So, in this panel, I'd like the three panelists to join me on stage. Please come, so I'm not alone. This panel, I think, is quite important and interesting. There was a journalist from Folio de San Paolo. He was here yesterday, and he, please sit whenever, and the journalist said, I liked the event, but quite theoretical. I don't have anything to write about. And I said, yeah, come to my panel, because my panel is about case studies. So this panel is about things people are doing. And I will say two things that are absolutely redundant. And because they are redundant, it's what makes this panel interesting. So Amazon bought Whole Foods, and this is quite related to artificial intelligence. So now, I was in London, and Sunsbury created a new distribution systems to deliver e-purchase.  So Sunsbury is not competing with Amazon or Whole Sales, because they don't have artificial intelligence. The reason why I say this is redundant, because this is one of the case studies we hear all the time. We hear corporate uses of artificial intelligence for the market that are really interesting. But how many case studies you have based on inclusion? How many cases you can point if someone writes 20 million and say, tell me three things that should people have done to tackle inclusion and artificial intelligence? And we have very few ideas. So in this panel, we have three people that will have ideas of what they are doing to tackle the issue of inclusion in artificial intelligence. And I think it's quite rare. I call it strawberries in the desert opportunity to have these three samples of people and their cases on what has been done. Then we're going to open for questions. You can question them. And if you have an interesting case, please share, because it's rare moments where we have these case studies about AI and inclusion. So I have here with me Lukas, Lukas Santana from the Zabaf Social, he's from Brazil. He's wearing a t-shirt about racial affirmative actions. We'd say, if something is black, the thing is good. And he's going to talk about algorithm discrimination or profiling in bank images. We also have here Arisa Ema from the University of Tokyo. And we have Mark Sermon from Mozilla Foundation, who will share all the stories and I'll leave them to present themselves. Anyone who wants to start? Mark? In this order or let's see, whoever the slides up first is going to start. Let me look for the gods. The gods said okay. The message from the gods is coming down. All right, everyone. Good? All right, okay. I guess my slides up first, so. Hi, everybody. If you hadn't just had a break, I would get you to stretch. It's been a long day. So I guess I'm going to provide four very quick provocations of case studies. And so it's sort of, but it's a long one theme, which is really most of this conversation, the characters in the story have been big companies or to some degree governments and regulation. But if we want to look at inclusion or agency or self-determination or however we're going to talk about where we as people fit into this cyborg world or this world where computing is pervasive and wraps around us, and we have to look at ourselves as citizens or ourselves as people who aren't companies who aren't agents of the government and how do we shape and how do we shape what the reality becomes and put ourselves into the story, not just as the computed, not just as the users. And so it's one of the reasons that increasingly I think about the need for a social movement or an environmental movement for this digital environment, where citizens actually are shaping what's going on. And obviously in that world, the tactics that have worked in other movements. I mean, I grew up in the environmental movement and the peace movement in North America, but many movements have their own traditions of how they're trying to shape reality. The question for me and where I want to look at the case studies is how could we imagine if we wanted a movement for a healthier digital environment or one that is more humane and more empathetic, what would be some of the tactics that we would try? And it's something Mozilla is really investing in is trying to encourage a movement like that to grow and support people who are doing probably the kind of work you guys are going to talk about. So I'll just give four quick case studies of types of tactics we might imagine to tackle some of the challenges we've talked about over the last day and a half on AI. And so the first is this idea that especially in the context of machine learning, there's a huge kind of centralization of power and oligopolies, which have a lot of negative impacts, but include that if we wanted to create a counterbalance to most of the big AIs, you know, like Alexa or Siri or these things, we actually don't have the access to the big data sets that those companies have. And so there's a barrier to competition. If we wanted something like a Firefox or like some social alternative or some alternative from different parts of the world, we actually don't have the data to start with. So one opportunity as an intervention is this one I mentioned yesterday, is that we would imagine cooperatives or commons of training data. So it's not one entity that creates the kind of training data that would allow you to build quality systems, but it's actually collectives. And so we actually tried an experiment with that, something called Common Voice. And what that was or is actually is an attempt to create a training data set for voice recognition that has two benefits. One is it's a commons, so anybody can use it. And you can if you go to that site, speak a sentence and help train the machine or help quality control by listening to a sentence and say, did it sound like that or not sound like that? And I think we got in a very short period of time, just kind of going out to our community 10,000 hours over the course of a couple of months of training data on voice. And so the benefits of course are that somebody could then use that. That's a free open source voice recognition training data set. But also it means that we can start to have languages that are not going to be represented by the kind of larger corpus of, you know, the mainstream platforms, languages other than English, much smaller languages represented, you know, through that kind of open source approach. So that's one thing, you know, that, yes, I guess, Mozilla is an organization or a company of sense where we're a nonprofit, but really is a citizen action of people coming and contributing to this common. So that's one thing we could imagine doing is more cooperatives and more commons like that. The other is to think about, like in many ways, the challenge of not trusting the kind of products we have or the technology we live inside of. And, you know, one of the things where this comes up a lot is in this talk about auditability or verifiability. Do we know, like, what bias was in the algorithm or how algorithm made a decision about me or also in, you know, how is my data being used? Is it being used for the purposes that I, you know, that I wanted it to be or being used in other ways? And so an interesting idea is to kind of take a page from the history of different ethical products like organic food or fair trade and look that you could tackle some of these questions or at least attempt to tackle some of these questions about verifiability, auditability, about understanding how my data is used at some systemic level by building basically fair trade marks or trust marks. And so we actually just supported a paper with a group of folks called ThingsCon on building a trust mark for IoT, which really then extends a lot into AI. I mean, the two kind of often go hand in hand. So that's another strategy as citizens we could use is to look at nutrition labeling or trust labeling as something that we then either encourage companies to do or over time regulate and force companies to do on their products. And that's a way to start thinking about that over a number of decades at a kind of a large scale. And so that's a real thing that we're exploring to see whether we could make happen in the industry, but driven as a citizen initiative and an initiative that involves researchers like some of the kind of people who are here in the room. And then a third piece, and these are really provocations of like what other tactics could we think of? These are things that we're thinking about as we imagine kind of more of a citizen movement around these issues is to be more demanding customers and know that we actually have market power as people or at least those of us who choose to opt into buying these technologies as people who are buying them by making choices and also like being vocal about what is good and bad and what we want and especially being vocal about what's stupid. So one thing we just released this week is a holiday shopping guide for IoT things which helps you know, what things are spying on you that you're buying for Christmas. So in there you can find out that there is an Adidas soccer ball that you can buy that has a microphone, a camera, you need to register an account to use the soccer ball and all of these kind of things. And it's funny, but it also is starting to say, let's build a lexicon of knowing what we're buying. Especially as we imagine embedding AI and sensors in basically all kinds of products that we use. And that's a very tried and true strategy from other movements is looking at consumer power as a way to influence how the market behaves. And then the last piece which relates back to the, the fact that Greenpeace was my opening example is really to shift the narratives and look at citizens as especially artists, people who can imagine a different future. It was just actually upstairs in the museum and it says on the wall, you know, what different futures can we imagine? What different tomorrows can we imagine? And so much, I think even here as we're critical of where things are going with AI and want to look at inclusion, we so much look at the narrative that's being written in Silicon Valley as, you know, a narrative we're trying to negotiate inside of instead of looking at other possible tomorrows. And so one of the things I would like to see way more of and we're trying to support is basically design fiction and other kinds of things where you're prototyping a different reality. So, you know, there's a group of artists we've worked with including SuperFlox to basically develop IoT products and AI products that won't ever exist. But they do imagine a very different kind of future. And so this one, if you go and see the video, I'll give you the link at the end, is about this, basically this voice assistant that this woman has that she can dial up the different mood settings of the voice assistant. And then when she actually calls for customer service, you know, she starts it out nice and she starts to get more and more annoyed and she keeps telling her voice assistant to get meaner and meaner. And this is a sort of like fantasy digital assistant she wishes she had. And of course, it's talking to the AI of the company and they're seeing these battling AIs get angrier and angrier at each other. The value of this, I think the value of art as a part of a political strategy in this is imagining that the future that is being painted before us doesn't have to be the future.  So we can imagine a different future, I think, by blowing up the imagination. And the last thing I'll say, because it says I have no time, is that I actually feel, you know, pragmatically there are ways for us to intervene and shape things.  