A very good evening to all the educators who are joining us on the live session for international boot camp on coding artificial intelligence and robotics, especially for the school educators. So we are here with one more wonderful session for all of you in day number five, where we are going to talk about the gesture control robots. I hope you are all ready to make your robot move with gestures, we will be shortly starting our session just in few moments. Till the time you can let us know in the chat from where you have joined us. And the name of the country. And I would also like to call up my colleague Tina. Hello Tina. Good afternoon. Good afternoon. To welcome all of you to international boot camp program level with the knowledge of artificial intelligence and robotics after the completion of this boot camp program. On behalf of enters 10 period family, I welcome you once again to this international boot camp program level to I request all the educators to share your name along with your country name in our chat. Thanks Christina, we can see, of course, Christina has joined us from crochet, and I would like to share that now. We have of course, being a part of one family and now I feel very familiar with all the educators seeing their activities in the telegram, getting their issues resolved and next day again coming back and talking to them on the live session. So it's an amazing interactive process to make it little more amazing. We want all of you to please join our telegram group. So you can scan this QR code and of course, join our telegram group, where we would be helping you with the your issues supporting the problem you are facing apart from it. This would be one of the medium for our communications towards you for the complete boot camp as well as the further developments which are going to come up with the stamp idea as well as the new boot camps aligned for you. So I hope you have already joined in to the telegram still if not you can of course join us moving ahead with the part of how exactly we came up with this wonderful boot camp so we would like to thank our all collaborators who have helped us to reach to the maximum educators around the world. Stamp idea is of course hosted. The boot camp art part has is the co host which is our AI and robotics technology part from Bangalore. Also, the complete boot camp has been supported by Niti I hope that is at all innovation mission which is a integral part of the central government education system in India to take you further I would request Tina Tina can you hear me. Sure. So it's our pleasure to have all in the educators forum and American Indian Foundation to be as our community partner they help us to reach our maximum. We have many international partners like purpose smart education Palestine National Institute of Education Singapore apex coding Academy Egypt, it to nature and it to stream you a all of our collaborators and stamp idea had made had made a wonderful effort to bring all of our educators to this platform. Now, it's time to discuss about basic objective behind this international boot camp program. The objective of this boot camp program is to empower the educator educators by deepening their knowledge and understanding on coding artificial intelligence robotics and thus enhancing them with all the necessary skills that they required on this 21st. century. This international boot camp program will definitely give a hands on experience to our educators on this latest technologies like robotics artificial intelligence machine learning in a fun and very interactive manner by learning more about these new day technologies like artificial intelligence robotics coding. Our educator will become more confident to talk about this technologies in front of a society. Last but not the least, we are making our teachers to become an ambassador of their own institution. Now, now why you need to participate in this boot camp program. We are giving you an opportunity to learn with our international educator, so that with this through this program, you can join in our international educators community also you can share your doubts you can discuss among yourselves and not. As I mentioned before, you can acquire the skills that they you required on this 21st century centuries. And one important thing I want to add here is that we will be giving you all the teacher resources and we will are giving you the your students our learning resources also by this by the completion of this boot camp program. You will be earning a badge and a certificate aggregated by art park and stem art park stem media and stem dot org. I think I use it's time for you to take it forward. Thank you. Yeah sure Tina. So now I think we are on the part of the schedule. So, just a sec. So now we are going to tell you about an overview of the schedule. Actually, we have covered so many things during the boot camps in the level one and the level two. Right now let's see what exactly the things we have covered in the last days and what we are going to cover it today. The first day of course it was an amazing to start with the introduction of robotics where we talked about the basics of robotics, making a robot move in forward in the backward directions. The next day of course on the 28 June we started with self driving robot which is one of the very interesting activities where our educators came up with amazing projects, making their robots move by showcasing the different card recognition cards in front of the camera. The next day of course one of a very important topic of the robotics that is line following robot, which helped us to make a quirky move in a line so that of course it can be utilized for making big and amazing projects. The next day, we came up with a chapter or I should say we came up with a session on AI delivery robot, where we used our own learning of recognition cards, as well as we used our previous learning of line following and came up with an amazing concept where we were able to stop our our quirky in different locations with just showcasing the numbers here the numbers were working as a house number so we made a quirky delivering the robots in different house numbers like house number one house number two and house number three. And today we are here again coming up with a wonderful session on gesture control robot, where we would be using some hand detection techniques and machine learning concepts to make our robot move in the different directions. We have and coming amazing session and an important session on third July, where we would be discussing about your certificates your batches and the resources how you would be getting them. Apart from it, it is going to be very much entertaining to see the different projects made by the amazing educators who joined us in the complete bootcamp journey. So without a further delay, we can move ahead with our today's session. Although we have covered this learning management system related things so many times but still we wanted to cover it again because still this is a very important part, because from here only you would be getting certificates you would be getting batches and everything. So let me just show it to you. So here basically you, you would be getting an access of the complete elements that is learning management system or your own dashboard on AI dot this time period dot com, where you have to just sign in yourself by clicking on the sign in option. You have to, once you have signed in with your credentials you have to click on my courses and click on the educated bootcamp level to see all the learning content and the recorded videos. Once you are here, you would be able to see all the learning content related to each and every sessions, along with this, there will be embedded videos which you can see it here. A very important part that we want you to fill a daily feedback form after watching the sessions live or recorded. That means even if you are not able to watch this sessions in on live medium, you can watch it later at as per your convenience, fill the form as per your convenience and submit the activities to acquire and earn the wonderful batches and certificates for you. So today, of course, we are going to cover two activities where we would be working with hand detection, as well as we would be working with machine learning. So now, I think we are pretty clear on this part and I would now like to share my screen and move ahead with our today's part. So still if you have not joined our telegram channel, please join us so that of course we can support you with your queries. So now, as we like as we have already taken the session in the level one, but of course I wanted to cover it again to give you a little more idea so that we can use this concept again today. So now, let's discuss about the machine learning. So here you can see a small diagram, which is giving us a four stage process for a human learning, because it becomes very much important if we are talking about the machine learning that how exactly a human scheme with this concept of machine learning, and to understand the history of course we have to understand how exactly the human learning process goes on. So if you'll see it clearly, basically we have four different stages. The first is sense environment. Second is analyze the information. Third is decide and act, and for is increase your knowledge. So now starting with the first. So we can consider it today as an example, where we want to understand a machine learning process, we want to understand in human learning process with a with a process I should say with an example like if I last time we discussed about cat and dog that how exactly we learned about the cat and dog. But today if I say that if you want to understand it through the postures. So I'll just turn on my camera here to show the things up here. So here you would be able to understand that. Of course, from very childhood what happens is whenever any guest comes to our home or we go somewhere. We start observing the different gestures made by the humans. Like if I want to say hi or hello to everyone.  So this happens with our childhood and we observe these things and we adopt these things right. Similarly, if you want to stop anyone, of course, what we what do we do we bring our hand and put it like this that we want you to stop here. So there are many other gestures. Like if you know about if we generally do namaste right so we do namaste like this. So any person doing this, we are able to understand yeah that person is basically greeting us. Or of course. So similarly, there are different gestures, which we have learned from our childhood. Now, after understanding how the gestures work, what happened like now if you're going on a road. And sometimes what happens, like someone stops you or someone say waves his hand to say hi.  But if I say if you are standing on a road. Okay, and there are I can say the cars in the buses are going on. Okay, on the road. So you also wave hand like this right. What does that mean. So certainly that means that you want the bus to be stopped or car to be stopped so that you can about that bus or a car. So now what happens with the time we increase our knowledge, understanding about the different gestures and as per the scenario we understand that what exactly that gesture means. So the same gestures can also can be understood in a different way in a different scenario. But as we are humans, we are capable of understanding the things very well, we are able to take the decisions perfectly. Now, moving ahead to a part that how exactly we have understood all these how exactly we have learned all of these things as a human. So of course we have used our different senses, like visions, touch, smell, taste and sound. So here using these senses of course we can, we have given a data to ourself to train ourself on, on the different gestures on a different understanding to come up with a particular output or I should say with a particular decision whenever we require to do so. Now, in this complete process if you'll see the first time when if you'll see a baby you, if you're going to just wave your hand, you're not going to get a response from the baby because of course for a baby, they don't have an understanding that the waving hand means hi. So what exactly happens there is their parents or teachers tell them that see that person is waving his hand or her hand that is showcasing that he is greeting you. Okay, so then in that case what happens even if you might have seen generally people also hold the baby's hand. Okay, and like ask them to do so. Now what happens here is basically this is a training, basically we are training the human babies to do a waving of hand to say hi. So this is how exactly the things work that we train, then of course the training comes up. Once there is an analysis of information by the machines by the humans, then there is a decision and action taken upon it. And finally, we increase our knowledge that yeah of course, waving hand is only not the way of greeting there is different ways like saying hello or saying Namaste, or even some