WEBVTT 00:00.000 --> 00:08.000 Hello, good afternoon everyone. 00:08.000 --> 00:14.000 First of all, you like to apologize for the delay because you have a bit of a technical problem. 00:14.000 --> 00:19.000 But without further ado, we would like to start our next presentation with the public, 00:19.000 --> 00:25.000 where we have the women of sex in the three gone 2.0, which will be brought by Luria Derex. 00:25.000 --> 00:32.000 So without further ado, the stage use yours. 00:32.000 --> 00:36.000 Thank you. It's groovy. 00:36.000 --> 00:38.000 Oh, no, I liked it. 00:38.000 --> 00:42.000 You're not going to like it, but it was fun. 00:42.000 --> 00:45.000 Hi, everyone. Thank you so much for coming today. 00:45.000 --> 00:52.000 Thank you for having me the second year in a room to present another time, 00:52.000 --> 00:58.000 another woman from the computer science history. 00:58.000 --> 01:00.000 So we are a bit late, sorry for that. 01:00.000 --> 01:05.000 My computer is not working anymore, but that bad because I don't know my text, 01:05.000 --> 01:07.000 so it's going to be fast anyway. 01:07.000 --> 01:10.000 I'm kidding. 01:10.000 --> 01:19.000 So this is a bit challenging because this is the second version of my conference of my talk. 01:19.000 --> 01:24.000 It was really a big challenge for me. It was really hard last few months, 01:24.000 --> 01:29.000 so I hope you're going to enjoy it and you're going to discover a new women. 01:29.000 --> 01:32.000 So my name is Luria Derex. 01:32.000 --> 01:34.000 AKA, they've got on the internet. 01:34.000 --> 01:36.000 I'm a first-class developer. 01:36.000 --> 01:41.000 I want some medals, and I'm also a streamer on Twitch about coding and technology. 01:41.000 --> 01:46.000 So this is now the most difficult part and technical part of the conference. 01:46.000 --> 01:51.000 So on the social networks, Twitch is there to go with one on the score. 01:51.000 --> 01:55.000 On Twitter is two on the score and on Instagram is three on the score. 01:55.000 --> 02:00.000 I love to do this joke. I do it every time. 02:00.000 --> 02:03.000 So let's get started. 02:03.000 --> 02:06.000 I want to start by asking you a few questions. 02:06.000 --> 02:10.000 Do you know who invited the compiler? 02:10.000 --> 02:14.000 Normally, if you listen to my last talk, you should know it. 02:14.000 --> 02:21.000 Who invited the assembly languages, who developed the ARM architecture, 02:21.000 --> 02:26.000 and who made the protocol that allowed the worldwide web to exist. 02:26.000 --> 02:32.000 These persons are rarely taught in books and in schools. 02:32.000 --> 02:34.000 Why do they have in common? 02:34.000 --> 02:36.000 They're all women. 02:37.000 --> 02:43.000 In my past talk, I see our way more focused on the 19th century in the beginning of the 20th century. 02:43.000 --> 02:50.000 This time I will focus way more on the 20th, and especially the period between the 50s and the 80s, 02:50.000 --> 02:55.000 which were a really important period for women in computer science. 02:55.000 --> 03:01.000 So I wanted to begin with the question I always get at the end of my conference. 03:01.000 --> 03:06.000 How can we encourage women to be more interested in computer science, 03:06.000 --> 03:10.000 and to feel like they belong in it? 03:10.000 --> 03:16.000 I recently started to read a book called Legal Homsubliye by T.T. LeCoc, 03:16.000 --> 03:22.000 and this book really changed my vision of the world and the history. 03:22.000 --> 03:25.000 Unfortunately, it's only available in French. 03:25.000 --> 03:27.000 I'm really sorry guys. 03:27.000 --> 03:33.000 If you have some references of books talking about the representation of women in the history like in general, 03:33.000 --> 03:37.000 I feel free to let me know, and I would love to read and share them. 03:37.000 --> 03:39.000 Let's go back to the book. 03:39.000 --> 03:47.000 Since the first pages I was hooked and here is the first quote that I stood out to me. 03:47.000 --> 03:50.000 What's the early pre-history? 03:50.000 --> 03:55.000 Imagine it was nothing more than a copy of the social organization than you. 03:55.000 --> 03:59.000 And I think that you begin to understand where I'm going. 03:59.000 --> 04:01.000 This is not a news. 04:01.000 --> 04:04.000 Women are hidden and invisible in the history. 