A little bit about myself, I am a professor at Insofi, the International School of Engineering. I am based here in Bangalore, we also have offices in both in Hyderabad and in Bangalore. I, our, a little bit about Insofi, basically we are Education Institute mainly focused on corporate training. We run weekend programs for corporate training as well as corporate training. We also do consulting and R&D. So, prior to joining Insofi, I was in consulting with Ernst and Young and even prior to that, I was with IBM Research and early in my career, I also work with startups.  So, you know, you might be wondering about the topic of this talk, right? Behavioral biases. So, I'm trying to use the term, you know, behavioral biases as a proxy for, you know, to distinguish from, you know, biases in machine learning, right? In machine learning, we have this concept of bias and variance. So, I'm not talking about that kind of bias, right? I'm talking of human factors. So, with that, let me get started. Let's look at a typical, you know, what's happening in data science these days, right? Why is this becoming more important? So, if you look what's happening, when data science initially, you know, was limited to a few companies, right? There were the Googles, Facebooks, the Netflix, right? And, but what's happening today is data science is becoming much more, you know, going down the ladder, right? You know, even a decade ago, right? It was pretty much folks, for example, in traditional businesses like insurance risks, logistics, e-commerce, digital marketing. That were the main users of data science. Today, that's not true, right? Today, it is, you know, industries like healthcare, physical retail, manufacturing, even small businesses, as well as even support functions like customer support, finance, procurement, HR, they're also looking at using data science. And what's different about, you know, the organizations in the lower two boxes versus the ones in the upper two boxes is the kind of people you are talking to are very different, right? The people you talk to in the upper two boxes, they understand data, they understand data science, right? The people in the lower two boxes, you're talking about people who are good at running a traditional business. They don't really understand data science. They don't understand probability as well, right? So you got to talk to them at a different wavelength. And that sometimes creates a mismatch of, you know, because the difference in their understanding and the understanding of data science folks, that creates a mismatch of expectations.  What are the reasons for those pitfalls showing up? How we can avoid them? And just being aware, just being aware of these pitfalls, right? You know, upfront, you know, will help improve the chances of success. Or, you know, say that, okay, this project is not really, has a low probability of success, let's not do it, right? So, so let's understand, you know, what, you know, how a typical data science project gets started, right? So, all right. So a typical data science project, you know, in an organization that's, you know, in the lower two boxes, right? It might start, let's say, with the CEO or a senior VP, right? A person at that level, talking about, hey, he or she read something in the, you know, in the latest journal or online, said, hey, this organization in this, in my industry is using data science. I should also do something with data science, right? And that's how it starts. And, you know, I've called this person a hippo. It could, you know, basically a highly paid person's opinion, right? If you've heard that term, right? So these are the people who basically think that they could think that, okay, if I do data science, I do AI, I do machine learning, and I talk about it, my stock price may go up, right? So that's, that could be some kind of a motivation for them. They're also, you know, also expecting, you know, an ROI, right? But they're not sure, you know, what ROI to expect. So they go and say to their, you know, they catch one of their line of business management and say, okay, can you, we want, I want to do a data science project in this company. Do you have some projects you can think of where data science would, you know, help?  He's telling the line of business person.  So the line of business person, he also doesn't know much data science, right? So he goes and talks to, you know, either his vendor, right? Your favorite IT vendor and says, okay, can you help me get some consultants on board and staff, staff up a data science project, right? Well, that I, you know, you never ask a barber if you need a haircut. So don't ask a consultant, you know, if I, you know, need help. So, you know, some data scientists comes on board and they start, he tells them, okay, go and talk to the users, right? So there's end users. So these, each of these persons has their own, you know, things that they want to get out of this. And let's see, you know, what are the pitfalls that happen in this, you know, in this kind of a project? The first pitfall is failure to set the right expectations. Now, let, let's see what I, let's, let me take an example, right? Heard of IBM Watson, right? IBM Watson is IBM's AI platform on top of which they have developed certain specific solutions in certain verticals, right? So that's IBM Watson. And last, last month or end of July, an article appeared in the news, stat is one of the, in the, it's a online site for medical information. And an article appeared in stat, which is quite widely followed, saying that IBM Watson gave incorrect cancer treatment advice.  I mean, machine learning is not supposed to be 100% correct. I mean, if it can probably treat cancer 80%, maybe that's good enough, right? But that's not, you know, the way the medical professionals see it, right? And once it appeared here in stat, it came in the Wall Street Journal, right? So this is what appeared in the Wall Street Journal, right? The big blue spent billions on its Watson AI product with focus on cancer care. Sometimes it didn't say more than what doctors already knew. Sometimes, how can you expect machine learning every time to tell you something more new than what doctors already know? You can't. And most of the time, you know, it's going to tell you what the doctor already knew, either and confirm the belief, which is a good thing. There's a second person saying what you believe in. Or sometimes if it tells you something new, that's also great, right? But how do you expect it to always say something new, right? So then they say, in some cases, Watson was stripped up by a lack of data in rare or recurring cancer cases and rapidly evolving treatments. Of course, that's going to happen with machine learning, right? If you have rare cases, you're never going to have enough data, evolving treatments, if your context is changing, if you don't have enough data on things that are changing fast enough, obviously, machine learning is not going to give you the right result. Nothing to be surprised about. But, you know, that was something that was totally the way a data scientist looks at it, the way IBM looked at it, and the way the medical professionals looked at it. You know, there was a big mismatch of expectations, right? So, of course, IBM, you know, responded to this, and there's an article by the VP of Watson also online. But there was a Twitter posting by Andrew Ng. So, he's basically saying that, you know, it's important to set realistic expectations, right? So, the keyword here is setting realistic expectations, right? So, this is really one of the first pitfalls, right? Data science is, you know, about making statistical guesses, right? Probabilistic guesses. But sometimes stakeholders expect a sure thing. Let's try to understand this a little bit more, right? So, there's a behavioral bias called the certainty preference, right? And why does this happen, right? Because in a traditional business, the people whom we speak to, right, they are typically, you know, they're used to seeing, basically, software applications which are rule-based.  They have some input, there are some rules, and based on that, they spit out a decision. What's that? You know, that's not machine learning, that's not a statistical output. You know, that's a very consistent output that you're getting, and they're used to seeing these kind of things. So, when you go and tell them, I can predict cancer, or I can predict customer propensity using machine learning.  That'll be correct, and that is not going to be, you know, moody like a human is, or going to make wrong decisions because they're sleepy or hungry or something like that, right? So, they expect, you know, almost perfect kind of decisions, right? So, this is, you know, this is what happens to people in the traditional line of businesses who haven't been exposed to machine learning to date, and we, you know, as machine learning is going more and more lower down the ladder, we are seeing more of this happening, right? So, the way to, you know, set the expectation, the way I see it, is to tell them, you know, this is, the output is not going to be 100% correct, right? A machine learning project is more like an R&D project than a software implementation project, and it really needs to be planned as such, right? So, all right, so, once, you know, you have set this, you know, expectation, then what next, right? You need to figure out how to, you know, what to expect, you know, what, you know, they're going to ask you, right? How much accuracy can expect from this, or you need to figure out and tell them this is how much accuracy I can expect from this, and then they will say, okay, okay, go ahead, or they will say, oh, it's not worth it, right? So, the other pitfall is failure to baseline, right? For example, if the IBM Watson people had known that this is what the doctors were expecting, they might have tried maybe a different approach, right? So, the failure to baseline is, you know, one of the major challenges. I mean, if you don't know where the line is that you need to cross, how will you know your chances of success, right? You might end up putting a lot of money into something, you know, which has very low rate of success or low expectation of success. So, you need to know the threshold. Let's take an example of speech, right? In speech, today the accuracy, many of you would have attended yesterday's talk by Anand, right? The keynote talk yesterday morning. So, we talked about how speech recognition today has reached an accuracy of about 95%. And this, when it hit this 95%, right? That is when it reached what is called, you know, tipping point, right? You might have heard of this book, Tipping Point by Malcolm Gadwell, right? He really, that book is really in the context of startups, right? Something suddenly at some point, something in the startup has been running for, let's say, 2, 3, 4 years and it's not successful, but suddenly something changes in the external market and the startup suddenly becomes successful, right? The same thing happened with speech. The point it reached 95%, that's when human adoption quickly took up, right? Because for humans, 90% is just too much error, you know, for me to correct, but 95%, 5% error, I can live with that, right? So that 95% was a tipping point. The same thing is true in many applications of data science, other applications of data science. You have to know what is the baseline or what is the tipping point for your solution to be accepted by the users, right? And this is very different in consumer and enterprise, right? I'm actually borrowing from, if you attended the 2PM talk by Om Deshmukh, he referred to this, right? 80% accuracy might be good enough in some context, whereas in other context, you know, even 95% is not good enough. So it's important to know what's your baseline and what's your tipping point. And with consumers, it's typically, you know, when you're doing a marketing or consumer, their tipping point is fairly low. For example, let's say you're doing email marketing, right? Your marketers are using some rule-based system to send out emails every day. They're getting a 2% click rate. If using data science, you can improve that to 3%, they'll be satisfied. 