you can inspiring others to do the best that they can to be their best selves it's not about management it's not about ordering people around it's about sharing positive things it's about sharing your journeys it's about sharing your experiences like i said it ties hand and hand with being that role model be that person be that nurse that you want to be be that nurse that you look up to be that person that you see and you think wow i want to be more like them be that person and then someone else can come and look up to you and be like i want to be like them and then we just have this knock-on effect of amazing fantastic good leaders in the world wouldn't that be amazing yes so some little tips and advice for being a good leader or what i think is a good leader you might be different but i think you have to have a lot of empathy you have to be a good listener good communication skills eye contact body language you have to work at different levels as well so start from the bottom work your way up to the top so you know what it's like to be in that person's shoes so if you're speaking to let's just say i was a hca before so i've been a hca then i'm going to go on to be a student nurse then a nurse i know what it's like to be a hca so i can empathize with hca's because i've been there i know how tough it is and i know how hard they work so that's going to be a massive benefit i think just having that underlying knowledge and experience from before and in building on that i think also with being a good leader you have to share your journey share positive message share all of the amazing changes that are happening in your area so that other people can see this and be like yes i want to do this i want to do this maybe we should implement this into our area that's you paving the way again to be a good leader also encouraging others like i said encouraging others to become their best self egging people on none of this competition malarkey that i see sometimes on social media where one person's jealous of another person no that's not leadership that's jealousy it's terrible so please erase that we want to big each other up we want to encourage each other to be amazing someone's doing well you big them up you say do you know what well done a massive i'm genuinely genuinely happy for other people when i see them win awards when i see them get really good marks when they're passing with firsts i'm so proud of everybody that haven't even met and i'm there like oh my god this is amazing well done because i think it's just lovely it's lovely lovely lovely to share positive messages to encourage others to keep them going keep them motivated and just be proud of other people and i also think that you should have integrity and empower other people so that is it from me these are my own thoughts feelings experiences of leadership other opinions are going to vary between person to person and we're all unique amazing individual human beings are what might work for one person might not work for another person so my advice to you is go and find your inner leader


Melissa Hart from the University of New South Wales and my research is in the area of urban climate. My past research has been observation based, so looking at temperature observations across urban areas and trying to quantify the urban heat island magnitude. So that's temperature difference between urban areas and rural surrounds. We tend to find that urban areas are warmer than rural surrounds and it's called urban heat island because it tends to show up sort of like an island of heat if you think of a city that's not sort of too complicated geography and is just you have urban areas and then perhaps grasslands pastures surrounding it and you get this island of heat in the middle. And it's caused because we replace sort of natural landscapes, rural landscapes with lots of concrete and lots of buildings that absorb more of the energy from the sun and then they hold that energy for longer. So in the rural areas, as I said before the rural areas cool off at night they cool off quicker whereas the cities they retain that heat for much longer and then also because you have the complicated geometry within cities as I said the heat gets trapped as well within the street canyons. We also find that cities tend to have less vegetation so you don't get that cooling aspect of vegetation that you have in rural areas. Plus cities tend to release their own heat through what we call anthropogenic activities or human activities so that might be from cars if you walk down the street you can feel it if a bus goes past and the exhaust hits you in the face and you can feel the heat from that. From heat released from buildings so from air-conditioning vents and then heat released from people of ourselves.  The anthropogenic sources it tends to be a smaller amount. It's more sort of the changing of the land use that has the bigger impact. The work I did in Hong Kong were particularly interested in quantifying what the urban heat island magnitude was so what the temperature difference was in the city between the city and the surrounding non-urbanized areas. We're then also being interested in looking at where the hottest parts of the city are and what are the characteristics land use surface characteristics associated with the hottest parts of the city. So you know how much vegetation there is what the building density is like to see where the really hot parts are. I've been working on cities in the US in Hong Kong and now back in Australia. The work we did in the US was Portland Oregon and we found that vegetation cover was the main factor that was influencing the urban heat island magnitude within Portland. So as soon as you hit the more vegetated parts of the city the temperature difference in temperatures in the urban areas were much cooler. Particularly for that city we found that yeah particularly tree canopy cover was incredibly important and it yeah we had a significant cooling of the city in areas where you had lots of trees. Hong Kong was a little bit different because you've got these very very tall narrow street canyons and so you the influence of the buildings is much greater than the vegetation. So we found that the hottest areas tended to be those areas where you had really dense buildings. Minimal vegetation but mainly because there's no room for the vegetation. Lots of vehicles so lots of heat emissions from cars and people and such and really tall buildings as well. So buildings that basically street canyons that are tall and narrow and that are trapping heat within those street canyons so they're not being able to cool off. We're particularly interested in the impacts of urbanization on sprawl versus density. So if you were to grow a city or if you had to if you had growth within a city whether you're going to grow it externally or whether they're going to grow within the city and so there've been some studies that are done on that but there's still a lot more to be done so I have a student that's going to be doing a modelling study for Sydney. Differ's depending on city, geographic location, climate, time of day, time of year but short answer is yes it does differ. We looked at some longer term temperature trends in Hong Kong between the urban area and the rural surrounds and actually found that on some days, some hours of the days particularly found that we were getting temperature differences of up to 12 degrees Celsius which was a significant difference. They tended to be differences in minimum temperature so the early hours of the morning. It tends to have the greatest impact overnight so it tends to be more that the cities are slower at cooling down overnight than the rural areas so they stay warmer throughout the night but those temperature differences are important particularly if they, depending on the time of year but it's the minimum temperatures from a human health perspective that have the greatest impact on heat related mortality, morbidity and such so if you do get increased minimum temperatures in the city during the summer time then you've got an added burden on the urban population because if you've got a hot day that then it cools off at night the body has the chance to recover from the heat but if you've got a hot day and it stays quite warm during the night then your body doesn't have the opportunity to recover then you've got a hot day the next day and so it's that accumulated impact so within urban areas where we've got these often much warmer minimum temperatures than rural surrounds this I guess this added burden for people living in urban areas versus those in the countryside. Which parts of the population are most vulnerable to heat stress? It tends to be the elderly also those that are homeless those that living alone and then people that have underlying health health conditions as well but mostly the elderly. Well I guess the most important thing is that when you are looking at climatological records of temperature that the weather stations that are in the middle of the cities are not included but if you're looking at sort of climate change and sort of global climate change and wanting to know how greenhouse gashes are affecting their climate then temperature records from stations in the middle of the cities are usually not included it's stations that are not under any influence of urbanization because you wanted to want to just look at influences of sort of other forcings not urbanization. The data I look at is in that I'm actually interested in the urban urban measurements but those that are particularly interested in global climate change then they'll either look at the data that's from non-urban areas or homogenized data sets so Australia they're a concept data set so they'll concentrate on those sites whereas for me I like the sites that are in the urban area because I'm trying to look at yeah I want more more sites in the urban area but that's purely for looking at urbanization not looking at sort of climate global climate change. I think that needs to be understood that there's a lot of work that's done under developing these data sets that are just purely to be looking at global climate change without the impacts of urbanization and so there's papers documents galore that you can read about this where you can see that that they look at each individual site and they'll look at any outside influences and they'll discard sites which have moved in time or which I've got a significant urbanization impact on them so yeah it's it's not just taking every site in the world and looking at how the temperatures change you've worked in a couple of different countries has that influenced you in the way you approach science communication? I was in the US first but I I don't know I was a postdoc and I think I just sat in my little postdoc bubble bubble and did my research so wasn't there I did some work with high school students but beyond that not so much it wasn't till I moved on to a faculty position that I started yeah I guess communicating more in Hong Kong we worked a lot with the local government so the Hong Kong Observatory and such but beyond that I'm I'm probably not a good post a child for science communication professionally yeah it's been just yeah governments or local organizations and such one thing that I thought was interesting from my work in Hong Kong was that the Hong Kongers in general didn't have a lot of general worries about climate change it wasn't number one on their sort of environmental issues radar and I believe that came down to the fact that they have an incredibly bad air quality problem there and that that just was number one for everybody there because it's something that you see almost every day and you experience whereas climate change I guess unless you it's really difficult to experience stay you can't experience day in day out and so for Hong Kongers yeah whenever they were asked about environmental issues that greatest that worried them it was always a quality that was number one actually did an air pollution meteorology PhD first up so that was just I think I did an undergraduate degree undergraduate course sorry in air pollution meteorology that I really liked so I did my PhD in air pollution meteorology and then it was just pure chance that when I was looking for a postdoc post after finishing my PhD there was one that was advertised this was in the US that was looking at the impacts of both air quality and heat and urban heat island on urban populations so that opened up the urban urban climate sort of door for me as well have you ever had to ever had much interaction with people who reject climate science and how do you respond to it yeah always taxi drivers how many people say that I sometimes I kick myself because I get in a taxi in a taxi driver starts chatting and says so what do you do and sometimes they'll say oh climb I'm a climate scientist and then it starts and I'm like oh I did I say that way and so I actually have started lying a little bit then occasionally the other day I got in a taxi and I just said oh I work at a university and then he said what do you do at a university I said I work with graduate students but I didn't take it further because I just was tired and I should you know it's not every single taxi driver that comes at me being a climate change denialist but I guess there's a level of frustration as well just when I come across people in the general public that I guess yeah I feel that a lot of people have more trust in the media than the science than scientists and that frustrates me and and I get frustrated by the amount of time that done denialists often given within the media as well I think that yeah there's a lot the media can do what would be your advice to scientists about how to better communicate the climate science to the public honestly I think I should somebody else should be giving me this advice too because I'm sure I don't do it I know that I don't do it as well as I should so we've had a lot of talk within our center about engagement with schools there's a lot of people that do go into schools already within our center but we're talking around I guess yes seeing if we can expand that a little bit further I would have loved that as a school student I had never met a scientist but apart from my science teachers until I got to university so I would have just loved to have somebody in it come in you know from the core piece of science research and talk about it in schools I yeah I loved science at school I mean that's why I yeah carried on through university and science I loved it but honestly I didn't have the best I came from a public school with not the best science teachers unfortunately but there was something in there that kind of got me hooked but I would have loved the opportunity to have scientists come into schools what are the most interesting questions in your field of research at the moment it's that link between sort of urbanization and climate change so if you think that people within urban areas you have that I guess that double whammy added burden of you live in an urban area so it's going to be warmer but then you've got global climate change you and have a warmer climate so it's it's quantifying I guess that the the relative impact input impacts of global climate change versus urbanization because when it comes to sort of adaptation or mitigation there's a lot of things that we can do that can help both so within urban areas it's so easy you know greening cities and changing building practices and things that can help to mitigate the impacts of both human emissions of greenhouse gases have had having a significant impact on our climate and it's relatively recent just in the last 100 odd years and the changes are sort of unprecedented within that timescale and it's something we should be definitely worried about and looking at


So first of all, of course, I'm going to ask the audience before we start the panel if they have some general questions to ask to the speakers. Okay, no question. Oh, yeah, one question. Go ahead. How can all the techniques you use be used efficiently in communication? So that was a more specific question, not a general question. But how do you think the techniques that have been shown today could be used efficiently? Maybe this is the best question for Hongli in communication. I explained one example. I explained one example in SDN, right? Using machine learning to routing decisions. Yeah, that's just one example and there are many other examples. So in general, I believe anything related to decision in telecommunication, if it's very hard in traditional ways, then maybe data-driven machine learning can play a very important role. And it's getting started in all the areas in telecommunication. People are starting to collect data, et cetera, and try to define problems. This is true for our own company Huawei. In our lab we have several projects and quite a number of people working on this area. And I believe it's going to be more and more important. What about cellular? What about the 5G? Because the network and computer science oriented approach is quite clear from your presentation. But for 5G, there are also issues like resource allocation, resource management, and other things like, for example, antenna parameter setting. So far, such kind of problem has been done by human knowledge. If you have an antenna, you want to decide the angle of the face, the pilot's power, et cetera. So far it's based on human knowledge or based on simulation. But we think that, for example, in such a case, machine learning can help to do a better decision. And in fact, we have done some experiments for this and we observed some improvements. 