We live in a world where our daily routine involves multiple contact points with the digital world. We have computers assisting us with communication, travel, entertainment, and whatnot. The digital online products (apps, websites, software, and so on) that we use seamlessly all the time help us avoid mundane and repetitive tasks. These software have been developed using computer programming languages (like C, C++, Python, Java, and so on) by programmers who have explicitly programmed each instruction to enable these software to perform defined tasks. A typical interaction between a compute device (computer, phone, and so on) and an explicitly programmed software application with inputs and defined outputs is depicted in the following diagram:
Though the current paradigm has been helping us develop amazingly complex software/systems to address tasks from different domains and aspects in a pretty efficient way, they require somebody to define and code explicit rules for such programs to work. These are the tasks that are easy for a computer to solve but difficult or time consuming for humans. For instance, performing complex calculations, storing massive amounts of data, searching through huge databases, and so on are tasks that can be performed efficiently by a computer once the rules are defined.
Yet, there is another class of problems that can be solved intuitively by humans but are difficult to program. Problems like object identification, playing games, and so on are natural to us yet difficult to define with a set of rules. Alan Turing, in his landmark paper Computing Machinery and Intelligence (https://www.csee.umbc.edu/courses/471/papers/turing.pdf), which introduced the Turing test, discussed general purpose computers and whether they could be capable of such tasks.
This new paradigm, which embodies the thoughts about general purpose computing, is what gave rise to AI in a broader sense. This new paradigm, better termed as an ML paradigm, is one where computers or machines learn from experience (analogous to human learning) to solve tasks rather than being explicitly programmed to do so.
AI is thus an encompassing field of research, with ML and deep learning being specific subfields of study within it. AI is a general field that includes other subfields as well, which may or may not involve learning (for instance, see symbolic AI). In this book we will concentrate our time and efforts upon ML and deep learning only. The scope of artificial intelligence, machine learning, and deep learning can be visualized as follows:
