There are two Tajin restaurants in Doha, one at the centre roundabout on Selwa Road and the other at the Sukh Al-Waqaf. Both restaurants use the latest wireless technology, which includes write-on hand-helds, to manage table service and to keep an up-to-date inventory of stock. The technology means fewer staff, which keeps down costs, means better customer service and increased profits. It's very important, especially in our restaurant, because I can save the cost control and I can control also the staff and I can see what the staff is doing. And I can watch if any order late and I can also check if the customer pay or not pay. So many things. The two components of the system are the restaurant manager point of sale terminal and the write-on hand-helds. The system was supplied to Tajin by Khmera Solutions. The write-on hand-held is the only system in the world that uses handwriting technology and the objective of the system at the end of the day is to replace pencil and pad. Let's look at how this technology works in more detail when a customer is ordering a meal. Each staff member has a code and a password and all transactions on the hand-held can then be tracked back to that particular waiter or waitress. Serving staff make each order by writing on the screen. They enter the number of customers and their orders. They can even select how a particular cut of meat should be cooked. Let's take filet mignon, which is a steak. You would be writing FM filet mignon. And then the system would prompt force modifiers, let's say rare, medium, well done. And you would be forced to pick one of these force modifiers, so it's much quicker and training is minimal. Once the order is complete, the staff member enters it into the system, which is then transmitted by wireless to various locations in the kitchen and recorded on the point of sale terminal. Beverage orders go to the beverage section, meals to the chef and desserts to the dessert section. There's no need for serving staff to enter the kitchen with orders. It's all transmitted instantly by wireless technology. I take orders, and it's easier to take orders from the system. I don't need to go to the kitchen to take orders from the order. I take orders, and it's easier to take orders from the kitchen, and it's easier to take orders from the order. If the waiter or waitress stands in a predetermined spot, the hand-held records the orders as per the table seating. So when the food and beverages arrive, there's no need to ask customers who's ordered what. The staff member already knows. If an item is not available, the hand-held will show that immediately. Let's say you order a cheesecake. If a waiter was using paper and pen, he would just write cheesecake and then check it and come back to you. However, with the hand-held, he is notified immediately that there's no cheesecake. So he would tell the customer, sorry sir, we have no cheesecake. Would you like something else? These small examples are where you have up-sales.


Hi, in this video we'll observe how to configure Bitescout Cloud API server. In the last video, we have gone through setting up API server in Internet Information Server, IIS. To begin with, let's explore the API server publish folder. Here, we have all published files for API server. We're mostly interested in the web.config file. It's the key file for setting up various configurations. Let's open a web.config file with any text editor. I am using visual studio code. In the web.config file, under configuration, app settings, it contains all settings. To begin with, we'll be going through some key settings. Storage type, this represents the output location of generated files. It can be either local or s3. If the storage type is local, output files will be stored locally in the specified folder. We also need to specify additional settings for local storage such as storage path, base URL, lifespan of stored files, etc. On the other hand, if we have configured storage type as s3, then output files will be stored on AWS s3 storage. As expected, we have to provide s3 related settings. For example, bucket name, AWS keys, server region, etc. Let's see the contents of the local storage folder. Here, we have a local storage type. Let me grab the path and open it in file explorer. As we observe, it contains several folders with output files inside. Please note that these files are temporary and will be deleted after specified duration. One important note here, we have to give right permission to an IIS user so that files can be generated at the specific location. Follow these steps to check rights. Right click on the output folder and navigate to properties. I have already given rights here. We can check by going to the sharing tab and verifying allowed roles in the share menu. Folders specified by local storage settings contain nested folders with output files. Let's observe them. Now moving to other configuration settings, base URL will be used in API response to refer to output files. Log exception type represents error log type. It can be either none, local, email or both. Naming is self-explanatory. In case of local error message logging, we need to provide an output folder path for error logging. Let me open an error log in file explorer. As of now there are no error files, lucky me, but in case of error that will be stored here can be very useful for diagnosis. In case we also want email to be sent to the developer when any error occurs, we need to provide email details such as email to, SMTP configuration, etc. Finally, we need to set all license keys for internal byte scout SDKs used. You will receive all license keys upon purchase of API server license. And that's all. See you on board.


Welcome to Toffy TV, it is the Everton News Daily. Marco Silva has given his pre-Darby press conference and was in quite a bullish mood ahead of that game on Sunday. He was asked about it being Everton's Well Cough final and I thought his answer was brilliant. He said Liverpool celebrated like they'd won the Well Cough final with that lucky goal at the end of the last one. So it quite clearly means the same to both teams. And he's absolutely right, listen. The rivalry is there. We come from the same city. We want to beat each other. If we'd have scored that goal, we'd have celebrated. I had no problem with Clop doing what he did. He is what he is. And in that moment he was caught up in it. I just hope if it happens to us on Sunday, Marco does exactly the same and just gives it the big one. It's going to be obviously a very tough game Liverpool in excellent form at the moment at the top of the Premier League. And it's going to be a big ask for Everton to try to fair that first Darby win for nine years, which is absolutely, or eight and a half years, absolutely shocking. David Moyes was the manager the last time we won a Darby.  And it's up to these players to get rid of that horrendous record and do it on Sunday. It's going to be a difficult game. We're not in great form, but we've just had a good win in midweeks of Liverpool. So it's going to be a tough one, but why not? You know, we've got us in a bear pit, which I expect it will be with our supporters at the weekend. And the players respond to that. Who knows what can happen? You know, the Darby match at Anfield in December was very close. It was very tight. Both had opportunities. Everton played really well. Match Liverpool, but for the silly mistake I picked for it, you know, it would have been a 0-0 draw. That's the confidence we have to take into Sunday and hopefully it's our turn this time to be, to be fortuitous and get the results and, you know, try and kick on for the rest of the season. But good to see Marco coming out fighting. I like Darby's, took a lot of criticism. So good to see him actually smiling. I like his response of laughing when they asked about Klopshain. You know, Klopshain, it was our welcome final. I thought that was really good. A couple of things he did reveal. He said Layton Baines is definitely unavailable for the weekend. He's got a back injury, but he did say that yet he mean it will be available. He said he's in better condition than he was for the card of game. And he also said, of course, what we knew K2 will return to the squad. Other than that, I think I've got a fully fit squad for the weekend other than Layton Baines. So, you know, we've got the opportunity to go out and get rid of this horrible record. And let's hope we step up to the plate and do it. You know, and Liverpool will know the pressure will be on them, especially if Manchester City can win at Bournemouth on Saturday, then Liverpool go into that game now and they need the three points and it's up to us to make sure that they feel the pressure on Sunday. Yeah, let's see what happens. Megan Klopp has said that he expects it to be loud and wild and wants his players to be able to respond to that. So let's make sure it is loud and wild and make sure that they do feel like they're up against it at the weekend. Thomas Munier has obviously been linked quite heavily with Everton over the last few months. Has said that he expects to stay at PSG. He said he's aware of Everton's interest. Again, this link, you know, he's done well for PSG. Is he, he's 27 years of age. Is he someone at Marcel Brands would want? Over Marcel Brands be looking at Marco Silva, be looking at a younger player. I don't know. But again, this link has been there and it'll be interesting to see exactly what happens in the summer. Mason Holgate, who is doing quite well at West from Jalvin, also retains in the summer. We'll have to wait and see whether Marco Silva and Marcel Brands think the heat has got a future. Jose Baxter has been speaking to the BBC about his career. Said that Bill Kenyth saved his life for want of a better word really or want of a better phrase because he was so down. At that time said he had, you know, he had depression and had suicidal thoughts as well. But said that Everton's offer of a contract and the offer to train back at Finch Farm as a dream come true for him and to help them get fit and focused and of course he's now playing for Oldham under Paul Scoles and doing really well. So, you know, I thought that was a nice positive story. I took a look to him, you know, for the rest of his career. It's always good when you see people turn their lives around. So hopefully he'll be on the up now. Also, don't forget to back evident in the community. The opportunity is there at the moment to do the goodness and zip slide if you fancy jumping off the top balcony and flying into the park and stand. There are still lots of places available. So go on to evident in the community's fundraising page and sign up quite simply. You know, we've done a zip line a few weeks ago in Wales. It was scary, but it was exhilarating. So if you fancy doing that over the goodness and park pitch make sure you sign up for it. It's a great cause and obviously any penny, you know, every penny that you raise goes towards evident in the community. It is a fantastic charity. That's it today. Not much happening because it's obviously everything's geared towards the Barbie. Let's hope the Blues can do it for the first time in eight and a half years. Fingers crossed. If you haven't subscribed, please do so when it is just a sec. If you want more videos, get over to Peyton. Thanks for watching. See you soon on Toffy TV.


Everyone who was born here or who has come here knows that this place is special and absolutely unique. However, there are a few ways in which we could be more like others in this country. On the issue of political and democratic reform, your government acknowledges more should have been done sooner and more needs to be done now. Your government will pursue comprehensive reforms that will ban corporate union and third party donations, including donations in kind to political parties, impose a maximum donation limit for individuals to political parties comparable to other Canadian jurisdictions, ban donations to political parties from outside British Columbia, including foreign donations. Ban funding to a provincial political party from a federal political party. Restrict the role of money influencing collections through third parties. Ban loans to parties by any organization other than a Canadian chartered bank or credit union. And apply these reforms to local government candidates and political parties. The results that British Columbians delivered in the May election require cooperation. Your government is committed to working with all parties in the legislature. Following referenda in 2005 and 2009, there remains a desire by many members in this place to revisit electoral reform. With the confidence of this house, your government will enable a third referendum on electoral reform. It will require extensive public consultation to develop a clear question and will ensure rural representation in the legislature is protected. It is vital that any referendum reflects the views of British Columbians, not just its political parties. Additionally, your government will work with other parties to strengthen lobbyist legislation and regulations. Members, we gather for the first time since British Columbians sent you here following an unprecedented outcome in the May election. British Columbians want a stable government and in sending us this result, they expect us to listen and find a way to work together. They expect us to collaborate while respecting the dignity, rules and traditions that govern our constitutional monarchy, our democracy and this legislature. British Columbians voted for parties that spoke to the importance of economic growth and jobs, strengthening our social programs and protecting our environment. They have told us to find a better balance to move forward on all these priorities. The election results also exposed a growing gap in understanding between rural and urban BC. We have an obligation to do everything we can to bridge that gap because urban and rural communities cannot succeed without one another. With that in mind, instead of focusing on areas of disagreement, we should reflect on who it is that we are and what we share in common. We are fortunate to reside in a part of the world that is unmatched in many ways. We have Canada's most diverse population of First Nations and indigenous peoples whose cultures have shaped our province and with whom we seek justice, reconciliation and partnerships in economic growth. An abundance of natural resources, the bedrock of British Columbia's economic success. A system of clean transformational power that is the envy of the world, created by previous generations with the opportunity to transform our economy into the cleanest in the world. A super, natural and biodiverse environment that we enjoy today and must protect for future generations. A diverse population that is open-minded, independent and prepared to embrace change. A place where we have the freedom to be ourselves, thriving urban communities and strong rural communities that connect us to our land and wildlife. A province built on hard work, leading in jobs and economic growth and with a financial foundation that is the envy of North America. Your government worked diligently to create jobs and economic growth in the province and thanks to that effort, your government will confirm a higher than expected surplus at public accounts in July. This unanticipated surplus provides reason to consider moving our fixed election date to the fall, to ensure British Columbians are fully informed on our province's fiscal position before a general election. While other provinces have created structural deficits, BC's unmatched record of five successive balanced budgets has created a structural surplus that puts us in an enviable position. This moves up the anticipated elimination of our operating debt to 2020, one year ahead of schedule and accomplishment not achieved since 1976. Growing surpluses allow us to return dividends to British Columbians, but our sound fiscal management must be nurtured and not taken for granted. Your government recognizes the importance of labor stability in the province and will continue to share the dividends of economic growth in public sector labor agreements. Over the past decade, most labor issues have ended without disruption. This is a sign that the labor code is fair and as such, no further changes are contemplated by your government.


In this video, we're going to talk about how you can get a cybersecurity degree and is little as six months while attending WGU. And WGU is also known as Western Governors University. There are other universities where you can do this at, but I'm going to be focusing on WGU in today's video. And we're also going to be answering questions like is a cybersecurity degree from WGU worth it? We're going to be talking about the pros and the cons of getting a cybersecurity degree, how long it's generally going to take to get a cybersecurity degree, depending on how much time you have to put into it, and all other related questions that you might have. And this whole process of accelerating your degree is something I only revealed in my private consultations up until recently. But now not only am I revealing it for you, but my friend Josh Madicor and I actually put together a spreadsheet that is going to make this whole process insanely easy. And we're going to use this spreadsheet to sort of go through this process and you can follow along. I'll put that spreadsheet down in the description as well as the pinned comment below. So definitely check it out. And if you appreciate me putting all this effort into this for you to make this super easy for you, go ahead, gently tap that like button and let's jump right into it. Now the first step is for you to choose the degree which you want to accelerate, which you've already done because you're watching this video, you've chosen cybersecurity. Now there are certain types of degrees that are much easier to accelerate than others. That is something that I've gone over in different videos. But with that being said, cybersecurity is one of the best degrees to accelerate. And there are countless testimonials on Reddit, YouTube, Google, blogs, et cetera, all over the internet on forums of people who have been able to get these degrees in less than a year using the methods that I'm going to reveal in this video. Then after you've chosen the degree, you would want to go to what's known as an accelerated or competency based university. Now this is something I've talked about extensively in other videos. There are other universities that are pretty good. For instance, Thomas Edison State University is a great one. Depending on the major you want to go to Southern New Hampshire University and Excelsior College is also pretty good. And then after you've chosen the university, which in this case we are going to choose WGU because not only is it one of the most common ones, but it's also one of the ones that people have a great experience at. Then you want to move on to the real step number one, which is to transfer in existing classes and experience. So this can include AP or IB classes, college classes that you've already taken, certifications, military experience, even work experience. Sometimes chances are you've already tested out of some of these classes. So make sure you get that information together and send that into WGU. Once you've sent your transcript into WGU in a short period of time, they should send you back a paper that will tell you all of the classes that you have remaining. And it will look something very similar to the spreadsheet, which I have linked below. So definitely check that out and follow along. I will try to keep that spreadsheet updated, but of course things can happen in the future and there might be a few little things here and there that are different. And after you've gotten that back, you're going to go ahead and move on to step two, which is you want to start testing out of classes online, right? So you are not enrolled in the university yet. They might kind of try to pressure you a little bit to enroll. Do it. Don't enroll until several steps down the line. Now with a BS in cybersecurity from WGU, you need to take 34 classes. And you can very comfortably test out of one class per week, even if you have a family and a full-time job. Many of my students tell me that, you know, especially for the earlier classes, you can easily test out of like a class every day or two. Now, one of the great things about a competency-based university like WGU is it's competency-based. So one of the things that that means is that if you are competent, it's very easy for you to test out of those classes.  