places we shake the hands. So those also are a part of you can say the greeting. So there at the end of course we got an understanding that greeting can be done in many ways. Now, if someone is coming in going to put forward his hand or her hand to you. It's not like you would not understand what exactly the meaning of it you understand that basically that is for shaking the hand. Okay, so I think you might have got an idea how exactly the human learning work. Now let's understand the machine learning. So as per the definition machine learning is the process of machines learning how to act themselves without any human intervention. So that means if just assume that there is a robot, and I wanted to work automatically. So if in case I am going to wave my hand. So my machine should also wave its hand to give me a greeting, or in case I am giving this expression from my hand, or this, that I am asking the machine to come forward. If I'm just saying like this. So I'm asking my machine to go backward. So these all decisions if taken by the machines automatically. That means those machines have been gone under the machine learning process to take the decision. Now how exactly these machine gets an understanding. So the first is of course we give an input of images or sound information into the model. That means we tell them first we give them a lot of images see this is the hand and if you are saying like this that means go back, if you are doing like this, this is come back. Now arrange the pictures in a data set so that we can give an input in different labels that this is hi, this is namaste, this is hello. So that next time, if we are testing it we are able to get a desired output. Now, these input are sent to the model for training. Now in model basically algorithm operates on the input to get the output to get to output the higher level information, basically to find out the higher level information inside it. That means, if of course the person is static like this that does not mean that this is high. Okay, but if we are waving it then only it is going to be said as high. So this is a very granular information which is taken out by the algorithms. Okay, of course, the algorithm works on the pixels of the images and find that what exactly it is. Then the final things comes up as an output where basically we tested that whether whatever the training has been given to machine is machine performing good or not. If machine is not performing good again, I have to go back to the initial position, give little more data so that of course I can get an amazing output. Okay, so now we are going to start with an activity. So I hope you all are very ready with your pictoblocks and I would just share my screen. So now we are going to start with an activity where we are going to make our quality actually move with different hand gestures. So I'll just share my screen now. Okay, so I hope my screen is shared. Okay, so the first thing in a very important part is of course that we should have a pictoblock we should have a porkey. Okay, and the very important part that you should have signed into the pictoblock. Okay, so if you have signed into the pictoblocks, then it means if you have signed into the pictoblocks that means you are now able to use the different extensions provided in the pictoblock. So today we are going to start working with first the hand detection. Okay, then we are going to move ahead with machine learning. So I'll just click on this add extension option at the bottom corner. I hope you are all able to locate this add extension option. So now what I want is I'll just click on this and I'll add an extension which is human body detection. Okay, so as we know that this extension is can be worked with offline pictoblocks that is you do not require of course the internet to use it. So you would require a little patience so that of course all the model details are loaded in the pictoblocks and can be used for making a wonderful project. So now I have added an extension called as human body post detection. And now here the first and important part is I have to bring up a block which is going to help me with the input data. Right, because till the time I'm not giving an input my machine is not going to work. Since I have to take an input I have to turn on the camera. Also I have to use the block which says when green flag is clicked. And now here, if you'll see I'm going to add a block from settings that is show detection. So here it will observe this complete extension blocks that it has been basically categorized into three different part one is settings one is post detection and the third is hand detection. So if I'm talking about setting is basically I have to set an environment so that of course I can use the utilities present in other blocks of this extension. Now since I've already added to turn on the camera I've also added to show the detection. So the next part is I want everything to be working on live basis that means on an instant, like if there is any change on the footage I want to see the changes on live. So I'll just click it here. I'll click it here on forever.  Of course machine can analyze machine can come up with the solution, but the most important part with the machine is you have to give a command so that they can initiate their process. So to initiate the process, I have a block which says analyze the image from the hand. Now they have initiated the process, but how exactly I'm going to check whether the process is working good or whether I'm going to get some output or not. So what I can do here is I'm going to add a condition. I'm going to go to control and I have a block says if then else. So basically this is a block we have already discussed it but again I would like to draw your attention here that this block is basically we are using many times whenever we want to work with condition. Since condition is a very important part in the machines because there can be many possibilities and while making a project it's very necessary that we consider all the possibilities. So even they can be a negative possibility or a positive so we have to consider both of them and also allocate an output related to both of them. So right now I'm going to add a block here which says is hand detected so this can be true or this can even be false. So this is why I'm using a condition that if this condition is true that my machine has detected some hand in that case what I want is I want my machine to do something. Okay, so let's say I'll add from looks say hello. Okay, sorry. Okay, if in case it does not detects any of the hand I want no hand. Okay, so now let's keep this inside the forever and let's see what happened. So I'll need to just change my camera just a moment. Okay, so now I just click here. So right now you can see it's saying no hand detected. Because yes there is no hand right now right on the screen. But if in case I bring my hand what is going to happen. I'll just stop this. Okay, just give me a second. So right now you can see it's saying no hand detected. Okay, but as soon as I bring my hand. It is going to say hello. So you can see it's saying no hand detected no hand detected and as soon as I'm bringing my hand, it is saying that say hello. So now of course we did this part, but the problem. Okay, let me just clear the screen first. So I'll just hide the detection. Okay, and then bring show me. Okay. So now you can see what we did till now was we detected the hand and I was just asking it to say something. But now if I want to work with quirky. So what I'm going to do is I'm just going to bring my quirky into play. Just give me a second. My quirky I'll just switch the camera. So that of course it is visible to you. So I hope my quirky is now visible to everyone. So although I would just showcase this thing to you, even once I have like completed it. Okay, so first, I need to select a board. Okay, so I'll just select a board here. That is quirky. I'll click on connect. And I'll just connect my quality with Bluetooth. Okay, once I've done this. Now the next part is I have to use certain blocks. So for as of now I'll just remove these blocks, because I want certain different things to happen. So generally, as I said if you're talking about hand gesture, we do this part when we want to stop something. Why not to bring a robot part and bring a stop robot. If in case a hand is detected. Also, if you want to make it little more interactive, I can add a display part. Okay, which where I would be making everything okay just to say where I would be filling up red light. What would happen if hand is detected of course the display matter it would be this and the court is going to stop as what is going to happen, as I want if there is no hand, I want my quirky to keep going on. Okay, so, and also, so I'll just put it here. Go forward. And of course 100% speed is going to be very much for my small board so I can try 30%. Okay, although I don't know like it is still going to be very, very high. Okay, but still let's try it. So now if I'm going to press a green flag, you can see it's moving right as soon as I put my hand, you can see it stopped as soon as I kept my hand. You can see how exactly it is working. Okay, so it's moving. So it's it has detected the hand on the last part. So now it is done. And I want to stop. So I'll just put my hand. So now you can see how exactly it is able to basically detect the hand and move. So let me just show this part on stopping the share. So right now, I will just bring this keep it here. Okay, and I'll just put it leave it and now as soon as I bring my hand it's getting stopped. Okay, so camera in camera your hand should not be visible. Okay, I'll remove my hand and it will again move. Again, I'll bring my hand, it is going to stop. So using basically hand gestures, I'm able to stop my vehicle. So it is going out of so I'll just bring it here. Okay, and now I'll just stop this. So now I hope you might have got the idea that how exactly basically using my hand, I'm able to stop this. Okay, I'm able to stop all the this part of quality. Now what of course, if you're talking about the activities. Of course, if you're talking about some technology, where we want not only to stop it. Okay, we want it to work with different gestures, like if in case of course if I'm putting my hand in even different gestures, the corky is only going to detect a hand, whether you are putting your hand with an open palm, you are putting your hand with a single finger, or you are putting your hand with like fist closed. All the things is the hand. So whenever it is going to detect a hand, it is going to stop itself. But if in case I want, so I'll just bring the new file first. Of the picture blocks is downloading a data since 10 minutes. So I would request, gentlemen, ma'am, please restart the picture blocks and please check. Hope the ramp is not getting overused, because if there are other applications working on background, then sometimes it makes little difficult for people blocks to get open so please, from the task manager please switch off all the other tasks, which are not required at the moment, and then restart the picture block. Okay, so now I'll just bring up a new project here. Let me just. So I have like right now turn, let me just turn my court here. So I'll just share my screen now. Okay, so now you would be able to see that we are going to talk about now about the teachable machine. And why we are talking about teachable machine is as we know, like, it's an amazing platform where we can just train our own models to get the outputs right. Okay, so I'll just. Okay, so basically what are the different types of medal models you can work on teachable machine. So you can work with image model. Okay, you can also work with the pose model. You can also work with an audio but right now audio is not something which is going very good so we generally prefer doing it with I should say this. The image model. Okay, image model is something where we can work it very well. So what we are going to do I'm just bringing my picture blocks back again. Okay, so we are going to use the teachable machine platform. But for here this is very important that we have to understand that we can add a machine learning extension here using an add extension, but we have to move to the teachable machine to create a model. Once you click it here on the machine learning extension. Okay, you would observe here that there are no blocks added. I think you would you are able to see there are no blocks added. But there is actually two different clickable buttons are here, like create a model and load a model. So what am I going to do it here is I'll start with creating a model. Okay, so to create a model, of course, once I click it here, it is going to open. So once you have clicked on create a model, it is going to take you to the part of teachable machine where we are going to train our model. Now, we can use of course the post project and you can also try that. But of course right now what we are going to do is we are going to use the image project. So that we can work with it very easily. Okay, so