Welcome to the Business in Hawaii show. I am your host, Gaila Nyonegita, and my guests and I are live from our home offices on Oahu, Hawaii. If you want to tune in live to any of the Think Tech shows, we are live at www.thinktechhawaii.com. While there, please subscribe to our programs and get on our mailing list. The theme of Business in Hawaii is to share with you stories of local businesses by local people, and our guests share with us their expertise, trials and tribulations while building successful businesses right here at home. In the Think Tech Studio today is Zachary Marita, educator at New Valley Middle School. Not only is Mr. Marita a music instructor, but he is also the winner of the 2018 Farmers Insurance Dream Big Teacher Challenge and the President of Hawaii Youth Percussion Ensemble. Mr. Marita is an innovator who breaks the mold when it comes to educating our young people. He inspires the students to become entrepreneurial thinkers using their passion for music. I am so honored to have Zachary Marita on the show today, and I want to welcome you, Mr. Marita. Thanks for joining me. Happy to be here. Thank you. You know, before we started the segment, I was telling you about just how amazed I am that you are able to take music education and combine that with other valuable life skills like entrepreneurial spirit, like building a business, like thinking and designing through a business plan. And I wanted to ask you, what inspired you to deliver education that's so interdisciplinary, right? Tell me about that. Tell me about your passion. One of my beliefs is that school education should help students prepare for life. It's not for the future necessary. It's because they're living life now and they're alive now, and we have to bring them those skills that they are gonna need to be successful and to really have an impact on a community. And if they continue just doing textbook work, worksheets, even in band, if they just play the same song over and over again for like three months at a time, that's not helping them. It's gonna give them some value at that moment in time and they're gonna feel good and they're gonna get the A, but really it goes beyond the grades. It's amazing. You are president of the Hawaii Youth Percussion Ensemble. Tell me about that organization. It started about six years ago to give students in Hawaii an opportunity to pursue their passion and percussion. As a percussion is my main instrument. And it's something that I really advocate for. I really believe in that anyone can do it if you have the passion, if you have the desire. And it's for a committed group of students to share their love of drumming. Very nice. I know that you have some photos to share with us today of some of the work that you've been doing with your students in hype. I understand that's what you call it for short pipe. Could you tell us about some of the things that you folks do? So the first picture is a screenshot for video classes that we're now doing during this time of distance learning to give students an opportunity to still have music as part of the curriculum. So sometimes I do step in but for the most part we have our hype students that do teach other students. We actually have a teacher from another school who was really interested in learning about percussion. So she joins in every week as well. That's fantastic. I know we have I think a couple of more photos that kind of showcase what you do with hype. Yeah, so the next one was a project that we launched called Drumming for Healing. And it was in honor or in dedication to our healthcare workers and first responders just because they're doing so much. Yeah, so using music as a way to give back. So we had 30 video submissions from 25 different people. We had some people from out of state as well. We had two math teachers participate. One was elementary school, one is at Pro City High School. So a whole bunch of people, some that didn't even really have music background but they wanted to be a part of this project. And it was really exciting and it came out really well and we were happy about it. So how did that distribution happen? Drumming for Healing, was it sent out over social media? Yeah, so I put out a call on social media and we use this program called Soundtrap and also we use iMovie. So I'm not a very techie person. So it was something that I had to research. I saw all these cool virtual ensembles and I thought, hey, we should try to do one. And I was able to figure it out and we got lots of participation. So yeah, it was good. It was a nice thing to do for our community. I think one of the key things that I'm hearing from you is that you bring together people without any music experience, build their passion for music and deliver amazing things. Do you often draw in people without any music background? On social media, a couple of years ago I started on Twitter and Twitter has been a lifesaver for educators. Like there's so many people on Twitter, educators willing to share and through that collaboration, through those connections, all these ideas come up. And it's something that I really believe in and I hope that other people watching this will also sign up for free resources. There's monthly chats sometimes and just discussions that go on that really elevate learning and education for our students. Fantastic. I know we have a couple more pictures about hype and I'd love for you to tell us about it. So this one is also related to the COVID-19 pandemic that we're in where there's a lot of live musicians who are really not getting any income right now because concerts, live concerts, symphony performances have been canceled. So there's this organization, the New Music Solidarity Fund. They raised about $100,000, but within a week they actually ran out of all of that funds because so many people were applying for it. So I got some of my students and they made quotes about music and why music is important to them and they made these posters to promote this fund to support our live musicians. Now, you must have a secret to how you get students because I'm homeschooling two of my own. But how do you get students to jump into a project? Of course that's not part of their required curriculum and get them to be involved in that. How do you do that? It like for anyone else, parent or a teacher, it is a struggle and it's something that we have to continuously find what is relevant and what is purposeful and what means something in our world. And we have to get the students to see that value and realize that their impact or their work can have an impact on others. And once they feel that, like they see someone post even just a simple comment on something that they did, they feel validated. So we have to continuously find ways for them to do something, to create something and not just think about it. It's not just the knowledge spitting out of information. So I understand that a lot of this work that you're doing with hype and even with just a group of students that kind of hang out in your space is not even part of your required curriculum over at New Valley. Is that correct? I saw my best to kind of leave everything, everything in because everything is connected. I try as much as possible but sometimes with education and with public student, there is restrictions there and you try to find ways that we can go beyond what the system allows for us. I know that some of your students have shared that you gave them a place to express themselves and to dive into other interests. And I know that you were kind of leading them down to a business building project. Can you tell me about that? Yeah, well, it was actually a student, someone that you know very well who had the initial idea of launching a business because like you mentioned, I wanna find ways for my students to find relevance and money is always a motivating factor for anyone. And so they thought that a business could be a perfect place to start with. So they started brainstorming what could a music percussion business do? So they came up with things that they could charge for performances. They could write their own music and do publishing. They could make sticks and mallets, produce sticks and mallets and sell it. So they're still in the brainstorming stage but one way that we're practicing it is through the online teaching lessons. If they get good enough, that is something that they could definitely pursue as something that has value and that people would wanna support, especially if they have a curriculum that they actually work on getting lessons, like what would lesson one be? What should we teach for lesson two? And having all of that mapped out, there is real value to that that other educators could want as part of their learning. Well, again, I commend you for, you know, taking student interest and just going with it even though it doesn't fit a traditional, perhaps a traditional music curriculum. And as a parent, I'm so appreciative of educators that are thinking outside the box and of course bringing together the different disciplines and showing our kids that they can have passion for something and all of it just kind of fits together. We are going to take a quick break but when we come back, I wanted to talk about some of the other things that you are involved in, which are equally as amazing. So if you bear with me for just a minute, we're gonna take that short break. This is Business in Hawaii, we'll be here back shortly. Aloha, my name is Mark Shklav. I am the host of Think Tech Hawaii's Law Across the Sea program. Being a lawyer has many aspects and I try to cover them every time I do a program of law across the sea. Not everything has to do with law or being a lawyer per se. Some of it has to do with the people you meet, the things you see, the places you visit. And that's what I try to combine in Think Tech Hawaii's Law Across the Sea. Thank you for watching, Aloha. Welcome back, this is Business in Hawaii. With me today is Zachary Marita, educator, innovator from New Valley Middle School. Ms. Marita, I, you know, we talked about the Hawaii youth percussion ensemble for which you represent and which you've inspired many middle school students to participate in. But there are a number of other things that we know that you're involved in, some of which are very, very significant grant opportunities. Can you tell me about some of those other projects that you're involved in? One of the projects that we just kind of wrapped up was a movement called Hashtag 808 Educate. So it is a group on social media that started on Twitter, but that has now moved also to Instagram and Facebook, where we wanna connect educators in our state, public, private, and charter school to just have a platform, have a place where they can share ideas and resources to have positive conversations that are gonna elevate our state of our education. And we had a challenge to get 808 educators on social media through these