Live from Las Vegas, extracting the signal from the noise. It's theCUBE, covering IBM Insight 2015. Brought to you by IBM. Now your host, Dave Vellante and George Kilburn. Welcome back to IBM Insight everybody, this is theCUBE. This is day two at IBM Insight. Check out ibmgo.com, it's a digital social experience. Rob Thomas is here from IBM, CUBE alum. Rob, it's great to see you again. We just saw you last month at Strata and Hadoop World. We've seen a constant cadence from IBM this year, so it's great to have you on again. Yeah, great to be here, Dave. George, good to see you as well. Thanks for having me back. You're welcome, big show for you guys. Focused on analytics, it's one of your biggest, I don't know if Insight is the biggest show you do, but this is the biggest analytics show I think in the business. I don't think there's a bigger one. It's like four times the size of Strata and a lot of excitement here. Yeah, it's a very different crowd. So I was kind of reflecting on all the events that I've been to this year. Started with Spark Summit, where obviously we had a big presence and that is very much purely a data science type crowd. Strata to me was different this year. It was more of what I would describe as a biz dev crowd for whatever reason, which tells me maybe that means Hadoop has matured a bit in that respect. And I think what you see here is a little bit of a combination of those two, plus you see a lot of the Fortune 500 here. These are enterprises that are deciding we have to do something. And it's a really good mix of folks. And I think a lot of people forget less than 30% of companies have adopted things like Hadoop. Spark is way below that right now. So what you see here is this is the Fortune 500 that's trying to figure out where they go from here. No, it's true. I was writing nice comment on the Cubius that I'm riding down the elevator on Monday. It's like, oh, JP Morgan Chase. Oh, Citi, oh, AIG, oh, South Africa National Bank. All your big customers are here. You don't get that at the other shows. Like you said, you get the hoodies at Spark Summit. Strata really has become a biz dev show. A lot of vendors, you know, showing it where. Vendors talking to vendors. Well, I mean, there's still some practitioners, but remember it used to be the data science show early on. Yeah, exactly. So what does that tell us about how the market is shifting towards Spark? You guys had some Spark announcements recently. Maybe fill us in on those. Yeah, we've been busy since we made the announcements back in June. We continue to build up the Spark Technology Center in San Francisco. We announced this week that we've now enabled 15 products in IBM on Spark, ranging from commerce applications to some cloud applications to things like SPSS, you know, advanced analytic algorithms running natively on Spark. So we've been very busy. You think back to the strategy that we described to the world on Spark. It was one is we're going to invest in the open source through the Spark Technology Center. We've certainly been doing that aggressively. Two was we talked about getting IBM running on Spark, which is, you know, an example of what I just shared. And then third was around how are we going to educate the world and help drive the data science revolution? So we've been very busy on that aspect as well. So yeah, I was saying off camera, we had Mike Tamir on and talking about that imbalance in supply and demand. And he was saying, I'm all worried about that ever, that there being a glut in data science. There's so much demand right now. I wonder if you could comment on that, what IBM is seeing. So it's been crazy. So we committed in June, we said we're going to educate a million data scientists. We are at 310,000 as of this week. And that's through avenues like Big Data University. It's through offline courses we did. We know we did a two day course at Strata. Think about it. That's pretty amazing in a four month timeframe. The summer, I thought people were just to take the summer off. I guess that doesn't happen anymore. That's how much interest there is in this topic. And I think part of that is because traditional IT skills are under threat, is my view. Between cloud and data, that puts a lot of pressure on traditional IT operations, that type of thing. And so what I see is these kind of folks coming to the table saying, it's time for me to do. I'm going to reinvent my skills and data science is the place to do that. So one follow up on something that you said earlier, you said enable a number of products on Spark. You mentioned SPSS an example. Just taking that example, what does that mean to enable products on Spark? So think about it this way. Historically, if you were doing advanced analytics, whether it was with something like SPSS or something like SAS, step one is move the data into SPSS or move the data into SAS so that the programmer can actually then work within that environment. What we've done is we've taken all the intelligence of SPSS, the machine learning, the algorithms. We've pulled them out of SPSS and we've built them directly on Spark using things like ML lib. So now you can run those same algorithms where there's a lot of people in the world that know those and you don't even need to use the SPSS engine, if you will, you can run them right there on Spark. And the reason that's valuable is think about it. We view Spark as the analytics operating system, pull data from anywhere, whether it's a mainframe, a warehouse, a database, you can now use your SPSS algorithm in one place and access all those data sets. So you're extracting sort of capability and function from the app essentially in that example, embedding it directly into the workflow. That's right. So it can scale? It can scale and it's much faster. You think about it, you no longer have to work with a data engineer to say we need to move the data from here to there, it's just, you just go and you run. If Spark was, I'm sorry, if SPSS was sort of one of the first, so it's a tool, enabling tool, then we go sort of up the hierarchy, the 15 apps that are enabled and then the next ones that come after that, what do they look like? We want to enable all of IBM on Spark. We use the analogy back in June of, Spark is exactly what Linux was back in the day for operating systems. We think it will be that fundamental to how it plays a role going forward in every enterprise. So I'm not sure you're gonna be relevant in the future if you're not building your portfolio on Spark. Let me give you one example of why. We enabled DataWorks, which is our cloud data prep service on Spark in the last six months. When we announced DataWorks last year at this show, it was 40 million lines of code. Enabling on Spark, it's now five million lines of code. Think about that, was that 87%? 90%? It's so much simpler and it's so much faster to work with data-rich applications on Spark. So it's changing how we build products and it certainly will change how clients use products. So when you talk to guys, so when IBM makes big moves like that, you got to pay attention. When you talk to the hyperscale guys, the big, you know, Googles, for instance, they'll say MapReduce, we did that. We knew that wasn't going to sustain. So we moved on to in-memory and now we're on to something else. We won't tell you what we're doing. But so relate that to what's going on with IBM because you guys played in Hadoop, but it wasn't like, okay, we're all in. There's a billion-dollar investment in Hadoop. You're doing that with Spark, right? I don't think you've quantified that investment, sort of my quantification. But where do you see as sort of the next? I mean, you guys obviously have insight on that. Does Spark, does the investment you make in Spark, is that leverageable into whatever's next, you know, Spark 2.0 or whatever we call it? Can you give us some glimpse of what you guys are thinking there? I believe it is. And the reason that we've organized the way that we have for Spark is we built the Spark Technology Center. And the best way to think of that is separation of church and state. We do nothing in there that's not open source. Everything we do in the Spark Technology Center goes back into the open source. We have other parts of IBM that will do enabling of IBM products, value out around that. But the point of doing that with that team is one is it gives us the ability to attract some great talent, which we've been doing from outside. And two is that gives us the focus of skills to take on whatever comes next. And there will be something next. Probably pretty soon.  And so that will certainly evolve over time. So it's the framework, the model that says we're going to do everything open source. We'll innovate with that open source community. Whatever it is will lead. Yes, and I don't think any other company, our size, has made this significant of a bet on open source. Oh, but this is why the open source community, Rob, says, oh boy, here comes IBM, because you guys are putting your money where your mouth is. You're bringing development resources, not just marketing. A lot of companies come in and say, okay, we're going to make a dupe enterprise ready. You heard that a lot. And then what you guys are doing is really moving the industry and shaping the industry. That scares a lot of people, you know? But you're not making apologies about that, are you? Yeah, I'm a little surprised that it scares people and that I think our reputation's pretty good in this space. I think Linux would not be where it is today if it weren't for IBM. Yeah, I don't think people are afraid that you're going to fork the code or go proprietary. No, you've got a track record there. I just think it's culturally, hey, this is our little sandbox. And now all of a sudden there's this big adult playing in it. We're actually much more immature than we appear. But IBM was- Point, case in point. John Thomas in the Q and A. I'm the best evidence of that. But someone like IBM was necessary to be the sort of the commercial sponsor, the big commercial sponsor from what's still seen as a little bit on the fringe. But my question to you is, if I understood you correctly, so Spark is not your future proof forever layer. Spark is your current analytic operating system. And you can see, if I understood you right, other projects coming along, maybe Flank or something that works near real time. But even abstracting away from sort of naming projects, if you want to take a look at the apps that you've created so far, IBM's created so far, what is that analytic framework going to look like? So I think obviously it will continue to evolve over time. I think Spark will be here for a long time. That's why we made it. But you think I'll keep them back to the Linux analogy. Supportive Linux and as Linux rolled out, it formed a lot of ecosystems off of that, right? And I think the same thing will happen here. I think you'll, you know, right now we're focused on applications and data rich applications. I think a very logical extension for Spark is going to be around IoT, Internet of Things. And you've already seen some of those, but that probably explodes. I talked about one of the keynotes this week. 50 billion devices by 2020 is our estimate for connected devices. That's going to require a new approach to technology and a new layer. Does that mean, yeah, just new layer on Spark and new obstruction or does that mean reworking Spark itself? I don't know. Every client I meet with, I get, as I walk out of the room, I get a list of, here's all the things that we want in Spark. And I hand that to the guys in San Francisco and we go make progress on it. The biggest ones right now have been around Spark SQL and the back end and a little bit on Spark streaming, those are the most common requests. Those will change over time.  