Here we are. Okay. Thank you very much, Alissa. So for this office, our GSOC office, we had some Zoom not cooperating with us, so we had to switch from meeting room. Thank you very much, everybody, to join and to the people who are going to listen to the recording. I personally have three news items to share. Hey, Roushikash joined. That's great. You finally found us. Yeah, I've been a little bit troubled. Thank you for joining, Roushikash. Okay, so I have three points to share. One, I will take it offline, just mentioning. Normally, all evaluations for projects are in. It's only with Jenkins File Runner GitHub Action that I think, and I need to reach out to the mentors there that they complete their homework before end of the week. Roushikash project, so you filled in your evaluation. Roushikash is done? Yeah, I filled in mine. I filled in the evaluation form, but I need to update my project page, so that is still pending. Okay, good. So this follows. If the link is there, we can fix that afterwards. So no real worry for that. So I need to check that the mentors fill in their part on time, so this must be filled for next week for your project. By Monday, I think, no. Roushikash project is shifted by one week. Oh yeah, sorry, sorry. He's shifted by one week. So I need to chase Yeeming in... I cannot pronounce his first name, I apologize, but the two mentors for Yeeming's project need to complete theirs. So the other important news is that there seems to be a consensus based on what Duluth told me, that we will hold the final presentation of the projects in an online job that we will hold Wednesday, October 5th at the same time as these meetings. So this is a 15 hour UTC. So I think at least the four contributors are available at that time, and a lot of the mentors are available at that time too. And Alyssa is available too to do her job magic. So that means I will send out the announcement two weeks prior to the 5th? Yes, so I believe this is next week. If I check that, well, it will be a busy week. Yeah. Okay, so this said, and the last announcement, I believe that as the summer, at least here in the northern atmosphere is over, it's raining, it's getting colder, darker. So no, it's not raining in your pot, Adrian. Well, okay. Now I wanted to... We're reaching the end of Google summer of code 2022. Projects are wrapped up, wrapping up, finishing. So I think this will be the last office hour that we will hold for this session or this Google summer of code edition. Unless there requests that we continue for some particular topics. For my part, just as a conclusion, this has been a great addition, a great experience for me personally. And I'm very happy to see that all projects were concluded positively and really contributed in great amount to the community with very important work. So it's been a pleasure to lead these office hours in the work. Thank you very much for all the mentors that have been key to all that, and also to all the contributors that have been key to do the actual work and to contribute. So thank you for everybody. So this is my conclusion, the things I wanted to share. So now I open the conversation to whoever wants to add something. I wanted to thank all my mentors and you have also helped me a lot, you know, motivated me and during my presentation. So this has been a wonderful opportunity. I've learned a lot during my summer. I've learned a lot about programming. I learned a lot on how to communicate with people, kind of improved my confidence and learned a lot about time management. That was one of the biggest factors. So getting up at 8.30 in the morning and attending was kind of very difficult for me. And yeah, this journey has been a very great journey. Thank you for this opportunity once again. I hope we'll have the opportunity to do for a face-to-face meeting one day. Yeah, I'm looking forward to that. Great working with you. Go ahead. I have to say there's a lot of great teamwork and Rishikesh led to this, which is a lot of sacrifice on everybody's part to make this whole thing work together in improving the project. So great work, everybody. And thank you so much for being a part of the program. To Rash. Yes. That's my turn. So I want to start by thanking everyone right from choosing me to work on this project and selecting me as part of this big program. Because if I would not have joined this program, I would not have learned the things that I've learned at the pace that I've also learned. So that is extremely valuable for me. So it's like a treasure that I got to learn during the summer. I would say it with pure confidence that this has been the best summer ever professionally, because there was some summer where I went to my hometown and had fun. So there was that as well. But professionally, this is the best summer ever. I had lots of great time with my lovely mentors. I get to learn from them. And I had weekly meetings with them trying to understand what are we going to do next. We plan things. Then I asked for doubts. Then I delivered something. Then I asked for more questions, then got feedback. So I improve my communications as per, as compared to what, how I was before the program. And the programmatically also I learned lots of things. So the whole tech stack that is involved, I got to learn a little bit more from each aspect. So this has been only possible because of the whole Jenkins admin team, first of all, to keep us on our toes on weekly basis, asking for updates and keeping us in check. And of course, to the mentor for being so helpful. It goes without saying because they've been really helpful. Thank you so much, Tadrian. Thank you so much for your help and everything. And thanks to Jake as well for being very helpful all throughout as well. And also to Aditya Shrivastava for all his help and tricks, tips and tricks along the way. So this has been a long speech. So I've been I'm filled with gratitude. Thank you so much for choosing me. I'm looking forward to be with you