platforms. And then we also connected with Henry Capono, who put on a concert at the end of that challenge. Yeah, so we did meet the challenge, we're actually about a thousand educators strong right now. So there was really exciting to have that performance dedicated to our educators. He did shout out, we were able to take song requests, people dedicated certain of his songs to their team teachers, some of them did it to the entire school, some principals that shout out to their teachers and their students. So it was a really nice event to honor our teachers. Wow, fantastic. I know that we have a couple of photos of yet some other things that you've been doing. Could we put that on? Tell me about this one here. So Henry Capono, this is the concert that he did. It's called Dukes and Sundays at Henry's house and he does it every week. And it's in support of his foundation. He has a Henry Capono foundation, but this is specifically dedicated to educators. And then that student in the front is a New Valley student, also a member of Hype, who is actually neighbors with Mr. Capono and kind of introduced the concert and said what the purpose of it was. Fantastic. And I think we have one more. Yeah, so this one is a project called April Madness that relates to March Madness for college basketball. And what I did is instead of having basketball teams going at it with each other, we did it for classical music to support symphonies and orchestras. And then it was a month long event. And at the end, it was Beethoven Symphony V that came out as the proud favorite. Wow, wow, fantastic. Was there another one? There is, yeah. So this one was to me really special because our students at New Valley composed a full program. I think it was eight pieces of their own music. And each song, they dedicated to a public school educator. And then they presented their performance live at McKinney High School for the Hawaii State Teachers Association Hololulu District Institute Day. Wow. And then there's- And a voice something additional as well? One more, yeah. It's another project that I'm working on with some educators called Voice where the initial idea was to get $5,000 for our students to recreate education. So to go against the status quo, to not do what we've always done, but to really truly innovate from the perspective of our students, our learning. Just amazing. And I think it's such a valuable lesson that you're teaching to our young people to understand the idea behind innovation. Because innovation really is the start of some of the most successful businesses. And some will fail, and failures are good too. Failures are great learning experiences. I think it's an amazing skill that you're leaving with them. I wanna talk about the Farmers Insurance, the 2018 Dream Big Teacher Challenge, which you won a huge grant. I believe it was $100,000. Tell me about that. I know it was statewide. So it's a grant from Farmers Insurance Hawaii to put on what is called the New Valley Music Olympic Invitational. And we were able in our inaugural year to have over 170 students representing 20 different public, private, and charter schools in the event. We were so lucky to have had it early March. Right before all of this happened, we were able to have the competition. We were able to have the winners concert. And I really feel that it has strengthened our arts community. We even had a guitar ensemble. We had a harp group as well. But those are instruments we normally get to see, but to see them and to see their teachers really proud, proud of them putting the hard work to showcase their talents. It was an awesome event for music in our state. And I know I could just go on and on because I'm pretty aware of the time that you invest with our young people. But I know a recent, and I saw you do a presentation recently for a... I wanna say it was a mini-conference online. I mean, given our current situation, conferences now look a little different. It was a webinar by the Teach for America Hawaii. And I believe it was an iLabs series where you and along with other educators were getting together and pitching ideas about how education can be redesigned. And you gave a presentation on your ideas to build a performing, a community performing arts center. Tell me about that project. Okay, so this is like huge. And after winning the $100,000, I really believe that anything is possible, that if the community wants it and we can rally around the right kinds of people with a great kind of vision, then we can bring to life anything. Now, so even though when I did my research, there was a performing arts center in Florida that cost $160 million to build 10 years ago, but I'm pretty sure ours would probably cost $200 million that there is so much value to this. And when we keep talking to people and I keep getting students involved, there are so many connections to other disciplines where maybe it's not just a performing arts community center that we can bring in other subject areas, history, math, science, maybe media. There can be a media program attached to this and we can have hubs on each island or maybe in different communities, we can have various gathering places. Yeah, and I want it to be a place where students feel valued, students feel connected where they feel like they can truly explore their passions while getting guidance from professionals in our community. So is this an opportunity for you to join? Does the Farmers Insurance Grant and this project come together in any way or are they completely separate? They're completely separate, but this could definitely be a home for the Music Olympic Invitational. I can see that. This project, though, is very large and it's going to require a pretty good deal of research, of design and tell me about how you're bringing your students in to those ideas. There's a principal on the big island. Her name is Janice Blaber and she was in on the presentation as well and she had mentioned that it would be perfect to have the DOE have a course, like a design course where students could use this as their main project and where we can get students input, they can design it, they can maybe even build it and help in building it and maybe they can volunteer or they can work at this community performing arts center as training, real-life training. Also, there's so many facets, so many connections that we can make with this one building and I'm really excited to what could happen. And I think this is a photo that we have up right now of your, was this the first annual? Is that correct? Yeah, first annual. Very nice, very nice. I actually had an opportunity to attend and it was amazing giving students an opportunity to take their talents and I know that it meant a lot to them. I've always admired educators but I think what you're doing is taking that one step further and really engaging students in an opportunity to learn just so many skills and drawing in other educators to bring in their specialty areas to help you build that. The community also has a lot to gain by your ideas. So tell me what the future is for the community performing arts center because I'm really excited about that. I'm really hoping that we can talk like, it's really who you know. So really finding the right people that we can talk to that have had, I think everyone has had connections to music and going up maybe they had that special place in music where they played a band instrument or they went on some trip that was life changing for them. Yeah, and just because you're not, you don't have a career in music. There are so many ways to appreciate it and to support it. And this definitely can be a way to do that where there is music in anything and everything in our lives. And through this pandemic, so many musicians have used music as a way of healing. Like there was that viral video in Italy where musicians came on their balconies and they started playing together. Again, I talked about the virtual ensembles that people are doing that are really just giving people hope and lifting people up and showing how important and how timeless music really is. Well, I certainly hope that all the good work that you're doing isn't just limited to middle school because I think that you have so much to share with just the community in general and not just middle school students but elementary school students, high school students and beyond, I can really see that. I think a lot of times what we know is that being an educator sometimes is a thankless job. And I think that it's important for you to know that you have made impact on people's lives. And I have an example of that. There are three who claim to be your favorite students who would like to remain meaningless, who wrote you a letter and I'm gonna read it to you. It says, dear Mr. Marisa, thank you for all that you have done with these past three years, from eight-hour rehearsals to online teaching sessions. You have always been there to support and inspire us. We consider you as a teacher and a friend and we have accomplished so much with your help. And we enjoy working with you. We have had so many exciting experiences in your music room. Even through tough times like right now, you still came up with ways to make our education system better. Thank you for all the memories and because of you, our future is really bright. And they wish to remain nameless. I, again, am very excited about all that you contribute to our community. Tell people out there how they can get in touch with you if they're interested in joining you in your project for the community performing arts team. Before I say that, I wanna thank those three students. It really means a lot when students share how they feel and they show appreciation. Cause like you said, yeah, it's not something that a lot of kids do at this young age. So I really thank them for that. As far as connecting, you can email me at Zachary underscore marita at nvms.k12.hi.us or probably easier to connect with me is on social media. I do have accounts, I reactivate accounts on Instagram and Facebook for our challenge that we had a couple of weeks ago. But my main one is Twitter at Zachary marita, Z-A-C-H-A-R-Y-M-O-R-I-T-A. So those would be the best places to connect. And I'd be happy to start conversations with anyone out there who is willing to support. We wanna grassroots effort. Like we want thousands and thousands of people to be a part of this because then they're gonna have a stake in it. And once it's built, they're gonna wanna support it. I wanted to thank you again for joining me and I will be on the lookout for all the good that you do. Unfortunately, we are out of time. I wanted to thank Zachary marita again for joining us and a big thank you to our production staff back in the studio. If you would like to be a guest on our show, please let us and subscribe and leave a comment below. Business in Hawaii airs every other Thursday at 2pm and we look forward to seeing you here soon.


Every town has its own set of folklore and history. In fact, I'm sure the town that you grew up in had tales of haunted locations or crazy neighbors who were perhaps witches. Shoot, as we've discussed here in Atlanta, we had the folklore of a vampire at one of our music locations and down in South Georgia, they even have the legend of a werewolf. One town in particular here in America is famous for one of its urban legends, an urban legend involving the Boy Scouts. But before we go any further, you know what to do. Hit that subscribe button and give us a like. If you would like to help support the channel, we do have links to our Patreon down below. And I also would love for you to go check out one of our producers, Tiffany Monroe. Tiffany Monroe is a master Reiki practitioner. Her email address is located down below if you would like to use her services. Welcome to SOTaric Atlanta, my name is Bryce, and today we're going to be talking about Boy Scout Lane. You probably noticed that our set looks a little bit different today. That's because I am still in Nekomas, Florida. Please don't forget that I will be back with David Zublik on Tuesday on his channel on thedarkoutpost.com. And then hopefully by the time we get back into Atlanta, I will have more videos for you for the rest of the week. Boy Scout Lane is an abandoned road in Stevens Point, Wisconsin. For those who are not from the United States, Wisconsin is in the northern part of the United States. It is pretty much considered to be in the heart of America. The Good Old Midwest. You see in 1838, a man by the name of George Stevens moved to this area of Wisconsin to be a lumberman. George was from New York State. And you see, George was pretty clever because he picked this point along the Wisconsin River to open up an outpost or grocery store. And this was because all these men were coming into Wisconsin to try to work the lumber. And they were using the Wisconsin River as a bit of a highway. Again, we've talked about that a lot in our stories down here at Georgia, the importance of rivers. And so this outpost that was created by George Stevens ended up growing the town of Stevens Point, hence where we get the name. In 1858, 20 years after George Stevens created his outpost, the town of Stevens Point was actually incorporated. To presence, the total population is around 27,000 people. So this is a pretty good average size, what we would consider all American town. The town of Stevens Point has about 20 parks and a 26 mile long jogging and biking path. This is a fantastic town to raise a family. Definitely a town, in my opinion, that seems like a place where people probably leave their doors unlocked. Stevens Point is also semi famous for its world's largest trivia competition. You see, it pulls in about 12,000 people alone just for this one trivia competition. That means there's about 400 teams that come to Stevens Point to compete. And for the young people watching, or parents of young people watching, Stevens Point is also the location for season three of the Disney show Live in Maddie. I'm not familiar with this show because I don't watch Disney nor do I have young children in my house, but I'm sure for some families that might ring a bell. Now Boy Scout Lane is a 2,500 foot lane in Stevens Point, Wisconsin. Now many people say it's the name Boy Scout Lane because apparently the Boy Scouts did own some property around the lane at some time. Now the Boy Scouts of America were founded in the year 1910. Most people are familiar with the Boy Scouts. The Boy Scouts mission has been a mission to teach young men self-reliance. In fact, they say to prepare young men to make ethical and moral choices over their lifetime by instilling in them values of scout oath and law. They also teach a lot of self-development through many outdoor activities, including camping. You have to earn badges. We also know the Girl Scouts have an organization as well. However, we're not going to focus on the Girl Scouts because this story is about the Boy Scouts. I listen to the Girl Scouts or the brownies for like a week. I'm just really not into camping. But anyway, Boy Scout Lane might have been owned by the Boy Scouts. That could very well be where the name comes from. However, legend states that in the 1950s, a group of Boy Scouts went camping in this area. It was one of their adventures where they could earn their badge or whatever it is that the Boy Scouts do. Well, anyway, the story does not end well because apparently something happened to all the Boy Scouts and they died that night, leaving this area, this Boy Scout Lane, to be incredibly haunted. And of course, as most legends go, there are lots of different alternative stories that explain the hauntings. The first story is that it was the bus driver himself that drove the boys out to this desolate road for camping and went crazy and murdered them all. Another story is that perhaps it was the Scout Master himself. I guess the Scout Master is the grown-up in charge of the boys that went a little berserk and murdered all the boys. Although there's a whole other story that says that the boys woke up in the middle of the night and took their lanterns and decided to go explore the grounds. And one dropped his lantern and the whole forest ruptured into a fire, killing all of the little boys. Now it was definitely hard to find evidence to support any of these stories. In fact, it seems more logical that the road got its name Boy Scout Lane because the Boy Scouts probably owned this property. I couldn't find anything to indicate whether there was an actual catastrophe that happened in the 1950s to cause all these young children to die. However, that doesn't mean that there isn't something there. In fact, many people who have been on this property claim that they feel like somebody is watching them. They also claim that they can see little lights everywhere at night, like they're the boys holding their lanterns roaming around through the forest.  Now today in 2020, the land has been bought and is privately owned, so please don't try to go to the land to see for yourself because it is private property and it's not worth getting arrested for. But you let me know, do you think that there's some validity to this story that perhaps there was a huge catastrophe in the 1950s that led to this land being haunted or do you think this is just an urban legend? Like a snowball growing and growing and growing until all of a sudden there is no truth left to the tale. But that's the case. What about all the people that have experienced paranormal phenomenon on the property? Alright guys, once again, I'll be on with David Zublik tomorrow on his channel on thedarkoutpost.com. We are going to be talking about vampirism and its supposed origins in the Bible. I've got some really exciting topics that we're going to go through on his channel in these coming weeks that I'm super stoked about. And then once again, as the week goes on, you'll have more videos again on this channel as well. Thank you so much to Josh McCabe for doing our music. If you would like to purchase our opening song, that link is also in the description box below. And thank you. Thank you. Thank you to Todd Roderick for helping me produce and edit this video. Please go and give his band the Flying Mystics a like and a subscribe as well. Alright guys, thank you again so much for sitting through this story. Hopefully it was a really good distraction or a fun distraction from all the craziness that is going on in our world right now. I hope you guys are all staying safe and taking care of yourselves. And I will talk to you soon. Bye.


Valerie. Oh, yes. Say hi to the vlog. Hello. How are you? Welcome to a new vlog. We're doing some family day. We are outing things We are. Honestly, I'm feeling lazy. So I think I'm just going to Montage this. We're taking our doggy. I'm gonna get Starbucks. My parents are gonna go to this Maple Street Biscuit company. It is like good if you like breakfast, but I don't so I was like, okay I'll go. You can get me Starbucks and they're like fine. So I'm gonna get the Starbucks. We brought Piper. She loves her outing. Valerie already said that her tail is dirty So it sucks, but it's fine. It's fine. She's going pee now Okay, so the craziest thing just happened I was gonna just do like a montage or whatever because we're at the Maple Street bakery Biscuit place and I ran into my old high school friend Kayla. She's been on my channel and stuff I don't want to like vlog because she's like with her boyfriend and everything But yeah, it was like so cool. Like she's actually one of the people that like provided me with like one of the best nights of my life I need to tell her that in just a minute. I will let me just go tell her that real quick But I got my Starbucks. All right update just told her feeling the egg bites today. So I got the kale and egg Mushroom bites and it's right here. So I got this and then my iced coffee We're rocking and rolling here All right guys, so we made it downtown shoot an afternoon downtown poop, which is fun But we're here in downtown celebration at the farmers market. It's about to rain. So Yeah All right, so don't mind Sherry's hair we got caught in the rain There's Valerie So this is a blanket and it didn't have color choices. We already have one. It's tan in the picture. It looked gray So let's just hope this is really gray. It's not great. We'll just have another can one It'll just have to go with the rest of the stuff here And I think it's bad It's tan wait really Look great in the picture Color choice Wait, yeah, I guess Oh, well, it's the same one I have but it's a good blanket Well as I pointed out Sherry we do have wood in here. Yes, so it could come and it comes with a nice little separate bag See We love serenity To the dog park it is Saturday as you guys saw I went to spend Had to run like an air into the pharmacy and now we're here my hair be looking crazy, but Yeah, we are just going to go to the park for probably like an hour She really wanted to