It will continue to evolve. Okay, the requirements are changing so quickly. So the question is, you know, can the Spark ecosystem respond without making a mess? And I think we're seeing kind of a mess in Hadoop, the Hadoop ecosystem right now. You're drawing parallels to Linux. We, Furrier and I, for years have been saying, will there be a red hat of Hadoop? You know, and it looks like Hadoop is more like Unix. You know? Yeah. There's really a fragmented ecosystem, or worse, a lot of people tugging at each other. So, you know, will there be a red hat of Spark? Is that what IBM will be? I don't know. I think one thing that's been unique about our approach here for Spark, at least, is we've started with community, and by community I mean not only open source, but in terms of partnerships. We're partnering closely with TypeSafe, closely with Databricks, closely with Galvanize, who I think was on before. And the whole thought here is, this is going to be done as a group. This is not IBM's thing. We're not confused about that. But we need a group to work with. The guys at TypeSafe, you know, inventors of Scala, incredible guys to work with. Databricks has been an incredible partner, and together we kind of sit down and say, so what is the enterprise need? And it's a great collaboration that we have going in. But what is IBM's thing is making money on top of open source, which so many people are struggling to do. One of the themes that we talked about at Strata Hadoop this year is it's, the market's overcrowded, it's overfunded, and it's profitless. That's not your model for open source, right? It's like the exact opposite of that. And right now you've got companies that are spending two to make one, you know? And it looks like they're pointed up, but they're losing altitude. And so if the funding dries up, it kind of, it gets really interesting for you because you can pick up companies cheap. But you've got a business model around open source. Can you talk about that a little bit? Yeah, I think, first of all, Red Hat is a successful company. At the same time, if you look at Red Hat's market cap versus every other software company that was born that time, it actually doesn't really look like much of a success. Yeah, it's amazing because it's open source, but that's really the only one. Because there's really no way to drive investor returns. And Whitehurst is going through a TAM expansion right now. You've got to expand as TAM, and it's pissing some people off, frankly, but it's the right thing to do. And so that's difficult. So I do think there's very profitable business on open source, but they don't look like what we're used to. Facebook is built on open source. And what they deliver and what they use, their customers, open sources are relevant to them because it's just an enabler to an end, right? And I think that's the kind of model you will see IBM end up in where it's not about us trying to monetize the open source. That's really not an objective. It's about open source provides speed, open source provides innovation, and then we can build creative business models off of that. We've talked about partnerships with the weather company as an example here that's been covered a lot. Do you sit that on top of something like Spark? It changes the access to data in the use cases. And so it's not about us, hey, you got to pay us for this open source. It's about, we've got these great data rich applications that ingest weather data. That's how you make money. So it's a totally different approach. Well, and one of the first guys that ever point this out was Peter Goldmacher way back when said the guys were going to make money in Hadoop slash open source big data. Other practitioners that are applying these technologies. So is IBM a technology company or are you a practitioner in analytics? Or enabler of the practitioners?  So that's your sort of practitioner play, I guess. But it's a different model than what we used to was oh, I make widgets and hardware and software and services and I go sell that. Your approach is to apply analytics to change industries. It is. So maybe we're a little bit all of the above there. I think the most interesting part, at least to me, is we can deliver very unique outcomes working in different industries. One example that came public just at the conference this week is the work we've been doing with American Airlines where we've really modernized and moved their flight operations work to real time. And that's using things like streaming. It's also using other technologies like we've discussed here.  And it's not because we're charging them for open source. It's because we're saying together we can build this data-rich application based on IBM technology, which, by the way, leverages some open source. And we wouldn't be able to move those projects as fast if we didn't have a role in the community. And they'll pay for that value, absolutely. So it really is, I mean, it's almost like we get enamored with Spark because it's a shiny new toy, but it's just this analysis layer. And you have these analytic data feeds, one of which might be the weather company, and it can be applied. And you have other data-rich services and perhaps software components. And then the whole thing is composable by your industry experts. And that sounds like the solution. That's right. And I think there's a lot of services work to be had in the next five years in this area. Traditional consulting, or a lot of the consulting business in the past has been IT integration. That's probably gonna be less interesting services going forward, but there's a lot of services kind of in the vein that you're describing, George, around composable analytics. How do you actually get to these different types of use cases? Like the examples that we talk about typically involve a decent bit of services, sometimes from IBM, sometimes from our partners, because it's not necessarily out of the box. You don't get landmark analytic outcomes that are out of the box. There was, it's funny when you said American Airlines because that was the first commercial online app that was built in conjunction with American. The first one was, say, Jair Defense. And that was, the reason I bring it up is it was a joint venture, it was a huge amount of money, but over time it became a packaged app. Do you see these data-rich analytic apps migrating from being fairly service-heavy to being rather packaged? I don't know, and the reason I say that is the minute that they're packaged and it's that easy to buy them and use them, there's no longer a competitive advantage in doing it. Everything that we do, last year at this conference, Conoco Phillips was talking about the work that we've done with them on oil exploration. If you package that up and then every oil company could buy that same exact outcome, then suddenly Conoco Phillips would not have an advantage. So then you have to move on to the next thing. So there's always going to be a next, and so thinking that there's going to be real packaged apps here that drive great, I don't know, I'm skeptical. So, okay, it's not that there won't be packaged apps, it's SAQ, IBM's interest in them will be in the next wave of hard apps, of hard problems to solve that add competitive advantage to the companies. Yeah, so certainly I think that space will, that space will grow. There will be a lot of applications. We're going to do a lot with solutions now. I guess my main point was that the use cases that excite us are always going to be a step ahead, and it's going to be hard to package that up, I believe. Understood. So, then that brings the question, how do you scale your business? I mean, it seems like the solutions business is repeatable. Right. But you're saying your customers want you to preserve their competitive advantage. So it's not just speed, there's some unique IP. Is that IP developed by them, enabled by you? Add some color to that. I think the way you scale is through cloud delivery, because, and as you build data assets over time, you start to monetize through data assets, as an example, we kind of do that through some of the partnerships that we have today. And those set of data services is something that you can monetize over time. So my point is we deliver the outcome, but then we've got a scalable platform that we're building everything on, and that's how we get leveraging. But it seems in talking to people today, it seems like IBM traditionally, in the last 15 years, has been very services-led, and it seems like you're extracting knowledge from your industry services expertise, and you're actually putting that into software. I mean, that's a very clear trend. Yet you're saying that you're still preserving that competitive advantage for your customers. So am I correct that the services-led emphasis component is shrinking in favor of software, that scale? Is that right, or am I misinterpreting that? I think that's fair. I think what you end up with, though, for some of these use cases is something that's probably, I'll call it, 80-20 in terms of software to services. As opposed to 20-80. As opposed to 20-80 or 100-0, which is, you know, all software, which is the ideal business. It's just, I think it's hard to get extravagant and extraordinary outcomes without having some service assistance. It's not traditional service, it's not systems integration. It's data science work. We've got the largest private research staff in the world still. You give us a set of data, we can find things that you don't know about, and we can build interesting applications. So data science is a service. Yes, okay. But continuing on that thought, if you're working deep within an oil and gas firm about upstream exploration, and you build this expertise about how to really find this effectively or with great efficiency, that model is something that's largely your IP because your data scientists are working in conjunction with the customer to do it. How much of that model stays with you? How much stays with the customer?  