I have a really beautiful slide here on this screen of the GNU mascot, the Tux Linux mascot and the Firefox mascot at Fisley, the open source conference here in Brazil. And, you know, we do have a history in the free software movement of being a citizen movement that did shape big parts of how the internet have worked out. It's also backfired on us a little bit. But I think there is, to me, the free software movement is, you know, like the prehistory of the environmental movement. You know, you didn't, you had environmentalists organized for well over 100 years. But there's phases and phases and phases. And I think we have to imagine a new phase where in the free software movement, we had something where we were able to shape and build digital reality. I think we have to reimagine that now with strategies like these and others we can come up with so that we get a future that is different than the one is being painted and the one that looks like it's unfolding around us. So that's my provocation to you is what are those strategies and how do we reinvent that movement? Thank you very much. Arisa, would you like to go next? So hello, my name is Arisa Ema. I'm from Japan, the University of Tokyo. So I came on the other side of the earth. It took like 30 hours to come to here. But I really enjoyed having attended this symposium so far. So I would like to talk not specifically about the technology, the AI technology, but how it's kind of like a story. So how I created my colleagues or like the engineers, the social scientists to be involved in a group to make more people included in this AI ethic, to consider the ethics and policy in Japan. So the title is like breaking down silos. So as you can easily imagine, there's a silo has been created among the engineers do the engineer's job, the social scientists are doing their jobs and the policymakers are doing their jobs. So in the 2030 or 40, the IT, the AIs has to be considered in the... We thought that it would be really important to include many people in Japan, the many stakeholders to discuss the AI ethics. And this picture became one opportunity to build that kind of community. So this is the journal cover design of the Japanese Society for Artificial Intelligence. So it's official journal cover. So before the renovation, so it was used to be like this, the originally dull cover design. So you could see in the library or so. But in 2014, they changed the cover design to this like more attractive animation like magazine. And this, I think you could easily imagine, this was criticized by from the gender perspective. So there's a lot of things that has been discussed, but the thing is that this is woman plucked. So it's kind of like anthropomorphism of like the vacuum cleaner. So she's cleaning the room, but she's plucked. And some say that she has a hollow eye. So it seems like she's not willing to do this kind of job. So it's, and so, but however, this picture was kind of chosen within the community. And it was, so, sorry. So it was, it got like a first prize within the community, but they saw that this was really nice because it feels something like nostalgic way. It's traditional Japanese. So you could see there's like the library and they used to have this kind of girl cleaning in the room traditionally. However, the thing doesn't, so the criticism become bigger and bigger. And the worst thing for them is that they want, the news went into the global. So the BBC News wrote the article saying, Japan artificial servant girls broke sexism rule. So this kind of, oops, sorry, article went into the global. And so, however, so I wasn't involved in this community in this editorial board at this time. But the thing I found is that there was two opinions. So one say that this is really good. It's really nostalgic and impressive cover. However, they found out when they released these issues, the public thought that this have more gender issues. And it has like a, you need to think about the political collectness and the other thing. So the AI researchers realized that they have social impact on what they represent. And so they started to discuss their social impact with not only within the community, but including the philosophers or the anthropologists or social SDS researcher like me and law or sociology. So we are creating this kind of community in right now. And I think this was a really good opportunity to discuss this. And also we actually wrote the paper together about this topic and do like ethics and social responsibility. What should we do? And who to be included in this kind of communication. So this was 2014 and the ethical board was actually organized within this Japanese Social Society for Artificial Intelligence in 2014. And I was invited to the society, the ethical committee. And we started to discuss, well, what we can do to say that the public that we're not the much scientist. So the conclusion is that why shouldn't we create the ethical guidelines and show it to the public and to start discussion. So you could see these nine articles and the most of them is, you know, it's the common thing that it's really more like the code of ethics to the most engineers, the contribution to humanity or the fairness security act with integrity. But the unique thing is that the article nine, Abidance of Ethics guideline by AI. So it says that AI must abide by the policies described above, above means one to eight in the same manner as the members of JSI in order to become a member of a quasi member of society. So here, in this guidelines, we treated AI as not just a tool, but they aims to treat AI as our partners, not like human beings, but as a quasi member of society. So this article captured a lot of interest and also the criticism, like how are we considering about the rights and also like the obligation or the duty and so that kind of thing. So we were not thinking so much in depth, but I think this is kind of like a unique view, way of thinking about how we treat AI, how we build the relationship between AI or how we collaborate with AI in the future. And also this is another, it's not the same route but also the government is also interested in creating the AI R&D principles. And recently they actually released the guidelines and you could read it in the English, in the website. So I just read the core message from that R&D guidelines. So it was created by the Ministry of Internal Affairs and Communication Japan and it consists of 33 participants and it's not only from the engineers or policymakers but it also includes the lawyers, the social scientists, I'm also involved in there. And so it has, so it also has the nine principles here. So the guideline aims to protect the interest of users and deter the spread of risks, thus realizing a human-centered wisdom network society by way of increasing the benefits and mitigating the risk of AI system through the sound progress of AI networks. And also it, as you can see, they list the principle of collaboration was a first principle. And as you can see, the transparency, safety, security, privacy issues is listed. Also they created the users guidelines, well, usage guidelines. They want to create usage guidelines but before that they did some kind of case studies in these five, six fields. And in Japan, as you can see, we want to within, so not only within the stakeholders like the engineers, scientists, the public or the policymakers, we also want to be involved in the global networks like this symposium. And in October, we held the symposium called AI and Society and we invited the person mostly from the West and we discussed about how we could build the beneficial AI. And to do so, we have the partner from all over the world, like the IEEE or the CFI or the West. And also in Japan, we have a Japan Deep Learning Association which promotes or the enhanced competitiveness of industry by deep learning certification systems. Or like we have like a research center and also the organization who do the research on the general AI and that kind of. So we have a good community building right now and we would like to, I think I'm the only Japanese here in this symposium, so I would really love to have some kind of networks within this, the person who is living, who is now here. And this is my, this is the last slide I have. So I think Japan has really unique ecosystem regarding like the robotics, artificial intelligence. So as you can see, this is Apollo, the pet robot and also the eyeball, the Sony released the newest pet robot. And also, can you tell which one is the robot? So this kind of robot, he created the robot and he also created this telenoid robot. So the very opposite robot. So we imitate in the human beings and just the element of human beings. So these are kind of doing crazy things, but I think maybe we could learn from these cultural differences. And so for example, here is like the box, there is a avatar or the VR and this is the young lady's inside the box and it's kind of like a chat box. But I think there contains a lot of like a gender problem and other things. However, I think, well, we need first to talk about why there exist needs of this kind of thing and what's the problem, what the ethical issues. And that will be very, very interesting to start with. And this will raise many other questions and maybe sometimes solutions to have the mutual understanding among the cultures. Thank you. So hi everyone, my name is Luca Santana from Zabafo Social. And despite the theme of the talk, it's not a good case of the AI or machine learning by the bad one. According to National Bureau of Economy Research, black people weights 35% more for riding in Uber. And according to Harvard Business School, people have 16% more to cancellations in Airbnb. And these are cases of digital discrimination. These two examples are not exactly about AI, it's not exactly about machine learning, but these are examples of how people interact with each other through the technology. The technology should be a good or a natural space, but can perpetuate a bad behavior of our society. Yesterday was said that AI is a black box and really is, sadly. To talk about the black box and the virtual invisible racism, the Zabafo Social media campaign about photos of black people in bank of images. So let's see the first video. Why is white the standard? Why do we need to write the word black to find black people on the internet images search? Can you stop, please? That's what happens on the photo stocks that feed the advertising and editorial market in Brazil. This is the second one. Can you play the first, please? So we made these videos, we sent to bank of images, received some answers, some responses. So as you can see, black is not the standard, is not the standard in white society, is not the standard in algorithms too. And algorithms, artificial intelligence and machine learning are all around us and will be even more. The bias will continue to discriminate people just like happens every day in offline world. But now is invisible and that is dangerous. Now we're going to see the second video with the results with the campaign and think about how to change the situation. The second video, please. Why is white the standard? Why do we need to write the word black to find black people on the internet images search? That's how it happens on the photo stocks that feed the advertising and editorial market in Brazil. Desabafo Social, an NGO that fights racism and for black representativity has recorded videos of real searches and invited the photo stocks to talk about the problem. Some photo stocks replied, others ignored the cause, avoiding dialogue. They repositioned their communication, though. The public opinion helped spread it through, drawing attention from an even bigger institution. We sat at the table to discuss the problem. All those who will look for and question why white is the standard. Bank of images, searchers, we take the first step. But I want to remind you that changing the algorithm is changing reality. So, algorithms are not neutral. So, algorithms are not neutral. There are some European standards that discourage the force. Their decisions made by algorithms must be understandable and explainable. IEEE, which I remember, has a huge role in it.  