Hi, beautiful. There are so many oddly satisfying curly hair TikToks on TikTok. And we need to watch them together. These TikToks have so many likes, so many views.  And whoa, this is also really satisfying. Let's check them out. That was so cool. I can't even digest what just happened. How are you using your hair to sculpt? I mean, I always say doing hair is like sculpting, but this is like next level sculpting. This is crazy.  Is that all her hair? Is it like hair extensions? I don't even understand.  What? The artistry on that is like insane. Like, did you watch that? And that has 3.5 million likes. It is worth that many likes. I can confirm the hype is real and the hype is hyping. I also have so many questions. If you're the girl in this video and you're watching this, please comment down below. Do you wear that out? Is that look like a thing that you actually wear? If you had a mask made out of hair and I saw you in person, I would have to just hug you. That is so fun and interesting and entertaining and just the coolest thing ever. And then she got so creative and used the pomade color to actually put a design on. I don't know what the design is. It kind of looks like an alien head at the top, kind of like a sort of bear thing going on. Honestly, I'll take anything. That was really, really cool and very artistic and very impressive.  Oh my God, that was a lot. It came out with the most perfect spiral curl ever. And if you have the patience, like I say go for it. I need to see what her hair looks like when it's styled though. Oh my God. I think that is some of the most courteous, luscious, longest hair I've ever seen. Oh my God. And with the wind blowing, everything is just like, easy breezy, Beyonce. Like this just made me wanna like cry because like I will never achieve hair that is that beautiful, that luxurious and not long in my lifetime. And what the, it's not fair. I know where this is going. Everybody's like, rush it. That's like an intrusive thought with everybody who like watches curly hair videos. Like, can you brush out for me? Like can you straighten it? Like I wanna see it straighten, please. I love it too. Like I love a transformation from something that's really curly to really straight or really straight to really curly. Either way, it's just so good. Oh my God. Her curls are gorgeous. That is definitely her natural hair color, which is really impressive. And honestly, that is such a look. Whoever kept telling you to brush it was right. And that is the look you should always have. Like look at her brush it out and it just becomes even better. It went from the most beautiful hair I've ever seen to like the most beautiful hair I've ever seen, if you know what I mean. Like it just like elevated to the next level. I hope you actually start wearing your hair poofy like that because people would be like, who is that girl?  They are the party. I'm intimidated by them because they're just so gorgeous with their curls brushed out. So any curly hair girls out there, please brush your curls out for us, please. Everybody loves it. I know you probably don't, but like we do. That was such a different look than what she started with. How do you have such healthy hair? How do you girls do it? She is straight in that curly hair, bone straight. And the after look is actually quite cute. I prefer the brushed out curl moment in the beginning. I know. She sounds insane because it's a crazy look, but she looks like a lioness if you will. What look do you guys like better? Do you like the curly kind of brushed out look or the straight in flat and like refined better? I mean, I'm kind of torn. Both are beautiful and her hair color is insane. Like that is also definitely her natural hair color. People who naturally have that ginger copper color. I'm clapping for your parents. Good job parents. That is so pretty. With the yellow headband and the orange hair, everything kind of just comes together and is such a vibe at the end. Ah, impacable. I've actually seen this before and I have mixed thoughts about this. Like she is taking a strainer and diffusing her hair with it, which is like kind of a really inventive, interesting idea. But I'm worried about the heat from the strainer heating up really hot and like burning her hair. Curly hair is always quite dry.  But I don't know. It doesn't seem like it's gonna be any worse, any better than like any other hot tool. So I don't think it's like that serious.  But I do get a little worried about it. But that look she ended up with is so beautiful. And actually, maybe actually second thought, maybe the heat from the strainer is helping her curls become curlier. Cause it looks like she has pretty straight hair to begin with and she needs all that heat to make those curls last and to make them nice and strong and defined. Maybe it was a good idea. I also love how shook she is in this video.  I think it's a fine idea. You can also use a strainer that's like, I don't know, did they make, well those are like plastic strainers but those will probably melt. No they won't. Pasta is f***ing hot. That plastic is not gonna melt. So you can probably use a plastic strainer. But then again, don't listen to me on that one. I am not a professional strainer or I don't know what's going on in the strainer market or if any of them can be using your hair. So it didn't come from me. Okay, just try it out yourself. Listen, I know that con air brush is damaging. So are a lot of tools. But if f***ing works, my friends that use that brush, they got the straightest, silkiest blowouts ever. And they all have like curly, curly hair like this girl. Also her curls to start with are magnificent. Oh my God, they are so shiny. I can't stop staring at that color.  Like it is stunning.  Please tell us. The straightener, I'm gonna really love it. But what do you guys think? I think absolutely straighten it. Like yes, because it looks so good so far, but I need to see that finish result where it's really smooth. And also I feel like you need more products in your hair. When your hair is curly, you end up with that super fluffy blowout at the end. It usually means your hair is like not weighed down enough from products. Heavy products can really help smooth the curly hair blowout because it like puts a lot of sh** on. The hair strand and it weighs it the f**k down. And that is what we want. You know, if you use too many light, light products or you don't put it enough, you'll get this kind of like more poofy look. It all depends on your hair type and hair texture also, but that is usually the case. Hair products are your best friend. I'm trying to do it in small, thin sections just so it gets extra straight. Cause I always try to rush the process and do giant sections and it never looks good. Ah, it's so satisfying watching curly hair get straight. For some reason when I said that one time, everybody got so mad at me. I'm sorry, I can't like when people straighten their hair. Wait, what? Like, damn. I can't even say that I like the straightened hair. Look how much movement I'm getting. Look at the difference between the flat iron bits and this part. Honestly, the fact that I used the brush straightener first made this a lot easier. Final strand. And here is the red hair straightened. It's so shiny and bouncy. Oh my gosh. Do love this color though. I think it's hot. It's smooth, it's straight, it's sleek, it's gorgeous. And I'm loving the look. I'm loving the vibe. I also really like it curly though. I don't know. I think for me, your curly hair is just top notch and the straight hair is pretty, but like anybody can do that. You know? Curly hair for me. I was the most gorgeously satisfying thing ever. Again though, I liked her hair before I was better. Like I love how big it was.  That before look like, oh, it's so good. Oh, a foam is so good for curly hair. Um, whoa, you hear that? She just proves that it works miracles. Like that is insane. She put that foam on there and immediately those curls were just like and clumped together and just made the most perfectly defined waves ever. Incredible. She looks so good before and after. And her hair just screams health. Like just healthy, healthy, healthy. I need her secrets to keeping her hair this gorgeous. Something tells me she never, ever straightens her hair or curls it or anything or does a silk press. That look was beautiful. Curls went from curls. That looks so good on him. I love this ginger color with his complexion and his skin tone. It looks beautiful. I love the way he used the products. Like putting in the hydrating wave cream in the shower like that and then putting on the foam after is just such a good idea. I mean, the end result speaks for itself. Like he looks radiant and gorgeous. And I love that he used a pick to get those curls fluffed up. I love a pick. You will find a pick in every drawer, in every place I do hair because I love just teasing up those curls and making it fluffy. To me, the only type of sad curly hair is when it's flat and drooping down. I want it to be like the bigger, the better. Those genetics are genetic-ing. Ah! I mean, I thought everything in this video was gonna have curly hair but she made it look like she had curly hair. She like did the reverse of everything we've been seeing and she went from straight to curls. And it actually looks really natural and real on her. And I like the curls better than I like her straight and hair, to be honest with you. Her hair is as thick at the top as it is at the bottom, which you never see. Like it literally looks like she has tons and tons of extensions in her hair but she definitely doesn't. And it makes no sense to me. And oh my God, I can only imagine how long that took her to curl her entire head like that. That must have been forever but the end result was totally worth it. And I hope she wore this somewhere. If she used curly hair products and the right technique I bet she could make her hair quite wavy naturally without having to do too much. Like not having to curl your entire head with a half an inch curling iron.  Gotta be honest with you. But that was spectacular. Okay, that video I don't even know. That had 770,000 likes. I don't understand why. I mean it was definitely enjoyable and her hair is definitely extremely beautiful and healthy like every single person we saw today. But like what? That is some major bounce back on that curl though. Usually people have to like scrunch their hair to make it look like that. Hers, even if she brushes it straight it's going right back into place like that. I need to see this dry. Is there a video of her dry hair?  Clearly like zero dead ends. Like what is the secret you guys? What am I doing wrong with my life? Genetics, man. 10 out of 10 results. What about a 10?  She had me convince she had straight hair but honestly anybody can have curly hair with some good hair products. Wave time. You can really craft and shape hair into waves or curls with pretty much any hair type and the right technique. Everybody has some kind of wave pattern in their hair unless you're like part of the 1% of the people who literally have none. Then I'm sorry but you'll probably never have some waves with just some hair products in your hands. You probably have to use a curling iron.  She literally got the most beautiful curls ever at the end. That was so satisfying and unbelievable. Her hair looks so much thicker.  This is the hair I always thought I had but then I saw a random curl in my hair so now I'm thinking maybe I do have curly hair. I just got out of the shower. Now I'm just gonna scrunch it a little kind of. I have this strident lotion and I also have this smoothie. Maybe it just dries flat, I don't know. Let's put in this stuff. This is super sticky. Who is she? Stop. Maybe I should put in a little mousse or something. Okay, I found this old mousse. Should I do more? I don't know. Yeah, that's too much. Babe, this is curly, right? Over here you see that it's a bit more flat, a bit more straight, so let me know what I should do with that. Over here, look at all those curls. That is crazy. Apparently I do have curly hair then. Oh, not totally satisfied but it is kind of satisfying that she went from straight hair to wavy hair. Next time just skip all those creamy conditioning products that's gonna weigh your hair down. A foam or a mousse is going to be best for people with that really light wave if you're trying to enhance it because it's so lightweight. It gives you that definition and that frizz protection and that hold without kind of like bringing your waves down. But good try and I am very impressed with the fact that you tried this out for the first time and that's what your result was. Good for you and I hope more people would try to make their hair curly from straight hair because I swear. Most of you have wavy hair, you just don't know it. Okay, this is enough today. I'm starting to feel really bad about myself.  How was your hair like 35 inches long and it's so perfectly curly? I mean probably because you wash your hair like this, like this looks complicated and she's like only getting the root of her hair which is a really good idea with the shampoo and then most likely just conditioning the bottom of her hair. Ah, my God, I so badly wanna do her hair. Like I wanna touch it, I wanna feel it. Like it looks just so perfect. Insane, that is so satisfying. Um, I am blown away. I am shook. That was extremely, extremely satisfying. These people today had the most beautiful curly hair I've ever seen in my life and I am severely jealous. That was just beautiful. If you guys wanna check out any of my curly hair products you can do so with the links right down below or go to xmodohair.com. That's all for today guys. Thank you so much for watching and don't forget to live your extra life and I'll see you next time. Bye.


This research paper provides a systematic review of the neuropsychology of generalized anxiety disorder, GAD, highlighting key findings such as potential cognitive deficits in GAD patients, neural correlates of GAD, and limitations of existing research. The paper uses the Prisma methodology in draws data from multiple databases, including PSYC info, Scopus, PubMed, and Elsevier. It also discusses clinical neuropsychology strategies and treatments for GAD, such as cognitive behavioral therapy, CBT, mindfulness, and medication. The paper recommends future directions to enhance the understanding of GAD's underlying cognitive and neural mechanisms. This article was authored by Evgenia Kintoni and Paula Suarez Ortiz.