Well, it's three aspects. It's models, it's methodologies, and then it's the data. The data always stays with the customer, and it's really hard to get value out of the models or the methodology without the data. What we carry forward is we got the models to do this so it can become repeatable back to Dave's point. We got the methodology so we actually know how to drive the outcomes, but data has to be part of the equation which the clients retain for themselves. That's why I think it becomes a pretty good relationship where they do get a unique benefit and then we can go work with others as well. Okay. All right, so what's next for you? We've seen you several times this year. Yes. You're going to keep this cadence up? You're going to accelerate that? Is there a flywheel effect going on? We have data Palooza coming November 10th in San Francisco, November 10th through 12th. We're pretty excited about this. This is three days, built a data product, and it's almost sold out already, remarkably. And it's going to be a huge turnout. It's really a first of a kind. And we're doing this with a bunch of different partners, some of the folks that I mentioned before. And then we have a world tour starting next year where we're going to hit 11 different cities. This is all about data science training, build a data product in three days. I don't think we've ever seen anything like it in the industry actually, so really excited by that. And that's in November? Yeah, November 10th through 12th in San Francisco is the first event. At Galvanize. At Galvanize, that's right. Exactly. And then we hit the road worldwide starting in January. What's 16 look like for you guys? What should we be watching for? I think we'll just take vacation that year, right? We should be all done. We done? Yeah, let's take, I think, awesome. You'll be game over at that point. Yeah, I'm just wondering, wait. Look, 2016 for us is taking spark to the next level. I talked about some of the things that we've been working on in terms of it being enterprise sufficient, enterprise grade. It's come a long way. 2016 is the breakout, I would say. I've talked a lot this week around a big data maturity curve where clients spent the last five years basically using things like Hadoop to reduce cost. And we are quickly moving to the exponential part of that curve that says self-service analytics. How do you make data available to everybody in an organization and ultimately building new business models on the basis of data? Spark is going to be the fuel for those two phases and that's going to be the big thing. IBM on the steep part of the S-curve, that's exciting. Hang on. It's escalating, quick. One quick question then on that. Are we moving to Spark because of greater development and administrative simplicity that Hadoop is, someone explained it to me with all the different components. You know, you have these failure domains, security admin, all these different things that can go wrong with all the components where it's with Spark it's somewhat more unified. Is that partly why you see the maturity curve going towards Spark? There's two reasons for Spark. One is the unified programming model to your point. That's how you can reduce the time it takes to build stuff. And the second big piece is machine learning. Spark is built for machine learning. It's why we contributed system ML, it's how we're maturing that part of the ecosystem fast. Spark is about machine learning and it's hard to get to self-service analytics or new business models if everything requires people. So you need the automation that comes with machine learning. So those are the two reasons in my mind. All right Rob, thanks for stopping by again. Always a pleasure having you in the Cube. Great to see you guys. Great stuff. Thank you. All right, keep right there buddy, we'll be back with our next guest. This is day two, we're winding down here at IBM Insight, right back.


That's correct. Sorry. Yes, it was. Okay, can you guys hear me? All right, thank you. Hi, my name is Ken. My surname is, so I'm a Singaporean. So today I'm going to talk about process automation using an open source tool that I built maybe over a year ago. So let me segment my talk today into three parts. The first part I'll spend a few minutes to talk about, you'll show some photos about my open source journey since this is about GitHub and the open source community. And the second part I'll spend a few minutes to talk about what is process automation, specifically the branch called robotic process automation. And then the last part I'll spend maybe a 10 minutes or so to talk about the tool that I've developed and how you can use it. It'll be open source being free and basically the technical details. There may be five minutes left just for Q&A, all right? Okay, so let me see. Why is this not showing? Okay, so yeah, my open source journey started in 2015, so I'm like a late boomer, right? This was me, 2015, three years ago. So at the time I was at DBS, that's where I first signed up my GitHub account. I think it's January 2015. So this was me at DBS and I was developing test automation tools for manual testers, kind of like replacing manual testers job in a sense. So what I do there is to basically write program that whenever the software releases, there's upgrades. We no longer have 10 people sitting there to test the application, specifically trading application. Let's say we trade the forex, US dollars, Euro and so on. We don't have to test 1,000 test cases manually one by one. So I just write code basically to automate that away. So, but at the time I thought that I want to do something more exciting that maybe I want to do process automation stuff but not on staging system. I feel that there's a more challenging value in trying to do automation in production system. So I was there for one and a half years and I left DBS basically after I was... I just selected to be the agile team lead for the new agile team, but I tendered my resignation and then I basically packed my bags, went to Eastern Europe with my wife for one and a half years. Then I came back. So during that time I was spending all my time to develop the open source software. Pali is because of interest.  So this was me, I was 95 kg and this is my weight chart. So basically I can also talk about weight loss if you want to hear about it.    So that's my key takeaway. So the first place I went to was Serbia. This is a Nikola Tesla museum. Nikola Tesla makes his name in the United States but actually where he's born is from Serbia, the eastern side of Europe. So when we were there, we visited his museum. Let me zoom out. And this is, this is his urn. Basically. So I was there in Serbia for one month plus. Then I moved on to, this was Belgrade. Then I moved on to Novi Sad. Novi Sad is a beautiful place with about 300,000 people. And this is their fortress. This is the Novi Sad beach. It was summer at the time when we went. Then after that we went to Budapest and the locals will pronounce it as Budapesh with a SH. Although to us we pronounce it as Budapest. This is the co-working space there. It's called Impact Hub. So I was working there on this open source process automation stuff and this was Sunday night I think it was maybe 11, 11 p.m. So I got to catch the last bus and then I just took a photo before leaving. I like Hungarian food very much. So I started to learn how to cook Hungarian food. They are kind of curry. They are meatballs and stuff. Alright. This is a home-cooked dinner I made for my wife. Usually I'm the one cooking for some reason. She doesn't really cook. Then some of you may have come across this software called Prezi. It's for making what you call PowerPoint slides in a web-based version. So I went to Prezi headquarters in Budapest and they were having a talk on machine learning using H2O. Now they are running the deep water version. So this is the Prezi HQ. And they are doing a talk on sparkling water. But now the next phase for their product roadmap was actually deep water. This is a meetup at one of the start-up centre. So this is me. Okay. So after Budapest I came back, continued developing my code and I can't stay in Europe for too long because Singaporeans, you can stay maximum three months and then you've got to do border runs and so on. So basically I was staying there four and a half months, five months, then I came back. After that we rest for a few months then we went back to our next trip again. We started off with Chiang Mai because it was still too cold in February 2017 when we were about to go there. This was Chiang Mai and it has changed a lot. This is a tree house which I do my development every day. So every morning I'll wake up, have breakfast, go to this tree house cafe and do coding and end of the day. Then I'll meet my wife. Okay. And this is the Prata. They have their local version of Prata. There's also time for parties. This is a music festival in Chiang Mai and authentic Thai Pad Thai food. After that we went back to Novisat again. This is the same fortress as just now but this is where the sun is setting and I think this is the moon rising and the night view. Also attend some of the machine learning talks in Novisat. So this is our Euro trip for a year. Okay. Now, Lee Se Do. So what got me started on to move towards automation stuff was in 2016, I'm not sure, or 2015. But there was this time where Lee Se Do was having matches with AlphaGo and I'm a Go player for 15 years already. Maybe my rank is 1Q around there.  