go out of that. I need a call to make her grooming appointment for next month I forgot to do it when I was there. Okay, so we're back from the park Obviously just sitting outside and I've been playing on my phone stuff like normal But I think I'm actually going to start reading this again I started it and I watched the whole like Netflix series on it So I kind of know the story, but I wanted to read the first one before I read the sequel I was gonna edit a video and get it up today because it's Saturday and I haven't uploaded a video in a while But it was like five minutes. So Yeah, I'm just gonna continue on the video because I'm not gonna upload a five-minute video tomorrow I was going to be super fun because my friend Kyra is coming over I think we're gonna have a pool day and then we're gonna go see the new Barbie movie. I am so excited I don't know. I've just heard really good things about it and Obviously, I'm now in my pink era. Yeah, I just wanted to catch you guys up. Feel you guys in and chat a little bit The next day. All right, so it's next day. We're at the pool. Kyra made it. Where is she? There she is and And yeah, we're just gonna hang out. There's some alarm. Maybe it's construction Someone's house is on fire. That's great. Do you have your cereal gloves? It is time for the photo shoot so I Don't know. I don't know Kyra's gonna be the creative director here and tell me wait show us your camera Oh, it's so cute. It looks just like the G7s And then wait how oh I see I see nobody has the flip-up screen and everything Okay, so we just got done taking photos, which I wasn't like expecting to take photos and I didn't want to get my hair all But that's why it's like up in this bun, but Kyra, I Got some good ones. She was like, can I use your camera? I thought it was she was meaning like the G7X But I was like no, I have like my other camera. She's like that's fine She brought her SD card but forgot her battery as you guys know I was like, is it okay if like we use this one and she was like, oh, yeah, I don't care I'm like, okay. So then we started using this camera. This is the m50 with the 11 to 22 millimeter lens and We got some good pics of Kyra and she's like, do you want any I'm like I wasn't really planning on like getting any today, but I knew you wanted to use my camera So we took some of me and like do you ever just feel like yo? I feel like I feel like this but then when you look at it's like that's not it Cousin this one. She was I was like, I want to just try this She's like, okay, and then she takes it. It's like me laying on my stomach. I'm like watch out for the back And you know what? You know what? It looked really good picture here. Yes But I was like, okay, that's the one I don't need any more success We are going to have Panera for like lunch. It's kind of like a later lunch, but it's fine So basically I am going to make a tiktok and vlog this event So it's gonna be kind of hard for me, but it's fine. I want to do both So bear with me if I don't get that much vlog footage I'm vlogging and doing a tiktok. So if you want to go see this process you can go over to tiktok But basically I had to cut the bottoms of my hands because it was like raining and they got all wet And I was like, no, thank you Say look a little cray-cray But it's good. I wanted to wear this outfit so bad So it is what it is. I finally get I finally have a purpose to wear this outfit So it's fine if the bottoms look a little Jagged. All right, so I got the pic too like normal and Kyra got this sandwich. It is kind of small I'm editing this video now and I wanted to give a little review of the movie So if what you're thinking the bargain movie is it's probably not I Definitely wasn't expecting this type of movie. It's I don't want to give any spoilers away But I will say it's a very like feminist movement movie movie Excuse me. It's so good. It's like a women empowerment. It's funny There's some emotional parts if you do get like emotional well, like there's only one But it's not like really made for kids I mean kids can go see it like if you're a parent That's like kind of strict on what your kids see like probably it's not made for kids But definitely I would be allowed to see it if I was like five years old because I've been watching things that like are not age Appropriate. It's just like my parents have always just explained to me what was going on and like so I could understand it Like rent and everything. Oh, oh my god. Oh, is it a lizard?  Okay. Anyway, I like freaked out over a lizard Yeah, really good movie not what you're expecting It's made for adults the only reason why is because there's underlying messages about the movie. It's not just like oh Barbie and Ken There's just like underlying things that you would probably need to explain to a kid if they didn't really like understand it And there's like references to that like probably more adults would understand But I think a kid could see it like there's no harm and a kid seeing it, but overall it was a really good movie I think you should support it and I'm loving all the pink like I'm definitely in the pink era now because of Barbie So definitely go see it if you guys enjoyed this video Which is like a mash-up of a bunch of different days give this video a thumbs up. Don't forget to subscribe I should be having more videos come because I have two weeks off So I'm planning on vlogging more fingers crossed because I do want to break But I also want to film some content so pop on over to it tick tock So you guys can check out some of my content there and I'll see you guys in my next video Bye


Hey everybody it's Hari Swaminathan from optiontiger.com looking at Thursday's action on the SPX and very very weird because the market started off okay, was on a nice climb so you could have gotten a small trade here especially you know once it got into the green you know trade may be up till here not a whole lot just a small trade however news events I believe it was about the US China talks came out and boom the markets just started crashing and so even if you miss this bar and you got it here that's from 2981 you would have stayed in the trade all the way till here and so that would be 2958 so that's about 20 points plus this would have been a fantastic trade again even on Wednesday after the Fed announcement the markets tanked and so you could have cashed in over 5000 dollars on that trade and at least 3000 dollars on this trade so at the end of the day you have to watch for these kind of moves you know when you're as I said morning session might present one opportunity afternoon session might in this case it was driven by some comments made by President Trump I believe whatever the case might be if you're watching the markets and if it's tanking like this you've got to take the trade and so you know these were these were two days that you could have made excellent profits on the SPX algo itself so you've got to watch for these kind of opportunities thanks


And I like to frame it with this question, what is required to consider something alive? Well, often sort of the first question is something that requires that there's actually a structure. So for example, a body or a cell. So we can draw some sort of structure here. And then when you have a structure, something like this, you have an inside and an outside. One of the purposes of the structure is to keep the living parts of the cell on the inside, to keep them together, to help them function. And so what are some of those functions? So they can include things like getting energy. A cell or organism needs energy to be able to grow and reproduce. This energy, if it's going to grow, it means that it's creating more biomass and that takes chemical reactions. And chemical reactions take energy to constrain. There's an increase in entropy or disorder generally in an environment, but something that's alive has this structure. So it actually has to be able to resist that entropy by expending energy. So we call this energy metabolism. So we get our energy from eating organic molecules and reacting it with oxygen to produce carbon dioxide. And we have all these enzymes within us that capture that energy for use to build our cells. And the cells are also reproducing. That reproduction requires basically a recipe for how the structure or cells can perform their metabolism. They extract that energy. So basically this recipe is what we call inheritance. And for us, that's our genetic material and how it's arranged and the chemical environment around that. So basically for something to be alive, we have to have a structure that can harvest energy to grow and reproduce. And the instructions for doing so need to be passed on. One of the key things is for this energy and metabolism and the chemical structures to keep working properly is that the inside of the cell needs to be a fairly consistent environment. The environment changes through time. And the wall or structure that separates the life from the environment helps the organism keep this consistent chemistry. And it can be things like for larger organisms things like temperature. We have a body temperature. And this consistency is called the homeostasis. So homeo is same and stasis is state. So a lot of times organisms keep a consistent chemistry within the cell. And sometimes they can't do that or they don't have enough energy for metabolism. And when these properties break down, you actually have death as part of the cell. When the requirements for life are not met, the organism dies. So we can take these ideas and make it more of a list of things that are needed. So we can annotate this with the words people normally use. Structure is one of the key aspects. The internally consistent environment is this homeostasis. The harvesting of energy is metabolism. And the reproduction and the inheritance are words that people normally use. So what we have is five properties. Structure, homeostasis, metabolism, reproduction and inheritance. And those are what makes something alive. Thanks for watching.