They are the agents that can change the situation. And I'm glad they are here. So, these machines should be trained with diverse data and should be built by diverse people, which they are in right now. Nothing is going to change if the same black boxes are made by white men from Europe and North America. And nothing is going to change if the same people are here discussing how this box can change with technical language. We need to pay attention to what black feminists are saying. We need to pay attention to what trans feminists are saying. We need to pay attention to what Africa is saying. We need to pay attention to what Latin America is saying. And what and how are they complaining? So, right now we need to think. You need to build some new and more black boxes and let's eradicate the machine together because change the algorithms, it changes reality. Thank you. We're ready for some questions from the floor. Thank you very much for the three presenters. I think we have in Spanish some ideas on what can be done to address the issues of inclusion. You can do stuff. You can create data. You can train data openly. You can attack the silos of communities and make stakeholders talk together and reach more consensual and more inclusive decisions. You can start a debate with those who create algorithms and point them to the direction they might not have spotted and some of them might reply to that. Questions on the floor? Please raise your hand. We have mics on both sides. Comments. There's one there. Victor Akiwande, IBM Research Africa. I just want to talk about one initiative that I really like, which is the AI for all initiative. I think more and more we need to see such initiatives take ground. Initiatives that actually try to spread, educate a generation, a next generation of AI technologies and actually push for that to happen. One of the challenges is the very, very few AI experts.  This is also a real challenge as well. For example, researchers, professors, they leave research academia and then go to industry. How can we come to an understanding that it's actually possible for them not to... More and more we need to see more researchers, academics actually work in industry but not exactly leaving academia completely so that we have some way of ensuring that that knowledge is actually spread across and not just concentrated within silos. More hands? Thank you. Only to congratulate your work and also to share that this story from Jezebaphos Ossial remembered me of the famous the sheer lay cards which were used by Kodak as a standard for photography since the 50s if I'm not wrong. And only around 2000 they made a new card with women with other colors because the whole standard of revelation of films that we use for half a century was based in a white woman. So until today when filmmakers try to use and film black people they have to adjust these and this is an example of technology reproducing the bias of a racist society and I think algorithms are not free of that so congratulations for raising this. I hear someone there. Is this on? Yeah, these are such amazing examples. Thank all of you so much for sharing them and I am really especially encouraged by the work that Jezebaphos Ossial has been able to do following the project for a while and it's really great to see it getting more visibility and calling attention to that inequality. I just wanted to kind of like add on to the table. I know I've mentioned it before at the conference but the work of Joy Bulamwini who's with the Algorithmic Justice League who's been looking at the intersection of race and gender in Algorithmic Bias and so one of the suggested readings for this conference was Kimberly Crenshaw's classic paper on mapping the margins and on intersectionality which is very, very specifically as we all in our different respective spheres take forward the analysis and the critique and especially the audit of Algorithmic Systems and the auditing of Algorithmic Bias which means taking systems in different domains of life and analyzing the way that they distribute harms and benefits among different kinds of people. Joy's work and other people's work can really make us focus on the fact that we can't only look for single identity categories when we're looking for bias. In other words, Joy has demonstrated that in the face recognition and gender classification systems the three most popular ones they do best at mapping sort of white male faces, second best at mapping white female faces, third at black male, and fourth at the bottom is black female faces. So if we don't pay attention to the way that intersecting inequality of race, class, gender and other axes, disability and so on the way that they work together even when we're constructing our audits we're not going to actually be effectively monitoring the inequalities that AIs are reproducing. We have one question here. I was very intrigued by that by these nine principles or whatever you want to call them. When you said that you see that AI is also bound by the first eight and that you see that as the idea of regarding AI more as a partner than as a tool and I would just like to ask you to tell us a little more about that to elaborate on that because there's that huge discussion about attributing agency and free will and what will you do AI and that's very contested and it seems to be a very different concept that you have and I'd just like to know more about that. Yeah, so yeah, I think this is really controversial the nine article is very controversial and we actually when we created this principle we had a debate about whether we should include that into the principle. However we thought we there's if you go to the website on this ethical guidelines there's is writing some statement or like explanation why we include this article nine and he said that considering that the Japanese people traditionally considered like the robots as the friends like the Raya Mon and the Astro Boy and other stuff rather than Terminator and that and many Japanese researchers actually aims to well read that kind of cartoons and animation and their motivation to research is to create that kind of friendly AI and so that's why he wanted to include well most of the AI researchers wanted to include this into the article. However so like whether we put the robot rights or like whether the robots has duty or that kind of issues is really very kind of getting messy right now. However so we were not actually seriously discussing that into the depths. However by writing down this article we want to start the conversation with the public well what is the what is the relationship between humans and robots and how we could promote what is the friendly AI or what is what's the element we should study or like so what is the security what is privacy and that so on. So this is just a start to discuss the thing and I think we are just writing the code of ethics and how we motivate to create that kind of AI. So next step what we have to do is like to think about the AI ethics so is it really so what to so right now we the ethical board only have like a social scientist and also the AI researchers but we need to include the lawyers and the policy makers and other stuff to discuss this kind of big issue so we are still on the in the process of discussing it but I think posing this kind of concept like AI as a partner is really interesting to discuss and it has something to say from this East Asia. So I think we have one question there here and then I'll get two more and then five more and then I close. Hi Joe from Amnesty International it's more just a comment just to say thank you for the inspiring examples I couldn't agree more that what is needed here is building a real movement of citizens who are in various different ways challenging the power structures that exist at the moment and trying to empower people to understand how these systems are working and how we can hold them more accountable and more transparent. I mean I think it has to be a combination of what we've already been talking about some of the long term research that builds the evidence base but as well as that combined with some of the great sort of short term campaigns on particular companies like in the example that you gave with the Shutterstock and others and I think looking forward a little bit I would also think I also think it's important that we that we demand transparency through freedom of information requests and testing the legal limits of the existing legal frameworks to be able to reveal as much as is possible in order to then subsequently find where there are gaps that require new policy solutions to resolve. I will just speak, those who can hear can hear. I also just want to echo everyone and thank you for this really inspiring examples. Lucas the work that you're doing is fantastic. It reminds me in 2013 when Google had a similar problem with auto search complete because if on Google for example if you typed in female scientists it would be interesting didn't you mean male scientists because the category of female scientists did not was too rare for people to be searching for so they presumed that you had made a typo at that point. But I do actually want to highlight the point that I also find it really reassuring that when you reach out to these companies there is equal willingness for them to have a conversation. I think oftentimes this becomes this very stakeholder thing like the moral vanguards of whatever and I think it's important to realise that these are questions that are equally hounding people who are working with big databases and algorithms and sometimes we just have to trigger them into having those conversations. Sometimes it needs more pressure more public policy work or public advocacy work but I find it really reassuring that there is an opening up of space for conversation like that so thanks for highlighting that in that instance. Thank you very much. I just love the examples that you were talking about and I know that Mozilla is not a regular kind of a company but it is still a company. I'm just very curious about how are you imagining the kind of examples that you are giving as having democratisation potentials just because of the cost that goes into these things. So just to give a brief example, two weeks ago I was at the University of Illinois Urbana-Champaign campus which is one of the five supercomputers in the world so I visited deep waters for example and they told me that this supercomputer which is housed in a land grab university in the US and supported by the university is so expensive that their students can only afford to use 8% of its computational power whereas everything else has to be given to the different kind of people who can afford to pay for it. We talk a lot about empowering civil society to take back the tech. We talk about how we should all be using AI applications but the incredible amount of cost that goes into computation makes it almost formidable for a lot of smaller organisations in the global south to work together towards it. I'm just curious how you would respond to it but also give us an idea of what next steps could be for smaller organisations especially organisations that are not necessarily dealing with AI as the core focus to kind of reappropriate and get assimilated into this network and ecosystem. I have time for more two questions. There is one person there waiting lots but we start there and then we end there. Sorry for the short time. Let me make it easier for you, it's not a question it's just a quick comment. To people talking about AI as a partner versus as a tool this is a narrative that is not very well known to a lot of people but it's actually very, very common in East Asia and I think that we have to keep an open mind when we think about this and approaching this because it's something that is very kind of new and unique but I feel like Arisa was telling about why is this happening? There are so many different considerations and this is research that I do about the emotional need for meaningful companionship through artificial intelligence and there is a, I mean the hologram at the bottom people testified that it was the most meaningful relationship they've ever had and the robot that looked like a weird kind of kid thing is a robot that is used to prevent dementia for the elderly and there is this really interesting narrative about AI becoming a partner which I think is going to become much more prevalent as we progress. It's very common in a certain area right now but I think it's really going to change and I would like to encourage people to think about this kind of thing and also to think about the implications of including AI not just having an eye on inclusion also including AI in the sense of AI becoming a quasi-member, even member of society. Thank you. So let's get a last question over there and then I'll go for a final round of closing remarks. Thank you very much to all the speakers. Your presentations were very interesting. I guess my comment, my name is Juliane and I'm from Uganda and my comment is in relation to case studies, the African problem would be how do we make the few case studies