now here you can see it's a class one and a class two options. Okay. Okay, thanks. Like wonderful. It's amazing to see the comments. Okay. I'm going I have to make my gesture control robot. So if I say that I have to make my robot move in different diets directions right so one can be left, one can be right, and I can also train my model on stopping it and going forward. Right, so we can take these four classes and water classes. So basically classes are nothing but the different labels of the data. So if we are saying that this is a go class, that means we have to give the images only related to go class. So right now I'll make a class that is go. I'll make one more class. Right. Okay, and here from here basically you can add more classes. I'll make it left. Okay, and I'll add one more class that is a stop. Okay, no, sorry. Stop. Now I've added four classes now have to also give the images. So of course last time we worked with one of a cat dog classifier where we basically uploaded the data, but today we are going to use a web camera. So I'll just click on web camera and you will be able to switch on your web camera. Okay, and here what you have to do it is little tricky that what you have to do is you have to keep the background clear. Okay, so once you'll be keeping the background clear then only you would be able to get a good result. So I'll try my level best to do so just give me a second. So I'm just turning my laptop so that of course I can get a clear background. Okay, so I hope this is going to work. So this is pretty clear. Okay, so this is how exactly we are going to use the model. So of course I have to be little like this. Okay, so what we are going to do is of course for go we have to train in certain gestures. So right now what we can do we can take this as going right, we can take this as going left, we can take this as an stop. Okay, and we can use this as a go. So there are four things and we have we are going to use four of them. Okay, just give me a second. So now it would be a little more easier for me just give me a couple of. Yeah, so now I would be able to train it perfectly. So now how I have to trade, you have to see this very carefully that there is an option to saying hold to record. Okay, as soon as I'm going to click on this it is going to start recording the photo. So what I'm going to do I want whenever I show this that is a sign of victory. So I want my machine to go forward. So I'll just give the images I'll just try to take the images little in a different ways. So that it is much more comfortable. And how many images we have to give you have to take a record that you have to give images around 150 to 200, not more than that. Otherwise it is going to consume a lot of RAM. Now for right, of course, I have to show this angle. Okay.  Actually, for me, I have tilted my laptop on the left hand side so it's getting a little bit difficult, but not a problem will try level best to do so. Okay, I think this is going to work. Yeah, this is going to work. So for right, we are going to train it with this. Oh, I'm sorry. So by mistake, I added the data here. So I'll just delete this. So we cannot put two different data in win label that is one class. So now here I'll click web camera, and I will now give. Okay, again, I'll give somewhere 180 major. Okay. Okay, now what I have to do here is I have to add for left and for left it's pretty easy. So now I'll click on left. Okay, so I have given more than 200 but I'll delete some of them since like I don't want my machine to get lag because if you're giving the images that of course going to use it and it is going to of course take the ramp. So I will delete some images. So I'll try to keep it around 180 only. Okay, now coming to the stop. So stop is very simple. I want this we can feast it so we can use this as a stop. Okay, so I'll directly choose web camera and I put this to hold to record. And you have to also make sure right now I'm using only my one hand. Okay, but if in case you are you want to use both the hands. So you is it okay for each five major not of course not because see I'll tell you why is it not okay. When we were working with cat and dog. They of course had a very large difference in their images. Right. But when we're talking about human hand. Because there is not a big difference in these images if you look to it closely. So that is why we cannot give very less images we have to give minimum 150 to 200 images in order to get a good result. Okay, so I hope this is not perfect. Okay, yeah. So now, coming back to the part. I'll just stop the video again here. So I have given the images with different angles. So to basically train the model. Now I'm going to click here on train the model. So as soon as I click on train the model, you have to understand what is going to happen. So the first important thing is going to happen is the machine is going to find the similarities between all the images in each class. If you see this message that is pages unresponsive that means it is taking a little ram so you have to click wait. Okay, because there are two chromes, two cameras, so many things running on my PC so sometimes the PC cannot handle but you have to press wait only always. So now important point which I want to drag your attention that it is going to of course find the similarities. I've chosen the actually wrong color. Let me just bring it here. They will closely observe this part. So are you able to see a number growing? That is 10 out of 50, 11 out of 50, 12 out of 50. So what exactly this number is? Basically this is we are calling it as epoch. That is the number of time the machine is training itself because in a single training of course we cannot get the wonderful result. So it is going to train itself. Now every time there is a training going on, you will observe there would be some calculations on back end where first the similarities between the same classes would be found than the difference between the other classes images would be found. So that of course machine can understand what is the difference between the images given as an input data and what is the similarities between the classes that is like all the go plus would only have two fingers. If all the right hand images if you'll see the thumb would be somewhere at the downside. So these type of calculations happen. Now once the calculation is done, you would be able to see here. I should say the testing part. So now right now it is getting very confused. Okay, again I would require to do the same setup so just give me a second. Okay, so right now I'll do the same. Okay, right now it is not able to find any answer. But let's see whether it's able to see my hand and find. Yes, this is a stop. It has trained itself correct. It is left perfectly correct. It is, you can see I will try to show. So it is detecting it right now that there is a percentage also I'll tell you what exactly that percentage means. So here you can see this is go. While I'm not able to show my hand completely but still I'll try. So right now you can see, while I'm giving a right hand, it is basically not so sure that this is a right side turn or not. So what it is giving if it is giving me a percentage that whether how much percentage my machine is confident. Okay, so right now if I show this, it's understanding it left. Okay, if I show this this it's understanding because now you have to understand that I have trained my machine with a certain background. While right now I'm changing the background. If I'm changing the hand, so my machine is not going to work good. So you have to make sure that you are working with the same background. If you want to work with different background then you have to train the machine as well with the same background. Okay, I hope this part is pretty clear. So now we have also tested it. And now I'm going to just clear all the drawings. And the next part is we will be exporting the model. So I'll just click on export model. And here basically I have to click on upload my model. So I saw this was one of the teachers doubt that it is showing something else. So no, this is going to be upload model always once it is uploaded it is going to show you that the model is completely uploaded or up to date. So I'll just click it here. If in case that might be a case when you are seeing something else that means you have clicked on the model link. So you don't have to click on the model link. You have to basically copy that modeling and you have to copy and basically paste that modeling over the picture blocks. Okay, that's superb. I can see her shell shimpy as mentioned it's working that superb. So now you can see I've got this modeling. Okay, I hope you all are able to see this. Let me just again mark this. So basically what I've got is this model link I have got. Okay, now I have to copy it from here. So that of course I can take it to I can take it to my picture blocks. So here you can see it is saying my cloud model is up to date. Okay, and now I'll just clear the drawing and copy this. And I have to take this to the picture block. So I'll just close the teachable machine right now. I can open the picture blocks. Now here are important part that I have to do is I have to click on load a model that is second option paste to the model. Okay, and then click on load model. So it will take a little bit of time you have to be patient with it because patients with it because you can understand we have of course used so many images we have trained our model in so many images. Right now it has uploaded everything. Okay, now you'll observe that this block. Okay, let me just zoom it and show you that this block has all the four classes what we had. Okay, that means our complete information has been successfully transferred here. Okay, so now the next part comes up that how exactly I'm going to make my quirky work with this, how exactly I'm going to use the script. So as you know, the first and a very important part, whenever we are working with artificial intelligence becomes that I have to take input data. I'll just zoom it out actually. Yeah. So it this block is somewhere going to help me to turn on the camera. Once the camera is turned on. Of course, I have to also check right whether it is working good or not. So this thing what I'm going to do is I'm not going to get for like for ease what I'm going to do is I'm also going to bring in recognition window. So you'll observe a small change that there will be a small recognition window coming up on top so that there is no so problem of basically the stage. So now what I'm going to do here is I'm also going to add a block which says when dream flag is clicked. Now these three blocks are going to only help me to set the settings part. Now the testing part has to be taken care under the forever as we would be working with the things in a real time. And now the things come up related to the condition because we have four different conditions here right that it can go it can stop it can take right or it can take left. So I would be using a condition and I already have this block with me is identified class. Let me zoom it out a little bit or let me just use this option. So you will observe here this that is identified class from the web camera in go is go. Okay, so basically what exactly going to happen is I'm going to bring that I want at this part that my court he should move ahead, but how I'm going to get the So here I have to select the board. So there was again a question that I'm not able to see the court he pallet. So that is very important that you select the court he is bored. You have to click on board, you have to select the court key, and then only you would be able to find this. Now what I'm going to do it here is I'm going to bring a robot. Okay. And I'm going to click it here go forward at 100% speed. Okay, but of course as we have already seen that 100% speed with going to work a very high so right now making it 30 so that it is very understandable by us and we can easily change our actions. And to make it more beautiful let's bring some color of green. Okay. Now else. Now here you have to understand right now we use if this is one condition. Now in the else part you have to use one more if else only, not if you have to again use this blog, because there are four different conditions. Okay, so we would be putting one condition inside the other. So right now I'm going to put like this. And now I'm going to check it for the second. So if the identified class from the web camera. Now we have tested go. Let's keep it stop. Okay, and now if it is a stop what do I want is, or let's keep it left. Okay, and now I would be, I would try to have some motion on my quality for that I have to go to robot. What I want I wanted I want my party to move on the left side. So from the drop down I'll select this and again to make it little slow, I'll make it 30. Okay. The next is to bring a little good look of quirky let's put some color on it. So I'll put this mustard color. Okay. Now again model is still loading in pictor blocks. Okay. So just what you can do is you can just stop the process and you can just click back again on load a model and re paste the link. Also, please check that you have copied the link correctly that is very important because sometimes a part of the link or a one even character is left to be copied. So it becomes little difficult for machine to understand. Okay, now again, let's make a ladder of if it's here. Okay, so I'll just bring if it's. Okay, and now we are going to put the next is the right. So I'll just bring that block and I'll put it here that if it is right. So what do I want is again the same block. So we'll just go to robot. And I'll just drop this down here and from the drop down I'm going to select right.  