My name is Rishabh Gore. I work as a technical architect in Microsoft. Let me get my system ready, sharing the screen, perfect. Okay, so today we are here for a very interesting topic called Responsible AI. Every organization out there, they are committed to the advancement of AI driven by ethical principles that put people first. And today we'll look at the implications of this and what are the possible repercussions for the same as well. A little bit about me, wait a second. So a little bit about me. I work as a technical architect in Microsoft. I'm privileged to be a part of a very small and interesting group in Microsoft called the Microsoft Technology Center. Internally, we call it MTC. We are very small. Like I work in the Indian MTC, the India subcontinent. And we are a very small team of four technical architects and one director for the entire Indian subcontinent. So all of us have to be a cross domain, specializing cross domain solutions. Like me, myself, I look after all of our IoT service offerings, the entire application development scenarios in Azure and the local, local offering in Power Platform. So in my day-to-day job, we host customers day-in, day-out on deep technical discussions. So we have specialists in Microsoft who specialize in particular technologies, but whenever there's a requirement of a deep technical discussion, and this could be around architecture, this could be around designing, this could be about rapid prototyping. That's where we come in as MTC. We show to our customers the so-called art of the possible when it comes to technology. You can find me on LinkedIn at GodRation. This is my last name and the first name. So, and I know it's like pretty early, day one of the DevCon. And I know it's DevCon, but my entire talk is not going to be code heavy. Too very frank, I won't be showing any code at all. So this talk is just to get your thought process started, to get your brain juices flowing if I may. So sit back, get a cup of coffee and just let's go. Like we'll get started now. So if you think about any solution, any big data IoT solution out there today, it's all about three components there. It's things, this could be your sensors, your microcontroller units, this could be anything. It's things, they are generating vast amount of data. Now, what you need to do is you need to actually work on top of the data, analyze the data to get some insights. That's the second stage. You need to get some insights out of the data by doing some sort of analysis there. Now, once you're done with the analysis, that's when the next stage comes in, you need to turn those insights into action labels. So that's stage three of the process. Now, pretty straightforward piece, because now what will happen is your actions will in turn generate more data thereby completing a digital feedback loop. Now, what's really interesting about this entire slide is this is a very, very easy concept to understand. But if you take it one step further, if you really think about implementation, that's where the entire sort of complications comes into play. That's where you really started to think about how can I go about really implementing this solution at scale? It's a very complex piece. In fact, before going into how to do this, a general question, first we need to ask ourselves, what makes AI different from any other technology and basically why responsible AI? So there are multiple reasons here. First, it's the pace of innovation around this technology itself. It's absolutely incredible. For example, in 2018, we achieved human parity when it comes to machine translation. In 2019, we achieved human parity when it comes to general language understanding. So just think about how rapidly it has transformed. This is just the last five years that I'm showing on this slide. Secondly, it's the proximity of these AI technologies to human intelligence itself and about how we personally experience the world around us. So the whole concept of sensing. And lastly, the biggest difference from any other technology is AI's path to both harm and good that is driving today's conversation. In fact, let me take you 30 years back today. 30 years back, every business out there was looking at software as a way to redefine how it ran its operations. There were systems of record, they were getting created which were able to manage every core process in the enterprise. So this could be from accounting, payroll, to resource planning, to customer management. Now, this change, it was foundational to the digital transformation of an organization. But as big a change as it was, the entire digitization of the core processes, it didn't alter the primary business of a company. It just made it much more efficient. Now, in the past decade, however, systems of record, they have been extended to the so-called systems of engagement. Now, what I mean by that is now it's redefining how companies engage with their customers, how the customers themselves use the products or buy their products and eventually how, eventually even what those actual products themselves are. So along the way, software evolved from being focused on efficiencies to being becoming a core aspect of every business out there. Actually, if I really think about it, it quickly became the part of business which where differentiation happens between your competitors. So for example, let me give you a simple example. Now think of Netflix, Airbnb, Uber or Amazon. Now, even referring to these companies as media company or a real estate company, or a transportation company or retail company, in our own head, it sounds pretty weird because they are in a sense truly software-driven companies because they understand that software is the primary function of their organization. So modern companies or modern organizations out there, they really need to actually look at software as something that's infused through every aspect of their business, a critical component of their operational efficiencies. So what has happened is, okay, this was something that we have been seeing for the past 10 years, but the full digitization of companies, it has led to a very interesting secondary effect which is the proliferation of data. The systems of record, they transformed what traditionally were paper files into digital stores where all the business data reside behind the company's core processes. Then systems of engagement, they added a vast amount of data with respect to your product usage, your customer outcomes, one of those things, the customer interactions with your products. So this in turn created the perfect environment for systems of intelligence. Now systems of intelligence, they leverage vast amounts of data generated in an enterprise to create those expert systems. Now what has changed now because traditionally the term artificial intelligence, it was reserved for describing very special occasions when the machine was able to perform tasks that are normally associated with human intelligence. Now as powerful as reporting on analytics can be, they are definitely not at human intelligence level. So what happened? Why is everybody using the term AI day in, day out now? I think the primary reason for this is the growing sophistication of the techniques available in particularly in the area of machine learning as technology improves exponentially, it allows us to compute more and more data together. So an interesting piece is in news systems of information can actually join my existing system of intelligence now. For example, I can connect any of my IoT enabled devices directly to the cloud today. So those things are already there. And because of this entire change that we are seeing today, it becomes very imperative that we look deeply at the responsible development and use of AI that prioritizes people in the center to accelerate the positive outcomes. Now let's look at what possibly can happen if it's not taken care of. So on a weekly basis, there are local headlines everywhere in the world related to the concerns regarding the use of AI. So let's look at some of the repercussions. So for example, this was one article that I was able to find focused around fairness. So this is a concern given that the African-American community, they are already over scrutinized by law enforcement in the US. Now this suggests that facial recognition technology, it's likely to be overused on the segment of the population on which it underperforms. So think about it, it's a very negative paradox here because facial recognition algorithm, they are not tested for racial bias. So this is something that can creep into your system. Another interesting piece, this one.  