more visible such that the AI and inclusion discussion can happen there as well. Thank you. So should we do a final remarks? Mark, do you want to start? Sure. Thank you for all the comments and for inspiring to hear you guys as well. I think on Nishan's question maybe as then to wrap it into some other things in Mozilla as maybe most people here already know is a non-profit and was kind of founded as a non-profit with this idea that there was going to be no other way to take on Microsoft in the browser market than through open source and through volunteerism and Firefox has been successful a different economics than in the beginning but I think that's still the roots that we imagine people getting together to challenge the centralization of power on the internet is something that is worth doing and can have an impact and so there's two parts of that in how we do it and the first part relates to your question Nishan. One is as a social enterprise and then the other is more trying to encourage things like what Lucas is doing as a social movement part of the organization. So we have both of those aspects of the organization. On the social enterprise and the technology piece I think the computing power one is one of the barriers in terms of people getting into AI whether it's civil society organizations, small entrepreneurs even medium-sized companies in places outside of the big five tech companies in the US and the one that we're first poking at is the machine learning big data sets as a thing that maybe we can democratize more and that's even a tiny experiment so that's a theory of one thing to do you know the question of how you deal with the computing power one is there large parallel distributed systems that citizens could build so could you imagine SETI at home but for citizen AI or for small company AI or as cooperatives so I think those are exactly the kind of questions where huge resource centralizations whether that's through whatever NSF or DARPA funding is paying for that supercomputer or what Facebook is able to do in data centers we do want to find ways and push on that to counterbalance power and I think it doesn't have to happen we may fail but I think there's a lot of people starting to say now how do we reconceptualize the idea of free software as a distributed disruptive force in the era of AI so we don't have all the answers to that that common voice or other things that are starting to ask that question but to your other point how do smaller civil society organizations have an impact I mean that's amazing you guys build on the history of basically consumer activism in a way that is very fresh and related incisively to specifically the everyday experiences we have of biased algorithms and like you're not talking about the abstraction of algorithms these are the products we all think people did an image search of any kind in the last month in this room right these are the everyday realities of the technology we live inside of and so I mean that's the other side of it you don't have to get into open source AI to have an impact on how this is doing I mean you guys are having an impact so I give the word to Arisa and Lucas and just technology if you saw a woman at the end of the movie is Moniki she's sitting over there so I can also talk to her after the break Arisa do you want to closing remarks Lucas so about the question of Sasha yes you are 100% right about it we need to we need to bring more conversations and bring more problems this campaign was about one about race it's about social ideas a race and Joe a race we talk a lot about race and this is why we did this and about talking to talk with companies after this video after this campaign we had a day with Google to talk about this campaign about all the other campaigns with other problems so we are trying to talk and start a conversation with companies too which is really important so thank you so like when I came from Japan the discussion in this symposium this conference is somehow really new to me because Japanese we have many people living from the Philippines, Brazil or the Koreans however Japan we talk to Japanese and every Japanese well East Asian people have the same color and we say that there is an algorithm bias but it's not a real problem however when I hear many discussions here I think it's really important and I think Japanese also have to consider and contribute to this kind of communication so I would really love to continue this kind of discussion and maybe we could talk about AI as a partner or tool and that kind of things next time like tomorrow or this afternoon thank you so thank you very much for coming one last advert tomorrow 9.30 to 10 there's breakfast here so there's something the program called informal meetings and other things there will be food so you can talk and eat so 9.30 tomorrow there will be breakfast here thank you very much for the panel thank you very much for the audience and see you later, bye bye


Subunit 3.5, Implementation, and Phase C. Implementation includes the final phases of the system lifecycle. During implementation, the system design is completed, the system components are built and purchased, the system is integrated and tested, and then operated throughout its defined lifespan. As you enter implementation, there are two primary tasks to be performed. One is to complete the system design. One example for additional design considerations for a flight system is the launch vehicle interface. So again, you went through a preliminary design review in Phase B, but now you have to complete your design. And so during this completion of the design, one of the things that will happen is NASA doesn't usually assign a launch vehicle to a mission, in many cases, until you get into the implementation phase. I think there's a consideration that not all missions make it through the first phases and get to implementation. So either they end up running over cost or falling behind schedule or there's some assessment that they can't be technically completed. And so there's a hesitancy to actually assign the contract for the rocket to launch this satellite until you get into implementation.  So once the rocket is chosen, now the team has to go back and complete the design of the satellite to make sure it will properly interface with the rocket that's been chosen, the launch vehicle, and that it also is going to be able to survive the launch environment. Every rocket has a different launch environment, the vibration environment, the acoustic environment, and again, the technical interface between the spacecraft and the launch vehicle. So the satellite team needs to go back and refine their design to make sure it's going to be compatible with the specific vehicle that they're going to be launching on. So once the design is complete, the remainder of this initial part of implementation or phase C is going to be focused on procuring or building all the required system hardware and software components. As these components are delivered, they must be evaluated or verified to ensure that their capabilities and performance meet defined detailed requirements and that the interfaces meet the defined interface requirements. So again, you're building all these components or buying them separately. Now you want to make sure you've defined how they need to perform, say a thruster. You've defined how much thrust needs to come out. And you want to take that thruster you just purchased and make sure that it performs the way you wanted it to. You've also defined how heavy that thruster can be. So you want to put it on a scale and make sure that in the end, the requirement you had for how much mass the thruster could have is being met by the thruster that you purchased. So you're verifying that this thruster you bought can meet all of the requirements for performance and capability that you've defined earlier in your requirements development process. And so the other thing is, remember, this thruster has to plug on to the system and so it might have to interact with the power system and has to interact with the rest of the propulsion system and those interfaces were defined in requirements. And again, you want to make sure that this thruster you purchased will be able to meet all those interface requirements. Now, again, in this phase, you're not actually building up the satellite yet. You're just procuring the individual components and making sure that they meet their requirements. The actual building of the satellite will come a little bit later, but at this point, your verification is all being done at the component level. Phase C activities include developing a baseline detail design that's compatible with all the requirements down to the lowest level that you've defined in earlier periods. Completing plans for system integration and testing. This will be the part where you're going to actually put the system together and test that at a full system level, it operates properly and you have to have all your plans laid out in phase C to be able to do that. Procuring or building all of the system components so you're verifying the capability and the performance of each one of the components against these requirements. You're also looking again at the component interfaces to make sure that they're all clearly being met by the component that you've actually purchased looking back at your requirements. You're preparing the launch site check out an operations plan and the post launch operations and activation plan. So even though you're far away still maybe from launching, you are starting to make sure that the system as it's maturing that you've got a clear path on how you're going to actually launch and operate the system and that's being defined at this early stage here in phase C. You're going to now go into implementation and actually take all the work you've done on requirements and design. You're going to finish up your designing and now you're going to actually start to build. And so when you think of a curve of cost, you could see that over the time in this lifecycle cost has been going up right in pre phase A. You were spending maybe a minimal amount of money in phase A and then in phase B you were continuing to increase your staff to bring on more people to do more analysis more experts in certain fields to help you get to a higher level of fidelity. And now once you've been approved that confirmation or KDPC key decision point C to move on into implementation now you're really ramping up because now you're going to start to put out procurement sometimes right after the key decision point meeting people will run out in the hallway and start making phone calls because sometimes there are long lead parts pieces of equipment for the spacecraft that have to be procured very early you guys are going to take a long time to manufacture and they could become something that holds up your schedule and so as soon as you're told you're go to go through implementation you'll immediately start to buy your long lead parts because your design has been maturing you know what you need you're now given the go ahead which means you have the money to go through the implementation phase and so you're moving on at that point and so in phase C you see a picture here of the fuel tank for one of our missions this was from the Solar Dynamics Observatory which is on orbit in geostationary orbit this is what's happening in phase C you finish your design and now you're actually bringing the parts together not the full spacecraft but the parts of the spacecraft and you're testing them at the component or subsystem or element level so here you see a fuel tank it's got heater ribbons around it the silver ribbons are actually heaters on the tank there are thermistors connected so now that tank is going to have to connect up to both it's a propellant holding tank but it's going to have to connect up to your electrical system your computer systems a lot of connections to this tank but you're only working at the component level to make sure the tank is right it's built right and that it survives its environment so what you're going to do is take this tank and you're going to put it through and vibrate it you're going to put it through heat and cold and you're going to do all the things that it'll in the environment it will need to survive both for launch and