Well, in our simple model of a very simple task here, let's see how we can use AI and why we should use AI. When we discussed about machine learning, we talked about the situations that women are good to deal with different kinds of data. And have problem when we have to deal with big data. Traditional programming, okay, it's great with big data. We have big computers running big programs to deal with big data, no problem. The problem is that with traditional programming is to deal with different kinds of data. Text, images, sounds, movies. And AI is great to help us in these kind of situations. Let's see one example. If you have a situation that you are the one that will take the decision, but you have to deal with lots of data, lots of input data in different kinds, how we can transform this decision, how we can work to work together with AI and human. Yes, how we can augment the human capabilities using with AI, okay. And for that, we use a pattern in artificial intelligence that we call recognition to help you to understand that data, to get some knowledge from the data, and then you took the decision. But you use the AI to help you to recognize what is on the data. Let me give you an example. For example, if the data is image, you will use computer vision, for example, with the learning to get elements, objects on that image. If it is a picture, for example, to classify that image. Yes, and then you provide an answer and a probability, and maybe you as a human that has to make the decision, you can only deal with the images where the probability is low, okay. And all the other. It's okay because the recognition can do this for you. And you can combine, and now maybe now you can deal with lots of big data, not only a reduced number of information. This could be, for example, the data could be text, you have to read lots of different contacts, news, articles. You can use natural language processing with the learning to recognize, to understand that data, to recognize the speech, to recognize sentiment of that. And work together you as a human with the recognition, the AI with this pattern of recognition, to make the decision, and then continue the process to actions, outcomes and the feedback. Another example, prediction. You have to define, for example, what is the sales price, as we have seen in supervised machine learning. That is the price that I have to put a condo when I am announcing to sales. Yes. And to define the price, maybe you need lots of information. And not only the number of rooms or the borrower where the data is. You can use prediction models to help you to define which number you have to use, for example, to announce a new condo, the price of the new condo. And this is a regression, you will use regression to do that. You can also build a prediction model understanding, for example, text from Twitter, to understand what is the mood of the market regarding your brand and any other different kinds of predictions that you can use. Again, you are augmenting humans, giving AI the capacity to predict and knew the one that judge and make the decision. Working together, human and AI. Another example here is discovery. And what is discovery? It's trying to find some hidden, some patterns that you usually cannot see directly in the data, because we are talking about big data, lots of different data. And when you have this need to understand, to find some patterns, to segment your data, we will use this kind of discovery. Maybe the technique in this case is not supervised machine learning, maybe it's unsupervised machine learning. It doesn't matter. But if you have this problem that you have a bunch of data and you want to find some new hidden structure, you want to find some new patterns on that data, because you want to maybe you want to launch a new advertising campaign. But what kind of segmentation will you use? The traditional demographic segmentations, or you want to find new kinds of segmentations. And then you can combine discovery patterns here in artificial intelligence to build and to discover this pattern. And you make the decision, which are the clients that will receive your advertised, your ad to really try to push them to buy your products. Yes. And after making the decision, you're going through the actions and get the outcome. Those are three examples, first three examples of the use of patterns to apply on tasks, on products that you have in your workflow, recognition, prediction and discovery.