15% improvements in overall management. So it depends on how you define the efficiency, overall efficiency, et cetera. But it's possible to define and also make improvements, for example. Is this just an optimization approach? Not optimization, but using reinforcement learning, for example. We can do a better control to improve, for example, throughput or improve some KPIs. Yeah, better than existing method. Yeah, so there are other cases like this. Another question from Maxime. So the progress of learning nowadays seems to be very fast and in a wide audience, there are many people who are afraid that maybe machines can become too smart.  And more practically, do you think your work has the potential to really disrupt some fields of the world as we know it? No. And of course, I think technology will disrupt the world. But people have that all the time. But in ancient machines, personally, I don't see any queue that they are closer today than they were 20 years ago. But maybe they are, but I just don't see that. Maybe a good question I was asking is that maybe people think that we are destroying our own future in terms of jobs. Which comes very often in many discussions, and I think in your interactions also with people. What we're doing is destroying our own job. Is it true? I can speak from the interactions we have with doctors. And one recurring question they ask us is, well, it's really nice your technology, but are you trying to replace us? No, and it's really the kind of question they ask today. So what you say is really important for us because we need to explain them that the algorithm is important in the interaction. With the human and not as a replacement of the doctor. It means that we need to figure out a way to design this interaction. And doctors are not specifically educated priori to interact with algorithmic results. So there is much room for development in this direction, I think. I was, for some random reason, I was at some big biomedical meeting in Lyon a couple of weeks ago. They invited me. I don't know anything about the topic, but the doctors didn't seem too worried. And I think they also have to take into account for things such as medical decisions. You have things of elusive ability. Reliability, explicability and all that that are not well mastered. Explanation, you know, responsibility and all that. So I think there's a long way to go before you can take a human out of the loop and I think you actually can't. I have a more general question is that AI is there since the 50s. It's not a new topic. Whenever you talk of why is the AI hype now, they tell you it's because of the computing power. Is it the only reason why now AI is increasing in terms of momentum? You're seeing it now everywhere, but it's been there all the time. I mean, there's always people who've been working on neural networks, stuff like this. No breakthrough and now, for some reason, everybody's on it. What's the reason? I speak too much. It has always been there, but they didn't work at all for a long time. Why? I think there have been several factors. Of course, there's the availability of data and the availability of computing power, but you also have the technical stuff like neuralness now. The thing that works now is essentially CNN. CNN has been here since the mid-90s, maybe early 90s. They worked quite well on specific domains. They don't work well on natural images, but they worked quite well on their own domains. They started working a lot better when there was enough computing power and data to make them work better. I think it's a mixture of scientific advances and a mixture of data and computers. But the scientific progress is there, and I think it's the key behind it. Yes, it's the combination of the three. Also, one more to add is some good examples like AlphaGo. It's good and bad. It gives people some confidence and also wrong impression. People can easily imagine that a machine can do anything. It can beat a human champion. But in fact, it's not always the case, but also I think it's important. If I look at the projection, things can only be better because computing power is increasing every time. Storage, I would say, ability is also getting cheaper and cheaper, so we're going to have more and more data. So there's always going to be a good combination for AI to explode in the next year. Is there a limit to it? Yes, you want to... I think that maybe there is a hype right now about artificial intelligence due to AlphaGo, for instance, just like you said. And that when people will realize that artificial intelligence is not able to do what is promised to everyone, then they may be downfall like we've seen in the past. And maybe it will happen in the coming years. We don't know. And maybe then after, it will still rise up for the reasons you said. But not maybe at the same pace as it does right now. I don't know. I agree, but I think you still have to take the opportunity right now because I think there's so much excitement. And there is scientific progress. But an example of expectations is the automated cars, right? So automated cars are not deployed. The only ones that I know that are deployed are the Uber cars in Pittsburgh. So in Pittsburgh, when you call Uber, you know, one time, sort of, I don't know, many times, it's an automated car that comes. And I was in Pittsburgh last week and you see them driving around. But there's a driver sitting at the wheel. I think he doesn't drive most of the time. He's sitting at the wheel. And it's not clear to me when real automated cars, if ever, will come on intelligent kind of intelligent cruise control type things. Yeah, but so who knows. But I'm sure we will have this thing. But the scientific knowledge stays. So you never go back down like that. So I think we have learned a bunch of things. So another question I have since we are in a mathematical, I would say center here, is basically the relation between mathematics and AI. So there's two issues I see there. The first thing is our industry is using basically a lot of AI cooking recipes without really understanding it. How do you foresee this problem of using things that we don't really understand with the risk that maybe sometimes it will not work for the customer, basically because we're not fully sure about the theory behind. And second is when do you think a neat mathematical theory, let's take the example of deep learning will emerge from your point of view in trying to explain. And the third one is basically, and I know that Gilles is maybe going to be going to ask him, is when do you think we're going to find the FFT of AI? Basically, where we could implement something of a mathematical, I would say theory of AI with its algorithmic fast free transform. So let's go for the first question is basically, do you think our industry is taking a huge risk? ICT, I think a bit less maybe because it's not nuclear bomb for the moment. But basically do you think we're going too fast in industry by incorporating algorithms for which we don't have a full understanding. And sometimes the network may fail because just the 5% of the cases that we didn't understand and predict. Yeah, currently training neural network is a kind of art, right? You need to accumulate some expertise or knowledge in order to train a good model. And there is no theory as I explained in my talk. And we hope there will be some new discoveries in theory to really guide the construction of neural networks. But it's difficult to predict how long it will take to have a better theory to explain everything into learning. But even without theory to guide, still people will try to different models and different architectures with more data, etc. So that means it's less efficient if there is no theory. But maybe still it can help people to build neural networks to apply the technique to different problems. Let me say differently, I mean would you buy a product where somebody tells you we have an algorithmic size, it works but we don't know why. But this is the case in many scenarios, right? But people don't care. If it works and we know the fundamental, it will not break something important, right? And it's still within one territory. If that's the case, I think it's okay. But the question is how can we assure that it doesn't break? Yeah, there is one issue. Some people like Professor Dietrich is proposing the idea of robust AI, right? Even for telecommunication, it's the case. You want to do something really bad, right? That's something still not clear for self-driving cars, the case. And even the accuracy is very high still but there is a risk. And then there's another issue because we always, when we train a model, it's always on average we can