and interact with all of you all throughout coming years. That's the plan. Thank you so much. Right. Thank you. Thank you, Raj. I hope to see you to continue seeing you around and contributing to the community and sharing your experience. Or I think the end of this program, it just meaning the end of this program, but it's a beginning and a lot of a long term relationship. So don't go anywhere too far, though. That's a good point. Yes. Somebody wants to add something to it. So we will be doing a recap of this whole program. So Dharaj and Rishikesh, your feedback here is really valuable. You know, exactly what you said earlier. So I'm going to be taking feedback from you guys, you know, so what you can relate here. I mean, literally just reiterate what you said here and put in writing so that we can share with, you know, future contributors and future mentors because we're always in need of more mentors and art admins. So your feedback here is also valuable for us and help us out as well. Okay, so I'll be reaching out next couple of weeks. Listen in everybody listening. So normally there will be a blog post. I post the blog post from Dharaj and ask not to merge it yet. The one from Yeeming has already been merged, which is because I, okay, it's merged. My idea was to publish the blog post about the same time so that we can tweet about it. But don't forget to prepare and polish your blog posts. You can still continue to update them. Unless somebody has another opinion, I'd like to publish them at the beginning of the week of October, just before or about the time we have the job. Devash. Yes, thanks. So my question is, we would be adding our recording of the final demo in that blog post as well, right? Yes. So there will be a global blog post. This is the one we're going to work together with with Alissa, linking to the various blog posts from all the contributors. And there we will also include the recording of the job in the various pieces. Is that did I phrase that correctly, Alissa? Yes, you did. So well, somehow somebody was a little bit too impatient and already merged the blog posts from from Yiming, but that's not a problem. Yeah, we'll work with it. We'll work with it. So, Deraj, your blog post is ongoing, you can still continue because you were, you wanted to add things to it. Hoshikash don't forget to work with it. I'll remind you before when we have the job, this is about the time where I would like to publish them. Okay. And last question. You say that global, you talked about global post, blog post. So what is it going to have? So we'll recap the process and the program itself this year will include your project blog post. We will also include the final job. So it's very general. Very general, just an introduction. Okay. Okay, got it. Thanks. Hoshikash, go ahead. So by this week, I need to update my project page because that is the link I have submitted. So I almost drafted and edited my project page, but I'm not sure if that is no, that means the exact requirement because I've gone through others project pages, but you know, I didn't find like I kind of didn't help me out. So I'll push my changes once because it also has does I'm not sure if the recent changes have to be merged into the, you know, for Google to, you know, review it. I'm not sure about that. You. Yeah, Hoshikash, because I don't see a PR open. So it's probably in your repository. So I didn't, I didn't create the PR yet, but I worked on it. I'll send my PR as soon as like tonight. Yeah, you can, you can open it. So we have time to read you and maybe give you some, some, some tips. But you can create it and and just either ask me or I'm not sure that you can update the tags on the PR, but you can either add a do not merge tag on it or change the subject with work in progress. Just as a draft, if your draft is even better, I see the Roush has the three ones. So it's a draft PR with work in progress in the title and the tag do not merge. So we have three anchors that the PR does not get merged. Yes, extra careful. Extra careful. Yeah, sure. So it doesn't slip away. Hoshikash is that clear? Yeah, yeah, yeah. I'll send my PR as soon as possible. I've always, I almost worked on it. So because I'm not sure, do I need to submit the final one? Or you know, I can update it even after it, you can update it afterwards. That was my doubt. Yeah, yeah. It's it's there is no hard deadline to it. It's only an important record. Probably the people from Google will have a look to it to see for me. It's all good. Okay. So I think we can conclude, we can now get ready for winter. But no, people in India don't have other seasons. So the reference is not


The Cricket World Cup is going on and Glenn Maxwell from Australia set a ton of records in his historic innings that happened in crazy dramatic fashion. This breakdown is brought to you by DraftKings. A lot of people reached out as soon as this happened and requested me to do a video on this and I appreciate that very much. I never thought I'd be getting instant cricket requests to do videos, but it's awesome. So I appreciate it. Here's the scene. I'll set it for you. Australia was down bad. They needed 201 runs to tie the match, 203 to win it, and they had seven outs already. At this point, Cummins comes in. He's a bowler.  And Max, he's at 22 off of 26, and things started to look pretty scary because he floats that one up. Afghanistan gonna make the catch. Oh, a little bit of a slight miscommunication at the start, because the fielder thought Khan was gonna get it.  That's upsetting. And then the umpire actually calls him out on that ball. LBW says, yep, that was gonna hit the stumps. Ah, wait a second, wait a second. Let's review it.  