Building Intelligence

Summary

In this chapter, the author's goal is to delve into the key differences among various machine learning (ML) algorithms and to highlight the scenarios and situations in which each can be most effectively applied. By understanding these distinctions, we can better navigate the complexities of artificial intelligence (AI), ensuring that our investments yield meaningful, reliable outcomes and drive real value for our organizations. Classical AI, also known as symbolic AI or good old‐fashioned AI, refers to an approach to AI that was dominant from the 1950s to the 1980s that was characterized by the use of symbolic representations of problems and logical reasoning to solve them. These models are rule‐based and rely on explicit programming of knowledge and rules by human experts. ML is a subset of AI involving the development of algorithms that enable computers to learn from and make predictions/decisions based on data.