Nemanja Rodojkovic introduces a four-hour course on AI fundamentals in Python. He traces AI from early computing to today, defines intelligence as reasoning and learning, and explains why current AI systems are narrow specialists like AlphaGo, not general intelligences. Machine learning, the math of learning from data, powers these systems.
Chapters
Hello and welcome to the course Foundations of AI in Python. My name is Nemanja Rodojkovic. I am a senior data scientist with a broad experience in developing AI solutions within a range of industries and multinational companies, primarily those belonging to the Fortune 500 list. I will be your course instructor and help you understand the foundations of artificial intelligence. Lets go. On planet Earth in this moment, you probably hear the word AI at least three times a day. AI stands for artificial intelligence, which is a field of research that goes back to World War II and the first computers. It lay somewhat dormant for decades and literally exploded in recent years. How come? Well, technical advances have only recently made it possible for almost anybody to crunch massive data sets using powerful algorithms in almost no time and at a minimum cost. Today, AI infused systems are beating humans in most complex games, driving cars on their own and creating works of art. But how does it all actually take under the hood? That's what you will learn in the next four hours. We don't promise to immediately turn you into an AI miracle maker, but you will get a solid understanding of the foundations on top of which even the most complex AI systems are built. Let's start with the core concepts. Let's first define intelligence as such. One of the widely accepted formulations defines intelligence as the ability to reason, plan, solve problems, think abstractly, comprehend complex ideas, learn quickly and learn from experience. So AI systems are systems that possess these capabilities. Are we there yet? Not really. So if you're scared of machines taking over the world, don't be. It's not likely to happen in the foreseeable future. But we are making great progress. We usually talk about two major subtypes of AI. The AI that mimics human like intelligence is what is commonly called artificial and general intelligence, or strong AI. And as we have mentioned, we're not there yet. What is being developed and implemented by 99% of AI practitioners in the industry and academia today is a subset of AI called artificial narrow intelligence, sometimes also called weak AI. Why narrow? Well, because these solutions are designed to solve only one specific problem without any capacity to be translated to another one without rework. Alphaco, Google's algorithm that beats the best human players in Go. A game more complex than chess couldn't even start a game of tic tac toe. You can call it a one trick horse, but it's a very powerful trick nonetheless. Finally, when we talk about narrow AI, 99% of time we talk about the good old machine learning, which we'll explain further in a moment. So what is machine learning? Simply put, it is the process of applying computer algorithms to capture the behavior and behavioral patterns of systems and processes based on the input and output data collected from these systems. Under the hood, it's just good old mathematics. But AI and machine learning sound so much cooler. Finally, why is this course taught in Python? The reason is simple. Python is extremely simple to learn, flexible, versatile, and has the fastest growing community and ecosystem of libraries for machine learning. Now.