Now, even if you are of a different gender, even then it was causing issues. Another very interesting one is this one. Now, we see examples of, this is more of a diversity or ethnicity issue. So basically in this picture, this Asian man, he's trying to put his picture onto a system. In this case, it was a passport software and software was repeatedly declining his application saying that his eyes were closed. Now, a very important point here is this is not somebody doing intentionally something, okay? This is not, like nobody's actually intentionally putting these issues into the software that they're actually building. It's just that the software that was developed to identify whether a person is uploading a valid image or not on their passport or for their passport application. The dataset that was used for that identification scenario, it did not have a representation from every ethnicity and that was causing this issue. Another example, and again, this is something that you actually will pay. So this is more of an NNP example of Turkish language. Now, Turkish is a very special structure when it comes to their language that is their third pronoun. It does not have a gender. So when we input a sentence to the third pronoun to a machine translation system, so this could be Bing translator, this could be Google translate. So in this case, the translation software, it needs to map the third pronoun to either a he or a she. Now, what we see is that the algorithms, they are trying to put their own biases in these translations. For example, as you can see on this screen, a doctor or engineer was a he, but a nurse was a she. Again, nobody deployed this, but it just happened because the dataset that was used to train this, it had more representation from that sector. The last one, again, this is more about accountability, very, very crucial. Like for example, when a driverless car crashes, who gets the blame and who is going to pay for the damage? So as you can see from all these examples, if ethical checks and balances, they are not in place, it can lead to a lot of issues without us ever even realizing that we are doing it. And we can be the developers who are going to develop this. And in fact, that is why responsible and ethical practices, they are crucial from the grounds up. So in the next, I think we're done with 10 minutes. I have around 20 minutes and then we'll take a 10 minute Q&A. So in the next 20 minutes, what I'll do is I'll show you the approves that Microsoft suggests and what I have personally learned from my interactions with customers in the Indian subcontinent day in, day out. And again, whatever you'll see, I work very closely with the engineering team in Redmond and it's something that we have been doing from the grounds up globally across our different subsidiaries. So when you talk about responsible AI from a Microsoft perspective, it's a built down into four different pieces. I'll talk about three of them. The first one is to have an AI strategy that top down approach. Second one is to enable an AI ready culture. This is where we talk about having that culture from the grounds up. The third one is around having technology which is able to do that which is something that I will not be covering today because again, technology, we all understand that. We all understand how a rest networks or how image networks. But the fourth aspect which is very crucial is where I'll talk about, like I spent significant time there and I'll talk about the six principles that we have announced as Microsoft. So that's around responsible interest for the AI. So let's go into the first one which is about defining an AI strategy. So again, the whole idea here is very simple, very straightforward to enable true AI transformation in your organization. As Microsoft, we believe that you need to bring AI to three different pieces of your organization. So the first one is you need to bring AI to every application out there. Now a simple example that I can give you is of the organization can add a simple chatbot on top of your existing website or an application that interacts with users in more natural ways. Enabling you to so-called, to create a very involved customer experience and help your employees maximize their time as well. Or it could be as simple as adding a predictive analytics to a range of different applications, making it easier to plan ahead or decrease the operating cost. Now, while this is great, but for true AI transformation, you can't limit the impact of AI to your applications only. You have to bring AI to every process, be it internal, be it external. A logical place to start on this is to empower your technical development teams. By giving them the tools, they need to quickly and easily leverage. They can help bring AI to your processes and bring you major results. For example, enterprises today, and I have talked to them, I have talked to the CDS of multiple organizations who are already doing this. They are leveraging intelligence solutions to, let's say, help their marketing teams monitor their brand by tracking user feedback or improve their seller efficiency by prioritizing the so-called lead generation or help their finance or operations to reduce cost and optimize their operations by data-driven insights as well. So having AI in every single process, that's very important. And lastly, you can't transform by bringing AI to trust your developers. Every department needs to partner with your developers. In fact, if you read any of the gardeners' latest high trends for the next decade, citizen movement is very key there. So the whole idea here is, how can I democratize AI to every single employee? How can I bring AI to my business user who does not understand computer programming? How can I enable him to use a sophisticated train model? And again, the entire premise of transfer learning actually comes here or giving your team explainable AI models comes here. So all of this is on the verge of how to enable AI strategy organization. So from technical employees to non-technical users, we believe AI will give them the power to transform how they work and thinking more innovative based than before. So next, let's look at, next we want to discuss the qualities that characterize an AI ready culture and demonstrate how change management can make this culture transformation a reality. So you might ask, okay, what exactly makes an organization's culture AI ready? So the whole idea here is from our perspective, fostering an AI ready culture, it requires three different pieces. First, it requires being a data-driven organization. And it's not only about accessing the entire data state, it's not about creating those data lakes and then dividing them into different data warehouses and then going to data maps. No, it's about ensuring that the data in that data warehouses of absolute highest quality. It's about ensuring that you have the best and the most complete data as the foundation for your AI systems, which