Hi, everyone. First of all, thanks for coming. Today, I'll be talking about how you can generate actionable intelligence from open source data just using AI. So a little bit about myself. I am right now in the advanced analytics practice at PWC. Before this, I was working with the Indian Army with various applications of AI, and I worked on several cool projects like actionable intelligence gathering through AI. I've also made a bracelet which tells you when you're going to have a stroke and all that. We'll not talk about everything, we'll talk mostly about how you can use only open source data to generate intelligence because that was most of my work. So now, before I get started, I think I should tell you all that open source data is extremely difficult to work with, right? So there's this quote which I wanted to share with you guys. So this should stick with you for each and every data science project, okay? I see a lot of people actually ignore the data in their machine learning projects. Your data, you have to work 90% on your data, 10% on your machine learning code. You need to extract as much information as you can from the data and that's very important. Without that, you're basically nowhere. Machine learning is very easy. There's one line of code to any machine learning model, so that's not a big thing. Now, open source data gives horrible results. Okay, straight off the bat, if you work with open source data, it gives horrible results. The best results, okay, how many of you have done any courses online for certifying? Okay. Okay, so the thing is, when you work with data given in certification courses, you get good results. When you work with competitions, you get worse. When you work with real-world data, you get even worse. And open source data is garbage, so I made a very neat visualization so you can understand how open source data results are. So as you can see, not very good. You can't even make it out what it was. So fine, let's get started. So open source data in a nutshell is basically data which anyone and everyone can use, modify, share, and they're free to do whatever they want with it. Nothing big. But the thing here is open source data is an ever-expanding source of data. It increases every second, every minute, and it's a growing data source. Now, can anyone here tell me what is the number one factor which affects the performance of any machine learning model? Any machine learning on any deep learning model? Quality of data. But you have 10 good quality data versus quality and quantity of data. Not just quality. You need a lot of data. So even, I guess, logistic regression will beat the topmost algorithms if it has more data to work with. So here's where we come to our first paradigm which we're going to look at is large data versus relevant data. So when working with open source data, let's say you get 100 rows which you think are relevant to your use case. Chances are only 40% of them will actually be useful. But open source data is very tricky in the sense that it disguises itself as being useful. So you need to really know what you're looking for. You need to really do your research to understand what was going on here. And you'll look at this in the later use cases that I covered. So my main point in this talk is I'll talk about a few other stuff which I have done with the Army and with other stuff. And basically I can guide you through what challenges I faced. And hopefully you guys will not be working on such critical projects but you can apply it on anywhere you want. So the second thing you're coming to is analytics as intelligence. So when I was first contacted by the Army, they told me that they wanted a social media intelligence platform. And I was like, fine. So I went home, I did some research. I saw a few of the top tools. I thought, okay, what could, it's not that difficult. I'll just take the best features from every tool and I'll put it into one, I'll show it to them. So here's a sample dashboard, right? So this is a social media tool which is available. You can see, it's giving you tremendous insight. You can see the audience interaction here, the audience impressions, how many retweets you're getting. It's giving you insight. Another dashboard, same. This is giving you a more textual format but it's giving you more detail as well. So you can again see your interaction, how your brand is wearing the sentiment and all that stuff. So when I presented it to them, they said simply that it's not acceptable. This is not, this is nowhere close to what you're looking for. So can anyone tell me why? Okay, I'll give you guys another clue. They said this is analytics, not intelligence. Yeah, so sure, this is, someone said not actionable. Yeah, so this is not actionable, right? Okay, sure, you can see on 1st Jan you had good customer interaction also on 1st November. Now intelligence on this use case would be why did you have good customer interaction here? What was it that you said before that you didn't get any customer interaction? Is there anything you can do? Is there anything driving your customer interaction? So that is where the crucial intelligence comes in, the answering the why, which many people lack or which many tools lack, not people. And in fact in many data science scenarios also, people don't answer why. They try and give you analysis and they just leave it at that. You're supposed to infer. So that's what we mean by analytics was intelligence and we'll be moving towards intelligence, right? So we'll be covering a few use case. Before that, let's just go to the open source workflow. Now, here the first two points are very, very, very important. Not just for open source, but I think in any data science project, any AI project, and I think they are heavily ignored by people. The first one is research. Doesn't matter what domain you're working for, especially when people are working in consultancies. I see they, because they have to work on a lot of domains, they don't do too much domain research. They just go from project to project and that's not acceptable. Any data science project, you need to know what you're looking for. You need to know what exactly a problem statement is. Now, second thing, this is more towards open source. You don't know your problem statement when you're working with open source. Mostly if you get a project in a company or something, you are given a problem statement, right? Okay, this is your data, predict risk. This is your data, predict customer interaction. In open source, you don't have a problem statement. So for example, when I was working with the Indian Army again, I had data from Twitter and all that sources and they told me, okay, get intelligence. This is not a problem statement, getting intelligence. You have to formulate stuff through which you can act on it, right? The other two approaches, I'm sure all of you do very well, understand and clean the data and try various similar approaches when you aggregate it to get results. Now, three things to consider before we get started. In open source data, you must be good at getting something from virtually nothing. So I said virtually because there's always data. There are always patterns in data. You just don't see it. It's hard to see, but that's why we have machines and machine learning, right? To see what we cannot see, there are always patterns. You need to understand that and you need to torture your data. You need to get them out. Second thing is, again, it's very important. You need to understand your problem. If you don't, you're nowhere. And the third and the most important thing is stop trying to reinvent the wheel. How many of you here would say that you're good coders? Seriously, one guy. Come on, man. At least a few people.  In open source data, in data science, most of the stuff has been done. There are so many great researchers who have already done their stuff, done their research. Why do you want to start from scratch? Any problem you're having, 99% of the time, someone has done it, someone has done their research, someone has presented a paper, a lot of cases you will find GitHub repositories having it. So you should always look for if someone's already done something and then work along with it rather than trying and making something from scratch. So let's get to our first thing. Region estimation. So while social media intelligence was my problem statement, first thing I thought was, okay, I have all this Twitter data and they wanted to know location of people were using Twitter, right?  So they said, okay, we can't give you until you give us a POC. So what I had to do was I had to look at Twitter data and predict location. Now I had no training data, no testing data. Now this is where formulating the problem statement comes in. You need to understand how to formulate the data, put it in the problem statements all the way so you can use machine learning. So here's our workflow. So the first one is, first and the last ones are just open source search. There's nothing to do with data science. The three in the middle, activity-based predictions, slang-based predictions, and network approximations, these are what we'll actually use to get a person's location, right? The first one is open source search and this is something which we have to do. Like suppose if you were on Twitter and you're not giving a location but you're on Quora or Reddit or some other place and someone asks a question, how is it like living in Mumbai and you answer, I've been living in Mumbai for 10 years. Why am I approximating a location then? I can obviously see that, right? So, and you'd be surprised that even the safe, you have one username, how many other websites have people with the same username? I'll show you guys a quick demo of that later. So the first thing we'll do is activity-based predictions where you'll try to get your location based on your activity, like when you're active, when you're not active. Second thing is you look at slang, and third thing is network approximation. So what we do, I'll just show you a quick video of how I'm approximating the location. So what I did was when it comes to region approximation, identifying a country is very simple through activity because we live in different time zones. It's easy to identify which country someone is tweeting from based on their activity. But once you get inside a country, like inside India, it becomes difficult. You lose your accuracy. For every interaction, for every prediction, we don't take the number one thing with the model that's predicted. We take the top two cities. So suppose my model is predicting Mumbai and Kolkata, I'll take those two, draw a line between them, and keep a pointer in the middle. I like to call this the slide-point approach. And then I move the pointer based on what my other models are telling me, right? So, I'll just... How would I play this video? The video, please. Sorry for the bad animation, but this is how the workflow is. We start with region estimation where we try and estimate a country first, which comes pretty easily. It's not that difficult. So for here, let's say we have identified India, right? Now that we have identified India, we need to go deeper, and here's where the real problem is going to start. Let's go deeper. Yeah, so here we're going to try and predict the cities. So let's say we take five cities as our sample set, right? So we are trying to predict within these five. And the first region estimation model says, okay, it's either Mumbai or it's Kolkata. So we'll take these two cities, we draw a line between them, and place a slide in the middle. Now the model says that the probability of Mumbai is 0.6. We move the pointer towards Mumbai. Second model, Slang Detection 1, says it's Kolkata. We move our pointers to towards Kolkata. So like this, we get the results from all our models. And the final, as the closer the model, the pointer is towards the city, the better confidence I have, the smaller radius I can give you. Okay, so this is actually where he is located. The farther it is, the worse it's going to be. So I think I'll show you guys a demo before I actually start with this. I'll take questions. So this is the model for tracking. So in the start, we'll just look at one person who we used to be very interested about with this guy. So if you see his location, he has given it