I was basically shocked. So after the few series of games I basically go online to buy Lee Se Do's book and just share a copy with my friend. Okay. Yeah, so let me now go to the next part I'll talk about. The first part was just my background. Now I'll talk about automation and process automation. Okay, before that. So after the Euro trip I came back last year and then I happened to meet a director at AI Singapore. So I was saying, oh, I've developed this automation software. It has gained considerable traction in the open source community. And then I was saying, okay, since I'm going back to work, why not you guys at AI Singapore just take whatever I've developed and use it in your processors and whatever. And so happened at a time he was looking to hire engineers. So that informal meetup became the interview and I just got in and started working there sometime in December last year. Okay. Now, I'll touch on a little bit about AI Singapore before I continue. Okay, basically, AI Singapore is a government-funded organization. It's a nonprofit. We don't raise funds, but more of helping the Singapore ecosystem to build up local AI capabilities. So our programs, if you go to our website, AI Singapore, you can see a few things under programs. There's doing fundamental research, doing some grand challenges which we tackle challenging problems in healthcare, fintech, and urban solutions, for example. And then we have 100 experiments and AI apprenticeship where we try to tie up researchers from ASR and the various universities with some of the private sector companies to help them build AI MVPs. So if you want to know more about AI Singapore, you can just go to our website. Okay, going back to RPA, right? Okay, so, okay, for automation, if we look at the whole span of the IT industry since the 1960s, the whole entire 50 years, 60 years, essentially automation itself. IT itself is automation. When you try to reach that process, you are essentially automating the backend whatever happens. For example, let's say you file income tax to IRAS. Some companies actually do the auto fouling so their employees don't have to do the fouling themselves. So all these backend things that are going on is actually automation. But in the last few years, there's a rise in the new type of automation called robotic process automation. So that type of automation actually is built on legacy IT systems. Instead of trying to integrate, you have system A, you have system B, C and B, instead of trying to integrate every of the systems together through the backend, through the APIs, we actually try to integrate them through the front end. So you see the difference. The traditional IT integration, we have all API calls, we have all the rest services, and more recently, the Facebook GraphQL is picking up. But in process automation using the RPA way, the robotic process automation, we do it in a way that we make the behavior of a user. For example, let's say you receive an email from your boss to do something. So after you read the email from your boss, you'll get the relevant information out and then you'll log into, for example, application A to do certain steps. And after doing certain steps and getting certain results, you log into application B and do the next few steps. And after getting certain results, you log into application C and then you do the remaining steps. This is an example of a workflow that if you are able to map this sequence of business logic into some rules, actually you can automate the process and repeatedly do it many times again and again and again. So this thing is being used in the market now in mostly in banks and accounting firms because they have the skill to do large volume transactions and they have the skill to invest in automation for RPA so that they can tell, like, make things more accurate at the same time they can handle a larger workload with the same amount of workforce without hiring more people. So that is the background of process automation. Okay. But there's a gap in the market now. Traditional commercial process automation software costs about 5 to 10,000 a year per user. So that is very costly for any SME to even try to use. Because even if you try to write the first automation script you've got to pay so much cost just to use it. But I'm not saying that those commercial tools are not good, they are great, but I'm just saying that there's a gap in the SME space smaller companies which they have no access to process automation tools. So in that respect, I think maybe an open source version of those type of tools will make it easier for those smaller companies. That's the reason why I left DBS to just basically spend one year to develop and then, you know, it got subtraction on GitHub. Okay. Now I will go into the tool. Okay. So if you type Google tag UI, you'll see the link to the tool. It is spelled tag UI in the sense you are trying to tag the user interface. So tag UI. The first result would be the tool. And if you type there's also a Chrome extension if you like to use a Chrome extension. So I also make a Chrome extension that you can download to help you with the automation. So, okay. Now I go into the introduction. So basically this is an open source tool that's free and it is under MIT license. Later on it will be Apache 2. So I joined AI Singapore with the goal of adding ML and AI capabilities to this tool while keeping it open source and free to use. Not just by Singapore community, but anywhere else in the world you can try to use it. So some of the key features I developed with a focus on web applications because more and more we have the traditional application type of apps moving from legacy applications into web based cloud solutions. Some of the browser you can run on Firefox, Chrome, PhantomJS. You can do visual automation. You can do OCR. You can automate besides websites. You can automate your desktop application for example Excel, Microsoft Word or your Outlook email client. And you can write your automation in 20 over human languages. Initially I built this tool just to write my automation. But later on after I made the tool, I basically write an automation flow and run it to let it build itself. So essentially I write a few lines of code and then I let it run and build the language definition itself. How it does it is it goes to Google Translate and then one by one you slowly get the keywords and submit to Google Translate and get the corresponding matching keyword for Hindi, for Polish, Hungarian and so on. And then at the end of the building process it will be able to get all the languages converted for use. But the only one, the only two languages I did manually is Chinese and English because the rest I'm not familiar to really manually check through. The rest are actually generated languages. And you can unzip and run on Mac, Linux and Windows. So this is slightly different from the traditional RPA software because the commercial RPA software is mostly based on Windows as the Windows Microsoft API is more supportive of backend automation. But I try to build it based on cross-platform open source way so I support Linux, I support Mac OS and also of course Windows. Recently I added Python and R integration to the tool which means that right from the automation script itself you can run Python and R code your machine learning libraries and get the results and basically do more complicated stuff. You can run by schedule and advance API course of order, the typical developer stuff. I'll just download and do a demo. It's very easy to use. Once you go to a website you can just unzip and run it. So let me see this. It may be too small. Okay. So let's say I want to run assemble automation flow. Okay. So let's say I want to log in, I want to go to Yahoo and do a series of steps, capture some screenshots and then save the result to somewhere for example. So I can just run it from command line this way maybe using Chrome so I can see the automation happening in front of me. Wait let me queue it. Okay. So right now what you are seeing, the typing and clicking search, getting the results and saving screenshots is actually done through the automation script. So after searching for GitHub I capture a screenshot and then I go to some other, you can see what's happening here. So I capture the screenshots, I go to some other web page and type some other stuff over at the new web page. So all this clicking and typing is automated. So this will be an example and then I will just jump in to show a video for some other examples. Okay. So in this example I will, I showed a using of the Chrome extension to do a recording of automation and then replaying it and in this case is to record and replay account creation. So now I'm recording using the Chrome extension and then I go to this web page key, my username and password and then save the recording. After that I'll replay it. So after saving and exporting the recording what you'll get is actually text based files with English like language click, get started click something else, you know enter user email, enter password blah blah blah. So after running through exporting the text file you can just do your updates like maybe I want to wait for a few more seconds to capture the results and show the results and then you can just run it after that. So imagine doing this by sending an Excel spreadsheet to this application. You can actually instead of registering 500 accounts you can just use the Excel spreadsheet to key in the accounts you want to create and then pump it to the application to this tool and let it automate the creation process. Okay so alright I have a few more minutes. I will show another video maybe this one. Okay and I will jump straight into the example. So let's say this okay now I will show an example of automation of downloading let's say you have a survey you want to automate downloading of the survey results. So this will be an example so whatever you are seeing now that's happening in the front end is actually automation script including typing of the passwords and so on and the user ID, the logins and then after logging in you do certain things and you download the CSV file to your desktop and then you use it for the next step of the workflow process. So all these things can be stacked up to form a more complicated workflow but this is just a very short workflow to show an example of what it can be done. Okay I think I got 2 minutes left so any questions for the floor? Alright okay cool so do we have any questions here? Can't say again it's alright. Okay cool so thank you very much Ken so thank you very much.