improve or we can achieve very high performance, right? And then there is maybe an issue. For example, if it's in self-driving cars, it's an issue about how should we put our trust on machines, right? It's not only an issue in computer science, it's an issue in social science or other fields, right? And that's also one important question. I don't have a very clear answer about that but it's beyond technology in some sense, right? But within the boundary of technology, still, with current deep learning or machine learning, it's average maximization and there is no theoretical guarantee from the viewpoint of math. Yeah, and clearly this question about having an algorithm that works well but you cannot explain why is critical in medicine. That's what you said is that if you have an algorithm that predicts the diagnosis or whatever but you can't explain to the doctor why is this decision the right one, then no one will use it. This is as simple as that. So we need to develop explainable and interpretable machine learning systems. And this is related to what I said before is the idea that we need to create this interaction between artificial intelligence and human intelligence and to create this interaction we need this interpretability. And for this interpretability to emerge I think my intuition is that it will require mathematics. It will require a deeper understanding of how it works. So at least I would say this is one of the big challenge ahead for the theoretical development. Well, I think you need really reliable things with empirically provable at least reliability so you can some confidence in whether it works or not. Otherwise people just won't use things. I think people have been, I mean they have been accident with self driving cars. So part of it was that people maybe were too confident in the decisions of the car. So I think reliability is a research issue. I think to me any research issue is a mathematical component in any case because otherwise you can't model it. I think it's a real research issue and after that the explainability is also a research issue. And so I think there are interesting topics on that. I think as you said there are people at CMU who work on this. There are people who work on this from an empirical point of view as well. You know having some sort of monitoring system that tries to look at the data and how good it is for the underlying algorithms. I think those are very important. I think there must be still plenty of areas where you don't need. When I step into a plane I have no clue how it works. I have no clue how the engine of my car works either and I still use it. So maybe for doctors it's a bit different and certainly there's a responsibility issue. You may not want an explanation always but if there's an incident you certainly want to be able to point at the responsible party. Whether it is the person who sold you the software or it's a part of the software. But I'm not sure you need the customer to always understand how it works. But for doctors it's probably true. And another question if we have to invest in mathematics to make AI work and have a full understanding. In which particular field of mathematics do you think we need more investment? Statistics, optimization, algebra which would be the part where you think we would need or require more and more. I would say a bit of breakthrough there. It's a tough question. Well if you look at the major trend certainly probability theory and statistics would play an optimization would play a very important role. But I would add there are other areas which would be important as well like reasoning in AI. So currently AI is mainly based on machine learning. But one view is that to further expand the capability of AI ideally we want to have reasoning or inference capability added into machine learning. And then clearly humans when we do reasoning we don't follow the logic in maths. And maybe we use a kind of different logic. Some people propose like natural logic etc. A lot of works I did before but actually no really helped improve AI. And maybe one possibility is a kind of new logic different from traditional logic in math. But very close to human natural logic maybe and really can help enhance AI. That's maybe one possibility. Another possibility is knowledge and this is related to symbol manipulation etc. I don't know whether it's strongly related to math but it's also likely math can contribute. And there are several other areas I think moving forward in math if there is any advancement may also help AI a lot. I would say that to my opinion it's really important to do a lot more of mathematical biology and especially mathematical neuroscience. And use mathematics as a language to describe on the same ground artificial intelligence system and biological neural systems. And using this common language I guess it will favor the emergence of new ideas in the field of machine learning through this understanding of how the brain works using mathematics. And it will require probably to develop fields such as dynamical systems on very large graphs, statistical physics of disordered systems that are purchased by external inputs. So it's non equilibrium thermodynamics and things like that. I think it can be very powerful to describe these disordered systems and have a mathematical view of how the brain works and then have this common language. I have no clue but I'll say something anyway. I think analysis is important in the mathematical sense is important. When you look at machine learning, I'm not a machine learning person so I'm very naive. But I never really understood kernel methods and all that until I looked into it and this thing about using functional spaces to describe data. In machine learning you have data people often talk about feature vectors but they are not vectors because there are not things. There are arrays of numbers but there are not things you can add or subtract or multiply by. And the way people went around it in machine learning communities by building functional space over those values you can reason about. And I think doing something similar in the deep learning realm for example is important. The few kind of theoretical analysis I've seen of CNNs. Assume that you have linear operators and some linearities over some input vectors and those are not input vectors. Images are particular structures and convolutions are particular operators and that's what makes CNNs work. So I think there's a lot to do there. I think there are a few people like CNNs. I think there are things that interest me that are intriguing also from a mathematical point of view. If you look at these image generation stuff, deep dream, whatever. And they seem to have pieces of images that are remembered from the training set. You see a face of a dog somewhere in the image or it has been the same for other images and these things. So why? And I think that's a mathematical problem. Do these things really remember somehow represent some of their input data? I think that's interesting. And of course there's all the business. I like CNNs but I'm not a great believer so I don't think it will solve the world. I don't think this is the solution that one should explain because I think people take as a take on the thing that CNNs work. I don't think that's the case. But I think still it's an interesting problem to try to understand as much as they work or not why they kind of work. There has been a little bit of work there but I think it has been not very so sexual. I think there are very few attempts that are convincing and why these things can learn the functions that they learn. And I think there's plenty of effort to do them. Especially if you are part of the converted, if you think that this is the solution to everything for the rest of the mankind. But I think it's generally interesting. Okay, are there questions? Yeah, actually I've seen a couple of papers lately that connect neural networks with renormalization in physics. Real space renormalization is where basically you have... So physics have this problem where they have situations where they have many, many particles, millions of particles. They won't somehow deal with the situation. So what they do is they break it up into groups. So you sort of replace the group with one sort of average, so to speak. And then you take groups of groups and groups of groups and so on. And there's an analog between that and when you have a neural network and layers. As you go from layer to layer you have features and features and features and features and features. I don't know if you have any say about that or are you familiar with this connection with renormalization in physics? I've read the paper. But yeah, there is much work to be done ahead. It's really more like an analogy at this stage, I