4%, they'll be happy, right? So in that, in those cases, you know, the tipping point is, you know, not very strict, but you gotta figure out what the tipping point is. And this is one of the pitfalls, you know, people, you know, do, they don't figure out what's the baseline. They're putting a lot of money, putting in a lot of money into things where there's very little hope of actually reaching that baseline, but they haven't, you know, figured out, they haven't tried to really identify the baseline. So now let's say, you know, you've done this, you've identified the baseline, right? What next, right? You have to get a team on board, right? So when you're looking for a team of people, right, you know, you need a bunch of data scientists, right? And when we talk about data scientists, we talk about people who know a mix of math and they need to know programming, right? Typically, two skills we look at, but they also need to, you know, it's just not, it's also the attitude that matters, right? So let me give you an example, right? You might have heard of this. A traveler came to three men, you know, working, all right? He asked each of them what they were doing. The first man said he was laying bricks. The second man said he was building a wall. The third person said he was building a cathedral. Which of the three workers do you want in your team? Third one, right? So that attitude also is very important, right?  It's somebody who also has a right attitude to solve a business problem to formulate a business problem. So how do you formulate this business problem, right? The challenge is that the education of data scientists today largely consists of working with standard data sets, right? And in the standard data set, you have a decision variable. You have, sorry, you have decision variables and you have a target variable, all right? And then with that, you know, you can say, okay, I got 1% higher accuracy than the other person on Kaggle or you can say, okay, I published a research publication in this conference. But, you know, in the real world, it's different. When you go in the real world, right, nobody tells you what's the objective or the target variable. The business problem says, make me more money, reduce my costs, reduce my headcount, help me do more with less people, right? And you have to figure out what your decision variables are. They say, okay, go and get the data sitting in the data warehouse. Go and pick whatever you want. You have to figure out what data is relevant. Nobody tells you that these are the variables you need to look at. You have to figure out what is the target variable. You have to figure out what you want to optimize, right? So it's very different. An example that I was working on, we were doing a project for a large telco in India and they basically came to us, says we want to improve the revenue from prepaid customers, right? So this was about three years back. So that was before GEO came in and messed up everything. So in those days, you know, there was still money in optimizing your things. So, you know, they said, okay, the data is there in the database, go take what you want, right? And sorry, the font is not very clear, but essentially what's out there is that they wanted us to optimize their prepaid plans. So they have some, you know, prepaid plans in the market, which is things like, this is an STD coupon for 14 rupees, STD pack for 14 rupees, which allows you to, which is valid for 28 days, which gives you a discounted STD rate of 30 paisa per minute, all right? Now, this is an open market plan. What they said is, can you come up with some, can you come up with some, you know, customized plans, which will be given through SMS, which will be offered only to some users or some segments of users. Can you figure out how to target these plans? So the decision variable was, they said, okay, you figure out what's the plan to use, and you know, but the point, you figure out, you can change the coupon rate, you can change the rate, you can change the validity within certain constraints, but you have to figure out, you know, what's most effective to change and by what amount to change it, what we want is more revenue at the end, right? So here, nobody's telling us what's the decision variable. So, okay, the team said, okay, let's look at the data, and they said, okay, the way we formulate the problem is let's focus on your STD rate. Let's make that the lever of the decision variable. And what's the target variable? They said, whether the first, they said, they came to me and said, okay, the target variable is whether somebody accepted the plan or not. That is the propensity to pick up the plan. Does that make sense? No.  If they make the target variable as the propensity to purchase this STD plan, they might end up giving this plan to the customers who are already taking this plan, and they will get 100% propensity, but because it is a lower rate, they might actually see a drop in revenues. We don't know how, we don't know, right? What we have to really target is not the propensity, but actually the revenue increase, right? So these are the kind of challenges that we see when we work with data scientists who are relatively inexperienced, and this is one of the pitfalls that we had to deal with. I think yesterday you saw the same Einstein's quote yesterday evening's talk. So you need to spend a lot of time just thinking about the problem and formulating the problem. That's actually the hard part, right? Solving the problem is much easier these days. It's changed, obviously, 10 years back, we didn't have nice packages like we do today, which can make it very efficient to solve problems. So now, as we go ahead, the thinking part, the formulating part is going to become more and more important. The solving part is going to become more and more easier. Yeah, yes, good. Yes. I understood your question clearly, but we are talking about revenue of this company, right? So we're not looking at other companies at all, right? No, yeah, right, yeah. Okay, yeah, yeah. So yeah, so we had to do an experimental design. We had to look at it segment-wise. So for each segment, we decided, okay, we'll make within each segment, we'll do an A-B test. We'll give the offer to some customers, not to give it to some customers, and using that A-B test, we had to do it. The next challenge that I've noticed is availability bias. Typically when the client says, go look at all the data that's in your data warehouse, take what you want, right? We don't actually get all that we want, right? The person, whoever we speak to, kind of the DBA or the database person, or the marketing person, they will basically say, this is the data I deal with, so I know about this data, you take this data. And what I've seen is the average data scientist will basically just focus on that data, and they will be able to make sense of that data, but what they have a hard time making sense of is what is missing, what variables are missing that are important. That they have a very hard time doing. So it's like this cartoon here we see, based on the data you read in the newspaper, right? You're heavily influenced by the data you see. You're not trying to think more broadly about what are all the other factors that influence the target. So it's important to not just look at the data you have, but try to figure out what else is important. I was also doing a project for a call center, for one of the large banks in Australia, where they already had a cross-sell model, right? And they came to us saying, can you help us improve the cross-sell model? So they were using the normal regression and decision trees. So we looked at the model and what we felt is, maybe we can try and improve the accuracy, right? But we still, you know, we don't know how much improvement we'll get. We'll probably, you know, these guys already have a good team. So I'm not sure how much, we can't say how much improvement we can really get. Is it worth taking up? The approach we took was, let's try to figure out what data is missing and try to make that data available for the decision tools. And since this was, you know, the people who came to us really for the call center people, they were making offers while the customer made an inbound call or they made an outbound call, they would make offers to their customer, right? Cross-sell offer. What we said is, once the call center agent has the customer on the phone, let's ask the customer two or three questions. For example, these are credit card customers. So what we said is, we'll ask the customer, what do you like about your card? Do you like the reward program more or do you like the low interest rate? So that's a very, you know, question that tells us a lot about what the customer cares about, right? And it also, you know, tells us, you know, are they more interested in, you know, reward program means they're probably somebody who's, you know, not, you know, cares much about their interest because they have enough savings, whereas if a customer who's, you know, cares more of the interest, we know that they're more likely to be interested in other loan or credit-related offers.  Right? So that, we did that. And that, you know, using that, we could improve our cross-sellerate by 20%.  All right, so now you have all the data, or sorry, another example I have is a retail example, right? Suppose, you know, many of us, you know, might be looking at retail data. In a retail case, one of the challenges they have is pricing. How do I determine, you know, how much discount I should give to maximize my overall profits, right? The thing is if you are only looking at some people, some data scientist might take that data and say, okay, I'm looking at the sales of this item and, you know, the price of that and I'm trying to fit in a regression model. All right? But that's not good enough, right? You also need to look at other things. Maybe the customers are coming to the store because there's a big sign outside that says even more on sale, right? You need to know those things. But then there's not captured in the data. The store manager has made the decision to, you know, put a big sign outside that says even more on sale. Is it captured in your data? No, it's not captured in your data. How do you, you know, kind of reach indirectly? How can you make an inference that something like this is there? One way is to look at the total footfall into your store or total sales, total number of transactions on that day. That says there's probably some promotion going on, right? And then normalize by that, right?   What next? Now, you have your team. They're going to build some models, right? And typically, you know, today, when we get data scientists, right? Today, it's a bit more, people are more data scientists are more aware of all these techniques, right? About three, even three, four years back, we used to hire a lot of people with the statistics background. They used to have a BSC, MSC in statistics, right? And one of the challenges in the training of statisticians we found was they trained in regression models, but they don't know much anything else, pretty much.  