What's going on everyone? Sam here with late sports cards ripping 22 23 Bowman University Inception a hobby a 16 box full case break number three random hit style here's how this is going to work we have our 32 participants in a random.org window we also have a blank random.org window which all of our hits are going to be copied into as we do the break Dylan is going to be typing in descriptions of every single hit so say it's victor women yama base auto he'll type in victor women yama base auto once all 32 hits have been shown all the boxes are open they'll be transferred back into here there will be 32 we're going to roll the dice whatever say it's eight times going to roll the names eight times the hits eight times copy them over to the results tab whatever hit is next to your name is the hit that you are going to receive so the way it'll work with the base and the numbered cards to is the way the pack is set up is we have numbered card two autos for base the top auto will receive the numbered card from the box and the bottom auto will receive the four base cards so they'll be all individually like packaged so good luck here everybody let's do our last case of the morning man it's sad to go it's been a great morning great morning yeah me too me too coffee that the freaking heart and do that is like my favorite pole of all time it's the heart and love of it and I don't unless I get obsessed and fixated on another card that's in something for some reason I don't think anything's going to top that unless they put like a 2015 ray lewis shield into flawless and I pull that that's going to be like only thing that time so all right here we go number one uh random hits should be clear here we go box one box uno it's going to be a jarris walker to 299 uh we'll be going with the first auto of katie johnson katie johnson get you a levis uh one of one that'd be pretty sweet that'd be pretty sweet chain for second auto is gonna be will anderson nice 42 of 50 on the gold good luck on that one everybody alabama now at houston texan third overall pick when they traded back up that's awesome menace of a defender will also be getting the four base cards base box do a great way to start with the will anderson great way to start uh jackson smith and jigba to 299 nice one there seahawk now we'll be going with the first auto of oscar to sheboy nice to 75 for kentucky the what player of the year two years ago i want to say that's that grats on that one oscar to sheboy uh second auto is going to be fuchsia 27 of 75 metas buzellas that's what i'm saying mark that'll be getting the jackson dart alaya boston and sam hartman filipowski to 199 we've got gold of hunter dickinson nice one there for michigan to 50 the transfer google that be transferred to kansas i don't think i'm the right guy he did let's go baby transfer to kansas that's what i thought nice one there uh justin adam alola behind that justin adam alola he's on kansas now that's what i thought i remember seeing that he was kind of a stud too anthony rickerton in there marie full of quarterback auto too it's feel like a young or shroud or anthony a rich i didn't even need the second my second dose of caffeine today it's a whole garden there's no need now uh to 199 mackay ashton lingford for boston college we've got auto is a mackay ashton lingford mackay ashton lingford and second auto is going to be ryan holinsky for north western ryan holinsky north western what do you think this is already good yeah your dad on the keys is going to be uh that's going to be uh it's going to be a show that that's going to be a show right there that's going to be him having to explain all this to him and watching him do that god luck with that godspeed for us clashes are going to be on looking for everything jay starling to 99 for Notre Dame will be paired with the first one of mark mitchell for duke nice one there to 199 jumbo jersey auto let's go mani insurance purchased hell yeah mani uh tyrese hunter is going to be your second auto tyrese hunter for texas pretty easy actually 10 of 10 what's what's better 10 of 10 uh the ability to insure to purchase insurance the ease of that or uh bowman university inception as a whole rice young nice one there and a drew timmy and a jamir gives good base there mark armstrong 250 oh nice color here is nick smith junior and jordan walsh on the dual patch auto for arkansas nice one there two of five that is pretty huge right there actually uh you can make this one this one this this one's nasty nasty this is a good really good break so far she way uh willy anderson gold now this two of five awesome dude we've got a brand's uh q2 99 on the blue auto and then mj rice kaleb love jackson smith and jigma and shrubbery the end yeah very nice case so far for sure they're been taking another uh tiered spot tonight thank you guys helping forest out a lot for sure i know he'll be very uh very appreciative of that yeah so far this has been great man great flawless tiered get knocked way down mega dual case down to 23 thank you guys on that we also have uh 2012 nt and prism tiered full case tonight all right onto the next box one to give uh on the picnic picker that uh you know by the way see okay we'll just we'll just figure out sent for us tell him now i can't never do that to forest mackaya ashton langford i'll look at him and be like look at me i'm the breaking manager now nice one there we've got base auto is a uh Hansele manual nice one there oh that's cool he's the guy who plays with one arm that's sweet oh no way yeah that's really cool i didn't know he was in nice that's really that's awesome actually sick yeah he'll buck on that one got to kwan thin for a Toledo to kwan thin kwan thin kaleb williams base and a jordan walsh i remember watching like uh like a tick talk about him or something yeah exactly he's pretty big on yeah oh yeah he's good he would he would just he would kill me yeah nice one here one of five jordan walsh for arkansas nice one there one of five that'll go with the first base auto of um isaiah nair isaiah nair for texas and jeremy roach is the second auto nice one there for oh my hat got delivered nice got a new hat that'll be rocking tomorrow on stream everybody uh you get ready for that one we've got uh clayton tune anthony black uh tiger cambell and jeres walker jaren hall 299 i don't have a santa cruise banana slugs cap now there's jet howard nice one there oh wow wow that's tough that really comes down to me versus floris we've got uh kade mac namara for iowa nice one there kade mac namara karen marcus sassard julian phillips do not have an akron zips cap i do not shame nope no i do not no i do not i like i don't think new era doesn't have a lot of uh you might switch in these two trash cans uh new era doesn't have a lot of like college teams and i i prefer new era over pretty much anything so uh it's tough to find uh like new era stuff zavier worthy to 199 got blue is chris livingston for kentucky 299 that's one there on the blue uh second auto be phillips uh okara for ohio state phillips okara nice auto on that one now i have like so this is actually kind of nice on the house that i'm renting um basically there's like the way my like hallway is set up is it's like an l shape and then there's like a hall closet on the corner of the l like the inside of an l and it's basically like a bunch of a bunch of l shaped um like shelves and they're just all on there now i've not bought a new era hoodie what's up d mills oh is that quack it's a quack you're still going by quack unfortunately got spencer sanders to 75 uh auto is gonna be a um tracing pots for minnesota soft that's awesome you have to look into that fan out for sure and another oscar to sheboy this time a base auto so we have a base auto and we have one out of 75 yes actually pulled one a okay in the first uh first break we pulled a uh women yama redemption yes anders van dyke and walker howard ah man they made you change your username and password here's kaleb love to 299 we've got first auto is cameron ward for west uh washington no wv u so washington state probably plays at west virginia now wazoo and gold tony grimes 33 of 54 a and m place is full of jerseys that's awesome as i i'm in on that i'm so in on that uh j j mccarthy avery anderson vince iwachuku and jordan adison i'll go with that second one yeah i i would for sure once it comes in manny i would i would get it graded why not honestly you know there's a jordan walsh to 199 we've got mark mitchell is going to be your first auto mark mitchell for duke followed by a patent thorn base auto from michigan state it's going to be a jamir gibbs base uh chance westerie anthony rickardson and mackay ashton lingford uh cam wittmore at a 50 nice on the gold oh cam wittmore grady dick on the dual 35 of 99 a great case dude huge scratch on the back oh that's annoying cam wittmore grady dick the 99 you can bag this once we're done uh kiki rice is going to be your base auto nice one there for ucla kiki rice got a amari bailey davier worthy kyle filipowski and stetson benet uh mj rice to 299 base auto is a nice brice sensiball nice one there i know he's a um projected first rounder and hendon hooker to 100 on the inception auto nice one there 23 of 100 now i lion that's a really really really good um case so far jeres walker i'm jay rice kaleb love and jackson the smith and jigba i have some toothpaste on it oh god you're on 31 right yeah oh because all right no there's two autos good we're good here we go good here my band saw there's amari bailey to 199 we've got auto is zack harrison nice one there for ohio state zack harrison and gold brandon murray 34 of 50 for georgetown the hoyas oh i get it oh i know exactly we were talking about i i may not have owned any cds but i owned a lot of disc video games a lot of playstation 2 games oh yeah i know exactly we were talking about the toothpaste the trick or the banana trick base and base all right guys good to go all right again guys we've got our 32 participants over here in the um random order window we now have our 32 hits over here as well for the katie johnson base auto down to the brandon murray no extra hit in this so uh everybody just will go home with one auto and either a base car four base cards or a numbered card as well roll the dice we're going to go 10 times 1 2 3 4 5 6 7 8 9 10 that's shawn l down to ajf and 10 times on your bless you it's 1 2 3 4 5 6 7 8 9 and 10 hunter uh dickinson auto to 50 down to justin adam alola base auto okay we've got shawn l with 100 dickinson auto to 50 Todd D brandon murray brandon you uh patin thorn base auto james p with the handsill emmanuel uh and day quan uh finn base auto ross k uh brian qth 2 99 lee m katie johnson base auto connor h with the ryan holinski shane with mark mitchell patch auto 175 of 199 nice one there's shane uh uh oan h uh mataz uh buzella sado to 875 27 of 875 this guy man i got it to 75 mathew b mark mitchell base auto ronald r cameron ward uh michael b jeremy roach james p chris livingston victor o brice sensaba and philix aparo uh james p oscar to sheboy to 75 uh mathew b with kade McNamara james p willanderson gold to 50 nice one there james jeremiah e hendon hooker to 100 chris g with the tracen pott's base auto and the isaian nayer base auto uh travis w with the tyree's hunter uh chris g with the nix smith and jordan wall's dual patch auto five gorgeous card there uh james p with the kiki rice delmus l mackay ashen langford talon h tony grimes eugene b zach harrison uh james p also with the cam whitmore and um grady dick dual patch auto in 99 uh todd d oscar to sheboy base auto james p with the jet howard base auto and ajf with the jaden and adam alola base auto i got these copied over guys we'll uh nothing coming up after this


Clean water is one of our most important resources. Our very survival depends on it. But where does it come from? If you look upstream, you will probably find a forest. Forests are vital to our water supply. They not only influence how and where rain falls, they filter and clean our water far better and more cheaply than man-made solutions can. So protecting our forests protects our clean water. Remember, when you turn on a tap, you're tapping into forests. Look after forests, because every drop counts.