guess. There's also been work where they tried to use, you know, spin glass type stuff and you know, polynomial functions with coefficients and random coefficients on the sphere and all that. It's interesting to do that but it's not so convincing because as far as I understood the paper in question, they removed the nonlinearities. If you remove the nonlinearities the neural network doesn't work so well. But then, otherwise, what they are trying to do is interesting. They are trying to get at the structure of the critical point of the... Yeah, but I think there are real technical problems in that paper. But trying to do that is interesting. Yeah, there are connections with spin glasses. Marc Mésard has been doing some connections with this physics. And they try to look at how energy flows per layer. And it's a question of our energy and what they call free energy goes in from layer to layer. So I think it's a good path but I don't know what's going to be the result. That's like the paper with Le Cun and Banarou. Because the analogy is the layer, the structure, the critical point for spin glass models. But it doesn't tell you at all that when you minimize you will go down to the local minimum. So you have to take it to the ground assault. But I think there are clearly interesting things to do. And even if they are not the answer to everything anyway, it's an interesting problem. Yeah, I have another question on this black box approach. I think one of the fancy things about our industry is of course that whenever we have difficult problems to model, the black box is nice because we can't spend time. And of course whenever it's too complicated we just use it as a black box. I have an issue with this because it's also from a scientific point of view. I think maybe you saw some talks of Condès is if you look at how science has been made until now is that usually you bring some experts in industry to develop a theory and use data to validate that theory. And now we're going to a realm where we use data to create theories basically. How do you see just change of paradigm in the way engineers and researchers are facing with science in the sense that we use data to create Maxwell equations instead of finding Maxwell equations and then using data to validate it in terms of how we are training our students and how also we're doing research at the moment. I think it's an excellent question. And maybe data is something that is putting us away from knowledge and putting us away from wisdom maybe. Yeah. That was short, yeah? Yeah, so I think I answered your similar question in my talk. So let me just follow his Geo's point and maybe it's just one step. If the problem is too complicated, we don't know how to model this in a mathematical model and maybe data-driven approach can help a lot. And so this also going back to the previous question, deep learning is being used in different fields in optimization, et cetera. And to me it's not a surprise because it's just a technique for function approximation with data. But we use different functions in different fields for different purposes. And if there is data and the problem is too difficult to model in mass and then maybe machine learning, deep learning can help a lot. This is true for telecommunication problems as well, right? But longer term, once for something, for some problems maybe if we have enough knowledge, it's also likely. We don't know, but we can build a new theory based on the experiences we have gained in machine learning, deep learning, right? It's likely and there is a kind of chasing game. So we're first to build something that works and then on top of that we can build a new theory. That's also likely to happen in the future, at least for some areas, some problems. So we don't know, but personally I'm a strong believer in math. So I think it's necessary to understand the details when necessary. And even it takes time or even it's very challenging but it's at least likely to happen for some areas. Maybe Jean, but he's tired. It's the end of the day. We probably all have the same kind of training. So of course we all want to model things with mathematics.   But I don't see those are new ways of doing science at all. I see these are useful tools for certain things and if scientists can use them, good. But to me science is about making theories about physical phenomena. And we are in a bit different realm as well for engineering and science are different. So in science you have natural phenomena, you observe, you want to explain. In engineering we build complex systems and we want to analyze their behavior and understand them. And the two I think are probably a bit different. Okay maybe one question because I think it's going to be time, it's already 45. So do we have other questions? Yeah, maybe Miriam and we'll finish by that. Why ask the question about why do we make machine learning into communication? It's the following. In communication I give you a message, you transmit it, you don't know where it is. In machine learning you understand the underlying structure of information. How can this help you? For example you do a test classification, you know the context, you know the keywords. How does this help you to compress the text inside it? This sort of interaction, how we change the paradigm of communication. Not just pipelines and bits and coding schemes and that's it. So I don't know if you have any insight about this one. I didn't get your question very well. So yeah, so clearly for instance you talk about compression and using unsupervised learning. In autoencoders for instance you can build up new compression systems that can then be used in communication but it's more sequential engineering than global. Do autoencoders work better than classical methods? I don't know, it depends. Maybe she was asking the question what do you think about the recent Elon Musk project about connecting the brain to AI. It calls this neural link. That was your question. What? Take the fifth. But I know I think in itself it's a good question to look at the limits of how AI can help improve networks and communications. And I think the whole idea of looking at how the message can be in plus the user behind and the whole point to point. But I think the biggest gain are from a network point of view rather than a point to point. So basically it's going from separate layers designed to more cross layers designed because of the correlation of information, because of the utility of information.  But whenever you're not fully compressed, meaning because you don't have long bits, then doing the whole modeling, a lot of people work on something called cross layer. Meaning basically try to combine, but then it gets complicated to understand how layer one or layer two, layer three things. Why don't consider all this as a black box? Because we like black boxes now. And basically start learning the whole thing and basically to be able to build a system which takes into account what's happening. Meaning you have finite packets, you have everything finite. And maybe instead of trying to model each part like we're doing and trying to connect it into cross layer design, which is a bit complicated. You could do it as a black box stuff. But if you have prior knowledge, why don't you use it? Even if you aren't fine-tuned things, I mean me, naively I would build in whatever knowledge I have and then maybe I can tweak it by using one of these methods. But I've never understood why, when there are people, for example, in robotics, try to learn robot kinematics. I've never understood why you want to learn robot kinematics. You know the kinematics, you build the machine. Of course you know it imperfectly. And you may want to adjust it, but I think when you have good knowledge of things, unless the systems are so complicated that the knowledge gets confused when you pile it on top of each other. I think to me, I would use it. The black box ID, to me, is very strange. The question I'm asking here, that's a good question. If you have prior, it would just reduce, I would say, the learning space. Which is important for these things. But do you have enough data in any case? No, because you have a non-convex optimization thing that may work because the gods are happy that they are. It may not because it doesn't work. And my understanding of CNN is that, you know, you change the initial parameters, you get very different results. And I'm not especially, so I may be wrong. The fact that you can get more data doesn't count. If you get an infinity of data, I guess there are some consistency things that will tell you that it may work. But in general, you don't get an infinity of data. So to me, it's not clear at all. Okay, I think we'll stop here, except if there are other questions. Let's applaud the speakers.