everywhere. So that's not really a small radius to look into. So let's try and get his location. So we used to keep tracking him a lot. In fact, I used to keep tracking him. So the thing is this guy roams a lot. And every time I track someone, I maintain a record in the database. So I can understand where he is moving and what his pattern of movement is. So with this, I've already loaded the result for this one because he has a lot of data, so it takes time to predict. So the moving pointer you can see here, this is the latest location which we found as per his latest tweet. And you can see quite a few places where he was actually tracked. Now, one in particular is you can see the one in the middle in around eastern China. There's a big round circle around him. That's because we weren't able to predict his location very accurately. So the radius on that thing is around, I guess, 300 kilometers. Yeah, so this is a 337 kilometers radius in which you're predicting. Otherwise, at other places, we can go down to 28 kilometers as well, where we'll be able to get his location. So one thing which I tried to do at... I spoke at Google DFS last Sunday. So what I tried to do there was, I tried to get one of the speakers who spoke before me and I tried to predict a location which was... And it came out correctly. It was the same thing for ODSC, but how I did not find any speakers matching because most of us have already given a location. So this guy was, again, the closest one which I could get, but, again, his location is present. So again, I'll go with the same example which I went for the Google DevFest. So Priyanka Sinha is a researcher at Tata TCS, right? So now hopefully this will make you understand how this thing works. So let's try and get a location. Hopefully my net is working good. So you can see the bottom part of the screen. The models are actually trying to approximate the location and the location radius keeps getting smaller and smaller based on how high each model's confidence interval is. So we go to 300 kilometers using just machine learning model. After 300, to get more insight, that you need to do a little more domain research using some backdoor, some cybersecurity knowledge, using IPs and all that stuff. You can pinpoint it more. So we have already down to 48 kilometers. Hopefully it'll go better. Okay, it's gone. So you can see just one second. So you can see your location is like shown here, Kolkata. And the thing is we don't use a pinpoint thing, right? So we, although the radius is, it's triangulated through a lot of models, the result might be the same when you search for the multiple times, but it actually gets deviated. So for example, I've searched for a widely testing also. So if you can see, there are so many places around here where she was tracked, right? So although all the models are predicting the same place, it's not that pinpoint accurate that always didn't predict on the same place, even if I search for your name again. So that's just a slight take-up. Otherwise, that's how it works, right? And I'll show you open source analytics as well. So this I just did it for my username, right? So just for my username, you can see there are so many websites where this user is present. So out of these, I'm only present on three. But again, it can give you a helpful insight on where your users are also present. So you can do more research on that. Now moving back. Okay, so like I talked about region estimation, the first step is trying to estimate which country the guy is coming from, right? So this is actually pretty simple. We look, we all, different countries are in different time zones. So estimating location through that is really straightforward. You can see the activity graphs from Mumbai, London, and San Francisco. You can clearly see the difference of why I want a machine, right? So this was pretty simple. So for international level prediction, for this presentation, I took five cities, Mumbai, London, New York, San Francisco, Canavera. And I couldn't gather too much data because of a lot of logistic reasons. So can anyone tell me what sort of models do you think will work better, generative or discriminative? Even when you don't have a lot of data? Yeah, so what we found out is if your data source is less, which is very likely because most of you guys will be using Twitter APIs and stuff like that to get your results. The API has rate limitations. You won't be able to get too much data, especially because if you get 1,000 rows, only 200 or 300 are going to be useful. So it's very difficult to get data. So as long as you have less data, generative models will work better because they work on the distribution. So that's better. But if you manage to get bigger data discriminative all the way, definitely. We just wanted to put it out there. Now we talked about large data versus relevant data in the start. So I, like I said here, just cleaning the data and getting rid of stuff which can hamper a model will push the accuracy of 59 to 71% without changing the model, without doing anything. So keep in mind the data which you are collecting is I'm getting all the users from Mumbai and for every user I'm seeing what time it reads and mapping it on a plot. So it's like for every hour there's a percentage of activity plotted. At 1 to 2, he spent 22% of his time or 22% of his tweets. So can you guys help me out with this? What sort of data do we not want? What sort of people do we not want in this data? Yeah, people working in AXIS, but there's no way for me to find out that. So that's not something I can do in data cleaning. That's not something I can... Very good, very good. So one good point is if there are too few tweets, that will just skew his graph. So for example, if there's a guy like me whenever tweets and today I say, okay, there's a giveaway, so we give each other and I tweet at 12 o'clock. My graph will be 0% for the entire day except 12 o'clock that's 100% activity and that's not something reasonable which we can actually feed any model. So that's one. What's the second one? Bots, fake tweets is fine. Their activity is still going to be same for the most part. Bots is a big problem. So the two biggest problems which we'll face is robots, cyborgs, or humans, and rare users. By cyborgs, I mean most company Twitter accounts are cyborgs, right? So for normal stuff like answering queries and all, they use robots which the bots answers simple stuff or sorry, we'll get back to you. Please reply to this mail idea and all. But if it gets complicated, the humans handle it, but they are very difficult to detect. When it comes to robots, I had to train my own support vector machine classifier using different instances from its profile and getting information. So robots are going to destroy your model and tell you right now because once you gather collecting data there will be a lot of robots in there and you need to get rid of them. The second thing which you need to get rid of but it won't be that difficult is rare users. Like he pointed out, if someone is just waiting for one day, you can put a cutoff if you have less than 30 tweets from the last month, I'm not interested in you. So that's one thing which you can do. So let me just show you some of the accuracy stats which I got just from this data. So if you see the international, this is the balanced accuracy which I'm showing you. So if you see internationally the worst we are doing is 77%, which is not so bad. And you can still work with it. We are getting as good as 81% for some classes. So not that bad, but when we go to the local level we are doing as bad as 50%. So that's not something which you can actually work out of. We need to improve this. So what do you do to improve the results? Get more data. We don't have more data. So forget that we have the option of getting more data. I've used a knife bias to get this. What can I do more to get better accuracy? Anyone? Deep learning? No, we have it. I'm saying assume we have data. You guys will work on it properly. So deep learning, anyone? Okay, you are a good crowd because we will not use deep learning for this. It's too trivial of a problem to use deep learning too. You don't need to use deep learning for everything. I'm saying you guys are a good crowd because when I was at Google DevFest everyone was shouting, deep learning, deep learning. Now we'll move to the second piece which is slang detection. Before I get into the problem statement I'll just... This is the basic NLP pipeline where you have a document, you tokenize the words, remove stop words, you do stemming, bag of words, TFIDF to get the topics, model creation and model evaluation. So I think everyone should be familiar with this, the basic pipeline for slang detection. The basic NLP pipeline. So this is clear enough. So let's get into our next topic which is slang detection. Now another thing I thought of was India is a very diverse country. So I have lived in Mumbai, I've lived in Chennai, I've lived in Delhi. So I know people talk differently in every place. They all speak in English but there's a difference in how they talk and the same difference is used when they are actually speaking on social media. So one interesting example I can give you guys is when you go to the south, like Chennai and all, Bangalore also in fact, the tweets tend to be spoken in fuller sentences. So tweets will be, hey, there is no cake at this party. For the same tweet if you move north, Mumbai for some instances, the tweet, they don't use full sentences. Your tweet would be, there isn't any cake so that isn't and all that stuff. So I thought maybe I can look out for stuff like that and slang terms like bye and all that stuff which people use in different parts, it changes from place to place. So that's the problem statement. So now if you look at it, then this is a sentence classification problem. So which is the best model for text classification? RNNs, right? Because you have a memory layer so you know what the sentence, you know the meaning of the sentence, you know. So that's why RNNs are better. But no, we don't want to use RNNs here. Why? So if I get short sentence, a lot of the time for the case of Twitter it might be fine because the sentence is sometimes maybe 8 or 9 words. So if you have a Facebook post where there are huge posts, why not RNNs? So, yeah, so the slang words thing is the thing is you don't really care what the sentence is meaning. We don't care what they're talking about. We care if there are some words present inside them which we can give us an insight on where they are from. So RNNs are going to take too much computation time, too much time to train, plus they're doing something which we're not even interested in. We don't want to know the meaning. So RNN doesn't suit our needs. So we are going to work with CNNs here. So how do you work with CNNs for textual data? So the idea is the convolution layers will fire when they see a particular pattern. And to get that pattern, how do you set the pattern? Like if you want the pattern to be 2, like 2 words per pattern, like, 3 words per pattern, something like that. So size of the phrase. So by changing your kernel size and concatenating the output, you can actually train your CNN to look for phrases of a particular size. So that's how CNNs can be used here. And they are actually pretty good when, and it did give me some really good results for classification for text. An ideals, although this is again, I'm sorry, this is again vary from person to person, but an ideal scenario, 2 grams or a size of text of 2 is best. So just a bonus slide for vectorization I highly suggest to use Google's glove. It has a vocabulary of around 400,000 words. It is a very heavy model. So if you download models around 1.8 GB, something like that. So that is something you have to be careful about. But the reason I like this so much is because we are looking at slang terms and I highly doubt Google has trained their model on Bhai and all that stuff. So it's very easy in glove to represent unknown words as vectors, or randomized vectors. So that's why I really like this. Again, you guys don't need to do your own word to vex stuff and all that stuff. You use what is pre-trained stuff is available for glove. Use that. No need to generate your own stuff. The next thing is networks. So the idea here is this is not