Catalogue Search | MBRL
Are you sure you want to remove the book from the shelf?
{{itemTitle}}
5,291
result(s) for
"Artificial Intelligence - history"
Sort by:
Artificial intelligence in healthcare: past, present and future
Artificial intelligence (AI) aims to mimic human cognitive functions. It is bringing a paradigm shift to healthcare, powered by increasing availability of healthcare data and rapid progress of analytics techniques. We survey the current status of AI applications in healthcare and discuss its future. AI can be applied to various types of healthcare data (structured and unstructured). Popular AI techniques include machine learning methods for structured data, such as the classical support vector machine and neural network, and the modern deep learning, as well as natural language processing for unstructured data. Major disease areas that use AI tools include cancer, neurology and cardiology. We then review in more details the AI applications in stroke, in the three major areas of early detection and diagnosis, treatment, as well as outcome prediction and prognosis evaluation. We conclude with discussion about pioneer AI systems, such as IBM Watson, and hurdles for real-life deployment of AI.
Journal Article
Artificial intelligence : an illustrated history : from medieval robots to neural networks
\"From medieval robots and Boolean algebra to facial recognition, artificial neural networks, and adversarial patches, this fascinating history takes readers on a vast tour through the world of artificial intelligence. Award-winning author Clifford A. Pickover (The Math Book, The Physics Book, Death & the Afterlife) explores the historic and current applications of AI in such diverse fields as computing, medicine, popular culture, mythology, and philosophy, and considers the enduring threat to humanity should AI grow out of control. Across 100 illustrated entries, Pickover provides an entertaining and informative look into when artificial intelligence began, how it developed, where it's going, and what it means for the future of human-machine interaction.\"-- Publisher's description.
Book
The Mechanical Mind in History
The idea of intelligent machines has become part of popular culture. But tracing the history of the actual science of machine intelligence reveals a rich network of cross-disciplinary contributions--the unrecognized origins of ideas now central to artificial intelligence, artificial life, cognitive science, and neuroscience. In The Mechanization of Mind in History, scientists, artists, historians, and philosophers discuss the multidisciplinary quest to formalize and understand the generation of intelligent behavior in natural and artificial systems as a wholly mechanical process. The contributions illustrate the diverse and interacting notions that chart the evolution of the idea of the mechanical mind. They describe the mechanized mind as, among other things, an analogue system, an organized suite of chemical interactions, a self-organizing electromechanical device, an automated general-purpose information processor, and an integrated collection of symbol manipulating mechanisms. They investigate the views of pivotal figures that range from Descartes and Heidegger to Alan Turing and Charles Babbage, and they emphasize such frequently overlooked areas as British cybernetic and pre-cybernetic thinkers. The volume concludes with the personal insights of five highly influential figures in the field: John Maynard Smith, John Holland, Oliver Selfridge, Horace Barlow, and Jack Cowan.Philip Husbands is Professor of Computer Science and Artificial Intelligence in the Department of Informatics at the University of Sussex and Codirector of the Sussex Centre for Computational Neuroscience and Robotics. Owen Holland is Professor in the Department of Computer Science at the University of Essex. Michael Wheeler is Reader in Philosophy at the University of Stirling. He is the author of Reconstructing the Cognitive World: The Next Step (MIT Press, 2005).ContributorsPeter Asaro, Horace Barlow, Andy Beckett, Margaret Boden, Jon Bird, Paul Brown, Seth Bullock, Roberto Cordeschi, Jack Cowan, Ezequiel Di Paolo, Hubert Dreyfus, Andrew Hodges, Owen Holland, Jana Horáková, Philip Husbands, Jozef Kelemen, John Maynard Smith, Donald Michie, Oliver Selfridge, Michael Wheeler
eBook
A brief history of artificial intelligence : what it is, where we are, and where we are going
\"From Oxford's leading AI researcher comes a fun and accessible tour through the history and future of one of the most cutting edge and misunderstood field in science: Artificial Intelligence The somewhat ill-defined long-term aim of AI is to build machines that are conscious, self-aware, and sentient; machines capable of the kind of intelligent autonomous action that currently only people are capable of. As an AI researcher with 25 years of experience, professor Mike Wooldridge has learned to be obsessively cautious about such claims, while still promoting an intense optimism about the future of the field. There have been genuine scientific breakthroughs that have made AI systems possible in the past decade that the founders of the field would have hailed as miraculous. Driverless cars and automated translation tools are just two examples of AI technologies that have become a practical, everyday reality in the past few years, and which will have a huge impact on our world. While the dream of conscious machines remains, Professor Wooldridge believes, a distant prospect, the floodgates for AI have opened. Wooldridge's A Brief History of Artificial Intelligence is an exciting romp through the history of this groundbreaking field--a one-stop-shop for AI's past, present, and world-changing future\"-- Provided by publisher.
