Good morning, everybody. Welcome to my talk. My talk will be about being radically candid and transactional analysis that leads to effective leadership. My name is Michel van Velde. I am the founder of Wanshu, a creative and digital agency based in the Netherlands. We work for international clients. I'm also board member of the Drupal Association. Today I'm standing here on behalf of my agency, not the board. I have to say this. I'm also board member of the Economic Board in Utrecht. I'm standing in front of you with a story about what happened to my company, and particularly to me as a leader of the company. I started and founded the agency in 2006. And we were, I can honestly say, successful from day one. I landed my first client on the day I registered my agency. And I jumped on the train, and the train became a rocket. And I was living the brilliant life of successful entrepreneur until 2017. And this is where I started to learn everything about true leadership. And I'm going to share you, well, everything I've learned in the last two years. In 2017, we lost a major client. We knew this was coming, and we had to find another and replace that client to keep the operations going. That took a couple of months longer than we expected. And at the same time, one of my colleagues got into a burnout. Another colleague went on skiing holidays, and she got into a skiing accident. And these are like three hurdles I had to come across early in 2017. I won't go into detail, but I was facing somewhere around May or June, I was facing 17 hurdles. And I remember one day early in the morning, I was standing under the shower, and it felt like Mike Tyson had done a workout on me. I felt really stressed out because of all the hurdles I had to come across. So being an entrepreneur, I decided to take matters in my own hand and steer the company into the new bright ocean and go for growth again. I made a lot of mistakes in terms of my leadership skills and in terms of my communication skills. And this is what I'm going to talk about today. There is a happy ending to the story. 2018 was a record-breaking year again. So we're back into the clear. We're doing absolutely fine. But it took 2017, took a lot of strain and toll on me. And this is what this talk is about. This talk is about leadership and effective communication. And believe me, I will share some valuable insights that will help you not only from a business perspective, but also from a personal perspective. All right, let's talk about leadership. This is, when we talk about leadership, what leadership looks like. You're right in the top. And the second bit, this is what leadership feels like. The difference between leadership versus management is quite simple. Management is working through routine processes. Leadership is like walking into the void. You have to find new territory. You have to come across hurdles that have never been done before. And management then takes over and turns this into management processes and goes to the daily routine. So leadership is leading the agency into, well, new waters, new territories. And that's walking into the void because you have no idea what you come across. Leadership is, and this is what McKinsey found out, is like oxygen. Leaders are investing more and more into leadership skills because they know it's true value to their company and their organization. So leadership throughout the company, it's not only the management team or the board of directors. Now leadership now is necessary throughout all parts of the organization. So let's take a look at the Drupal community. You want to come up with a new module and you've got to convince other developers to do so. That's leadership as well. All right, so I'm going to walk you through some question and some pictures. And the question is these people, I'm going to show you some photos for like a couple of seconds per photo. And I'm going to ask you the questions to you. Are these leaders yes or no? Well, he's a leader, yes or no? All right. I knew this was coming. I had to be in the air, of course. I had to do it. Remember this guy? So think about it. Were these leaders? A second question is, are you a leader? I think you are. Every one of us is a leader. Could be within the Drupal community. Could be at your work. Could be at home. Leading your children into a new life. So leadership is not just from management or a board perspective. Leadership is found in every position in life. So here I was. Well, Mike Tyson just done his workout on me and was not feeling very happy. So I started to do some self-analysis. And I came quite a long way. But I also learned there's some pitfalls to doing self-analysis. The pitfalls of self-analysis is that it's very difficult to be objective. You need to be accurately on both your strength and your weaknesses. But you tend to heavily rely on your strength and downsize your weaknesses. That's just a basic, natural thing. So I think I got to about 70%. And I knew I was missing something. So I said, OK, if I don't have it in me with a lot of self-reflection, let's start reading some books. So I came across, well, first it was a podcast. And then I found this book which is called Radically Candid. Radically Candid is a book. It's written by a woman. She'd been working with Google. And she had a colleague who was not performing really well. And that person had quite a bit of problems at home and everything. And she was really nice to him. She was not open or candid and telling him that he was not performing really well. So as the year went by, she was taking over some of his work. And at the end of the year, the review came up, are we going to lengthen your contract? Yes or no? She said, sorry, we're not going to lengthen your contract. So why not? I don't think you perform really well and et cetera, et cetera. And then he says, but you never told me. Because you were too nice and too sweet, and you were not openly and radically candid to me. So I said, wow, well, this is it. I have to be radically candid to my colleagues and stir them into the blue ocean. This is what I'm going to do. That didn't work out. It really didn't work out. It just turned against me. So yeah, being candid is good. But how you communicate, that's where the essence is. So I was like, okay, this is not particularly working. Try some other books and I was like, okay. And then I remembered this picture. This is Tiger Woods. He's the number one golf player. He was the number one golf player in the world until he made some mistakes. I'm not going into depth on that one. But I remember this picture and I said, okay, you are the number one in the world, but you still have a coach behind you. And I thought, okay, so behind every leader, there is a coach. And I said, okay, so let's go over that bit of ego and he says, okay, I'm not good enough. Gotta learn. So I found a coach, brilliant guy. He's into business economics, but also psychology at the same time. Leadership and organizational change. So I still remember going there for the first time, went over to his office and well, we started talking. He says, why do you want to do this? Because it looks like you're really busy as well. You know, I want to change. So well, that's the first thing I wanted to ask you. Do you want to change this? Yeah, I want to change. So we entered into some role plays and I immediately found that I was stuck in terms of my communication. And then he entered me into the world of transactional analysis. TA, it's called. You can Google it, it's really cool. But we're gonna deep dive into it in a minute. So transactional analysis, every form of communication can be seen as a transaction. You know, it's a transaction in between people. I want something from you. You want something from me. And we have a conversation about it. I want you to do your job properly. This is a transaction as well. So what I learned during that first session that I was stuck in what they call the drama triangle.  If you haven't heard from me yet. Okay, so problems you experience evoke emotions in you. You know, I was heavily emotional because my company was not doing well. You know, and I've been riding the success wave and suddenly there was no more water on the wave. And it's like, okay, so it's heavily emotional. This will basically got me into, well, there's a bit of fear, some anxiety, some tensions, angers, some frustration. And that emotion evokes a certain behavior. And which kind of behavior that varies from person to person. Some will fight, others will fight. That's basic, you know, human nature. You either fight or flight. Some other will paralyze, you know. And all, in all cases, it's reactive behavior. You have to respond to your emotion. And basically you respond to your emotion, sorry. And not the problem.  That's difficult. I'm not gonna say this talk is going to be lead you into the future. Like, yeah, this is going to be difficult to do. But if you start, you know, learning this, this is great. So you don't want to feel annoyed by the emotion. You want to get rid of the emotion. And when you act on the basis of emotion, you're in the drama triangle. And here we are. Okay. So this is the drama triangle.  Colleagues has made a mistake. I'm radically candid. And I say, okay, you haven't done your job right. So here I was persecuting somebody, not doing his job properly, you know. And this is basically in the right hand corner, the persecutor, it's all your fault. I was critical. I was controlling. And I was taking the superior position because I thought that was the way into leading the company into a better world. In 80% of the time, people will react as a victim. Oh, I'm sorry. You know, you are always late. You know, that's pretty much the persecuting position. Oh, I'm really sorry. Bridge was open, you know, and the tires of my bicycle and, you know, and my radio clock's not working properly. We had a power outage. I don't know. It's poor me, purely powerless, hopeless and stuck. In 80% of the time, your reaction will be because you feel sorry for the person. You're like, okay, I'm gonna rescue that guy. Let me help you. You know, so you got to start doing his or her work. So if you start doing that, if you start rescuing, this is what I was doing, you know, start rescuing. First, I was persecuting, I saw the victim part. I was rescuing to the max. Problem was, it messed up my personal agenda. So instead of working nine hours, I was working 12 hours a day.  The colleague, the victim colleague, had already left, you know, you know, see, it's time is up. You know, and I was still there doing his job and my job, and do you hear it already? I'm already persecuting the guy again.  Are you the entrepreneur who's always away instead of at home with the children and stuff? She's persecuting me and guess where I am? The victim, yeah. So I started as a persecutor, you know, then I started being the rescuer, then I started persecuting again. I ended up being the victim. That was not really the place I wanted to be. So I asked my coach, okay, how can we get out of this? All right, so when you, in the drama triangle, you will often see that or wait for somebody else to move. You know, to get out of the drama triangle, to get into action, and because he or she, they basically must change. It's not you, it's the other person, you know, it needs to change the way they work.  And I got this brilliant little line from my coach. This is a life saver, you gotta write it down.   