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A novel approach to hybrid AI aimed at developing trustworthy agent collaborators.The vast majority of current AI relies wholly on machine learning (ML). However, the past thirty years of effort in this paradigm have shown that, despite the many things that ML can achieve, it is not an all-purpose solution to building human-like intelligent systems. One hope for overcoming this limitation is hybrid AI: that is, AI that combines ML with knowledge-based processing. In Agents in the Long Game of AI, Marjorie McShane, Sergei Nirenburg, and Jesse English present recent advances in hybrid AI with special emphases on content-centric computational cognitive modeling, explainability, and development methodologies. At present, hybridization typically involves sprinkling knowledge into an ML black box. The authors, by contrast, argue that hybridization will be best achieved in the opposite way: by building agents within a cognitive architecture and then integrating judiciously selected ML results. This approach leverages the power of ML without sacrificing the kind of explainability that will foster society's trust in AI. This book shows how we can develop trustworthy agent collaborators of a type not being addressed by the “ML alone” or “ML sprinkled by knowledge” paradigms—and why it is imperative to do so.

How to make AI capable of general intelligence, and what such technology would mean for society.Artificial intelligence surrounds us. More and more of the systems and services you interact with every day are based on AI technology. Although some very recent AI systems are generalists to a degree, most AI is narrowly specific; that is, it can only do a single thing, in a single context. For example, your spellchecker can't do mathematics, and the world's best chess-playing program can't play Tetris. Human intelligence is different. We can solve a variety of tasks, including those we have not seen before. In Artificial General Intelligence, Julian Togelius explores technical approaches to developing more general artificial intelligence and asks what general AI would mean for human civilization.Togelius starts by giving examples of narrow AI that have superhuman performance in some way. Interestingly, there have been AI systems that are superhuman in some sense for more than half a century. He then discusses what it would mean to have general intelligence, by looking at definitions from psychology, ethology, and computer science. Next, he explores the two main families of technical approaches to developing more general artificial intelligence: foundation models through self-supervised learning, and open-ended learning in virtual environments. The final chapters of the book investigate potential artificial general intelligence beyond the strictly technical aspects. The questions discussed here investigate whether such general AI would be conscious, whether it would pose a risk to humanity, and how it might alter society.

An expansive case for bibliography as infrastructure in information science.Cats, Carpenters, and Accountants argues that bibliography serves a foundational role within information science as infrastructure, and like all infrastructures, it needs and deserves attention. Wayne de Fremery's thoughtful provocation positions bibliography as a means to serve the many ends pursued by information scientists. He explains that bibliographic practices, such as enumeration and description, lie at the heart of knowledge practices and cultural endeavors, but these kinds of infrastructures are difficult to see. In this book, he reveals them and the ways that they formulate information and meaning, artificial intelligence, and human knowledge.Drawing on scholarship from areas as diverse as data science, machine learning, Korean poetry, and the history of bibliography, de Fremery makes the case for understanding bibliography as a generative mode of accounting for what has been received as data, what he calls “carpentry-accounting.” Referencing a well-known debate in the Anglo-American bibliographical tradition that features a willful cat, he suggests that bibliography and bibliographers are intentionally marginal figures who, paradoxically, perform foundational work in the service of the diverse disciplinary ends that formulate, however loosely, information science as a field. When we attend to the marginal but essential work of accounting for what humankind has fashioned as recorded knowledge, it becomes easier to consider the ways that human accounts can serve and, sometimes, injure us. Relevant to scholars and students from the sciences to the humanities, Cats, Carpenters, and Accountants is a highly original argument for bibliography as a marginal but foundationally powerful force shaping information science as a field and the ways that we know.