Parents want teachers to explain how they instruct children. They become annoyed when the teachers are silent or surly. Parents counter with explicit, common sense questions: how do teachers arouse interest, design curricula, reinforce discipline, assign grades, designate textbooks, and select technology? This book examines the parents' questions, the answers they elicited, the allies they attracted, and the improvements they initiated.

Parents have questions for school administrators. They want to know how they hire teachers, erect facilities, select learning materials, protect students, allocate budgets, use data, make forecasts, measure progress, and compete with for-profit schools. This book examines the questions they pose, the answers they elicit, the allies they attract, the adversaries they arouse, and the improvements they prod.

Parents had questions about the tests their children took at school. They considered them to be common sense questions. They posed them to the businesspeople, publishers, and politicians who championed tests. They also posed them to the school administrators, teachers, and union leaders who criticized them. This book examines the questions the parents posed, the answers they elicited, and the changes they prodded.

Parents asked educators about their children’s learning. Frustrated when they were ignored, they asked politicians to put pressure on the educators. They were then surprised when the politicians provided personal advice about the optimal way to nurture learning. They were even more surprised when the politicans prescribed changes to instruction, curriculum, textbooks, technology, school safety, teacher retention, student behavior, school funding, and even the menus for school cafeterias. More frustrated than ever, they intensified their barrage of common sense questions.

What artificial intelligence can tell us about the mind and intelligent behavior. 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. Levesque considers the role of language in learning. He argues that a computer program that passes the famous Turing Test could be a mindless zombie, and he proposes another way to test for intelligence—the Winograd Schema Test, developed by Levesque and his colleagues. “If our goal is to understand intelligent behavior, we had better understand the difference between making it and faking it,” he observes. He identifies a possible mechanism behind common sense and the capacity to call on background knowledge: the ability to represent objects of thought symbolically. As AI migrates more and more into everyday life, we should worry if systems without common sense are making decisions where common sense is needed.