If I tell that person you're always late, I'm statistically wrong. And he will say that's not true. And we have an argument about the statistical fact that I was not right. Not about the problem itself. So you have to be statistically right, factually right. This is okay, in 80% of the times I've been noticing going over your timesheet, you know, you're late. And this is the brilliant line. Remember this one. What can you do yourself to be on time? And then if they wanna truly play that victim role, I don't know. Well, go figure it out. You know, do some research. There's Google, uh-huh. Google has the answer to everything. But by answering that little question, I was empowering him to change. I went on with my day-to-day business and just left him and he's like, well, he's empowering me. You know, he felt strong. And I was enabling him to change. So that was good. So this is basically the persecutor and the drama triangle in a different perspective, which I am going over right now. When you start your communication, you know, you have to be talking from a standpoint, this is difficult, but there's a lot of emotion in you. And I told you, you have to push all that emotion out. Because if they see emotion, you're already in the drama triangle. So push your emotion out, be factual and state, I'm okay, you're okay. Because if you are the persecutor, you'll be, I'm okay, you're not okay. That's drama triangle. The rescuer says, okay, I'm not okay, you're okay.  You know, you're in a plus-minus situation. So then I was, no. So you've learned now about the drama triangle. I'm gonna show you a quadrant now. This is the second thing you'll be learning today. So here I am, I'm okay, you're okay. This is difficult.  And when you start a position from I'm okay, you're okay. You will have a healthy conversation. If you're starting the conversation from I'm okay, you are not okay, you will be in the drama triangle. For sure. If you are in a position, I'm not okay, victim, you're okay, you're in the drama triangle. If you're, I'm not okay, you're not okay, there's drama all over. You don't wanna be there, I can tell you that. There could be another position. This is an ego position, I'm okay. No, no, I'm very much okay.  That's drama triangle as well.  That was really fun to read actually.     So you're always late.   So, okay, now bear with me, I'm gonna deep dive into transactional analysis. Based on some observations, there was a, there was a psychologist called Eric Byrne, I believe it was somewhere in the 1950s or 60s and the psychologist was talking about the psyche of people and it was all based on, now I've got the name of the former psychologist, ah, I've got the name, sorry. But anyway, in his conversation with his clients, he found out that people were changing ego positions. So, in some times they were having an adult position, they were talking from an adult position and sometimes they were talking from a really childish like position, you know, the free-minded creative position and sometimes they were like in a parent position. So he found out people, even within one conversation could change in ego statuses. So, it was Freud, yes, I got the name now, Freud in the past was talking about ego statuses as well and they were talking about the ego statuses like the it. And nobody was understanding what Freud had written down. So Byrne came up with the modern form of psychology called transactional analysis. So, let me talk about the parent position. You know, the parent position reflects the absorption of over years of influences of actual parents. When you read the book, I'm okay, you're okay, you'll be learning that a child from the age of zero to about five is recording a lot of images from their parents. The way they act, the way they communicate and they all store them in the parent position. In his life, the person will have leaders like for example, their manager, their CEO, their teacher and all that kind of behavior from leaders around them, they will store in their parent position as being the truth and they will act accordingly and they are heavily recorded and you cannot change that. I still remember my parents saying, wearing white socks, that's not done, you gotta wear colorful socks.  But as life changes and over time, you start questioning all those scripts that you have within your system. That's what the adult position does, you know. This is where we hope to be as an adult and it's dealing with all the vicissitudes of everyday life and it's basically also regulating the activities between the parent and the child position. In short, behavior, thoughts and feeling which are in direct response to the here and now.  Okay, and then there's a child position. Child position's a really interesting one. This is where we go basically back to our childhood. It's child-like, it's not childish and this is where the creatives, the free-minded people are roaming, you know. They have no boundaries, you know, they come up with crazy ideas and stuff and those are not regulated by daily life or anything that's being stored in the parent position. If you read the book, I'm okay, you're okay. You'll be deep diving into these situations.  When the parent says to him, when I tell my kids to say, okay, clean up your room, you know, this is quite demanding, you know, top-down position, that would be, okay.  So, but when I talk to my kid from, and this is the parent position, you have the steering parent, you have to clean your room, and you have the feeding parent.  So, I would like to advise you to clean your room, you know, it's a mess, you know, but the clean room, and your kid says, okay, you know, sounds good, so it's a different approach. So, the lessons learned for me is by identifying whether you're stuck or sucked in the drama triangle, you can deviate from your position, and you can always continue from an adult dialogue position. Within meetings in my office, I now can easily identify in which position people are.  And then they're like, hey, wow, they're listening to me. When people are in the child position, they want to be listened to, and once you listen, they move automatically to the adult position, and you can have an adult conversation again. So it's really interesting to identify in which position people are. Okay, so I'm gonna walk you through some key ideas. Great leaders identify other people's treats, and act upon them. So yeah, I got the question earlier this morning, it's like, okay, why are you into psychology? So well, I want to be an effective leader, and effective leadership is about communicating effectively. So I have to identify the traits, I have to identify in which position people are to be able to communicate effectively. If I'm a steering parent, and the other person is in his child position, we'll end up in a drama triangle, for sure. It won't work out, it won't be effective. So you have to get into the same position to be effective. But that requires some studying, reading the book, I'm okay or okay, and you'll be fine. So okay, key position number two, key ID number two, good leaders solve problems, and great leaders first ask questions to make sure the right problem is solved. I got to look at the time, I got two minutes left. There's a parable that reflects this. There's a couple of blind men and an elephant. This story originated in ancient India, and it's a story of a group of blind men who have never come across an elephant before, and who learned to conceptualize what an elephant is by touching it. So the first one is just grabbing the till, and it says, yeah, that's her rope. And the other one is just grabbing the stomach, and it says, no, it's a wall. And the other one just grabs the leg, and says, no, it's a tree trunk. So they all have a different perception of what an elephant is. So every now and then, a couple of colleagues come into my office, and they have a problem, and they wanna discuss it with me. And based on what they're saying, I can say something or just the way to go. But the first thing I'm going to do is ask questions. Because each one of them has a different perception of what the problem is. And we have to find out, and I just say to them, okay, what's the elephant? And they're like, okay, yeah. So we gotta talk about the problems first. I'm gonna ask questions, and questions, and questions.  Not based on the trunk, or the till, or whatever, or the stomach. No, the whole question. So all I'm doing now, save me a lot of time, is just ask questions, and they come up with a solution. I'm not bringing the solution to them, otherwise I'll be in a different position. Okay, great leaders set the standards, and they act accordingly. That's really important. If you don't set the standards, and you don't act accordingly, they won't follow you, and they will follow the standards. People will view your actions, and they will draw their own conclusions. So this is a lesson I learned. Every month I give a presentation to the whole company about the state of the organization, and everybody walks out with a different conclusion. And they have their own opinion about it, and their own conclusion, and then I have to rehearse, and rehearse, and just, sometimes I gotta rehearse myself about five times to get the message across. So we're all aligned. People are, everybody's different, and every brain is different, so everybody will consume the information in a different way. This is an interesting one. This is what Einstein wrote. Everything is energy, and if you match the frequency of the reality you want, and you will get that reality. This is not a philosophy, this is physics.  It's not gonna happen.  This is almost religion, you gotta believe in this. So everything is energy, your thoughts begin it, your emotions will amplify it, your actions give energy to the momentum, and people will feel it. Absolutely, and I believe this, this is my religion. Same as the drama trials, part of my religion as well now. Okay, some tips and tricks. I don't know if you've ever heard about push and pull communication. What happens when you're in an argument, and the other party is just trying to blow you away with arguments. He's pushing, you know? And you can push back, you know?  What you can do is, if you wanna reach a certain outcome, you start pulling, it's basically asking questions, you know, and by asking questions, you can change his thoughts on the subject.  So that's the push and pull strategy.  And when you give feedback, you know, we're in America, I had to do this, the hamburger model, you know, you start with something positive, you know, your conversation is like, hey man, you're doing a great job, you know, but as of late, I see you're coming in, you know, quite a bit late, and let's see how you can improve that, but keep up the good work.  And this is a really effective way of communicating, and it's called the hamburger model. So you can deep dive into that as well. So I hope this will give you some insights. It changed my life, you know, I'm far more effective. Once you had a record-breaking year last year, the energy is excellent at the office right now. I've been teaching this to all my colleagues, you know, so they all know about the drama triangle, and every now and then it says, okay man, it feels like during the drama triangle, you know, maybe we should step out of it, you know, oh yeah, yeah, cool, you know, how can you improve your life, how can you improve your work, you know, and people are feeling really empowered, and the company is just going superb again. 2017 is behind, I've learned my lessons, and I'm very proud to have shared this with you. Thank you very much. Okay. And don't forget about the contribution possibilities and opportunities on Friday. There's mentored contribution, first-time contribution workshop, and general contribution. Thank you. If you have any questions, you can come up to me and I'll have a one-on-one with you.