Hello and welcome back to the channel for part two of the pike peak grip versus drift challenge or drift first grip challenge If you guys followed the first episode guys, no, I drifted the Z up and well Pikes peak one drift car zero So here we are staring at the dashboard and the start line here of This challenge on grip once you guys guessed it down in the comments We are taking the time attack Evo here off of the mountain here at pikes peak So make sure you guys follow me on all social media all that's found in the description box below So like I said a bunch of you guys Did guess it right that we are gonna be doing the pikes peak Evo or the time attack Evo here on pikes peak still let's Get ourselves in the gear and Go full send we are rocking it in sequential mode We do not need our clutch We're gonna try and not die and of course we are rocking first person because When you are racing and Drifting 90% of time first person is the best view, but I feel like Driving racing in first person is exactly what you guys want to see right now. I was trying not to die Don't remember really any of the course This car is pretty quick But it does take a little bit to spool up Well, it's happy So we got to keep it in the higher revs if possible a little trial braking And that's our first off but Not too terrible than off. So we're okay That off was could have been a lot worse made a few offs with the 350 also You guys do remember I am running on sim damage. So Turning radius not the greatest Gotta remember I'm on 900 degrees not a 540 like some of the race cars. I run Just trying to be smooth and fast I could probably be running this a lot faster, but we're trying to survive Have not driven this car since I downloaded it back when I got a set of course in the beginning on PC There's that turbo infamous turbo lag of this car We are at 100% boost. So nothing we can do about that so maybe trail brake like that and Keep on power Kind of does help a little bit. Oh, it also gets a little tail happy that way so We're not doing Too terrible right now could be doing a lot worse Little clutch kick to get the power up there. Maybe it's those rpm spooling little clutch pop Doesn't hurt. Oh There's that another off And a hard hit into a rock, but look at the rock was there. We beat off the mountain It did cause to it caused some damage in our front Seem to be okay. The wheel seems to be Still responsive and centered so seem to be okay with that not be dipping tires in the dirt Like that in this car There's that turbo lag and like I said, I'm trying to do this basically on the seat of my pants Not really sure what's coming up So don't have this course memorized Hit clutch to set a break. So that's not that's not definitely not something you want to do because that could be in real life the Difference between life and death on something in this line here at the little split box So we still got more summits of climb Hopefully pike speak doesn't take a second win on me here for today I'm also trying to remember where I went off in the 350 so I don't do that again Because how how would that be going off in a silver Evo off the cliff? There's our little off parachuter hang glider guys just hanging out bop of the clutch Doing a lot smoother and a lot better than I thought we were gonna do so My confidence is building a little bit in the car right now But we know what could happen if I get too cocky Done this before very sharp right-hander. Oh, there's a bit of a lock up Bit of a lock up into the guardrail that probably would have wrote the car off in real life Luckily we're in a set of Corsa We're still feels okay Take this a little bit more cautious up here. I'm having definitely some difficulties on these tighter hairpins All the drive car and super grippy front with all the down force and this will run off last time. Nope That's not where we went off of the Evo Or the oh my Hmm No, we saw that right We're losing it up here in the ladder section of this Will is still somewhat. Okay a little bit off now I Got I think I may have damaged my brakes because it is not stopping how it was before so unfortunately, we got a bit of a break problem and This is where we went off with the 350 not can do that again Rip to the 350 down the valley But that was much harder than was because this is where the tighter turn is get a little bit squirrely bopping in first Got a little lifelike helicopter. It looks like still got Plenty of road to go Cruise that a little bit slower Coming the brakes trying to Should break stay warm So they actually slow me down. We are having a little bit of break issue I'm unsure if it's break fade or if it's from the impact, but we are having some break Issues as you can tell and we're trying to finish this run Can't let the mountain Get me twice We have to be close to the finish Like we are somewhat very close. I'm on the brakes there Definitely got way tighter than I thought I was going to see I'm okay with going off on Little parts like that because there's some runoff of dirt and sand and not a cliffside If I wouldn't have slowed down for that, we would have been clear off the mountain side As I say that no Think we can think we can still save this Come on. Oh, we managed to feel saving get back on the road Our steering wheel is definitely not centered now, and it's very loose. I just want to finish this run I don't want pike speak to claim me again I could be driving this a lot slower, but I'm trying to be super quick That's that We managed to make it all the way up Pikes peak in the time it's at Evo and we managed to survive and not horribly die so Mountain pikes peak one me one That makes two runs up one run failed So make sure you guys follow me on all social media all that you found the description box below If you're not a subscriber, please do subscribe to the channel. I'll wait and And I appreciate all the support So make sure you guys come back for the next video I hope you guys enjoyed this because I want to see some other cars ripping up pike's peak Let me know down in the comment section down below and as always I think guys come back and watching. I'm evil rabbit I'll see you guys on the track


Aaron, can you just describe your frustration right now with what went on this weekend? To describe, you know, it's just disappointing all around. Just offensively, you know, I had a couple of chances up there for runners on base and it's not getting the job done. You know, I'm being a guy in the middle of the order. No, this lineup and the leader, you know, you got to step up in those situations.  So it's pretty frustrating. We're not able to, you know, capitalize on the ones like that when you got to get out of the rules.  I know Fulmer's got a good fastball, you know, hard slider, kind of looks just like the fastball and trying to get something out over and, you know, those couple of sliders took off on me and, you know, made some bad decisions. I'm not intentionally the one that's, you know, hanging up there at the top of his own.  I plug in my best when I'm aggressive and just missed two pitches off the plate. Aaron Boone has been for the most part overwhelmingly positive when it comes to this offense, even when you're in a rut that this offense is going to turn it around. Why do you think this offense will turn it around? Do you think this offense will turn it around? Oh, of course I do.  And, you know, that's where we just got to dig down deeper and, you know, make some changes. I'll figure out, you know, you just can't keep coming to the plate trying to do the same thing, expecting different results, you know.   So it's just about us, you know, doing our homework, prepare, you know, forget about the series, learn from it, and I'll get ready for the new one. We can take another question for Aaron if there is one. Lindsay Adler, please let me.  What do you say to your teammates at this point? Do you feel like you guys just aren't clicking? Like, how do you try to help, I guess, in baseball terms turn the page out of this series?  So it's just about us being honest with each other. And, you know, we see somebody make a mistake. I'll tell them, let's pick it up and let's go.  Let's pick it up and, you know, keep rolling. Something that I've noticed is that when you guys do have like a series or a game like this, where something starts to go wrong, then everyone sort of gets deflated.    That's one thing and basically you got to have a short memory. You know, some of the best and most successful teams and players have a short memory where, you know, they make an out, they make an error, they do something wrong, you know, they forget about it and move on and get ready for the next play because that next pitch is the most important one in the game. So I think it's just about us, you know, like I said, just have a short memory, be honest with each other, pick each other up and move on to the next book.