on orbit you're going to test it at the component level you're going to make sure that this component of the spacecraft has been built properly you go back to your requirements that you originally set back in phase A and B and you make sure that you can verify that the requirements have been met by the specific part of the spacecraft and that's what phase C is really focused on is that all the components meet the specifications that they were set out to meet so you can guarantee that you can more or less guarantee when you put the whole spacecraft together it should be able to operate the way you expected the primary technical review in phase C it's the critical design review and there's also a system integration review and we'll talk about both of them so the critical design review we talked about a preliminary design review back in phase B and so similarly there will be a series of lower level component and subsystem reviews that are looking at the design maturity here in phase C but that series of reviews peer reviews and subsystem reviews will all lead up to a major system review called the system critical design review and so at this point you're going to be looking at all the the detailed design work that you've done and the external review technical review team will be coming in to assess whether your design is going to be able to meet the requirements that have been laid out for it with adequate resource margins and an acceptable level of risk and so they'll be looking at things like you've defined a certain mass for this whole satellite can't be heavier than a certain amount or the launch vehicle won't be able to launch in and so they're going to see that does your current design and the way you've laid this out look like you'll be able to complete the work to build the spacecraft without going over that mass limit that you have for the system design the interfaces and making sure that they've been matured enough to proceed with the fabrication, assembly, integration and test of the system they'll make sure that the system documentation is mature enough to allow for proceeding with that fabrication, assembly, integration and test as well so do you have all your test plans and all your test procedures identified and the integration process is well defined so that they can say that it looks like you can mature through and get the system actually built and integrated they'll also be looking to see that your system verification and validation plans are complete all the way down to the lower level to make sure that you're going to be able to both verify and validate the components all the way up through the subsystems and all the way through the full system as you start to build up the spacecraft during the next phase they're going to look for adequate technical and programmatic resource margins and make sure that again you can within the money and the schedule you have available complete the development and not have to end up asking for more funds or not meeting your launch date or whatever your other constraints are they'll also be looking at your risks to make sure that all your risks to mission success have been identified, assessed and that you have plans and the resources in place to effectively manage those risks they'll also be looking at your safety, reliability quality and other concerns that may impact your mission success and that they've all been identified and considered so this is all again right before you're going to go off and start to integrate and build the system this critical design review is kind of the key review to make sure you're ready to take all those components and start to build them into an actual spacecraft and so this is really a major milestone in the development of a new system and this is where you may have already prior to the critical design review started to procure some long lead parts and there's always a process that says gee, if I haven't finished my design should I really allow anyone to go off and buy any long lead parts because maybe those parts will be the wrong parts once I've completed my critical design review because the design may have changed a bit well in some cases it'll take years to develop flight hardware and so sometimes you have to build some of the components before you actually get through this review but what'll happen is that'll have to go through a review cycle of its own to make sure that every one of the stakeholders approves that yes you can buy some of this material early because it really is critical to do early and the requirements in that area are pretty well defined and we assume it would be safe for you to go ahead and buy those things early otherwise the critical design review is really that key review where after that review you're kind of given the go ahead to buy all of the rest of the components because after the critical design review has been approved it's now assumed that you have a design that you'll just be procuring and building from and so at this point you kind of have to go ahead to go ahead and get all of your other components purchased and then you'll be able to start to put the system together now again at the end of the critical design review the design review team is going to want to know back to the preliminary design did I close out all of the actions so did the design team look back at all the preliminary design review actions that were assigned and did they close them all out to the satisfaction of the review team so again that'll be kind of one of the factors that are reviewed in critical design review as well the second technical review that happens in phase C is the system integration review this review is a predicate to going into the integration phase of the technical development of the system so right you've got all these components and you like to now put them together and create a system out of them and so a system integration review says okay let me bring that technical group of experts in one more time and say are you really ready to do that and so you've passed through your critical design review you're headed off and now you're ready you think you've got everything ready to go ahead and start to integrate and so the system integration review the technical review team will look at things like your integration plans and procedures to make sure that they've all been completed and approved internally by the team so they're going to want to look for that the managers have all signed off on their plans and that you're all ready to go you've got all your procedures on how you're going to actually take the components and turn them into a system your component verification results are satisfactory so when you did previous to this you've done a lot of testing of the individual components that you're bringing in to verify that they meet their requirements as defined at the lowest level and so the system integration review includes looking to say did they all pass the reviews are you really ready to go forward and start to integrate them into a full system integration procedures and workflow have been defined and documented integration personnel have all gotten required training a lot of satellite integration happens in clean rooms very clean environments and very closed in spaces there's a lot of heavy equipment involved so a lot of the integration people that'll be working on the project have to have specific training on working in close quarters working with heavy equipment working in a clean room environment and so you have to make sure that everyone's certified to be in the environment that all has to get done and the technical team wants to make sure that that's done prior to actually starting the integration so it doesn't hold anything up and again the technical team is going to want to know that any open actions from the critical design review have been closed out as well now one of the things to keep in mind is that these big major reviews there's also they're the key ones they're the ones that are going to allow the system to go to the next level of maturity if you pass through them there's also test readiness reviews so whether you're going to be testing a component or testing a full system there'll be some test reviews done right before you go into each test phase to make sure that all the defined procedures are clearly you know they're all approved they've been all developed well before you go to that specific test and start to perform it so those are all done at a very low level with engineering peers just a few people sitting around a table sometimes and making sure that you're ready to go into that test but so what we're talking about are the major reviews where a big external team is going to come in and really make sure you're ready to proceed there are no additional resources for this subunit feel free to skip ahead to the next video


Predict the next three NBA Champions. Celtic, Celtic, Celtic. Three Pete, Warriors, Pacers, Nuggets. Bucks. No, not Miami Heat. You think the Heat for real? OKC is too young of a team.


Prigozin demands urgent action to repulse in regions bordering Ukraine. In an attempt to attract the attention of the military political leadership of the country, the founder of Wagner Group, Yevgeny Prigozin, is sounding the alarm, insisting that it is too late to think about the security of the Russian territories bordering Ukraine. Urgent measures are now required. Prigozin confirmed to the media that on the basis of the Wagner Group in the Kursk and Belgorod regions, the training of the militia is underway. There are no incursions from Ukraine. However, the situation in the Bryansk region is different. On April the 6th, Ukrainian military group tried to break through there. Yes, indeed, we are preparing the Kursk and Belgorod regions, he commented. Perhaps the guys who have received certain skills from us are more active in organizing patrols and protecting their territories. Therefore, the enemy mainly makes raids on the Bryansk region. As Prigozin emphasized, as regards the global security of border areas, it is necessary to seriously think about this now. Now I would say it is too late to think about it, but you need to take urgent measures. The founder of the Wagner is sure. In this regard, he clarified that if there is a request from members of the public, Wagner PMC will be ready to provide appropriate assistance. Today we have the capacities that are necessary for training, deployed and operating. Prigozin added, quoted by his press service.