He thinks his day is done, but nope, turn around. It was gonna go over the stumps. You're still in there. Umpire got it wrong according to the review. And then he floats this one right. Oh no, he puts it down and Max's got a cute little smile. And I think at this point, he said, all right, screw it. Let's just go big. He's at 29 runs off 37. He says, bam, sweep. Go that way. I'll go this way. I'll go that way. I'll go that way. I'll go this way. I'll go this way. He's hard to bolt to because he's gonna step forward and he's gonna take charge of the ball. That is crazy.  Wish you would have caught those balls. Damn, this is looking not good. But he's cramping up. So Doc's gonna put an elbow in his butt, then place his thumb in that dent. Then the other elbow in that same. And then, you know, robot loosened his leg up. That's, that's doctoring. Elbow to thumb, to elbow, and then wiggle the leg. He's cramping up. He had a field for like four hours before this. Now he's hitting, take some pills. Didn't play the match before because he got in a golf cart accident.  He's cramping up, goes back out there, says, I'm fine. Doc did the elbow, thumb, elbow thing. And look at that gorgeous shot. That's six and he's just going big. That kid catches the ball. This guy comes up says, don't celebrate that, throw it back. In cricket, they have to throw it back. They have to use the ball, but he's still excited. That kid didn't care. Oh, he touches that. And now he's starting to cramp a little bit. Uh-oh, the elbow, thumb, elbow is wearing off. He's running across to the other side to grab his run and it's starting to hurt. And then he took a swing there and he's like, I gotta run again. Ah, not feeling good on the ground. He's just going to stretch it out himself, do a little yoga.  Using his legs. Oh, I don't know if he can use his legs anymore. This was the last time he really tried to use his legs. Look at that, steps forward. The leg cramps and then a little bit of the leg there runs, Cummins is like, oh, good, good, dude. Oh, now wobbling across like a stiff back grandpa. Oh no, using the bat at his cane and just trying to stretch it out. And he realizes, hey, I think my legs are done. He's got to go Lieutenant Dan the rest of the way. Just arms and look what he does. He just flicks that for four Cummins laughs. He's like, holy smokes, man. What are you doing? And that one he sends straight on, right back where it came from for four. Yep, that fan doesn't even know what he's about to see. Look at the replay of this one. Does not use his legs at all. He just stands there and it's all timing and arm speed. Look at the difference. On the left you have how he wants to hit this ball. He wants to step forward. He wants to attack it, drive it. And on the right, he didn't do any of that because he got hurt on the left. So he had to stop doing it. So he's got to go no legs the rest of the way. And he's trying to move still. He stills like, all right, I can get across the field to the other side.  You don't know if you're gonna bump into anything. He's hurting pretty bad.  Cummins is like, dude, you don't, okay. If you want to wobbling, wobbling, wobbling, gets back just in the nick of time. Oh my goodness, cramp attack. Go still, lay down. Oh, like a tremor inside his body. Oh no, not good. Cramp attack. He's laying down like he just got attacked. Dot comes out. This is gonna need much more than the elbow, thumb elbow. He's like, what leg is it? He's like, I don't know, both of them. Zampa comes down because he would hit next. They need 56 runs from the next 59 balls. Glenn Maxwell is at 146. He turned it on so much. He says, hey, I can stretch, get back up those stairs. Zampa, don't need you. It's all right, fine. These guys, Afghanistan, have a team meeting like he's a, he's buying, he can't move guys. As this guy can't walk, he can't even stand. Can we just get him out? Shouldn't even take strategy. Just get him out. He says, oh, gonna take a lot of strategy. Kids, look at that one. Oh, coming just laughing at him. All the boys up in the, he's like, I just wanted to flick it. I just wanted to flick it a little bit. Missed it. Reverse sweep, get some. He's still, he's still hitting them with different shots. Now on the left, reverse sweep with legs. Pretty powerful. On the right, reverse sweep with that. On the left, or now he's stepping forward, moving. The other one, Lieutenant Dan, no legs. Just kind of standing there. Come on, come on. Getting exciting.   Look how cool this is.  It is very impressive because prior to being legless, all of his boundaries, the blue balls, came from balls way out in front. And any ball that he had to let travel deep, he didn't get a boundary on. He didn't get a four or a six, but he can't stretch and he can't move his body to get in the position that he was doing before he went legless. So now he's changing his entire style, letting the ball come to him just using timing and torque and hands and still getting the job done. It's crazy impressive. Baseball fans, it's like if Kurt Gibson came out and hit like four home runs instead of just the one. Look at that. Just stiff man, Mr. Roboto. But at this point, well, look at this smile. He kind of goes, mm, okay, that feels a little better. And then he's gonna go for a run at the end of the over here so he can remain on strike and look at the fist pump. Yeah! He's like, I'm back up. Cummins doesn't have to hit.  Must, oh yeah. I think the leg is starting to do better the pickle juice is working. The cramps are going away. It's still hilarious how he's doing it. I mean, it's just kind of, this guy's just nodding. He's like, oh, okay, I don't know what to do here. That's kind of nuts. They need 25 runs off the next 30 balls. He's yelling at Cummins. He's like, come on, go, go, go, go, go.  He's like, yep, I'm feeling better. Now this is Cummins batting. And an important part to note about this is Cummins is his partner up there, but he's a bowler first. He can whack it around a bit.  