human beings enjoy categorizing things. It facilitates our understanding of relationships between things or concepts that might seem unrelated, as well as creating initial connections that in turn build relationships. It's no surprise then that personality quizzes are so popular, although the majority of them are either outrageously inaccurate or somewhat true at best. The Myers-Briggs test however has gained much popularity as a deeply insightful and accurate profile of characteristics within a person. Created by Isabel Briggs Myers and her mother Catherine, the test based on Carl Jung's psychological types categorizes individuals into one of 16 types. These types are four-letter acronyms divided on the different ways a person perceives and relates to the world and others around them. The four-letter acronyms can be defined as the following. Introvert versus extrovert. The first acronym characterizes individuals as either introverted or extroverted. A misconception of introverts is that they are reserved or shy but it is actually how they derive energy from their inner world and needs to be alone to recharge. They may feel drained of energy when spending too much time with others. Extroverts on the other hand are energized by interaction with people. Their energy comes from outer world activities as they prefer to move into action and make things happen. Intuition versus sensing. Sensing types tend to pay attention to the physical world around them. They notice facts, the practicality of things, and trust experience heavily. Intuitive types prefer to work with the abstract. Paying more attention to meaning and patterns, they are more interested in new possibilities or thinking about ideas than reality and hands-on experience. Thinking versus feeling. This pair describes how individuals prefer to make decisions. Thinkers prefer to make decisions logically by being impersonal to avoid outside influences. They look for logical explanations, then apply it when making decisions, and believe that truth is more important than tact. In contrast, feeling types weigh personal opinion and other points of view when making decisions. Concerned with harmony and how the people involved will be affected, they lean more towards tact than telling the cold truth. They like to approach decision-making through compassion and heart. And lastly, judging versus perceiving. These traits express your orientation to the outer world, what others tend to see. Judging types are commonly seen as orderly and live to have control over their lives through organization. They are seen as list-makers, task-orientated, and are usually work-before-play kind of people. Those who are a perceiving type, however, appear to be flexible and spontaneous. Instead of organizing their world, perceiving types look as if they want to adapt to it, mixing work and play and keeping plans to a minimum. So, those are the four categories of the Meisberg's personality types, and individuals' final type is made up of one of each of the categories, then combined. In the description below are links to various sources, including an online test to determine what your type is. Take the test and comment below in your type. I'm an INFP. What personality type are you? Also, how accurate was your profile? Thanks for watching.


Hello and welcome to Station 204. For your space news today on November 27th, 2019, we're gonna be covering China landing on Mars. We're also gonna be talking about JAXA out at Ryugu. But first, let's get started with our space traffic, finding out who's putting what where. We start this week down in Boca Chica, Texas, at SpaceX's Starship Test Facilities. During a Starship Mark I pressure test, near maximum operating pressures an anomaly occurred, sending the forward bulkhead on a rapid unscheduled suborbital flight with a maximum altitude of 100 to 150 meters. No report on his horizontal distance, but still an impressive feat nonetheless for a vehicle that was reportedly not going to fly to any height. Now for an intentional lift off, we're headed to the Xishang spaceport in China for the launch of a Long March 3B. Leaving the pad at 055 universal time, the hypergolic propelled vehicle lofted two Bidu navigation satellites into a medium Earth orbit, continuing to build out China's Bidu navigation constellation. Now parts of this Long March 3 rocket landed on a nearby village, and that's definitely smoke that you don't want to be breathing in, let alone being around. Now this all should be alleviated soon when launches are moved to the Xishang space launch site on Hainan Island. So then China will drop its spent boosters into the ocean. Jumping a little west and a few days ahead, a Soyuz 2-1V launched from site 43 and the Plesiysk Cosmodrome in Northern Russia, doing so at 1752 universal time on November 25th. The Soyuz successfully deployed a payload for the Russian Ministry of Defense, which they said would observe the status of other Russian satellites in orbit. Very similar to the launch of a secretive Russian payload in 2017. On November 26th at 1824 universal time, we got a little rumble in the jungle. An Arion-5 carrying an Egyptian government communications satellite, Tiba-1, and broadband satellite in Marsat GX-5 leapt off at the pad at the Ghana Space Center in Kourou. Carried to a geosynchronous transfer orbit, the two satellites separated from the upper stage at 25 minutes and 34 minutes after launch, respectively. And mission success was declared. This was Arion Space's 250th mission since the first launch of an Arion-1 in 1979. And here are your upcoming departures. Now I've got another space news bonanza happening for us this week. There's so much to talk about on, off, and going away from the Earth. So let's get you up to eight kilometers per second so you're at a speed to see it all. NASA is aggressively moving programs forward to send humans back to the moon to stay. NASA's commercial lunar payload services selected five more companies that can provide the ability to deliver 10 kilograms to the surface of the moon. SpaceX, Blue Origin, Series Robotics, Sierra Nevada Corp, and Tyvek Nano satellite systems joined nine other companies, including the likes of Mastin Space Systems, Astrobotic, and Lockheed Martin. Each company provides its own lander. For instance, Blue Origin is offering its blue moon and SpaceX is offering starship. NASA, as fast as they're moving to return to the moon for good, is still waiting on Congress to approve a budget that includes clips. A hover test of an engineering model of China's own Mars lander was declared a success at the public unveiling of the planned mission. To be launched in the July 2020 window on the powerful Long March 5 launcher, this mission will include an orbiter with science instruments, a lander module, and a rover contained inside, which if they're successful, they'll be the second country to land on the surface of Mars, which historically has a success rate of under 50%. China is also expanding its scientific collaboration with countries in Europe, but the United States has legislation in place that strictly forbids NASA's participation. Boeing's first spaceworthy starliner departed its factory last week and made the several kilometer trip to Pad 41 at the Cape Canaveral Air Force Station. Once out, Pad 41, crews lifted the capsule onto the Atlas V rocket, it'll ride to orbit and began connection closeouts that'll continue for the next few weeks. Still on track for a December 17th launch, starliner will not carry a crew on board for its first flight, but will have an instrumented dummy named Rosie in honor of the World War II icon, Rosie the Riveter. The Alpha Magnetic Spectrometer is one of those rare experiments that's easier to do in space than on the ground because it's easier to cool. That is, of course, until the cooling system breaks. And now we have to do some really complex spacewalks to fix it. ISS Commander Luca Parmitano and Flight Engineer Drew Morgan stepped outside to remove the cooling pump, which forced them to destroy the cooling lines so that a new cooling system can be installed. And now to talk a little more about this week's space weather, here's Dr. Tamatha Scove. Space weather this week continues to be a bit on the disturbed side. We are coming down from a solar storm fizzle that occurred last week, but it did bring Aurora down to many parts of the world. And as we switch to our front side, son, oh look, there's yet another coronal hole that's gonna be rotating into the Earth Strike Zone here over the next couple of days. And it could keep us at disturbed conditions and continue with this high latitude Aurora over the next couple of days. Plus, if you look, you can see a bright region that's kind of growing in the middle of that coronal hole. This bright region does have solar cycle 25 polarity. So once again, we are seeing consistent signatures of the new cycle. It isn't here yet, but we're getting stronger and stronger indications that it's just around the corner. As we switch to our far side, son, this is the view of the sun from Stereo's view and it's actually looking at the sun from the side. You can see the sun has been pretty much quiet. There's not any coronal holes and not a lot of bright regions, but over the course of the last 24 hours, we see two bright regions that have emerged on the far side sun. And this is good news for amateur radio operators and emergency responders because it helps boost that solar flux and helps radio propagation hopefully get up out of the poor conditions. Did you catch the bull-eyed explosion over Southern Wisconsin on November 22nd? This is the view over Madison, Wisconsin, captured by the University of Wisconsin AOSS rooftop cameras. The origin of this bull-eyed is most likely the Alpha Montserratids meteor shower that briefly turned into an outburst, the intensity of which has not occurred for more than two decades. Now, typically the Alpha Montserratids, also called the Unicorn Meteor Shower, only produces a few meteors each year. However, on this night, conditions were very different. Astronomers Esko Leitinen and Peter Geneskens published a paper recently on the unusual meteor event writing that the conditions are nearly identical to the outburst 24 years ago, which had about 400 meteors an hour. According to Leitinen and Geneskens, the outburst is caused by a dust released by a long period comet, but the comet's identity remains unknown. Now, several eyewitnesses to the Wisconsin bull-eyed said they saw a brief flash in the sky that came out of nowhere and the bull-eyed was so low that they even heard the explosion as it broke up over the water. Too bad the solar storm that was occurring at the same time wasn't strong enough to bring Aurora down to Wisconsin