BOOK
A 25-Year Retrospective of the Use of AI for Diagnosing Acute Stroke: Systematic Review
Stroke is a leading cause of death and disability worldwide. Rapid and accurate diagnosis is crucial for minimizing brain damage and optimizing treatment plans.
This review aims to summarize the methods of artificial intelligence (AI)-assisted stroke diagnosis over the past 25 years, providing an overview of performance metrics and algorithm development trends. It also delves into existing issues and future prospects, intending to offer a comprehensive reference for clinical practice.
A total of 50 representative articles published between 1999 and 2024 on using AI technology for stroke prevention and diagnosis were systematically selected and analyzed in detail.
AI-assisted stroke diagnosis has made significant advances in stroke lesion segmentation and classification, stroke risk prediction, and stroke prognosis. Before 2012, research mainly focused on segmentation using traditional thresholding and heuristic techniques. From 2012 to 2016, the focus shifted to machine learning (ML)-based approaches. After 2016, the emphasis moved to deep learning (DL), which brought significant improvements in accuracy. In stroke lesion segmentation and classification as well as stroke risk prediction, DL has shown superiority over ML. In stroke prognosis, both DL and ML have shown good performance.
Over the past 25 years, AI technology has shown promising performance in stroke diagnosis.
Journal Article
AI & I : an intellectual history of artificial intelligence
\"An intellectual history of the first 50 years of AI written by a major figure who has been involved since AI's inception\"-- Provided by publisher.
BOOK
From Einstein to AI: how 100 years have shaped science
Looking back a century reveals how much the research landscape has changed — and how unclear the consequences of scientific innovation can be.
Looking back a century reveals how much the research landscape has changed — and how unclear the consequences of scientific innovation can be.
Journal Article
AI basics
\"Dive deep into artificial intelligence, from what it is to how it works. Readers discover the history of machine learning. They also learn how AI is used currently and explore the technology's future\"-- Provided by publisher.
BOOK
From past to future: a review of methods for assessing physical activity energy expenditure
Background
Physical activity energy expenditure (PAEE) assessment is important for helping individuals maintain energy balance. This study adopts a technological evolution perspective to systematically examine the historical evolution and current progress of PAEE assessment methods, aiming to clarify future development directions.
Methods
This study employs a narrative review methodology. The “Past” section presents a chronological account of the historical development of the PAEE assessment methodology, whereas the “Present” section synthesizes recent advances in the application of intelligent technologies to PAEE assessment based on a systematic literature search. Relevant literature was identified through comprehensive searches of the Web of Science, PubMed, IEEE Xplore, and China National Knowledge Infrastructure (CNKI) databases.
Results
The historical evolution of PAEE assessment methods can be divided into three periods: initial emergence (late 18th century to mid-19th century), gradual exploration (late 19th century to early 20th century), and steady development (mid-20th century to late 20th century). PAEE assessment enters the intelligent era now, with the application of artificial intelligence (AI) technology primarily focused on two main areas: machine learning (ML) and computer vision (CV). However, there are still some shortcomings. Therefore, future efforts should focus on advancing technological innovations in intelligent PAEE assessment, expanding the application scenarios of intelligent PAEE assessment, and mitigating the ethical risks associated with intelligent PAEE assessment to enhance the effect of AI in PAEE assessment.
Conclusions
The historical evolution of PAEE assessment has undergone three stages: initial formation, gradual exploration, and steady development. Currently, the application of AI technology in PAEE assessment is mainly concentrated in the two major fields of ML and CV, but it still faces many challenges. In the future, it will be necessary to promote technological innovation, expand application scenarios, and mitigate ethical risks.
Journal Article