I'm going to be honest about this, when it comes to debate, so I myself, I'm a skeptic. My experiences with debates, I've always seen the two sides almost like in a shouting match and it gets very personal and it's quite insulting in some ways. So, I'm interested to know how can debates be used as a positive thing to help interfaith dialogue as such? Bismillah ar-Rahman ar-Rahim, Alhamdulillah ar-Rahman ar-Rahman, peace be upon you. It goes without saying that a normal debate shouldn't be the way that you describe it. People have to sort of put forward their reasoning and argue with, at least, debate with one another without it turning into a shouting match as you put it. And when we see the example set by the Prophet, the Holy Prophet, Sallallahu Alaihi Wasallam, and how he conducted his debates with other people, whether Jews, Christians, atheists of the time, we see that it was very amicable, it was respectful. We see that even whether it was the Prophet, Sallallahu Alaihi Wasallam, or the other Imams from Ahlul Bayt, A.S., even if the other party were to insult them or use derogatory terms against them, they wouldn't respond likewise. They would make sure that they keep the environment, the atmosphere calm, and they try to use reasoning to refute the argument of the other side without resorting to any of those conducts that the other party may have used.


Hello, everybody. I've never spoken to so many people, so it's absolutely terrifying to be up here. I'd like to thank Vicky for that nice introduction. I've never been called very esteemed before. So, yeah, I'm here. As Vicky mentioned, I'm here to talk about the Python role in unlocking the secrets of the universe with the James Webb's telescope. So I'm sure you're all aware that yesterday was the first release of the science images taken with JWST. I'm very glad we could organize President Biden to present the very first one on Monday just in time for EuroPython. So, yeah, the timing has worked out very well. When I was first asked to do this, I thought, oh, this is great because there's so much Python involved in what we have done with Webb. And I thought, oh, I have so much to talk about, but when I actually sat down to do it, there was just too much. Okay, so I'm only going to give you an idea basically with my experience, colored by my involvement with the mid-infrared instrument. But bear in mind that 90% or more probably of the analysis tools, the simulators and so on, were developed in Python, okay, and used Python. So the way I'm going to structure this talk a bit is to give people an introduction to the telescope and the mission and what's been going on for the last few months, and talk a little bit about the Python tools we use to calibrate the data and to do simulations, which we use for testing and so on, and commission. And then finally, I showed the images from yesterday for those who haven't seen them along with a few others that weren't released as part of the announcement but have since been released. Okay, so the James Webb Space Telescope, it was built on the success of the Hubble Space Telescope. Okay, it was designed and built to answer questions Hubble just couldn't do because Hubble looks at a specific part of the electromagnetic spectrum from kind of near UV to infrared, to near-infrared. But Webb is an infrared instrument or a telescope, a pure infrared telescope, so it looks at a different part of the electromagnetic spectrum. It was designed to see the very first stars in the universe. Okay, so it needs a big mirror to collect enough light to see those. And the big mirror also provides fine detail in the images so you can resolve a very fine structure in your images. It needs to be cold because it's an infrared instrument. Most are an infrared telescope. Most people will understand infrared as being heat radiation. So if you want to observe heat in the universe, you need to keep your telescope very, very cold because if you don't, if this thing was on the ground, a great analogy I once heard was it'd be like standing in the middle of a football stadium with the floodlights on at night trying to see the stars. You just wouldn't see anything. You'd be overwhelmed by the heat from the telescope. Okay, it's a big thing. All right, it has a footprint of a tennis court and this is a picture taken from ten years ago when the full-scale model was here in Dublin at the Museum of Modern Art in Kilimanum. And this is that picture and in front here you see a lot of the scientists from NASA and European Space Agency and Ireland who were involved way back then in, you know, 2011, I think this was, but you can really see the size of the telescope, okay, and it's not trivial to put something like that in space. And it was overall, you know, in the announcements and so on yesterday, you heard a lot about the US involvement, but really there was a big European involvement in it as well and Canadian. So Europe supplied two of the science instruments in Canada, one of the science instruments. Indeed, the platform, the telescope and so on, was built by NASA who also provided one of the instruments, but the mission, the telescope itself, was put into space by Europe. It was launched on an Ariane 5 rocket. As I mentioned, it's an infrared telescope, so it looks at sort of heat from things, okay, that's a very kind of general description, but it's looking at the part of the electromagnetic spectrum, which we feel is heat, but it's actually looking at colder stuff in the universe and there's good reason for this. So Webb's four primary science goals or themes are shown here. There's the birth of stars and protoplanetary systems, the assembly of galaxies, the first light and re-ionization history of the universe, and planets and the origins of life. And they are pretty big questions to answer for a mission, okay, but these can only be answered using an infrared telescope the size of Webb with its instrument suite, okay. So it was from the ground up designed to address big questions in each of these four fields. Now I'm not going to go into detail about the science, because this is a Python conference after all, but the four science themes are just sort of how the telescope was designed. It's also a general observatory. You can observe anything in the sky and with that mirror and with that sensitivity and detail it and provide and the suite of instruments it can revolutionize pretty much all areas of astronomy and astrophysics, which is why the scientific community and the astronomy community is so excited right now and we got the first taste of this yesterday and again I showed the images at the end. So it was launched in December 2021 on Christmas day from Kourou in French Guyana. It was launched about lunchtime European time and it was the worst Christmas day I've ever had. I couldn't enjoy anything because I knew this was coming and I was so nervous and yeah I had lots of drink after it I can tell you when I went up safe. But it was launched and it was extremely successful launch. It's one thing you maybe don't hear so much about that the European, I don't know why it's flickering, but the Ariane space company did such a great job on the launch that they didn't need as much fuel to correct the path of the rocket after the launch which meant that that fuel or the path of the telescope rather which meant that the fuel that was supposed to have been used or could have been used wasn't which means they're available to extend the life of the observatory and instead of a mission lifetime of 10 years because that extra fuel is there and it was the only consumable it's now looking like 20 years okay so that's all from the quality of the launch and I was inserted into orbit. This is the final real look we got of Webb as it was moving away from the upper stage of the rocket and this video was taken by an instrument that was developed here in Dublin by a company called Reiltra Space Systems Engineering and it's just showing Webb being pushed off to where it's going okay and what happens I won't let it run because it goes on for a few minutes but you start to see the solar sail deploy when Webb started to get power and that was it that was the first indication that everything had gone so well because it was on such a perfect path that it could start to deploy you know as I said it was a big thing it had to be folded up to fit into the nose cone and this was a very nerve-racking time this deployment of the telescope took two weeks and was very slow and methodical and systematic and there was nearly 300 single point failures in this process so any one of those goes wrong and that telescope is completely useless okay so it was a phenomenal piece of engineering but they got it up there and deployed the telescope and it went absolutely perfectly okay and this was the day in January when when the last deployment went or it was successful and what's on the left here is kind of a visualization tool that feeds telemetry from the telescope directly or that takes telemetry directly from the telescope to show what's happening and this is the moment when the final latch went in on the primary mirror and we had a fully deployed telescope and there was celebrations in the mission control room in the space telescope science institute in Baltimore where Webb was controlled from so it was a great day so what's been happening since then this was back in January well the telescope has undergone a six-month commissioning phase where everything gets tested out to make sure the mirrors work okay make sure the instruments work okay and it's a lot it takes a long time and because you have to do this right the first was the mirrors okay this is a this is an amazing image on the left is an actual selfie taken by the telescope with one of the instruments so each you'll see that to fold the mirror up there's there's mirror segments okay that have to come out and fold together so each one of those mirror segments is a single telescope in itself okay and what you need to do is you need to bring each one into focus first and then bring them into focus together to get the best out of the telescope and what's on the left is the selfie and what's on the right is a star in the middle of each mirror and you can see that they're distorted you know the mirrors were just that completely out of alignment and putting everything back into alignment and getting everything phase in the center and focus is a very very methodical process and it and it takes about two or three months two months but they got there and this was the first real image we got from web where now you're seeing and this is the same selfie just with all the mirrors focused that everything is pointing in the right direction they're all lit up because they're all pointing into the into the telescope people and you get this image of a very boring star okay but it's just showing the sharpness you can achieve that spikes are just the fraction spikes because of the optics of the telescope but what jumps out of this image I don't know if it is clear on the on the screen with the lights but there's just that background filled with galaxies okay it's so sensitive that no matter where you point the telescope you will get just field of galaxies okay and actually for commission and for science it's great but for commission and it was just so annoying right they're photobombing everything once the first instrument was focused then they could move on to the rest so there's four science instruments on mary and a fine guidance sensor which which kind of you know keeps the telescope pointing in the right direction as it's observing and this was the first image released for all of the instruments including mary up here on the top right which is which is what the instrument I worked on and again I mean this this is a field in the large magillanic cloud which is nearby galaxy so it's another galaxy but the images are filled with stars and galaxies the thing is so sensitive it collects so much light and it has such fine resolution that it doesn't you don't know where you point it if you integrate for any little if you expose the instruments for any short period of time you're going to get this rich field of stars and galaxies it's amazing and so this is kind of a close-up now of what the previous best space-based infrared instrument could do compared to James Webb in the mid-infrared wavelengths where mary observes so on the left is a spitzer space telescope and on the right is the James