An influential scientist in the field of artificial intelligence (AI) explains its fundamental concepts and how it is changing culture and society. A particular form of AI is now embedded in our tech, our infrastructure, and our lives. How did it get there? Where and why should we be concerned? And what should we do now? The Shortcut: Why Intelligent Machines Do Not Think Like Us provides an accessible yet probing exposure of AI in its prevalent form today, proposing a new narrative to connect and make sense of events that have happened in the recent tumultuous past, and enabling us to think soberly about the road ahead. This book is divided into ten carefully crafted and easily digestible chapters. Each chapter grapples with an important question for AI. Ranging from the scientific concepts that underpin the technology to wider implications for society, it develops a unified description using tools from different disciplines and avoiding unnecessary abstractions or words that end with -ism. The book uses real examples wherever possible, introducing the reader to the people who have created some of these technologies and to ideas shaping modern society that originate from the technical side of AI. It contains important practical advice about how we should approach AI in the future without promoting exaggerated hypes or fears. Entertaining and disturbing but always thoughtful, The Shortcut confronts the hidden logic of AI while preserving a space for human dignity. It is essential reading for anyone with an interest in AI, the history of technology, and the history of ideas. General readers will come away much more informed about how AI really works today and what we should do next.
The Last Summer Story of Lucy Christalnigg and the End of a World BY Nello Cristianini A True Story - It is the summer of 1914 and the Austro-Hungarian Empire has just taken the road which will lead to its ruin. In the remote resort town of Gorizia, general mobilisation starts in August, and involves also Countess Lucy Christalnigg, racing car driver, on a mission for the Red Cross. During the journey between Klagenfurt and Gorizia the Countess meets her tragic fate, while the world is sliding into war. After 100 years, this story brings back to life a forgotten episode which had caused great sensation at the time, describing the
Kernel methods provide a powerful and unified framework for pattern discovery, motivating algorithms that can act on general types of data (e.g. strings, vectors or text) and look for general types of relations (e.g. rankings, classifications, regressions, clusters). The application areas range from neural networks and pattern recognition to machine learning and data mining. This book, developed from lectures and tutorials, fulfils two major roles: firstly it provides practitioners with a large toolkit of algorithms, kernels and solutions ready to use for standard pattern discovery problems in fields such as bioinformatics, text analysis, image analysis. Secondly it provides an easy introduction for students and researchers to the growing field of kernel-based pattern analysis, demonstrating with examples how to handcraft an algorithm or a kernel for a new specific application, and covering all the necessary conceptual and mathematical tools to do so.
This is the first comprehensive introduction to Support Vector Machines (SVMs), a generation learning system based on recent advances in statistical learning theory. SVMs deliver state-of-the-art performance in real-world applications such as text categorisation, hand-written character recognition, image classification, biosequences analysis, etc., and are now established as one of the standard tools for machine learning and data mining. Students will find the book both stimulating and accessible, while practitioners will be guided smoothly through the material required for a good grasp of the theory and its applications. The concepts are introduced gradually in accessible and self-contained stages, while the presentation is rigorous and thorough. Pointers to relevant literature and web sites containing software ensure that it forms an ideal starting point for further study. Equally, the book and its associated web site will guide practitioners to updated literature, new applications, and on-line software.
Kernel methods provide a powerful and unified framework for pattern discovery, motivating algorithms that can act on general types of data (e.g. strings, vectors or text) and look for general types of relations (e.g. rankings, classifications, regressions, clusters). The application areas range from neural networks and pattern recognition to machine learning and data mining. This book, developed from lectures and tutorials, fulfils two major roles: firstly it provides practitioners with a large toolkit of algorithms, kernels and solutions ready to use for standard pattern discovery problems in fields such as bioinformatics, text analysis, image analysis. Secondly it provides an easy introduction for students and researchers to the growing field of kernel-based pattern analysis, demonstrating with examples how to handcraft an algorithm or a kernel for a new specific application, and covering all the necessary conceptual and mathematical tools to do so.
Where did SARS come from? Have we inherited genes from Neanderthals? How do plants use their internal clock? The genomic revolution in biology enables us to answer such questions. But the revolution would have been impossible without the support of powerful computational and statistical methods that enable us to exploit genomic data. Many universities are introducing courses to train the next generation of bioinformaticians: biologists fluent in mathematics and computer science, and data analysts familiar with biology. This readable and entertaining book, based on successful taught courses, provides a roadmap to navigate entry to this field. It guides the reader through key achievements of bioinformatics, using a hands-on approach. Statistical sequence analysis, sequence alignment, hidden Markov models, gene and motif finding and more, are introduced in a rigorous yet accessible way. A companion website provides the reader with Matlab-related software tools for reproducing the steps demonstrated in the book.
An influential scientist in the field of artificial intelligence (AI) explains its fundamental concepts and how it is changing culture and society. A particular form of AI is now embedded in our tech, our infrastructure, and our lives. How did it get there? Where and why should we be concerned? And what should we do now? The Shortcut: Why Intelligent Machines Do Not Think Like Us provides an accessible yet probing exposure of AI in its prevalent form today, proposing a new narrative to connect and make sense of events that have happened in the recent tumultuous past, and enabling us to think soberly about the road ahead. This book is divided into ten carefully crafted and easily digestible chapters. Each chapter grapples with an important question for AI. Ranging from the scientific concepts that underpin the technology to wider implications for society, it develops a unified description using tools from different disciplines and avoiding unnecessary abstractions or words that end with -ism. The book uses real examples wherever possible, introducing the reader to the people who have created some of these technologies and to ideas shaping modern society that originate from the technical side of AI. It contains important practical advice about how we should approach AI in the future without promoting exaggerated hypes or fears. Entertaining and disturbing but always thoughtful, The Shortcut confronts the hidden logic of AI while preserving a space for human dignity. It is essential reading for anyone with an interest in AI, the history of technology, and the history of ideas. General readers will come away much more informed about how AI really works today and what we should do next.
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