04:04.000 --> 04:08.000 And how do you want someone to feel like they belong in something, 04:08.000 --> 04:14.000 if there's nobody else like them? 04:14.000 --> 04:19.000 So what's young girls today like our representation and role models? 04:19.000 --> 04:25.000 We need to celebrate the women figure. 04:25.000 --> 04:29.000 And I have a little dream and I'm going to talk about that. 04:29.000 --> 04:33.000 Imagine you have a kid and you ask the kid, 04:33.000 --> 04:36.000 now draw me a scientist. 04:36.000 --> 04:39.000 They will surely draw something like this. 04:39.000 --> 04:41.000 Thank you, Dali. 04:41.000 --> 04:45.000 Right? So a white male with blues. 04:45.000 --> 04:47.000 I don't know how to say English. 04:47.000 --> 04:50.000 I think the same word with hair like and stain. 04:50.000 --> 04:55.000 Now imagine that you ask the same kid to draw a computer scientist. 04:55.000 --> 04:57.000 And they draw you something like this. 04:57.000 --> 04:59.000 Would it be amazing? 04:59.000 --> 05:01.000 This is my big dream. 05:01.000 --> 05:03.000 Okay, this is Grace Hopper. 05:03.000 --> 05:05.000 I'm not going to talk about her today. 05:05.000 --> 05:08.000 So I needed to say her name at least once. 05:08.000 --> 05:11.000 It could be any women, but yeah. 05:11.000 --> 05:16.000 So this is what I'm secretly dream of. 05:16.000 --> 05:25.000 So all the young girls that don't have to become a computer scientist or programmer. 05:25.000 --> 05:32.000 But they should feel like they have the option to dream and explore the field that inspire them. 05:32.000 --> 05:37.000 And programming in computer science should feel like natural and accessible options. 05:37.000 --> 05:44.000 So I also started an open source project. 05:44.000 --> 05:46.000 Yeah, open source. 05:46.000 --> 05:47.000 Yeah, sorry. 05:47.000 --> 05:48.000 Cold impacts. 05:48.000 --> 06:01.000 So this is kind of a week like which goal is to try to regroup all the information, all the resources about women and technology in one place. 06:01.000 --> 06:08.000 Because algorithm doesn't help us to find resources about women and technology also. 06:08.000 --> 06:13.000 So if you want to contribute, I don't know if the QR code is big enough. 06:13.000 --> 06:17.000 But if you want to contribute, of course, don't hesitate. 06:17.000 --> 06:22.000 And I also have an issue open because if you're trying to go to the website right now is not responsive. 06:22.000 --> 06:26.000 I fixed this, but I'm not a DevOps and I have a problem with Docker. 06:26.000 --> 06:29.000 If you have some knowledge, don't hesitate. 06:29.000 --> 06:31.000 It's really welcome. 06:31.000 --> 06:33.000 Oh yeah, you have it here. 06:33.000 --> 06:34.000 Nice. 06:34.000 --> 06:45.000 Okay, starting with my iconic and like favorite questions, do you know any women in computer science? 06:45.000 --> 06:50.000 Don't be shy, you can just shout names. 06:50.000 --> 06:52.000 Yeah. 06:52.000 --> 06:53.000 Yes. 06:53.000 --> 06:55.000 Margarita Milton. 06:55.000 --> 06:56.000 Yeah. 06:56.000 --> 06:59.000 I don't remember her name. 06:59.000 --> 07:04.000 I don't remember her name. 07:04.000 --> 07:06.000 Thank you. 07:06.000 --> 07:09.000 Yeah, that's true. 07:09.000 --> 07:11.000 I don't remember her name. 07:11.000 --> 07:12.000 Her first name? 07:12.000 --> 07:13.000 Goldberg. 07:13.000 --> 07:14.000 It was Adele Goldberg. 07:14.000 --> 07:15.000 True. 07:15.000 --> 07:17.000 Thank you. 07:17.000 --> 07:18.000 Second one. 07:18.000 --> 07:19.000 Thank you. 07:19.000 --> 07:22.000 I don't know her. 07:22.000 --> 07:25.000 Okay, I have to do it. 07:25.000 --> 07:31.000 No, I think I'm going to talk to you after please stay. 07:31.000 --> 07:34.000 Thank you. 07:34.000 --> 07:36.000 Okay. 07:36.000 --> 07:38.000 So let's dig in it. 07:38.000 --> 07:43.000 And I'm going to begin the talk by asking another question. 07:43.000 --> 07:47.000 Who can code in assembly here? 07:48.000 --> 07:49.000 Wow. 07:49.000 --> 07:50.000 More than I expected. 07:50.000 --> 07:53.000 Are you still happy in your life? 07:53.000 --> 07:54.000 I'm kidding. 07:54.000 --> 07:55.000 I'm dreaming of coding. 07:55.000 --> 07:56.000 I'm just jealous. 07:56.000 --> 07:57.000 Okay. 07:57.000 --> 08:00.000 I will try to learn one day. 08:00.000 --> 08:02.000 It's easy. 08:02.000 --> 08:05.000 Who can code in X86? 