Futureproof
Futureproof

Author: Kevin Roose

Publisher: Hachette UK

Published: 2021-03-04

Total Pages: 256

ISBN-13: 152930475X

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A New York Times bestselling author and tech columnist's counter-intuitive guide to staying relevant - and employable - in the machine age by becoming irreplaceably human. It's not a future scenario any more. We've been taught that to compete with automation and AI, we'll have to become more like the machines themselves, building up technical skills like coding. But, there's simply no way to keep up. What if all the advice is wrong? And what do we need to do instead to become futureproof? We tend to think of automation as a blue-collar phenomenon that will affect truck drivers, factory workers, and other people with repetitive manual jobs. But it's much, much broader than that. Lawyers are being automated out of existence. Last year, JPMorgan Chase built a piece of software called COIN, which uses machine learning to review complicated contracts and documents. It used to take the firm's lawyers more than 300,000 hours every year to review all of those documents. Now, it takes a few seconds, and requires just one human to run the program. Doctors are being automated out of existence, too. Last summer, a Chinese tech company built a deep learning algorithm that diagnosed brain cancer and other diseases faster and more accurately than a team of 15 top Chinese doctors. Kevin Roose has spent the past few years studying the question of how people, communities, and organisations adapt to periods of change, from the Industrial Revolution to the present. And the insight that is sweeping through Silicon Valley as we speak -- that in an age dominated by machines, it's human skills that really matter - is one of the more profound and counter-intuitive ideas he's discovered. It's the antidote to the doom-and-gloom worries many people feel when they think about AI and automation. And it's something everyone needs to hear. In nine accessible, prescriptive chapters, Roose distills what he has learned about how we will survive the future, that the way to become futureproof is to become incredibly, irreplaceably human.

Interpretable Machine Learning
Interpretable Machine Learning

Author: Christoph Molnar

Publisher: Lulu.com

Published: 2020

Total Pages: 320

ISBN-13: 0244768528

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This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model-agnostic methods for interpreting black box models like feature importance and accumulated local effects and explaining individual predictions with Shapley values and LIME. All interpretation methods are explained in depth and discussed critically. How do they work under the hood? What are their strengths and weaknesses? How can their outputs be interpreted? This book will enable you to select and correctly apply the interpretation method that is most suitable for your machine learning project.

Ripple-Down Rules
Ripple-Down Rules

Author: Paul Compton

Publisher: CRC Press

Published: 2021-05-30

Total Pages: 196

ISBN-13: 1000363589

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Machine learning algorithms hold extraordinary promise, but the reality is that their success depends entirely on the suitability of the data available. This book is about Ripple-Down Rules (RDR), an alternative manual technique for rapidly building AI systems. With a human in the loop, RDR is much better able to deal with the limitations of data. Ripple-Down Rules: The Alternative to Machine Learning starts by reviewing the problems with data quality and the problems with conventional approaches to incorporating expert human knowledge into AI systems. It suggests that problems with knowledge acquisition arise because of mistaken philosophical assumptions about knowledge. It argues people never really explain how they reach a conclusion, rather they justify their conclusion by differentiating between cases in a context. RDR is based on this more situated understanding of knowledge. The central features of a RDR approach are explained, and detailed worked examples are presented for different types of RDR, based on freely available software developed for this book. The examples ensure developers have a clear idea of the simple yet counter-intuitive RDR algorithms to easily build their own RDR systems. It has been proven in industrial applications that it takes only a minute or two per rule to build RDR systems with perhaps thousands of rules. The industrial uses of RDR have ranged from medical diagnosis through data cleansing to chatbots in cars. RDR can be used on its own or to improve the performance of machine learning or other methods.

Rules, Reason, and Self-Knowledge
Rules, Reason, and Self-Knowledge

Author: Julia Tanney

Publisher: Harvard University Press

Published: 2013-01-08

Total Pages: 425

ISBN-13: 0674071727

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Julia Tanney offers a sustained criticism of today’s canon in philosophy of mind, which conceives the workings of the rational mind as the outcome of causal interactions between mental states that have their bases in the brain. With its roots in physicalism and functionalism, this widely accepted view provides the philosophical foundation for the cardinal tenet of the cognitive sciences: that cognition is a form of information-processing. Rules, Reason, and Self-Knowledge presents a challenge not only to the cognitivist approach that has dominated philosophy and the special sciences for the last fifty years but, more broadly, to metaphysical-empirical approaches to the study of the mind. Responding to a tradition that owes much to the writings of Davidson, early Putnam, and Fodor, Tanney challenges this orthodoxy on its own terms. In untangling its internal inadequacies, starting with the paradoxes of irrationality, she arrives at a view these philosophers were keen to rebut—one with affinities to the work of Ryle and Wittgenstein and all but invisible to those working on the cutting edge of analytic philosophy and mind research today. This is the view that rational explanations are embedded in “thick” descriptions that are themselves sophistications upon ever ascending levels of discourse, or socio-linguistic practices. Tanney argues that conceptual cartography rather than metaphysical-scientific explanation is the basic tool for understanding the nature of the mind. Rules, Reason, and Self-Knowledge clears the path for a return to the world-involving, circumstance-dependent, normative practices where the rational mind has its home.

Rule Based Systems for Big Data
Rule Based Systems for Big Data

Author: Han Liu

Publisher: Springer

Published: 2015-09-09

Total Pages: 127

ISBN-13: 3319236962

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The ideas introduced in this book explore the relationships among rule based systems, machine learning and big data. Rule based systems are seen as a special type of expert systems, which can be built by using expert knowledge or learning from real data. The book focuses on the development and evaluation of rule based systems in terms of accuracy, efficiency and interpretability. In particular, a unified framework for building rule based systems, which consists of the operations of rule generation, rule simplification and rule representation, is presented. Each of these operations is detailed using specific methods or techniques. In addition, this book also presents some ensemble learning frameworks for building ensemble rule based systems.