I'm Cooper and I'm Sydney and February's What's Neat starts right now! The What's Neat Show is sponsored by Caboose, sharing our passion for trains since 1938. This is What's Neat for February 2019. I'm your host Ken Patterson and this month we've got a really good show. First of all we visit with Intermountain in Longmont, Colorado. We talk briefly with Ron Angstead who shows us around a little bit and tells us some exciting news coming up for 2019. We also look at Art Lortz, beautiful O&3 layout. Now I've modeled in that scale for the last couple of years and it was a breath of fresh air to see Art's completely finished layout running. It was just beautiful. We also go to the Colorado Model Railroad Museum out in Greeley, Colorado and we look at the electronics and the electrical systems and the computers that run that layout. That layout can run itself with up to nine trains on a single track main line and it's absolutely fantastic to listen to Randy Palmer explain how the system works in depth from start to finish. We actually take a few trains out on the layout and I've got to tell you it's fascinating. So with that I want to say thank you to all the folks that watch the What's Neat this week podcast on Saturdays. We've recently won an award from James Wright, the People's Choice Award for the best video podcast. Thank you very much James for that. And also Lionel Strang won top honors for the first place in audio podcasts. I'd also like to thank Caboose in Lakewood, Colorado for sponsoring the What's Neat show. We've recently filmed the What's Neat this week podcast number 60 out there at the store. It was a breath of fresh air to see all the products, trains, wall to wall, everywhere you look. It's a great place to visit. If you haven't checked it out you need to do that. Also you can order online at MyCaboose.com where they've got over 145,000 different items and skews of available model railroad products. So with that I want to say let's start now the February 2019 What's Neat. For this segment of What's Neat I'm standing with Ron Eggstead in the middle of this beautiful facility here in Colorado. Where are we? We're in Longmont, Colorado, aren't we? Yes sir we are. Ron, now you've been working with your father and inner mountain for a good, I want to say almost 30 years now hasn't it been? Yeah that's correct. I was gone for a while for about 10 years with another full-time opportunity but I've always been a director of inner mountain, I've always been very interested and passionate about what's going on here and we're excited. We're really excited that Ken made it over here to Longmont. I think he endured a massive snowstorm in Kansas last night, is that right? That's more fun than anybody should have. It was great. But you've been here with your father side by side and you're known for some of those magnificent locomotives and freight cars in end scale and H.O. You guys are very diversified plus now you're setting up some new ties in China and you're expecting some of the most exciting stuff to come, isn't that right? Yes it is and thanks for the kind words, we appreciate it. We've always had a high standard, a high bar, we want to keep improving our own product line and helping to improve the hobby and my father and I have had the opportunity to work together for several decades and we have a lot of fun. Ken was asking me, what am I most passionate about and what do I like most about the hobby? It's the people. I love the trains and I love the history and I love seeing and riding on real trains but the research and understanding and learning about the different eras and the different regions of actual prototype trains and modeling has always been exciting to me and I love seeing layouts in different cities when we go to shows and stuff. That's awesome. Now you don't really model anything yourself but are you tempted, are you thinking about it? Well I do have a small secret private collection. I don't run my own layout but we do have test tracks here and I have a lot of friends with layouts so it's been a passion for me and I love the hobby. That's fantastic. It's great to be here and see all these machines. It's like micro-engineering times five from what I've seen in the past and this isn't going to go anywhere, anywhere soon and I want to say that in the next nine months you're going to have new engines and new fray cars and a whole list of things that's going to be new, right? Well that's a great question and a great point and we've talked off camera a little bit about that Ken and you're right. I have personally been to China twice in the last three months. We've qualified and visited a number of factories, we've qualified three of those and we've got nine fray car projects already on the books and purchase orders that have been placed and those will start shipping soon. We've got the Chinese New Year that's going to slow things down a little bit but early in the spring we're going to have a lot of product flowing. That's awesome. I'll tell you what, I say it over and over again and I'm going to say it again. This is the best hobby in the world and it's because of guys just like you that make it good for the rest of us. Thank you for taking this moment for the What's Neat guys. We appreciate it. We appreciate everything you're doing for the hobby too Ken, it's a pleasure. Thank you Ron. Thank you for coming to visit us. Alright and that's this from right here in Inner Mountain. This segment of What's Neat, I'm standing with Art Lort in a beautiful narrow gauge empire. Now this is awesome. This is the same scale that you've watched me model over the last two years on What's Neat trying to work it into my HO layout. But Art has built this from the ground up to be a narrow gauge layout and a standard gauge layout. HO and three beautiful stuff. Art, I am so impressed with this. I don't even know where to start but tell us a little bit about this layout. Kind of what's behind it inspire us. Well it was inspired by being on Marshall Pass when it still had some track on it. And like I said I got hooked on Marshall Pass and I think right then I decided that was what I wanted to model. So over the years there have been many, many books published about Marshall Pass and the Salida area and surrounding areas like Canyon and stuff like that. So there's no lack of material to learn about it. This is so exciting. I'm telling you now you've been doing this for how many years? Well this layout and this location almost exactly 20 years now. Just a few months more than 20 years. Some parts of the layout were built at my old house and moved out here in pieces. This big chunk of Marshall Pass came over in one piece and the area right along the wall over there came out in one piece and one small section of Salida Yard came out in one piece. The rest of it has all been added since. Now I notice you've got standard switches and you've also built stub switches on those. Does it work pretty good for you? Stubs are a bit of a problem. It's hard to get them to work right and it's probably why the prototype quit using them. But yeah if you're careful you can get a stub switch to work okay. Now we're surrounded by layout in front of us and you've got the lower grades here where the creeks and the water is but up here in the mountains behind us this is tall as we both are. So do you operate this layout with a crew? We do. We typically have a crew of 8 to 12 people that come over from time to time to run trains, have an operating session and perhaps you've noticed we have a car-card yes I saw that system and we do operate with NCE DCC system. Now you said in the 70s when you were modeling this type of stuff I guess you were getting that old brass you know the coffee grinders that you would sit there and tweak and try to make work. That's pretty close. And then what happened when I got into it is we had Blackstone models arrive on the scene and kind of changed the world. It did. Made it easy for a lot of people to get into this. Blackstone changed things a lot and yes I did and I still do have some brass models it's hard to keep them tweaked sometimes and I even have a couple of old MDC model diecast models that I've reworked a little bit and one of them is still in operation the other one's kind of been retired. I know there's a couple cheerleaders on the sidelines that want to see the MDC castings and those things come back because those are out there somewhere. This is awesome you are the first narrow gauge HON3 layout that we've had on what's neat other than the stuff that I've shown the viewers. It's a breath of fresh air to see it from your perspective done very professionally. It's awesome. So I want to thank you Art for sharing this gorgeous work with the viewers of What's Neat. You're most welcome. Thanks for coming. Okay. For this segment of What's Neat I'm at the Colorado Monterey Railroad Museum here in Greeley and we're in the nuts and bolts of the operation here with Randy and Randy's going to start up the computer system and make this layout go and I'm going to let him explain what he's about to do. Randy tell us about this. So what we've got here is we've actually got multiple computers but we run the layout on one computer at a time. The other computer is just a safety backup in case something goes wrong with the first one. I can switch to a second computer and get up and running very quickly. What we've got is we've powered up the DCC and the CMRI. I have booted the computer up so now the computer is ready. So now all I need to do is go into what we're going to do right now we need to move a train. So we're going to go into play mode. Okay. So what we're going to do here is I'm just going to go down to our little icon down here for play mode. I got to find which one is first one. When I boot this up the play mode immediately comes up and initialized all the nodes on the layout. It's asking if I want to do a test on each node and for this we don't need to do the test. What the system is doing now is cycling the entire layout five times in a row to make sure everything's up and running everything's happy. Once that's done computer systems up and running. We are now running in play mode. All the switches and everything are controlled out at the fascia. This allows us to move trains around clean track stuff like that. This is what we do when we just we need to move things around. The screen here shows the entire layout all the switches all the signals on the entire layout. It will show occupancy. That's what these reds are down here. Our trains are actually on the track. The screen shows all the boosters that we have. They're all green. If a booster word is short its name would turn to red. We also have a way when we're doing cleaning track and stuff we can mute the sounds on the layout so that we don't have to listen to all the sounds all the time. I also have test modes on this program so that I can turn all the signals to green on the entire layout. I can turn all the signals to yellow. I can turn all the signals to red. Or I can tell the signals I want them to cycle. And in the cycle what it will do is every few seconds it will change it to a different color so we can go out and make sure all the signals are working properly. Randy where did we get a system like this if we wanted it? This was actually a hand. I wrote this entire system. It's written in Microsoft Visual Basic 6. I wrote the entire thing. The reason I used Visual Basic is because I wanted this screen to show everything you want to see. This is fantastic so now what do we got to do to run a train? Right now the system is up and running. We can go move a train. We actually need to move a train to prepare for our docent run this afternoon. So we can just grab a throttle.  We can throw the switches out there on the layout right there where they're handy. Or if we want to we can actually throw the switches from in here. Now does the CTC board over there also throw switches? Right now in play mode the CTC machine is not running. Okay. The lights and everything on it are running but for play mode the CTC machine is not used. That's smart so it's a separate system where this can just run the layout. This runs just the layout. This is magnificent. Now how long did it take you to come up with how to do this? This was actually the first program that I wrote and it probably took me five or six months to write it. Get it to see all the occupancies, work with all the switches, work with all the signals. The hardest part was drawing the whole screen out because every switch that you see on the layout is actually consistent of nine different parts that I had to hand lay to the right position on the screen so that when you throw a switch it looks like it's thrown. That's amazing. You said you've got 19 NCE power supplies powering up the layout broken up into segments. Yes. There's 19 around the layout that power up the whole layout. This is a lot to keep track of. I would say that every model railroader needs one of you. We all need a Randy in our layout rooms but what a magnificent system that you can share with us. It's almost something that we can't have but the knowledge that something out there is like that. It's amazing. You made it. Yes. It was a lot of fun to write. It was a lot of excitement the first time I got a signal to turn colors. I was like, yeah, it actually works. You've got a passion for trains and electronics both, don't you? Yes. I love writing computer programs and stuff. That's been my whole career is computer programming, computer databases and so I love to figure out how to get the logic to work and all of it to work. Wow, that's great. So we're going to go run some trains, right? Yes. We got to go move a coal train. Okay, let's do it. This is going to be fun. Yeah. We like that train. 