Now for our final activity, we're going to go through the next several slides and you will have to vote on whether or not each scenario is an example of supervised or unsupervised learning. And then be prepared to explain what you think the supervisor or unsupervised. Remember what the difference is. Unsupervised is where you just try things out and kind of come up with some rules as you're going. Unsupervised is where you get a mentor or coach that kind of teaches you the rules or helps you figure out what the rules are. So we've got those two types and I'm going to give you six scenarios. On your assignment document, we're going to go to the next slide types of learning. And first I want you to think about some examples of supervised learning in your life. So maybe it was like riding a bike. Maybe it was cooking. Think about things that you learned to do because somebody helped you learn it. Then we're going to go through these scenarios and you're going to put down supervised or unsupervised. And then kind of think about why you made that choice. Scenario number one. You see someone playing your favorite song on a piano. After they leave, you sit at the piano and press random notes on a piano until you start to recreate the melody from the song. So do you think this is supervised or unsupervised? Write down your answer. Scenario number two. Supervised or unsupervised. You go to a swim coach who helps you learn how to swim in a pool. You start practicing basic strokes but then learn more and speak makes. So do you think this is supervised or unsupervised training? Scenario number three. The elders in your family show you how to cook meals from recipes passed down from generation to generation. You watch them make the meals first before practicing yourself. So is this supervised learning or unsupervised learning? Write down your answer. Scenario number four. A toddler watches all of the adults in their life walk around the house. Eventually, after a lot of falling and stumbling, they learn to find their balance and start walking. Supervised learning or unsupervised learning. Write down your answer. Scenario number five. You know that there are spices in your kitchen but you're not sure what to do with them. You add a little bit of different spices and taste as you go until you settle on a taste that you like. Scenario number six. You study different vocabulary words for science by creating flashcards. On one side is the word and on the other side is the definition. As you study, you gradually start to memorize the terms. Let's be supervised or unsupervised learning. We've now seen two types of machine learning, supervised and unsupervised. In this unit, we'll mostly be looking at supervised learning with training data. But the human brain is pretty powerful. Do you think AI bot will ever learn enough to rival our own intelligence? We haven't reached that point yet, but it might be possible in the future with something called neural networks. Hi, I'm Dion. I'm one of the creators of Forthought AI. At Forthought, we build artificially intelligent tools that people can use at work to be more productive. To make a learning machine, early computer scientists look for clues by studying other things that are good at learning. And it turns out that nothing is better at learning than the human brain. Our brains are made up of special cells called neurons. A neuron has two ends. Input signals entering on one end, they're combined together inside the neuron and leave the other end as a single outlet. All of the billions of neurons in your brain are connected to each other in what's called a biological neural network. It's how your brain processes information and recognizes patterns. Early AI scientists decided to mimic human neurons by making their own simple artificial neurons you saw through. Nothing fancy, just multiple signals going in as inputs, passing through the neuron and getting combined and processed by some simple math into a new signal going out. And it's a good start. But one neuron alone doesn't do much. The full potential of this idea is only unleashed when the artificial neurons are connected together to make an artificial neural network. This is what allows computers to recognize images, drive cars, and make some truly weird art. To see how a neuron works, let's build a movie recommendation system that uses critics reviews to guess how much you'll like a movie. Then we'll use your feedback to make the system better. Here are three movie critics, Ali, Bowie, and Casey. Each one rates a movie anywhere from one to five stars. Now let's build a single artificial neuron. Each of the critics ratings enters on this side as input. Some calculations are done in here and we get a single output. In this case, it's a movie rating. Here's the first movie. Ali gives it one star, Bowie gives it five, and Casey gives it a four star review. At first, the critics' opinions are carry the same weight and are counted equally. The inputs enter, but there's some basic math and outcomes of recommendation. Now, let's watch the movie so we can give it our own rating. Shhh, the silence has crashed and crashed. Uh, okay, that was weird. Let's pretend you really liked it and gave it a five star rating. The rating you just provided is now used to train the neuron. Based on your rating, the weight of each critics opinion is recalculated. Your rating is closer to that of Bowie and Casey, so their opinions get more weight. You didn't agree with Ali's single star review, so that weight goes down. Now let's train the neuron again. Here's another movie. And here are new ratings from our critics. And this time, the neuron will give more weight to these two ratings when calculating its recommendation. And here's the output. Now let's give it a watch. Well, at least I was short. Let's give it a rating. Our new rating adjusts the weights again. This process repeats over and over until we've trained a system to know our preferences and recommend movies that will probably enjoy. In this example, there's just one neuron. That's far more simplistic than most systems. Powerful neural networks have millions of neurons arranged in layers. There are input layers, any number of hidden layers, and output layers. The output of one layer of neurons becomes the input to the next layer and so on. Many real world media, music, and shopping recommendation systems work like this using ratings from millions of everyday users. In those neural networks, everyone has a hand in modifying the weights. Neural networks have so many other uses. They're working behind the scenes on big problems like throwing healthier food, predicting floods and forest fires, aiding wildlife conservation, and even detecting and curing disease. At the end of the video, Dion mentioned several examples of how AI is being used to solve problems in society. If you could pick a big problem for AI to help solve, what problem would you choose? Both of you are a Simon document and we're at the wrap up now and you have a place to answer this question. Also, we're going to revisit our question of the day how is machine learning similar to human learning? So, at the assignment wrap up, you have it. I'm going to give you some time to answer these two questions. When you're finished, you are ready to turn it in.