And this is one of the main reasons why you are able to graduate in less than a year and in some cases six months. Now, you might be thinking, oh, employers aren't going to like that. They're going to know that you're sort of like, you know, finding a loophole in the system. Well, with a lot of degrees, employers actually don't care. And with some degrees, they actually prefer people who go to WGU. So there's many degrees out there where they just want you to get a regionally accredited degree. They truly do not care where it comes from, how long it took you to get it, etc. They just need you to have that piece of paper. It's one of these weird things that we're sort of holding onto as a society for some weird reason. Clearly, people can learn a lot of these skills without going to college, but there's still a lot of businesses out there that would prefer somebody with a college degree. And you can sort of see why from a liability standpoint. Imagine if you're hiring somebody for a cybersecurity type position, if there is a breach that occurs, and they see that you hired somebody who doesn't have a degree, you know, it might be one of those things where they get fired, or maybe if you're working for a different company, they could potentially even sue them. So yeah, it just kind of looks good if you have a degree, so to speak, even though it absolutely doesn't matter. I mean, there's like 14, 15, 16 year olds that are like hacking government databases that probably know more about cybersecurity than your average cybersecurity professional. But with that being said, here is a comment from one Reddit user that got this degree, and it basically says yeah, they just a lot of the times people just want to know that you have a semi relevant degree, and they don't really care where it comes from. So the way that you would test out of most of these classes is you would use a third party company such as study.com, straighterline.com or Sophia.org. Additionally, you can get certain certifications which will help you test out of these classes as well. Now in my experience helping people study.com is the one that's just the most straightforward and it's the easiest. And there are people out there that just take this super seriously, like they, you know, they take this one from here and they're just extremely strategic about it. But I found that just telling people to do study.com is going to reduce the friction and just make it a lot easier. But there are some classes that study.com doesn't offer. And in that case, we went ahead and we filled them in with straighterline and Sophia.org classes that you can take as well. And you can see here that many of the classes you can actually test out of with different CompTIA certifications. Now I was able to contact study.com and they were nice enough to give a coupon code to me where if you type in Shane Hummus at checkout, you will save 30% on your first three months using study.com and it'll also help the channel out a little bit as well. Now after you've tested out of as many classes as you can, you want to make sure to transfer those into WGU. And at this point, you're still not going to enroll. You're going to move on to the next step, which is pre-study all the classes that are left. Now the reason that you're going to do this is because once you enroll in WGU, you're going to pay per term. And last time I checked, they charge about $3,500 or so per term, but definitely double check me on that because it changes depending on the program you're in. And you know, they're probably going to raise their prices in the future with all this inflation. But yeah, you do pay per term. So you want to try to get done with the classes as fast as possible. Ideally, you want to get done within one to two terms. Now, there are many different strategies you can use to do this. The resources that I recommend the most are actually just checking the WGU subreddits. So for instance, there is the main WG subreddit, which is r slash WGU. And then you've got the WGU MBA subreddit, WGU business management subreddit, WGU comm sci subreddit, etc. So you will find different strategies on taking these classes as fast as possible and learning the information as fast as possible as well. Outside of that, there's plenty of other free resources or really cheap ones. There's free Code Academy. That's really good. A lot of the time Udemy will have some good ones. Khan Academy is great. That's a free resource on YouTube. I do believe they also have a paid version that's even better on their website.  You definitely don't need to buy a new one. So yeah, there are many different ways for you to pre-study these classes, but you basically want to make sure that you sort of just know all the information before you even start. Finally, step four, after you've made sure all those classes are transferred in, you've made sure you pre-study the classes, you are going to enroll in WGU. And then you are going to complete the remaining classes as fast as you possibly can. Now a typical bachelor of science in cybersecurity is going to take about four years. Now there's tons of different testimonials, threads on Reddit, YouTube videos, et cetera, of people that have graduated really quickly. But realistically speaking, this whole process can very easily be done in less than two years. In many cases, if you take it seriously, you can do it in a year or less. And the time that you are actually enrolled in WGU in many cases will be less than six months. Now some people take it to an extreme. They're super strategic about it. My friend, Josh Madakor, for instance, graduated with a computer science degree in less than two months. And there's even videos of people who graduated degrees in like seven days or something like that.  But yeah, a ton of these people start off with zero cybersecurity, zero IT experience, and they are able to get their degrees much faster than a traditional university and much cheaper as well. Now while you are enrolled in WGU, I would say definitely check out the WGU subreddit, like I said, that's always a great resource. Also use your instructors, especially for the more difficult classes like the capstone written project, digital forensics, or IT application. All right, so now we're going to quickly jump into the cheat sheet for the cybersecurity and information assurance degree. So as you see here, we're kind of just going to go through each one of these kind of step by step. So in this particular case, you can sort of see all the classes you can transfer in from study.com. You also see all the classes on the right here that you can transfer in through certificates or certifications. And there are a few classes that you cannot transfer in through study.com that you can transfer in through straighter line or Sophia.org. So in this particular case, we're going to just go to sort of step one where you transfer in classes. So the classes that you would transfer in, and again, this is just an example, this is not necessarily going to be what you've done in real life. So let's just say you took one semester of some community college, and you also had some AP classes that you could transfer in. So you took AP English, maybe so you were able to transfer in English 104. And then let's say you also took AP Math, so Math 101 got transferred in as well. And you did one semester of college where you took communications and economics as well.    Now you would move on to the next step, which is you would take all of these study.com classes. And if study.com didn't offer it, you'd take the Sophia.org or straighter line classes. And these are of course much cheaper and faster than taking them at the university itself. So you would not have to take that many classes in order to finish. So it looks like one, two, three, four, five, six, seven, eight. So it looks like there's eight total classes that you would have to take to finish. And yeah, that is definitely doable in one term. So you would want to pre-study all of these classes, check out the WGU subreddit in order to figure out how to do that. And then once you feel confident, you would enroll in WGU and you would try to knock these classes out as fast as possible. Now a few other things that I wanted to mention is some of the downsides of going to a school like WGU, which is completely online. You can kind of do it whenever you want to do it.  But in this particular case, the cybersecurity network at WGU is relatively good. So for instance, the cybersecurity club has thousands of members. And there is a pretty strong community of people who have graduated with cybersecurity degrees from WGU as well.  And you are going to have to put a little bit extra effort into it in order to network effectively if you go to an online university like WGU. And then the second one is make sure you focus on building a portfolio. So a portfolio is basically a demonstration that you have the skills that you say that you have. There are many different ways for you to build a portfolio, but having a good portfolio is almost like a cheat code as well. Now lastly, is a cybersecurity degree from WGU worth it? Absolutely. I think it is a phenomenal choice. It's one of the best degrees that you can get at an online accelerated or competency based university like WGU. Is it for everybody? No, it is not for everybody. There's nothing in this world that is just perfect and it's for everybody. There are pros and cons of going to WGU, which I talk about in this video right here. But with that being said, WGU is regionally accredited. It is respected by most employers.  So you have to too. But those types of people are the exception to the rule and you probably don't want to work for them anyways. There is countless examples of people that have left testimonials on YouTube, Reddit, YouTube comments, different forums online that have gone to WGU, many of them that have gotten the cybersecurity degree and they are super happy about it. The most common demographic of people who go to a school like WGU to do this are people who are already in another career. They're typically established. They're a little bit on the older side. A lot of them have families and they want to go to a school that is flexible enough for them to get a degree without having to drop everything they're doing in their life for four to five years. And to be that type of person, you do have to be relatively self directed, right?  So it's not a very social process. And so you're going to have to have sort of the self control to sit down and do the work. But with that being said, there is a trend of younger people going to colleges like this now as well. And there's no surprise. I think I read an article the other day. It said 50% of Gen Z is thinking about not going to college. And the reason for that is because the value of a college degree has been going down for decades. And the price of a college degree has been going up. And at some point it has to reach a tipping point, right? If two lines are going like this, at some point they will intersect. And for many degrees out there, in my opinion, it's already reached that tipping point where it's just not worth it.  And I like to call these different options alternative education. I don't consider WGU to be like a traditional university. And this is going to be a massive trend over the next 10 years because people are just tired of traditional colleges. They're tired of having to go to college for 5.1 years on average. They're tired of the old outdated stuff that colleges tend to teach. They're tired of colleges telling you what you should think instead of being a place where you can explore all different types of options.  So that being said, definitely check this WGU video out. It'll answer a lot of the questions you have. Gently tap that like button, hit the subscribe button, ring the notification bell and comment down below any thoughts, comments, criticisms, et cetera that you have on the video. And I will see you next time.