Let's add some colors to the quirky. So I'll just click it here and I will fill it with blue. Okay. Oh, sorry. So how exactly I'm filling the color is I'm opening this matrix I'm selecting a color and there is an option to click here that says fill. Okay, that is going to fill the complete matrix. Now the last is if it's the do we have any more conditions, of course not in that particular scenario what we can use it we can directly use if we do not require if it's now, because there is only one condition left. So what am I going to do it here is the next again I'm going to check. And right now I'm going to check it with stop. Okay, and if I'm bringing a stop of course I can bring a color of red generally stop signify that and then along with it I want stop room. Okay, so if I just zoom my screen zoom out my screen, you would be able to see all the four conditions go left right in stop. Okay, and that has been even mentioned with a particular color. Like three mustard blue and red. Now the things come up that how exactly I'm going to start. So I was what I'm going to do it here is I'm just trying to set my camera here. I'll go to settings audio video settings. And I'm just going to change this. Okay, also along with this I want to, I want to show you the party working on my camera. Let me just put my camera little down we hope it is good now. Yeah, it seems good now. Okay, so let me just put my coffee here. Okay, my, this is also ready. So now what I want to do is I want to connect so for that I have to turn on the quality. As soon as I turn on the quality. I can see a light blue color. And now I'll click on connect. I'll go to Bluetooth port. I'll see quirky Edd six. That's the name of my own quirky click on connect. And as soon as it gets connected. You can see the green the blue light has turned out to be green. Now if I soon as I place this green flag. So you can see this recognition window coming up. So I was talking about this. Okay, so what I want here is I'm going to just a moment. They can see if even I'm not showing it okay so it is going to do something. So let me just bring stop. You can see it got stopped. You can see with the left it is taking the left direction. Go forward. Stop. Okay, then of course we can make it so right now and trained it on back of course even we can train it to go back. Let's make directly a left turn here. Okay, I'll just stop it. And if in case let's try right. So it is trying to take right. As soon as I put to stop it is going to stop for right it's taking a little bit of problem. But of course, because my hand is not sufficiently visible. Okay, right now it is able to find it stop. So I hope you have got an idea that how exactly I'm able to control my robot with gestures. Right, and I think that's pretty cool rate that we're able to meet my robot move with the hand gestures. Now how exactly I did I think it is very pretty clear to everyone, but I would again like to show you and demonstrate you this with not sharing my screen. Okay. I hope this is clearly visible. Blue light is showing but forward not moving. So there is an high chance that you have kept this okay blue light is not for right now if you have coded it in my way. So blue light I have kept it for right inside. If you kept it for forward you have to make sure that you first make the forward movement and then bring the display like that is going to really work good. Okay, so let's see this again once by I should say seeing on a big screen. Okay, so I'll try this connecting for that again I need to turn on the party. Once I've turned on the party. I'll just click on try again to check my blue and here I go connecting the party. And let's start making my party move in different directions. So let me just start it from here and let me start it with go. You can see it is coming as go. Let me just stop it by showing my stop finger. And now let me just make it go left direction and let's try a right direction and stop it. And then a small right and stop it. Let's make it go forward stop it take a left turn and go back to the initial position so I'm trying to take it to initial position and I hope you might have got a fair idea how exactly it's working so. Even what you can do here is you can right now I'll just share my skin and I'll tell you a very important thing that if I am not showing anything right. So it is still going forward and it is still detecting go so instead of that what you can do is you can even train the machine with a blank okay like a blank background that if there is nothing. Then I want my quirky to be stopped at a place. Okay, and then in that way machine is going to respond you even for your blank background that if in case you're not showing anything. Still if it's not moving, then just first check whether your quirky is able to move with only go forward direction that I mean to say is just connect your quirky and check it. With a block which says go forward. With 100% speed. Okay stop and left is too similar. So you can choose any of the colors if not a problem so of course I can change this color to green yellow, even white is a good color. I think you if you have not seen how exactly white looks actually for I like the white color on quirky a pretty good. So let me just show it to you by connecting it. Okay, if you're finding any problem with movement, you have to make first check that whether it's standalone it's moving or not. That means, is it able to move just with a single or not so you can see this is a white bright light coming out of a quality. Okay, so even you can set, I can say the brightness to it and clear the skin and many more things as per your instructions. So I hope this is pretty clear and you might have definitely got an understanding how we can move ahead with the gesture control robot. So I'll stop my share here for a moment. Now, an important part here I would like to tell is I can just share this model link. Okay, so that it can be used by everyone, even if you're using it phone. So how exactly would be working on phone is of course you cannot create the model but of course you can use our model. Right. So let me just paste this. So Tina I'm just pasting this for you on chat. So this is the link which you would be shortly getting on your chats. Okay, so now the things come up that if in case you are working on phone. So in that scenario, you have to directly copy this link. Okay. And once you've copied this link you have to paste it on the load model option. And then you have to load it. Okay, simple go forward is not working how to start. So I'll tell you what you can do is you can just first check that everything is working fine. So for that, let me just try to show you a troubleshooting. Okay, so I'll just share my screen. So what I want if in case it's not working for you, don't worry. The initial step is connect your quirky with the servo. I'm sorry, not servo serial port basically. Okay, Bluetooth connected fine. Okay, you can add let me first answer the first question that if your motor is not working, you have to connect your quality with the serial port that is USB C type cable. Once you have connected with a USB C type cable, then I want you to please click on upload firmware option. Let me just show it to you. So you would get an upload firmware option. Here just above the stage part. Okay. And you can do this only on your laptop desktop. So you have to upload the firmware. Okay, connected with USB C type cable and upload the firmware and again test it is still you are facing any problem related to the motor motion. You can join our doubt session. Of course, we are going to help you and support you there. The second question is how can we add more classes. So right now, you won't be able to add more classes to the same model, because that training has been completed and stored in teachable machine. If in case you want more classes. Okay, so you have to train the model from beginning in the teachable machine. Okay, so this is a way out you can do. And of course I think the things are pretty clear still let me just share my screen. So we used this machine learning extension, using which we created the model. And we have even get shared you the model links by clicking on load model, you have to paste the link. And this is very important educators who are using the smartphone. Okay, now here you can copy and paste the modeling which is shared. Not talking about some application so your machine learning is getting very much used an object and any male classifiers, it is getting used it identifying the different shapes and the gestures. So we'll answers that whether the people are wearing mask or not. Also in the healthcare industries, where it is getting helping the doctors to detect the different diseases. Okay. Making the games just we did one of it, like making a rock paper scissor game so today but we did it a little different. And I hope this is very clear and to everyone. Now, if you are still have not joined our telegram group please join our telegram group, so that of course we can help you support you in that, and also please do not forget to fill up the feedback forms. Especially for all the educators in India, you have to use the first QR code. Okay, and for the educators who have joined us, especially through the community of meet the IO, please fill out the second form. You can just scan the QR code from here and also you can directly fill up this feedback form in your dashboard. Those educators are finding a difficulty of course we are looking to the difficulties and for other educators who have already shared us. Our technical team is working on it and definitely I can assure you that we are going to support you with the issue till the time it is not getting resolved. So you have to only keep a patience with us. Definitely all the issues will be getting resolved whatever it is whether it's a technical whether it's related to robot it's related to your PC. If you want to have a word with our target educators, please join us on the doubt sessions at 6pm and 10pm. And then of course we can support you on that. So that was basically all of which I wanted to cover and I hope you have understood correctly. There was a small question that how exactly we can add the classes. So, let me directly open the teachable machine. I show it to you you can add the classes in the teachable machine by clicking and add class option I think this was even answered by Pat Scott thanks Pat for answering this. So you can just click on add classes option here. Okay, so this is an add class option. This is basically going to help you to add more classes. I recommend you not to go more than seven or eight classes with at least 180 or 150 to 200 images in each. Otherwise it can become little difficult for your machine to handle those things. So I hope these things are pretty clear still you have anything you can of course let us know we are going to support you with that. So that was basically all from my end. So yeah, thank you for joining us on the day number five. We would be coming up with a closing ceremony where we would be talking about the, I should say the certification batches and of course how can you become an instructor and of course implement these robotics and a concept for your own students. So do not forget to join us live in coming class which is going to happen for on the third of July. So I hope you have all enjoyed the today's session definitely you have got a good understanding of the things. So that was all from my end. Thank you everyone thanks for joining in. Okay, let me just take one question how can we get the model link. Once we have already trained after closing the window. So after closing the window if you haven't taken the modeling, you have to retrain it basically because once you have closed the window, the complete things have lost. So if you want to take you can take our model link and work on it. But yeah, there can be little difficulty because the background I'm using and you are going to use would be little change right. Okay, so thank you and over to you Tina. Thank you, Ayush. It's really amazing to see Ayush with a single AI powered quirky kit, we can make so many real time projects like self driving car line for over guests and robot and so on. We all are super excited to make our own guest control robot using quirky and Victor block hope all our educators enjoy today session. Thank you all our dear educators for joining today. See you all on Sunday.