because of paramount importance. Second piece is to empower your people to participate in the transformation, the so-called democratization of AI and to create an inclusive environment that follows cross-functional, multi-disciplinary collaboration. I mean, the whole premise of the citizen movement that Gartner has said is around the fact that your business user should not be dependent on your developer. It should be a team sport, it should be a fuse team. So that's what it is. So fundamental to empowerment is enablement, giving people the space, the resources, the security and the support to improve what they are able to do with AI. Now empowerment also requires allowing room for errors, encouraging experimentation and that's something that we are doing. And in fact, just after this slide, we'll go into how exactly I personally at your house, if we have approached customers in the Indian subcontinent or in the Asian territories when it comes to driving these changes. And you will see what are the repercussions of those as well. So I'll show you some interesting case studies there. So lastly, it's about creating a responsible approach to AI so that you address the challenges that are questions, you address the challenging questions that AI presents in front of you. So from our perspective, this third key element of an AI agriculture is to foster a responsible approach to AI. Now as AI continues to evolve, it has the potential to drive considerable changes to our lives, raising complex and challenging questions about what future we want to see. So these are questions that deserve a dedicated discussion. So let's go ahead and explore them more deeply. Before that, I'll just look at the chat window in case there are some questions. They are known, it's absolutely fine. I see there are no questions, it's good. Yeah, you can post your questions as Haimath has already mentioned. I will respond to them, but we'll have it dedicated to you at the end as well. Again, this is a very interesting discussion. You can, your questions could be around anything to be very friendly, it could be around app dev, could be around GitHub, I'll answer all of those, no worry. So with that, let's move forward. So yeah, so last piece, and again, this is the most central piece and this is where I'll show you something very interesting now. So I got a confirmation about my speech at the event sometime in, I think in December and at that point of time, I started preparing for it. So I went through three, I went through multiple articles in Howard Business Review and this is a very interesting piece. Now, look at these three different articles. All three of them, they are actually talking about the repercussions of responsibility or what if ethical practices are not there in your AI. All three of them, they are actually posted within like two months, the last two months and look at this interesting piece. All three of them talk to different aspects of your organization. Is ethical principle, getting ethical principles in your system is a solution responsibility? Is it a strategical decision or is it something that should be done with technology? Again, the entire premise is people don't even understand today. When we are working on any AI model, our approach is to get the maximum accuracy, get the best results, but we don't care about these things. And in fact, I'll show you what actually happens where you don't do that. So as Microsoft are AI journey, so in light of this entire responsibility, we have seen that organizations, they are finding the need to create processes and structures to guide their internal AI efforts, whether they are deploying third-party solutions or developing their own solutions as well. Now, we also recognize that every organization will have their own beliefs and standards in their AI journey. So what I will do in the next 10 minutes is share our principles and then we'll straight away open for question and answers, okay? So Microsoft, our journey started in 2016 as some of you might be familiar. We put forward six principles that guide our development and use of AI. These were fairness, reliability and safety, privacy and security, inclusiveness, accountability and transparency. So next, we put these principles into practice and then finally we use tools and resources that makes it easier for developers and data scientists to identify and mitigate potentially harmful resources or issues in their process, in their building of the data science life cycle process. So let's talk about these guiding principles. Okay, so first principle is around fairness. So for AI, this means that AI systems should treat everyone fairly and avoid affecting similarly situated groups of people in different areas. So basically the idea here is, for example, when AI systems are required to provide guidance on, let's say, medical treatment or loan applications or employment, they should make the same recommendation to everyone with similar symptoms or similar financial circumstances or similar professional qualifications. But unfortunately, because AI, as I mentioned earlier, because AI is designed by humans and it's trained using data that reflects our imperfect world in the world in which we live in, they may reinforce those biases. So as Microsoft, I mean, we encountered this when we were talking with a large financial lending institution in India and they were developing a risk scoring system for loan approvals, okay? And what happened was, we trained an existing industry algorithm using the customer's data set. And then what we did was we actually trial the system in a pilot program with the customer. And in fact, what we did was we had a side-by-side proof of concept with human loan approvals as well so that our pilot program can be validated as well. Now, when we conducted the initial audit of the system, we discovered that yes, the system was very well because it was giving loans to only low-risk, so basically only approving low-risk loans, basically. But interestingly, what we were able to find was only approved loans were for male borrowers. There was not a single female borrower there. And why was that happening? Because the training data for this, it reflected the fact that loan officers, they historically favored male borrowers. And again, the idea was this data set was like 200 years old. The company was established since the 1800s. And inspecting the system, it allowed us to actually identify this and connect that bias before the system was actually gone into production or into deployment. So, okay, what are the recommendations from our info to make sure that fairness is taken care of? First is we recommend that you understand the scope, the spirit and the potential uses of any AI system. Next is you ensure that the design teams, they reflect the diversity in the world. That was one of the major issues which caused this one. Third, you can identify and mitigate bias in data sets by evaluating where your data is coming from, understanding how it's organized and testing it to show its true representation. Next, you can also identify and mitigate bias in machine learning algorithms as well. It's not just that data will always give you those biases and this is where intelligibility, and this is something I'll cover in the later slide as well, intelligibility of a particular training model is also very important. And lastly, you should leverage human review, which is something we did in this scenario as well. So, human review by trained employees to understand the meaning and implications of the AI research. It becomes extremely crucial when you have to use AI to inform consequential decisions about people or wherever human beings are involved. So, again, very important concept around fairness and what can happen if it goes wrong. The next principle