that night, but some people at higher latitudes got lucky. Not only did they manage to catch the Unicorn Meteor Shower, but they caught several of these meteors streaking through Aurora-lit skies. Gorgeous. For more details on this week's space weather, including when and where to see Aurora, how your GPS reception will fare and what's gonna happen for emergency and amateur radio, come check out my channel or see me at spaceweatherwoman.com. Just as SpaceX is revolutionizing how we launch and land rockets with Falcon, JAXA is now ushering in a new dawn of asteroid mining with its own Falcon. Hayabusa 2 follows its infamously problematic predecessor of the same name, which led JAXA's control room on a roller coaster of emotions. The largest solar flare in recorded history damaged its solar cells. That caused a slew of electrical issues. That led to propulsion issues. It had two reaction wheels fail. It had a fuel leak. It had navigation problems. And all of this before yeeting its landing probe into the inky blackness of space. And that's just the appetizer. Now, in stark contrast, Hayabusa 2 has had a nearly flawless mission. After five years and a whole lot of explosions, Hayabusa 2 is officially returning from near-Earth asteroid Ryugu with both surface and subsurface samples in hopefully unprecedented amounts. Not only will there be a huge scientific payoff, but the path to asteroid mining will definitely become more feasible. Unlike previous sample return missions, Hayabusa 2 is the first spacecraft to successfully collect subsurface samples using projectiles ranging from bullet-sized to literal bombs. Future mining crafts could build upon these very innovations to establish a more efficient method of collecting and returning valuable materials. Plus, we'll learn more about the near-Earth asteroids that would be prime candidates for prospecting, with Ryugu itself estimated to contain over $80 billion in metals, water, and ammonia. Now, my favorite part about this charming rubble pile is what's on the inside. This asteroid contains materials left over from the primordial solar system that evolved in such a way that makes asteroids a bit like old home movies. By studying how the materials have evolved and interacted with each other, we can essentially play the tape back as to how terrestrial planets formed. With these samples, scientists are hoping to answer some long-standing questions about Earth, such as, where did all this water come from? And where did all these organic compounds that make this little thing called life possible come from? So how much material are we actually talking about? Well, we won't know until it gets here. But the success of Hayabusa 2 and JAXA's rapid growth signifies a promising future of space exploration. From launching their first satellite in 2005 to pulling off a first-of-its-kind sample return mission not even 15 years later, the prospects for the mining and maybe even human exploration of our solar system have certainly increased. Thanks for watching this week's Space News. Just wanted to let you know that our next live show will be happening on December 14th, right back at our usual time of 1800 universal. Now, we're gonna be talking about everything that happened in 2019 in spaceflight. And take a look at everything that's hopefully gonna happen in 2020. So make sure to join us. We've got a lot of ground to cover. And of course, we wanna give a huge shout out to all of the citizens of tomorrow. Without your help, we wouldn't be able to make any of this possible. No news, no interviews, no letting off steam. So if you got something from us and would like to give something back, you can head on over to youtube.com slash tmro slash join. And you can also head over to community.tomorrow.tv to find other ways to help. As always, hitting that subscribe and notification button, liking, sharing and getting us in in front of as many of your friends as possible helps us pursue the core of our mission, which is to get everyone excited about space. And that wraps up this week's Space News. Until the next episode, remember, keep exploring. Let's get started with our space traffic finding out who's putting what where. And that's just the appetizer. Is that, I'm drawn blanks here. Is that good? Texas, it's SpaceX's Starship, Starship, Starship. So two has had a nearly flawless mission. Was that too douchey or was that okay? Okay, it was, it felt douchey. Yeah, it's gonna upset some people.


Hello and welcome to the session. In this session we will describe events as a subset of sample space using characteristics of the outcomes like union, intersection and complement of an event. We will make use of main diagrams to understand union, intersection and complement of events. In our earlier sessions we have learned about the meaning of sample space and event of an experiment. We know that event is always a subset of the sample space in probability. The universal set is the sample space of the experiment and circle represents particular event. Now suppose we have a sample space equal to a set containing elements 1, 2, 3, 4, 5 and 6 and yet outcomes of an event E is a set containing elements 1 and 2. So we can show it in a main diagram. For this we draw a rectangular box which represents sample space S and we write the elements of sample space in this rectangular box. Now event E is the subset of sample space elements of event E are 1 and 2.  So here we have drawn a circle which represents event E we will write elements 1 and 2 inside this circle. Now all the other elements of sample space S which are not in event E lie outside this circle. So we write here 3, 4, 5 and 6 and we share the circle representing event E which represents sample space S and event E which is a subset of sample space S. Now let us discuss union of events. Now let A and B be any two events. Now union of these two events is the set of elements which are in both A and B and here the repeated elements are written only once and it is denoted by union B. Now we show it with the help of main diagram. Now let sample space S is a set containing elements 1, 2, 3, 4, 5, 7 comes for event E is a set containing elements 1, 5, 2, 3 and event E is a set containing elements 1, 4, 5 and 7. Then in union B is a set of elements which are in A or in B or in both A and B and here the repeated elements are written only once. So this is equal to set containing elements 1, 2, 3, 4, 5 and 7. Now here you can see the repeated elements 1 and 5 are written only once. Now all the elements of set A and union B are contained in sample space S event A union B is a subset of sample space. Now let us draw its main diagram. Here first we have drawn a rectangle which represents sample space we will draw two circles representing set A and set B and these two circles will be intersecting circles because set A and set B have elements 1 and 5 in common. So here we have drawn two intersecting circles representing set A and set B. Now in this intersecting portion we will write the common elements that is we will write 1 and 5 here. Then we will write the remaining elements of set A and set B. So we will write 2 and 3 here and 4 and 7 here. Now all the other interface S which are neither in set A nor in set B lie outside the two circles. So here the only element is 8. So we will write 8 outside the two circles. Now here we have to represent A union B. Now here the shaded portion represents A union B. Also you must note that A union B means either or for example we can be asked find the probability that either of the events occur or find probability P of the event A or B. Now let us discuss intersection of events. Now let A and B be any two events then intersection of these two events is the set of elements which are common to both A and B that is we include only those outcomes which are in event A as well as in event B which is denoted by intersection B like in this example which we have discussed earlier we see that the elements 1 and 5 are in both the events A and B thus 1 and 5 are common outcomes to both the events thus A intersection B is a set between the elements 1 and 5. Also we see that A intersection B is a subset of central space S. Now let us draw its ring diagram here the diagram is same as above but the shaded portion will be different and we shade only that part the circles which is common to both A and B. So here this is the required ring diagram where the shaded portion represents A intersection B. Now you must know that in questions it can be asked find the probability of getting both A and B events of A and B that is probability of event A and B it means here we have to find probability of the event A intersection B. Now let us discuss complement of an event. Now let E be any event. Now complement of an event E is the set of those elements of central space S which are not in event E and it is denoted by E complement. Now earlier we have discussed an example where central space S is a set containing elements 1, 2, 3, 4, 5 and 6 and event E is a set containing elements 1 and 2. Now here E complement will be a set containing those elements of central space S which are not in event E and those elements are 3, 4, 5 and 6. So E complement is a set containing elements 3, 4, 5 and 6. The elements 3, 4, 5 and 6 are not in event E. Now earlier we have drawn a ring diagram representing central space S and event E which is a subset of central space S. Now here we have to represent E complement. Now here the shaded portion in this ring diagram represents E complement. Now you must note that we can be asked to find probability of an event not E or to find probability of an event E complement. In both these cases we have to find probability of the event E complement. Now let us discuss complement of two events. Now complement of set A union B is the set of those elements of central space S which are not in set A union B and it is denoted by A union B whole complement. Now from this ring diagram can we find A union B whole complement. Now only element 8 is outside the two circles representing A union B. This means 8 is not in complement is a set containing. Now let us represent A union B whole complement in this ring diagram. So we have the following ring diagram and here shaded portion represents B whole complement. Now you must note that if it is asked to find probability of an event neither in A nor in B then we have to find probability P of an event A union B whole complement equivalently it also means not in A and not in B. So we can find of an event A complement and B complement or probability P of an event A complement intersection B complement. So in this session we have discussed union intersection and complement of events and this completes our session. Hope you all have enjoyed the session.