Hi Ho! Welcome to the tea of the day. Today's tea is another odd mix that I decided to go crazy with. We're a little MINTASTIC today and I've got like 15 mint leaves in here and I'm throwing a little bit of the cuckoo-looking nut from Serendipity's in New York. One with a couple of sage seeds. So I went a little bit crazy today. Wow! It's good! But this is a lesson in blending. Don't go psychotic with one of your flavors. This is just a little too minty. I can barely taste anything else in here because there's so much mint. So when you're blending, 15 leaves is fine if that's all you're going to do is mint and another by itself. Otherwise blend, keep it even. If you have a little bit more on one that's fine but don't go crazy.


This is Stink Tech, Hawaii. Community matters here. I'm Marcia Joyner, navigating the journey. Navigating the journey is dedicated to exploring the options and choices for the end of life care. And to assist people to talk about their wishes. It's time to transform our culture so we shift from not talking about dying to talking about it. It is time to share the way we want to live at the end of our lives and to communicate about what kind of care we want and what we don't want. We believe that the place for this to begin is not in the intensive care unit. So together we explore the various paths to life-ending. Together we can make these difficult conversations easier. Together we can make sure that our own wishes and those of our loved ones are expressed and respected. So if you're ready, join us. We ask. Navigate the journey. And today we have someone that is absolutely very special who has written a book at peace, choosing a good death after a long life. Dr. Sam Harrington. And he is world-renowned, a physician, retired physician. And he has written this marvelous book, so we're going to talk to Dr. Harrington. And he is in May of all places, a whole world from us. So, good morning, Sam. Good morning, Marcia. Thank you for having me. Of course it's evening in Maine in the sun it's setting, but that's the beauty of the life on the mid-coast of Maine. Where exactly, when you say mid-coast of Maine, where is that? Well, it's about halfway between Portland and Eastport. And we're on the east side of Penobscot Bay on the name of Deer Isle. And we're on the south of the town of Stonington, which is the most important lobster fishing community in the United States, because it lands more lobster than any other community in the continental United States. So, you know, of course, being in Hawaii, which is half a world away, our question is what's the weather like? It's beautifully cool and changeable. But, Sonny, the weather, there's an old saying that if you don't like the weather in Maine, wait five minutes, or you can walk around your house, it'll be cool on one side and warm on the other, and never hot. Oh, never hot, but how cold? How cold is it? Well, it gets very cold in the winter. About 50 degrees right now, which is shorts and t-shirt weather for most people who are on here. Well, and we won't go into that one. I can't imagine 50 degrees. Yes. So, tell us about you, about Sam. I have read so many great, great reviews about your book. So, tell us about you. How did you get started in this going down this path of a good, choosing a good death? I started, I grew up in Milwaukee, Wisconsin, and I practiced medicine in Washington, D.C. for 30-plus years. And growing up in Milwaukee, I observed my mother, a pediatric nurse, care for many of her neighbors who were dying at home. Not many, probably two, or three, but it was a formative experience for me. I went into medicine, went into internal medicine and gastroenterology, became a gastroenterology, performing, putting tubes into people toward the end of their lives. And I realized over the course of my 35 years of training and practice that medicine had changed from the time I went into practice to the later in 2013. And it had gone, the arc of medicine had gone from what I would describe as a healing art in 1982 to a commercial enterprise in 2013 with an explosion of technology that we were committed to using. And on a smaller scale, I also witnessed my parents age, dwindle and die, and understood my parents did not want us to do things to them at the end of their lives. They wanted us to do things for them and to disconnect between what my parents wanted and what we were doing to patients in the hospital, in the intensive care unit. And I don't mean every patient. I mean elderly patients who had the expectation that they would get out of the hospital and be well, but that was a false expectation in many cases. So that disconnect between what we do to people and what we should do for them informed me and inspired me to write this book, which I started in about 2014 while my father was dwindling and then rapidly finished about in 20 months after he passed away quietly at home. So you say that your father objected to all this extra stuff and wanted to be at home and live much longer than had he had all that extra medical surgery and what have you. Well, there is that. That's a component of it, but it's a more difficult sort of sophisticated issue. He wanted to be, he wanted to die at home, like 80 to 90 percent of elderly patients would express that preference. And he instinctively understood that at home one has to say no to hospitalizations, to say no to aggressive medical care. And so the book is written to help other patients understand that choice, to understand when it is appropriate for them to say no to hospitalization. And it started with a conversation with my father about a medical condition that he had known as an abdominal aortic aneurysm. And that's a ballooned blood vessel in the abdomen which gets to a certain point and threatens to rupture. And when it ruptures, catastrophes happen and most patients will die. And an elderly man like my father at the time would be unlikely to survive emergency surgery. And he knew this because he was a lawyer but he had had, one of his clients had actually died of an aortic aneurysm and my father worked feverishly during the last few days of that man's life to get his estate in order. My dad was a tax and estates lawyer. So he knew that if he allowed this aneurysm to rupture he would die. And yet, so I felt it was my job and that of my sister to have a repair done to prolong his life. At age 88 he was very vigorous. He was in perfect picture, perfect health, at least on the outside.  Whereas surgery would be a permanent cure but is a major operation and risk multiple complications and a prolonged hospitalization, maybe convalescence in a nursing home, all of which he was not very interested in. So the good news is he did in fact accept our reasoning that he should have the temporary procedure. He lived three years and he met twelve great-grandchildren. But at the same time, after three years, he started to dwindle and he had two more years that he did not appreciate, during which he became dependent on others. He lost his independence and he lamented that and he wished that during those years, his mantra was, I had lived too long.  When he was told that the aneurysm was re-expanding, he called his doctors and said that he would not have it repaired a second time and that he would accept his fate and he thanked them all for their care and ultimately a few months or a year after that, as he continued to dwindle, he went into hospice care and seven months after that, he passed away quietly at home. So whether one has aggressive treatment like that or not is obviously a personal choice and the results frequently are excellent. As in my father got three excellent years out of that procedure and he remained vigorous, but by putting off the inevitable, he also dwindled and became dependent on others which he did not appreciate. So there's a trade-off no matter what we do in medicine and particularly at the end of a long life. If we treat an acute illness, we are going to cause patients to suffer the consequences of chronic illness. In his case, just dwindling, which we call in medical business, geriatric failure to thrive. So that's, or otherwise, I would describe it as dying of old age, which is pretty much where he was. So I just want you to know that having read your book I changed everything. Your book is a great guide pathway to a good death. I hadn't thought about it before, even though we talk about navigating the journey for, well, we've been on the air for a long time. I had not really given it a personal thought, but I'm 80. And I had decided after reading the book that I am old enough to die. Therefore, I do not want any more dexascans, no more mammograms, no more CT scans, none of that stuff. I want to be calm. Well, I've never been calm, but to be at peace and to take every day as it comes, just enjoy every day. But I did decide I'm old enough to die, so I don't need all that stuff. I think that there's a lot of wisdom in that. I think everybody will come at it from a slightly different perspective and a different emphasis. But recognizing the limitations of medical care are very important. And after a certain age, dexascans and mammograms and colonoscopies, which would have been my expertise, certainly have much less to offer and certainly offer some potential for complications as well as potential for over-treatment. And over-treatment leads to complications. Well, that was where I was in your book, is the over-treatment. I hadn't thought about that before, but once I read it and I thought, oh, yeah, all of that over-treatment. And not only that, but it raises the bill, you know. I understand nationwide that it's for old people. The bill is $10,000 a day in the hospital. So if I'm in the hospital, all those $10,000, what does that do to your estate? What does it do to leave your children but bills? Okay. Well, those are very good points. Those are very good points that in the book I stick to the clinical aspect of things. But I will add on to what you just said, that Medicare pays about $550 billion a year or spends it on people over the age of 65. And $130 billion is spent during the last six months of life. And I'm not arguing that that's too much money or too little money. I'm simply pointing out that if one hundred of billions of dollars is spent in the last year of life, the last six months of life, then some of that must be useless or futile care because after all those patients died. And I think that's something we should think about as a marker of how much we do to patients at the end of a long life, when in fact we should be trying to help them be more comfortable and have a higher quality of life rather than committing them to these prolonged hospitalizations. And you also make the point that spending $10,000 a day would be a burden on the average family. And the concept that one does not want to burden their family is one of the characteristics of a better death to leave their family without more work and more burden. Can I go back to my father's discussion about an aortic aneurysm for a minute? Because I think that that helped me understand the conversation, even as a physician, we had had conversations about end of life decisions such as do not put me on a ventilator, do not start hemodialysis, do not give me some other aggressive form of treatment, do not do heart surgery on me. Because my father looked at the aneurysm and he turned to me and he said, why would I want to fix something that's going to carry me away the way that I want to go? And that was a vision that he shared with me that was much more meaningful than simply saying, don't put me on a ventilator, because it meant he wanted to go quickly and decisively. He did not want to linger. There's a lot to be said to be interpreted in what he said. And the vision of one's death, how we see it and how we communicate it to our families, the way to start a better, more in-depth conversation and have a better understanding of what we're really trying to say to one another. I think that and everybody's going to have a special vision of their own about how they want to die or what they foresee their illness would be one man in my book drank too much and smoked too much and he knew he was going to die of one of those illnesses. He was quite past and about his medical care until he had colon cancer and he decided that that didn't fit his vision. So he would fight that particular disease. He did. He was cured by surgery and after that surgery and the convalescence in a nursing home he went back home to his wife and on every subsequent visit to me, which was basically a brief checkup to ask if he had any new problems, he reaffirmed that he would never go back to the hospital and when he had dwindled enough to justify hospice care, we arranged that for him. So I'm trying to emphasize that a vision can really inform a patient and their family as to how to deal with end-of-life decisions or the decisions leading up to end-of-life care. I hate to interrupt you like this, but we're going to take a minute break and then when we come back, I want to talk about that, beginning the conversation and who do you talk to, how many people in the family is it a decision. Those are the kind of things I'd like to talk to Dr. Sam Harrington and we will be right back. This is Think Tech Hawaii, raising public awareness. I just walked by and I said, what's happening? Do you watch my show, Bird Zone? I sang this song to you because I think you either are cool or have the potential to be seriously cool and I want you to come watch my show where I bring in experts who talk all about easy strategies to be healthier, happier, build better relationships and make your life a success. So come sit with the cool kids at Out of the Comfort Zone on Tuesdays at 1. And we're back and we're talking to Dr. Sam Harrington, my new best friend and Dr. Harrington, I just let you know we only talk to my best friends. Okay. So in the book you talk about hope and the hard conversation. Do you remember that part of the book? You were talking? Yes. That part of the book deals with the idea that hope is used as an excuse to not have the hard conversation. We don't want people to give up hope. And I, in particular, one of my best friends an English journalist had been diagnosed with pancreatic cancer at age 62 and he lived three years. We were visiting toward the end of that third year without knowing how sick he was. But I overheard his wife in the other room talking with my wife about how they couldn't stop treatment because that would be equivalent to giving up hope.  And as a doctor I said to myself, well, this is the terminal condition. He is going to pass away in the next few months. But I didn't want to tell him that. I couldn't. He was not my patient. He was a friend. And I agreed with his wife to a certain degree that spontaneously saying, oh, this is the beginning of the end wouldn't help. But that is the kind of conversation that I would have with a patient in my office under more formal circumstances trying to help them understand that hope is important but hope with ineffective treatment is cruel and hope with futile treatment is wrong because that leads to basically treatment that is not going to be of benefit and therefore simply tortures too much or too strong a word just leads to more suffering. So we hope is something that we have to deal with and fit into the conversation in such a way that we don't delay the conversation because we are trying to promote false hope. False hope delays comfort care and false hope delays the reality of dealing with an illness and the reality of the hard conversation. Well, now, I'm skipping through the book simply because of time. You also talked about restarting the heart and how violent that is and that never occurred to me that that was... because that has bothered me since I read this. Certainly. Well, CPR is a technique that came into existence cardiopulmonary resuscitation, CPR, in the 1960s, 1970s and it involves pressing on the chest to circulate blood and blowing in the mouth to circulate air when a patient has collapsed and their heart has stopped. And CPR does have an important role in medicine but it is not nearly as effective as people believe partly because nobody knows... I mean, we don't tell patients the absolute truth about it and partly because CPR on television is surprising 75% of people who want to go survive and are perfectly normal a few minutes later which is absolutely not the case. CPR as performed breaks ribs, bruises the heart and usually doesn't work. So if my father, especially in elderly patients, so if my father had asked me what CPR would do for him, I would have told him that at age 85, the biggest study showed that 82% of patients who collapse outside of the hospital do not survive the trip to the hospital and are pronounced dead on arrival despite CPR being performed. And of the 18%, 13 of those patients will die before they're discharged. Of the 18% who survived to get to the hospital, 13% will die before they are discharged and of the five who survived to discharge, two of them will be relatively normal and three of them will have suffered significant neurologic damage among other things. So there's really about a... I would tell my father that he had a 2% chance of coming back to his apartment and when he was given that information he decided that he would do not resuscitate papers and avoid CPR. Now you mentioned... You mentioned in the book you said do you really want their last thoughts to be a violence? And that really struck me, their last vision, their last thoughts, the patient, to be this violence that is being perpetrated on them. Well, I think... I appreciate that comment. I think that... Well, no, you wrote it. I know. And I meant it because what we do to patients, futile care is very difficult to define but it is easily seen in retrospect. And if we look at the last treatment that our loved one received, be it CPR or two weeks in an intensive care unit or difficult chemotherapy, if we look at the last treatment that our loved one received and we say to ourselves, I wish we had not done that to my mother, I wish I had not done that to my father, but then we have described futile care and the point is to avoid it and foresee it and then subsequently avoid it. Well, you know, you must come back and visit with us some more because we're just getting started and it is a marvelous book and it's, for anyone that cares, it's a roadmap. Yeah, I'm calling it a roadmap to choosing a good death after a long life and my daughter is a hospice nurse and they try, or she does anyway, because she says it's an honor to be with these people at the end. They really give as much as they can so that your last memories are pleasant and they are not this violence. You are not in the hospital with lots of tubes and people you don't know. So will you promise me you will come back and visit some more and we'll talk some more about your book? Certainly. Thank you so much. It's been a real pleasure. I love you. Thank you, Marcia. I appreciate it. I love you.


This paper discusses the use of piezoelectric transduction of 011, oriented single crystal lead magnesium niobate lead titanate, PMNPT, thin film discs to excite the tangential mode and family of elliptical compound resonant modes. These modes are self-aligned and un-patterned, allowing for high performance in a variety of applications. The paper also demonstrates the ability of these modes to achieve high dithering rates and displacements, making them ideal candidates for MEMS gyroscopes and timing references. This article was authored by Ozana Turk, Kilian Shambour, Haasong Park and others.


five videos in five days. Who would have thought? Not me. Currently drinking a hazy IPA from a well-known California brewery.  I'm not putting them on blast though. Everyone is entitled to their off days from time to time. Still no symptoms and while I find them interesting I'm going to stop repeating info I find on the CDC and World Health Organization's websites. Not only is it depressing but anyone who wants to know can look those numbers up just as easily as I can. Now onto something a little happier. We left the house today. Park by our house was a little less crowded than usual but there were still plenty of people there. It was nice to see people out and about eager to resume their routines. Also we had a social distance hang on the stoop with some of our neighbors. No joke this is one of the best things that happened all week. Is that it for today? Yes. Forgive me for phoning this one in. Remember when I said everyone has their off days? I do too. Next week will be better I promise.


I Look out There's none of the motion lights went on, you know front or back So I just figured he heard an animal or something Didn't think anything of it until 7 30 this morning A monkey comes in the bedroom. I found out what he was barking at. I found out what he was barking at So She said come here hurry hurry. I said well, I can't hurry. She said well come here. We looked out I got out Get away from the Sun here If I can check it out the freaking tree lamb came down. Well, that's kind of like You know pretty much a tree really Just freakin broke right out of the tree man and so It was laying out here in the middle of damn Street See where they They cut it up. That's what happened.  So let's see if her dad's trunnin since to the front eighties trucks just f-150 but he's got a Fifth wheel in the back of it get up in this thing. Hang on a second. Don't bite I don't know if he's got turbo flows or flow masters. I never really looked at it, but It doesn't have the headers see if we can get this thing to charge a little better. I Know what's that? That's my little vlog for this morning The things that we go through I'll throw a clip in already through the clip in never mind. I Stay tuned for some more videos. I got some video of the hospital I was vlogging right after I got into the emergency room. So I did a couple short clips there Monkey took a couple clips that up for you guys today. So Keep an eye out for that. I got another life hack. I'm gonna put on for you guys and Thanks for all your support. I appreciate it guys and we'll chat soon. All right, I'll do a live stream Maybe this weekend We'll just see what's going on. So Hopefully they won't charge us for that. Shea bearer the myth man legend gone for now Don't forget to check out monkey 1000 channel.  She's been busy as we all know, but she's working on it So she did a vlog this morning. So keep keep an eye out on her channel. Thanks guys appreciate it The legend I'm gone for now. Bye. Bye guys Let's hear what cost doing Look at that. He's a jester Huh you just


usual way. Today I'm going to talk about one of the projects that we do at JetBrains Research and as I think you all figured it out it will be about Kotlin and Kotlin code anomalies. Well I'm not here to officially promote Kotlin, we have specially trained people for this, but we have to understand what they're looking at. So another show hands how many of you tried Kotlin? Whoa this is awesome. Anyway, so brief introduction. Kotlin is a general purpose study type programming language that both features object-oriented and functional features. So it was designed to interoperate this Java fully and originally was targeted JVM and Android platforms but now it can be compiled to JavaScript and to even native code via LVM. It's open source, it's relatively young but have active and continuously growing community. So we should focus on the tooling around this language. For most people Kotlin looks like a more streamlined version of Java with its extension functions, code routines, properties, null abilities, analysis and different features and when people use these cool features their code generally becomes more clean and concise but there are always people who do things differently. For some reason we have an implementation of fourth compiler in Kotlin. You know this stack-based language from the 70s. Who knows why it was written but as GitHub states it's written in Kotlin, 100% and sometimes it doesn't look like Kotlin at all. We call this code fragment anomalies in a way that their good code it's syntactically correct it may even work very well but it doesn't look like the code that other people write in this language and these code fragments are actually of great interest to language developers because well they could show some previously unnoticed compiler bugs or highlight some compiler performance issues or even could be used to get hints on how to improve the language further. So the informal text description that we've got from the Kotlin developer team was take all Kotlin code in the world and bring us some weirdly looking programs. And like in risk management it's pretty hard to plan for known unknown and it's much harder to plan for unknown unknown when you don't know what you're looking for. Before we clarify what this could mean and how to achieve this let's see what can be done in this field. If you Google code anomaly detection you well you find something some of the papers listed here the first two of them are based on static analysis the first one presents group miner tool which tries to detect anomalous objects interactions so it takes the code builds a directed acyclic graph that features construction method calls and their dependencies and tries to apply graph anomaly detection techniques to find a typical areas in this graph. The second one uses somewhat similar idea but they build the users models of objects using their sequences of method calls and they also apply graph-based anomaly detection techniques. They are very helpful if you're trying to find bugs in your programs so you these approaches basically target language users not language developers that we are intended. The next papers are based on dynamic analysis the guide use tool tries to run your program and store every value of every expression that it counters and it tries to induce some invariant rules and when these rules are violated for example some expression gets a value that well it differs a lot from all its previous values well it's considered a candidate anomaly. The last one here also runs the program but collects traces of system calls but nevertheless we have a huge data set in mind and well projects have all kinds of weird dependencies and who knows how they're supposed to be run so I think we should limit ourselves with static analysis only. Okay getting back to the task at hand what should we analyze? Originally we targeted the regular Kotlin that works on the JVM so we have the source code and the byte code produced from it. We should analyze both of them because analyzing source code gives us patterns in incorrect language use while analyzing byte code provides us with compiler issues but what's best we can combine these analysis together for example we could search for code fragments that were not anomalous in the source code representation but became anomalous in byte code representation and that's clearly an issue of some kind. Next at what level should we look at the code? Well obviously looking at operators single operators or lines of code doesn't make any sense because they don't capture structures complex enough to form an anomaly.  Classes also seem like a good choice if you want to look for anomalies in inheritance in function signatures or control flow for example to long chains of function calls whatsoever. Files could be used if you want to search for anomalies in class interaction and projects seem too large and to the main specific you can actually analyze the project without knowing what it was created for. So moving on to how. There's data science and there's anomaly detection techniques. We could use a standard task of anomaly detection on vectorized data and then when we get some anomalies we should somehow classify them according to the type and that's another task another challenging task. Speaking about code representation it's well it's really a hot research topic right now but basically all these approaches fall onto two categories. The first one are explicit features basically software metrics like the height of the ST or collision coupling metrics and some natural language processing features like bag of words or their derivatives. These features are very descriptive so you just look at the vector value that you've got and almost always you have a good hypothesis on what's wrong with this code and why it was considered anomaly which is very good but these features are hard to choose because well first of all software metrics are very well gray area because they mostly rely on opinions on what good code is and this is highly subjective. Also it's hard to choose them because when you don't know what you're looking for so you have to specifically specify precisely specify what are you looking for with these metrics. There's also path-based presentations training approach that basically allows you to traverse the tree and collect all the nodes types in this syntax tree that you encounter and then use these paths to do some research on it. An authority here is implicit features that mostly could be described as n-grams, some kind of neural networks processing, ST, hashing and different kinds of distributed representations while they obviously lack expressiveness. You get some vector of numbers and you can understand what this really means but they can capture some properties that were not obvious beforehand so they could be very useful too. Speaking about classic anomaly detection task you can do a lot actually when you know things about your data. Our data is all Kotlin code in the world and you can hardly make any assumptions about it you don't know your distribution type for nothing so we have to be really careful here when applying these techniques. The first two here are just a brief overview. The first two here are very popular outlier detection techniques. They are good because they're assigning a normal score to each of classifying objects and not just performing binary classification. Also they do not make any assumptions about distribution of the data which is good in our case. Some clustering algorithms including this here well also could be used for anomaly detection since they don't prefer that each element should be put in some cluster. Autoencoder neural networks is really fun too because basically it's a neural network that has input and output that has the same data as input and output and using its hidden layers it tries to learn the identity function basically. So the first part of the autoencoder is the decoder which tries to reduce the dimensionality of your data and well create a hidden layer of values and the second part is encoder that tries to reconstruct your data from these hidden reduced dimensions. And if you get reconstruction error, if you observe reconstruction error meaning that your predicted value differs a lot from your actual value that's normally candidate and you can just do it like that. There's also one technique here which is not unsupervised learning technique. It's semi-supervised learning meaning that you have to get some labeled data to train the classifier but it also used for outlier detection and well basically if we get some results we can pass them to this one class SVM and get more results. So there's a number of algorithms available to solve a task. So let's talk about what we have done so far. The workflow here is pretty straightforward. We get code from GitHub via GitHub API. We cloned and changed the compiler a bit to serialize all syntax trees that it builds. We ran feature calculation on these trees. We ran algorithms on these features. We look at the result and repeat. At some point we take all anomalies that we've got, go to Kotlin developer team, show them and see if we need to change a course. Today I'll talk about three experiments that we've performed last year. Well now the data is different. For example for last year the amount of Kotlin code has doubled on GitHub also but anyway we fetched all repositories that stated Kotlin as their main programming language that were not forks of any repositories and it was created before March and that left us with remove the duplicates and that left us with about four million functions and 9,030,000 files. We decided in these initial experiments we decided to stick with functions as our analysis level. Well that was a design choice. So the first experiment was very straightforward. We used explicit features and collected 51 of them describing different aspects of the code as you can see here. So we get for each of four million functions we get a vector of 51 numbers which we managed to reduce to 20 using the PCA. It's a reduction dimension reduction algorithm which led us with pretty much the most of the variants that we needed but much more friendly computation time. We ran a local outlier factor and isolation for us on it. The contamination parameter here is basically the proportions of outliers in the dataset and this is another design decision because we have no idea how many anomalies is there. So we just set it to some small number like one hundredths of a percent to keep the results observable by humans. That led us with one hundredths of a percent of four million four hundred functions which we manually observed and selected 322 anomalies that could have been of interest to language developers. Another experiment used explicit features. We used n-grams namely unibuy and trigrams on the syntax tree. This simple image shows the biograms and trigrams on the syntax tree but the idea is quite similar to these past based representations but simpler. And we use out encode a neural net to detect outliers here. It was a very simple architecture with only one hidden layer. We experimented with the compression rate of the hidden layer and got around 360 anomalies. So we brought these anomalies together and removed the duplicates and still got a lot of anomalies that we were not sure were useful or not. We looked them through and manually labeled them. We got 23 types. We created a simple web interface that allowed Kotlin team developers to rank these anomalies one by one from one to five. And 12 out of 23 types were considered to be very useful meaning rank four and five. This table shows this E1 column is experiment one, E2 is experiment two, errors rank. As you can see here most of the anomalies that were considered useful were lots of something. It's not that fancy and weird as we anticipated to see but developers still found these anomalies useful and use them in their tests. Some examples. This is an anomaly with this is a function with a when statement having about 120 case branches. I sincerely hope it was automatically generated. This one is a function with 22 generic type parameters. Actually this one is very useful because our recent finding was that compiler does a lot of work around generics and in some complex, real complex cases it could even reach exponential complexity inferring types. Code examples like this are actually very useful for tests. This is a weird test function. Actually this fourth compiler that I've showed before actually also have fallen to this strange code constructs group. And this one is good. This allowed us to file a bug to a parser. This obviously incorrect 400 plus lines of code actually break the parser with the stack over flow error. So we filed a bug. And the third experiment here that I'm going to talk about is features both static source code analysis and byte code analysis. Still we don't want to compile any code ourselves but we need byte code. What do we do? We created a tool that crawled GitHub and tried to find released Java files for projects. It downloaded the files, grabbed metadata from it and tries to search source codes for these packages. That way we managed to grab around 40,000 source files and byte code for them so we can compare the anomalies. The approach for the same using add grams and auto encoder that work and we looked for anomalies in one case and not anomalies in another. That led us with 38 conditional anomalies which one example that I will show you is these 10 lines of code function that turns into 4,500 byte code instructions. This is not actually a bug in a compiler as it turns out. It's a bug in the framework or not a bug but design feature. Someone wrote a very complex bind function that was inlined and someone wrote a code that inlined it nine times in a row so it resulted in a huge byte code. That's an interesting example because looking at the source code by itself you can have a guess that that's some kind of a weird code. This was our initial experiments. Now we are trying different algorithms for anomaly detection, different code representations. Now we have some labeled data and we are finally free to use semi-supervised learning like active learning or some other fancy stuff. A lot of our pipeline is still made by hand for example clustering and labeling of these obtained anomalies which should definitely be automated. We can look at different structural levels not functions but classes. On the other hand we can look at feature-specific anomalies. For example look for anomalies in loops or look for anomalies in function signatures and so on. We could look for anomalies in object interactions and some other ideas that were presented in the papers that I mentioned before. We can go deeper in the compiler and see which optimizations produce anomalies of any kind and so on and there's always Kotlin for Android, Kotlin native and Kotlin.js each of which is a completely different world with different anomalies. So to sum up even our first very straightforward experiments showed some useful results. Our work is open-sourced on GitHub. Also here is our research group page. If you in some way interested in our work feel free to drop us a message and thank you very much for listening. We have here the question was about whether we tried some other algorithms to instead of auto encoders. Well for implicit times implicit features we haven't. Well basically we tried to see we have a lot of stuff to try and we wanted to see if any of it works because when we got the test description just find the anomalies we weren't sure if we succeeded all. So we just tried this and this and this and this and got something working. Yes now we are doing different well more scientific approach when we try different algorithms on the same data and compare and stuff but I'm not sure I can talk about it yet. Other questions? Yeah the question is whether anything that we found so far well influence the language design decisions. I'm not sure that's the short answer. The more detailed answer is that you don't really I think you can't really see that obvious what makes people who take decisions well do this. So we have presented several anomalies that could have led to some decisions but well I'm not sure if they will be taken into account but already we had some feedback some positive feedback on some anomalies that were well they could be used I hope they will be used but I think that takes time and some process it doesn't work so bad. Thank you. Thank you.


Adipose tissue is a multipotent organ containing several types of cells, including adipose-derived stem cells, ADSCs, with the ability to differentiate into nerve cells. These cells can be used to treat nerve injuries through a variety of techniques, such as harvesting, isolating, and delivering them to the affected area. The release of neurotrophic and angiogenic factors from ADSCs has been shown to improve myelinization, regeneration, and decreased nerve fibrosis. Additionally, ADSCs are easy to obtain in large quantities and can be processed prior to implantation at the injury site. This process includes acute inflammation, hypoxia, and co-culture with Schwann cells, which promote neural differentiation of ADSCs. Finally, ADSCs and Schwann cells share many common mitogenic and differentiation factors, suggesting that they may work together to maximize the therapeutic effects of ADSCs. This article was authored by FISA Arif, Muhammad Fazla Ramon, and CML Fareed Khan. We're article.tv, links in the description below.


Alright everybody, we're here with Ben Pratt from Massachusetts. Ben, how are you? I'm fantastic! First time, first time. Crazy. Very nice. Now Ben, I've known you for a little while, I should say, in full disclosure format.  I am. I am. Just turned full time. Been doing it pretty much my whole life, but full time officially. Very nice. And obviously, TAM is a place for magicians to congregate. And this is your first TAM though. This is my first everything. First Las Vegas, first TAM. Definitely shaking like a leaf on a tree. But it's been really crazy and amazing so far. It really has. Very nice. So first time in Las Vegas for a magician, I thought it was like a pilgrimage that you guys all had to do. It's a pilgrimage and I'm missing out when you live in a tiny little town of a thousand people, you know? And with no money, it's the way it is. So would you mind showing us something? Yeah, sure. Chris, what's your favorite color? Let's go with blue. Oh, yellow. Perfect. That's good. It would have been said yellow because it's the only color I have, but that would have been a trick, by the way, if you said yellow. But fine. You can check it out if you want. Sure. That's fine. It's like super squishy. Yeah, yeah, yeah. So cool. You've sort of, like, the sawing lady in half, you've heard of that? Sure. Yeah.  That's a weird thing. That's a weird thing. Pretty much, if you take this ball, you place it inside of this hand, this one goes like this, you blow it and it actually transfers over to that hand. I know. Can you hold out your hand like this? Sure. Yeah, that's good. We're going to take the ball, we'll shove it inside of this hand like this. Take this one, squeeze it nice and tight. Don't let it get away. You've got a, got a, got a squeezy death grip there. I won't touch you already. Watch. It vanishes from the hand. Open your hand. Check it out. You got the two. Oh yeah. That totally just happened. That totally just happened. And if you ever want to get rid of one, it's really cool. Hey, I'm going to throw it. Souvenir. Okay, never mind. Yeah, you can keep that one. All right, so let me ask you, what are you looking forward to at TAM this year? Well, I've been to one thing so far. I went to the Dr. Google thing and actually that was really fascinating to me because I know a lot of people that have been through chemo and stuff like that, personal family members and stuff. And, you know, I've heard both ends of the spectrum now and it's really cool. It's really interesting to know and understand a little bit more than I did. And it's great. Like I'm learning all kinds of stuff here all the time. Very nice. And you got a deck of cards in your hand. Are we going to get treated to one more thing or are you just, is that just like a nervous habit that you shuffle with the deck of cards? Oh no, I shuffle. No, no, no. Here, we'll do something here really cool here. Very, very good. Make sure the camera can see. Yeah, yeah, well here, we'll go like this. Can you take one for me? It's not a full deck, but it's okay. Go ahead, show it to that one, look at it. All righty. Are you got it? Yep. All right, good. We're going to use a couple of the cards here. Now, do you like Snapple? Yes, I do. I love Snapple. I love Snapple like nobody's business. Actually, I love collecting little caps underneath my real facts. It's what they're called. Real facts. And actually, I have a whole ton of them in my office with the whole jar. And if I find a really good one, I'll totally post it as a status on my Facebook and I have no shame, but my favorite one to this day is real fact number 847. A Venus flytrap can consume an entire cheeseburger.  I call shenanigans on it, but I thought it was random and cool, so I made a cartridge out of it. Can you go ahead and place it back anywhere you'd like? That's perfect. Thank you. I appreciate that. I'm not a hard guy to work with. You have to use your imagination for this. I use a lot of imagination. It's like the flytrap. Let's see if the flytrap can find your cheeseburger. Okay. It's kind of hard to do, but I'll let it explain for itself. Should I step back? Am I in danger? No, I think you're okay. It caught one card in between. What was your cheeseburger? I believe it was the King of Spades. Sure enough, there it is. The King of Spades, just like that. There it is. That's all I got. Ben, thanks for joining us. Have a great time of your first time. Hope to see you back next year. Let's do the regular. Thank you.