Webb space telescope and you can see the improvement is unbelievable and with mary you start to see lots of interstellar medium features and so it's very well resolved and these were taken in a few minutes or you know a few tens of minutes so no no exposure time basically and now that's kind of how commissioning was going and then we you know could there are instruments were commissioned and so on and now I kind of move and tell you a bit about well how do we get these images because what's shown here on the right that's not how the images come down from the telescope okay you don't just get these beautiful things they they look a bit more messed up and this is a very simple example so on the left here um yeah I don't have a laser thing but on the left is a raw image okay and you can see there's a lot of crap in there right this is a simulation of a galaxy core but if you look around you see all of these stripes and bits and pieces and these white things are cosmic rays hitting the detector okay there's lots of you can see the sort of tree ring structure up here so there's lots of detector effects all of that is due to the effects introduced by the detector by the by whichever instrument you're using what you need to do to produce the images that are released that are used for science is get rid of all of that okay to produce something like what's on the right now there's still some bad pixel areas but that's okay because you can set up your observation to move the detector slightly and fill in those but you want to remove all of the effects introduced by the detectors and by the telescope the telescope will introduce them as well and leave just the pure image of the sky that's well calibrated so you know the flux of what the object you're looking at you know the wavelengths and the position on the sky and so on and that's where the instrument teams come in like myself are you know like the team I worked on where before launch we did years of testing of other of our detectors and so on to you know understand all of these effects that were introduced how we could correct them in orbit understand the behavior does do these effects change in time create calibration files which we could use in orbit to correct the data and you know refine those in orbit and this basically what I'm describing is a calibration pipeline okay what you understand various different effects and you create a pipeline where you send in raw data you correct one effect you correct the next effect and so on until you remove everything and it's what we call a calibration pipeline and this is kind of a schematic of the stage one pipeline and on the right is just a series of steps to introduce our understanding of the detector on the sorry on in the green is just the list of steps what the gray is shown is sort of the reference files so these are the calibration files that we've created based on our understanding of the telescope which are kind of can easily be changed but the calibration step the operation that that does is fixed we just change the values based on our understanding these calibration files are created by us and we're finding commission and the blue is just a description but I just giving you an idea of what I'm talking about this is how we get from raw data to to the to the public release data so we've been over in in Baltimore at the the control room where I showed you and I'm working in the space telescope science institute for the last few months working on commission in myri where we were looking at all of these effects we're finding everything creating new calibration files making sure everything worked okay and the instrument worked as expected um and this is the first main area where python comes in now I'm not going to show any of it because I think it's still proprietary but all of these call all of these checkouts we were doing all of this commission and tools we were testing and running and so on we're all developed and built in python okay they live on a get on a proprietary github repo um which the which our instrument team you know develops on and it was used to basically commission myri okay and other instruments were exactly the same so when we finish this process our instrument is science ready and images such as the one you saw yesterday can be taken okay we tick a box it's the the instrument is science ready and those observations can be taken other instruments have to do the same and the final check out was a few days ago where the very last science mode for James Webb was commissioned so it's fully ready for science now um but what you will see here is that there's actually a bunch of science modes okay so I've kind of talked about the various steps in calibration but really in practice you don't want every astronomer doing run these steps themselves you need some sort of software package that will take the data from the telescope automatically run a 3d steps and make that process data available to observers and scientists around the world so they can do the science okay our job is to make is to remove most of this from there stop them worrying about it okay it's true that a lot of scientists will rerun everything maybe tailor some of the steps maybe tailor some of the values and that's great if you if we need to do that we need to have a flexible software package that can do that which we do but there's basically 17 science modes for a range of different uh kind of purposes across the four instruments it's a big pain in the neck okay but a software package was created that basically takes the data figures out from you know metadata in the in the files what's been observed what mode is being used sends the data into the specific pipeline paths and processes everything and sends it straight to the archive where astronomers and scientists can download it and this is called the JWST calibration pipeline okay it's a pipeline software suite it does have some c plugins because speed is required for a very small part of the of the pipeline python and slowing things up quite a bit but it's but 90 like you know more than 90 percent is written in python it automatically processes data that's sent from the telescope and as I said you know it it decides which data it is sends it through the correct path and you know calibrates it combines it if necessary and produces the science ready products that astronomers will use to win Nobel prizes hopefully and why is it in python well there's a there's a substantial I don't know if there's any astronomers in the room but there's a substantial knowledge of python in the astronomical community we've had tools like astropy and of course numpy and sci-pi for years now and you know it's it's perfectly suited to to astronomical data analysis because in the end are just images okay and spectra which are two and one you know they're just numpy arrays basically so and the benefit of having everything together like this as well in a single calibration pipeline is that it it's easier to maintain different teams can work a lot of the instruments have the same calibration steps so the teams can you know interface over the best way to do things that will suit all of the instruments so you've an additional expertise in there and you can also you know it's developed in the open for years the jwsc calibration pipeline is being openly and publicly available so the community could get used to it start using it and you know play around with it a bit it's very flexible it's because it's written in python and i go through it in a minute and the the steps are python classes and so on so it's trivial to move stuff around to better improve your calibration for things like Hubble people don't realize that Hubble had a lot of science modes as well but pretty much everyone had their own independent calibration pipeline which were these monolithic chunks of code that were written in different languages and it wasn't straightforward if you wanted to change something you just couldn't do it okay because it just you sent in something at the top and your product came out at the bottom whereas here you're going through it step by step you can save at any point um you know rerun from or you know pause the pipeline rerun whatever you need to do okay so before i get into how it works the just just some information for yourselves the observation astronomical data files are in the so-called fit standard okay and it's basically what you have is you have a header with some metadata that gives you things like what instruments were used um you know the dates just just lots and lots of data about the observation uh metadata about the observation and then you have a data extension which is your actual data like the image shown here and you can have additional things like if you have an error image you can have that in your fits extension okay so any any software to process these things has to be able to read these in and output them and run them through a pipeline we do that uh for JWST for the calibration pipeline this is done should i say um using software data models so these files are read in to a python object where members of the python object correspond to different aspects of the input fits file so if i read in the file as before just using this very trivial um so it's the JWST package so if i import the data models uh from the package i can just open my fits file and my image my data is just a member of that object so i can access a true model that data or if i want to check out any of the metadata i can just go through the meta um member and find whatever i'm looking for and it's very flexible and very very useful um and it's you know it's uh because it's python you can you can import uh you're sorry the defined by yaml schema so you can just start with a base and add as you need to go and create data models for every kind of file that JWST pipeline uses such as the raw data such as the output data such as the calibration data and actually it's extremely useful for the for the for the instrument teams because we can use the data models to produce the exact format required by the pipeline to calibrate the data um all of this is done through a package called stpipe so basically as i said we'll go through series of steps series of steps at one level is called a pipeline each step is a python class that's based on the step um the step class and the idea behind this is that it handles everything and leaves the instrument teams and the community um free to work on the scientific stuff so this handles everything from data input to um person configuration settings um input and output file management accessing the calibration files okay that's a big thing those calibration files live on a server and this package will automatically recognize from the metadata in the data model what is needed fetch that from a calibration file server takes all of that worry away from astronomers and the instrument teams so we don't need to worry about it we can just focus on what we want the corrections to be and the data to to do okay it handles logging and um it has a consistent interface for for users across all of the pipelines across all of the instrument models okay so this is this is what the JWST calibration pipeline is based on and i'm just going to give a quick example i've never given a python talk before so i don't know if you're supposed to show code or not so maybe this is a big faux pas in the python world but i don't know if you can see it but basically this this is a very simple calibration step and it it uses the step class as an inheritance step class okay it's called a fringe step all it does is divide a science image by your reference array um it's set up with it so it just takes an input opens it with the data models gets the calibration file using this one line uh will fetch the calibration file from the server or if it's on your local computer it will just get it from there it logs everything if there is no calibration file it will give you a warning and move out you know stop the step and and set it in the pipeline that it was skipped it won't crash the pipeline the pipeline will keep going but you will get this warning that this step has been skipped the fringe the calibration file is then opened as a data model a fringe model in this case and the correction is done in a separate function that's imported and as i said it's just a division of of two arrays and returns the output model so this is a very simple version but it gives an idea that if you know