So all he's trying to do is survive and look at that, just blocks that one, con tries to kick it, it get past him and Max, he's like, let's go, I'm up. We get a run and I'm up, I'm feeling better. I'm gonna end this thing. This is the final over. The first ball goes for a big smash, kind of misses it, but looks at Cummins afterwards and he's like, hey, don't worry, I got this. I feel pretty good. And I saw the ball well. So look, he got a dot ball, the first ball of the over, the next one, boom. And you see him using his legs a little now? Watch him step into this. Oh yeah, thank you, pickle juice. That's six. How about it? They're scared.  We're gonna lose and it's gonna be historic. That is no leg movement at all. Still six. Now he needs nine runs from the next 21 balls. Basically, they're gonna win the game now. And he says, yep, and I'm gonna win it now. They go outside, he goes over there. What's that, deep cover? Cricket fans, let me know because there's a lot of positions. I'm still learning those. It's kind of hard. Cummins is clapping and then the last ball, he's like, here's six to give me 201 runs to give the team enough runs to win. They're going nuts. It's one of the greatest, I mean, it's one of the greatest single performances in cricket history. Afghanistan can't believe it. Dude went headless. Cummins is like, are you not entertained? Are you not entertained? Look what I did. Warner got 18, head got zero, Mars 24, Marnus 14, zero, 201. No one else on his team got more than 24 runs. He got 201 of the 293. And also look at Pat Cummins. 12 runs, but he saw 68 balls. He kept it alive. He kept it going. Very, very cool. I like it. It's, you know, because it's similar physicality to baseball, but it's kind of like what if Kurt Gibson had to hit forever?  This, they're doing that in the heat for two hours straight after standing in the field for four hours, nuts. Very cool. Thanks for tuning in. If you enjoy, subscribe to anyone that reached out and requested for me to do this. I appreciate you. It was a ton of footage. It took absolutely forever, but I enjoyed it. And I hope you guys enjoy it as well. And good job, Maxi. This one was brought to you by DraftKings. Thank you to DraftKings for sponsoring the breakdowns. I appreciate them as always. And I appreciate you guys for tuning in and watching. Subscribe to the channel if you are so inclined. It helps. Goodbye.


Hello. I am really thrilled. I'm Tim Freundlich. I'm on the board and also the president of Impact Assets, which is the hat I'm gonna wear to cover my bald head today. I'm gonna talk a little bit and tee up John Sturer who's the new CEO of Calvert Investments. Calvert, as I said a few remarks last night, I mean this is an institution that has been in the arguably the impact investing space truly for 40 years. So I always try to kind of think as we approach. I've been doing this for about 18 years now. Like we have to think in decades as impatient as we are, as much needs to get done. We also have to be in touch with the shoulders upon which we stand. And I very much think Calvert Investments is such a place. When I was there starting in 97, they were already doing a ton of microfinance, ton of community development, financial institution investing. They were doing venture investing in social ventures all across the United States and the world. And this is 18 years ago and they'd been doing it for five or six years already. So I just, I can't, can't sing their praises enough. John, I met him at the beginning of this year when he took over his, the CEO. He had just left AMG funds, which is a $60 billion asset management firm. And he's taken over the reins and totally impressed me in terms of his leadership and vision and the refresh. I think he's going to, they've done a lot over the last 40 years. I think they're going to do a lot over the next four. I'll just say that. And he's going to come up here and talk about that really special thanks to him too for the work he's done in the collaborative stance with Impact Assets and bringing some of our new investment notes to market in the next month. So please join me in welcoming John Sturer, the CEO of Calvert Investments. Thanks a lot. All right. Thanks a lot. Thank you very much, Tim. I appreciate it's great to see everyone. Yeah, and I wrote down two lines from the program introduction so that I could try to deliver the comments that you came for. One is impact through the capital markets and the other is how to effectively drive change. I'd just like to talk about that a little bit. What change are we trying to drive? And what role do you all play in that? The change we're trying to drive is to get major corporations worldwide to move their behaviors in a way that's consistent with the evolving social norms that we're all helping to influence. This is the group of people really that is on the leading edge of that. You're the social entrepreneurs. You are the innovators. You're on the ground and you're helping to codify the kind of social norms change that we need. The kind of change that will create a more sustainable environmental system for the planet. The kind of change that we need that will solve some of the tremendous social injustices that our global capital markets suffer from. So you're the leading innovators. You're driving the change. Now, how do we at Calvert use the global capital markets to help impact investor behavior and to further drive the change that you're starting on the ground? What does it mean to work in the global capital markets alongside of the world's largest financial institutions where trillions of dollars are moving daily between companies? And what role do we all play in trying to solve climate change and social injustice? We find it at Calvert necessary to compete on their terms in the respect that we need to produce the financial results that are competitive with all major investors. But we also must find the public companies that are behaving differently, that are changing their operations and that are treating people on the ground with care and justice and that are moving their operations forward to have a much less impact on the environment and reduce greenhouse gas emissions and help solve the devastating