 So thank you, Yulin, and thank you for everyone coming. So I'm Anglia Yang. Currently, I'm a data scientist and also a student which involved in research with computer vision and artificial intelligence research work. So today I'm going to give a brief introduction about artificial intelligence from my perspective and I will get involved a little bit about the hot topic, deep learning, okay? So I know the AI now is a very hot topic, both commercially and technically. So but many people get an illusion, just the AI is like this, almost human-like with self-consciousness and although can make a lot of impossible thing. And but actually currently to be honest, the artificial intelligence is at a very first stage and basically it's just a problem solver. It's sort of cold and a bunch of logic operations and algorithm embedding, but it does make powerful performance in multiple tasks. That's why we chase for the further investigation in the future and it also gain a lot of concentration. So according to me, I think artificial intelligence is just research work and investigation to enable our computers to function in an intelligent manner like our theory from Apple and also the other like chatbot and also the basic one is it's just a models and yes, problem solver which can in some way can imitate human brain cognitive processing mechanism. Later I will mention that. And also it's cause currently the press and media they were focused on like deep learning, machine learning, data mining, thought of things cause they are really hot topic, but actually for AI it's a large domain to investigate. It includes our knowledge representation. The typical ones is the Watson developed by IBM. I believe everyone know about that and also about some searching method like genetic algorithm sort of things. They are also about like robot playing soccer ball or just solve puzzles and also about learning about this is mainstream for studying and application about machine learning and also kind of reasoning. So it's a large domain not just about learning. So it's a comprehensive task of artificial intelligence and also this is machine learning the most concentrated one and actually machine learning is study of computer algorithms. So kind of programming and kind of mathematics and which enable our computers to improve automatically based on data not based on the human designed rules. So it can do some automation work from our data source directly. So this could be concentration and also the focus of current AI research and also application usage cause we have so many unlabeled data source on the website in our real life and that is a lot of potential and opportunities there. So for the next I'm gonna talking about learning and learning I think maybe you've heard about the support vector machine and also others like Bayesian network those thought of very classic and they're useful classifier but currently we most mention about the neural network cause it's the most human like methods and algorithm which give the most promising powerful performance on multiple tasks. So I'm gonna involving some knowledge basic about neural networks. So for neural networks it's just inspired by the human mechanism about our cognitive system. Our brain was composed by multiple or millions of neurons and the neurons can be activated by the stimuli of the input signals. Then it will give the path to the signal and to pass the information from one neuron to the next. So it will construct the whole processing procedure to make us realized an object or some kind of things that is currently happening. So this is initially was inspired by the human but so later we developed in like 20th century about perceptron. So perceptron is very much more like the single one neuron we give inputs then we just do a simply wait to sum up then we give a hard limit for activated or inactivated then we give the output. So for this we can do the simply give the prediction of this is yes or no just like a binary classification but we all know this is very hard to handle with the difficult tasks like when it face with images or our speech or voices data it's very hard to just use a linear sum up to handle such kind of complicated work. Then we in the 2012 the Yosha Benjo and also Yanlacun they build up the so-called deep learning. So deep learning basically is also neural networks but with much more layers with so-called layers and also the other image or speech processing units then we construct complicated enough for handling and understanding this kind of like images or video or speech and now on the data set of face the performance of deep learning can surpass the human level for recognizer face and also we can learn from the most comprehensive application is AlphaGo and this year is I think the master but the original one is AlphaGo. So AlphaGo actually is not just about deep learning though deep learning play a very important part in there but AlphaGo actually is a very typical application with comprehensive artificial intelligence techniques because basically it will recognize the whole play situation with its camera so it's a vision-based detection and recognition we need to capture like our image we need to capture like our human eyes saw the picture then understand what's going on with the current stage then we use the further reasoning or we call it competitive strategy to choose which is the best move for the next step or even for the rest step and it will also involve searching because for Go it cannot be a rule-based because depends on the other competitor it will generate like different choice for your next move next place for play this chess so it will use searching to search for optimal solution then it will give your move and there's a whole strategy because the goal game is composed of multiple optimal choices so this is basically the AlphaGo but also it based on the deep learning work to help us recognize the picture the scenery and also help us to choose better performance with multicolor trees and these are the state of art AI research results okay and this is the basic notes for your recommendation and yes next welcome Aisha and if you've got any problem about the deep learning and the computer vision related work you can contact with me thank you