So that's what I call, you know, man with a hammer syndrome, right? You know, one or two techniques, and you always want to apply those one or two techniques. It's really important to understand, you know, have a good understanding of all techniques. So make sure that even if one person, you know, you can't expect one person to know everything, but try and build that diversity in the team, all right? So now you have, you know, you've set up your team and you start working on the problem, right? And what next, right? Your team comes up with some, you know, comes up with some analysis, right? So going back to the telecom example, right, where, you know, the client has asked the team to look at what's possible in terms of improving my revenues by optimizing prepaid plans. So my team came back to me and said, customers, once they opted for the STD pack, increased the spend by 30% compared in the following month, compared to those who did not take up this STD pack. So this was their observation. This is a correlation. But based on this observation, this, they recommended, therefore pushing this offer to more customers will increase revenues further. Now this is clearly correlation, but does it imply that taking up this STD pack is going to increase revenues? It doesn't. In fact, correlation versus causation, this pitfall is taught in statistics, right? But people sometimes have a lot of difficulty taking what is taught in the classroom and seeing that it is relevant to the business problem they have, right? So what's happening here is, you know, there's some other factor. For example, in this case, it's probably the customer intended next month that because they're traveling or because the family is traveling or something, they would make a lot more STD calls and they ended up buying the STD pack. So because of some other factor, they bought the STD pack and so they were anyway going to call more, right? So it's basically saying that, you know, causation is not correlation. Yeah. So distinguishing between causation and correlation is not easy. You have to look at multiple things. What we looked at is, we looked at it at a segment basis, right? So we already had customers segmented. Some of the segments were as small as 100 customers so within each segment, we don't expect any bias in terms of or, you know, we expected fairly homogeneous two segments, right? So in both segments, we do, you know, we give half the customers or some x percent of the customers, we make them this offer, special offer, right? And then we record how much is the uptake rate in each of those segments. So that, in that case, we have really compensated for this effect because both segments, we are not looking at each person, but we're looking at a whole segment. At the segment level, the segments are homogeneous. The same percentage of people who might call, who might want to call long distance is there in both segments. So that's compensating or not normalizing for it. Yeah, yeah. Obviously, this is not something they, this is obviously a discussion I had with the team so it was well understood in this thing. Yeah. How am I doing on time? Okay. So now, you know, and so you are getting close to, you know, solving the problem, right? So what next, right? There are also human factors to look at, right?  You also have to look at human side or psychology. In fact, I think psychology should be taught to all data scientists because all the data scientists today, right? We have to deal with human decisions. Either you're working on consumer side, right? Like you're a Facebook or a Twitter, you need to know how consumers will behave, right? If you're working inside an enterprise, you need to know how your enterprise users, your decision makers will like or dislike what that, you know, what this black box is telling you, right? So psychology matters, right? You need to understand, you know, for example, something like you have probably heard of this, right?  You have a $20 bottle of wine. You want to increase the sales of $20 bottle of wine. The trick is keep a $100 bottle of wine next to it, right? No way data science can tell you this, right? You need to know psychology, human behavior, right? So in marketing, it's important, right? You need to understand marketing because so much of what data scientists do is with consumer behavior, right? Another thing was, for example, you're trying to predict the effect of some promotion, like some advertising, like, for example, this is an ad for a Dow soap, right? A fairly old ad from the US. So marketing is interested. You know, there's a quote, you know, where the chief marketer says, 50% of my advertising budget is wasted, but I don't know which half, right? And so this is for the Dow soap, right?  So you do the analysis and you say, the new promotion, sorry, the new campaign you launched where you're advertising the soap has no lift on sales or very negligible lift on sales. But you know, the marketer is going to say, well, it may not have an immediate lift on sales, but it's going to create brand awareness and that brand awareness is going to stick for a long, long time, right? So this is something, again, you only know from a domain expert, right? So these are kind of things you need to be aware of So it's not just, so marketing is one, but more broadly, right? You need to understand psychology, right? In an enterprise case, you might see, okay, users are not following your recommendations. What do you do, right? So in the several, almost a decade ago, I worked for a project at IBM, internal project where we were helping assign, build a tool for assigning practitioners to projects. So IBM at that time had a large bench and they were interested in minimizing bench and there was a lot of mismatch in the skills available on the bench and the skills needed for the projects. So we built a tool to help do a global optimization of that, right? When we launched it, we launched it initially as an Excel file, we would send an Excel report with recommendations, matching recommendations. Users didn't like, I mean, they weren't really looking at that much. They said, okay, Excel file, okay, I don't know how it's created, how it is, what's the basis for this, right? But then we said, okay, let's put it in a UI, a web-based interface. And that made a huge difference. People who are hardly looking at the user file, they were, the web interface made it much more interactive. We were able to give or put a lot more features when you click on a certain person, right? They were able to see, understand why this person is being recommended because we had those explanations in an interactive way. So that made a big difference, right? So, explainability is very important, right? Third thing was, you know, do you understand what users are measured on, right? If they're measured, for example, on revenue and you are trying to optimize profits, right, they may not like your recommendations. So that's very important, right, to know, sorry. All right, so now we have understood, you know, human behavior. I've talked about, like, some nine different pitfalls and biases, right? And many of these come together. And when they come together, it results in what's called, you know, it's the Dunning-Kruger effect, which is basically the effect, you know, in our normal language, we call it overconfidence. But basically, this is an effect where you are more confident than what you should be, basically because you don't know what you don't know, right? So it's very important to know what you don't know. So data scientists might say, okay, data, but, you know, after they build the model, the end result is not so great, right? Because the possible limitations, you know, the data scientist team did not really think about those possible limitations. So with that, you know, pretty much covered, I have ten of these. So I'll just summarize, you know, and typically in the order in which the surface in a project, right? First is failure to set the right expectations, right? Second is I want 100% correct or I don't want it. Failing to baseline, you don't know what accuracy you need to reach for it to be acceptable to your users. Bricklayer mindset, right? You think you're trying to solve a regression problem, but really you need to think of it more holistically. Problem formulation, like the telco example I gave you, what's the target variable. Availability bias, trying to get, you know, the data you have, also look at, figure out if there's some other data that's relevant. Man with a hammer syndrome where you only know one or two models and you end up using the wrong model. Causation versus correlation, failure to appreciate the human side, learn psychology, right? Overconfidence bias, especially when many of these things come together. So my ten biases and pitfalls, all right? With that, I think that was pretty much the last slide I had. I have, I'll just talk a little bit about my organization. We do, we are basically in Sophie, right? We are based in Bangalore and Hyderabad. We do training, we do R and D, we do consulting. We have 15 full-time faculty, all of whom we have a PhD from top universities. And we work in these, all these areas. These are some of the clients we have worked at. And, you know, if you have any requirements, we have a booth outside. You can figure out, find out more about us. And with that, I will take any questions you have. It's not understanding the objective. So the question is, how do I get to the objective, right? So, see, you need to talk, talk to, right, true. And that's I think it's important to have, see, the way I would do it is typically I would go and have a conversation with, you know, the hippo, right? In this case, the highest paid person who's sponsoring the project, right? Have a conversation with them, understanding what they want. And then I would talk to the other extreme, the users, right? To understand their challenges. And then come up with a proposal, try to figure out what's the meeting point. What are the, you know, because of CFO or the hippo is talking at a high level, these people know the low level challenges. The meeting point, right? That's, that intersection becomes important. See, I think, when it comes to consumer behavior, you never have enough features, right? There's always a lot about the customer you don't know. I think it's basically a matter of speaking to domain experts, figuring out how they speak to the domain experts, understand, you know, what they think are believe are the important variable. Don't just think to speak to one person, speak to another person. I think it's a good example. Each will tell you something, put it all together. That's the way how to, you know, figure out what, what's relevant. And then the second part is, you know, having figured out what's relevant and how do you, is there a way, is that data captured in the organization? Right? If it's captured good, if it's not captured, maybe you need to talk to the IT team to see how it can be captured. Sometimes it might involve doing some, to capture that. Maybe it might involve doing some customer surveys. But in the long, you know, in the long run it's all worth it because, you know, in fact, even if your model fails, that data that you collect from consumer surveys that you collect from maybe testing, it might be useful, it's useful for some other reasons. So used to? Yeah. Right. Very good point. Yeah, we, they will typically talk in the context of, you know, the process and they don't think outside of the limitations of their process and yeah. So that is, I haven't figured out a good way to yeah. There's no formal, yeah. There's no formal, yeah. When I talk, I'm talking more about features, missing features. Yeah. Yeah. So what we typically do is look at what's the best practice in industry, right? Retailer, right? Look at globally what are the best examples, what are retailers worldwide doing? Take that as a benchmark and starting with that, then you know, figure out, is there something missing for this particular, let's say, Indian context, is there something else I need to capture? For example, if it's like, let's say a big basket delivering to your doorstep, maybe that's an opportunity for the person delivering to ask one or two questions, right? And they can collect that additional information. Maybe they can, I mean, there's obviously questions of privacy, but they could just visit, I mean, they come to your apartment, they could see, you know, what, how many bedrooms it is or, I don't know, things like that, right? Understand, collect some more data about this. With data science, it's the brains that matter, right? Any, some pre-existing solutions you buy rarely work, yeah. Focus on the brains and spend 5% on your, there's a, somebody, Avinash Kaushik, he's kind of the guru for web analytics.