whatever the develop like whatever the instrument teams or the scientists want to do is done in a function that's imported all of the other things so just the input and the output the fetching of the calibration files and you know reading with configuration personal configuration parameters for this step but but all of that is handled by this st pipe package and and the step class which makes it very very straightforward and each one of the sort of steps the calibration pipeline is step it's just a series of steps so the pipeline class is just a list of step classes that are run through and again very straightforward if we want to reorder these steps we can just move them around if we want to remove one we can just skip it or remove it i'm just giving an example here again you're taking your your input reading it as a data model and this this is where now you're you're starting to check for the different modes so you read the metadata from the data model you see that it's a mirror observation and you go through the mirror part of the pipeline okay and there's logic like that built in all over these pipelines it's extremely useful extremely powerful extremely modular and because it's compared to some of the stuff you guys do i'm sure it's extremely simple okay it's just a series of steps um so in the end what you want is you want to take all remove all of those detector things which i showed which is done in the first stage of the pipeline these second stages just do flux calibration wavelength calibration and astrometric calibration and so on okay which are just you know registering the images on the sky and making sure you have the correct flux and and then the third is stitching everything together so you know in an observation you will take a bunch of exposures um calibrate them up to level two stage two and then stitch them all together to produce a final mosaic such as the images you saw yesterday at level three and all of this is handled by the calibration pipeline wow ten minutes i'm gonna skip the simulators uh that's okay i'm sure everybody wants to see the images and not just me rabbitting on um okay uh so there are i mean i'm not very quickly go through the simulators but that that was basically a for the pipeline but you know to test the pipeline to prepare for commissioning to to test our commissioning tools we needed kind of real data not real data but simulated data at the sky because all of our ground test data is on calibration sources that's not representative of what you see in space okay it's good for you know checking your uh you know understanding your instrument and so on but if our commissioning observations are going to be of something in space then we need to create simulated data of something in space and that again is all python all python packages in miri team we we developed this miri sim miri simulator where we could know what we're going to observe during commissioning simulated with miri test our tools and in the end it was it was used pretty much by every part of the miri team um and what we had in the end sorry i'll just what we had in the end was something like this where we had knowledge of what we were going to observe in the sky which is on the left knowledge our simulations on the right which incorporates our understanding of the instrumented sensitivities and so on and this is what it actually looked like in commissioning so on the left is a simulation and on the right is the actual observation during commission and there's a slight offset in angle but that's just because we were out by a degree um on our way we were out we simulated a day uh two days later then was actually observed but you're looking at this the field on the right and it's almost perfect okay so our understanding of the instrument was extremely good because we could take that simulate what we thought this guy should look like and it was basically the same okay so really uh really allowed us to develop our it was perfect for developing our commissioning tools post pipeline so after the pipeline is finished and the science products come out um again there's a suite of um python tools to analyze the data and the images and the spectra and whatever uh these are all like a lot of these would be quite common throughout the astronomical community um okay the images now everybody wakes back up um so i'm on the knife we had uh president biden reveal the very first image uh science image with taken with awst and it was i don't know anyone watches a few okay that's good because it was it was really weird but um but it gave a whole nother level of publicity to the uh to the community not to the instrument and someone obviously not in this room because there's about five people who put their hands up but but no it was really something else and we didn't know that president biden was going to release the first image when he found out on sunday but this is the first image just uh it's the um jwst first deep field okay it's the furthest into the universe apart from the cosmic microwave background which is the first light that's absolute first light in the universe but this is the first sort of stars and galaxies that the deepest images stars and galaxies we've ever had this was done with 12 in 12 hours with james Webb okay 12 hours that's nothing the Hubble deep field ultra deep field took weeks of constant observing okay okay spread out over years but it's a weeks uh exposure and this is 12 hours this was done just in just over one shift one console put it here that way um the detail in there is phenomenal if you download the full resolution image you could scroll around it all day you're seeing a massive galaxy cluster in the center and you know Einstein's general theory of activity told us that if there's a mass in space it deforms space time so if you have a very massive object in the universe it acts like a lens because the life from behind it sees the deformity and gets bent around it and that's what you're seeing here so these are lens galaxies and this isn't even lens galaxies this is the same galaxy just seeing a different part of the lens here okay so phenomenal stuff the galaxy cluster is about four or five billion light years from here you're seeing stuff in this image that's it was emitted 13 billion light years ago over 13 billion light years ago so the universe was still only a few hundred million years old again this is just in 12 hours with james Webb you could scroll around and look at all sorts of funny images or funny objects like this thing here again this is just deformed by the by the gravitational lens you're seeing objects in here like these red galaxies which aren't visible in Hubble okay because they're so far away the light from those has been redshifted redshifted means that the light has just lost energy as it moves across the expanding universe and Hubble physically can't look at these galaxies because it can't see in these wavelengths this is why james Webb was built to see those galaxies the image that was released was taken with near cam but on the left is the mirror image so this is the with the instrument we worked on and this sees even further back now we actually don't understand we don't know a lot of the objects are in the Miriam is because we need to identify them which is probably why it wasn't released yesterday in the in the announcement but Miriam has the potential to look even further back again okay because it's longer wavelength stuff that's further away gets redshifted to the longer wavelengths and we also saw Stefan's quintile yesterday which is a small galaxy cluster interacting galaxies nearby relatively nearby the one on the the one here is actually a foreground galaxy okay this isn't associated with these three with these ones here these are interacting so they've been in a kind of a cosmic you know dance for millions billions of years and this is the image taken with near cam so you can see the white is basically the stars but if you look with miri you start to see the interstellar medium and you can see all this dust being spun around the galaxies and in that dust there's lots of stars being formed because they're you know there's turbulent motion in there and there's compression of the dust and gas and stars will form in there and this is a near cam miri composite but the Miriam image itself is insane okay it looks like fireworks and again this this is so this is kind of a zoom in on the top tree here but this galaxy on the left again is a foreground galaxy it's pretty standard looking for for a spiral but on the right here you can see really how the interstellar medium is being disturbed by the interactions of those galaxies and you can see that there's these shock fronts and clumps and everything and here it's like there's you know it almost looks like the gas swirling or the dust is swirling into the center and this is a super massive black hole in here this bright object which is only visible in Miri or which becomes very apparent in Miri okay and again photo bombed in the background by all of these galaxies that are even further away okay which were you know consistent pain in the neck the next image was the southern ring planetary nebula this is a dying star this is a star like the sun which is in its last stage of revolution and it's throwing off the outer layers of the star exposing the hot core in the center the hot core is on the left is a near cam image and you can see the hot core star which is white dwarf basically or a proto white dwarf and the layers of the star being thrown out into the interstellar medium and the right is Miri but the detail in the near cam image is just phenomenal okay you can see all of this layers and you know there's dust and so on in here or there's just gas and dust and just layers and layers of mass loss from the from the star as it dies basically it was always known that this was a binary star but actually in Miri you can see the binary okay the resolution of JWST is so good you can see the binary in there next to it it's a dusty star so it's not visible in near cam but Miri resolves it it's phenomenal stuff this was released yesterday as well it's the spectrum of an exoplanet atmosphere okay so this is a hot Jupiter basically a big gas giant moved in front of its whole star while the web was observing and due because of the changes in the stellar spectrum we could infer the content of the stellar atmosphere and what was found was that it's filled with water vapor okay so steam basically this is right next to its star so it's very hot so it's filled with steam now this was kind of known what wasn't known or what what's different is that these water features shown here these bumps are smaller than expected the reason is that there's clouds in the atmosphere okay so this is actually inferring the presence of clouds in the atmosphere of this hot jupiter because that the the clouds will inhibit the formation of water vapor okay so just from this one observation uh and this is you know it will observe things like you know candidates that have potential for life and and so on but this is a very kind of standard hot gas giant and it's already you know producing these amazing results this wasn't released yesterday this is in our commissioning paper that was released today on the archive this is jupiter jupiter shines out in the infrared okay and i'm just showing you this it you know it doesn't look anywhere near as impressive as the Hubble image does it right it looks like someone shot a torch behind it but telescope or the telescope James Webb is a big unit okay it's a big thing but to observe something like jupiter it needs to be able to track it on the sky so it doesn't move in target analysis so it can actually follow jupiter as it moves along and this this observation when you're kind of demonstrated that and you can see the moons which are also bright the red spot so you're actually seeing a mission from jupiter itself now not just the reflected light from the sun um and you can see its rings okay you can see the faint rings here uh you can see lots of detector stuff in the background that's just because jupiter is so bright okay it's not um there's nothing you can do about it when it's so bright it's saturating the detector and someone this one was my favorite this is the kareena nebula the gas and dust in the kareena nebula um so there's massive stars up here that are irradiating this gas and dust and you can see the the gas and dust is evaporating off into