is around reliability and safety. To build trust, it's important that AI systems, they operate reliably, safely, and consistently under the normal circumstances and in unexpected conditions as well. How they behave and the variety of conditions they can handle reliably, it largely reflects the range of situations or circumstances that developers can anticipate. So, basically, that's where the bias comes in. If you're not even thinking of a particular scenario, it won't be there in the model itself. So, again, the example here was, this was with a Southeast Asian mining company. They were designing an AI system to identify if their drivers were alert when operating heavy mining machinery. And that's where we actually had to implement a very staged implementation process so the system can be thoroughly tested before the large-scale implementation. Again, what you need to take care of here is, one, you need to understand your organization's AI maturity. Second, you need to make sure that because it's Microsoft, we recommend developing practices for auditing AI systems. If you can use that, and again, I was just, I'm talking about Microsoft, but again, Amazon is doing the same as well, Google is doing the same as well. So, at the end of the day, the industry leaders, as industry leaders, we are doing one of these things so that any organization out there can use this. Third, you should evaluate when and how an AI system should seek human input. It becomes extremely crucial in almost every scenario. It's not actual replacement, it's a collaboration. So that's very crucial. And finally, we recommend developing a robust feedback mechanism for users to report performance issues. And again, if you think about it in a very traditional sense, feedback should be eventually reaching the training algorithm stem sets so that it can retrain the model and go back. So that's our reliability and safety. Next one, a very, very crucial piece. It's crucial to develop AI systems that can protect private information and register tax. And again, I'll show you a very, I'll tell you a very interesting case study here. So the whole idea here is AI becomes more prevalent in the industry today. Securing important personal and business information is becoming more critical in complex. The privacy and data security issues, they require especially close attention for AI because access to data is sort of essential to AI systems and hence it becomes very crucial. So the interesting example here is, this was Microsoft's own, so we released a chit chat bot on Twitter. It was called A. So basically we taught the bot, the chat bot to learn from online interactions so that the bot could better replicate human communication and personality traits. But what happened was, and this was something we did two years ago, but what happened was within 24 years of the deployment of that particular bot, users on Twitter, they realized that the bot can actually learn and they begin to feed the bot with electronic. So turning that entire bot from a very polite bot to a weaker for hate speech and we had to remove that. So again, here the important pieces are you need to comply with relevant data protection, data privacy and data transparency laws. This could be GDPR, this could be the California Privacy Act and all of those things. Second, you need to make sure you design AI systems that maintain one, the anonymity and second, the integrity of personal data. And lastly to protect the AI system from bad actors, you need to make sure that they are designed in accordance with secure deployment and moreover, they should be designed to identify abnormal behaviors or to prevent the manipulation and malicious attacks that can happen on that particular pace. In this case, the chat bot that we built. The next principle is inclusiveness. And again, there are already, I mean, we know that for a fact that there are around one billion people with disabilities around the world. And for them AI technologies can be truly game changer. AI can improve access to education, the government services, employment, information, all of those things for them. So intelligence solutions, like for example, a simple solution for real-time speech-to-text transcription or visual recognition services or predictive text functionality, they're already improving or empowering those with hearing, visual or other impairments. So in this case, we actually were working with the Australian Department for Human Services and the DHS there to build an AI system that could augment the so-called overloaded call center operators. Now, with inclusion in mind, we conducted user research and we discovered that some citizens did not have the ability to call the DHS due to either because of their disability or because of the lack of phone service, meaning that they were, they could only access services by in-person appointment. So what we did there was, again, making sure that we created a multimodal, multi-channel, intelligent chatbot that can not only improve accessibility for those groups, but also provide a more convenient experience for every single individual. So these are the four principles. Now, underlying these principles are two foundational elements which are essential for ensuring the effectiveness of these four are taken care of, which is transparency and accountability. When we talk about transparency, whenever AI systems, they are used to help inform decisions that help, they have tremendous impact of people live, like people's life, for example, in the healthcare sector. It's critical that people understand how these decisions were made. So a crucial part of transparency, as I mentioned earlier, is what we refer to as intelligibility or the so-called useful explanation of the behavior of AI systems and their components. So again, in this case, we actually partnered with a large healthcare provider in India to develop a more accurate risk model for detecting cardiovascular diseases, cardiac diseases. And we had around 32,000 patients' records and we were able to build a risk model that was significantly more accurate than the model that was used previously. But again, what was important that we wanted to actually ensure that the providers, they could understand how the system scores patients one. So basically what we did was we worked together to create an interface that explains the result of the so-called explainability of a particular model. So the interface uses three categories, dietary, medical, and activity. Again, not to go too much deeper here, but the whole idea is it showcases the healthcare providers how it scores patients, enabling providers to develop the most effective treatment plans for their patients specifically. The last principle, again, this is the second last slide that I have and then we'll open up for question and answers. So the last principle is accountability. We believe that people who design and deploy AI systems, they must be accountable for how their systems operate. The need for accountability is particularly crucial with sensitive use cases, for example, facial recognition. Now, it's a very interesting piece because recently we have seen there has been a growing demand for facial recognition technology, especially by the law enforcement organizations who see a lot of potential use cases of this technology, like for example, finding missing children. Now, however, as Microsoft we recognize that these technologies, they can potentially be used by a government to put fundamental freedoms of people at risk. For example, enabling continuous surveillance of specific individuals. So to that end, we have publicly called for regulations on this. In fact, we have, we have, we are like very vocal advocates of this, that this should be a good issues when it comes to any technology out there. So it's