problems associated with climate change. As we hear about impact investing going mainstream, it's good news, but it's also very concerning because we must also really understand what companies are doing throughout the world. On one hand, it's terrific to have a global information system that can inform us about what's happening in India and Pakistan and throughout the world so we can see global supply chains. But we need to know the reality of what the companies are doing and we cannot depend on the financial industry to provide us with metrics that tell us about that. We need a social fabric of people like yourselves to help define the standards and tell us what's happening on the ground. We must maintain independence and objectivity as we seek to use the global capital markets to move our common agenda forward. Critically, we need impact investors and networks of impact investors like those of you in this room. For Calvert's part, we're investing in you through our high impact program and we are asking large global corporations to do more business with small and medium sized enterprises globally. We're asking major global corporations to think about how they impact local communities, how they treat people from those who need protection as indigenous people to the poorest people in the world, as well as people with unique cultures that don't necessarily agree with the Western ways. So this is a major effort that we're all involved in together. Now let me talk a little bit about what 2015 looks like because I think when we look back at 2015, we have the potential to see this as a transformative year, not only for your work, but that of the global capital markets. And I just like to cite the work that the Pope did in writing the encyclical and getting major publicity for the needs of the poorest people in the world and for the needs to solve the impacts of climate change. I'd also like to share with you the activities of 10 days ago in New York as we worked to pass the sustainable development goals in 2015. It was a tremendous event, very different from what we've ever seen at the United Nations before, where we had the leaders of the largest nations, not just the finance ministers, but the Angela Merkels, the Barack Obama's and everybody from around the world coming together with public companies. I was in a meeting with those people, with Mark Zuckerberg from Facebook, with Paul Pullman from Unilever. Our special guest was Bono from U2 and he said metrics three times. So really, we have a unique situation in 2015 where we see a confluence of events coming together to focus world leaders, public policy players, private enterprise on driving sustainable development goals worldwide. It's extremely encouraging, but I just want to close and reaffirm how important it is for the people who care, the people who are really trying to drive change to have independence and objectivity and not let this become completely co-opted by those who are coming into the space most recently. What can you all do? Well, at Calvert, we are very engaged as an activist in trying to move public companies forward. When we see behaviors by corporate management that we think need to be changed, we contact them, we speak with them directly and when necessary, we file shareholder resolutions and take it to a vote. This is the role of the investor in the capital markets. We vote our proxies not just for management, we vote our proxies for the people in an effort to solve these major problems and you are the people, as I said at the beginning, who are helping to redefine the social norms that we're asking the companies to live up to. We need you to join the movement. We need you to engage. We need you to vote your proxies. We need you to contact company management alongside of the players like Calvert. The more voices, the better and we are getting success. We're at a tipping point and we need everybody, particularly those of you in this room, to join in. So thanks for being part of a fantastic movement. Appreciate it. Thank you.


Well big changes are underway on the third floor of the Sanford Bemidji Medical Center. Josh Peterson was there and got a tour of the changes. Changes in the air at Sanford Bemidji Medical Center where on the third floor a major renovation of its nursery is underway. The new 10-room nursery which is expected to be completed by the end of the year is all part of a response in the changing needs of Sanford's patients. We are doing a lot of construction and remodeling up within our labor and delivery unit here to better meet the needs of our community. One of the reasons Sanford Health here in Bemidji is expanding its nursery is because of the number of drug exposed newborns has increased. Substance abuse and pregnancy is rising and so our nursery is expanding to fit the needs of the additional babies that are coming in. We're expanding our services to treat those moms more efficiently and effectively. What looks like a skeleton and an endless sea of wires will become a 10-unit private care nursery. It moves the pediatric patients' rooms as well as the nursing station. Then next year an OB operating room will be added to the floor. But the layout isn't the only thing that will be new. A new approach for the mothers will also be implemented. Over the last year we did implement couple care which is the new trend for the model of care for infants and parents. So we're able to keep babies in with their moms during their stay rather than utilizing the nursery as much. Even though the expansion of the nursery does eliminate some rooms, Sanford Health sees the design and practice as the best option for its patients. In Bemidji, I'm Josh Peterson, Lakeland News. Construction on the new nursery should be completed by the end of December. The entire project should be wrapped up by the end of next summer. If you've enjoyed this segment of Lakeland News, please consider making a tax-deductible contribution to Lakeland Public Television.