Hello everyone in this video. We're going to go through charge point stock which is SBE is the ticker symbol for this. We're going to go through some of the core fundamentals of the company some of the research Where I think these stocks going to go for the future whether I think it's got potential in the long term And we'll go through a wide range of information in a step-by-step Presentation format. I've got to say that obviously I'm not a financial advisor I'm a business teacher in my daily life, but I'm by no means an expert when it comes to the stock market So as always, you know, don't do your own Research there, but what I've done is here is compile of compiled Some excerpts of research and I'm going to get straight it straight into this To respect your time and we'll get right into this All I ask is if you could leave a like on the video to help it open the YouTube algorithm It'd be great. Thank you for that if you do it But I'll get straight into it as I say just to respect the time So should you buy charge point stock? We'll go through this now first thing to mention is charge point is a is an EV charging company and Also, it's one of the leading or it is the market leader in charging stations here So one part one part I wanted to go through is is some of the main Aspects of it and really is is ultimately it's the ease of use We talk about and this is what maybe nobody's talk people are talking about the fact that yeah the building Charging stations and the EV sector is growing there for the charging stations grow. Yeah, it's brilliant It's fantastic, and it's a great observation But also in addition to that you've got to start to think about the fact that The ease of use that they're trying to produce here and the producing an integrated approach They're making it easy to use these char points and that's what's really important here is Building that accessibility and ease of use is going to help create more customers that come back and use them Because it's easier and people like to save time people like to save money Yes, but people like to save time and people like ease of use just as a product and and you can see that across industry Customers are more demanding of ease of use Technologies think of you know, you can you can think across industries here I'm sure you can think of examples of where ease of use is really important. That's just key to it Okay, and then you've got three areas here that they're they're Strategizing in and it's not just in terms of your your EV consumers, you know electric vehicle Consumers actually Charging up their vehicles, but you've also got them into interacting and integrating with businesses and fleets as well and and the good idea in terms of businesses is obviously there is a growing kind of growing current towards more ethical ways of doing stuff and companies are trying to become more ethical because Customs are demanding companies to be more ethical and with that in mind What we've got here is a charge charge point who's obviously trying to be environmentally friendly In terms of charge charging electric vehicles. Now. This is a good way for companies to show that they're economically Friendly and economically friendly environmentally friendly and economically friendly and actually following ethical You know procedures and trying to be more ethical as a company So there's a there's a great kind of selling point for charge point there and as the market leader as well But with charge point being the market leader the most recognizable Well businesses are going to want to go with who is the most recognizable and that is charge point at this moment in time Okay, not to say that other charging station stocks aren't going to do really well I've made videos on those and I think they've got this the space for these to grow as well But I think that this one is the market leader and it's got a lot of sustainable potential with that But like I say, there's three areas businesses fleets and drivers And then you've got the ease of use point here So so what else here? So we've got access to hundreds of thousands of places to charge with one account Okay, we're leading the charge actually we started it Brilliant slogan that you you think after creating the world's largest and most open EV charging network We'd be satisfied, but you're wrong the way we see it There's still some so much more we can do together join us and getting all all people and goods moving on electricity Okay, so there they've actually built up a massive amount of charging stations already Across the country, which means that they built a massive infrastructure To start off with and they're only getting started with this infrastructure They're building more and more and once they've got them in place then obviously as people switch more to electric vehicles They're going to be used more and more profits and and revenues going to come from that But ultimately they're building up the infrastructure as to when that demand starts to occur. They've got that infrastructure already in place So they're not playing catch-up so to speak there now obviously that EV Switch to electric vehicles does need to continue to happen for the company to Grow further Exponentially it's obviously heavy heavily links to a growing electric vehicle market because it's electrical vehicle charging stations So is dependent on that, but there's no inclination or indicators It tells me that the you know where the electric vehicles are not going to be bought more in the future in terms of the are going to be So there's nothing that tells me that the and okay And then we got we do it all where the only network with an integrated portfolio of hardware Cloud services and support services delivering the smoothest possible Experience for all businesses and drivers when everything works together EV charging is better for everyone That is brilliant because what they're doing there They're involved in the fact the the hardware side of things for the Charging stations themselves, but they've also got cloud services and they've got their own charge point app I believe or you know so that they've got a range of Areas here. It's an integrated approach. It's not just the you know charging station itself It's a package of a different areas where the where they're trying to build it to make it easier for the customer to use So for example, you can see in this picture, you know people are able to find out exactly where their closest charge point Is and that is that is that is really good in terms of what they're trying to do that and obviously as I build that Infrastructure these charge points become more accessible So the building that that software up there alongside the hardware essentially Another point here 86 million charges delivered and counting So it says he is smart charging is and always has been our soul focused After more than a decade and millions of charges delivered We're still pioneering new ways to make electric mobility the easy choice. By the way, this is going to be a long video I do apologize. There's just I just really like this company and I want to go through it and give it It's it's it's fair to have attention. Shall we sort of speak? So if you can like the video and subscribe to the channel, that'd be great because it does help me out a lot And there's a small channel here, you know, you're helping this grow and thanks to all the new subscribers This channel's been growing very fast recently. So thank you Right. So back to this 86 million charges delivered and counting So so ultimately they are growing as a company. They are scaling up their operations and This is a continuous process, which is brilliant No, I don't want to read through all of this But I've just it just shows an indication of the facts that they do have an experienced leadership team involved in charge point that is driving that growth and expertise in the company So you've got the president and chief executive or executive offer officer As a member of the company board of directors, he currently sits on the board of the YMCA of Silicon Valley Joint charge point in February 2011 bringing one 30 years of technology technology industry experience of 30 years in that industry and is involved being involved in other companies as well Again, none of the two really You know really Impressive leadership team individuals here So most recent the head of the policy and communications for ride sharing app uber Brilliant. So he's wet for one of the one of the fastest growing companies There so so interestingly, you know, they've all been basically Highly experienced, you know, I got here 25 years of experience in strategic planning now that is key strategic planning is Sometimes underlooked because it's not it's not seen as flashy and you know Strategic planning is what makes companies succeed in the sustainable long-term So if they in what it seems as we go through this they have got the strategic plans in place to grow the company Okay, so bear that in mind Now some interesting stuff here It just says on the website again as an excerpt so 100% focus on EV charging Better than anyone else our solutions are designed and tested by people who drive electric over 200 EV drives at our headquarters alone More than 70 charge pointers have been working on electric mobility with us for five plus years The entire charge point leadership team drives electric. We welcome you to join the charge So they've got people involved in the company that actually believe in the vision of electric vehicles, which you may think oh well It's just it's PR But if you think about it you for a company to really expand you need people in the company that actually believe in its purpose Okay And you've got 2.5 million places to charge by 2025 so they are building that infrastructure 352,000 tons of greenhouse gas emissions avoided. That's a good marketing Point they can put that within their marketing in terms of what they're doing there First EV charging provided to be an energy star certified for energy efficiency brilliant So it adds to create about credibility of the company and here it says and this is really important awards for Environmental achievement from the United Nations global clean set climate change business channel Akterra and more so they are definitely seen as this credible market leader in the charging Space here and the winning these awards. You know that the they're building themselves up as this credible Market leader in the industry Which which again it just in boldens their reputation as as the leader in this area And I found this recent article. This is fairly recent Okay, and it was just about the facts that they're working on You know trying to make it more accessible for people to use charge point So they've looked at this and it now charge point works with Apple CarPlay So with that with Apple CarPlay you can actually EV charging is part of the CarPlay experience as well Just connect your iPhone to a vehicle and it works with CarPlay and open the charge point app to find stations nearby and even Start a charge or from the vehicles built-in display So so brilliant. So this is about ease of use and they're looking at working with obviously Different tech providers that can help make this more accessible, which is good that they're doing that I just thought it's quite a notable kind of Blog post to kind of mention here. I think this is really important as part of their strategy and we talk about electric vehicles Okay, but it's it's also not just EV vehicles It's it's the fact that the tech in built-in the vehicles is always improving as well and as the tech Improves in the actual vehicles. We're talking about technology Then obviously if they can do things that match with charge point Then this is a good kind of thing to be doing really Another thing here is the the growth of the company. So it's it's growing directly proportional to EV So if you