important to recognize that facial recognition technology is not like, it's not going to be the last technology with sensitive use cases or corner cases. It only serves to highlight the importance of one, remaining vigilant and second, accountable for faults and harmful uses in all the emerging AI cases, use cases that we'll see in future as well. So at Microsoft, we have developed these six principles to guide our use of AI with the aim of respecting collective values when helping society realize the full potential of AI. We encourage organizations to do the same as well. From holistic, like from holistically transforming businesses or industries to addressing critical use cases or critical issues that are facing humanity, AI is already solving some of the most complex challenges that are out there and redefining how humans and technology interact with each other. So with that today, what I did was I, I can have very brief 32 minutes, I am outlined some of the steps that we have taken or we are taken to prioritize responsible AI. And in hopes that our experience can help others as well. However, we recognize that we do not have all the answers. It's very true. For example, the Twitter chatbot that we created. And every organization out there, it has to have its own beliefs and standards. As organizations and as a society, we are steps to a responsible AI. It will need to continually evolve to reflect new innovations and lessons from both our mistakes and our accomplishments by engaging with AI in a responsible manner. We can ensure that it fulfills the promise to create a better future. So thank you all for attending this session today. The next step for all of you is to share what you learned today within your organization. So as to discover what opportunities AI presents and for you yourself and for your business. And now with that, we'll open up for questions. I see there are a few questions. None in the Q and A, one in Okay, one in the chat window. Hey, if there are any questions, maybe you can ask me those, it's okay. Well, Rishabh, I could see one of our attendee has raised one question. Are there some open source projects following the guidelines you're talking about? I mean, if you have any idea about any open source project, which is also following the guidelines that you have described during your session, it would be great information for the person who is looking for it. Okay, like at the top of my mind, I don't have an answer for that question, but I'll be there in the discourse over it. I'll make sure that if I find some link, I'll share those. Whatever I discuss today, it's more or less around case studies between our customers. Again, because I personally work with customers day in, day out, so that's what I had. But yeah, if I am able to find some answers, I'll make sure I share those links with you. Yes. Hey, Monty, how are you? As we wait for another question from our audience, I have one question. You brought up a really interesting case study where the loans in India, your training data was mail-oriented and therefore your AI models were mail-oriented as well. Thankfully, you looked at the data and saw that it was lopsided and it wasn't approving women. But what are techniques that people can use to understand is there bias in their models, whether it's sex or age or whatever? Yeah, so pretty good question, Eric. Basically, one key aspect is around explainability. And again, every big organization is actually working on this. All the trained models that are out there, they have that component, they are okay. If my model is giving some particular result, why exactly is that result coming in? That's one beautiful way to actually think about actually learning it from machines. The second piece that I mentioned in my talk as well is about having checks and balances, having some humans in the loop who can make sure that, okay, when my system is going into pilot, when it's at a proof-of-concept stage, at that time, if you have those checks and balances before going into production. Because I mean, our chatbot, it went into production and then we faced those issues. It was actually on Twitter and that's when we realized people can also learn that it is learning. It can be taught to speak in a different way. And it became a disaster for us as an organization. So would one technique being make sure that those human checks are part of your test plan and you validate? Absolutely. Think about it. Absolutely, but at a massive scale, it's just not possible to have human checks and balances when it comes to machine learning. If it was a typical software developer lifecycle, yes. But when it comes to machine learning models, it's not possible to have human checks and balances at every stage. And that's where it becomes important that you reevaluate all the decisions that your AI algorithm is taking. And that's where explainability becomes key concern then.


When we quantify frictional head losses in internal flow, we sort them into two categories. First, major losses, sometimes just referred to as friction losses, which represent the losses associated with friction along straight lengths of pipe. The other type is minor losses, which are associated with anything else. These are usually points of transition, the pipe changing area or changing direction, going through elbows, bends, or other fittings, especially valves. These minor losses are referred to by what we call their minor loss coefficient, represented here with the letter K. In practice, you could probably consult the manufacturer for a published loss coefficient or you could determine the loss coefficient yourself with a simple flow test. For our purposes here, we will be using the charts and graphs of general loss coefficients available in section 6.9 of our textbook. Those charts and graphs give loss coefficients based on some of the physical characteristics of the fitting in question.


What's up everyone, Takedown here. Welcome back to another video. We have just returned from our vacation and we usually do this once a year. We'll go to Kingston and we absolutely love it. There's a lot of cool things that we like checking out while we're there and a lot of stuff that we enjoy doing there. So we try to go once a year and usually anywhere from spending two days to maybe a couple of days there. But while we're there, we always check out different places to add things to our collection. And we did pick up quite a bit this year compared to previous years. And we're going to be showing that in today's video. So let's get started. So here's an overview of what we picked up when we were in Kingston, which is way more than what we did last time. So up first, let's just start. These are actually Funko. They're like VHS boxes, these mini boxes, but there's a mystery figure inside, kind of like the soda cans are. These were, I think, $20 each, but we actually got them on sale. And I think one was 17, one was 18. So just a couple of dollars off, but we're going to open these up in a second as well. We also found this here, this little Winnie the Pooh figure. It's actually Ashley's birthstone, which is really cool. And I picked it with the nicest one. This was only $4, so a nice little Winnie the Pooh figure for her collection. We also found this, I think it was $17 at Hot Topic. And it's just a cool Winnie the Pooh honey pots, but you can also get a figure inside, which is awesome. And before we even went and got to Kingston, we did go to Walmart. We found these little almost like keychain things. So she got a Mickey Mouse and a stitch, which is awesome because she loves stitch. So at least she got one that she really likes. At the same Walmart, we did find this Walt Disney with magazine that has Mickey Mouse on it, which is really cool. So she picked that one up. I think this was $17 or around that mark. The same Walmart, we finally picked up another one of these icon lights. I do have the yellow question mark cube, but now we have the pow one as well. So that's a really cool addition. I think these are about the same as a Funko. I think they're around $17 as well. Other places actually sell them for around $25. And when we're in Kingston, I did find the mushroom and the boo ghost, but they were $25 each. And I thought that was overpriced when I already picked this one up for 17. So I decided just to stick with the one. At my games, I actually did find a lot of these Funkos. So she finally found a snow white that she liked. This was 1799. She also found Maleficent. I probably butchered that 1799. And then the hundredth anniversary Cinderella, which was 1499. And they had a deal where if you get three, you get a fourth for only $2. So mine ended up being the cheapest. It was Doctor Strange for $11.99. So this one was only $2 with the sale, which was awesome. She also picked up this really cool stitch t-shirts, which is HimHangry, which is awesome. So that was really cool. I think we got that at Spencer's.  