Welcome back to my channel. Well, you know, I'm very interested in chat AIs and all these chat bots going on in the industry today. So last week, well, last time, I think two weeks ago, I did a test where I asked ChatBT, Bing AI Chatbot, and Googlebot to write a recursive function in Python. And the result was that Googlebot was not able to write the code at all. So if you want to watch the video right here, I posted up two weeks ago. And you can see the result. So this time, I think just yesterday, we heard, well, actually, I read the news. There's an article saying that Googlebot is now able to write software code. Let me show you my screen here. We can take a look at artificial intelligence. Chatbot Googlebot is now able to help write software code. And that's great news, right? That's what we expect. And you can read the article here in full detail. But let's go ahead and see what that can really do. Last time we tested these right here, if you can see the same instructions. This time, I'm going to rephrase it just a little bit differently. And I hope that this will make it much clearer for these IAIs. So we're going to ask to write a recursive function again at Python, accepts two lists of numbers, and returns the list that has the least total gaps between the values of its elements. So for example, if we're given list A, 1, 2, 5, 9, and list B, 1, 2, 4, 8, the function should return list B because the total gaps between all the elements is only 4 as opposed to 5 and list A. So I'm going to copy this same paragraph instructions here. I'm going to paste it to one of those apps and we'll see what it gives us. So let's go over to Chat GPT first. I'm going to paste it right in here. And here we go. And let's see what happens. OK, as before, it's really fast. It is respond right away. And looking good. I do like the comments, the documentation, the explanation as well, just like before. OK, well, it doesn't even further, but it provides a modified version, which is very interesting. And I gave that example. Perfect. OK, so let's go ahead and test this. This is the modified version up here. And this is the original version. It doesn't really matter which one, I guess. But you do see that it is a function and it does the recursive call right here and right here. So that looks good. I'm going to go and just load the modified version down here. OK, so again, this is our base cases. Then so let's try this one. Copy it. All right, and then go back to the IDE and we'll paste it right above here. And then I'm going to call it down here. OK, so we'll do a result. It is going to be leased. Yeah, it is. And 1 and 2 and print the result. OK, so let's give it a try and see what happens. Well, it does it differently than I want to expect that. Let's go back and see what happens. Then not there, right? Yeah, it's supposed to print 1, 2, 4. And there's a comma missing here. I'm not sure what that is. OK, so somehow it's not giving me what I wanted, unless it's understanding my insertion incorrectly. Let's try the first one. OK, let's copy that and go back to this. Again, I'm going to turn all these off. And we'll add the second, the first one, the same function, least gaps, and 1 and 2 and OK. And let's see one more time. OK, so somehow it's like combining the two lists together and it's not doing what it's supposed to do, unless it doesn't understand my instruction correctly. OK, so yeah, the expected result should be just the end 2 here, right? It's appending what's before and then to the end 2. So it gave us a new list, and that's what I want. So for some reason, that one does not give me what I want. Oh, well, OK, let's try the other one. Let's go to the Bing chat, Bing AI. And here is the Bing chat. I'm going to again choose the precise mode and ask the same question and still thinking. So here we go. We see if this one understands what's asking. It's short, so it seems to be what it's supposed to be. Let's give it a try. It's much shorter, as you can see. So let's copy this. Is it this one here? Oh, I guess. I'll copy this and go back to the IDE. I want to paste it right above here and we'll call it down here just like before. So a result is going to be the least gap has 1 and 2. I want to print the result. OK. So let's try this one. I'm going to clear this console and let's see if this is correct. Wow. Yay. Bing AI actually understood my question exactly how I want it and returns the correct result. So great job for that one. OK, so now let's go ahead and do the same thing for Google Bard. OK, so cross your fingers. OK, here we go. I'm writing here. And so thinking. Great, he actually writes code better than the last time. And it just throws everything right at me. So it provides the PyDocs here, which is good. And our base cases. Hmm. OK, yeah, it doesn't really do the recursive call as I expected. Instead, it's using a zip function, which is a built-in function in Python that does the recursive call in here, I guess. So it's not understanding what I want it to do, because I want to write its own function like it does here and then call it, right? And then not using a built-in function like zip here. But let's see if this is correct, though. I mean, at least it writes a code and returns something what I wanted. Let's copy that. And let's give it a try and a bar over here. So it's called least gaps, same as before. So I'm going to go right down here, 1 and 2. And it should return the end 2. OK, let's clear the list and run. OK, so it does work. But again, this time it's not really using the function that it created, right? So it's supposed to call the list gap and does it recursively. Instead, it's using a for loop in here. And we'll call this a list comprehension, which is not what we want. So I'm going to ask again and say, you cannot use the zip and see what happens. OK, just curious. Rewrite using a without list comprehension. I'm afraid it's going to use a regular for loop. Yeah, it's still going to use a range function. It's used for loop. So yeah, it's not doing what it's supposed to do. It does give me the correct result back because this actually works. But then it didn't understand what it mean by using the recursive function that it creates. Maybe I should have said use the recursive function that you could call the function that you create, which in this case, so I guess it's more like a training process still. But theoretically, it should understand exactly what I wanted. OK, so what do you think, guys? Well, my impression is that chatGPT is still as fast as before, very quick to respond. The downside is that now it actually misunderstood the question and it gave me a wrong answer. I mean, it gave us a new list as opposed to returning one of the lists back. So that's incorrect. Google barred huge improvement. It was able to write the function as well as provide some documentations as well. The only thing is that it did not call the function. It created recursively. Instead, it uses a for loop and a list comprehension and then to do that process for us. So it does give the right result. Huge improvement, but it did not understand by what I mean by creating a recursive function and use that function. So I guess it's still learning in that department. But it's a huge improvement. And so Bay AI does exactly what I asked and it produced exactly what I expected as well. The code is very short and sweet and that's perfect. So the winner for this time is Bay AI Chatbot. Well, thank you and I hope you enjoy. Take it now. Bye.


The older you get, the harder it becomes. It doesn't get any easier. No.  No, you're running out of time. Absolutely I do. I just feel like you have less time to help correct your mistakes. So I kind of get where you're coming from, but oh my gosh, you're so young. No, no. Because I have less time to make mistakes. So maybe I can correct them, but if I make the mistake, how can I build off that? What do you mean when you say you have less time to make the mistake? Well, I'm already supposed to be so far ahead in my life. I'm not there. So I've got to catch up with myself. It's so crazy. And where we think we should be. And now I can't make the decision because I'm so afraid I'll make the wrong decision that won't give me where I want to be.


From the SiliconANGLE Media office in Boston, Massachusetts, it's theCUBE. Now, here's your host, Dave Vellante. Hi everybody, welcome to this CUBE Conversation. My name is Dave Vellante. We're here in the CUBE studios in Marlboro, Massachusetts. We're going to talk about storage and some of the trends that are going on in storage. And things have changed quite dramatically. It's not just about what media you're using today. You've got a lot of other considerations. Cloud, on-prem, incomes the edge and it really drives new considerations for customers. Sandip Aurora is here. He's the director of North American Storage and Big Data Solutions at Hewlett Packard Enterprise. He's going to talk to me about some of these trends, the customer point of view, and what HPE is doing to solve some of these problems. Sandip, thanks very much for coming on theCUBE. Dave, thanks for having me, super excited.  What do you see as some of the tectonic shifts in the storage marketplace? Yeah, Dave, so listen, we've traveled around the continent here and I spent a lot of time with customers in North America. And what I hear from customers is their center of universe revolves around being able to map to the cloud journey. And what does that mean for their data? And I look at our cloud operating model and I map that to HPE's own point of view. Our point of view is bringing the intelligent data platform to our customers. And when we talk about mapping the cloud operating model to a customer, what does that really mean for us? When I talk to customers, they tell me three things. It means that you have extreme cost efficiency, you've got super ease of use, and you've got resource optimization. How do you utilize them in the best manner? So let me ask you on that one. Big data is in your title. And one of the things that we observed early on in the big data days was it was about bringing five megabytes of code to a petabyte of data. That sounded great and it was great, but it also caused problems because you're pushing now, storage is everywhere. I mentioned the edge. So I'm sure you're seeing that with customers. There is no more perimeter. Storage is just everywhere, wherever you want it to be. So when you talk about the cloud operating model, are you talking about bringing that experience to your data wherever that data lives? Yeah, it's a great question. It used to be that you had an accounting system and that had a database and that was delivering you a ton of data that you could analyze and store and read and write. And now you've got data that's being produced at the edge. You've got point of sale systems. You've got autonomous vehicles. You've got data that's being produced on the cloud itself. And you've got data that's being produced at the core. So what we're talking about is not just the automation of bringing that data in, but also how that data is being utilized. And to us, the way we map that challenge is through intelligence. So when you talk about, let's talk about the breakdown of those three things, cost efficiency, ease of use and resource optimization. Let's start with cost efficiency. So obviously there's TCO. There's also the way in which I consume, right? The people I presume are looking for a different pricing model. Is that, are you hearing that? Yeah, absolutely. So as part of the cost of running their business and being able to operate like a cloud, everybody's looking at a variety of different procurement and utilization models. One of the ways HPE provides a utilization model that can map to their cloud journey, a public cloud journey is through GreenLake. The ability to use and consume data on demand, consume compute on demand across the entire portfolio of products HPE has, essentially is what a GreenLake journey looks like. And let's go into ease of use. So what do you mean by that? I mean, people, they think cloud, they think swipe the credit card and start deploying machines. What do you mean by ease of use? For us, ease of use translates back to how do you map to a simpler operating and support model? For us, the support model is the key for customers to be able to realize the benefits of going to the cloud. To get to a simpler support model, we use AIOps. And for us, AIOps means using a product called InfoSight. InfoSight is a product that uses deep learning and machine learning algorithms to look at a wide net of call home data from physical resources out there, and then be able to take that data and make it actionable. And the action behind that is predictiveness, the prescriptiveness of creating automated support tickets and closing automated support tickets without anybody ever having to pick up a phone and call IT support. That InfoSight model now is being expanded across the board to all HPE products. It started with Nimble. Now, InfoSight is available on three parts. It's available on Synergy. And a recent announcement said it's also available on ProLiance. And we expect that InfoSight becomes the glue, the