Antarctica is teeming with unique plants and animals that could face extinction with environmental change. Some of them have amazing adaptations that have helped us to improve our own lives, such as anti-freeze proteins that help us to keep our ice cream creamy instead of crunchy and anti-tumour and antibacterial compounds that we've used in pharmaceuticals. But decisions about environmental regulation in Antarctica, such as the establishment of marine protected areas, currently require consensus agreement by nearly 30 countries and that's not easy to achieve. So a proposed marine protected area in East Antarctica, for example, has failed to gain consensus approval after more than seven years of discussions. Meanwhile, Antarctica's environment has been changing very rapidly. Parts of Antarctica are among the fastest-warming regions on Earth and scientists predict we're soon going to see very rapid ecological changes. Ice will melt, plants will grow and new species could establish. Most Antarctic species are thought to have evolved in almost complete isolation for millions of years. Antarctica is surrounded by the enormous Southern Ocean with the strongest current in the world, the Antarctic Circumpolar Current and very strong Easterly Circumpolar Winds. And so for a long time, we've thought of this ocean as an almost completely impassable barrier to plants and animals. In fact, a lot of my recent research has been focused on trying to understand just how plants and animals could have survived in Antarctica for so long, for example, through past ice ages. Many seem to have survived around warm volcanoes and some possibly even beneath the ice. I've recently found traces of DNA from plants and animals in soil from volcanic ice caves like this one. But I've been starting to suspect that Antarctica might not be as isolated as we've always thought. We've assumed that because we see very different plants and animals in Antarctica to elsewhere, such as penguins and mosses in Antarctica and trees and parrots in Australia, and because there's an enormous ocean between Antarctica and the rest of the world, nothing is getting in or out. This concept of long-term isolation has also made us think that Antarctica will be reasonably protected from an influx of other species with global warming. We've been worried about the sorts of species we might take with us when we go, for example, through seeds attached to our clothing and shoes, but not particularly worried about what might be able to get there on its own because we think that nothing can. But my research is showing that plants and animals are actually much better at crossing oceans than we've given them credit for. We are not the only species that can raft or fly. This floating seaweed, for example, is now known to be able to travel thousands of kilometres at sea and can act as a raft carrying many other hitchhiking plants and animals with it. And I've even recently recorded rafts of seaweed which normally grows around temperate regions like New Zealand and Chile, crossing south into Antarctic waters. And that makes me think that many plants and animals are actually getting to Antarctica all the time, but simply aren't able to establish at the moment because it's so incredibly cold and inhospitable there. And if that's the case, we can expect to see very big changes in Antarctic ecosystems in the near future. As ice melts and oceans warm, plants and animals will arrive in Antarctica that were never there before. And the species that have been in Antarctica for millions of years could be rapidly outcompeted and replaced. Such changes are going to create very big challenges. How much should we interfere? Should we try to keep Antarctica as it is, protecting its unique plants and animals? Or should we allow and maybe even assist ecological change? Because after all, environments are changing everywhere and many plants and animals are going to have to move to survive. Unfortunately, we don't yet know enough to be able to confidently predict just how ecosystems will change. The disappearance of a tiny worm or a seaweed species could destroy entire fish stocks. We need to urgently prioritise research on Antarctic ecosystems if we're going to be able to predict and manage rapid change in the region. We actually now have top-notch scientific tools for making such predictions. Genomic data will allow us to pinpoint where plants and animals that have reached Antarctica have come from. And combining that information with high-resolution environmental and oceanographic modelling will help to show us where we should focus conservation efforts. But critically, we can't afford to sit around tables arguing for years on end. Decisions about environmental regulation in Antarctica do need to be based on strong scientific evidence but have to be made as rapidly as possible. Antarctic treaty partners and UN organisation colleagues also need to communicate and cooperate more over areas of common interest. To achieve the best possible management and science outcomes and to safeguard the future of Antarctica and of ourselves, we're going to have to learn to get along, to set long-term goals and to coordinate international efforts because while nations continue to bicker for years on end, Antarctica continues to change. Thank you very much.


Hello. Hello. Hello from KubeCon. This is our slightly delayed open office out. There's a delay actually. Yeah. Yeah. Okay. Welcome everybody.  We have been here for, you probably want to, I'll put in. We are talking about Jenkins X. We've had a fantastic week. There's been a huge amount of excitement around Jenkins X. Carlos did a fantastic talk. I think there was around, how many, about 900 people? Yeah, 900. Yeah, 900 people for Carlos's talk. It has been pretty huge. We're very, very excited. We're going to have a little write up as well when we come back and show a bit more of the details. Liam's just showing some of the conference there. For anybody that hasn't been to a KubeCon, it's hugely exciting. The energy here is just immense. Positive, it's very, it's all around a fantastic conference. So yeah, we'll do a bit more of an update on that and maybe just do a blog on the Jenkins X website when we're back next week. But yeah, so far everything has been positive. There's a lot of excitement for Jenkins X and so we're obviously very excited too. Any other highlights? James, can you talk or anything? Right, James is struggling with his headphones. Rob, what's been the highlight for you at KubeCon? I think actually the highlight for me has really been Carlos's talk. Obviously about Jenkins X, so that's really, really good. I mean, it's just the general part. So it's a real excitement around the earth and people are really increased and excited about Kubernetes in general. So without Jenkins X delivery, it's great. And actually we've come across a number of projects which we think are going to work really well with Jenkins X and accelerate the growth around that. So it's good. Will you join it? Did you join it? Yeah, actually, so there was a quarter with some of the Bitnami folks actually yesterday and they come up with, they've actually now got this like an app or something, it's a distribution of Prometheus and Grafana and a number of metrics out of the box that actually they're going to distribute in a nice easy way to consume. So that seems like a perfect win that we can try and get, try and add in as soon as possible. What was that chap, James? Is it Shipper? Is it Shipper? It was Shipper and it was from booking.com. Let me just find the link. This looks very cool. Yeah, and so this chap came over. He really loved this chap, came over and started talking about what they've been doing around Canary. And there's a link that James is going to find. We think it's called Shipper. And it looks like a great fit for Jenkins X. So it's just kind of in as soon as possible. Yeah, Bob's just popped it in the Hangout link and can add it to the Google Doc afterwards as well. But yeah, maybe everybody's interested in what would probably be hopefully doing around Canary deployments as well, then have a look at that link. We should be hopefully in the new year get that in as soon as possible, really. Yeah, Shipper looks very cool. What's been your highlight, James? I think the general buzz about, well, Kubernetes, Jenkins X, CICD. Lots of people have asked about serverless Jenkins, which has been a lot of people who are maybe not ready to go full on CICD with Kubernetes, but they are using lots of Jenkins. And lots of people have been very excited about moving to serverless Jenkins. So you don't have any Jenkins Masters to manage anymore, which is awesome. There'd be lots of excitement about that, but I think preview environments keep blowing people's minds when you can see the light bulbs go off in people's heads when they realize how awesome preview environments are. But no, it's just been awesome. Lots and lots of chats about lots and lots of things. One of probably the most common question that I've had, which is actually surprising, actually, is how do we build our Docker images? And I've kind of packed a little bit every single time at the moment where you remount the Docker socket so that Gareth will be looking at a mechanical and Google Container Builder. So basically, I've been telling everybody it's going to be sorted as soon as possible, but I don't know if that leads into Gareth.  