08:05.000 --> 08:06.000 Don't answer. 08:06.000 --> 08:08.000 So this is really the name of the language. 08:08.000 --> 08:09.000 I wasn't sure. 08:09.000 --> 08:16.000 So I think that the thing that I can do that is approaching assembly languages is playing the game. 08:16.000 --> 08:18.000 Human resource machine. 08:18.000 --> 08:21.000 And I'm looking at the last level. 08:21.000 --> 08:23.000 Thank you. 08:23.000 --> 08:24.000 Okay. 08:24.000 --> 08:30.000 So now for the people who doesn't know what the language assembly languages really fast. 08:30.000 --> 08:35.000 So this is a language that is just one step above binary code. 08:35.000 --> 08:41.000 And every and unlike compiled and interpreted languages, 08:41.000 --> 08:48.000 every assembly instruction is directly matches binary instructions. 08:48.000 --> 08:56.000 So this is an example of a program to display hello world for Linux. 08:56.000 --> 09:04.000 And as it is easy, every instructions depends on the processors architecture. 09:04.000 --> 09:06.000 Okay. 09:06.000 --> 09:09.000 Let me apply. 09:09.000 --> 09:21.000 So from the 1947 and the 1953 British couple working at Beerbeck created three computers just on day-on. 09:21.000 --> 09:28.000 The arc, automatic relay computer, the sec, simple electronic computer and the apex, not the game. 09:28.000 --> 09:32.000 All purpose electronic x-ray computer. 09:32.000 --> 09:36.000 So Kathleen Booth and her husband were working like this. 09:36.000 --> 09:42.000 He built the computers and she programmed them and wrote the programs. 09:42.000 --> 09:45.000 Returning from a trip in the United States, 09:45.000 --> 09:53.000 the couple starting to write, started to write 28 pages document titled, 09:53.000 --> 09:59.000 General consideration in the design of all, I'm sorry. 09:59.000 --> 10:04.000 No, it normally, in all purpose electronic digital computer. 10:04.000 --> 10:11.000 And it was describing the improvement they made about the arc computer. 10:11.000 --> 10:18.000 And this is also the first time that we spotted an assembly language in the history created by Kathleen Booth. 10:18.000 --> 10:20.000 So let's wrap it. 10:20.000 --> 10:27.000 So Kathleen Booth is a computer scientist, she invented the first assembly language and little anecdotes. 10:27.000 --> 10:35.000 Her research on neural networks led to programs that mimic how animals recognize shapes and patterns. 10:35.000 --> 10:36.000 Just that. 10:36.000 --> 10:37.000 Okay, go. 10:37.000 --> 10:39.000 You can do it. 10:39.000 --> 10:42.000 Let's continue. 10:42.000 --> 10:45.000 And we're going to speak again about computer. 10:45.000 --> 10:50.000 So before computer machine, computer was a job title. 10:50.000 --> 10:54.000 And it was about long and boring calculations. 10:55.000 --> 11:02.000 This term appeared in the 17th century and literary means one who computes. 11:02.000 --> 11:08.000 At the end of the 19th century, this job was dominated by the women. 11:08.000 --> 11:14.000 Sadly, these workers were a lot invisible, and especially the women. 11:14.000 --> 11:18.000 But it was really a common job in astronomy. 11:18.000 --> 11:23.000 But only every researcher, every scientist, engineers, 11:23.000 --> 11:31.000 relayed on computer, human computers to verify the calculations and the formulas. 11:31.000 --> 11:34.000 Oh, I'm going to first. 11:34.000 --> 11:39.000 Do you think that this is not the case anymore that this kind of people are invisible? 11:39.000 --> 11:40.000 I don't know. 11:40.000 --> 11:41.000 I don't think so. 11:41.000 --> 11:44.000 And let me give you an example. 11:44.000 --> 11:49.000 Who have seen the movie, Oppenheimer, released in 2023? 11:49.000 --> 11:50.000 Okay, a lot. 11:50.000 --> 11:53.000 Did you like it? 11:53.000 --> 11:54.000 Oh, wow. 11:54.000 --> 11:58.000 I heard one, yes, the one. 11:58.000 --> 11:59.000 Okay. 11:59.000 --> 12:02.000 So let's remember a little bit of the movie. 12:02.000 --> 12:07.000 You're going to see the whole life cycle of the bomb through the movie. 12:07.000 --> 12:14.000 You see the scientists speaking about physics and formulas to create the bomb. 12:14.000 --> 12:20.000 You're going to see the military organizing Los Alamos where they're going to test the bomb. 12:20.000 --> 12:25.000 You see the other people transporting assembling the bomb. 12:25.000 --> 12:30.000 Also the people who charge in the communication during the test of the bomb. 12:30.000 --> 12:33.000 They are all men and only men. 12:33.000 --> 12:36.000 And what are the women in the movie? 