automation AI glue that goes across the entire portfolio of HPE products. So this is a great example of applying AI to data. So it's like call home taking to a whole new level, isn't it? Yeah, it absolutely is. And in fact, what it does is it uses the call home data that we've had for a long time with products like three part, which essentially was amazing data, but not being actioned on in an automated fashion. It takes that data and it creates an automation task around it. And many times that automation task leads to a much simpler support experience. All right, the third item you mentioned was resource optimization. Let's drill down into that. I infer from that there are performance implications as maybe governance, compliance, physical placement, can you elaborate? That's in color. Yeah, so I think it's all of the above that he just talked about. It's definitely about applying the right performance level to the right set of applications. We call this application aware storage. The ability to be able to understand which application is creating the data allows us to understand how that data needs to be accessed, which in turn means we know where it needs to reside. One of the things that HPE is doing in the storage domain is creating a common storage fabric with the cloud. We call that the fabric for the cloud. The idea there is that we have a single layer between the on-premises and off-premises resources that allows us to move data as needed depending on the application needs and depending on the user needs. Okay, so that brings me to multi-cloud. It's a hot buzzword now. Some people don't like it, but it's a reality. And so you've got data on-prem. You want to look like the cloud operating model. You got data in the cloud, the edge confuses things even more. And so what is your perspective on multi-cloud and then I have a follow-up for you? Yeah, for us, multi-cloud means the ability to be able to run your business, whether it's on-premises or off-premises, based on the needs of the requirements of the application and the business user. We don't want to force a model down on our customer's throat. We want them to have optimization across both models. And the way we do that is using a couple of different products. We've got a product known as Cloud Bank, which maps to store once. Store once is our purpose-built backup appliance. Where a customer can store a backup copy of the data on-premises, and then a backup copy of that on a public cloud like Azure, AWS, or Google. Similarly, we've got products with Nimble in three parts that allow us to have tight integration with both public and private cloud domains. And in the future, the idea is to bring all of that together where the automation and the orchestration allows customers not to worry about what product they're using, but more about what are the requirements of the application. Because sometimes it's going to want to bring data back. Whether it's peak and I want to put into the cloud for bursting, I want to bring it back for more control. Whatever it is, when it comes back, I want to have that cloud operating model. That's where the AI ops fits in, that you were just describing. Yeah, absolutely. Okay, and so let's get into more specifically what HPE is doing. You referenced some of the things that you and your partners are doing, but what specifically are you doing from the standpoint of products? You mentioned what I call a data plane and control plane. What do you have there that we can actually buy? Yeah, absolutely. So what we have, as I talked about earlier from an AI ops point of view is our product called InfoSight. And InfoSight is available to all customers that today use three part Nimble or ProLand service. As long as you have a valid support contract, it comes available to them. So I remember when HPE acquired Nimble, you said one of the things you're going to do is take that technology and push it across the portfolio. So that's something that you've really done in a pretty short timeframe. We have, and what it does, it gives us the opportunity now not just to look at call home data from storage, but then also look at call home data from the compute side. And then what we can do is correlate the data coming back to have better predictability and outcomes on your data center operations as opposed to doing it at the layer of infrastructure. And you also set out a vision of this orchestration layer. Could you talk more about that? Are we talking about across all clouds, whether it's on-prem or at the edge or in the public cloud? Yeah, we are. We're talking about making it as simple as possible where the customers are not necessarily picking and choosing. It allows them to have a strategy that allows them to go across the data center, whether it's a public cloud building their own private infrastructure or running on traditional on-premises sand structure. So this vision for us, cloud fabric vision for us allows for customers to do that. And what about software defined storage? Where does that fit into this whole equation? Yeah, I'm glad you mentioned that because that was the third tenant of what HPE truly brings to our customers. Software defined is something that allows us to maximize the utilization of the existing resources that our customers have. So what we've done is, we've partnered with a great deal of really strong software defined vendors such as Commvault, Cohesity, Cumulo, Deterra. You know, we work very closely with the likes of Veeam, Zerto. And the goal there is to provide our customers with a whole range of options to drive building a software defined infrastructure built off the Apollo series of products. Apollo servers or storage products for us are extremely dense storage products that allow for both cost and resource optimization. What's the nature of these technology partners, partnerships that you do in engineering integration or is it just kind of going to market together? Yeah, we bundle our partners into three main categories. We've got a set of complete partners.  We create a joint pricing list and we bring them in to the family. We've got a set of partners as part of the Pathfinder program. The Pathfinder program, our partners that we've made strategic, HPE has made strategic investments in. And then the third set is partners that we resell through HPE. So depending on which partner it is, they fall into a different bucket and we have all sets of resources including engineering collaboration to make sure that the customer is buying a solution as opposed to a product. That's great, Sundip. Thank you and thank you for watching. But before we go, how do people learn more? The way you learn more is make sure you contact your partner and make sure you come to Discover. So we'll hopefully see you all at Discover.


Hello and welcome back to the craft box. My name is Brent and today we are on episode 88 of The completionist craft the series and today is actually a very special day. We are crafting the Dragoon did death boots So yeah, obviously from Zasha over in the catacombs as always everything you need is on screen and all of the requirements as well and Luckily, we're gonna go and craft these bad boys up finally. These are what I've been wanting to finish off this set for caramel So yeah, we're just gonna quickly Before we craft them just go through the stats first just because it's easier to do Well, they're On well, I've got the other ones still so I can compare them between each other I just need to quickly find them. They are here So basically pretty similar as you can see over here We get one death damage or 1% death damage 12 24 max health Sorry 1% power pip chance one potent trap card So we retain the sharpen blade, but we also get a potent trap as well And also there is a set bonus which we don't get yet But if we got another piece we get five death accuracy then another one we get for death damage We get also a hundred and forty Pip conversion for death then if we have five pieces, we have 12% power pip chance extra, which is pretty dope Seven we have 6% death damage on top of the four that we already get and also 108 block and 25% stun resist so yeah pretty pretty insane But yeah, we might as well Go and craft these I need to take off these boots right I just had to go and un-stitch my boots because I forgot that I should probably not have them stitch So I can use them again later But yeah, there they are. We do not have them on currently, right? Do we not have them on? No, we don't have them on currently. We just have regular boots I can't I can't get over the new stuff at the moment. It's a bit confusing a bit confusing But anyway, let's craft them dragoons deadly boots. We have everything we need one of the Vanguard's it could be any set. It doesn't have to be the death ones I believe So you can farm them in the catacombs or you can craft them We've already done crafting the Vanguard's one alchemical extract salts and crystals you all get in the catacombs itself There are various ways to go and find them and I recommend just finding the best that suits you I Farm vigors Drake and we generally get them pretty quickly But yeah, otherwise, let's create this bad boy up. Oh Well, I'm excited. I am very very very excited And yes, we've already shown the stat what we give but now we now have 7187 health We have 138 damage. We have a whole bunch of resist 58 of it in fact 25 accuracy and That's basically all the same and we get a hundred and seven percent power put which is absolutely ridiculous and 98 I'm shadow rating which is the same as before. Um, but yeah, that is all for today I will be doing an updated an updated gear set up Very very soon. So stay tuned for that. Otherwise. Thank you for watching Remember to leave a like and subscribe if we subscribe press the wee little bell So you know exactly I post another video and remember to craft outside the box


Welcome my name is Rohit Loteliker, I am faculty at Insofi International School of Engineering and today I am here to talk about the challenges we all face in data science projects is not having enough raw material right and I am sure many of you have experienced this. You know you know over the last I think 10 odd years you know we always see this graph of you know data growing exponentially people talk about you know every year the data is growing some 30 percent right it may even be growing on your system or your hard drive but when you come to the data science the project that you want to do when you want to deliver insights to the business you always have this problem I do not have the relevant data so am I just getting unlucky or is my luck I mean you know or you know I am not understanding the problem that other people are you know saying that there is too much data right of course the problem I think we all understand is data is growing very fast but the relevant data the data that you need for your project that is not growing so fast right that is often that is missing you might even have a large number of observations but you do not have the attributes that you need I am sure many of you have experienced this so let me talk about you know what is it that we can do right how can we overcome some of these challenges and I think the growth of data science you know to come in the future years is really going to be dependent on you know not you know better algorithms but also on acquiring data right so I am going to do a case study talk about some of the experiences this is from a few years back at one of my previous organizations where we were doing you know this was actually with IBM where I was previously employed and you know IBM is a large company it has different types of businesses one of IBM's main business for the contact center business the business process outsourcing business and what is this business responsible for one of the things they did was to run contact center operations contact center operations for clients one of the clients was a bank in Australia a large global bank and IBM was running the contact center operations for this bank they were doing it out of Philippines now you know this was there was specifically a team responsible for credit card operations so the banks credit card customers they would call into this contact center whenever they needed some help and so these guys were here in Philippines helping these customers now now you know after servicing you know customer would call in for whatever thing they had to call in about and then these agents would service those you know customers now you guys I'm sure most of you get calls from banks right and you might probably have a bank relationship manager and that bank relationship manager keeps periodically calling you and tries to sound helpful so our madam I can help you with whatever your need is do they really mean that do they really want to help you no you are the prey they're trying to sell you something right and that's why they're calling you and their job was exactly that right the real job was to sell products so of course initially they would service the customer and whatever the customer needed but after that they would try and pitch pitch some of the banks products typical bank it had you know the say these were credit card customers