Sorry, you cut out there at the end, James. But yeah, it's definitely... It's very, very high on my priority list, and I'm very, very close to having it all working. It works. I've got Google Container Builder. Building images really nicely, pushing them into GCR. I'm doing that. I'm just trying to work out at the moment how to integrate it into Jenkins X so we can sort of enable it as a flag when you create a cluster on GKE. Very, very close. That'd be awesome. GCR support by default on Google Cloud would be great as well. Yeah, I mean, it seems to do that natively. As soon as you enable the Cloud Builder, it just pushes to GCR. Awesome. That's really cool. And it's super fast. It's so much faster than building on our own images. I think the serverless builds, I think they create that mono repo, or each of the mono repos creates about 10 builds of about two GKE each. So you can see why we run out of disk space pretty quickly. That's very cool. So, good. So we can actually continue telling people that that's almost it. We actually, I'm just chatting to Dan actually. I think he's managing the Kanako project, I think. And he's saying that he's been adding loads of caches that they've really quite shocked at how fast these builds are actually going. So that's really exciting stuff as well. He's taking a fancy job. I'm breaking up. Okay, I'll just keep... I think I might have lost connection too. We can hear you, James. Oh, awesome. Flaky Wi-Fi at the conference. Who'd have thought? Which video will be coming to my mind? Who knew? Who knew? Okay, so can anyone hear me? Yes! Yay! I think people are getting kicked out at the moment. Some region. Make your way. So, anybody... For us. So, I'll just mention briefly the progress on adding more Git provider support to Prow is coming along nicely. I got some good feedback from the Prow SIG testing community about that. In fact, apparently some of them that are there at Cube kind of been talking about it. And people keep asking, like, hey, when are you going to support more than just GitHub? And so, it sounds like there's a nice groundswell of community support behind this. And they had asked me about it. So I'm going to present at the next... Whenever the next SIG testing call is, what I've been working on so far and try to get some feedback and just kind of keep... Make sure that I'm staying on people's radar and that kind of thing. That's all. That's really good. Yeah. Cosmin, how's the... So the vault operator looks to be just about there now, right? Git talks, Dev mode looking. Is it close? Yeah. Almost. I guess in the installation it's almost there. It works. I figure out some issues today in the upgrade platform and it also to make upgrade platform working with Vault and also to move this GPG encrypted secrets to Vault, I would say. And then in the pipeline, we need to fetch secrets from Vault as well. Currently, we use Kubernetes secrets for that. But I did a lot of refactoring in the install. Recently, also clean up Git, rework the entire Git stuff now, make it clear with the pipeline and normal user, how you set it up. Yeah. So it's progressing, I would say. But should be soon ready. That's... As soon as the Dev Git talks is done, we should be able to do multi cluster pretty quickly after that, really. Doing GitOps for the Dev platform install is the first step to really nice multi cluster. And then for me, so the app stuff is also kind of waiting for Cosmin because, again, I'm trying to do the app stuff based around the GitOps for Dev environments and almost every single app you install except really trivial ones need a token or a password to connect to the service they're going to talk to. So I'm kind of stalled a bit, intentionally stalled a bit on that because I can't make much progress. But I've got a lot of the pieces in place. So we have a working tool that will ask you questions based on a JSON schema. So for everything that's in the JSON schema, it asks as a question. So I basically have all of the latest draft for the JSON schema implemented except some really weird education, which I don't understand how to implement when you're asking questions. There's some odd stuff around nested schemas and things like that that I didn't do. But the rest of it kind of works. So I'm going to integrate that tomorrow. And I kind of took a detour down writing an app for Slack, which is based around the Slack bot that James originally wrote, which is now working quite nicely. So it will direct message you with any pipeline, any pull requests that you create with the pipeline. And then it can also tell a chat room when a new pull request is created. And it will mention anyone you put as reviewers on GitHub to notify them that there's a new pull request created. And I'll do that. I'll do that initially as a room, but then we can also do that as direct messages. So it can literally just direct message any reviewers to tell them that there's a pull request waiting their review. And then I'll do some nice filtering, so you can kind of filter which repos you on, some which rooms and things like that. That sounds awesome. Is there a way to get notifications over the master build fields? Yeah, I'm going to do that. That's the last one because I realized really to do that we need a kind of history because it's not really very useful just notifying the person who last committed with the master build fail. You kind of need to notify everybody who committed since it last passed because it might be any of their fault that it failed. We don't really have that right now in Jenkins X and that's a really classic feature in Jenkins, right? So my thought is really that it's quite easy to work that out though because you just look back through the pipeline activities for the last success. And then you can just query the Git provider for all of the... I mean, I'm hoping that I can then take the... I know we stole the last commit shot in the pipeline activity and I'm hoping that I can then use that and maybe some utility and go to basically just work backwards through the commit history and pull out all the user names and put all the names of all the people who committed since then. Absolutely. Yeah, so, you know, because I've got code now to, like, resolve Git users to Slack users, so it's just a case of, you know, running it all through that same code, basically. How's the code work to associate a Git user with a Slack user? Are we using the... So, Git Slack basically identifies people by email address. It's Slack clearly retrofitted user identity into the system or global user identity, right? They clearly started with user identity per workspace and then retrofitted the whole unique stuff in because, actually, under the covers, you really just reference everybody by some kind of ID, like, you know, 10-digit alphanumeric thing. So, whenever you mention someone, you kind of have to call a method on the Slack API to get their user ID out, but they have a thing called Git user by email address. So, you can always just use that, I think. So, what I've done is I'm pulling the email address from Git and then I'm checking the user CRD to see if... No, sorry, I'm pulling the user name and the email address from Git provider. Then I'm checking the user CRD to see if there's an alias... See if they've got their Git user name on the user CRD and if they have override it either with a Slack user name or the email address from the user CRD and just as a backup using the email address from the Git provider, which I think that will work for most people, but, like, for us, we quite will often have our personal email address as... Or, you know, if you work for a company, you'll often have your personal email address as your GitHub user email, but then you'll have your corporate one for your private Slack or whatever. So, yeah, so it's just kind of making it possible to do that. That's great. One question to the application secrets. Do you want to store them in the same... Like, in the same system, whether we store all the... I think so because the way it works with GitOps is when we do the install of the apps, they literally get installed in the same release as the platform and all the platform components. So, to me, it seems to make most sense to just keep that consistent and install them all into the same place, but I'm also, you know, open to ideas on that. And do you need to share these secrets also to, like, let's say developers because, currently, we haven't built all the user management into default. It's just one admin service account. I don't think we need to share them because the person who would add the app would provide the secret and then the app would use the secret. So, it's entirely service accounts. It's not, like, personal accounts don't need to get it, but I'm not sure if that's the question you're asking. Yeah, I'm asking if we need to give access to the VoIP developers, for instance, if they need to create secrets from VoIP and then you need to separate what is, like, secrets for developers and what are, like, installation apps in secrets. So, developers don't need to