think about the fact that electric vehicles is is is projected to grow then obviously with that charge point is Expected to grow. Okay one billion Estimated one billion dollar revenue there Okay, so we're about here and it's saying that it year on year. It's going to grow fairly exponentially Okay, which is good to see and obviously as the company grows That's got a potential impact on the share price as well So it's saying from 2019 to 2020 we've had a bit of a dip because of the you know the the curve it Impacts there, but ultimately it's going to start to go back up again as we know EV becomes more Used and and that's something that we could see happen in the future. Okay So says a fossil additional tailwinds fossil fuel bans transit electrification Incentive programs. This is the main kind of realistic one in the fact that there is now and we see it It's happening now Incentive programs for people to go to electric vehicles and as these I take these kind of Intensify then that is just going to help charge point out even more and I don't see electric vehicle kind of The switch to electric vehicles stopping anytime soon I think it's just going to increase whether it's going to increase at this rate in terms of people switching to EVs or Whether it's going to increase a bit less than that either way it's going to grow from what it is now I mean that's to keep on that And it says here It's a global approach as well. It's not just about North America. They have built up a you know massive kind of Team and and kind of infrastructure in North America and they are in a lot of North American areas in Terms of in terms of that which is fantastic because even cars are growing there But also you've got operating in 16 European markets as well Partnerships with energy retails and lease and solution providers 133,868 ports accessible via roaming Integrations so what we're seeing here is ultimately Increase into different markets as well. You've got the European markets and you know, they're expanding a global level Which just adds to their potential profitability and protect potential customer Target market that they can reach as well There and as you can see here Market share there seven times closest competitor There, you know, they've got 73% here of the share here So they are actually really ahead of the game in that terms of being a market leader Not to say that some of these other companies like blink, etc. Can't be successful. They probably will But you know, they are the market leader in this area and for the price that the current price of The stock is quite cheap So just to go through this bit here. So the benefits, okay, what are the benefits of this company? Well, number one It's a market leader in the charging industry as we say second one is strong brand and reputation So they're building their brand. They're building their reputation with winning these awards with building strategic partnerships with being the market leader with having more prevalence in different areas of the Well, you know in terms of European markets and not and North America Okay, so they're having more prevalence that you know, they're being seen more there So their brand and reputation is always improving huge infrastructure. That's always being built So as we see more people switch to electric vehicles, then the infrastructure is largely built already So the potential profitability will explode You got the credibility factor as I mentioned before Integrated approach so they're not just seeing it in terms of if this is it's just about the Charging stations themselves are seeing okay. It's about the app It's about the software and the cloud kind of data behind it as well So they'll have it have this sort of integrated approach to make it easier for customers There and then it's a growing industry as we say the EV market is just going to continue to grow Um Now this is good. They have got a good leadership team as I mentioned before So they've got people you know in tech and different categories of skills And they've got a good kind of experienced leadership team here Okay, which is brilliant. Fantastic. And they say they've got 750 employees Okay, which is really good and they've got operations in you know, a range of different locations here, which is great um So they have got a good team my only concern Okay, my only concern is 40% technical staff I feel that that should be a bit higher Um, and I'm not trying to knock the stock here. I really like the stock. I'm just being realistic I'm not one of these hype YouTubers that hype up stock for the sake of it I'm being realistic. I think that 40 percent technical staff could be improved upon a bit I mean, um, I know it's different. I know it's really different But you know, you see you look at cii and g in terms of arrival They've got I think it's 80 to 90 percent all engineers or technical based staff You know, the the majority of their staff are either engineers or you know, you know, you've got kind of technological Tech, you know, you know that sort of skill area And they're 90 80 90 percent and their charge points only 40 percent I feel that this could be improved on a little bit just um, because I think he'll help their research and development and and approving their their Technicals of the product. Um, so I'm thinking maybe that can hopefully be improved upon that's my only concern because Um, you know, if you have another charging station stock that has a high level of technical staff, then Maybe they can Build better products and then that could be a real big problem for charge point So I feel that this needs to be a bit higher. That's my only concern. It's not going to be the be all and end all of the stock I'm just saying that that's my one of my main concern The other thing is is the the prices run up recently as well and we'll go through that in a moment there Okay, so this is the stock price at the moment in terms of 37 25 Okay, and you know in the past month you see there's a lot of volatility It started off at that, you know, it was at 35 here at 27th of November by the 2nd of december it went down to 30 Uh, just under 30 there sort of 27 or so and and you know, it's shot back up and it's kind of stabilizing around this price So what I would say in terms of a target buy price I would try to get it under 30 if you can if it does go under 30 again That will be an absolute fantastic buy. Um, but if it goes, you know towards the low 30s Maybe buy some and what I'm saying is is is that I'm buying this stock not all at once, you know I'm actually buying it at setting points and I'm waiting for dips I'm assessing the stock and I'm buying just a little bit each time really and kind of Establishing the position over time. This is one of the stocks. I'm invested in in the long term I would say don't buy all your stock all at once, you know for this particular stock I would say buy it over time, especially if you're holding it in the long term For example, I'm holding this for the next three five years this particular stock because I think there's got a lot of potential I'm just interested to see where it goes as well Anyway, that's the end of the video Please leave the video a like and subscribe to the channel if you knew I apologize for the fact that it's a 20 minute video I just wanted it to be informative and hopefully helpful and if it was Leave a like and it really helps it out And subscribe and I will go in depth in in other videos tend to be a lot shorter But I know I go in depth and give the company It's you know a good Information in terms of what we go over. Okay. Thanks for watching and I will speak to everybody later


Closing in on Japanese-held positions on New Guinea's second-largest island in the world, Australian jungle fighters struggle through some of the world's worst combat terrain. Boarding streams and climbing trails up the sheer sides of mountain ridges, they haul every ounce of their supplies by manpower alone. Fighting under exhausting conditions, these courageous Australians are playing a major role in pushing back the japs in this far-flung theater of war. In sunny Italy, part of which is blanketed by snow, New Zealanders fight on a front far removed from their homeland in the South Pacific. Taking a lesson in camouflage from Soviet ski troops, they move up, supported by Italian alpine units now fighting with the Allies. South of Rome, the beach at Anzio looks peaceful enough here, but actually this is the scene of some of the bitterest fighting of the war. German prisoners are still coming in. Here the Nazis lost 24,000 troops killed, wounded and captured. Some are from the famous Herman Gehring Division, poured into the lines to try and stem the fierce attacks of the Allies. Prisoners receive the same expert medical care that is given every Allied soldier. From minor wounds to major operations calling for blood plasma, friend and foe are treated alike. African Army nurses are right at the front to care for the wounded. They draw field rations just like the men and are prepared to stay as long as they are needed. This is typical of the spirit with which American girls are following the flag. Constantly under fire, the beachhead is attacked by a lone Nazi plane and the pilot is shot down in flames. Such is this latest glimpse of war in Italy as the Allies gather their forces for the blow the Nazis know is to come. Playing over snow-covered mountains on America's east coast, planes simulate the search for a lost pilot. By radio, contact is made with the rescue party on the ground. From the air, a doctor parachutes to earth with emergency medicines and supplies. A dog team sets out to meet the lost airman. Supposedly hurt but on his feet, he has made snowshoes from twigs. Now they've found him and life-giving blood plasma is administered. The doctor holds the precious bottle against his body to keep it from freezing. Dogs train for rescue work in arctic climbs haul the sled over the trail. They encounter many hazards but dogs and men are accustomed to such obstacles and here's how they cross a perilous ravine. Last comes the wounded pilot. Now safe and sound, he's on his way to the base. A naval academy located in southern Italy is again training large classes of cadets to become officers in the Italian merchant marine. Aboard training ships anchored before the academy, the cadets learn seamanship in one of the real old timers. Hand over hand, they scale the rigging like veteran salts. Here is one of the ways in which young Italy is being equipped to man the ships of the future. At an American army base serve a new purpose. They're loaded with rocket targets to be fired aloft to test the speed and accuracy of anti-aircraft guns. Hurtled through space at more than 450 miles an hour, the rocket targets travel at speeds greater than the fastest aircraft. This is a sample of the terrific barrage now being hurled skyward to meet the enemy's raiders. Asian-American sailors on leave at a North Atlantic base and enjoy winter sports with a tug of war. Healthy friendly rivalry between the fighting men of two great allied nations. Some of the British boys try to emulate Scandinavian ski stars. Snowshoes or skis a few days ashore as a welcome change for men who sail the seven seas. New United States aircraft carrier Shangri-La, named for the mythical base from which American fliers bombed Tokyo, is sponsored by Mrs. Doolittle, wife of the man who led the raid. Two years ago, President Roosevelt decorated Major Doolittle, now a Major General, for his daring feat. It was from the carrier Hornet that Doolittle and his 79 valiant fliers prepared for their surprise attack. President Roosevelt called the Hornet Shangri-La when asked to reveal the base from which the huge B-25 bombers took off. Now before a crowd of 100,000 people, Mrs. Doolittle launches the new carrier. 27,000 ton Shangri-La goes to join the great fleets of United Nations warships.