and this bank had other products savings account checking account insurance mortgages additional cards the usual portfolio so their job was to sell you know maybe an additional couple of products now the question we were asking ourselves so I was part of IBM and we was we said the IBM's I was part of IBM research and the contact center part of IBM came to me and said hey a doctor data science can you help us do something fancy and help improve our sales and so I said okay let me have a look let me see how data science can help so I flew down to Philippines and you know went to the contact center operations and try to look at what these agents are doing there so these the way these agents would work is you know they had CRM tool where they would see the customer's information so whenever a customer called in you know the customers profile would show up in the CRM tool and then these agents would look at the profile and try to get some sense of you know the customer you know is this family person is it a single person what is the income you know bracket what is the age bracket these kind of things get a general sense of the customer and then they engage into a conversation with the customer hey sir glad to glad to help you I see you're you know using your been using your card regularly do you find our reward program attractive you know try to get into a conversation and then you know in that process try and sell you know something else so that was what they used to do the question was we thought you know maybe we could build a propensity model that's the first obvious thing that comes to mind in the CRM system we saw this these agents are seeing you know these six to eight products that each customer they can pitch and they have to select first product and then maybe a second product to pitch to the customer there's no obvious you know thing in the tool that is giving them some idea what to pitch they are themselves doing their assessment of what the customer might need through that conversation and then trying to find some product that the thing they can tempt the customer to buy so propensity model was the obvious thing so we had a conference call with the bank in Australia and you know say we proposed we will we can build a propensity model and rank the products and give that ranking as a guidance for the you know agent to sell so that would appear in the CRM that ranking well what do the bank tell us well they told us they already have a propensity model okay but we don't where does it show up for the agents how are the agents the agents don't seem to be looking at any output from the propensity model they said well in the CRM system the products that show up they are ranked according to the propensity they're ranked according to the propensity but these agents were completely ignoring it well they didn't really care right I mean they had their own way of doing things I don't care you know we're going to do it our way as long as we meet the targets for selling these products well you know we're done our job right and that's and we told the bank hey these guys are not really using the propensity model they said fine as long as they using the you know meeting the targets the sales team doesn't care why should the decision support team in the bank care right so well so that was the situation all right so we had to really come up with a plan B they already have a propensity model can we build a better propensity model it's not clear that we can the guys in the bank have a decent team they're a domain experts they've been doing it for a while why should we as outsiders and IT company come in and be able to build a better model than a bank's internal team maybe we could but even if it's a little bit better it doesn't mean that the agents will use it it has to be substantially better for the agents to notice a real difference so we needed plan B and question was you know we had to come up with a plan B right and here is we are already whatever data is there that that the bank is already leveraging so a plan B has to be based on something else what can that be right so you know I said let me understand you know how they're trying to sell so this is what they do right we listened in on the calls so me and another colleague of mine and what did we learn three things this is what good agents did first they would look at the customer profile they would try to categorize this customer hey this looks like a family guy he looks like good income maybe he can be pitched some insurance or he can be pitched some you know additional card for a family member then they would strike up a conversation you know agent gradual flow not to make like this really selling to the customer but you know trying to solve a customer's aspiration right the customers have aspirations trying to understand those aspirations trying to position a product to meet meet those aspirations so they're getting clues to that conversation what is the customer really need what are their aspirations and then they match those aspirations to one of the bank's products something that they can tempt the customer to buy so that's you know that's you know this and you know data science have a role here does it have a role in the sales process beyond a propensity model which already was there that's a question we have to answer so this is what we did the question is is there what can be improved right if you're going to pitch something new you're going to first look at what is the issue what is the problem is there a problem in the first place what can be improved right the first thing was the variability in the sales of sales anywhere you go right sales is not easy especially in a contact center in a contact center you have a high level of churn these are kind of people who are just out of college one year two year out of college or maybe not even college may be just done high school and you know here they're facing a customer from day one they're not at all productive it takes months for them to become productive and that's because they have to understand what the customers are like and they have to understand what are these you know the typical customer of the bank what are their you know kind of aspirations what are the needs and then they get a sense of that they get a sense of different segments of customers and they have to figure out okay for this segment of customer this is the kind of products you know that they're interested in so this takes time it takes months right so there's a long learning curve and then there's a huge huge churn again contact center agents right if they're not leaving the company they're moving into a different account a different client within the company right to learn something different so this is this is a problem in all contact centers it's a problem in sales in general and you know how can data science help you with the question right so this was the first problem right the variability there were good agents I would say the top 25% were the good ones who are mature then you have the middle two quartiles and then you have the bottom quartile right so the idea was can we make the lower quartile pull them up right using data science or the second thing right these agents are getting into a conversation but were these questions really enough personalized or could it be made better can the questions be more targeted and personalized to extract more valuable information what do we mean by that right let's take an example here's one of the questions that these agents would ask the customer we see your you know using the credit card a lot and you have you know paying interest every month do you find the interest rate attractive this is one of the questions they ask sometimes they would ask this question even if a customer has just you know they see in the CRM system last month the customer paid interest they might ask this question but in the previous 12 months the customer has not paid interest they've been paying the balances in full every month this is just because the customer ended up being late that one month because of some oversight maybe not the best question to ask this customer because they know the kind of person who really has you know doesn't have the money to pay off the credit card but that is something it's very difficult for these agents to do because in the CRM system all they see is the recent data the last month statement if they really have to go back and look at the pattern for the last few months they really don't have time for that because only when the call comes in that's when they actually the CRM data pops up right so there's no way for the agent to really profile the customer what they're seeing is a very high level rough idea of the customer's profile they're getting so what data science can do is actually analyze the customer's profile over a longer time period not just that most recent data and come up with a better profile of the customer come up with better questions that the agent could ask the customer and that's what that's what we thought we can do so so okay so what is it that you know instead of agent coming up with questions what if we we built a tool that could guide these agents that could guide these agents what questions to ask the customer think about it machine learning is not giving answers machine learning is coming up with questions coming up with questions questions that will be valuable in understanding the understanding the customer better and then step 2 based on the response of the customer to those questions can it come up with an improved propensity score can it refine the propensity score that the bank already had just based on the response to those questions and that is a much better propensity score it's trying what the bank's propensity model with what these agents were trying to do trying to learn in fact trying to learn the behavior of the good agents trying to learn the best practices of the good agents making it available to all even the most recently joined agents so coming up with the questions and suggesting a product bit those are the two things in fact the three problems right the first thing is coming up with a customer propensity based on all the historical data of the customer's credit card spending over the last year two years based on the demographics coming up with questions to ask now we say we are saying here two questions coming up with two questions why just two questions just two questions because this is a you know you don't want to feel the customer like you know you ask running a survey to them you know sometimes the I get these surveys which are really long and you think you know you there may be four or five questions sometimes if you click on that survey you get in the email you think maybe I like this company maybe I'll help them and give them some feedback and that's great you click on that survey sometimes the surveys are short right sometimes they learn into several pages and at that point you just abandon it so it's important to just ask two questions maximum two and then based on the customer's responses based on the response to the question can we update the propensity score so we're solving three question three problems here not asking questions is not a traditional machine learning problem right coming up with the right questions to ask it's kind of a missing data problem but it's not received a whole lot of attention especially in live interactive scenarios like a contact center so these two questions the second and third problem were really important to differentiating what the bank was already doing and what we as IBM you know what we could bring to the customer the criteria was now how do we pick those two questions what is it what is it that you know what is it important what is the criteria for picking questions now you know these questions are not we have a question bank we came up with a question bank of 15 questions by talking to subject matter experts product experts of the banks products so we have these 15 questions the question was which two questions from this to pick and so we had to come up with a criteria right this is the problem formulation part what is the criteria for picking questions it is you know which are the questions you know you already have a propensity score from the from the banks historical data now the criteria has to be which questions are the most useful in helping discriminate between the products your banks propensity score might have given say similar scores you know two products might have very similar scores which question will help you know discriminate the propensity more further so that was that was the criteria so what we essentially wanted to do was to model what we were calling the utility of a question each question we said it has a certain utility we have to find out that utility and ask the question with the maximum utility first and then the next question so the model was like this essentially we took a systems approach what I mean as systems approaches you know don't combine everything break up your problem into pieces first piece was you have the propensity score from the old data which is the all the existing old data then that old propensity score is getting