Nello Cristianini – författare

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.

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.

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.

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.

Can machines think? This troubling question, posed by Alan Turing in 1950, has perhaps been answered: today we can converse with a computer without being able to distinguish it from a person.Machines can pass university exams and program other computers. ChatGPT, Bard, and other 'language models' have proved proficient at performing tasks far beyond their creators’ initial expectations, and we still do not know why. Trained simply to predict missing words in a text, such models have gained an understanding of the world and language that makes them capable of reasoning, planning, solving problems, as well as conversing almost flawlessly. Is this the secret of knowledge, and is it now in the hands of our creations? Perhaps we are no longer alone. And as we try to figure out how to share these powers with the 'aliens' who now work at our side, we can wonder what else they may learn tomorrow. Are we approaching a critical threshold beyond which machines will attain superhuman performance?Following on from The Shortcut, Nello Cristianini has authored another brilliant book – written like a gripping thriller – explaining the ideas behind a technology destined to change the world. If our worst terror has always stemmed from fear of the unknown, the cure, since time immemorial, is knowledge.

Can machines think? This troubling question, posed by Alan Turing in 1950, has perhaps been answered: today we can converse with a computer without being able to distinguish it from a person.Machines can pass university exams and program other computers. ChatGPT, Bard, and other 'language models' have proved proficient at performing tasks far beyond their creators’ initial expectations, and we still do not know why. Trained simply to predict missing words in a text, such models have gained an understanding of the world and language that makes them capable of reasoning, planning, solving problems, as well as conversing almost flawlessly. Is this the secret of knowledge, and is it now in the hands of our creations? Perhaps we are no longer alone. And as we try to figure out how to share these powers with the 'aliens' who now work at our side, we can wonder what else they may learn tomorrow. Are we approaching a critical threshold beyond which machines will attain superhuman performance?Following on from The Shortcut, Nello Cristianini has authored another brilliant book – written like a gripping thriller – explaining the ideas behind a technology destined to change the world. If our worst terror has always stemmed from fear of the unknown, the cure, since time immemorial, is knowledge.

The question driving Artificial Intelligence research has shifted. No longer do we ask whether machines can be intelligent, but whether they can match us, or surpass us entirely. And with that question returns an older, deeper fear: the fear of being surpassed. superhuman (adj.) That which transcends the possibilities and limits of human nature.transcend (v.) To exceed or go beyond the limits of. To surpass.After The Shortcut and Machina Sapiens, this is the third episode in a compelling trilogy of books on the new era of thinking machines by Nello Cristianini. Artificial intelligence is now reaching human performance levels in many of the cognitive tasks in which we excel. But what will happen next? Are we entering an era in which machines will be able to understand things that are incomprehensible to us? No intelligence is unlimited, not even ours, so we ask ourselves: what lies beyond human limits? But while we invest huge amounts of money in building a machine capable of competing with us, we refuse to accept that this is possible. We both desire and fear that encounter. In this new, visionary book, we will see artificial intelligence successfully pass extremely difficult tests, right up to the final test, in which they will tackle areas that are impenetrable even for our best experts. Will we be able to understand what lies beyond the limits of our intelligence?

The question driving Artificial Intelligence research has shifted. No longer do we ask whether machines can be intelligent, but whether they can match us, or surpass us entirely. And with that question returns an older, deeper fear: the fear of being surpassed. superhuman (adj.) That which transcends the possibilities and limits of human nature.transcend (v.) To exceed or go beyond the limits of. To surpass.After The Shortcut and Machina Sapiens, this is the third episode in a compelling trilogy of books on the new era of thinking machines by Nello Cristianini. Artificial intelligence is now reaching human performance levels in many of the cognitive tasks in which we excel. But what will happen next? Are we entering an era in which machines will be able to understand things that are incomprehensible to us? No intelligence is unlimited, not even ours, so we ask ourselves: what lies beyond human limits? But while we invest huge amounts of money in building a machine capable of competing with us, we refuse to accept that this is possible. We both desire and fear that encounter. In this new, visionary book, we will see artificial intelligence successfully pass extremely difficult tests, right up to the final test, in which they will tackle areas that are impenetrable even for our best experts. Will we be able to understand what lies beyond the limits of our intelligence?

This two-volume set LNAI 7523 and LNAI 7524 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: ECML PKDD 2012, held in Bristol, UK, in September 2012. The 105 revised research papers presented together with 5 invited talks were carefully reviewed and selected from 443 submissions. The final sections of the proceedings are devoted to Demo and Nectar papers. The Demo track includes 10 papers (from 19 submissions) and the Nectar track includes 4 papers (from 14 submissions). The papers grouped in topical sections on association rules and frequent patterns; Bayesian learning and graphical models; classification; dimensionality reduction, feature selection and extraction; distance-based methods and kernels; ensemble methods; graph and tree mining; large-scale, distributed and parallel mining and learning; multi-relational mining and learning; multi-task learning; natural language processing; online learning and data streams; privacy and security; rankings and recommendations; reinforcement learning and planning; rule mining and subgroup discovery; semi-supervised and transductive learning; sensor data; sequence and string mining; social network mining; spatial and geographical data mining; statistical methods and evaluation; time series and temporal data mining; and transfer learning.