You might have heard terminology from AI, such as machine learning, or data science, or neural networks, or deep learning. What do these terms mean? In this video, you'll see what is this terminology of the most important concepts of AI, so that you will speak with others about it and start thinking how these things could apply in your business. Let's get started. Let's say you have a housing data set like this, with the size of the house, number of bedrooms, and number of bathrooms. What are the houses newly renovated, as well as the price? If you want to build a mobile app to help people price houses, so this would be the input A, and this would be the output B, then this would be a machine learning system. In particular, it would be one of those machine learning systems that learns input to output, or A to B mappings. Machine learning often results in a running AI system. It's a piece of software that, any time of day, any time of night, can automatically input A, these properties of a house, and outputs B. If you have an AI system running, serving dozens, or hundreds, or thousands, or millions of users, that's usually a machine learning system. In contrast, here's something else you might want to do, which is to have a team analyze your data set in order to gain insights. A team might come up with a conclusion like, hey, did you know, if you have two houses of a similar size, of a similar square footage, if the house is three bedrooms, then it costs a lot more than a house of two bedrooms, even if the square footage is the same. Or, did you know that newly renovated homes have a 15% premium, and this could help you make decisions such as, given a similar square footage, do you want to build a two-bedroom or a three-bedroom size in order to maximize value? Or is it worth an investment to renovate a home in the hope that the renovation increases the price you can sell a house for? So these are the examples of data science projects, where the output of a data science project is a set of insights that can help you make business decisions, such as what type of house to build, or whether to invest in renovation. The boundaries between these two terms, machine learning and data science, are actually a little bit fuzzy. And these terms are not used consistently, even in industry today, but what I'm giving here is maybe the most commonly used definitions of these terms, but you will not find universal adherence to these definitions. To formalize these two notions a bit more, machine learning is the field of study that gives computers the ability to learn without being explicitly programmed. This is a definition by Arthur Samuel many decades ago. Arthur Samuel was one of the pioneers of machine learning, who was famous for building a checkers playing program that could play checkers even better than he himself, the inventor, could play the game. So a machine learning project will often result in a piece of software that runs, that outputs B given A. In contrast, data science is the science of extracting knowledge and insights from data. And so the output of a data science project is often a slide deck, the PowerPoint presentation that summarizes conclusions for executives to take business actions, or that summarizes conclusions for a product team to decide how to improve a website. Let me give an example of machine learning versus data science in the online advertising industry. Today, the large ad platforms all have a piece of AI that quickly tells them what's the ad you are most likely to click on. So that's a machine learning system, and this turns out to be incredibly lucrative. AI systems that inputs information about you and about the ad and outputs whether you click on this or not. These systems are running 24-7, and these are machine learning systems that drive ad revenue for these companies. So it's a piece of software that runs. In contrast, I've also done data science projects in the online advertising industry. If analyzing data tells you, for example, that the travel industry is not buying a lot of ads, but if you send more salespeople to sell ads to travel companies, you could convince them to use more advertising. Then that would be an example of a data science project and a data science conclusion that results in executives deciding to ask the sales team to spend more time reaching out to the travel industry. So even in one company, you may have different machine learning and data science projects, both of which can be incredibly valuable. You have also heard of deep learning. So what is deep learning? Let's say you want to predict housing prices. You want to price houses. So you have an input that tells you the size of the house, the number of bedrooms and bathrooms, and whether it's newly renovated. One of the most effective ways to price houses, given this input A, would be to feed it to this thing here in order to have it output the price. This big thing in the middle is called a neural network, and sometimes you also call it an artificial neural network. And that's to distinguish it from the neural network that is in your brain. So the human brain is made up of neurons. And so when we say artificial neural network, that's just to emphasize that this is not the biological brain, but instead it's a piece of software. And what a neural network does, what an artificial neural network does, is take this input A, which is all of these four things, and then output B, which is the estimated price of the house. Now, in a later optional video this week, I'll show you more what this artificial neural network really is. But all of human cognition is made up of neurons in your brain passing electrical impulses, passing little messages to each other. And when we draw a picture of an artificial neural network, there's a very loose analogy to the brain, and these little circles are called artificial neurons, or just neurons for short. It also passes neurons to each other. And this big artificial neural network is just a big mathematical equation that tells it, given the inputs A, how do you compute the price B? In case it seems like there are a lot of details here, don't worry about it. We'll talk more about these details later. But the key takeaways are that a neural network is a very effective technique for learning A to B, or input to output mappings. And today, the terms neural network and deep learning are used almost interchangeably. They mean essentially the same thing. Many decades ago, this type of software was called a neural network, but in recent years we found that deep learning was just a much better sounding brand. And so that, for better or worse, is the term that's been taken off recently. So what do neural networks, or artificial neural networks, have to do with the brain? It turns out almost nothing. Neural networks were originally inspired by the brain, but the details of how they work are almost completely unrelated to how biological brains work. So I choose very cautious today about making any analogies between artificial neural networks and the biological brain, even though there was some loose inspiration there. So AI has many different tools. In this video, you learned about what are machine learning and data science, and also what is deep learning and what's a neural network. You might also hear in the media other buzzwords like generative AI, unsupervised learning, reinforcement learning, graphical models, planning, knowledge graphs, and so on. You don't need to know what all of these other terms mean, but these are just other tools like getting AI systems to make computers act intelligently. I'll try to give you a sense of what some of these terms mean in later videos as well, but the most important tools and terms I hope you remember from this are machine learning and data science, as well as deep learning and neural networks, which are a very powerful way to do machine learning and sometimes data science. If we were to draw a Venn diagram showing how all these concepts fit together, this is what it might look like. AI is this huge set of tools for making computers behave intelligently. Of AI, the biggest subset is probably tools from machine learning, but AI does have other tools than machine learning, such as some of these buzzwords listed at the bottom. And of machine learning, the part of machine learning that's most important these days is neural networks or deep learning, which is a very powerful set of tools for carrying out supervised learning or A to B mappings, as well as some other things. But there are also other machine learning tools that are not just deep learning tools. So how does data science fit into this picture? There is inconsistency in how the terminology is used. Some people will tell you data science is a subset of AI. Some people will tell you AI is a subset of data science. It depends a bit on who you ask, but I would say that data science is maybe a cross-cutting subset of all of these tools that uses many tools from AI machine learning and deep learning, but has some other separate tools as well that solves a very set of important problems in driving business insights. In this video, you saw what is machine learning, what is data science, and what is deep learning and neural networks. I hope this gives you a sense of the most common and important terminology used in AI and you can start thinking about how these things might apply to your company. Now, what does it mean for a company to be good at AI? Let's talk about that in the next video.