Shweta here, XSSME, X Red Hatter, right now a student studying NLP, Artificial Intelligence from UK's University of Sheffield, and I was invited to give a short presentation. That's my area of specialization. I'm doing my dissertation on that. So, let's start with NLP. NLP is all the hype these days with chat GPT and all. All of us must have used it at some point or the other. I use it often for my assignments. So, let's move forward. This session we will be covering introduction to NLP, what NLP is all about, why NLP, what's the significance in like software, and what's the significance in open source. Then we have the hyped about the market topic, chat GPT. Why exactly chat GPT? I'm not all for chat GPT, so I will be displaying the cons of chat GPT over here instead of the pros, because all of us know the pros we have been using it. Then I will be showcasing some uses of NLP in the industry, what all use cases we can have. Since this is a very short session, I won't be able to showcase any demos, but I do have GitHub links for the code that I have already developed for a few projects of mine working on. In your free time, please feel free to take a look at that code and check out what exactly is NLP all about. Before diving into NLP, let me give you the basics of machine learning, because NLP uses machine learning extensively. Machine learning is all about having your systems make decisions for you without explicitly programming it. That's basically a main definition for it. We have two categories, supervised and unsupervised. Supervised is like when we use it with labeled data. We have index data that we use it with in the model, and there is unsupervised where there is no label for the data. That's when we call it unsupervised learning. When it comes to NLP, NLP is the field where we have systems, computer systems understand human language, understand speech, human speech, create language, and then analyze a huge amount of given text and make some sense of it. Get the context of what exactly is being said there and then try to process it and give us outputs, whatever the context is. NLP is a very, very, very vast field and the only four things that we would be concentrating on in this session, which are the major areas where it has been focused upon, the research has been focused upon. So those four things are, the first one is language understanding. All of us must have used it, Siri, Alexa, voice assistants. Those are the devices that actually interpret your human language and answer you accordingly or provide your outputs accordingly. Then we have text analysis and information retrieval. So for this, this actually is part of sentiment analysis and all of us use Netflix, right? Netflix. So the recommendations that we have on Netflix, that is based upon sentiment analysis algorithm and that's how the more data you give it, the more recommendation, like the more plus signs, the thumbs up signs you give it, for the content you are watching, the better it gets with the prediction. So that's how the sentiment analysis part works for Netflix. And then we have natural language generation. This is like chat GPT, all the chat bots, all the virtual assistants that we might have seen any, like even on bank websites when you go and ask them that what is the amount that I've spent this month and that chat bot will showcase to you that, yeah, this is the amount that this current month we have been, you know, expending or what exactly we have been doing with our account statements and stuff, it will showcase you all the details. So that is natural language generation because we call it natural language generation because it not only interprets what you are saying, but it also gives you an output in a human understandable way. So it's like a human language thing. That's why it's called natural language generation. Humans put the input in a human language form and then you can understand the human language in the same output. And future innovations, I would be talking about what all social implications like whatever social contributions NLP has had and how we are implementing it for social causes. So those are like the dissertation topics that I'm working on, that will be covered in the last slide. So why is NLP automation significant? Because it can help you understand human language. It helps you bridge the gap between human language, human speech and our automation processes and our machines. So that way it actually helps you beautify, I would say, customer experiences. It helps you bridge the gap between customers and humans and then it helps you make data-driven decisions like if you have a huge amount of data, you give it to an NLP model, it will make some sense of it and give you an output that this is the prediction for this given output that way. So that industry, it actually helps you realize even from feedbacks, even from huge amount of texts that, yeah, this is what the customer is actually trying to ask or this is what an open source developer is actually trying to achieve by doing this, by doing this documentation or writing this amount of data or like a text saying that this is what this code does. So it will be able to make sense of it. Okay, the examples now. So this comes from my experience working as a subject matter expert and also open source developer. So please take whatever resonates with you over here. So the first one is machine translation. Machine translation is like, supposedly when we were working on cases and there was a case from some region, let's say China or Korea and we don't understand the language, we would have to wait for the other region to come and then the engineer has to take up the case because there's always sensitive data on the cases. We cannot just put it on Google translator. That's not how it works. You can't do that. So that's why we would have to wait for the other region to come up. So in that way, we could have an API of our own. Red Hat could have an API of their own, an open source API maybe that would help us analyze these things and we wouldn't have to wait for another human to come up and work on the cases. And there is also like for open source, I would say localization of software as in let's say if there is a user interface, usually they do have it for some very good traffic websites when it comes to like train bookings, flight bookings, they do have it. But we should have it for more websites because English is the commonly used language but it should not be a barrier in any way. That's why if there is like a website that has a flight booking thing and then somebody is trying to work on it and they can't analyze the buttons or can't understand what exactly is happening there. So in those cases there should be like an NLP API. We could develop one. It's easy. It takes effort but then it would be like a long-term investment. I would say that that would help you like go through a lot of interfaces converting the language in just a GIF. Then we have knowledge base automation. We do have like our KCS. All of us must have used it at some point of time. So for KCS what we do is like we have a generalized search. We do a Google search as well for our KCS articles instead of that we could have like for each product company could have their own KCS base where you make the search. It should not be a generalized thing. You just use a few words keywords and then the NLP algorithm that we have developed, the NLP model