7 is the east A, 1407 UP cattle train. That's what you do. So Randy, we just saw you pull out the Rio Grande train out of that yard back there and that's the switch yard where the computer actually reads those tracks and knows what trains you've got. You've got seven trains. You had to pull out one of them. Now tell us what you're going to do for the last six trains. Okay, what we're going to do is the DOSM program has six parking places. We want to run seven trains today. Okay. So we pre-position the coal train out on the layout. So now what we're going to do is we're actually going to stop the play mode and we're going to actually start the DOSM program and what the DOSM program will do is it will actually read the layout and find all the trains on either its staging tracks or out on the layout. So it'll find seven trains. So we can do that simply by closing the program and then we open and I've got to get my glasses. I want you to get in there. Let me let you get closer in front of the computer. I'll go on the other side of you. Is that right? Okay. So we're going to start the DOSM program and what the DOSM program does, again it initializes all the nodes and then it'll cycle the entire layout five times. I see that. And while it's doing it, it's finding all the trains. So there are the seven trains that it found. Six of the trains are on dedicated parking places for DOSM. So we have this little sheet here where we went and confirmed where they are. So eastbound is and we have to tell it the right locomotive. So this is 1407, let's be up here 1407. And then on the B parking place is train 4811, 4811 and you guys wrote all this down here didn't you? Yeah. So that's what Michelle did. She went back there and found what trains are sitting at what parking places because we have to explain to the computer where the trains are. That's interesting. So west was 1404 and then the Christmas train 1101 and 75776 and then in Klamath Falls is a train we pre-positioned and it is the 5351 coal train and it is eastbound. Now let me ask you, is this a system what you've designed here where you could actually turn it into a product for model rotors to use somehow? Could you box this? We've discussed about this. This is actually custom written for the Colorado Model Railroad Museum but the theory could be applied to other railroads. I mean it's the same theory just you'd have to tell it what the tracks look like. Sounds like you need one of those open source Facebook page websites where you could share this with us and those guys would take it and run with it wouldn't they? Yeah, they probably could. It would take a little bit of work but... Sounds like the next new gadget that we all need. So now that we've told the computers where the trains are, it's all ready to go. All we have to do is tell it to continue. Once we tell it to continue, it starts the layout and what you're going to see is like here it's identified the 5351. It saw that the tracks in front of it are all clear so it's given it a green light to start accelerating. It is now moving. Wow. Wow. Hello. It's also noticed that the train coming eastbound or westbound out of the staging yards has free track in front of it so it's also cleared it to start moving and that would be the train down here at the bottom. So it's been cleared out to Lakeview. As you can see as the train starts to move it will turn red where it's actually occupying the track and as the tracks as it leaves tracks behind it they turn dark red. So you can see where the train's going and where it's been. Up here it lists all the trains and it lists what direction they're going, the train number, the name of the train, where the train currently is, what its current speed is and then what its approved speed is. Like I said, if a train is approved for green speed of 66 it doesn't go to 66. It steps up fourth rattle settings every four seconds to build up to that speed. And then every now and then you can see that all the current speeds go to pink. Every 15 seconds it talks to every train and tells it what speed it's supposed to be going. Nice. So this train's pulling out right here next, right? Yes. This is amazing. I know everybody sitting there looking at this saying I want one. Oh, this is a lot of fun to watch. Oh, this is awesome. This is amazing. We actually have three trains moving right now. This train's been cleared out of staging and he's going to come into Klamath Falls because this guy has gone out of Klamath Falls so it's now available. This train's coming into Lakeview. And so once these get on the main line, will this layout run itself all day? This layout will run itself all day. It also runs the CTC machine to emulate what it's doing because it's acting like it's a real dispatcher. Matter of fact, the subroutine that I wrote, I named dispatcher. And what it does is it does just what a real dispatcher does. It looks at the tracks in front of the train and to see if the train has a place to go and if so, it dispatches that train forward. The trains are watching the signals when they see a green signal, they start to go. I'm taking him home with me. I want this. This is amazing. This is amazing and you came up with this and this is really a big part of your hobby, isn't it? Yes. This is a big part. And I've spent a lot of time writing this program and perfecting it. It's a lot of fun. And now what we need to do is go out on the railroad and watch the trains pass each other because that's the best part. I want to. Yes. That's the best part. Okay. Let's go catch that. But this is absolutely awesome. Wow. So Randy, as I hear a prototype run by outside the building right now, we're watching the models come into a large, long passing siding and they're about to have a meat and your signals are going to help them do this. You want to talk about this? Yes. What he's doing is he's coming up at red speed right now. The computer's controlling the train. He's going to hear pretty quickly. He's going to throttle down to stop because there he goes. He's throttling down. See how he's taking? So this layout's doing this all by itself. The layout, the computer's doing all of this. Now he's going to sit here and wait for the steam engine, which is coming the other direction, to pass him. And once the steam engine passes, then he'll get a green light to proceed on down the mountain. Let's watch. Okay. Randy, I just saw the caboose go past, buddy, and I see these Rio Grande starting up. Tell us what's going on now here. Well, now that the steam engine is passed, the Rio Grande has a clear track in front of it, so the dispatcher has given him a green. He's now accelerating out of Quartz Mountain, going down the mountain. So Randy, coming up on the end of the day, when you shut this layout down, I understand it'll park itself. And I heard about another feature that when the executive director's got to do some business around here, she can push pause or something on this, and they'll park themselves. Tell me about that. Yeah, there's two options. First of all, if Michelle's got a group here that she needs to give a presentation to or something, she can press the button that says stop all trains, and all the trains will be brought to a stop, a gradual stop, and they'll just sit there and wait. Then she can give her presentation. When she's done with her presentation, she comes back, she clicks the button that says resume running. All the trains pick up right where they left off. So these guys are all going to find a sighting by themselves with the help of the computer, slow down and stop. Do the signals change, too? Yes. And the switches all switch? Yes. Signals all switch. The engines are paying attention to the signals, so when they see the red signals, they just kind of go stop and they wait for a green.  What do you do then? Four o'clock today, one of the things we wanted to do was the computer program needs to put the trains back in the parking places. So we'll pick up, we'll click a button that says park in staging. And the computer program will take and capture the trains as they come into the staging and we'll park them in their parking places. So there's three parking places westbound, three parking places eastbound. It will just park the trains back there. When we're done, the trains are all back in staging where they belong. Wow, this is absolutely amazing. This is going to be another one of those videos where I'm going to have to watch it twice. But this is far out, Daniel Coombs. Are you getting all of this? This is the most amazing technical segment I think we ever had on the show. Because this is Cal, right? Oh, yeah, the computer does a lot of thinking for itself. It also has a lot of safety precautions built in to protect both the layout and the trains. If it senses anything it doesn't like, it brings all the trains to a stop and asks for a human to come in and take a look at what's going on before it'll restart the trains. So it protects the trains. This is amazing, Randy. Thank you very much for sharing this with all of our viewers on What's Neat. Well, thanks for coming and seeing it. I really appreciate it. All of the model railroad products seen in this episode of What's Neat are available through Caboose in Lakewood, Colorado, or order online at mycaboose.com.


Hey guys, welcome back to my channel. My name is Sonali, if you guys don't already know me. And today I'm gonna be doing my first ever Snapchat Q&A. And I'm so excited to be doing it with my boyfriend. Hey guys, what's up? I'm Ryan. So this video is gonna be kind of like a boyfriend tag, but the questions are gonna be coming mostly from you guys and your Snapchats. I was wondering if you have any cute couple traditions you do and what's your favorite memory together? I don't know. I would say like going to Mountain Park.  So it's really cute. We like to go there a lot just to like hang out. What other traditions do we have? I don't know. Do we have any? Yeah, I don't know. I don't know if we have really any traditions. And then what's our favorite memory together? I don't know. There's so many good memories that we have together. But I would say just like exploring Atlanta or exploring anywhere in general with Ryan is just so much fun. We've been to like Miami. We went to Miami once. That was really cool because we spent just like a whole day there. It was like jam-packed. And I don't know, that was really fun. But yeah, exploring Atlanta is like one of my favorite memories with you. What's yours? My favorite memories together with Sonali are probably all the spring breaks we went on during high school. Just kind of like a week of being with the teacher. There is always fun. Yeah, that's so good. And with all your friends. And it's kind of like your whole hometown is that in Destiny. That's fun. Yeah, we usually went to Destiny for the average spring break. And oh my God, senior spring break was like so fun because I went in like a whole house with all my girlfriends. Oh my gosh, probably the best week of my life. And especially at the beach, like who isn't happy when they're at the beach, right? So the next question is what is your favorite store? My favorite is Hands Down Forever 21. I love Forever 21. Seriously, I'm about to go buy all of their body suits because I'm assessing over body suits right now. He kind of dresses like preppy kind of like. I'll go to like, I think sporting goods a lot. Yeah, athletic. Yeah, it really depends on what season it is, I guess. Yeah, I love Ryan and Heather Gray Athleticwear. That's just random, but it's really good in it. I got my Heather Gray Quarters at the TJ Maxx. I like that this one. Yeah, that's a good place to go. Next question is what are you planning to do after college? It's really scary to think about this because it's honestly like so close for us. It's like three years and that's after college. I'm majoring in radio television right now. So maybe something with that, but I'm not really sure at the moment. I'm going to school from marketing majors. I don't really know what entails after college, but hopefully something that's high-paying. So that's all I really want. Retweet. Maybe be a manager for Snow. That'd be so fun. This one says, how do you keep the relationship going with long distance? My boyfriend is going to university in the UK and I'm going to Orlando and he advice helps. That is so much worse than we have it. Obviously, FaceTime was a big, big thing for our relationship and it's also really important to just save money to go see them once in a while. He always flew on Spirit Airlines, which their flights are so cheap and honestly, I flew on them recently and I'm going to start flying on them so much more because they're so cheap and it's the same thing as