Given how fast things are moving forward, a book on AI published in 2019 may seem outdated. On the contrary – everyone interested in AI and needing an in-depth understanding should read this book. As well as discussing the benefits to be gained from AI, Russell also explains, in great detail and with clear examples, why our approach to AI today is not necessarily the best. His core question explores how we ensure machines remain beneficial to humans and addresses a fundamental challenge: increasingly intelligent machines that do what we ask but not what we intend.
Russell starts by demystifying AI and clarifying its capabilities and boundaries. He argues that AI may excel in structured environments like chess, where its logic navigates a finite set of moves. However, AI’s ability to predict outcomes falters in the dynamic tapestry of human decision-making, where actions are not necessarily bound by rationality. Today, dissecting complex social interactions to advise humans on decision-making is a formidable challenge for AI.
Russell also contemplates the possibility of artificial general intelligence (AGI), where a machine could learn and think like a human. Contrary to some experts, he believes we still need a major breakthrough and a human lifetime (about 80 years) until AGI is developed. Here, his arguments on the challenge of truly understanding human language make one wonder about the capabilities we have attributed to LLMs, especially ChatGPT. According to the author, current AI algorithms “can extract simple information from clearly stated facts, but cannot build complex knowledge structures from text; nor can they answer questions that require extensive chains of reasoning with information from multiple sources”.
Russell spends time discussing what could go wrong if we continue progressing down the AI path we are currently on and emphasizes the “unintended consequences” that superintelligent AI may bring just because humans were not specific enough when describing what they want and do not want.
This book is not technical; even in his suggestions about how to build a better AI development philosophy, most of Russell’s suggestions are around teaching machines human values. Russell argues we should strive to build beneficial machines that are purely altruistic, and that is only possible if machines learn not from data but from observing human behavior. When discussing how to do this, his suggestions fall short of being applicable, but this does not take anything away from the book’s brilliant questions and discussions.
This book is not specifically about AI but how it may impact the role of leaders in corporations. It is a light take on the role of AI-powered robots – as employees, as team members, even as a boss. It provides excellent food for thought on what it means to co-exist with technology in the workplace and the increasing importance of inclusive and purpose-driven leadership in organizations that rush to become algorithm-driven.
In this accessible, entertaining book, the reader can expect to learn the basic working principles of machine learning and why depending solely on data-driven AI may not get us to the AGI that everybody seems to be eagerly awaiting. The authors argue we need to “revive” the 70s vibe in AI: to get back to symbolic AI – a subfield that focuses on the processing and manipulation of symbols and concepts rather than numerical data –
to add reasoning capabilities to machines and be more inspired by how humans learn. It is an excellent antidote for the doomsday scenarios popularized in the media.
A little on the techy side, this book highlights how complex algorithms are increasingly being adopted by governments and organizations and how, given how many of them are violating basic human rights, we need to do a better job designing them. The authors suggest that more traditional approaches to solving this issue, such as laws and regulations, auditing mechanisms, and privacy watchdog groups, are barely moving the needle. Thus, we need to understand and improve the science behind the algorithms and design socially aware algorithms that take human principles into account.
AI is revolutionizing the world of business, at pace. How do executives guarantee this mass adoption benefits both their organizations and society? In Issue 13 of I by IMD, we explore how to lead effectively – and responsibly – in the age of AI.
There is so much talk about GenAI today, yet the clear, valuable applications that improve an organization’s productivity or innovativeness still seem limited. Even at IMD, we hear quite a bit about the implementation challenges to scaling seemingly successful AI proof-of-concepts. The book is a no-nonsense, practical guide to AI implementations that delivers a clear message. The author provides several practical tips and offers applicable templates for executives to use. While this book does not offer technical details on how AI works, it does provide a comprehensive overview of potential AI applications for business. It focuses on the strategic aspects of implementing AI. I found it particularly useful as it explains how the AI project management process differs from traditional software development projects.
Today, the dominant paradigm of “building better AI” is about increasing the data and computing power used in building machine learning so that systems are more accurate, capable, and can handle ever-increasing demand. Yet, the technological building block of this in-demand computing power – the microchip – is under immense geopolitical pressure. The book is an insightful look into our dependency on microchips, which are fundamental to everything from smartphones to military systems. Miller illuminates this technological race’s geopolitical and economic implications, highlighting the emerging Cold War-like scenario it drives. The book offers a deep understanding of the contemporary world’s intersection of politics, economy, and technology.
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