see the secrets any more than they do with the platform. Okay, so there's no sense to put them in the same port, I would say. Really, the purpose of this is more the other way, right? So, it's so that the app installer or the platform installer can provide a secret that the app or the platform can use. Okay, so that's, it's just extending to the app. Okay, yeah, I mean, if you can also do this today, actually, you just need to create, probably, an application kind of sub-path and then create an application name for something, yeah. And next week I'll try and do that. Fantastic. How's the impossible task wheel of the Git provider supports other than how you're getting on? Sorry to throw you right into that. Yeah, no, it's going well. I mean, there's a lot of pieces to that. I'm kind of, so I was working on, I was kind of working on GitHub Enterprise as like the first sort of, I don't want to call it low-hanging fruit because that feels like bad luck, but kind of like the easiest, the first obvious thing to do is add support for GitHub Enterprise. A little bit stalled on that at the moment just because Proud assumes a version of, or it uses, there's a couple of places where Proud uses the GraphQL API that only works with a version of GitHub Enterprise that's higher than the current version that we have at CloudBees that we can test on. So I'm working with, you know, some of the folks here at CloudBees to get that upgraded so we can, so I can start working on that. So that's kind of, that's a little bit stalled at the moment, but then while I'm waiting for that to get sorted out, I've been working on factoring out the Git provider logic and actually moving it into a separate repo and moving each individual provider into its own package and kind of trying to clean up some of the, right now everything's kind of just in one package, which is, you know, nice and convenient and, you know, you can just develop really quickly with all this stuff, but then there's some, there's a little bit of leakage. Some of the abstractions aren't really as airtight and as nicely encapsulated as you might want. And so I'm kind of working on getting all that taken care of and making sure that, you know, that those are all nicely encapsulated and that the individual providers aren't coupled to one another and that the provider interface isn't, you know, coupled to any of the implementations and that kind of thing. So I've been working, just working on that, trying to get that all done. Weird Git foo that I've never done before to try and pull that stuff out of the Jenkins X repo while preserving the history, which is being uncomplicated, but I'm kind of having fun wrestling with that. So that sounds fantastic. I also want to give you a shout out. So we had some amazing feedback. Bitbucket server, is it James? Yeah. So somebody came up to the booth and said, yeah, they're using Jenkins X with Bitbucket server, flawlessly. So a massive shout out because you were doing that when you actually, as a community contribution, that was before you couldn't join part of the core team. So I thought, just really wanted to give you a shout out and, you know, well done for that. Well, thanks. Yeah, I appreciate that. Any other, any other things? Anybody's got this? I see there's a few folks on the channel. I've not seen before. They want to say hello or anything? Any feedback? It's fine if you don't. Hi there. Hello. Hi. Nice to meet you. Nice to meet you. Just wanted to say thank you for this really great project. I've been using it for the last month or so on some new stuff at my work, and it's really sort of let us stand up a whole new team and get sort of all the CI CD stuff out of the way, which is something that we've been struggling with so, yeah, thank you. That is fantastic to see that. Thank you so much. Anything, any improvements, anything you have, please keep any feedback coming. I'd love to work with you and I'll always continue to improve everything that we're doing here. So that's great to hear. Awesome. Thanks for the work. Yeah. Cheers, Andrew. Thank you. Is there anything else from anybody? I had a quick question for Will, which is more just kind of something I've been thinking about, which is that with the Git stuff, quite a lot of the Git providers don't have half as richer an API as GitHub. And I've been adding things to the Git API that have been nice to have for various apps and things like that. So like what I was just talking about with the Slack app, I get the reviewers for the pull request so I can notify them on Slack. And, yeah, sure, it's fine if you don't have that. You can still use it. It just won't mention anyone for your review. But I was wondering if you thought about how we want to handle the fact that these APIs, some of these APIs, like say, we can't always use one that's really bad. I think Bitbucket's pretty crappy. At least the client we use for it, like most of the stuff just doesn't seem to be implemented. And I'm wondering if you thought about that. Yeah, a little bit. So one thing I've thought about, not so much in connection with Bitbucket, but in connection with Garrett, for instance, which only really provides the code review features. It doesn't really have very many of the social features that GitHub has. It's not an issue tracker. One thing that I was thinking about was trying to figure out, okay, so GitHub basically has conditioned us to think about all of these different things as Git that aren't really Git, like code review and issue tracking and all the social things. So one thing I thought about is, and I've been thinking about off and on, is how could we kind of decompose all of the components of Git that make up GitHub and try to figure out, okay, this is code review, this is issue tracking, this is whatever else those other things might be, so that we could do something like in the case of Garrett, you could have Garrett be your code review system, but then you could use it with an issue tracker, JIRA or something else, and that essentially it would be, to the other components in Jenkins X, it would be relatively transparent. You would still do things like update issues and create pull requests or change sets or whatever they're called in the particular Git provider, but they could be delegated to different systems and have that be kind of decoupled and those different components be pluggable rather than just assuming that it's all one big monolithic thing like GitHub. So I don't know if that gets us to what you're talking about, Pete, but I think that kind of, that at least allows us to start reasoning in that direction where we can think about, okay, how could we fill in some of these gaps and some of these providers? That's exactly what I was kind of thinking, but just supporting the Git provider like Garrett's a great example. You actually wouldn't, I mean, yeah, we could support Garrett, we do support Garrett, but like 98% of stuff won't work. So I guess the kind of more meta question was, well, is it worth it and do we actually kind of need a different approach? I think as well, we're currently using the ability to sync the pipeline status to commit some pull requests. We use that quite heavily for promotion. And maybe we should default to using just pipeline activities instead of the Git provider because, I mean, a lot of Git providers, you can Git clone and run a pipeline, but recording CI statuses on a Git provider is a fairly Git abuse thing. Bitbucket kind of does it and GitHub has a bit of that, but all the cloud hosting Git providers, for example, they're super simple. They rarely even have pull requests, never mind all the other features. So I think having a generic implementation of lots of the bits of GitHub that we can reuse on different Git providers would be really useful. They're interesting to me, like branch protection, which is kind of related to commit status. That is actually, I did look, and that is actually a really common feature. I think everyone has some kind of branch protection. So yeah, okay. Yeah, no, that's good. I'm glad we're thinking about it because it was something that was vaguely concerning me. Yeah. Awesome. So I'm probably going to wrap it up, but I just wanted to maybe finish off with pretty much a year to the date where the first commits for Jenkins X happens. Honestly, I'm kind of a little bit exhausted, but blown away personally of how Jenkins X has just taken off. It's a true community project. And it's been anybody's watching this back if they get to the end of this call. A big thank you to everybody that's been involved and hopefully we can encourage more people to continue building up and taking Jenkins X further. This year has just blown my mind and I suspect next year is going to be even better. So yeah, I just wanted to say I've finished it off with a big, big thank you to everybody. I'm looking forward to the new year, the new effort, the new features. Okay. If anybody else hasn't got anything to add? Maybe have a birthday party. It's super loud here. Yeah, but we should maybe have a... the next hangout should be on the birthday party one year ago.  Sounds good. I'm all in favour. All right, okay. Let's wrap it up. Thanks to everybody. Catch up soon. See you online. Cheers. See you soon. Cheers.