that we have developed that would give you the predictions of what exactly those articles should be that you are looking for instead of going to Google and doing the all generalized search. It would be faster. It would be more efficient. Then we have social media monitoring. This means trends like let's say there was a lot 4G trend that happened. A lot 4G vulnerability that happened a few years back and we were flabbergasted with a lot of cases over there and then each customer was asking the same question of how exactly can we resolve it. How exactly can we you know how exactly our product is being impacted by this. So for those things if we have those social media trends with us we would be able to allocate more resources accordingly and take steps whatever are like required at that point of time. Then we have virtual assistants. This could help us like for developers when we are doing open source contributions. If it's a new GitHub you know code then it becomes really difficult for you to debug instantly and understand which classes are being used in this code. So for that we could have virtual assistants guiding you to you know go through the module or find the module exactly where the package the module wherever the code could be. So it won't be a 100% accuracy thing because NLP doesn't provide 100% accuracy. It's around 80%, 70% depends on the model but that way we would be able to analyze the model. Then we have descriptive analytics and automatic insights. I'll go through them really fast. So descriptive analytics we could have like we have heard about Sonar. Have we heard about Sonar code quality tool. So those are like rule based things. Static rule based things. So they do not have any dynamic things going on in there.  At times it's not, I would say not at times I would say 50% of the time it does make a mistake in indicating that your code is correct or wrong. Sometimes those APIs are necessary and it indicates no this should not have been here. So that way we could have dynamic programming using NLP developing a code based quality based tool that will help us analyze our code better. And automatic insights it's again like feedback analysis and rating analysis whatever is given on cases or maybe on releases what exactly the customer is expecting or maybe the open source people are talking about. What should be the newer trend or what should be a newer feature that should be added. Those things we could take into account. Now moving on to GPT. So GPT is like generative pre-trained transformer. It's a huge topic and it's very difficult to explain it. But transformers are something that are better than neural network. If you have heard about neural network transformers are stuck ahead. And it is based on sequence to sequence modeling it takes larger contexts into account. That's all I can explain right now about it because it's really, really huge. And the cons that I have found while working on GPT is there is no understanding whatsoever. The model never understands what I'm trying to say. It doesn't get the context most of the times. Second, if you put like a question different phrase it won't be able to make sense of it. If you like even use two or the inner different sequence it won't be able to understand it or give you an answer accordingly. You have to be grammatically correct to get an answer out of it. Then you have bias. So if as a user I am adding something to the system the system learns right every time you give an input to it. You type something inside it, it learns from that input. So if I have bias as a user, if I am biased to a certain political let's say agenda and I'm adding that to the system then it will give me output with bias. And every time you access the system again you will keep on seeing bias because that's how the generative model works. So for each user it's different but if you as a user are trying it again and again you will be able to see that bias. You could try it. You will be able to see it instantly.  Of course it doesn't have any real time knowledge. It's not a human being. And there is no reason or explanation that's something that we have been working on in an LP research areas because reasoning and explanation are really really important at times because if like you are giving an answer to someone and you cannot justify why that answer is that way it doesn't make sense. So that's a very vast area of research in an LP and then misinformation and inaccurate responses any of you have ever encountered any inaccurate response. So for me any mathematical question you input any mathematical question because I am a student right now I'm working on multiple assignments. It always gives me an incorrect answer always. It's not like even 80% 70% it's 100% of the time. So I would suggest never use it for mathematical equations. Don't do that because it's still not developed for that kind of inputs. Ethical considerations whatever we are adding to it it's getting uploaded to the cloud. It's getting uploaded to the open AI cloud over there and it has all the data that we are adding to it. So of course it's like an ethical consideration because we are adding sensitive information at time and then it becomes very very very difficult to understand where that information came from and hackers can use it if they have access to the cloud. Now the last part of the presentation this is like the dissertations that I am working on. The social cost things from NLP. So there are two things that I am working on. The first one is EDoS EOSD whatever you call it. Explainable online sexism detection. Sexism has become very very very prominent on Twitter and Facebook and all those platforms. So we do work on I have been working on that and we try to identify like from a given text we try to identify whether it's sexism or not. What kind of sexism is there? Who is accountable for that sexism? Those kind of things we try to find using our research using our model. The second one is AI judge. This is a very I would say like that's like a 50-50 part of it that people would accept it. There are a lot of people who are against it because they think having something like an AI judge is a social implication that people would start losing jobs because if you have an AI judge what's the point of having a human judge and why would there be lawyers at all? What would be the case? So AI judge also is like a very interesting research area that we are working on currently. So what we do in these things is like we have something called trained validation and test. It's like for all ML algorithms. So you have if you have like 100 test cases, 100 cases that are given to you you use 80 as training, 10 as validation, 10 as test and then when you have real-time situations you give those situations to your model and then it will give you a prediction. That's how it works for AI judge or be it for EDOS the sexism part. So that's the research area that we are working on currently. I will be adding those GitHub links on the slides if any of you want to like take a look at the code. It has sentiment analysis it has an LP used pretty extensively and bias and all those things. So yeah you could take a look at it. We have worked on BERT, Roberta and what's the other one? Yeah the other forms of BERT itself because BERT is like the best one right now in an LP. So those are the algorithms that we are currently using. So that's it guys thank you. Sorry for the delay.


