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Author: Rosey Press
Publisher: Independently Published
ISBN:
Category : Computers
Languages : en
Pages : 0
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Book Description
Multimodal AI refers to artificial intelligence systems that are capable of processing and generating multiple types of data, such as text, images, audio, and more. This technology has revolutionized the capabilities of AI applications, enabling the development of more sophisticated virtual assistants and interactive systems that can understand and respond in more human-like ways. By incorporating multiple modalities of data, these systems are able to provide more comprehensive and nuanced responses, enhancing the overall user experience. One of the key areas where multimodal AI is making a significant impact is in the field of healthcare. AI systems can now analyze medical images, text reports, and patient data to assist in diagnosis and treatment planning. By combining multiple types of data, these systems are able to provide more accurate and personalized insights, ultimately improving patient outcomes and reducing healthcare costs. In the realm of autonomous vehicles, multimodal AI is playing a crucial role in enabling these vehicles to navigate and make decisions in real-time. By processing data from cameras, lidar, radar, and sensors, AI systems can effectively interpret their surroundings and respond accordingly, ensuring safe and efficient transportation. This technology is paving the way for the widespread adoption of autonomous vehicles and revolutionizing the future of transportation. Multimodal AI is also being utilized in customer service applications, where AI systems can analyze text, voice, and facial expressions to provide personalized and empathetic responses to customers. By incorporating multiple modalities of data, these systems are able to better understand and address customer needs, ultimately enhancing customer satisfaction and loyalty. This technology is transforming the way businesses interact with their customers and improving overall customer experiences. In conclusion, multimodal AI is a groundbreaking technology that is revolutionizing a wide range of industries, from healthcare to finance to education. By leveraging multiple types of data, AI systems are able to provide more comprehensive and nuanced insights, ultimately improving decision-making processes and enhancing user experiences. As this technology continues to evolve and advance, we can expect to see even greater innovations and advancements in the field of artificial intelligence.
Author: Rosey Press
Publisher: Independently Published
ISBN:
Category : Computers
Languages : en
Pages : 0
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Book Description
Multimodal AI refers to artificial intelligence systems that are capable of processing and generating multiple types of data, such as text, images, audio, and more. This technology has revolutionized the capabilities of AI applications, enabling the development of more sophisticated virtual assistants and interactive systems that can understand and respond in more human-like ways. By incorporating multiple modalities of data, these systems are able to provide more comprehensive and nuanced responses, enhancing the overall user experience. One of the key areas where multimodal AI is making a significant impact is in the field of healthcare. AI systems can now analyze medical images, text reports, and patient data to assist in diagnosis and treatment planning. By combining multiple types of data, these systems are able to provide more accurate and personalized insights, ultimately improving patient outcomes and reducing healthcare costs. In the realm of autonomous vehicles, multimodal AI is playing a crucial role in enabling these vehicles to navigate and make decisions in real-time. By processing data from cameras, lidar, radar, and sensors, AI systems can effectively interpret their surroundings and respond accordingly, ensuring safe and efficient transportation. This technology is paving the way for the widespread adoption of autonomous vehicles and revolutionizing the future of transportation. Multimodal AI is also being utilized in customer service applications, where AI systems can analyze text, voice, and facial expressions to provide personalized and empathetic responses to customers. By incorporating multiple modalities of data, these systems are able to better understand and address customer needs, ultimately enhancing customer satisfaction and loyalty. This technology is transforming the way businesses interact with their customers and improving overall customer experiences. In conclusion, multimodal AI is a groundbreaking technology that is revolutionizing a wide range of industries, from healthcare to finance to education. By leveraging multiple types of data, AI systems are able to provide more comprehensive and nuanced insights, ultimately improving decision-making processes and enhancing user experiences. As this technology continues to evolve and advance, we can expect to see even greater innovations and advancements in the field of artificial intelligence.
Author: Arash Shaban-Nejad
Publisher: Springer Nature
ISBN: 3031147715
Category : Technology & Engineering
Languages : en
Pages : 417
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Book Description
This book aims to highlight the latest achievements in the use of AI and multimodal artificial intelligence in biomedicine and healthcare. Multimodal AI is a relatively new concept in AI, in which different types of data (e.g. text, image, video, audio, and numerical data) are collected, integrated, and processed through a series of intelligence processing algorithms to improve performance. The edited volume contains selected papers presented at the 2022 Health Intelligence workshop and the associated Data Hackathon/Challenge, co-located with the Thirty-Sixth Association for the Advancement of Artificial Intelligence (AAAI) conference, and presents an overview of the issues, challenges, and potentials in the field, along with new research results. This book provides information for researchers, students, industry professionals, clinicians, and public health agencies interested in the applications of AI and Multimodal AI in public health and medicine.