the into the interstellar medium there's star formation going along in the dense parts of this um which you know can't be seen by Hubble because Hubble can't see into the gas and dust and near cam can't really either but mary can so if you look with mary you're seeing right into where these young stars are forming you can see the you know the plan is to to look at the formation of young stars and protoplanetary disks so look at the very earliest stages of planet formation and there's lots of other things here as well the ever present photobombing galaxies in the background uh outflows from massive stars okay you can see here's a there's a jet that's carving a cavity in the in the interstellar medium here just phenomenal stuff and all of all of these observations were done in one week okay one week and not even filled the week um this kind of thing is routine for the James Webb Space Telescope which is why the next few years are going to be extremely exciting um these these targets were also chosen because they're not on the list of targets that are going to be observed in the next year so the interest in targets have yet to be observed okay i'm going to leave it with this image because it's my favorite it took 20 years to build the telescope put it in space and now um yeah now it's very exciting time and all because of python thank you perfect thank you patrick that was amazing it's really hard to interrupt you when you're showing this is wow okay doesn't fit in my brain oh so we have time for a few questions if you want to ask a question you have to stand in that microphone that is there um anyone want to ask a question have to be quick um i don't think i i want to say a reminder if you are watching this conference remote you can ask questions right so if anyone is remote there is always a operator asking do you want to ask a question and you can get into a call and then we'll put you here in the screen live with everyone um so remember that for all the talks so first one uh so what are the interesting targets uh what there's a list of them uh so i mean okay i everybody's favorite thing that it's going to do is is the exoplanet atmospheres okay because potentially you could observe life and and we'll see so um web is not a planet finder uh you need to know what you're you need to know the planets before it will look at them okay and there's we have how many now three four thousand more probably exoplanets that are known some in you know some are rocky some are not of atmospheres all of this so they pick the best candidates of them they will be observed in the next year so really in the next year there's you know somebody only asked me yesterday in an interview you know will you find life well there's never been a better chance um potentially very quickly but yeah i don't want to get everybody's hopes up on that but yeah things like that i mean there's so many interesting targets so many cool next one hi first of all thank you so much for the for the presentation uh web is such an amazing uh a piece of piece of hardware and software um my question you mentioned something about the data pipelines being open and to the public doesn't also involve the data how do we get data okay so so everything that was released yesterday is publicly available okay that was these are so-called early release observations um the raw data from them and the calibrated data the fits files that will um will be available either today or tomorrow and they're free for everybody there's a second program called early release science which will also become immediately available to everybody um when they're taken okay so some have already been taken so that data will become available today or i can't remember today it's there tomorrow as well um but as they as they are taken in the first cycle they will become available the other programs the g so the so-called guarantee time and guest observer programs there's a proprietary period of one year for them so scientists have the right because they won that time to have the right to have it to themselves for a year to produce the science you know to produce papers and make the discoveries and someone after that it becomes publicly available right so basically in a year we can do just pip install jwst and we can well right well right now you can do that all right you can just type in anyone on your computer open type pip install jwst and they should install the software package um this the data that you want right so go get that data download that from an archive it's on mass it's called mass archive you can go and fetch it today or tomorrow and you can start playing around right cool so no that's two questions one more we have remote questions so only one more sorry for the other ones thanks for the great talk i was just wondering what is the size of the scale of the raw data uh to produce one image the scale it depends on so it all depends on how long you integrate so how long the exposure time is um for let me see now so basically the way the telescope works is it samples the detector uh at some readout time okay so it takes a sample takes a sample takes a sample and then you use that to calibrate your data and it depends on how many of those samples you take and if it's short it can it's only a few hundred megabytes and now you will have more than one you'll have more than one exposure right so you're talking a few gigabytes a couple of gigabytes but if it's much bigger then of course it just scales with the the amount of samples of the detector basically so it can yeah it can be tens of gigabytes probably for the biggest all right thank you okay thank you very much Patrick please give him a shout


My name is Hospital Corman Third Class Janet Rosas. I am a medical laboratory technician on board USNS Comfort. So I was born in Lindwood, California. I have lived all over Los Angeles County. My family is currently residing in Lakewood slash Long Beach. Most of my family is located in California right now. So I was actually going to high school in Lemora, California Naval Station. So everybody around me was prior Navy or active Navy somehow tied to the Navy and they basically motivated me to join and I'm very glad they did so. So I am actually attached to Naval Medical Center Portsmouth but the USNS Comfort is my platform so most of the time when the comfort goes out, so do I. So my day usually begins, we wake up, we go grab something to eat. We're working 12-hour shifts right now so we go in, relieve our counterparts, take on any patients that they might have been working on throughout the night, go ahead and do our quality control of the day and the patients start rolling in. We get samples, start testing them and pushing blood products out. My job is I work in the blood bank which means we receive samples for patients awaiting some type of transfusion whether it's for blood products. We do issue, you know, thought plasma, cryo, all the extra products as well. So we just receive samples, we test them, find compatible units and push them on out. Patients get transfused, we get everything back, review everything, make sure you know patients aren't having reactions and that's that's our patient care. I believe everybody was a little concerned at first. At this point I believe, I truly believe everybody has a routine established where you know it's just second nature to wash your hands after you receive any sample sanitized at every you know station. It's just become you know part of our daily routine. Yeah we still think about it but at the same time I believe we're taking precautions to really lower the risk of us getting infected. Our nurses have definitely you know established some very strict rules that I believe you know have made such a big difference. It's the little things in my book that are you know keeping us safe at this point and they've done a great job enforcing them. Like I said it's second nature now for everyone to sanitize, wash, social distancing, big one. We switch our masks daily, we are covered up completely when we're working with patient samples. It's just you know these are things we do at the hospital. They're just a little a few extra steps that we're taking here for the comfort. So I do talk to my family almost on a daily basis. They're doing okay right now. Everybody is mostly staying at home just keeping up to date. It's a little hard to be away from family especially during these times but they're very proud of me. They do believe that we're making a difference here and I think we really are you know. So that's that's what's important to me that they're safe and that we're doing a good job here.


Thanks René. Good morning everyone, how are you? Excellent. Well, welcome to day two. Thanks for joining us here today. Thanks for joining us at TechCon. Again, great event. I was really pleased with how yesterday went. I thought we had a great day. Talking about a lot of exciting stuff. Of course, we launched some new products yesterday. I'll talk a little bit more about that. Great presentations. It was good to have Massa here on stage. Mike gave, as usual, a very entertaining presentation about his own personal life and how these various animals are sniffing him to try and work out what's going to go on in his body over time. He actually pays extra for that, I've got to say. And, you know, we've heard a lot about technology. Great technical papers yesterday. Really strong lineup contributed by all of you. So thank you for that. And you also got to hear from a Meet My New Boss yesterday. It was great to have him with us and have him here on stage to talk about his vision of the future and why ARM was so important for that. Now, when I introduced Massa yesterday, I talked about how excited I am about ARM's future and how excited I am to partner with him. He is a fascinating guy who thinks in the biggest terms possible and it's a very exciting time to be in ARM and I think to be part of the ARM partnership. You heard yesterday about some of the technologies you might talk about where things are going generally. You just heard from Jim about computer vision. You heard from Greg yesterday talking about Moore's Law and some of the challenges in just making transistors ever, ever smaller and higher performance. So great technologies and we're committed to delivering those technologies. But really what they are when you think about that is the building blocks that other people are going to innovate around. Jim just mentioned that right now. We have all of these different components that can be put together on a chip. They are tools, they are building blocks for others to go and innovate with. Others, i.e. all of you hopefully here today, you're going to take those things that we do and create something really spectacular with them. So our job is to work on those building blocks, those tools and to deliver them to you, to put those tools in your hands so you can innovate and build even more phenomenal products than the kind of thing that we have today. So that's what makes my job fascinating. It's as much as I love the individual technology components that we're building is seeing what other people do with them and seeing how new products emerge. That's the really fascinating part. And as well as building the technology itself, it's enabling access to it, enabling higher and higher performance at lower and lower cost. That is really what enables innovation to thrive. And we're seeing that impact many, many different end markets. Now I think this phenomena is summed up really well here in this quote. Lots of people have talked about this phenomena. Now this quote is from Helen Mirren. Helen Mirren is an actor, one of the most famous actors from the UK famous for playing the Queen in a movie called The Queen. And you may be thinking, well, what's something like that doing thing about technology? Well, she was at an event where she was asked, you know, what's technology going to do in your sort of line of work? And she's talking here about the lower barriers to access, drive innovation, and that is in itself a phenomenal thing to see. And this demonstrates, again, how the combination of technology and other industries come together to create innovation. And we see this all the time. You look at the, yeah, Jen mentioned the volume of data that gets uploaded to YouTube. That's user-generated content, which 20 years ago would have been nigh on impossible to do. We're seeing in lots of areas, this example of technology in