Living organisms, such as animals and humans, naturally have some level of intelligence that allows them to be able to take meaningful decisions during their daily lives. On the other hand, computers are just electronic devices that can accept data, perform logical and mathematical operations at high speeds, and output the results. AI is essentially the subject of making computers able to think and decide like living organisms to perform specific operations.
As you can imagine, this is a huge subject. There's no way that such a small book will be able to cover everything related to AI. However, it is essential to understand how to use the basics of AI in different domains. AI is just a general term; its implementations and applications are different for different purposes, solving different sets of problems.
Before we move on to game-specific techniques, we'll take a look at some of the research areas in AI applications:
- Computer vision: This is the ability to take visual input from sources such as videos and cameras, and analyze them to do particular operations such as facial recognition, object recognition, and optical character recognition.
- Natural Language Processing (NLP): This is the ability that allows a machine to read and understand human languages, that is, as we usually write and speak. The problem is that human languages are difficult for machines to understand. Language ambiguity is the main problem: there are many different ways to say the same thing, and the same sentence can have different meanings according to the context. NLP is a significant step for machines since they need to understand the languages and expressions we use before they can process them and respond accordingly. Fortunately, there's an enormous amount of datasets available on the web that can help researchers to automate the analysis of a language.
- Common sense reasoning: This is a technique that our brains can efficiently use to draw answers, even from the domains we don't fully understand. Common sense knowledge is a standard way for us to attempt several questions since our brains can mix and interplay between the context, background knowledge, and language proficiency. Unfortunately, making machines to apply such knowledge is very complicated, and still a significant challenge for researchers.