Author: Blaž Škrlj
Publisher: Springer Nature
ISBN: 3031570162
Category :
Languages : en
Pages : 78
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Book Description
Author: Santosh Kumar
Publisher: Academic Press
ISBN: 9780128237373
Category : Computers
Languages : en
Pages : 375
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Book Description
Multimodal Machine Learning: Techniques and Applications explains recent advances in multimodal machine learning, providing a coherent set of fundamentals for designing efficient multimodal learning algorithms for different applications. The book addresses the main challenges in multimodal machine learning based computing paradigms, including multimodal representation learning, translation and mapping, modality alignment, multimodal fusion and co-learning. The book also explores the important texture feature descriptors based on recognition and transform techniques. It is ideal for senior undergraduates, graduate students, and researchers in data science, engineering, computer science and statistics. Presents new representation, classification and identification algorithms for data prediction and analysis on feature characteristics Discusses recent and future advancements in diversified fields of computer vision , pattern recognition, generative adversarial network-based learning, video analytics and data science Provides an overview of future research challenges and directions
Author: Man Luo
Publisher: Springer Nature
ISBN: 3031578163
Category :
Languages : en
Pages : 170
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Book Description
Author: Raymond M Russell
Publisher: Independently Published
ISBN:
Category : Computers
Languages : en
Pages : 0
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Book Description
Dive into the ever-evolving landscape of artificial intelligence with "The Artificial Intelligence Cycle," a comprehensive exploration of the latest advancements and critical considerations shaping the AI industry today. In this groundbreaking book, we embark on a journey through the reality of AI, offering a reality check to set more realistic expectations amidst the hype. From the emergence of multimodal AI, capable of processing text, image, and audio data simultaneously, to the evolution of smaller language models and the democratization of open-source advancements, we dissect the cutting-edge technologies driving AI innovation forward. However, the AI journey isn't without its challenges. Explore the impacts of GPU shortages and cloud costs on AI development, as well as the accessibility of model optimization techniques. Discover how customized local models and data pipelines are revolutionizing AI deployment, empowering organizations to tailor solutions to their unique needs while navigating the complexities of regulation, copyright, and ethical concerns. Delve into the realm of more powerful virtual agents, revolutionizing human-computer interactions and paving the way for enhanced user experiences. But beware the shadows lurking within AI implementation - uncover the implications of shadow AI and corporate policies, and learn how to navigate them responsibly. Whether you're a seasoned AI professional or a curious newcomer, "The Artificial Intelligence Cycle" offers invaluable insights into the past, present, and future of AI, equipping you with the knowledge and understanding needed to navigate this transformative era with confidence and foresight.
Author: Tongliang Liu
Publisher: Springer Nature
ISBN: 9819983886
Category : Computers
Languages : en
Pages : 574
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Book Description
This two-volume set LNAI 14471-14472 constitutes the refereed proceedings of the 36th Australasian Joint Conference on Artificial Intelligence, AI 2023, held in Brisbane, QLD, Australia during November 28 – December 1, 2023. The 23 full papers presented together with 59 short papers were carefully reviewed and selected from 213 submissions. They are organized in the following topics: computer vision; deep learning; machine learning and data mining; optimization; medical AI; knowledge representation and NLP; explainable AI; reinforcement learning; and genetic algorithm.
Author: Richi Nayak
Publisher: Springer Nature
ISBN: 9819711118
Category :
Languages : en
Pages : 409
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Book Description
Author: Swati V. Shinde
Publisher: CRC Press
ISBN: 1000896161
Category : Technology & Engineering
Languages : en
Pages : 411
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Book Description
This book explores the advancements and future challenges in biomedical application developments using breakthrough technologies like Artificial Intelligence (AI), Internet of Things (IoT), and Signal Processing. It will also contribute to biosensors and secure systems,and related research. Applied Artificial Intelligence: A Biomedical Perspective begins by detailing recent trends and challenges of applied artificial intelligence in biomedical systems. Part I of the book presents the technological background of the book in terms of applied artificial intelligence in the biomedical domain. Part II demonstrates the recent advancements in automated medical image analysis that have opened ample research opportunities in the applications of deep learning to different diseases. Part III focuses on the use of cyberphysical systems that facilitates computing anywhere by using medical IoT and biosensors and the numerous applications of this technology in the healthcare domain. Part IV describes the different signal processing applications in the healthcare domain. It also includes the prediction of some human diseases based on the inputs in signal format. Part V highlights the scope and applications of biosensors and security aspects of biomedical images. The book will be beneficial to the researchers, industry persons, faculty, and students working in biomedical applications of computer science and electronics engineering. It will also be a useful resource for teaching courses like AI/ML, medical IoT, signal processing, biomedical engineering, and medical image analysis.
Author: Michael Yang
Publisher: Academic Press
ISBN: 0128173599
Category : Computers
Languages : en
Pages : 422
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Book Description
Multimodal Scene Understanding: Algorithms, Applications and Deep Learning presents recent advances in multi-modal computing, with a focus on computer vision and photogrammetry. It provides the latest algorithms and applications that involve combining multiple sources of information and describes the role and approaches of multi-sensory data and multi-modal deep learning. The book is ideal for researchers from the fields of computer vision, remote sensing, robotics, and photogrammetry, thus helping foster interdisciplinary interaction and collaboration between these realms. Researchers collecting and analyzing multi-sensory data collections – for example, KITTI benchmark (stereo+laser) - from different platforms, such as autonomous vehicles, surveillance cameras, UAVs, planes and satellites will find this book to be very useful. Contains state-of-the-art developments on multi-modal computing Shines a focus on algorithms and applications Presents novel deep learning topics on multi-sensor fusion and multi-modal deep learning