the arts coming together. We see smartphone-recorded footage as commonplace on the news every night. And we see it in music as well. That clip of music that was playing as I walked on is an example of electronic dance music that's being created today by thousands of thousands of people all over the world. And when I grew up, synthesizers were starting to become commonplace in the creation of music. But if you wanted to make something like that, you needed lots of them, you needed expensive equipment, you needed a recording studio. And that was, frankly, out of the range of most people. And that particular clip was made by my son, who's 11. And he did that with nothing more than his laptop and some headphones. Fortunately with the headphones, because he makes a lot of noise doing it. Now, I don't know if he's going to become the next Calvin Harris, but I think it's quite phenomenal that a teenager, well, not even a teenager in his bedroom can create music and we're seeing that become a really driving force in innovation in the music industry. So it's happening all over the place. And what we're seeing through arms history is how the successive waves of computing, the successive waves of new technologies make it more and more accessible for people and computing gets closer and closer to people. We've seen it with the rise of PCs, we've absolutely seen it in spades with the growth of smartphones and we're now seeing it with the growth of the internet of things. So each successive wave of technology brings it closer to people and it creates more and more opportunities on a global scale. As well as the computing getting better, it's also becoming invisible. Now, when I went to university, if you wanted to use a computer, you really knew you were using a computer. It was a big thing. You maybe had a break time on it, book time, going to an air-conditioned room that some guy in a suit would be looking over your shoulder the whole time. What we're seeing now is computers are everywhere and the internet of things is going to take computing on a global basis and provide more opportunity for more people. So the opportunities are increasing, opportunities for innovation and entrepreneurialism. So I believe that the internet of things is going to create an enormous impact. It's going to create, I believe, more impact than everything else that's gone in computing before. So in ARM, we spend a lot of time thinking about IoT and what it's doing and how it's progressing. So in analysing this, we've talked to a lot of people, but we thought we'd go out and actually get some data. And this is a work that started more than three years ago now. The internet of things is relatively new still. It's in its infancy. There's still a lot of hype around it. I get comments to me all the time, are we always going to be five years away from the internet of things? So this gathering of data is helping us plot a course and see where we are. As I said, we studied this three years ago, and if you came to TechCon in 2013, you'd have seen me talk about this piece of work, a work study that we commissioned in partnership with the Economist Intelligence Unit, where we went out and we surveyed companies around the world. We went to talk to 750 different companies to ask them what they thought about IoT, where they were in their development of this technology, how they thought it was going to impact their business. So at the time, three years ago, IoT very, very new. So presented the results, stood on this stage three years ago, and one of the key things that came out of it was there was a lot more going on than anybody actually thought. One of the key statistics that came out of the report was this one. The 750 companies that we spoke to, 95% of them expected that IoT, that they would be using IoT in their business within three years. So here we are three years later, 2016. What do you think happened? Do you think 95% of companies are using IoT today? I'm not hearing lots of positive, overwhelming answers out of the audience here. So we've just repeated this survey to go and ask largely the same questions. We went to talk to another 750 companies, and what we found of those 750, 75% of companies are being impacted in some way by IoT. Now, I don't think that's bad. 95% of companies thought they would. Actually, 75% of companies are. If you've got a de-factor for hyper-newness, I think that's not a bad result. When you look into it, though, what you see is less than half of those companies that are being impacted by IoT have they had a significant impact? For the other companies, it's kind of minor so far. So that's not bad progress, I think. So what's going on? Why is it that all companies aren't using IoT? What are the barriers to it? There's lots of questions in this report, and you'll get to see that. The study does a breakdown of some of the statistics, and these are the key areas that people cite as barriers to the adoption of IoT. Security, unsurprisingly, and if you've been following the news recently, very unsurprising, is highlighted as a barrier to the adoption of IoT. That 26% of companies say that that is an issue for them. Cost is another issue. How do you create this end-to-end system? How much of this stuff have I got to do myself? Because there are great tools out there to help me. Now, these two factors, security and cost, are absolutely what we're trying to address with the technologies that we launched yesterday. Mbed Cloud is there to help with both security and cost. The innovations that we're providing at the IP level to enable you to build secure devices are absolutely there to help with both cost and, of course, security. So, you know, we're trying to work on that. Knowledge is an interesting one. Three years ago, the study highlighted a skills gap. People concerned about access to the right people with the right skills to help them develop IoT. Here, the category of people where the knowledge was at question was actually senior management. Senior management. You know what they're like.   Again, we know our product for that person. But if him or her needs help understanding what IoT is all about, then haven't come to this event, have them come to other events that many others in the industry are putting on to enable transfer of knowledge and learning about IoT. Because I think it's a technology that can positively impact every business. Now, this is a taster of the data. The full report will be out in a month or so. We'll publicize that. We'll be able to download it. And again, leave hard copies on the desk of your boss. You know, help with that knowledge transfer. Help play a role in getting people up the learning curve. So this report's great. I want to thank the economists and also IBM who we partnered with to put this together will make the data available soon. So it's good progress and I think this is very exciting. Mike mentioned this yesterday but our own journey in IoT goes back to about 2001 and it wasn't about wearables or tracking health, anything like that. It was about toast. And Mike mentioned this yesterday in his presentation back in 2001. He was out trawling for interesting people doing interesting things as he does. He came across some university researchers making what then was called an internet appliance to print the weather on your toast in the morning. That was interesting. It was a bit of fun at the time. But it did inspire an R&D project within ARM not to focus on the problems of toast but more crucial to the innovation process and that is coffee. So the team had one of these nice little coffee machines with the capsules and the problem was how do you track how many capsules are being used and when to reorder and can we automatically reorder the capsules when they're running out?  Now if you were here two years ago, 2014 Mike described this journey and how it's led us to what we announced yesterday with Embed Cloud. You want a device, you want it to measure something, you want to track data in the cloud, that sounds really simple. But when you get into the complexities of securing it and making it easy so that everyone can build products not necessarily like this but leverage the full capabilities of IoT there's a ton of stuff, a ton of detail that you need to work out and Embed Cloud is there to help solve some of the foundational problems against so that others can then go and wait on top. Now continuing this journey through Tecon if you were here last year you will know that toast or rather toasting was a central theme of my keynote but not on purpose. If you were here last year you may remember right in the middle of my presentation I hit click on the clicker and the fire alarms went off. Now fire alarms are pretty simple things they detect a problem they make a lot of noise everybody leaves the fire department turns up and works out if there's a real problem or not and if you were here I want to thank you all for diligently leaving the room in a very orderly way and also thank you for coming back afterwards when we did get the all clear.  Now these things do happen but like I say the technology is pretty simple and maybe using some of that computer vision technology that Jen was just talking about we can have something a bit more sophisticated in a high-risk area like a kitchen having a camera look at what's going on assess how much risk there is assess whether somebody has just walked away from the toaster these are ways in which we can make security and safety systems more intelligent by just analyzing what the camera is looking at not just streaming video into the cloud in a dumb way but actually analyzing the scene and looking for anomalies and then taking steps as a result of that then you could also argue well a smarter toaster might not burn it in the first place or a smarter human wouldn't have left the bagel in the toaster and just walked away but things like this happen unexpected events do happen and I think intelligent technology can help us deal with this in a quick and safe way but we're only going to do that with a full end-to-end system now what the report shows that we've just done with the economist and IBM is that industry really is serious about adopting IoT three years ago when we first did this it was unclear exactly how this was going to pan out but when I look at that report when I look at the responses to some of the questions it is clear that industry is very very serious about this whether it's bagels and toasters or coffee or healthcare or agriculture the benefits of IoT come from putting an end-to-end system together device, network getting the right information into the cloud analyzing it in the right way pairing it with other data combining that together getting some insight and some knowledge but to deliver that end-to-end system is still going to require a lot of innovation it's going to require innovation in devices in the network, in the cloud in the algorithms that run on top of this and I think that innovation is still very alive and well and absolutely necessary so I think as an industry we don't really know what it's going to take to put a trillion devices onto the internet and you think of it, a trillion devices that is a lot, that's going to generate a lot of data as we've seen from some of the projections and how we're going to deal with that through the network how we're going to deal with storing it how we're going to deal with analyzing it this is uncharted territory for the industry and it's going to require a lot of innovation so we're on this in terms of those foundational elements those building blocks, those tools and we're going to need the whole industry to work together to solve some of these these problems so what we're seeing right now Massa talks about this camera in explosion we are seeing we're in this space we're not post-camry and we are in it right now where there's a lot of experimentation going on because access to the key building blocks is really low cost and that experimentation is great we're seeing it in lots of different areas it's driving a lot of potential here's an example of some of that experimentation I came across a couple of weeks ago you may have seen this this is a collaboration going on between Google and Chipotle the burrito food chain who are trying to solve the problem of how you deliver burritos with drones they're working on the campus of Virginia Tech to deliver burritos to students now you may think this is the most first world problem you could