Ooh, she already cute girl. I'm teaching, two time baby girl you know. Wanted to show you how your wig will come. This is the wig, super cute packaging. They switched it up a little bit. Your package will come with their lace and milk band. It'll also come with a rat tail comb along with a edge brush and then of course your wig. I did get the five by five lace closure. Super cute, absolutely love, absolutely love that it comes already curled for you. And we're gonna get into this a little bit more. Just want to show you how it comes straight out the box. Hey y'all, welcome back to my channel. Today's video, as you can see from the title, it is gonna be a hair review video. Thank you so much, love me for sponsoring this video. If you're new here, hey, my name's Akira Jenae. Don't forget to subscribe if you haven't been there before you leave. So, they sent me over a beautiful five by five HD lace closure wig. This is their breathable cap in their brown highlighted loose wave glue-less wig. Super cute. Like I said, it's a lace front with curtain bangs. The bangs are already cut for you. Highlights are already in there for you. They've already curled the wig for you, like maybe. What more could you ask for? So, on the inside, I think I've already showed a video but you do get two combs on the side elastic band and then you get the comb in the middle. No, you get the comb in the bottom, in the middle, yes. These right here irritate me. I don't know why they're always in there. But yeah, this is a glueless wig so the elastic band is crucial in this. I'm gonna go ahead and try it on because I have not tried the wig on yet. I did go ahead and spray some lace tint on there. This is the lace tint that I sprayed. This isn't my favorite one. There's another brand at the beauty supply stores that I usually pick up but at the time this is all they had. So I did spray that and I let that dry. That is literally just two-tenth lace. I didn't bleach the knots because I didn't want to mess up what they already basically did for you curled and everything like that. So I wanted to kind of just show you how it is, a throw on and go type of wig. But yeah, definitely if you care about, you know, the knots and stuff like that, you can bleach them. It looked like they slightly bleached them a little bit but you definitely could go over them. So this is how it looks with the lace tint and everything on there. So yeah, I think it's pretty cute.  I'm gonna adjust these bands in the back to go on the second hook. We'll see if I need to adjust this or not. And I use their Slick Back HD Baud Cap. They came in there. Absolutely love these. I'm gonna throw her on. Ooh, she already cute girl. And she ain't even out of weight. Oh yeah, yes. Okay, I'm gonna move my cap up just a little bit.  I absolutely hate all that. So I'm gonna try to opt to not do it if that's possible. All right, so I got the comb in the bag down. My battery's about to die so I'm about to have to switch this out. I didn't pluck or anything but it probably could use a little bit of plucking. Just to kind of customize it. Like it doesn't look bad. It's not a bad hairline but definitely could use maybe just a little like the tiniest amount of plucking.  Sometimes people don't like to sometimes I stick them, sometimes I don't. It just depends but since I want this to definitely be glueless I'm gonna go ahead and stick them down on the side. So therefore I can pluck a little bit better with this on my head and it won't be sliding everywhere and you'll just keep it in place. So yeah, it's already staying in place.  I don't have many flyaways as you can see. I'm just gonna put a little bit at the top of my head and then use the hot comb to kind of just keep it flat so it doesn't move. So when the streets the dark out and come through and pull your back out I hope that's okay I can't leave you alone No, no It seems like forever when you sit and think about my heart out on my sleeve but I can't live with Pull back out your car No sunshine's made for chilling That's the motto Do we really wanna take it there? I scream you scream We can never play fair Thought you're on my team Don't you ever dare Make me favorite player If you wanna we can take it there Summer nights Get my favorite girl We can face the world We stay up until the sunrise Sometimes you get angrier This get dangerous Can't get up and get on my bride Three kiss seems like forever When you sit and think about it I put my heart out on my sleeve But I can't live without Okay, so this is the finished look Literally the install took less than five minutes The only thing that took a little bit is curling the bang I'm not a really good curler So this bang was kind of going inward instead of going outward like that So I had to kind of curl it to kind of match this side Like I said, that's why I'm glad the wig came pre-curled because I am not a good curler So that definitely took the stress off of that The hair is so soft, flowy I love the highlights Y'all know I'm a blonde girl at heart I recently just dyed my natural hair black So this blonde and this brown is really doing it for me I cannot wait to get some color back in my head next year So this is definitely my vibe Like she's cute And she's glueless I didn't have to add any concealer to the part I didn't do any spraying or anything like that So literally 10 out of 10 definitely recommend I got a comb right here And then I got the comb right here inside Like clipped in my hair And then I got the elastic band Obviously on as well So that is what's making this unit completely glueless Absolutely love She is so cute Like love me, I did it with this one So I do actually have a coupon code for you girlies Use AKLM for 28% off Also Y'all know Black Friday Also Y'all know Black Friday is coming up So definitely do not miss out on their Black Friday sales Okay, okay All this information will be down in my description box as well So don't forget to click down below Check out all the links Don't forget to like this video, comment and subscribe We are almost to 30k subscribers 100k is the go But we are almost to 30k So don't forget to subscribe As always, I will see you lovelies in my next video Bye guys