Delta. There's honestly no difference. Next question is, most Indian parents want their kids with other Indians. So I was just wondering if your parents care about you being in an interracial relationship and how did they react when you told them? They're really awesome about it. Obviously, they both want me to be happy and if I'm with a good guy, then that doesn't really matter. Mommy, come here. Do you want to answer this question? What is the question? I just want you both to be happy and you both to get along and you both to have a long happy life or have a long it lasts. And if you guys are getting married, just I will suggest pre-marital counseling. Sounds good. Thank you. Heard it from the source, ladies and gents. Bye. Okay, our question for you guys is what is your favorite Indian food? Because we're proud of one another. I'm going to be honest. I'm like almost totally Americanized. So I don't handle spice as well. So my favorite Indian food is butter chicken and naan. Probably his too, right? No, I like spicy food. Yeah? I love spicy food. So I don't know, I had some wings that I made that were really good. Oh, chops. So lamb chops, I think they were lamb chops. And I had goat curry. That is good. Because Indian restaurant I went to is good too. Yeah, goat curry. I honestly eat a lot of Indian food. It's just not super spicy. I don't see the point in feeding it because it's like torture for me. So the next question is from one of my best friends in my sororities. So if they're a little cray, you know what. Then Holly, I was wondering how many pictures do you have on your phone? That was Charlotte's sister, Marissa, let's see. Okay, 6,473 photos, 223 videos. And that's only because I deleted over 1 fourth of them. So I would have had way more, but it would just get too much. I have pictures from eighth grade. I only have 700 on my phone. What the heck? You cray. Hi, Charlotte here. I was just wondering if you would hunt Pokemon with me. Thanks.   I'll honestly probably jump on the bandwagon sooner or later, but for now, no. Are you ever gonna get Pokemon Go? Question for Ryan. I was wondering if you had any tips on being an Instagram husband. If you guys don't know what an Instagram husband is, pause this and watch the video in my description box below. It is probably the funniest thing ever, especially if you know a couple that the girl loves Instagram and the guy always has to take the pictures. So before she starts taking pictures, put a number limit on how many you're gonna take before you start taking them. Count down four, so she knows how many she has left. Always be ready to take a picture no matter what. I would say just breathe in and out when you get irritated.  This one's actually for your dog. I was wondering if she was ever gonna consider getting braces. Okay, if you guys don't know, my dog has really messed up teeth. Like, her, she is such a bad underbite and her teeth are really, really messed up. I'll insert a picture right here. She's so cute, but no, she's not gonna get braces because she likes the way she looks and I think it's cute.  So that said, are you guys ever gonna get married? Um, obviously I want to get married someday. You never know. And we don't like to jinx anything, right? My question is how do you guys deal with or cope with being apart for a long time in your long distance relationship? It's honestly not as bad as you guys think. You know what I mean?  I go to school all day. Like when we do homework all day. And I have a FaceTime, we go to sleep usually. I just feel like there's not that much time and especially for me like with my sorority and like school. And then if Ryan was like in Orlando with me I would just have to like balance all of that and it would just become so much harder. We kind of like like it just because it's easier for us to manage time between the things that we do have like right there. I would just say like I keep myself busy so my day planner is the day designer. What?  Yeah, they did. It was well worth it. Last night of dinner she penciled me in for this video in here. True story. How did you and Ryan meet? How did you? So unfortunately the sound didn't work on that one. I don't know why, but she asked how did we meet? Yeah, in middle school I don't think we're like Facebook friends. And then she's a cheerleader and I play football and one year we walk together at home I'd be like at home coming football game. Mm-hmm. And I always thought she was so weird. And then we weren't really that close until we had biology the other ninth grade. The way it became like best friend and then sophomore year I was so in denial of liking him. And then I was just like wait I actually like my best friend. Yes, it happens just like the movies. And then he was literally oblivious that I liked him for so long. It took him so long. And then told me. I was like tell me. Why would I tell him? Like oh my God boys literally need you to tell them everything. Like they can't get hints. They do not understand hints. What's the number one reason why you love each other? Love from Missouri. That's so cool. I love seeing like all the little filters. Get your orders from my whole family, Liz by the way. I would say because you're my best friend. Like it was awesome that we were first best friends like for like a whole year and a half or something like that. And then we started like getting into the relationship. So I don't know. It's just like so nice to have like my best friend and my boyfriend all of them. Actually she's really funny and she's the funny thing that was. Only me like 30% of the time. And not me. And sometimes you're annoying so. Hey Sonali, I love your channel and my question is what's your biggest advice for styling your dorm and how did you come up with a theme? Thank you. So I feel like for me styling my dorm was very essential because it's just awesome to have like a really decorated room. I think your bedding makes the whole room. So I just got like a white comforter and just dressed it up with like a lot of pillows and a lot of blanket. And I can link my dorm video down below that I featured 41 Winces products. They're so cute. So I would definitely recommend checking them out. But yeah, I would just say like focus on the bedding because I don't know, that's where you'll be spending a lot of your time, you know? Make sure you have a good mattress copter in your dorm because the beds are user-friendly. Don't hang like all this clutter on your wall. That's what makes sense. Like, I mean a poster here and there is fine but I would just say keep it clean. Whatever you do, don't make a gallery wall. I'm on that. Hi, it's Hannah. I was just wondering how do you find good off-campus housing because there's a lot of choices. First of all, just Google off-campus housing at your university. It's so important to tour it. Like it might look so nice on the website of it but it might not be as nice in person. So it's really important to tour it and then also look at the pricing of them and compare them all. That's how I picked the one I wanted to live at. I would say just find the best amenities for the price. Like where I live has like five pools and like the shuttle, same-off-the-drive downtown. Or Uber because that saves you so much money. And it's like safe. Like I'm in Europe and my college town there's a lot of dangerous places to live. There's a lot of muggings and where I live is pretty safe. Hi, Sonali. I just wanted to know if any tips and advice that you and your boyfriend may have on surviving long-distance relationships in college. So I was wondering how you and Ryan maintain a happy and healthy relationship while you're in Florida and he's in Georgia. So we text like almost every single like hour of the day. You know what I mean? We Snapchat, we update each other on our lives like as we're going, you know what I mean? Snapchat's so fun because it feels like I'm there with him without him being on FaceTime like the whole time at a bar, you know? Enjoy yourself. Don't just like Snapchat people the whole time around and waiting for you. Just being honest with each other. Tell each other where like you're going. Don't hide anything.  Like I'm gonna tell him and go into the frat party now. Hey, Sonali. I just want to know how you and Ryan told your guys' parents that you guys were dating. Love you, bye. Haha, funny story. So I actually hid my boyfriend for like two and a half years from my parents. Yeah, that happened. But I think they like knew the whole time, but like not really. My mom was actually the first one who brought it up to me and I just kept on kind of denying it, but then I was kind of tired of like denying it. And this was after I got into college. So I felt like I could just tell them because their main reason why they never really wanted me to date in the first place was because they thought a boyfriend would be a distraction from school. Obviously it wasn't a distraction because I did get into college. So I felt like I was just like, well, I'm almost out of the house. So here it is. I just said, hey mom, it's my girlfriend. White people. My mom's really relaxed a lot. I know a lot of white people would be like. Yeah. Hey, Sonali. I live in New York, but I'm from Atlanta and my sister and I bond over your videos. And we were wondering if you're ever gonna do a meetup in Atlanta. ATL. I've actually gotten that question a lot this summer. And I don't know, like, I don't think I will this summer, maybe next summer, if everything goes good and if I'm here over this summer. So possibly next summer, but no promises because I don't really know yet. Even in the summer, if you guys wanna meet me. Yeah, maybe. I'm sitting in the market outside. Comment whether you would wanna come to a meetup, either if you're in Atlanta or Orlando because I don't even know where I would do it. So I just wanna see in the comments if you guys would come. Would come. How long have we been together? It has been three and a half years on July 7th. Do you and Ryan ever have issues concerning race or religion or culture or anything like that? Please let me know. Thank you. Neither one of us is super religious. I mean, I'm Catholic and he is Baptist. So I mean, it's still Christian. You should let whoever you're dating believe that I wanna believe anyways. Race, like, no, not really. I definitely got some weird books at the mall. Really? Culture, I mean, he's really good about that. Like, he'll eat Indian food.  I'm definitely not opposed to American culture because I feel like I'm pretty Americanized already. So what are your guys' favorite Starbucks drinks? My favorite Starbucks drink is probably the Masha Green Tea Ice Latte or the Frappuccino Green Tea Frappuccino, yeah. My favorite's a cold brew with cream and sugar. He's so basic. He drinks coffee, guys. I have really relatable signs in my room, so I put first coffee. Then she said, Anne, have you ever had or do you have any pets? That is such a cute mechews! Oh my God. I do have a doggy. I used to have a black lab, her name was Lucky. And now I have a toy-sized Yorkie and her name is Sophie. I have a new name for her every day. I have four dogs and I have two cats. He has a zoo inside his house. The cats, we didn't really, but I thought I'm just trying to show up at our house and then like these pet arms would stay. And then the dogs, they're all rescue dogs.  Are you guys nervous about doing a long distance relationship in college and how did you manage your first year of an LDR in college? We were definitely nervous about doing it. Like, oh my God. Guys, I cried.  I was just nervous because I wouldn't have her like so close, like our houses are like five minutes away. Literally five minutes away.  Wanted to know how you fell in love as well as did you know he was the one? Do you picture your future with Brian? Like I said, he was my best friend and how can you not fall in love with your best friend? In reality, you do date to Mary. So if you're not dating someone that you eventually see a future with then why are you dating them? So yes, I see a future with him. I hope you guys really enjoyed this video. And if you want to see more of Brian then check out my blogs. I actually have some playlists already made up. So I will link my playlist of blogs down below. So I will see you guys in my next video. Bye guys. Bye guys.