"The authors of Machines Like Us explore what it would take to endow computers with the kind of common sense that humans depend on every day--critically needed for AI systems to be successful in the world and to become trustworthy"-- Provided by publisher
What can artificial intelligence teach us about the mind? If AI's underlying concept is that thinking is a computational process, then how can computation illuminate thinking? It's a timely question. AI is all the rage, and the buzziest AI buzz surrounds adaptive machine learning: computer systems that learn intelligent behavior from massive amounts of data. This is what powers a driverless car, for example. In this book, Hector Levesque shifts the conversation to good old fashioned artificial intelligence, which is based not on heaps of data but on understanding commonsense intelligence. This kind of artificial intelligence is equipped to handle situations that depart from previous patterns - as we do in real life, when, for example, we encounter a washed-out bridge or when the barista informs us there's no more soy milk.
At the core of symbolic artificial intelligence, known as GOFAI, lies the concept of a knowledge base. This system emphasizes the importance of structured information and reasoning capabilities, allowing for complex problem-solving and decision-making. The book explores the architecture, methodologies, and applications of knowledge-based systems, illustrating their role in advancing AI. It also delves into challenges faced in the development and implementation of these systems, providing insights into their potential and future directions in the field of artificial intelligence.