12:36.000 --> 12:42.000 The wife, the mistress and a random secretary at one moment. 12:42.000 --> 12:44.000 I don't really remember. 12:44.000 --> 12:49.000 So writing it started to ask myself this question. 12:49.000 --> 12:54.000 Why is it not interesting to show the women, the women, computers? 12:54.000 --> 12:59.000 Because you have to know that women were part of the Manhattan's project. 12:59.000 --> 13:00.000 There were a lot. 13:00.000 --> 13:02.000 And not just any women. 13:02.000 --> 13:12.000 At the beginning of the project, it was the wife of the scientist who were the human computers trying to verify and redo the calculation to see if this is right. 13:12.000 --> 13:17.000 But as the project grows, they're hired more and more human computers. 13:17.000 --> 13:19.000 But we don't see them. 13:19.000 --> 13:23.000 We never see them in the movie. 13:23.000 --> 13:25.000 Okay. 13:26.000 --> 13:28.000 I'm going a bit far as I think. 13:28.000 --> 13:29.000 You can do a little break. 13:29.000 --> 13:31.000 I just want a minute to try to. 13:31.000 --> 13:33.000 Okay. 13:33.000 --> 13:35.000 I have to breathe. 13:35.000 --> 13:37.000 After the word word. 13:37.000 --> 13:38.000 The word. 13:38.000 --> 13:39.000 Two. 13:39.000 --> 13:41.000 Many women made history. 13:41.000 --> 13:43.000 And especially at the Naka. 13:43.000 --> 13:45.000 The old Nasa. 13:45.000 --> 13:49.000 And you probably already know them. 13:50.000 --> 13:57.000 We begin with Catherine Johnson, who is a mathematical mathematician. 13:57.000 --> 13:58.000 Mathematician. 13:58.000 --> 14:08.000 Mathematician, who is famous for calculating the trajectory of the first American mission sending a human man in orbit around the earth. 14:08.000 --> 14:12.000 In 1962, the Mercury Atlas 6, friendship 7. 14:12.000 --> 14:17.000 And the little anecdote is that the pilot, John Glenn, didn't trust computers. 14:17.000 --> 14:28.000 So he specifically asked Catherine to redo all the computing to be sure and to verify, refusing to launch without her approval. 14:28.000 --> 14:31.000 We also have Mary Jackson. 14:31.000 --> 14:37.000 Mary Jackson was an American mathematician and aerospace engineer. 14:37.000 --> 14:46.000 And in 1951, she started to work at Langley Research Center as a human computer. 14:46.000 --> 15:02.000 Inside a team led by Dorothy Vulgan that you should probably also already heard the name, which was the first black division head at the Naka and later the Nasa. 15:02.000 --> 15:11.000 The name of the team was the West Area computers and it was only composed by black woman mathematicians. 15:11.000 --> 15:15.000 But when Mary was working there, she started to drink bigger and bigger. 15:15.000 --> 15:19.000 She wanted to do more. She wanted to do something big. 15:19.000 --> 15:21.000 And she wanted to become an engineer. 15:21.000 --> 15:29.000 So she started to take evening classes of mathematics and physics at the Hampton High School. 15:29.000 --> 15:32.000 But you see, this is too easy. 15:32.000 --> 15:35.000 So she needed a permission from the city, a special permission. 15:35.000 --> 15:40.000 Because the school was for white students only, of course. 15:40.000 --> 15:45.000 But she did it. She had the permission. She succeeded her training. 15:45.000 --> 15:50.000 And she became the first Nasa's black woman engineer. 15:50.000 --> 15:53.000 Congrats to her. She's amazing. 15:53.000 --> 16:00.000 So of course, you recognize this woman from the movie and the book Hidden Figure. 16:00.000 --> 16:06.000 If you didn't see or read those, please do it because this is really interesting. 16:06.000 --> 16:11.000 And this is just a master class. 16:11.000 --> 16:15.000 As we speak about Nasa, we can forget Margaret Hamilton. 16:15.000 --> 16:22.000 Remember this picture just here of her standing next to a huge pile of paper. 16:22.000 --> 16:24.000 Told it in her. 16:24.000 --> 16:29.000 I present to you the program Navigation Software of the Apollo program. 16:29.000 --> 16:38.000 That she wrote with her team at the MIT's Drapalab. 16:38.000 --> 16:48.000 In 1963, Margaret joined the Drapalab leading the team that was responsible to develop the software for the navigation, 16:48.000 --> 16:52.000 but also lending in or on the moon. 16:52.000 --> 16:55.000 Always program with that. 16:56.000 --> 17:01.000 Lending on the moon, which is a really important job. 17:01.000 --> 17:08.000 And her work was used for the Apollo program, but also for the sky lab. 17:08.000 --> 17:12.000 There was something really interesting in the way that Margaret coded. 17:12.000 --> 17:17.000 I mean, for that time, is that she wanted to rationalize a lot to her code. 17:17.000 --> 17:19.000 She wanted to be reusable. 17:19.000 --> 17:22.000 So modules software should be reusable in other cases. 17:23.000 --> 17:27.000 And for that time, that was really exceptional. 17:27.000 --> 17:34.000 And as we can see, after Apollo, it has been reused for Skylab. 17:34.000 --> 17:37.000 Both of internet. 17:37.000 --> 17:39.000 Yay. 17:39.000 --> 17:43.000 No, I didn't have, I wanted to say something, but I forgot. 17:43.000 --> 17:51.000 So today, the worldwide web is an entirely part of our life. 17:51.000 --> 17:55.000 In even the smallest details, we use it for everything. 17:55.000 --> 18:00.000 And mostly for useless things. 18:00.000 --> 18:05.000 But let's take a look at its ancestor, the OpenIt. 18:05.000 --> 18:10.000 So OpenIt stands for Advanced Research Projects Agency Network. 18:10.000 --> 18:15.000 It was the first switching packages network. 18:15.000 --> 18:20.000 And it has been created at DARPA. 18:20.000 --> 18:21.000 I always forgot. 18:21.000 --> 18:24.000 Defense Advanced Research Projects Agency. 18:24.000 --> 18:28.000 So the project started in 1962. 18:28.000 --> 18:32.000 It became a reality only in 1969. 18:32.000 --> 18:39.000 And the first official demonstration was in 1972. 18:39.000 --> 18:45.000 So you have to know that the main goal of OpenIt before was not social as it is today. 18:45.000 --> 18:50.000 It was for resource sharing, even if we still see resource sharing still today. 18:50.000 --> 18:59.000 The first users of OpenIt was engineer mathematicians, computer scientists, military, etc. 18:59.000 --> 19:03.000 Who knows this comment if you can read it? 19:03.000 --> 19:05.000 Yeah, okay. 19:05.000 --> 19:08.000 Who's still using it today? 19:08.000 --> 19:09.000 Okay. 19:09.000 --> 19:11.000 Interesting. 19:11.000 --> 19:23.000 But so you have to know that this comment, who is, will let you try to track down who is behind a domain, 19:23.000 --> 19:30.000 not really a person, but who is the owner of the domain of a certain domain? 19:30.000 --> 19:38.000 And this is Elizabeth Faylor, who invented this comment. 19:38.000 --> 19:46.000 So let me introduce to you one of the biggest figure, important figure of the OpenIt, Elizabeth Faylor. 19:46.000 --> 19:52.000 In 1972, she joined the team of Douglas Engelbart. 19:52.000 --> 20:02.000 I didn't see him coming though, but he's the inventor of the GUI and the mouse though, like that you know. 20:02.000 --> 20:07.000 And as soon as she arrived, she had a mission. 20:07.000 --> 20:11.000 She had to create the resource handbook of the OpenIt. 20:11.000 --> 20:16.000 What's the resource book of the resource handbook of the OpenIt? 20:16.000 --> 20:23.000 That's the question that Elizabeth asked to Douglas and the glass answer that he didn't know, 20:23.000 --> 20:26.000 but he needed it for in six weeks. 20:26.000 --> 20:28.000 Okay, great. 20:28.000 --> 20:32.000 But Elizabeth quickly knew what it could be used for. 20:32.000 --> 20:41.000 Create a whole map of the OpenIt, listing all the notes, listing all the people the organization using running the OpenIt, 20:41.000 --> 20:46.000 and like that make it more stable. 20:46.000 --> 20:53.000 With her mostly female team, she was responsible for organizing the network. 20:53.000 --> 21:09.000 And we can see the resource handbook as the browser of the OpenIt, the yellow pages of Elizabeth as the search engine and Elizabeth herself as the human algorithm. 21:09.000 --> 21:17.000 Without that, it was impossible to navigate through the OpenIt. 21:17.000 --> 21:26.000 And as OpenIt grew faster and faster, Elizabeth decided to create a people find the inside the network itself. 21:26.000 --> 21:32.000 And she developed a new service called Who Is That's Why I Talked About It Before. 21:32.000 --> 21:37.000 So yes, that was the original user profile system. 21:37.000 --> 21:42.000 So that's not my space, I'm sorry, it's who is. 21:42.000 --> 21:56.000 Even though the common has changed a lot from that time, it's still really used common and it's still part of the core of internet. 21:56.000 --> 22:03.000 But we are not stopping here, a busy women. She was also part of the first or R.C.F. 22:03.000 --> 22:07.000 So request for common or, no, I'm sorry, or F.C. 22:07.000 --> 22:12.000 This is hard to pronounce for me. The request for commands. 22:12.000 --> 22:18.000 The group that built the standard, the basic standards of the internet we know today. 22:18.000 --> 22:28.000 And she was in the group at the moment when they decided to create something new that it didn't exist at the time. 22:28.000 --> 22:30.000 The domain names. 22:30.000 --> 22:41.000 So because the network still continued to grow fast, fast, fast and fast, they needed to reorganize it because it was really chaotic. 22:41.000 --> 22:47.000 And it's really, I don't have time to explain you all the discussions around that, but it's really fascinating. 22:47.000 --> 22:58.000 So if you don't know, I really recommend you to go and to see how they decided to give the names for the domain names. 22:58.000 --> 23:10.000 And despite her incredible contribution, she's not mentioned in the ones in the French Wikipedia page of the open it. 23:10.000 --> 23:17.000 Okay, we are now in 1983, and a revolutionary invention is about to happen. 23:17.000 --> 23:30.000 So if he will soon, he's a British computer scientist, and she has been working at a con computer, LTD since 1978. 23:30.000 --> 23:42.000 When she started designing the instruction for the first risk processes, the con risk machine or ARM. 23:42.000 --> 23:54.000 The ARM1 processor was delivered on April 26 on 1985 and worked perfectly on its first tests. 23:54.000 --> 24:05.000 This processor became one of the most powerful core and by 2012, it was used by more than 95% of the smartphone. 24:05.000 --> 24:11.000 And I think that my data is a bit outdated because we are in 2025 now. 24:11.000 --> 24:17.000 Okay, let's play a little game. 24:17.000 --> 24:24.000 So I think that everybody knows who is, and you have two proposition of his name. 24:24.000 --> 24:28.000 All right, one is false, of course. There is a mistake in one of them. 24:28.000 --> 24:34.000 Who thinks that the proposition A is correct? 24:35.000 --> 24:40.000 A, okay, who think it's the B? 24:40.000 --> 24:48.000 Okay, majority voted for the A, of course, it was the A. I put a E randomly in his name. 24:48.000 --> 24:55.000 Well done, so this is Tim Berners Lee, one of the three inventors of the worldwide web. 24:55.000 --> 24:59.000 And now let's look at someone else. 24:59.000 --> 25:12.000 This women invited the STP protocol, spanning tree protocol, who allowed the worldwide web to exist. 25:12.000 --> 25:22.000 I have two names, so it's easier. Who think that her name is the first proposition, so Betty Olberton? 25:23.000 --> 25:26.000 Who think it's the B? 25:26.000 --> 25:31.000 Okay, that's nice. A lot of people already know her. Amazing. 25:31.000 --> 25:34.000 So of course, this is a radio, I don't know how to pronounce her name. 25:34.000 --> 25:38.000 A radio poem. 25:38.000 --> 25:43.000 So a radio poem is a computer scientist and network engineer. 25:43.000 --> 25:48.000 And since childhood, she loved puzzles, logic, and mathematics. 25:48.000 --> 25:54.000 She secretly dreamed of a boy that would beat her in math. 25:54.000 --> 26:06.000 It never happened. She always has always been the top first in her classes. 26:06.000 --> 26:16.000 We are now in 1985, and radio is working at the DEC, and at that time, the Ethernet was becoming a global standard. 26:16.000 --> 26:31.000 With one major issue, is that when the network is growing, at some point, the package, the package was starting to collide into each other. 26:31.000 --> 26:36.000 Which was really problematic if you want to scale up your network. 26:36.000 --> 26:51.000 So one day, the manager of radio came to her and said, let me, I want you to invite a black box, magic box, a magic box that would result that would fix the Ethernet box. 26:51.000 --> 26:59.000 That's finished without needing more memory and letting the Ethernet scale up without a problem. 27:00.000 --> 27:09.000 And what's that funny is that the manager asked her that on a Friday, because the next week he was going on a vacation, one week vacation. 27:09.000 --> 27:14.000 Because he thought that we would be a really hard problem to solve. 27:14.000 --> 27:23.000 The radio, the same night, in the middle of the night, with the solution. 27:23.000 --> 27:30.000 So really impressive of her, just half a day to find the solution of this problem. 27:30.000 --> 27:35.000 And so she writes everything, the proposition, the specification, everything. 27:35.000 --> 27:40.000 So she invited that day, the spanning tree protocol. 27:40.000 --> 27:47.000 And because she was waiting, because on Monday everything was done already, she was bored. 27:47.000 --> 27:58.000 She decided to write a poem about the spanning tree protocol inspired by one of the favourite poem of her mother. 27:58.000 --> 28:02.000 I think that I should never see a graph more lovely than a tree. 28:02.000 --> 28:06.000 A tree whose crucial property is looked reconnectivity. 28:06.000 --> 28:11.000 A tree which must be sure to span so packets can reach every land. 28:11.000 --> 28:18.000 First, the root must be selected by ID is elected. 28:18.000 --> 28:23.000 Leescused path from root are traced in the tree, this path are placed. 28:23.000 --> 28:29.000 A meshes made by folks like me, then bridges, find a spanning tree. 28:29.000 --> 28:38.000 I would never think to see one day the word LAN in a poem. 28:38.000 --> 28:45.000 But this is amazing. 28:45.000 --> 28:55.000 So without the STP, the worldwide web would never exist, or at least not as big as we have it today. 28:55.000 --> 29:04.000 And even now, even if the STP is not used a lot for legacy networks, 29:04.000 --> 29:10.000 modern and new and modern protocol are still extension of it. 29:10.000 --> 29:16.000 Some example are the or STP or the MSTP. 29:16.000 --> 29:19.000 All right. 29:19.000 --> 29:23.000 The Leesfun parts, girls and mathematics. 29:23.000 --> 29:31.000 So in my first talk, I talked about the gap in the 80s between the women and the computer science, 29:31.000 --> 29:37.000 especially in Europe and America, so still I'm speaking about Europe and America here. 29:37.000 --> 29:51.000 But we have another big issue, girls and mathematics, which is part of the problem that women are afraid to begin also in computer science. 29:51.000 --> 29:55.000 In 2000, researchers published a study. 29:55.000 --> 30:03.000 It was an experiment on newborn babies, which were presented an object and a human face. 30:03.000 --> 30:13.000 And they observed what the baby preferred, the face or the object. 30:13.000 --> 30:20.000 So the boys looked more at the objects and the girls to the human faces. 30:20.000 --> 30:31.000 And the authors of the study said that boys naturally have a systemizing brain, meaning they understand where objects are in space and how they work. 30:31.000 --> 30:36.000 Congrats boys, you have already a geometry degree when you're born. 30:36.000 --> 30:39.000 I'm kidding, I'm not even sure if it exists. 30:39.000 --> 31:00.000 But the girls, on the other hand, have more empathizing brain, looking for more focused on people and care more about others. 31:00.000 --> 31:08.000 And from this they conclude that girls aren't as interested or skilled in math and science. 31:09.000 --> 31:19.000 Okay, so this is funny to say that kind of thing, because when you look, you are looking through the history like from my talks, even from my talk. 31:19.000 --> 31:26.000 You can see that there were a lot and a lot of women in mathematician women. 31:26.000 --> 31:37.000 And I also find a paper who was, who disagree with the study, like say the same point per point, why? 31:37.000 --> 31:47.000 And here I have the statistics of the study. I'm sorry, I couldn't click on the study. I didn't want to either click on the statistics of this study. 31:47.000 --> 31:55.000 So here we have the stats. The study was made on 102 babies. 31:55.000 --> 32:03.000 27 girls showed no preferences between objects and face. 32:03.000 --> 32:11.000 And the boys, 19, has had a mobile preference versus 14 for no preference. 32:11.000 --> 32:20.000 So I don't know why they conclude so much thing I just said before, but it's interesting. 32:20.000 --> 32:29.000 And you have to know that this theory was widely shared by the media, right? 