Living organisms such as animals and humans have some sort of intelligence that helps us in making a particular decision to perform something. Our brains respond to stimuli, albeit through sound, touch, smell, or vision, and then convert that data into information that we can process. On the other hand, computers are just electronic devices that can accept binary data, perform logical and mathematical operations at high speed, and output the results. So, AI is essentially the subject of making computers appear to be able to think and decide like living organisms to perform specific operations.
AI and its many related studies are dense and vast, but it is really important to understand the basics of AI being used in different domains before digging deeper into the subject. 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 the following research areas in AI applications that have advanced tremendously over the last decade. Things that used to be considered science fiction are quickly becoming science fact, such as autonomous robots. You need not look very far to find a great example of AI advances—your smart phone most likely has a digital assistant feature that relies on some new AI-related technology. Here are some of the research fields driving AI:
Computer vision: It 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): It is the ability that allows a machine to read and understand the languages as we normally write and speak. The problem is that the languages we use today are difficult for machines to understand. There are many different ways to say the same thing, and the same sentence can have different meanings according to the context. NLP is an important 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 data sets available on the Web that can help researchers to do the automatic analysis of a language.
Common sense reasoning: This is a technique that our brains can easily use to draw answers even from the domains we don't fully understand. Common sense knowledge is a usual and common way for us to attempt certain questions since our brains can mix and interplay between the context, background knowledge, and language proficiency. But making machines to apply such knowledge is very complex and still a major challenge for researchers.
Machine learning: This may sound like something straight out of a science fiction movie, and the reality is not too far off. Computer programs generally consist of a static set of instructions, which take input and provide output. Machine learning focuses on the science of writing algorithms and programs that can learn from the data processed by said program.