Comparing AI, ML, and DL
AI is a broad field that studies different ways to create systems and machines that will solve problems by simulating human intelligence. There are different levels of sophistication to create these programs and machines, which go from simple, rule-based engines to complex, self-learning systems. AI covers, but is not limited to, the following sub-areas:
- Natural language processing
- Rule-based systems
ML is a sub-area of AI that aims to create systems and machines that are able to learn from experience, without being explicitly programmed. As the name suggests, the system is able to observe its running environment, learn, and adapt itself without human intervention. Algorithms behind ML systems usually extract and improve knowledge from the data that is available to them, as well as conditions (such as hyperparameters), and feed back after trying different approaches to solve a particular problem:
There are different types of ML algorithms; for instance, we can list decision tree-based, probabilistic-based, and neural networks. Each of these classes might have dozens of specific algorithms. Most of them will be covered in later sections of this book.
As you might have noticed in Figure 1.1, we can be even more specific and break the ML field down into another very important topic for the Machine Learning Specialty exam: DL.
DL is a subset of ML that aims to propose algorithms that connect multiple layers to solve a particular problem. The knowledge is then passed through layer by layer until the optimal solution is found. The most common type of DL algorithm is deep neural networks.
At the time of writing this book, DL is a very hot topic in the field of ML. Most of the current state-of-the-art algorithms for machine translation, image captioning, and computer vision were proposed in the past few years and are a part of DL.