refined getting refined based on the responses to the questions so based on excuse me based on what the customer answers you have an increment to that propensity score this increment could be it could increase the propensity score or it could decrease the propensity score here I'm talking about some product for that product the propensity is depending on the customer's response it could go the propensity could go higher it could go lower maybe it'll stay around the same so add these two you get the final propensity score so which question to pick is really the which is the one that which question has the most contribution in the second box here and essentially how much is the customer's risk you know propensity spreading out you have a certain propensity initial propensity base propensity based on the historical data based on a customer's response how much could be the spread in that propensity that spread is the utility of the question if the customer has let's say a question has to answers yes or no typically these questions are yes no kind of questions and let's say when the customer answered yes the propensity increased by 5% if the customer answered no then all then the propensity increased by 4% so there's a 1% spread that's not a good question it's hardly making a difference whether the customer answers yes or no hardly the propensity is going to change a good question is when there is a large difference between when the customer answers yes versus when the customer answers no so that was a utility metric now we have some some challenges here the challenges were we had a cold start problem we are starting this fresh we don't have any historical data on when I talk about historical data I'm talking about the responses to the questions we don't have any of that because we are starting this fresh so we have a cold start problem data is growing very slowly we did a pilot with a limited number of agents don't expect the data to grow very fast this is not like a web scale problem where you know we have tens of thousands of customers on your website very sparse data each customer is asked only two questions two out of 15 so it's a very sparse matrix so these are the challenges the cold start problem was you know we did it through business rules we said okay based on some business rules we will come up with the initial questions to ask the customer and then based as as you know the responses get collected we will refine we will be able to compute the utility of a question and that utility of the question will then be used you know to refine you know which question gets selected so let's talk about you know how we actually compute the utility let's say we have one question one of the questions is the one of the actual questions do you hold any other credit cards now in his now remember this these kind of questions are important you know in a credit card customers you know in the banks data there's the bank has no idea whether you have other credit cards or not all they see is that you know you have their credit card right so they have no idea where you hold other credit cards so let's say you have a product insurance product which you are trying to cross sell and then you have this question we found that this question the response to this question affects the probability that a customer will buy insurance why why do you think it might happen this was a model when the customer let's say you know in the initial thing you have sir this question is asked to a certain number of customers among those customers about 10% of them 10% of them are accepting the product insurance product so the propensity the base propensity before the question is asked is 0.1 or 10% then the question is asked to the customer now depending on the customers answer sometimes it's yes sometimes it's no people who answered no to this question 25% of them ended up accepting the card sorry the insurance product customers who answered yes they have another card only 4% of them ended up accepting the insurance product so you see that how this question is helping based on the response to the question we get a better sense of whether the customer will accept which product in fact this question while it is being was in this example it is for this insurance product it affects the propensity of other products as well so this really helps you you know re-rank the products some products you might see in going up in the ranking other products coming down the ranking it's not just insurance that gets affected so this is a model obviously other thing is matters if let's say say 95% of the customer said yes and only 5% of the customer said no even though this you know spread just 0.15 and 0.06 spread is large on a weighted basis the spread is still small so really look at it on a weighted basis so we weighted by you know how many people say yes and how many people say no so based on that we come up with a utility for each question so that we call the weighted spread is basically gives us the utility of the product of this question and it this is just for the insurance product we do the same thing for all the products and this gives us the utility for the question so we get a utility for all questions as we start you know the cold start problem is there of course but once you have you know start collecting the data and you know asking questions and collecting responses and we get this we start updating the utilities and over time the utilities start settling down into some you know the long term values this is what the interface look like let me explain this so we here we have a customer you have some profile data of the customer some of the those these are what you see here are the different products initials for different products like insurance additional card and so on these are the products so you know when the customer calls in the questions pop up right initially the first question pops up the first question here is is your current payment method convenient now this question is actually used for you know to understand whether the customer might be interested in taking a you know a current account or a savings account if the current payment method is not convenient you know then they might be more likely to go for a bank savings or checking account right so that was the idea then based on the response to this question another question pops up this is about maybe know if you have another credit card so that was the second question that popped up because the customer answered yes here maybe the customer answered no here maybe something else would have come up here so you have these questions and answer I mean you have these questions popping up these questions are based on the customer's profile so firstly they are personalized and they are selected to be the ones that are most discriminating for this kind of a profile and then each time the customer answers the question the off this is where you have the ranked list of products the top one is AC which is a checking account then the second one is credit line increase so each time the customer answers a question here the ranking will change here so what what happens in this way is that as the questions keep getting answered the agents they can actually see this ranking changing they see the ranking changing and they are interpreting that they are interpreting that a customer who answered you know who doesn't have credit cards maybe they are you know more likely to take up insurance so they are also learning they learning to connect customer profiles with the product fit internally in the mind they building a mental model of that when this is improving you know making the learning curve shorter so what are the outcomes we did an A B test over six weeks we did a pilot with a test group who was using this tool and a control group the test and control groups are balanced in the sense that because there's a lot of variability between the agents you know we make sure that the we have different tiers of agents in terms of the performance we make sure that the test and control groups have the same balance in terms of agent performance so this is what we found over the six week pilot we got about a 15 to 18% increase in sales in the test group which was using this tool interesting thing was the improvement in sales was maximum for customers who sorry for agents who are new experience agents who are good for them the increment was less which is what one would expect other thing that was important customer experience is customer experience we were you know the bank was concerned is will my customer experience be you know degrade you know this kind of probing might seem a bit structured and artificial will my customer experience degrade thankfully no customer almost hardly any customer declined to answer questions we had come up with the scripts in the way that it could be smoothly inserted into the conversation flow no customer hardly any customer declined to answer questions so essentially customer there was no customer experience impact agent experience novice agents the ones who are new within the first two or three months they are the ones who find this the most helpful it helps them reduce the learning curve and it helps them improve their performance so they were very happy to use this tool so given these outcomes where can we use this is it just for sales is it just for a bank's customer center in fact any kind of a sales process you have you can think you can apply something like this you structure the sales process in a way sales conversation in a way that it can be more formalized and once you have these kind of a formalize this conversation you can use this tool to sort of encode you know what your really good sales persons are doing encode their practices encode the best practices that is what that knowledge is being encoded in this tool it support that's the other big area in it support what do you have I mean all of us you know we have this issue we go to an IT team and sometimes the people we encounter are really good they fix your problem other times you know they're struggling to fix the problem I mean of course then many of them are new people but what if you know if the now now these IT support tools whether you know remedy and service now and all these they have a lot of you know fields where once the problem is fixed the IT support person can say what they did to fix it what was the root cause what was the diagnosis you know what did they do to fix it often that is not populated so it needs to use something like this you don't need to build a new tool you can use the data in that old tool but you may need to have these IT people put in the right kind of information into that tool what did they really do sometimes they'll have something like oh I talked to David he told me what to do and I fixed it that's useless right so discipline has to come in once a discipline comes in you have this valuable data that you can use customer service huge long service right and why can't you know I think amix I don't know any people from amix here American Express right yeah okay I get very big long service from you guys and all sorts of irrelevant questions we can really personalize the service make it shorter you don't have to ask every question to everybody right so do away with the service and what else health care health care systems right so we have now you know more people are getting into using decision support tools in health care advising doctors on you know based on the results of the test what might be the symptom you know what might be you know ultimately be the issue and what might be the remedy but sometimes you know one of the issues still is like the low confidence right and the low confidence is because there's so much variation I respond differently to you are you are you right each person responds very differently and sometimes more tests are needed telling doctors not just what the diagnosis is maybe or might be but if the confidence is low telling them which other excuse me which other tests and should be done to improve the diagnosis so these are some of applications obviously any application like maybe iot something right if you have an iot devices or cameras those kind of things this may not be the right one because those things there's no cost additional cost once you have installed the camera or once you're installed a sensor there's no additional cost to collect more data cost is only installing a new sensor so that might not be the ideal case to use this but anytime you have a human interaction those kind of things this is very very useful to minimize the amount of time that people have to spend giving you information what's holding us back right why aren't we seeing more of this happening I think there are two reasons right one is something called an availability bias what is an availability bias might have heard this it's a tendency to focus overweight data that is available and neglect data that you don't have this cartoon is saying that somebody in today's newspaper yesterday there was an airline crash and then you know like that whatever mh370 or you have one of these Boeing planes going down and then you're scared to fly for the next several weeks right you're overwaiting the immediate data at the expense of other data that is an availability bias the same thing happens in data science you know all of us data scientists we get you know trained on whatever data source like Kaggle or you know others data sources there you're given the data on a platter in the real world it's not like that you have to go and look for the data so we don't really get trained in terms of trying to identify what data extra data we really need to solve the problem and that is a gap in training of many data scientists the second thing in any organization changing an existing process trying to change a tool is not easy there will be a lot of resistance to that people are used to doing things in a certain way and this introduces uncertainty that people don't like so what can you do here for the bank what did we do I didn't talk about what we did for the bank the bank wouldn't let us touch the CRM system no way right so what did we do we built a parallel tool we built a parallel tool where the agents are using to see the questions a web-based tool all they had to do was log in through the browser and then it would show them the questions they would answer the questions there at the back end the two things were connected it was just a one-way feed they won't they don't want us to push data into the CRM either so it was a one-way feed where the data was coming from the CRM system so yes sometimes parallel tools are needed at least in the pilot phase and hopefully at that time the organization gets convinced at the end of the pilot that they can do something more permanent so I I mean I would really encourage people to try and change not just think of data science as you know working with the data you have but also trying to figure out what other data you need because that is responsible you know for the growth growth in data science otherwise data science is going to you know at some point the algorithms that we have what has been driving the improvements in deep learning it is data larger and larger data sets speech data sets image net it is a growth in those data sets that's really driving better algorithms it applies even if you have structured data business data in fact asking questions is really a sign of intelligence this is a book I bought sometime back nice book if you have kids you might want to read it asking questions is a sign of intelligence in fact a lot of our professionals doctors lawyers detectives the main work they do is asking questions that is really key to diagnosis really key to finding out who's the culprit machines should ask more questions in fact I'm going to close with this remark right by Yoshio Benjiro we need to really think of you know he's kind of a little bit negative view on deep learning but I want to focus on this part of the message right where AI needs to be extended to be able to acquire information we can just keep you know focusing on the algorithms you know getting better results on image net we have to go and help algorithms also acquire information from the ecosystem so this with that I think that was my last slide and I think I'm on time more or less ten minutes ten minutes yeah so we can have yeah ten minutes good so we can have a few questions now yeah also the the conversation is happening on the phone the the kind of questions we came up with when mostly the multiple choice questions yes no or like what like one of the questions was what what do you find more attractive the reward program or the interest rates or the interest free days so it's like typically multiple choice questions so the agent when the by the agent the questions yeah the questions where the question bank was created by experts subject matter experts so adding to that question like I have also built one of the model in one of the largest private sector bank and where I need to recommend the customers regarding the same thing it was a call center and we have treatment about the product so we tried like their past history and then exploratory data analysis and combination of that we were predicting that different product so here also it was kind of exploratory data analysis that you use because questions responses yes or no based on that it is happening yes I wanted to know we didn't do any exploratory analysis for the questions questions were I mean the sense we spoke to the agents we weren't looking at data it was conversations we spoke to the agents we spoke to the banks product experts and came up with the questions that we thought would be the most useful no no speech to text the agent was listening entering it into the into the drop-down list how did you come up with the top two questions for each customer how did we come up with top two questions so I explained that that tree kind of a thing where I explained you remember that tree kind of a diagram so if the so let's say that two responses possible responses yes or no right if the customer answers yes there is some probability if the customer answers no there is some probability right if that spread between the yes probability and no probabilities large so we say that the utility of the question is high and that utility the question with the highest utility the first question to be asked okay but you need to ask the question first right see out of 15 questions yeah you'll be asking only two questions in each customer correct yes so once you ask then he gives sr no answer then only can find the difference or impact the propensity score yes so at the first time yeah how do you know which two questions to ask huh yeah good so I said you know we I said we have that cold start problem right that is exactly that is that is what the cold start problem is to start with you don't have any data in that case we had business rules the first week it was kind of actually a tuning phase the first two weeks actually the pilot where I said six weeks right but actually it was eight weeks where the first two weeks was just a tuning phase where the questions were just based on business rules and we collected some data and then the six week pilot really started hello yeah I have a question so like just to have like see we are having set of questions for like the Egypt really the machine learning algorithms are required to predict like the which kind of question to be asked to a customer or it can be done it can be done just with the EDA when you say so when you say machine learning what the point is you don't need a sophisticated model in fact here we had a very simple model just a discriminant analysis okay and the second question is like as you have proposed like the this last slide like algorithm itself should acquire the information it means acquiring the data so do we have any kind of the research paper in that like the any other algorithm which is successful in acquiring the data or have be any progress towards it because this is the new kind of things which I am hearing it yeah so there is actually there has been work in the past but it hasn't received a lot of attention there's something called active learning which is about not acquiring features but acquiring which observations to collect that is one area active learning the other area is feature value acquisition itself which features to acquire that's another area of research they haven't received I think active learning was hot I think probably 20 years back people were talking about it I haven't seen much happening after that this borrows some ideas from that but the main thing is the focus of this has been on a live interaction in the contact center how is it applied there those have mostly focused on you know transmit I mean in those days 20 years back it was more about I've got some data which data do I need to transmit to a different place like you know because there the data transmission costs in those days were high so it focus on a different types of problems okay sir excuse me so my question is that a lot of a lot bigger portion of feature engineering is checking the data integrity so how did you tackle the falsification of data in this particular use case so the user if you ask a user do you use another credit card he might as well say you know when he uses a credit card right he might try to act over smart or you know try to read into the process yeah so how do you tackle those problems we didn't worry about it I mean yeah there might have been some could have been I don't know we didn't really think of worry about it yeah so do you think these are only corner cases or the impact could have been larger than anticipated see if a lot of customers falsifying right what will happen is your you know the question will not the utility of the question will go down right if customers are falsifying right because it will basically cancel out the people are telling the truth and people are falsifying their their their effects will cancel out and then the utility of the question will go down and then the question will not become important so yes a lot of people are falsifying then this will not really work so is there a better solution you know around this problem or is there a if you can have a larger set of questions so that even if the utility of such questions goes down some other questions substitute for this factor I mean the way we looked at it is why would a customer really kind of questions we asked we carefully selected it so customer wouldn't feel offended or wouldn't feel embarrassed or to answer it so we didn't suspect really falsification and it in fact our questions went through a regulatory process where the banks regulatory team reviewed it and it went to that process so so I don't think that we didn't suspect falsification really happening right thanks so can you give some more details about your pilot so you said for two weeks there was pilot done so it was a six-week pilot two weeks was kind of a warm-up phase and then the real pilot was six weeks so what exactly happened in pilot so is it like customers call and every customer was asked to random questions or like every customer is asked two questions which are suggested by this tool but I thought with this pilot you were learning and then no that to first first two weeks was more and first two weeks was you know that learning was that phase when we were just collecting data based on it was still through the tool but it was based on business rules some business logic was used to select the questions and here you're kind of dependent variable what you're modeling it becomes that did the customer took the product or not yeah the target variable is whether the customer took the product or not yes yeah there's a question here so you mentioned there's an update to the propensity model right so basically you add some the propensity model score becomes some delta plus or negative whatever right so is it memory less in the sense that the kind of question that you asked in the first versus the second right so the first one you ignore the memory right so and the second one impact the end propensity score more than the first one right so so by the call the question is basically is it memory less or stateless no there is a state like the first propensity after the first question maybe the propensity went up by point five sorry let's say point zero five and after the next question again it went up by another point let's say it went up by minus point one second question at the impact so the total impact would be point five minus point plus point zero five minus point one so the net propensity would go down by minus point zero five so let's say if you would extend this from two to say ten ten questions right so then do you see a problem in terms of this aggregation things that we do yeah so then what would happen is sometimes if you know the propensity score go more than one or less than zero in that case we thought of that what we would have to do is actually put a sigmoid function so the propensity is go stay in that range so it's not nearly additive right it's basically a let light is very something's go down something's come go down come down or whatever is the relationship that you have right so I'm not sure if this really works well for having more than two states or what yes that's true it will if you have more than many questions you'll have to modify this you know probably a multivariate rather than having a simple model like this with a univariate model a multivariate model would make more sense and I think there's one comment on the actual learning right so I think the snorkel database program in data programming the way to generate the data to capture propensity rather than it's still popular I mean a lot of active research is happening yeah alright I'll be around for some time thank you


So you wish you could organize your animations. Not a problem. At the bottom left, drag a new window above the timeline. Set it to dope sheet and change this to action editor. Click new, you're done.  To create a new animation, click this button here. You can see a list of all your animations by clicking here and select the one you want to work on. Hope that helps. If you enjoyed this video, please don't forget to like, subscribe and ring that bell. Hope you have a fantastic day and I'll see you around.