possibly think of but it isn't really about feeding students it's about how to solve the issues associated with delivering things rapidly especially something that was hot when it left the kitchen and the recipient was still like to be hot when it gets there so a lot of issues associated with that and they're using drones to do it interesting experiment and it's going to necessitate a lot of innovation in the capabilities of the whole system and in particular the drones there's going to be capabilities beyond the kind of low cost stuff you can buy today I was in fries at the weekend buying some cables and I kind of took a stroll down the drone aisle which is quite long and you can pay 30 bucks for a drone you can pay a thousand dollars for a drone I mean there's a ton of them out there relatively simple devices to do this kind of thing is going to require innovation I think drones are pretty cool I don't spend a lot of time playing around with them myself but I think they hold a lot of promise as a disruptive technology the kind of drones you see today are largely recreational they're not being used that much in commercial applications because the military are big on them but the regulations around using drones for commercial purposes are being relaxed just over the summer the FAA enacted some new rules which relaxed the restrictions you can now fly a commercial drone up to 100 miles an hour that's a lot faster than you can legally drive a car on the road around here you don't have to have a pilot's license anymore there's an easier way of becoming a pilot of one of these things but you still need to fly it within line of sight it will probably change over time they're letting people experiment with that issuing waivers to people and ultimately I think we will get to the point where drones are being used in many many applications and of course delivery being one of them but when you get to that point where drones really are truly autonomous and flying around out of line of sight of somebody ultimately controlling it drones go from being this kind of recreational thing a remote controlled toy let's face it a system that's going to require a ton of intelligence in it in order to operate and operate safely it's going to need to operate online, offline, be connected to a network be connected to a person cameras, the whole thing it's going to need a lot more computing within it if you do go and buy one of those drones in fries right now and take it apart you're going to find that there's a microcontroller in there and there are hundreds of drones out there Cortex-M3, Cortex-M4 running the algorithms that are looking at the accelerometers balancing the thing communicating with the smartphone or controller over some form of RF so it's complex the algorithms are pretty impressive keeping people in everything but it's a relatively simple device from a computing standpoint so to get to fully autonomous that's going to have to change the drone of tomorrow is going to need much more it's going to need to run a much more sophisticated software stack it's going to need to download maps into it as it's flying around it's going to need to talk to a network it's going to need to augment what it's told in advance of the location it's going to with what it can detect in its surroundings itself and to do that it's going to need a lot of sensors in it and probably cameras to look at what's going on and require a lot more sophisticated software running AI type algorithms to make sense of everything and operate in a safe way and then communicate back into the cloud what it's just done what it's seen so that the next drone that comes along has got an even better sense of the environment that it's flying into so a lot of ways in which the capability of drones and the computing power in drones is going to need to increase of course that runs off of battery so it's got to be low power you're going to see those improvements that are made spill over into other applications all of that learning is going to benefit other applications and self-driving cars is a great example because the challenges of drones are very similar to the challenges of making a car self-driving which you've got to worry about three dimensions with a drone but a lot of the basic algorithms you've got to solve are very very similar so in all of this it's critical we see computer vision as critical and that's why we're increasing our investment in this area we think that sensing and autonomy are going to be key capabilities for the internet of things generally and we're going to see those and drones and cars of course see lots of other applications as well let's take agriculture for a moment I think IoT can have huge benefits in the improvement of agriculture as this planet gets older more middle class there's going to be enormous strains on just feeding everyone and using water efficiently and IoT sensors are going to help with that but driving cost down so food ends up on our tables at low cost is also important and so autonomous vehicles being used to gather crops are going to become really important now some advances are happening there already and we're starting to see that and we're seeing in other use cases too anywhere where it's maybe remote it's dangerous like an oil rig or a big open pit mine we're starting to see autonomous vehicles and they've all got the same problems they need to sense what's going on detect the unexpected and make a very intelligent response as a result of everything that they're seeing so to get to this point where these objects can be truly autonomous and totally safe at the same time it's going to require a lot of advance a lot of innovation and we're not going to wake up one day and suddenly find that all the cars on the road of Santa Clara and San Jose are suddenly driving themselves around it's going to require a continuous development of technology a continuous step of advances in technology to get there so this concept of continual small breakthroughs has been entirely through ARMS history if I take you back about 20 years or so we announced a product called ARMS7 TDMI maybe many of you have used that in their design you've probably all used it in their product as it ended up in virtually every cell phone back in the 90s but when we developed that we were looking at a couple of specific problems that we were trying to solve digital cell phones were just coming about they had more sophisticated software so they wanted a high performance 32 bit processor but when you compile the code the binary ended up being really big so you needed a lot of memory system cost went up we came up with a way of compressing the code to get the system cost back down we also needed to debug the system and we found a way of abusing the JTAG interface on a chip to serially get data in and out and make debugging an embedded processor just like what it was when processors were standalone chips that plugged into circuit boards when I was at university if you wanted to debug a processor system you had an in-circuit emulator it's box about the size of a filing cabinet you could take the chip out, plug this thing in and tap away on it we can't do that clearly if a processor is embedded and that's where a lot of stuff and we've made announcements this week about improvements in debug a constant theme for us so these were two things that were seemed kind of relatively small at the time but through our history we're actually part of a succession of developments of technology a succession that has led to just an explosion of embedded devices and the ability to put an embedded computer anywhere and everywhere and that is making a huge impact now we need to keep that going we're totally aware of that and that is why we are doubling down on our technology investment when I look through just this year over the year to date and look at the products that we've launched as a result of R&D that's been going on for many years I think we've delivered amongst the most comprehensive line-up of technology in the first 10 months of this year that we ever have new platform for advanced mobile devices real-time processors for autonomous vehicles new embedded processors and of course, embed cloud so we're working on accelerating this roadmap as we talked about yesterday and one of the areas particularly we're working on is security because I think security is one of the biggest issues that we face as an industry and you only have to look at that recent news I was working on this presentation on Friday and it pops up this news story about the DNS server in the US being taken down by a denial of service attack and what was interesting about that was it wasn't PCs being taken over to do this it was IoT devices it was devices in the home that had next to no security in them that had been hijacked to perform this attack and that's a pretty scary thing and really does bring home the advance that has to go on in making IoT devices secure so a lot of challenges that we face as an industry and I think one of the best ways to really explore the security strengths and weaknesses of your product if you're working on anything that requires some degree of security is to go and hire somebody external go and hire a hacker to test your product I can tell you, your team will tell you it's brilliant, go and get somebody else external to try and break it and they'll probably find some issues there now we we're kind of exploring this theme further this week and tomorrow we have Charlie Miller up on stage giving a keynote, he's the guy that infamously hacked the jeep, took over the breaking system while it's being driven, pretty terrifying so he's going to talk about that so I think security, big, big issue we're investing a lot in that it's a very broad technology and it's one that has horizontal applications, we need a technology security solution for all types of products so it's a horizontal it's something that impacts everything and to get to these horizontal solutions often requires focusing on vertical and within our we drive a lot of our technology development around specific verticals and we're going to continue to do that so we're going to continue to work on mobile where making processes high performance and small and low power and integrating video and graphics and imaging, all of those things going into other applications if your TV is a smart TV, then almost certainly the processor that's in it started out life in a mobile phone mobile is going to continue to drive technology we're going to drive around infrastructure to deliver next generation networking and cloud service because that drives a lot of performance and of course we're going to drive IoT that drives ultra low cost, tiny tiny processors radio communication and drives solutions around this distributed security problem that I was talking about so these are the building blocks that we're working on and ways in which we're going about delivering them and what that's going to lead to is a continuation of this Cambrian explosion of devices what kind of devices, I don't know if you've got some spare time what I'd like, is this my I operate off my smartphone constantly there are times when I need some bigger screen real estate so if any of you could this up in the next couple of months I would appreciate that but whether it is this whether it is something else whatever you choose to work on we're committed to delivering those building blocks, it all comes from the tools and the building blocks that everybody else can go and innovate around we're going to make them better, lower power higher performance, continue to work on driving cost out to enable innovation so we are committed to continuing our role in the evolution of all of this technology we're committed to our business model based around partnership and committed to increasing the investment in our R&D to deliver more technology and to deliver it sooner we're committed to partnering with all of you in the industry because it is some of the parts that makes everything so phenomenal, we've got great products today but there is so much more we can do together in the future so Arm and the last couple of months might have changed hands, we may have a new owner but you heard it from me today you heard it from Massey yesterday together we are committed to continuing the great work that we've done over the last 25 years continuing to work with you so that jointly we can deliver the technology of the future Thank you so much