too much into machine learning. I won't talk about how I put graphs through machine learning. This is more into getting inside. So for some cases you get to a point and then you are stuck. So even machine learning is not always 100% accurate and for some cases you won't be able to predict your correct output. So do you guys know graph theory? Yeah, so there are a lot of big terms for graphs which are called, but I'll just make them simple like the actor networks, bimodal networks, semantic networks. These networks are fused with social media, give you tremendous insight into what's going on. So you might also be any e-commerce firm, any other firm which is doing social media analytics and if you don't look at these graphs, you are missing out on a lot. So I'll just teach you guys. So this is again a bonus. I didn't do it on Google first. So I'll just show you guys two graphs and how they are used. So actor networks are nothing. As the name suggests, actors are users in your social media. So these are these showcase who is interacting with who. So this is a simple actor network. So I made this when I was doing analytics for Pharma. We also did social media analytics for major pharma companies. So one of them is this is one of the outputs. So you can see the one in the every node represents an actor or a user and every edge represents an interaction with another user. So this is a very simple one. You'll not see something this simple. You'll see big complicated actor graphs which will help you understand. This guy is an influencer. This guy these many people are falling this guy. These many people are retweeting from this guy. It gives you a very good understanding of how your network works. Yeah. It is a biotraction graph. The second thing is a bimodal network. So the idea behind bimodal network is you know what your users are but what's the use in finding out who your users and who is actually an influencer if you don't know what they're talking about. So what this does is this takes a user as an edge and also takes a hashtag as an edge. So I'm using hashtag. It can take any common word as an edge but hashtags are very easily differentiable in Twitter. It's easy to build around hashtags in Twitter. So what we'll do is for every user we'll create okay how many times he's talking about a particular hashtag and other users who are talking about it. So in the bimodal network side you can see that there are in the exactly the background if you guys can read it the hashtags is empowering doctors. Right. So that is one hashtag so out of that you won't see any nodes any edges coming out of the hashtag. You'll see a lot of edges who represent users like the top company I think for Twitter's and Salman Oil and all that and they're tweeting about empowering doctors. So this is a very high level overview but understanding your networks and social media gives you a lot of insight into your data. So this was the first thing now this was obviously when you want to get your location down to much better accuracy you need to use a lot more stuff but this is a good guideline for example a lot of e-commerce companies and all they do social media analytics a lot of companies from pharma e-commerce I've seen a lot of people do that but in Twitter you must be aware only 8% of today's users reveal their location right. You guys are aware. So if you're doing analytics of your customers you don't know where they're from you know they're complaining you know a lot of people are complaining about this a lot of people are happy about this you don't know where they're from location. Even if you don't have an in-depth knowledge of how to get location to 9 km or something if you can predict someone city to 70 to 80% of accuracy that's more than enough for you. So you know where your base is from you know where your base is interacting from right. So this one is a little how would I put it this one is a little bit difficult to this was hands down on the most difficult things that I did with the army and any project which I have worked for. So the problem here was I can't reveal the exact problem statement. So I'll put I'll try and put it this way. So suppose there are there are I robbers or 10 robbers right and what they want to rob certain banks on certain days but they want to communicate to social media because not all of them have phones they don't want to be tracked and everything.  So what they'll say is hey let's come have cake XYZ place on Monday. Now my job was or what I wanted to do was I wanted to identify when the word cake and when the word any code word is used out of context and that could be signaling something bad like in this case a robbery I had different use case but again I'm just trying to simplify it. So at that time I was a new into NLP and all that stuff this more than two and a half years ago. So this was I thought this was what I thought the pipeline should be like you know again I thought this is particularly text classification problem no need to get too deep into anything simple text classification. So n grams I think all of you know anyone know okay so the thing is an N I heard of you know so I'll just quickly tell you guys so in NLP when your n grams is basically the phrase and N is the amount of words you're using in a phrase so if you have one gram it's just one word you're considering for your inputs if you're two grams two words three grams three words so ideally two grams are the best for generalization because one gram is two generalized three grams is too specific so two grams is your ideal phrase then which is what I have found again it will depend on top project to project like I said you have to research you have to know your domain. Second thing is noun phrase extraction now how does noun phrase extraction help anyone has heard of this no not named extraction okay so what this does is Abhi jessiki I have a statement called this is a dog right and my N grams is three so I'm taking phrases of three right so one phrase can be this is a it doesn't make any sense so the only thing noun phrase extraction does is it takes N grams in such a way that there's always a noun present in them or it makes sense grammatically like noun verb noun adverb noun adverb adverb noun something like that so with that you reduce your garbage inputs a lot. Second thing you do is theme extraction what is theme extraction again very simple nothing nothing too complicated all you do is you take all these phases you have extracted and assign a relevancy score to them and how do you assign the relevancy score it's through a very simple process called lexical chaining again and do you guys have an idea what lexical chaining is? lexical chaining is a very low level process what it basically does it joins a set of phrases and it gives them a score nothing much it just figures out how to join phrases that's it it's very low level you don't need to know it so what I thought was I was and I was being very optimistic I thought okay let's get these features and hopefully the perpetrators are using the same words in some other contexts and I might get something out of the themes there might be some other words which had to be associated with them which will bubble up when I'm doing the analytics unfortunately that didn't work so that's what I tried to do in the first I tried to get it out and I put it in a classification algorithm it didn't work out very well so another problem with the opposite topics will eventually turn out to be the same okay so I was again hoping that the people were tweeting in another context they might have added a few words here and there to like throw us off or something but even if they did topics eventually come out the same because it's a frequency based thing so you only get the top things word so that's not and as you can see the graph which will explain the next two points now this is a very simple graph which was done on arts dataset so even in this is this graph easy to comprehend someone said yes actually who understood this graph man I don't understand this graph so okay this is so a lot of data science is pretty stuff okay which doesn't make sense this is one of that you I really really can't make sense either you like make it more course or something so this is how you'll get your graphs especially when you're doing this because your data will be very large this is again this is for a very small data so what can we do okay so first the next thing is nlp classification really a best approach anyone can think of anything else yeah it is anomaly detection but it's text anomaly detection so that's what I said so the last slide this one this is an lda model you'd topics will eventually come out to be the same that was my problem here so I thought okay maybe this is not working and again here is that point of knowing your problem comes in okay so what are we trying to identify here out of context words but it boils down to one thing we are trying to identify the person who is tweeting out of context right and for the entire thing we have not considered him at all it's a profile we can consider that can give us some good insights okay maybe always tweets like this maybe there are maybe people who are using out of context make newer profiles and delete them soon so their profile age is going to be less so first thing which we can add is we can add all the user profile data to it and we can append it to our model and we can try and get better results so that's the first thing I did and yes we did get better results for this but still nowhere close to where we wanted to be but there was a significant improvement because earlier we were dealing with nothing almost so there was a significant improvement then I thought of another approach anyone again can anyone just think what else can we do here clearly this is not working out for us yeah so the time and all is considered in the numerical features so that we have already considered all that so okay so the next thing I thought is okay I'm hitting a brick wall here right so even I add as many numerical features like from the profile I'm not getting too high but the brick walls are only there for one reason to help us break them right nlp has to be used that's out of the question to forget nlp at entirely well what I did was instead of using nlp to classify if it's in context or not what I just did something like a multi-label classification right so you can see one pattern in the text come here for this at this three things which you want to identify name, occasion, place name, occasion, place so I took a set of trading data and I labeled them so we want to classify for multiple labels right I labeled them okay so this text contains a place this text contains a place and a time this text contains place and occasion so using all of that we generate additional features and we classify text and we put it put out what all the stuff is being associated with the text right so instead of our final model will be only numerical features so we'll have this profile data like he said profile time, profile age, what time it is and all that apart from that there'll be features okay how many times does he talk about location how many times he talk about location with respect to any other place or any other time how many of the times is he just talking about okay cake is he's talking about sweets or like every day what's wrong with him so that is one tremendous that again was a tremendous breakthrough for me that pushed the accuracy very high by very high I mean workable at least again there was a lot of work to be done but again so you have you will be facing a lot of break walls and you're working with open source data you have to break them down and that is again the boiling point of data science you have to work with data machine learning is the simplest thing in the world that you it used to be difficult if you have one line you write you give the colonel you give the parameters it's done machine learning is always the easiest thing the hardest thing is using the data and actually formulating the data to make the best out of it right so here's a bonus for you guys so I'm thinking you guys most of you guys are in businesses so context analysis is not going to be a big problem for you I don't think people actually do that so documentation is a big thing where a lot of stuff is happening you