Hello and welcome to NewsClick. Banking sector has been in use quite a lot lately be it demonetization or the NPA crisis or the recent comments by RBA governor and the different functionaries regarding the privatization of banking sector. So, today we have with us to discuss these issues all India Bank Employees Association's General Secretary Mr. Venkata Chiland. Welcome to NewsClick Mr. Venkata Chiland. So, all the bank employees unions have called for strike on August 22nd. Can you tell us what are the demands and why you have called for the strike? See it's a part of our continuous struggle. See basically the strike is against the government's attempt to implement the banking reforms policy. They want to say for example deny the capital to the banks and then privatize them. They want to make them a consolidated banks by merger of the banks and all these things are going on. But we have a different view from the banking trade unions. We believe that banks are very important if government is talking of development. For development money is required. Money is in the banks. Under the money is in the government hands is not going to help for development. They will use it for personal private profit in private banking. So, banks have to be in public sector. Second of our experience is banks have contributed a lot after public sector. Maybe white revolution, green revolution for a rural development and you name it and banks have done it. Now, if you privatize the whole development will be stopped. So, it is wrong to say that bank is a business. So, it should be in private hand. It's not a government job. It's totally wrong. Secondly, banks are dealing with huge money. Today, it is more than 106 lakh crores almost 80 percent of the total GDP. It is there as deposit of the bank and there is common people's money, domestic savings. So, there must be safety for the people's money. That is available only if it is in government banks. We have private banks. We have seen even now they take people's money and then things happen in the world. It is happening in India. It has happened. Secondly, they are not giving capital. For banks, today there is an international norms that we must keep some capital. If I had to give 100 rupees loan, I should keep at least 10, 12 rupees as capital. And these are all government banks. So, government should give capital. Now, government says no, no, I will not give capital. You have to make your own arrangement. Arrangement means that you have to go to private people and take capital. So, if banks have to do business, we are in search of capital. Government is not giving. This year they have given 10,000. In the next few years, banks will be needing more than 180,000 crores total capital because banks will be expanding. But if government will not give, banks will be compelled to go for private capital. That means privatization. So, they are creating conditions. And so, we are demanding government must give the capital. Government says no, no, we have no money. There is a budget constraint. We cannot give more. All these things, we have already given in the last six, seven years some 17 billion dollars. It looks very big for people like in India. Oh, 17 billion dollars. But throughout the world, governments are giving capital even for private bank, even America, for example, which our government is trying to copy now. There was a financial crisis, 2008, 2009. So far, the government has, United States government has given 2,225 billion dollars. We are given just 17 billion dollars and we are telling we are given. Russian government has given 70 billion dollars. UK government has given some 67 billion dollars. All governments are giving because the government wants banks. In India also, we need banks. Government must find a way to give capital. Secondly, it is not one way traffic. When the banks are earning profit, banks are giving dividend to the government because government is capital is with us. So, we are returning as dividend. And on profit of the bank, we are also returning tax, income tax we are paying. So, in the last seven, ten years, we have paid as dividend and income tax more than 180,000 crores and you have given 10,000 crores and you are telling no, no, it is too much. We cannot give. So, it is absolutely wrong. Government must give capital and banks must be enabled to contribute for national development. Secondly, they are also talking consolidation of banks. Why we need consolidation of banks? Is it that Indian banking is too much that we cannot develop further, we cannot grow further, we have to consolidate the saturation. It is not like that. Banks have huge scope to expand on our country. Globally, in India, the banking density is one of the lowest. Still, we have space to develop the banks in rural area for everything. So, when there is scope to develop, why do you want to consolidate them? They say, no, we want big banks. We want strong banks. We want good banks. We want banks that will help the people. It does not mean that we have to be necessarily big banks. And big bank, there is a risk to risk. One, big bank means they will take care of big people. The tendency will come. There will be insensitivity to small people. Small people need more of banking facility now. So, banks should not become big. Secondly, the banks are too big. They will take more risk because the money will be more. So, naturally, they will give bigger loans. Bigger loan means bigger risk. And so, we cannot afford. I am using people's money. So, I cannot take risk. And we have seen big banks in America. Lehman Brothers, you name it. Where are they? So, big bank doesn't mean strong bank. We need strong banks, good banks. People should be helped by the banks, not necessarily a bigger bank. So, that way also, it is not required. Big bank doesn't mean that it will be helpful to people. In India, there is no need for consolidation. We need expansion. And so, that is also wrong. We are opposing that. So, the NPAs, one of the reasons for push for privatization. NPAs are being shown as one of the reasons, saying that public sector banks make bad decisions in giving loans. That's why NPAs have come. What is the solution from the bank employee's side? We are getting people's money. We are giving us a loan. That loan is not coming back. I can understand agriculture loan. Rain has cheated. Rain is not there. Rainfall is not there. Other problems are there. Farmers are not able to get the produce. So, they are not able to repay the loan. I can understand. Some student has taken a loan. He has finished. He's not getting a job. So, he's not able to repay. I can understand. But big people, industrialists, corporate people, they have taken huge loan. And companies are making profit. But they say, no, no, we have no money. We are not returning.  Today, it is 15 lakh crores of money, big money. And these loans are not coming.  Now, it is no longer possible to hide it. So, everything is coming out open. And so, they say, no, no, we have to resolve the bad loan. We should resolve how I have given money. I must get it back. Now, they say, no, no, I want to resolve it, not by recovery, but by other method. What is the other method? No, no, you give concession. Earlier, they were giving concession on interest. Now, they say, in entire loan, I will give concession. I have given you 100 crores. Give me 10 crores. Or we will go to the court. We will resolve it. Resolvation means I will not get the entire money. For the remaining money, suppose after 100 crores, I get 10 crores. How to adjust 90 crores?  From where I will write off? From the earning. From where I earn? From people's money, I do business and earn. And that money will be used for writing out the loans of the big people. And some of them are having the capacity to pay. Kingfisher Malia, for example, 9000 crores. He has got bungalow. He has got property. He has got other business. So, we are telling deliberately he is not repaying. Declare people like him as a willful defaulter and make it as a criminal offense. I have the ability to pay. I do not pay. So, it is a criminal intention. So, criminal action must be taken. That is not being done. Now, government reserve bank themselves have said that only 12 people, this Rua, then this Bush and Steele, all these type, 12 people, 250,000 crores. So far, they were hiding. Now, it has come out openly. So, we will go to the court and reserve bank says, even if you go to the court, we may not get much money. Out of 250,000 crores, 60-70 percent money will not come. That means huge money will be written off. So, this is a loot of people's money, simultaneously. This is interesting. For them, you are willing to write off the money. But now you say, no, no, we are writing out. So, my revenue has come down. My profit has come down. I have to increase my revenue. And now, they are taxing the people.  You do not use your ATM card, I will charge you. You come to the bank inside, I will charge you. You withdraw money for more than five times, I will charge you. You want checkbook, I will charge you. Now, interest on saving deposit is reduced. Who is opening a savings account? Ordinary people, poor people. So, for them, you will reduce their interest.  So, it is completely unacceptable. It is very unfair.  I will give your money to somebody and I will give all privilege to him. So, these are all things. Plus, our own issues are there. You know, eight months ago, seven months ago, the demonetization. We worked a lot day and night. They said, very good, very good. But they have not given us the required payment over time, etcetera. Banks have spent a lot of money. When banks are in difficulty, at least the government should come because it is a government scheme. To implement government scheme, I have spent a lot of money. Banks are spent. So, we are demanding you reimburse. At least to that extent, banks will be saved. Now, simply they say, you give sub-derived. That is like Air India. Air India is operating in all un-reminerative areas and they are in loss and they privatize. Similarly, banks, we are helping people. Then we are in loss and then you privatize. So, this is no good. So, to draw the attention of the people to convey our protest and to save the banks in public sector, this strike has been called by all the nine unions under the United Forum of Bank Unions. In fact, we want to increase the awareness of the people. That is no longer only employee issue, bank employee issue. It is a national issue. It is a people issue so that they will also understand. They will support. Unless we can really arrest this trend of writing of the bad loans and taking stringent measures to recover the money, banks will be in difficulty. In India, banks are dealing with people's money. So, we cannot afford to play gambling with these people's money. That is why this strike. Thank you, Mr. Anupnath. Thank you very much. Thank you for watching NewsClick. Do keep watching us on newsclick.in.