So I've been looking for Thanos for years. And at the time, there were around $20 each for each can. These ones here, the Thanos is what I spotted first. It was only $9.97, so $10. So most of these range between $15, between $10 to $15, which is amazing considering they used to be $20 each. So we did pick up a Ghostbuster one here. Peter Venkman, Thanos for myself, Captain America, which I think is going to be really cool, Crash Bandicoot, because I can't find his regular Funko anywhere. And Ashley picked up two as well, Ursula and Ariel. So we're going to open those up and see what figures they are because there is a chase as well. And of course, while we're in Kingston, we had to go to Wilton Cheese to pick up cheese curds and brothers for the jerky. So this is one pound of regular and half a pound of hot and spicy. And that is the best jerky around. So here's the honeypot. We're going to open it up now. But looking at it, it has all of the honey dripping down, which is really cool. So Ashley is going to open it up now and we're going to see what figure we get inside. See. It's like Christopher Robin. Yeah, pretty good figure. Look at this. All of the figures that you could have got were actually really cool. So that's actually a really nice figure. So that's a great addition to Ashley's collection. Yeah, I like it. Even the honeypot just on its own was really cool. But the fact that you get a figure out of it was awesome. Now for these here, I didn't even realize Funko was making them, which is crazy. Obviously, they're going to be more movie related and things that probably came out on VHS. But I'm going to open up mine here, Jurassic Park, which is the Ian Malcolm figure. See what is inside it here. See if I can do it with one hand. Oh, that looks like just the regular one, which is actually really cool. That's a really cool figure. I like that Funko makes stuff like this. And then inside, it actually has a name tag for him. Dr. Ian Malcolm. That's really cool. Ashley's going to open up her stitch one here. And it's awesome because you get a nice little mini VHS case as well to display along with the figure. All right, so we know what's going to be a stitch one. Yeah, all right. And this is a hot topic exclusive. Yeah. Looks like the regular stitch. Yeah. Which still is a really cool and cute stitch. Yeah, I'll leave it in there. It's probably stuck in the ears, but that's really cool. Holding this belly and they have a little card here as well. Kind of like the other one. So these are blockbuster rewinds, which are really cool. And now we're going to open up those cans and see what figures we got. And hopefully we've got a chase out of one of them. So here are the cans. We're going to open them up one by one and see if we did get a chase or even just check out the figure. But these range between 10 to 15 dollars, which was amazing, considering they used to be 20 dollars each. That's just going to go ahead and open up the first one here. We're going to see what figure it is. Some of them are also exclusive ones as well, which is really cool. Open up the first one. This is the first one. It's the first one. It's actually a really big one. Yeah. And that does look like the regular one. There is a little thing inside here that will tell you if you got the chase, but no, just the regular one, which still that is a really cool figure. Yeah. So it's a nice one. That's still really cool. What's holding a little purse? Yeah, a little purse. That is really cool. That's a nice figure. Yeah. Nice. Yeah. So let's open up the next ones here. So let's open up the Compton America, which is also an exclusive one. So go ahead there. Let's see. Just the regular. Just the regular. Wow. That's nice. That's really nice. So it's the one from Endgame. Oh, is he he's holding Thor's hammer? Yeah. That's a really cool one. That's awesome. Love that he has the shield on Thor's hammer. That's in great addition to my collection. Nice. Next, the Ghostbusters one here. See what is inside it. That's cool. That's a nice figure. You have the Ghostbusters logo on the side. The pack on him. That's awesome. Bet you anything that Chase would have been him covered in slime. Yeah, that's guaranteed. So we're going to do we're going to do Crash Bandicoot next. So we're looking for the regular Funko for this for years and we've not found it anywhere. So at least I found this one and I think it was only $10. So worth it. Wow, that was awesome. This one's way bigger than the other ones. But finally, I have a crash to put on my shelf. That is awesome. That's a nice figure. That is amazing. I like that they're from Funko, but they're different than Funko. They're not the the same exact style, which is awesome. And the last one, Thanos, which I'm excited for, because this one was $20 back in the day and I couldn't find it anywhere. This one seems big. All right. No way. Yeah. We found the Chase, only 3,300 of these ones. Yeah, it's Thanos without the helmet. Yeah. Wow. OK. So let's check it with a chase. That is awesome. First time I were getting a chase. That is awesome. So it's him without the helmet. Still has the Infinity Gauntlet, which I'm so happy for. That's awesome. That looks amazing compared to with the regular one. So the regular one is him with the helmet on. There we go. It's him without the helmet. He's actually he has a bigger grin on here. This here, he looks angry. I think I'm going to go for the regular one, too, though. I think I want to get the regular one. But finding the chase is just awesome. Yeah. So finally, we did pull a chase, which is amazing. And I love that it has this suit. There's so much detail in it. So that is awesome. That is a highlight right there. But these are what we picked up when we were in our trip for Kingston, got a whole bunch of these mystery figures, whole bunch of other new additions to the collectibles. And we also got some treats as well. Hope you guys enjoyed this video. We'll see you guys in the next one. Please take care. Peace.


So have you ever wondered what the Kamasutra is really about? I mean clearly it's not a book about positions So what is it that made all the grand old sages of ancient India spend so much time on this one subject? Why are there so many thousands of books written about desire and arousal and pleasure? Well ladies and gentlemen, it's because in ancient times They believed that pleasure was the pathway to heaven. It was not just the way that you developed your spirituality It was also the way to develop every other aspect of your life So what are those secrets? What is that wisdom hidden in this book that you can use for your life today to benefit yourself? time to find out