either identify context of documents what news post blog post edit threads document classification is very important and one of the best algorithms which I feel right now are hierarchical attention networks how many of you are aware of these one okay only two fine so what are they so they try and mimic how we learn to understand documents right so how do we understand documents we know the fact that sentences are made up of words and documents are made up of sentences right so this will try to do the same thing so and even in even in one sentence not all words are equally important right some words will have more other words will not so what this will do is it will have two attention models so and each attention model has a bi-direction RNN RNN you guys must know and an attention model so what the bi-directional RNN will do is it will try to for the sentence level it will try to go word by word try and understand the meaning behind those sequence of words and give an output vector for each word now our attention layer it won't identify the important words what it's going to do is it's going to take the vector from every RNN try and calculate the sum and at the end of one sentence it's going to give out a weighted sum of all the vectors for that for that sentence now the same process is repeated for sentences to get the documents so you can see there are two layers of attention working on one top for another which is why you get the name hierarchical attention networks two attention in one hierarchy and how can you use them unlimited applications if you are able to tune them to your needs document classification is very important nowadays risk classification lot of contractual stuff is there so even we work on something which on which we get lot of contracts we try and identify risk with them so again almost lot of potential hans are really great so okay so I actually have only three moments so I am not going to okay so fine so the third problem which I had was identifying potential suspects so I'll just look at stuff where you if you if someone is interested in cyber psych or open source intelligence you can actually get into this so these are the ways in which you can actually get someone's location fine so in fact I've worked on all of them first is landmark so landmark detection is something which I did so Twitter people upload photos and Twitter scraps metadata from those photos so there is nothing you can do for that you can't get met with photo metadata and get the geocoordinates I took photos from of buildings from almost each country from each city and I said there will be a difference in architecture and if there is a monument behind then good and well and good but I thought every country every city will have different architecture associated with it so you look at the background of the photo other than you when you try and identify what sort of architecture it is and then see what location you are from other thing is billboards and signs short short way if you any photo you take you see a billboard you see a sign you see anything it can give us a lead on the location where you are from OCR simple low hanging route is something more on the cyber psych side so how do I put this easily basically sometimes you will your email address not even yours you want to do something else and you put our email id phone number something like that or low hanging fruit in the nutshell is basically anything which can give us a lead to a location right because right now we have nothing any small thing which we can give us a lead it's a low hanging fruit good things are high hanging fruit that's different mentions now mentions played a crucial role so in the demo I showed you where I zero in on the location there are a lot of people who we still contract okay so they don't either don't have enough data or they just have very very data and we just can't get a good location so we follow the energy that okay fine you can't get your location so the people you talk to the most must be closer to you the people who are you are retweeting the most people you are tagging the most I assume that they are closer to you so I can't get your location fine I'll get 10 of those location thus local location okay I can approximate where you actually are right so again it's not very specific but we are getting something out of nothing so again this is the entire base of open source data and back doors obviously if you do a research there are a lot of back doors for Twitter there are a lot of back doors for almost all Google to Reddit where you can get locations so okay I'll share one cool back door with you guys in Twitter how many of you have used the Twitter API okay good so in Twitter API there is an option to call location through GeoCode okay so what this does is in an area Joe Billow wants a tweet correct they give you their tweets but a lot of the times in there are some users in these tweets who have not enabled the location that's because Twitter is using it's own backend algorithm to calculate the location based on IP okay so you won't know this until you do your research and understand what's actually happening but that's a very interesting if you don't want to do all machine learning all this garbage by all means you can just get that you can just get a Twitter API call it and you can have your results although we want Twitter API is limited so you won't be able to get as much data as you want I'll be I was actually planning on releasing some data wrangling and data cleaning tools I don't like releasing machine learning models because anyone can do that I unfortunately I couldn't get them out by today but when I upload my slides on the conference page I'll be including everything right so I think we are done okay so the questions will meet you and thank you guys you have been really nice


Knowing how well Jill Lido had been pitching coming into tonight. What type of initial boost was it when you were able to take him deep first that bad? It was good man. I felt you know good. I He usually attacks his own He's been around the zone and he's been really consistent the last couple months or so and He gave me a good pitch hit the backed up slider. So if I'll get I get off to a good start tonight It was like you took a little bit of exception when he went high in and inside next step Yeah, I mean, you know, I go deep on them then next pitch 97 I mean, I don't even know how it didn't hit me.  So It's really frustrating. I don't know if he was trying to do it or not, but I Don't know if he's trying to get back just because you know, try to let one go or not, but I Know when it's in that area, it's it's you know, not good Was that frustration from that pitch or was it for the repeated instances of where you've been kind of pitched up in a lot? There's a little bit of both but first pitch you throw to me out there hit a home run off to use out my face.  It's a long season I believe in all these guys and you know It's the greatest thing about baseball tomorrow is a new day and we're gonna come out swinging and you know We know, you know, whatever pitchers out there for what's gonna get the job done And you know, it's a little rough spell, but we got this


Next question is from Kim Gettys. When the pandemic is finally over, what will be the first thing each of you do that you haven't been able to do? Fine dining. Really? Yeah, nice, like a, you know, white tablecloth expensive dinner where we're sitting in a nice restaurant, candlelit type of deal. Like that, I haven't had something like that on that or a movie. We're on a concert. Yeah. Yeah, I had like so many lined up this year too. It was like really frustrating. Yeah, I might be right there with you two on that because we do a lot of concerts and live, live sporting event too. Yeah, live sporting event. That totally, like all this stuff like made me appreciate sports even more now. Like I've been drawn back just because I can't have it. You know, it's made it even more of a bummer now that, you know, we're not seeing that as much anymore. Yeah, I've just to help myself mentally with the whole thing. I've accepted that it won't, not only that the pandemic won't be over, but that our life will probably never be like it was before. So I think that moving ahead five years from now, 10 years from now, you're just going to see people wearing masks. It's going to become a normal thing. It's going to feel, you already feel the social shame if you don't wear a mask. Even if you're out in an open field all by yourself, you can feel people looking at you like, what are you doing? What are you feeling the most? Because Justin says concerts, I say supporting events, fine dining. I feel like you don't really do any of those things. Oh, I love fine dining movies. Oh, okay. I love going to the movies, man. In fact, that was. So is that what you feel the most? I think so, dude. I think I would like to go to the movies, but I don't know. I don't know what it's going to look like. You know what I mean? I don't know what things are going to look like, but movies I miss. I love sitting in the theater with the sound and just the whole experience and anticipating a good movie. Fine dining is fun. I enjoy doing that. We do that every, I don't know. I'd say every few months we'll go to a really nice restaurant and just have that whole experience. Don't like concerts. Could care less about sporting events. Not that big of a deal. Maybe go to, you know, a busy park or beach. I feel like it's so weird. Yeah, have you guys gone done something outdoor like that and see people around you mass? It's such a strange. It's, well, yeah, cause the beach is open and it's, I mean, everybody's pretty much keeping to themselves in terms of distance, but you'll see some beaches where they're just like, everybody's clustered together. And it's like, that makes me super anxious. You know, it's like walking through that. You know, and I love working out in my garage, but I have to say now that I think about it, I think a good workout and a big, you know, like Iron Dungeon gym with other people. I missed that a little bit. I missed the gym a little bit. Like I've gotten really used to training from home and training from our studio for the, in the last year or so. And I think I was the one who vocalized liking, working in the gym more, but it's been nice having our own place, but I do miss that. And I feel at the difference, like I definitely get an extra level of motivation, training in a place where there's lots of other people training, you know? No, I missed Disneyland. That was a thing. It was like, it was almost on once a year, kind of a schedule at this point. Like we've been going in the last like three years, I think once. And that was something that like the kids, of course, always looked forward to. And then you see them experience it in a different way each time as they're older. And so you get to kind of like, cause you know, my youngest will now get to go on the crazier ride. And then, you know, and then see their faces. They're like, I like, oh, I shouldn't have done that. I was too early kind of a thing. Like I did that a few times.  Oh, wow, that was awesome. Wow, buddy. Like, ah! You know, it's really weird to me is that, so the way that I was raised in our culture and also the culture of my family, when you see family members, when you first see them and you greet them or when you say goodbye, we kiss each other. That is really weird to me now. If I see family members, we all say hi, but nobody does the kiss anymore. And that's really strange. It's a very, very weird thing. I almost feel like it's, I feel like we're not connected. No, that's a weird thing for me. I'm like, I'm a hugger. Yeah, I'm a touchy feely person in general. You have friends, family. You're not rubbing ears right now? Yeah, I'm like, it's just weird when you, like if you haven't seen anybody like, let's say, I mean, we just ran into Aaron. We hadn't seen Aaron in, you know, I don't know, maybe half a year or whatever.  Like there's always this weird moment when we get to, when you get to- What do we do? Yeah. Yeah, it's like, hey. Hey, you could walk towards each other and then you stop when you feel like it's about six feet. Then you're like, hey. Are they gonna fist bump? Are we just gonna air high five? Like what are we doing here? That's awkward.