32:29.000 --> 32:35.000 Even though it was heavily criticized by the community, the scientist community. 32:35.000 --> 32:41.000 And one big issue with the experiment is that newborn baby comes to the head. 32:41.000 --> 32:49.000 They can't choose to look at one object when they are up. 32:49.000 --> 32:54.000 So you have to know that the baby was hold, but they went so noise in the data. 32:54.000 --> 33:03.000 They don't even really properly report it how they maintain the head of the babies. 33:03.000 --> 33:09.000 And we also know that there is no other research teams that have observed the same behavior. 33:09.000 --> 33:19.000 And on the contrary, many psychology studies on children show that there are no differences between boys and girls. 33:19.000 --> 33:24.000 So today we have a one big problem with the girls and the women. 33:24.000 --> 33:32.000 We always hear them and I'm not good at math, instead of no, I'm not interested in math. 33:32.000 --> 33:48.000 So without knowing it, without realizing it, they still believe that they don't have what it takes to succeed in mathematics. 33:48.000 --> 33:53.000 I also have an anecdote about the low rent summers. 33:53.000 --> 34:01.000 The president of the Harvard University said something in January 2005. 34:01.000 --> 34:03.000 Oh, you have to read it. 34:03.000 --> 34:09.000 He said, so January 2005, low rent summers, the president of Harvard University said, 34:09.000 --> 34:17.000 the low number of women in science is because they are naturally not able to succeed in these fields. 34:17.000 --> 34:21.000 Tell that to Katherine Johnson or Dorothy again. 34:21.000 --> 34:27.000 And a lot of people use the study in the 2000 to support what he said. 34:27.000 --> 34:42.000 So it's really complicated because because of the media who took the study, it's really like stuck in our culture in our society. 34:42.000 --> 34:47.000 So this is already the conclusion. I have been really fast. I was really stressed. 34:47.000 --> 34:52.000 So you're going to be free. You're going to have a little break after that. 34:52.000 --> 34:56.000 And I would like to finish with two little sentences. 34:56.000 --> 35:03.000 A representation allows a person to identify with something and see new possibilities. 35:03.000 --> 35:08.000 A rule model, a person to admire someone and push their limits. 35:08.000 --> 35:13.000 Not everyone needs a rule model, but everyone needs representation. 35:13.000 --> 35:16.000 Thank you. 35:16.000 --> 35:33.000 Thank you. 35:33.000 --> 35:38.000 Those people have questions or you can come and talk to me if you want to. 35:38.000 --> 35:41.000 Thank you so much for your intention. 35:41.000 --> 35:46.000 And good luck for the cues to the death rooms. 35:46.000 --> 35:48.000 Samoa. 35:48.000 --> 35:54.000 Hello. So thank you for this. This is a second year and I really hope that you will do it every year now from now on. 35:54.000 --> 35:56.000 And maybe it will not be like this. 35:56.000 --> 35:59.000 Maybe even some kind of workshop or something else. 35:59.000 --> 36:04.000 So we can play, build some code, you have some fun and do more than this. 36:05.000 --> 36:09.000 Thank you so much for this. 36:09.000 --> 36:17.000 Every year is really, I was really happy to come here and see you with this talk because we need more of this things here. 36:17.000 --> 36:18.000 Thank you so much. 36:18.000 --> 36:23.000 And if you are a teacher, if you are getting workshops, you are teaching things. 36:23.000 --> 36:26.000 Don't hesitate to cite the authors, the inventors. 36:26.000 --> 36:31.000 People don't have to remember the names, but just like they are having female names. 36:31.000 --> 36:37.000 Women say women names inside syllabus books or during the lessons. 36:37.000 --> 36:40.000 It's always a good start and we need that. 36:40.000 --> 36:41.000 Thank you. 36:41.000 --> 36:44.000 Another question. 36:44.000 --> 36:48.000 Thank you. 36:49.000 --> 36:52.000 Have a good day. 37:03.000 --> 37:06.000 So I was wondering in terms of... 37:06.000 --> 37:08.000 I'm sorry I can't hear you. 37:08.000 --> 37:12.000 Can you just move a little bit silently when you're going? 37:12.000 --> 37:13.000 Thank you. 37:14.000 --> 37:19.000 Maybe you can come in as the question here is going to be easier. 37:19.000 --> 37:20.000 Thank you.