This is the fifth edition of Making Sense of Mass Education. It offers a nuanced discussion of emerging problems in an ever-changing world. Changes to the field of education have not slowed since the publication of the fourth edition. Of course, this edition offers an updated contemporary assessment of all the topics addressed in the book, but it also provides an extensive discussion of the important and rapidly changing areas that impact mass education and the professional lives of teachers.

Ever since we began to build software systems that interacted with humans, there have ethical concerns about the ways in which we interact with them. In [830], for example, Weizenbaum observes of the world’s first chatterbot that “ELIZA shows, if nothing else, how easy it is to create and maintain the illusion of understanding, hence perhaps of judgment deserving of credibility. A certain danger lurks there.”2 Fast forward more than 60 years, and this observation that a “certain danger lurks there” has emerged as a range of different concerns about the ways in which software (and hardware) systems are developed and deployed, and the range of data that modern data-driven systems rely upon. The space of machine ethics is vast, and a large number of texts, papers, and policy documents now exist on the subject.

Sensing is a key requirement for any but the simplest mobile behavior. In order for Robot to be able to warn the crew of Lost in Space that there is danger ahead, it must be able to sense and reason about its sensor responses. Sensing is a critical component of the fundamental tasks of pose estimation – determining where the robot is in its environment; pose maintenance – maintaining an ongoing estimate of the robot’s pose; and map construction – building a representation of the robot’s environment.

Robotic systems, and in particular mobile robotic systems, are the embodiment of a set of complex computational processes, mechanical systems, sensors, user interface, and communications infrastructure. The problems inherent in integrating these components into a working robot can be very challenging. Overall system control requires an approach that can properly handle the complexity of the system goals while dealing with poorly defined tasks and the existence of unplanned and unexpected events. This task is complicated by the non-standard nature of much robotic equipment. Often the hardware seems to have been built following a philosophy of “ease of design” rather that with an eye toward assisting with later system integration.

The ability to navigate purposefully through its environment is fundamental to most animals and to every intelligent organism. In this book we examine the computational issues specific to the creation of machines that move intelligently in their environment. From the earliest modern speculation regarding the creation of autonomous robots, it was recognized that regardless of the mechanisms used to move the robot around or the methods used to sense the environment, the computational principles that govern the robot are of paramount importance. As Powell and Donovan discovered in Isaac Asimov’s story “Runaround,” subtle definitions within the programs that control a robot can lead to significant changes in the robot’s overall behavior or action. Moreover, interactions among multiple complex components can lead to large-scale emergent behaviors that may be hard to predict.

Later chapters consider the algorithms and representations that make these capabilities possible, while this chapter concentrates on the underlying hardware, with special emphasis on locomotion for wheeled robots.

For autonomous robots it may seem like we can avoid needing the ability to make maps automatically. That is, it is sometimes assumed that a robot should be able to take for granted the a priori availability of a map. Unfortunately, this is rarely the case. Not only do architectural blueprints or related types of maps fail to be consistently reliable (since even during construction they are not always updated to reflect necessary alternations), but, furthermore, numerous aspects of an environment are not likely to appear on a map, such as tables, chairs, and transitory objects.

Although the vast majority of mobile robotic systems involve a single robot operating alone in its environment, a growing number of researchers are considering the challenges and potential advantages of having a group of robots cooperate in order to complete some required task. For some specific robotic tasks, such as exploring an unknown planet [374], search and rescue [812], pushing objects [608], [513], [687], [821], or cleaning up toxic waste [609], it has been suggested that rather than send one very complex robot to perform the task it would more effective to send a number of smaller, simpler robots.

Before delving into the harsh realities of real robots, it is worthwhile exploring some of the computational tasks that are associated with an autonomous system. This chapter provides a taste (an amuse bouche, if you will) of some of the computational problems that will be considered in later chapters. Here, these problems are considered in their simplest form, and many of the realities of autonomous systems are ignored. Rest assured, the full complexity of the problems are considered in later chapters.

Although many mobile robot systems are experimental in nature, systems devoted to specific practical applications are being developed and deployed. This chapter examines some of the tasks for which mobile robotic systems are beginning to appear and describes several existing experimental and production systems that have been developed.

For many tasks, a mobile robot needs to know “where it is” either on an ongoing basis or when specific events occur. A robot may need to know its location in order to be able to plan appropriate paths or to know if the current location is the appropriate place at which to perform some operation. Knowing “where the robot is” has many different connotations. In the strongest sense, “knowing where the robot is” involves estimating the location of the robot (in either qualitative or quantitative terms) with respect to some global representation of space: we refer to this as strong localization.

The use of machine learning in robotics is a vast and growing area of research. In this chapter we consider a few key variations using: the use of deep neural networks, the applications of reinforcement learning and especially deep reinforcement learning, and the rapidly emerging potential for large language models.

Although the vast majority of mobile robotic systems involve a single robot operating alone in its environment, a growing number of researchers are considering the challenges and potential advantages of having a group of robots cooperate in order to complete some required task. For some specific robotic tasks, such as exploring an unknown planet [374], search and rescue [812], pushing objects [608], [513], [687], [821], or cleaning up toxic waste [609], it has been suggested that rather than send one very complex robot to perform the task it would more effective to send a number of smaller, simpler robots. Such a collection of robots is sometimes described as a swarm [81], a colony [255], or a collective [436], or the robots may be said to exhibit cooperative behavior [607].

Robots in fiction seem to be able to engage in complex planning tasks with little or no difficulty. For example, in the novel 2001: A Space Odyssey, HAL is capable of long-range plans and reasoning about the effects and consequences of his actions [167]. It is indeed fortunate that fictional autonomous systems can be presented without having to specify how such devices represent and reason about their environment. Unfortunately, real autonomous systems often make explicit internal representations and mechanisms for reasoning about them.

Anyone who has had to move about in the dark recognizes the importance of vision to human navigation. Tasks that are fraught with difficulty and danger in the dark become straightforward when the lights are on. Given that humans seem to navigate effortlessly with vision, it seems natural to consider vision as a sensor for mobile robots. Visual sensing has many desirable potential features, including that it is passive and has high resolution and a long range.

This volume offers perspectives on examples of key ingredients in Plato’s writing: particularly of argument, allegory, images, and myth, of intertextuality, and of paradox, but also his characterization of speakers he portrays in dialogue, now through narration, now direct dramatic presentation, and his assumed readerships. All the essays included were prompted by perception of something problematic: either in a passage within a dialogue itself, or in the way scholarship had tackled or failed to tackle a topic. First come three approaching the corpus as a whole, three different vantage points. The next group of three focus on arguments and disputants within the overall argumentative structure of three very different dialogues: Gorgias, Cratylus, and Parmenides. A third group contains two studies of celebrated imaginative fictions – the Noble Lie and the Cave – that perform key but unstraightforward roles in the philosophical strategy of the Republic. The final six chapters discuss the Laws. They explore further literary and philosophical dimensions of Plato’s writing in the last and longest of his dialogues, nowadays yielding up more philosophical rewards than was once the case.

Relationship problems relating to sex and desire, including mismatches in desire and how to successfully address these; mismatches in preferred sexual activities and how to address these; the value of couples counselling for all couples.