This study examined the association between cigarette taxes and infant mortality rates globally. It found that increasing cigarette taxes by 10 percentage points was associated with a 2.6 percent decrease in neonatal mortality and a 1.9 percent decrease in infant mortality. This suggests that higher cigarette taxes may reduce infant mortality rates. This article was authored by Marta K. Rado, Anthony A. Laverde, Thomas Hone, and others.


My brothers and sisters, be aware of God, the way He deserves to be aware of, and don't die without having surrendered to Allah. We begin by thanking God. We thank Allah. We thank Him endlessly, because anything we have is not from ourselves, it's from Him. We thank Him for taking care of us, even when we forget Him. We thank Him for taking care of us, even when we neglect Him. We thank Him for taking care of us, even when we may be disrespect, may Allah protect us, be gentle and forgiving. We thank Him for giving us, even when we don't thank Him. We continue to praise Allah, because He's so praiseworthy. He's amazing. God is amazing. Subhan Allah. He's incredible. He's majestic. He's magnificent. He's tremendous. He's towering. There is no one like Him, so we praise Him. And our praise falls short, no matter what. Then we remind ourselves that we weren't made for this world. We weren't made for this world. Then what world was I made for? What world were we made for? Something much greater, much bigger, much more long-lasting. And you're going to live there one day. You're going to be there one day.  Are we forgetting the Gifter? Are we preparing for our next life? So we warn ourselves that you're going to go back to your maker, and he's going to ask you. And that question is going to begin in the grave. Who is your Rabb?   So the first question you're asked in the grave, man rabbuka. So 153 times a day you're saying, my Rabb is. So the first question you're asked, man rabbuka, who is your Rabb? The first verse revealed in the Quran to our prophet in the cave, you guys all know the story. I seek refuge with Allah from the accursed Satan, in the name of Allah the Merciful. Who is your Rabb? So today as we come this Friday, to find our Lord, to find our Rabb, to readjust, reacclimate our purpose of existence, to remind ourselves why I actually was created, right? The question for us today is, I want you to ask yourself, you probably have a lot of different kinds of shoes. What kinds of shoes do you have? What kind of shirt do you have? What kind of car do you have? What kind of hair do you have? What kind of physique do you have? Are you muscular? What kind of smile do you have? What kind of school do you have? What kind of bank and money portfolio do you have? What kind of socks do you have? I want these kind of socks, not those. These are cooler, these are nicer. What kinds of glasses do you have? And then we think, I want this kind of hair, I don't want that hair, so we work on it. I want this physique, I don't want that physique, so we work on it. I want these, what kind of grades do you have in school? I want these grades, not these kinds of grades. I want this kind of car, so we work on it, not that kind of car. I want this kind of look, I want this, so what kind? We have all these different kinds. And so, just like there's a lot of different kinds of shirts and shoes and shorts and pants and hairstyles, right? And physiques and clothes and car and schools. And you choose, Allah gave you the choice. Oh, I want this kind of hairstyle, I want this kind of pants, I want this kind of hat. What kind of hat do you have? I want this kind of... And so, the question for all of us today is, what kind of heart do you have? What kind of heart do we each have? Because there are different kinds of hearts. So what kind of heart do you have? Do you have? And these are all Quranic hearts, there's so many different hearts in the Quran. So just like we work and choose our hairstyle, you can choose the kind of heart you have. And so there's hearts in the Quran, there are rusty hearts. Ran, barran ala qulubihim.  There are hard hearts, there are hard hearts, like stones, like rocks, kal hijara. These are all in the Quran. Now ask yourself, I'll ask myself, we all ask ourselves, what kind of heart do I have? There's also dead hearts. There's also hearts with locks on them. Afala yatadabbaruna al-Quran. Am'ala qulubin aqfaluhah. Locked hearts with a lock. Where's the key? There are sealed hearts, sealed, like they're sealed, they're closed. Right? So there's all these kinds, and then there are other kinds of hearts. The kalba salim, the salim, the one that's flawless, the one that's sound. Then there's the heart that is mu'tuma innah, the one that's tranquil and peaceful. And then there's the heart al-muneeb, the one that turns to God.  You kind of ice-cream, you might like chocolate or you might like the vanilla. You might like the Ben and Jerry's full of all the stuff or you might like the the fruit flavors So you have a style you chose this and so What kind of a heart do we have? Because and that's all that matters And if we we are here we come on Fridays to remember our purpose of existence Turn to God he accepts those who turn to him Alhamdulillah. Alhamdulillah. Alhamdulillah. Thank you, Allah. Thank you for giving us Islam the recipe for happiness Thank you for giving us Islam. Yeah, Allah the recipe for happiness.   It's gonna become crusty and rusty.  You never cleaned it. What's gonna happen? No one's gonna be want to smell you. No one's gonna want to see you.  What happens to it? It becomes rusty and it becomes crusty and it starts to smell bad and starts to become moldy and it stinks and No one wants to get near it Because it's not being washed and cleaned and fed and given medicine and taken kids not nourished So how about your heart?  How about our hearts? Are we nourishing our hearts? Because if we don't nourish our heart the same thing happens just like our teeth Just like our hair just like our body Our heart becomes stinky Our heart becomes rotten our heart becomes rusty.   I Don't smell my own bad breath But everyone else can If I don't take a shower and I don't put deodorant on I Don't smell my I don't smell my odor, but everyone else can and the same thing with the heart I don't see all the ill and filth and grossness that's pussing and oozing out of my heart But everyone else can and so today we're here to wash our hearts and that's what this Religion is Islam Islam Allah is amazing. He's beautiful. He understands us. He made us. He designed us, right? He designed us. He designed Islam. He knows that we make mistakes Right and so that's why he's at Tawab He accepts those who turn to him and so we We need to wash our hearts two ways, right? So the first way The first way, okay Allah says in the Quran Fela yetadabbarun al-Qur'an. I'm a lot. I'm a lot of loob and a faluha Allah says he says Subhanahu wa ta'ala. He says yetadabbarun This is a very important word al-Qur'an. He didn't say yaqra'un al-Qur'an He didn't say do they not read Quran or are on their hearts locks He didn't say read the Quran. He didn't say Ya'fawun, memorize the Quran. Those are all important reading the Quran and rising that is all important But what causes the the lock on the heart is when we don't yetadabbarun al-Qur'an. What does that mean? One time I was sitting in a class and There was a teacher teaching in Arabic and there was a translator translating for all the English speaking students and some Someone asked the question. They said sheikh Tell me tell me what I need to do the bare minimum I need to do in America Just to like to be to be you know close to a lot the bare minimum so the teacher said You know other than your fard your fard prayers. He said and he said pray doha pray with it and and and read Quran with tadabbur He said pray the morning doha Sunnah Doha he said pray the witter before you go to bed Sunnah or watch it and then pray and then read Quran with tadabbur So the translator was there and so he translated he said the sheikh says you need to do three things You need to pray the doha you need to pray witter and you need to read Quran And I was like oh and he stopped Because he didn't he there was a key word that the sheikh said Ma tadabbur he said read Quran with tadabbur with what with understanding with contemplation So that's the first remedy to wash our hearts so our hearts don't stink So our hearts don't become rusty and gross where everyone else can see all this ooze Filth coming out number one. We're gonna read a portion of the Quran with understanding Are we in this is number one Maybe you're 10 years old. Maybe you're 12 years old. Maybe you're 15. Maybe you're 19 Maybe you're 22 and you've been coming to Saturday school and Sunday school and Muslim school in the Friday prayer And maybe you've memorized this amount of the Quran and read this amount of the Quran and this but did you ever read to?  It's amazing and the hadith says by Ibn Umar radiallahu anhu ma narrated by Tirmidhi in a Sahih hadith He says Don't talk too much without Remembering Allah Fa inna kathrata kalami be gayri dhikr Allahi ta'ala qaswatun lil qalb Because talking a lot without doing dhikr Of Allah Hardens the heart Wa abad annaasi min Allah Al qaswat al qalb al qasi and the furthest people from God are the hard hearts So we want to avoid hard hearts. We want to avoid locked hearts sick hearts rusty hearts Dead hearts, diseased hearts, and these are our two remedies number one a portion of the Quran that we read with understanding contemplating And two dhikr Dhikr has it's a spiritual nourishment You know when you brush your teeth with your toothbrush and you put toothpaste that's like the toothbrush of the heart The toothpaste of the heart, you know when you take a shower and you put soap. That's the soap of the heart dhikr You know when you take medicine because we're sick And it makes us feel better with Allah's will Allah makes us feel better through the medicine Using Allah makes us feel better connection with the medicine This is like the medicine of the heart That's right. This is like the medicine of the heart Now, let me ask you something.  No Do you know what ingredients are in ibuprofen? No But you still take it in benadryl. No And now you might say well vikr. I don't feel it. How is it making me better? How is it curing me? How is Allah, it's in the Quran The ultimate not the doctor said Allah said the one who created the doctor So we leave today relieve this friday prayer with two things We read a portion of the Quran every day if you're reading nothing Then make it one line If you're reading nothing make it half a page But don't go to bed without saying good night to god After he gave you everything all day, you don't say good night It's like it's like you're invited to a lunch or a dinner And then you drink your tea and you walk out And you don't say thank you to the host You don't say goodbye to the host.  Don't let shaytan trick you And make you have such a high lofty goal I have to read a whole 20 pages that we skip it. No start with one line start with one line Right adwamuha wa anqaal. Allah loves most of all the consistent even if small And the second thing we leave with today is a portion of dhikr every day Portion of dhikr Right dhikr is remembering God in the morning and in the evening and whatever you're doing today some some staghfirullah Ya Allah forgive us to clean the heart some some la ilaha illa Allah to remove the rest of the heart And some Allahumma salli ala Sayyidina Muhammad prayers on the prophet sallallahu alayhi wa sallam Right sallallahu alayhi wa sallam To help us and relieve us and make us feel better and these are prophetic prescriptions Quranic prescriptions they come from the ultimate being that doesn't help us manage our body.


How can biodiversity-focused organizations make the most of their conservation investments in the tropics? That's the question Oscar Venter addresses in a new paper in the December issue of Conservation Letters. I'm Nate Spillman, and I'm with the Society for Conservation Biology. And I'm joined by Oscar Venter from the University of Queensland. Oscar is the lead author of Acting Optimally for Biodiversity Conservation in a World Obsessed with Red. Oscar, congratulations on your paper and thanks for taking the time to talk with us about it. Thank you, Nate. It's a pleasure to talk with you, sir. First, my first question is, what is red and what is this objective? Well, tropical deforestation is one of the largest sources of carbon emissions globally. So the red is essentially a mechanism to provide the funds and the assistance to developing countries to help them reduce their rates of deforestation. So the real objective behind red is to contribute to mitigating climate change by slowing tropical deforestation. And what is the purpose of your paper? And I'll just give the title one more time. It's called Acting Optimally for Biodiversity Conservation in a World Obsessed with Red. Well, tropical forests are also home to about half of the world's biodiversity. So they're really important for conservation. But this biodiversity is not evenly distributed. It's concentrated in certain areas. So the way we view it is there are essentially two organizations working in considered tropical forests. The first are these new red actors and then more traditional biodiversity-focused organizations or actors. And the question that we address in our study is how can biodiversity actors influence red actors to ensure that their programs and their initiatives contribute to biodiversity conservation? Very, very interesting. Your study area is a Barao Regency in Indonesia. And I'm curious why you selected Barao as the study area. Well, we decided to address that question. We really should work in a real-world situation. So we partnered with the Nature Conservancy where they're developing a project in the Barao District in Indonesia. We chose that district because it's a very large area. It comes from multiple land uses. And there's a red project called the Barao Forest Carbon Program, so a red actor also working in the region in addition to the Nature Conservancy, which is a more traditional biodiversity actor. OK. And over to the first of your survey, why should a biodiversity-focused organizations pay attention to the study? Well, what we discovered is that there's considerable potential for the red actor to contribute to the Nature Conservancy's objectives in Barao, but it was important, or it was necessary, for the Nature Conservancy to actually engage the red actor to find locations and strategies that contribute to both of their objectives simultaneously. So the important point here is you can't just sit either by and expect red to contribute to biodiversity conservation. I see. Biodiversity. Do you see these conservation actors taking advantage of the opportunities within red, or do you find that they're still unaware about how they can use red to improve the way they pursue conservation in the tropics? Well, I think it's pretty clear that red is fundamentally changing the way tropical forests are conserved. And what we're seeing at the moment is that biodiversity organizations are actually engaging the red process. And in fact, they're acting as the developers for quite a number of red projects. What I'm not sure about is whether they've taken a systematic view of the best ways to engage the red process, and that's what we're trying to address in our study. I see, I see. With respect to that, what did you find to be the most effective strategy for conservation actors to pursue conservation in the tropics? Well, we looked at a number of different scenarios, but what really stood out as the most reliable way to ensure red contributes to biodiversity is to actually collaborate with biodiversity actors so that pool their funding and work together to identify strategies that are mutually beneficial. I see. And do you see any obstacles that are preventing biodiversity actors from taking advantage of red? I'm sure there's a few, but I think the first one that jumps to mind is really the transaction costs of these two different actors sitting down together to negotiate mutually beneficial strategies. I imagine in the real world that might be difficult to do, though in some situations, I think there is possible. So what more do you want to know about this topic? Is there something in your study that you came across that you feel needs further exploration? Yeah, I think we constrained our study into a single forested landscape in Indonesia. But of course, the world is made up of all different sorts of habitats, wetlands and grasslands. And I think it'd be interesting to know how can biodiversity organizations strategize with red across those different sorts of habitats. So, Asker, what is the bottom line of the study with respect to how red can benefit biodiversity in the tropics? Well, red is emerging as a significant new actor working to conserve tropical forests. The bottom line from our study is that biodiversity actors really can't sit idly by and expect the red mechanism to meet its objectives, the biodiversity objectives. Really, the mechanism does need to be engaged in some way. Okay. All right, well, I'd like to thank you for your time. It's a fascinating paper. The paper is titled Acting Optimally for Biodiversity in a World Obsessed with Red. It's in the December 2013 issue of Conservation Letters. It's freely available to SCB members via their SCB member phone page. I'm just log in and click on the link for Conservation Letters. So, Asker, thank you again for joining us. I really enjoyed the paper and I enjoyed speaking with you today. It's my pleasure, and I thank you very much for asking these interesting questions. No problem.


Russia attacking Ukraine with balloons Ukraine has accused Russia of turning to balloon power to support its invasion of the country. Moscow has begun launching aerial objects with the intention of tricking Ukraine's air defenses into firing surface-to-air missiles and exhausting valuable supplies, according to Kyiv. Suspected balloons that flew over Ukraine were detected in Romanian and Moldovan airspace, prompting Bucharest to scramble jets. Such flying objects carrying radar reflectors have been spotted over Ukraine's skies twice since the US shot down a Chinese surveillance balloon earlier this month. These fake targets were launched by Russia, said Colonel Yuri Ignat, spokesperson for Ukraine's Air Force Command. Kyiv suspects that the balloons are the latest tactic being deployed by Russia in a missile and kamikaze drone strike campaign that has intensified in recent weeks. They will exploit this when the weather is in their favor. The weather was blowing our way today, Ignat said. Ukraine's arsenal of Soviet air defense systems has so far prevented Moscow's fleet of fighter and bomber jets from establishing air superiority since Russian President Vladimir Putin launched his full-scale invasion on February the 24th last year. But its supplies of air defense missiles have been depleted, according to Western officials. While US and European allies have started sending NATO standard surface-to-air missile systems, more are needed in the coming months. Ukraine has not disclosed whether it fired air defense missiles at the balloons. According to the Financial Times, Russia has not acknowledged launching the objects. A second Ukrainian official said they believe that the balloons that drifted into the airspace of neighboring Moldova and Romania were Russian, although neither country has publicly speculated on their origin. Moldova's civil aviation authority said it had shut down its airspace for more than an hour, afternoon after it. Received information that an unidentified small object similar to a weather balloon was detected. Romania's defense ministry said it scrambled two fighter jets after its ground radars tracked an unidentified object with the characteristics of a weather balloon. But the pilots were unable to find the balloon in the skies or detect it on their onboard radars. The jets returned to base after half an hour, the ministry said. Jen's Stoltenberg, the NATO Secretary General, this week warned that both China and Russia were increasing their intelligence and surveillance activities, including through balloons.